Sea Turtle Rescue Forecast Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.sparse import csr_matrix, hstack\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import lightgbm as lgb\n",
    "from  datetime import datetime\n",
    "from sklearn.base import TransformerMixin\n",
    "import xgboost as xgb\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from collections import OrderedDict\n",
    "pd.set_option('display.max_columns', 500)\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [20, 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = pd.read_csv('train.csv')\n",
    "sample_submission = pd.read_csv(\"Sample_sub.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Capture_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201901</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CaptureSite_0_201902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CaptureSite_0_201903</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CaptureSite_0_201904</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CaptureSite_0_201905</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ID  Capture_Number\n",
       "0  CaptureSite_0_201901               7\n",
       "1  CaptureSite_0_201902               1\n",
       "2  CaptureSite_0_201903               5\n",
       "3  CaptureSite_0_201904               2\n",
       "4  CaptureSite_0_201905               3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rescue_ID</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>Researcher</th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>ForagingGround</th>\n",
       "      <th>CaptureMethod</th>\n",
       "      <th>Fisher</th>\n",
       "      <th>LandingSite</th>\n",
       "      <th>Species</th>\n",
       "      <th>Tag_1</th>\n",
       "      <th>Tag_2</th>\n",
       "      <th>Lost_Tags</th>\n",
       "      <th>T_Number</th>\n",
       "      <th>CCL_cm</th>\n",
       "      <th>CCW_cm</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Sex</th>\n",
       "      <th>TurtleCharacteristics</th>\n",
       "      <th>Status</th>\n",
       "      <th>ReleaseSite</th>\n",
       "      <th>Date_TimeRelease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000_RE_0060</td>\n",
       "      <td>2000-12-22</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1072</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00147</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.70</td>\n",
       "      <td>62.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>algae at rear of shell</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>22/12/00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001_RE_0187</td>\n",
       "      <td>2001-10-28</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_520</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>W442</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.85</td>\n",
       "      <td>31.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>multiple b's on front flippers&amp;  a lot of alga...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>28/10/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001_RE_0197</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1669</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE0376</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.80</td>\n",
       "      <td>49.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>clean</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>01/11/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002_RE_0031</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>Researcher_32</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1798</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00302</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.50</td>\n",
       "      <td>59.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1 b 3 CS+ calcerous algae at rear end of shell...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>11/03/02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002_RE_0118</td>\n",
       "      <td>2002-08-08</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Beached</td>\n",
       "      <td>Fisher_1918</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0113</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.70</td>\n",
       "      <td>33.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>very lively+ right eye is hanging out + swolle...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>08/08/02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rescue_ID Date_TimeCaught     Researcher    CaptureSite ForagingGround  \\\n",
       "0  2000_RE_0060      2000-12-22  Researcher_25  CaptureSite_0          Ocean   \n",
       "1  2001_RE_0187      2001-10-28   Researcher_6  CaptureSite_0          Ocean   \n",
       "2  2001_RE_0197      2001-11-01   Researcher_6  CaptureSite_0          Ocean   \n",
       "3  2002_RE_0031      2002-03-11  Researcher_32  CaptureSite_0          Ocean   \n",
       "4  2002_RE_0118      2002-08-08  Researcher_25  CaptureSite_0          Ocean   \n",
       "\n",
       "  CaptureMethod       Fisher                        LandingSite    Species  \\\n",
       "0           Net  Fisher_1072  LandingSite_CaptureSiteCategory_2  Species_6   \n",
       "1           Net   Fisher_520  LandingSite_CaptureSiteCategory_2  Species_6   \n",
       "2           Net  Fisher_1669  LandingSite_CaptureSiteCategory_2  Species_5   \n",
       "3           Net  Fisher_1798  LandingSite_CaptureSiteCategory_2  Species_6   \n",
       "4       Beached  Fisher_1918  LandingSite_CaptureSiteCategory_2  Species_5   \n",
       "\n",
       "            Tag_1 Tag_2 Lost_Tags T_Number  CCL_cm  CCW_cm  Weight_Kg  \\\n",
       "0         CC00147  None       NaN      NaN   64.70   62.60        NaN   \n",
       "1            W442  None       NaN      NaN   35.85   31.35        NaN   \n",
       "2          KE0376  None       NaN      NaN   51.80   49.20        NaN   \n",
       "3         CC00302  None       NaN      NaN   60.50   59.00        NaN   \n",
       "4  NotTagged_0113  None       NaN      NaN   34.70   33.00        NaN   \n",
       "\n",
       "       Sex                              TurtleCharacteristics    Status  \\\n",
       "0  Unknown                             algae at rear of shell  Released   \n",
       "1  Unknown  multiple b's on front flippers&  a lot of alga...  Released   \n",
       "2  Unknown                                              clean  Released   \n",
       "3  Unknown  1 b 3 CS+ calcerous algae at rear end of shell...  Released   \n",
       "4  Unknown  very lively+ right eye is hanging out + swolle...  Released   \n",
       "\n",
       "      ReleaseSite Date_TimeRelease  \n",
       "0  ReleaseSite_50         22/12/00  \n",
       "1  ReleaseSite_62         28/10/01  \n",
       "2  ReleaseSite_50         01/11/01  \n",
       "3  ReleaseSite_50         11/03/02  \n",
       "4  ReleaseSite_62         08/08/02  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LandingSite_CaptureSiteCategory_2',\n",
       "       'LandingSite_CaptureSiteCategory_0',\n",
       "       'LandingSite_CaptureSiteCategory_4',\n",
       "       'LandingSite_CaptureSiteCategory_1',\n",
       "       'LandingSite_CaptureSiteCategory_3'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data['LandingSite'].unique() #Unique  landing sites "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CaptureSite_1</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CaptureSite_10</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CaptureSite_11</td>\n",
       "      <td>CaptureSiteCategory_0</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CaptureSite_12</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CaptureSite_13</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CaptureSite_14</td>\n",
       "      <td>CaptureSiteCategory_4</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CaptureSite_15</td>\n",
       "      <td>CaptureSiteCategory_0</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CaptureSite_16</td>\n",
       "      <td>CaptureSiteCategory_4</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CaptureSite_17</td>\n",
       "      <td>CaptureSiteCategory_4</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CaptureSite_18</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CaptureSite_19</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CaptureSite_2</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CaptureSite_20</td>\n",
       "      <td>CaptureSiteCategory_0</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CaptureSite_21</td>\n",
       "      <td>CaptureSiteCategory_0</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CaptureSite_22</td>\n",
       "      <td>CaptureSiteCategory_4</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CaptureSite_23</td>\n",
       "      <td>CaptureSiteCategory_0</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CaptureSite_24</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CaptureSite_25</td>\n",
       "      <td>CaptureSiteCategory_0</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CaptureSite_26</td>\n",
       "      <td>CaptureSiteCategory_3</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CaptureSite_27</td>\n",
       "      <td>CaptureSiteCategory_0</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CaptureSite_28</td>\n",
       "      <td>CaptureSiteCategory_0</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CaptureSite_3</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CaptureSite_4</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CaptureSite_5</td>\n",
       "      <td>CaptureSiteCategory_4</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CaptureSite_6</td>\n",
       "      <td>CaptureSiteCategory_4</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CaptureSite_7</td>\n",
       "      <td>CaptureSiteCategory_4</td>\n",
       "      <td>Type_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CaptureSite_8</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CaptureSite    CaptureSiteCategory    Type\n",
       "0    CaptureSite_0  CaptureSiteCategory_2  Type_1\n",
       "1    CaptureSite_1  CaptureSiteCategory_2  Type_1\n",
       "2   CaptureSite_10  CaptureSiteCategory_2  Type_1\n",
       "3   CaptureSite_11  CaptureSiteCategory_0  Type_0\n",
       "4   CaptureSite_12  CaptureSiteCategory_2  Type_1\n",
       "5   CaptureSite_13  CaptureSiteCategory_2  Type_1\n",
       "6   CaptureSite_14  CaptureSiteCategory_4  Type_0\n",
       "7   CaptureSite_15  CaptureSiteCategory_0  Type_0\n",
       "8   CaptureSite_16  CaptureSiteCategory_4  Type_0\n",
       "9   CaptureSite_17  CaptureSiteCategory_4  Type_0\n",
       "10  CaptureSite_18  CaptureSiteCategory_1  Type_1\n",
       "11  CaptureSite_19  CaptureSiteCategory_2  Type_1\n",
       "12   CaptureSite_2  CaptureSiteCategory_2  Type_1\n",
       "13  CaptureSite_20  CaptureSiteCategory_0  Type_0\n",
       "14  CaptureSite_21  CaptureSiteCategory_0  Type_0\n",
       "15  CaptureSite_22  CaptureSiteCategory_4  Type_0\n",
       "16  CaptureSite_23  CaptureSiteCategory_0  Type_0\n",
       "17  CaptureSite_24  CaptureSiteCategory_2  Type_1\n",
       "18  CaptureSite_25  CaptureSiteCategory_0  Type_0\n",
       "19  CaptureSite_26  CaptureSiteCategory_3  Type_1\n",
       "20  CaptureSite_27  CaptureSiteCategory_0  Type_0\n",
       "21  CaptureSite_28  CaptureSiteCategory_0  Type_0\n",
       "22   CaptureSite_3  CaptureSiteCategory_2  Type_1\n",
       "23   CaptureSite_4  CaptureSiteCategory_2  Type_1\n",
       "24   CaptureSite_5  CaptureSiteCategory_4  Type_0\n",
       "25   CaptureSite_6  CaptureSiteCategory_4  Type_0\n",
       "26   CaptureSite_7  CaptureSiteCategory_4  Type_0\n",
       "27   CaptureSite_8  CaptureSiteCategory_1  Type_1\n",
       "28   CaptureSite_9  CaptureSiteCategory_1  Type_1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ = pd.read_csv('CaptureSite_category.csv')\n",
    "categ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29, 3) (18062, 21)\n"
     ]
    }
   ],
   "source": [
    "print(categ.shape,training_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-Preprocessing  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rescue_ID</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>Researcher</th>\n",
       "      <th>ForagingGround</th>\n",
       "      <th>CaptureMethod</th>\n",
       "      <th>Fisher</th>\n",
       "      <th>LandingSite</th>\n",
       "      <th>Species</th>\n",
       "      <th>Tag_1</th>\n",
       "      <th>Tag_2</th>\n",
       "      <th>Lost_Tags</th>\n",
       "      <th>T_Number</th>\n",
       "      <th>CCL_cm</th>\n",
       "      <th>CCW_cm</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Sex</th>\n",
       "      <th>TurtleCharacteristics</th>\n",
       "      <th>Status</th>\n",
       "      <th>ReleaseSite</th>\n",
       "      <th>Date_TimeRelease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2000_RE_0060</td>\n",
       "      <td>2000-12-22</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1072</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00147</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.70</td>\n",
       "      <td>62.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>algae at rear of shell</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>22/12/00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0187</td>\n",
       "      <td>2001-10-28</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_520</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>W442</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.85</td>\n",
       "      <td>31.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>multiple b's on front flippers&amp;  a lot of alga...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>28/10/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0197</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1669</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE0376</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.80</td>\n",
       "      <td>49.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>clean</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>01/11/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0031</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>Researcher_32</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1798</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00302</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.50</td>\n",
       "      <td>59.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1 b 3 CS+ calcerous algae at rear end of shell...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>11/03/02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0118</td>\n",
       "      <td>2002-08-08</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Beached</td>\n",
       "      <td>Fisher_1918</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0113</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.70</td>\n",
       "      <td>33.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>very lively+ right eye is hanging out + swolle...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>08/08/02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CaptureSite    CaptureSiteCategory    Type     Rescue_ID Date_TimeCaught  \\\n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2000_RE_0060      2000-12-22   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0187      2001-10-28   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0197      2001-11-01   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0031      2002-03-11   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0118      2002-08-08   \n",
       "\n",
       "      Researcher ForagingGround CaptureMethod       Fisher  \\\n",
       "0  Researcher_25          Ocean           Net  Fisher_1072   \n",
       "0   Researcher_6          Ocean           Net   Fisher_520   \n",
       "0   Researcher_6          Ocean           Net  Fisher_1669   \n",
       "0  Researcher_32          Ocean           Net  Fisher_1798   \n",
       "0  Researcher_25          Ocean       Beached  Fisher_1918   \n",
       "\n",
       "                         LandingSite    Species           Tag_1 Tag_2  \\\n",
       "0  LandingSite_CaptureSiteCategory_2  Species_6         CC00147  None   \n",
       "0  LandingSite_CaptureSiteCategory_2  Species_6            W442  None   \n",
       "0  LandingSite_CaptureSiteCategory_2  Species_5          KE0376  None   \n",
       "0  LandingSite_CaptureSiteCategory_2  Species_6         CC00302  None   \n",
       "0  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0113  None   \n",
       "\n",
       "  Lost_Tags T_Number  CCL_cm  CCW_cm  Weight_Kg      Sex  \\\n",
       "0       NaN      NaN   64.70   62.60        NaN  Unknown   \n",
       "0       NaN      NaN   35.85   31.35        NaN  Unknown   \n",
       "0       NaN      NaN   51.80   49.20        NaN  Unknown   \n",
       "0       NaN      NaN   60.50   59.00        NaN  Unknown   \n",
       "0       NaN      NaN   34.70   33.00        NaN  Unknown   \n",
       "\n",
       "                               TurtleCharacteristics    Status  \\\n",
       "0                             algae at rear of shell  Released   \n",
       "0  multiple b's on front flippers&  a lot of alga...  Released   \n",
       "0                                              clean  Released   \n",
       "0  1 b 3 CS+ calcerous algae at rear end of shell...  Released   \n",
       "0  very lively+ right eye is hanging out + swolle...  Released   \n",
       "\n",
       "      ReleaseSite Date_TimeRelease  \n",
       "0  ReleaseSite_50         22/12/00  \n",
       "0  ReleaseSite_62         28/10/01  \n",
       "0  ReleaseSite_50         01/11/01  \n",
       "0  ReleaseSite_50         11/03/02  \n",
       "0  ReleaseSite_62         08/08/02  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(categ, training_data, on='CaptureSite', right_index=True) #merge  categories and  training data\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rescue_ID</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>Researcher</th>\n",
       "      <th>ForagingGround</th>\n",
       "      <th>CaptureMethod</th>\n",
       "      <th>Fisher</th>\n",
       "      <th>LandingSite</th>\n",
       "      <th>Species</th>\n",
       "      <th>Tag_1</th>\n",
       "      <th>Tag_2</th>\n",
       "      <th>Lost_Tags</th>\n",
       "      <th>T_Number</th>\n",
       "      <th>CCL_cm</th>\n",
       "      <th>CCW_cm</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Sex</th>\n",
       "      <th>TurtleCharacteristics</th>\n",
       "      <th>Status</th>\n",
       "      <th>ReleaseSite</th>\n",
       "      <th>Date_TimeRelease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1511</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_569</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1828</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.13</td>\n",
       "      <td>50.57</td>\n",
       "      <td>21.09</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>White calcareous algae on carapace</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>18/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1514</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_125</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES0563</td>\n",
       "      <td>None</td>\n",
       "      <td>KES0416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.07</td>\n",
       "      <td>38.37</td>\n",
       "      <td>9.02</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Calcareous + green algae on carapace\\nBarnacle...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>18/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1532</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1343</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1833</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.20</td>\n",
       "      <td>52.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Clean turtle</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>24/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1533</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1551</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1831</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.90</td>\n",
       "      <td>48.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Green algae on carapace\\ntip of left supra mis...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>24/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1550</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1551</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES1432</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.60</td>\n",
       "      <td>31.20</td>\n",
       "      <td>4.29</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Thick pink patches of calcareous algae on cara...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_37</td>\n",
       "      <td>28/12/18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CaptureSite    CaptureSiteCategory    Type     Rescue_ID  \\\n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1511   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1514   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1532   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1533   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1550   \n",
       "\n",
       "   Date_TimeCaught     Researcher ForagingGround CaptureMethod       Fisher  \\\n",
       "28      2018-12-18  Researcher_30          Ocean           Net   Fisher_569   \n",
       "28      2018-12-18  Researcher_30          Ocean           Net   Fisher_125   \n",
       "28      2018-12-24  Researcher_30          Ocean           Net  Fisher_1343   \n",
       "28      2018-12-24  Researcher_30          Ocean           Net  Fisher_1551   \n",
       "28      2018-12-28  Researcher_30          Ocean           Net  Fisher_1551   \n",
       "\n",
       "                          LandingSite    Species    Tag_1 Tag_2 Lost_Tags  \\\n",
       "28  LandingSite_CaptureSiteCategory_1  Species_5  KES1828  None       NaN   \n",
       "28  LandingSite_CaptureSiteCategory_1  Species_6  KES0563  None   KES0416   \n",
       "28  LandingSite_CaptureSiteCategory_1  Species_5  KES1833  None       NaN   \n",
       "28  LandingSite_CaptureSiteCategory_1  Species_5  KES1831  None       NaN   \n",
       "28  LandingSite_CaptureSiteCategory_1  Species_6  KES1432  none       NaN   \n",
       "\n",
       "   T_Number  CCL_cm  CCW_cm  Weight_Kg      Sex  \\\n",
       "28      NaN   57.13   50.57      21.09  Unknown   \n",
       "28      NaN   42.07   38.37       9.02  Unknown   \n",
       "28      NaN   57.20   52.30        NaN  Unknown   \n",
       "28      NaN   51.90   48.50        NaN  Unknown   \n",
       "28      NaN   34.60   31.20       4.29  Unknown   \n",
       "\n",
       "                                TurtleCharacteristics    Status  \\\n",
       "28                 White calcareous algae on carapace  Released   \n",
       "28  Calcareous + green algae on carapace\\nBarnacle...  Released   \n",
       "28                                       Clean turtle  Released   \n",
       "28  Green algae on carapace\\ntip of left supra mis...  Released   \n",
       "28  Thick pink patches of calcareous algae on cara...  Released   \n",
       "\n",
       "       ReleaseSite Date_TimeRelease  \n",
       "28  ReleaseSite_68         18/12/18  \n",
       "28  ReleaseSite_68         18/12/18  \n",
       "28  ReleaseSite_68         24/12/18  \n",
       "28  ReleaseSite_68         24/12/18  \n",
       "28  ReleaseSite_37         28/12/18  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Creek    11408\n",
       "Ocean     6651\n",
       "creek        3\n",
       "Name: ForagingGround, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ForagingGround'].value_counts() ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['ForagingGround']=='creek', 'ForagingGround']='Creek' #Change creek to Creek. Hope you see the difference "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rescue_ID</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>Researcher</th>\n",
       "      <th>ForagingGround</th>\n",
       "      <th>CaptureMethod</th>\n",
       "      <th>Fisher</th>\n",
       "      <th>LandingSite</th>\n",
       "      <th>Species</th>\n",
       "      <th>Tag_1</th>\n",
       "      <th>Tag_2</th>\n",
       "      <th>Lost_Tags</th>\n",
       "      <th>T_Number</th>\n",
       "      <th>CCL_cm</th>\n",
       "      <th>CCW_cm</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Sex</th>\n",
       "      <th>TurtleCharacteristics</th>\n",
       "      <th>Status</th>\n",
       "      <th>ReleaseSite</th>\n",
       "      <th>Date_TimeRelease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2000_RE_0060</td>\n",
       "      <td>2000-12-22</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1072</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00147</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.70</td>\n",
       "      <td>62.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>algae at rear of shell</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>22/12/00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0187</td>\n",
       "      <td>2001-10-28</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_520</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>W442</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.85</td>\n",
       "      <td>31.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>multiple b's on front flippers&amp;  a lot of alga...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>28/10/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0197</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1669</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE0376</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.80</td>\n",
       "      <td>49.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>clean</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>01/11/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0031</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>Researcher_32</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1798</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00302</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.50</td>\n",
       "      <td>59.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1 b 3 CS+ calcerous algae at rear end of shell...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>11/03/02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0118</td>\n",
       "      <td>2002-08-08</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Beached</td>\n",
       "      <td>Fisher_1918</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0113</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.70</td>\n",
       "      <td>33.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>very lively+ right eye is hanging out + swolle...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>08/08/02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0119</td>\n",
       "      <td>2002-08-10</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Not_Recorded</td>\n",
       "      <td>Fisher_1918</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0114</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.20</td>\n",
       "      <td>30.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>large chip 6 LM</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_8</td>\n",
       "      <td>10/08/02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0214</td>\n",
       "      <td>2002-10-21</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_2013</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KA460</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>33.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>green and whitish-pink calcerous algae growth ...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>21/10/02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0215</td>\n",
       "      <td>2002-10-21</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1815</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KA442</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.30</td>\n",
       "      <td>37.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>calcerous algae growth-especially rear end and...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>21/10/02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0218</td>\n",
       "      <td>2002-10-22</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1815</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KA466</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.00</td>\n",
       "      <td>43.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>clean shell</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>22/10/02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2003_RE_0187</td>\n",
       "      <td>2003-06-09</td>\n",
       "      <td>Researcher_32</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1066</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE1184</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.30</td>\n",
       "      <td>43.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>10 LMS has a small chip&amp; right rear flipper is...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2003_RE_0322</td>\n",
       "      <td>2003-08-11</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1114</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE1289</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.30</td>\n",
       "      <td>43.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>b 10RM</td>\n",
       "      <td>2smb 3LLS&amp; 1sm b R supra</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2003_RE_0363</td>\n",
       "      <td>2003-09-02</td>\n",
       "      <td>Researcher_32</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Longline</td>\n",
       "      <td>Fisher_2065</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE1323</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.40</td>\n",
       "      <td>41.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>small scratches and saome flakings on the shel...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2003_RE_0411</td>\n",
       "      <td>2003-09-22</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_2013</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE1359</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.20</td>\n",
       "      <td>53.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>white calcerous algae on 5CS anmd supras&amp; inde...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2003_RE_0412</td>\n",
       "      <td>2003-09-22</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_2013</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE1309</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.20</td>\n",
       "      <td>39.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>pink calcerous algae 3+5 LLS</td>\n",
       "      <td>b scar 4RMS&amp; 2LLS&amp; 4LLS</td>\n",
       "      <td>many sm bs on rear shell and supras</td>\n",
       "      <td>Released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2003_RE_0413</td>\n",
       "      <td>2003-09-22</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_2013</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE1358</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.50</td>\n",
       "      <td>47.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>sm bs on shell</td>\n",
       "      <td>sm patches of calcerous algae on shell and su...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2003_RE_0424</td>\n",
       "      <td>2003-09-26</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_2013</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE1368</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.40</td>\n",
       "      <td>39.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>sm bs on neck and flipps</td>\n",
       "      <td>b join 3/4 LLS</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2004_RE_0300</td>\n",
       "      <td>2004-10-16</td>\n",
       "      <td>Researcher_10</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_838</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE1609</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.00</td>\n",
       "      <td>36.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Tip of LFF missing.</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2008_RE_0022</td>\n",
       "      <td>2008-01-23</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_257</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>3396</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.10</td>\n",
       "      <td>38.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6 CS&amp; 5 RLS. Brown algae on flippers&amp; neck and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2009_RE_0014</td>\n",
       "      <td>2009-01-06</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Beached</td>\n",
       "      <td>Fisher_2108</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>3889</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.60</td>\n",
       "      <td>43.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spaeared in the right side of the head . Brown...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2009_RE_0150</td>\n",
       "      <td>2009-02-09</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>By Hand</td>\n",
       "      <td>Fisher_325</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>3933</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.40</td>\n",
       "      <td>40.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A big spear gun injury in the left side of the...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2009_RE_0183</td>\n",
       "      <td>2009-02-17</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Beached</td>\n",
       "      <td>Fisher_1424</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>3946</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.70</td>\n",
       "      <td>24.50</td>\n",
       "      <td>1.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Left eye is infected. 7 CS&amp; 5 RLS. 2 huge remo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2009_RE_0390</td>\n",
       "      <td>2009-06-25</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1938</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>4048</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.00</td>\n",
       "      <td>28.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Perfect turtle.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2009_RE_0417</td>\n",
       "      <td>2009-07-19</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Collected Floater</td>\n",
       "      <td>Fisher_1045</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0206</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.20</td>\n",
       "      <td>39.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spear gun injury in the joint between nuchal a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2009_RE_0443</td>\n",
       "      <td>2009-08-04</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Beached</td>\n",
       "      <td>Fisher_1413</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0208</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.90</td>\n",
       "      <td>48.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fibropapillomas tumours in both eyes and eye l...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2009_RE_0452</td>\n",
       "      <td>2009-08-12</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Collected Floater</td>\n",
       "      <td>Fisher_1244</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0210</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.80</td>\n",
       "      <td>36.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Highly dehydrated and imaciated. Thick brown a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2009_RE_0520</td>\n",
       "      <td>2009-09-23</td>\n",
       "      <td>Researcher_32</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1564</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>4123</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Greenish algea cover on the rear of the shell....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2010_RE_0360</td>\n",
       "      <td>2010-04-23</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Collected Floater</td>\n",
       "      <td>Fisher_696</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0412</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.90</td>\n",
       "      <td>58.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Heavily infested with fibro virus on neck&amp; sho...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2010_RE_0754</td>\n",
       "      <td>2010-09-22</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_959</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KE5770</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.40</td>\n",
       "      <td>33.90</td>\n",
       "      <td>5.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thick green algae toward rear shells&amp; flaking ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2010_RE_0781</td>\n",
       "      <td>2010-10-02</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Collected Floater</td>\n",
       "      <td>Fisher_1705</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0418</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.50</td>\n",
       "      <td>39.60</td>\n",
       "      <td>6.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thick green algae covering the carapace&amp; shell...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2010_RE_0869</td>\n",
       "      <td>2010-10-28</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1035</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE5838</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.00</td>\n",
       "      <td>32.80</td>\n",
       "      <td>4.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3rd LmS is slightly deformed &amp; V-notch in the ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1369</td>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Jarife</td>\n",
       "      <td>Fisher_1550</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1783</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.27</td>\n",
       "      <td>51.13</td>\n",
       "      <td>18.97</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Notch on the 18th RMS\\nThin green algae on car...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>28/11/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1391</td>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_125</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES0472</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.47</td>\n",
       "      <td>38.67</td>\n",
       "      <td>9.50</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Tiny barnacles on shoulders, flippers and neck...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>30/11/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1392</td>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1550</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1791</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.93</td>\n",
       "      <td>47.37</td>\n",
       "      <td>17.79</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Barnacles on carapace \\nTiny barnacles on rear...</td>\n",
       "      <td>Admitted</td>\n",
       "      <td>ReleaseSite_0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1403</td>\n",
       "      <td>2018-12-02</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1343</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES1787</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>2.97</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Patches of white calcareous algae on carapace ...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>02/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1420</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Jarife</td>\n",
       "      <td>Fisher_1550</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1795</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.67</td>\n",
       "      <td>41.07</td>\n",
       "      <td>10.99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Patches of calcareous algae\\nPatches of barnac...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>04/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1421</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Jarife</td>\n",
       "      <td>Fisher_1550</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1796</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.07</td>\n",
       "      <td>45.53</td>\n",
       "      <td>12.99</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Small patch of fibro on eye\\nPatches of algae ...</td>\n",
       "      <td>Admitted</td>\n",
       "      <td>ReleaseSite_0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1422</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Jarife</td>\n",
       "      <td>Fisher_1550</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1797</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.53</td>\n",
       "      <td>62.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Fibro on shoulders, neck and on both eyes\\nPat...</td>\n",
       "      <td>Admitted</td>\n",
       "      <td>ReleaseSite_0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1434</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_915</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1803</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.57</td>\n",
       "      <td>39.87</td>\n",
       "      <td>9.33</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Calcareous algae and tiny barnacles on carapac...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>07/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1435</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_915</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES1695</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.50</td>\n",
       "      <td>31.50</td>\n",
       "      <td>4.70</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Calcareous algae on carapace\\nclean plastron</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>07/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1438</td>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1115</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KE6671</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.50</td>\n",
       "      <td>51.10</td>\n",
       "      <td>19.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Numerus BS around neck and shoulder, pink patc...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>09/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1446</td>\n",
       "      <td>2018-12-11</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1551</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES0987</td>\n",
       "      <td>None</td>\n",
       "      <td>KE8122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.47</td>\n",
       "      <td>49.10</td>\n",
       "      <td>18.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Wide gap between supras\\nGreen algae on carapace</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>11/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1457</td>\n",
       "      <td>2018-12-11</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1849</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1752</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.67</td>\n",
       "      <td>64.60</td>\n",
       "      <td>35.42</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Flaking carapace</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>11/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1458</td>\n",
       "      <td>2018-12-12</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1472</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES1010</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.20</td>\n",
       "      <td>34.23</td>\n",
       "      <td>6.30</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Green algae on carapace+plastron\\nFew tiny BS ...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>12/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1460</td>\n",
       "      <td>2018-12-12</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_915</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES1096</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.20</td>\n",
       "      <td>33.50</td>\n",
       "      <td>5.96</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Green algae on carapace and plastron\\nA few ti...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>12/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1462</td>\n",
       "      <td>2018-12-13</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1551</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES0296</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.57</td>\n",
       "      <td>44.47</td>\n",
       "      <td>11.88</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Chip on the 5th CS\\nGreen algae on carapace an...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>13/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1464</td>\n",
       "      <td>2018-12-13</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_92</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1788</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.03</td>\n",
       "      <td>42.81</td>\n",
       "      <td>11.32</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Notches on both supra\\nTag rip on LRF</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>13/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1465</td>\n",
       "      <td>2018-12-13</td>\n",
       "      <td>Researcher_20</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Jarife</td>\n",
       "      <td>Fisher_1550</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KEL0226</td>\n",
       "      <td>KEL0227</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.49</td>\n",
       "      <td>67.03</td>\n",
       "      <td>49.50</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Patch of calcareous algae on carapace \\n12 LMS</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>13/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1477</td>\n",
       "      <td>2018-12-14</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1550</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE8411</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>28.44</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>RRF half missing</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>14/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1492</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1551</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES1010</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.13</td>\n",
       "      <td>34.63</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>White patches of calcareous algae on joints of...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>16/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1493</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1364</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1825</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.33</td>\n",
       "      <td>41.00</td>\n",
       "      <td>9.52</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Heavy flaking on the scutes \\nA few tiny barna...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>16/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1496</td>\n",
       "      <td>2018-12-16</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1343</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KE8098</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.90</td>\n",
       "      <td>49.40</td>\n",
       "      <td>18.70</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Green algae on carapace\\nMissing tip of RRF\\nS...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>16/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1497</td>\n",
       "      <td>2018-12-16</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1472</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES1492</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.00</td>\n",
       "      <td>34.03</td>\n",
       "      <td>5.55</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Light pinkish calcareous algae on plastron\\nLi...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>16/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1507</td>\n",
       "      <td>2018-12-17</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1216</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KE8681</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.47</td>\n",
       "      <td>42.57</td>\n",
       "      <td>12.13</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>algae on carapace \\n12 LMS</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>17/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1508</td>\n",
       "      <td>2018-12-17</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1216</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES1306</td>\n",
       "      <td>none</td>\n",
       "      <td>KE6491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.93</td>\n",
       "      <td>59.97</td>\n",
       "      <td>31.55</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Green algae on carapace \\nSmall barnacles on s...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>17/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1510</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1550</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1796</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.03</td>\n",
       "      <td>45.87</td>\n",
       "      <td>13.07</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Tip of RFF missing\\nBruising on front flippers</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>18/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1511</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_569</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1828</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.13</td>\n",
       "      <td>50.57</td>\n",
       "      <td>21.09</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>White calcareous algae on carapace</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>18/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1514</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_125</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES0563</td>\n",
       "      <td>None</td>\n",
       "      <td>KES0416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.07</td>\n",
       "      <td>38.37</td>\n",
       "      <td>9.02</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Calcareous + green algae on carapace\\nBarnacle...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>18/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1532</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1343</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1833</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.20</td>\n",
       "      <td>52.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Clean turtle</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>24/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1533</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1551</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1831</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.90</td>\n",
       "      <td>48.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Green algae on carapace\\ntip of left supra mis...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>24/12/18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1550</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1551</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES1432</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.60</td>\n",
       "      <td>31.20</td>\n",
       "      <td>4.29</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Thick pink patches of calcareous algae on cara...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_37</td>\n",
       "      <td>28/12/18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18062 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CaptureSite    CaptureSiteCategory    Type     Rescue_ID  \\\n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2000_RE_0060   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0187   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0197   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0031   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0118   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0119   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0214   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0215   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0218   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2003_RE_0187   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2003_RE_0322   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2003_RE_0363   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2003_RE_0411   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2003_RE_0412   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2003_RE_0413   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2003_RE_0424   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2004_RE_0300   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2008_RE_0022   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2009_RE_0014   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2009_RE_0150   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2009_RE_0183   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2009_RE_0390   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2009_RE_0417   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2009_RE_0443   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2009_RE_0452   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2009_RE_0520   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2010_RE_0360   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2010_RE_0754   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2010_RE_0781   \n",
       "0   CaptureSite_0  CaptureSiteCategory_2  Type_1  2010_RE_0869   \n",
       "..            ...                    ...     ...           ...   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1369   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1391   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1392   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1403   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1420   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1421   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1422   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1434   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1435   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1438   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1446   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1457   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1458   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1460   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1462   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1464   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1465   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1477   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1492   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1493   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1496   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1497   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1507   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1508   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1510   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1511   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1514   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1532   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1533   \n",
       "28  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1550   \n",
       "\n",
       "   Date_TimeCaught     Researcher ForagingGround      CaptureMethod  \\\n",
       "0       2000-12-22  Researcher_25          Ocean                Net   \n",
       "0       2001-10-28   Researcher_6          Ocean                Net   \n",
       "0       2001-11-01   Researcher_6          Ocean                Net   \n",
       "0       2002-03-11  Researcher_32          Ocean                Net   \n",
       "0       2002-08-08  Researcher_25          Ocean            Beached   \n",
       "0       2002-08-10  Researcher_25          Ocean       Not_Recorded   \n",
       "0       2002-10-21  Researcher_25          Ocean                Net   \n",
       "0       2002-10-21  Researcher_25          Ocean                Net   \n",
       "0       2002-10-22  Researcher_30          Ocean                Net   \n",
       "0       2003-06-09  Researcher_32          Ocean                Net   \n",
       "0       2003-08-11  Researcher_25          Ocean                Net   \n",
       "0       2003-09-02  Researcher_32          Ocean           Longline   \n",
       "0       2003-09-22  Researcher_30          Ocean                Net   \n",
       "0       2003-09-22  Researcher_30          Ocean                Net   \n",
       "0       2003-09-22  Researcher_30          Ocean                Net   \n",
       "0       2003-09-26  Researcher_30          Ocean                Net   \n",
       "0       2004-10-16  Researcher_10          Ocean                Net   \n",
       "0       2008-01-23  Researcher_30          Ocean                Net   \n",
       "0       2009-01-06  Researcher_30          Ocean            Beached   \n",
       "0       2009-02-09  Researcher_30          Ocean            By Hand   \n",
       "0       2009-02-17  Researcher_30          Ocean            Beached   \n",
       "0       2009-06-25  Researcher_30          Ocean                Net   \n",
       "0       2009-07-19  Researcher_30          Ocean  Collected Floater   \n",
       "0       2009-08-04  Researcher_20          Ocean            Beached   \n",
       "0       2009-08-12  Researcher_30          Ocean  Collected Floater   \n",
       "0       2009-09-23  Researcher_32          Ocean                Net   \n",
       "0       2010-04-23  Researcher_30          Ocean  Collected Floater   \n",
       "0       2010-09-22  Researcher_20          Ocean                Net   \n",
       "0       2010-10-02  Researcher_30          Ocean  Collected Floater   \n",
       "0       2010-10-28  Researcher_20          Ocean                Net   \n",
       "..             ...            ...            ...                ...   \n",
       "28      2018-11-28  Researcher_20          Ocean             Jarife   \n",
       "28      2018-11-30  Researcher_30          Ocean                Net   \n",
       "28      2018-11-30  Researcher_30          Ocean                Net   \n",
       "28      2018-12-02  Researcher_20          Ocean                Net   \n",
       "28      2018-12-04  Researcher_20          Ocean             Jarife   \n",
       "28      2018-12-04  Researcher_20          Ocean             Jarife   \n",
       "28      2018-12-04  Researcher_20          Ocean             Jarife   \n",
       "28      2018-12-07  Researcher_20          Ocean                Net   \n",
       "28      2018-12-07  Researcher_20          Ocean                Net   \n",
       "28      2018-12-09  Researcher_20          Ocean                Net   \n",
       "28      2018-12-11  Researcher_20          Ocean                Net   \n",
       "28      2018-12-11  Researcher_20          Ocean                Net   \n",
       "28      2018-12-12  Researcher_20          Ocean                Net   \n",
       "28      2018-12-12  Researcher_20          Ocean                Net   \n",
       "28      2018-12-13  Researcher_20          Ocean                Net   \n",
       "28      2018-12-13  Researcher_20          Ocean                Net   \n",
       "28      2018-12-13  Researcher_20          Ocean             Jarife   \n",
       "28      2018-12-14  Researcher_30          Ocean                Net   \n",
       "28      2018-12-15  Researcher_30          Ocean                Net   \n",
       "28      2018-12-15  Researcher_30          Ocean                Net   \n",
       "28      2018-12-16  Researcher_30          Ocean                Net   \n",
       "28      2018-12-16  Researcher_30          Ocean                Net   \n",
       "28      2018-12-17  Researcher_30          Ocean                Net   \n",
       "28      2018-12-17  Researcher_30          Ocean                Net   \n",
       "28      2018-12-18  Researcher_30          Ocean                Net   \n",
       "28      2018-12-18  Researcher_30          Ocean                Net   \n",
       "28      2018-12-18  Researcher_30          Ocean                Net   \n",
       "28      2018-12-24  Researcher_30          Ocean                Net   \n",
       "28      2018-12-24  Researcher_30          Ocean                Net   \n",
       "28      2018-12-28  Researcher_30          Ocean                Net   \n",
       "\n",
       "         Fisher                        LandingSite    Species           Tag_1  \\\n",
       "0   Fisher_1072  LandingSite_CaptureSiteCategory_2  Species_6         CC00147   \n",
       "0    Fisher_520  LandingSite_CaptureSiteCategory_2  Species_6            W442   \n",
       "0   Fisher_1669  LandingSite_CaptureSiteCategory_2  Species_5          KE0376   \n",
       "0   Fisher_1798  LandingSite_CaptureSiteCategory_2  Species_6         CC00302   \n",
       "0   Fisher_1918  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0113   \n",
       "0   Fisher_1918  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0114   \n",
       "0   Fisher_2013  LandingSite_CaptureSiteCategory_2  Species_6           KA460   \n",
       "0   Fisher_1815  LandingSite_CaptureSiteCategory_2  Species_6           KA442   \n",
       "0   Fisher_1815  LandingSite_CaptureSiteCategory_2  Species_5           KA466   \n",
       "0   Fisher_1066  LandingSite_CaptureSiteCategory_2  Species_5          KE1184   \n",
       "0   Fisher_1114  LandingSite_CaptureSiteCategory_2  Species_5          KE1289   \n",
       "0   Fisher_2065  LandingSite_CaptureSiteCategory_2  Species_5          KE1323   \n",
       "0   Fisher_2013  LandingSite_CaptureSiteCategory_2  Species_5          KE1359   \n",
       "0   Fisher_2013  LandingSite_CaptureSiteCategory_2  Species_5          KE1309   \n",
       "0   Fisher_2013  LandingSite_CaptureSiteCategory_2  Species_5          KE1358   \n",
       "0   Fisher_2013  LandingSite_CaptureSiteCategory_2  Species_5          KE1368   \n",
       "0    Fisher_838  LandingSite_CaptureSiteCategory_2  Species_5          KE1609   \n",
       "0    Fisher_257  LandingSite_CaptureSiteCategory_2  Species_5            3396   \n",
       "0   Fisher_2108  LandingSite_CaptureSiteCategory_2  Species_5            3889   \n",
       "0    Fisher_325  LandingSite_CaptureSiteCategory_2  Species_5            3933   \n",
       "0   Fisher_1424  LandingSite_CaptureSiteCategory_2  Species_6            3946   \n",
       "0   Fisher_1938  LandingSite_CaptureSiteCategory_2  Species_6            4048   \n",
       "0   Fisher_1045  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0206   \n",
       "0   Fisher_1413  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0208   \n",
       "0   Fisher_1244  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0210   \n",
       "0   Fisher_1564  LandingSite_CaptureSiteCategory_2  Species_6            4123   \n",
       "0    Fisher_696  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0412   \n",
       "0    Fisher_959  LandingSite_CaptureSiteCategory_2  Species_6          KE5770   \n",
       "0   Fisher_1705  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0418   \n",
       "0   Fisher_1035  LandingSite_CaptureSiteCategory_2  Species_5          KE5838   \n",
       "..          ...                                ...        ...             ...   \n",
       "28  Fisher_1550  LandingSite_CaptureSiteCategory_1  Species_5         KES1783   \n",
       "28   Fisher_125  LandingSite_CaptureSiteCategory_1  Species_6         KES0472   \n",
       "28  Fisher_1550  LandingSite_CaptureSiteCategory_1  Species_5         KES1791   \n",
       "28  Fisher_1343  LandingSite_CaptureSiteCategory_1  Species_6         KES1787   \n",
       "28  Fisher_1550  LandingSite_CaptureSiteCategory_1  Species_5         KES1795   \n",
       "28  Fisher_1550  LandingSite_CaptureSiteCategory_1  Species_5         KES1796   \n",
       "28  Fisher_1550  LandingSite_CaptureSiteCategory_1  Species_5         KES1797   \n",
       "28   Fisher_915  LandingSite_CaptureSiteCategory_1  Species_5         KES1803   \n",
       "28   Fisher_915  LandingSite_CaptureSiteCategory_1  Species_6         KES1695   \n",
       "28  Fisher_1115  LandingSite_CaptureSiteCategory_1  Species_6          KE6671   \n",
       "28  Fisher_1551  LandingSite_CaptureSiteCategory_1  Species_6         KES0987   \n",
       "28  Fisher_1849  LandingSite_CaptureSiteCategory_1  Species_5         KES1752   \n",
       "28  Fisher_1472  LandingSite_CaptureSiteCategory_1  Species_6         KES1010   \n",
       "28   Fisher_915  LandingSite_CaptureSiteCategory_1  Species_6         KES1096   \n",
       "28  Fisher_1551  LandingSite_CaptureSiteCategory_1  Species_6         KES0296   \n",
       "28    Fisher_92  LandingSite_CaptureSiteCategory_1  Species_5         KES1788   \n",
       "28  Fisher_1550  LandingSite_CaptureSiteCategory_1  Species_5         KEL0226   \n",
       "28  Fisher_1550  LandingSite_CaptureSiteCategory_1  Species_5          KE8411   \n",
       "28  Fisher_1551  LandingSite_CaptureSiteCategory_1  Species_6         KES1010   \n",
       "28  Fisher_1364  LandingSite_CaptureSiteCategory_1  Species_5         KES1825   \n",
       "28  Fisher_1343  LandingSite_CaptureSiteCategory_1  Species_6          KE8098   \n",
       "28  Fisher_1472  LandingSite_CaptureSiteCategory_1  Species_6         KES1492   \n",
       "28  Fisher_1216  LandingSite_CaptureSiteCategory_1  Species_6          KE8681   \n",
       "28  Fisher_1216  LandingSite_CaptureSiteCategory_1  Species_6         KES1306   \n",
       "28  Fisher_1550  LandingSite_CaptureSiteCategory_1  Species_5         KES1796   \n",
       "28   Fisher_569  LandingSite_CaptureSiteCategory_1  Species_5         KES1828   \n",
       "28   Fisher_125  LandingSite_CaptureSiteCategory_1  Species_6         KES0563   \n",
       "28  Fisher_1343  LandingSite_CaptureSiteCategory_1  Species_5         KES1833   \n",
       "28  Fisher_1551  LandingSite_CaptureSiteCategory_1  Species_5         KES1831   \n",
       "28  Fisher_1551  LandingSite_CaptureSiteCategory_1  Species_6         KES1432   \n",
       "\n",
       "      Tag_2 Lost_Tags T_Number  CCL_cm  CCW_cm  Weight_Kg      Sex  \\\n",
       "0      None       NaN      NaN   64.70   62.60        NaN  Unknown   \n",
       "0      None       NaN      NaN   35.85   31.35        NaN  Unknown   \n",
       "0      None       NaN      NaN   51.80   49.20        NaN  Unknown   \n",
       "0      None       NaN      NaN   60.50   59.00        NaN  Unknown   \n",
       "0      None       NaN      NaN   34.70   33.00        NaN  Unknown   \n",
       "0      None       NaN      NaN   33.20   30.70        NaN  Unknown   \n",
       "0      None       NaN      NaN   37.40   33.50        NaN  Unknown   \n",
       "0      None       NaN      NaN   40.30   37.20        NaN  Unknown   \n",
       "0      None       NaN      NaN   44.00   43.20        NaN  Unknown   \n",
       "0      None       NaN      NaN   48.30   43.30        NaN  Unknown   \n",
       "0      None       NaN      NaN   43.30   43.60        NaN  Unknown   \n",
       "0      None       NaN      NaN   43.40   41.70        NaN  Unknown   \n",
       "0      None       NaN      NaN   58.20   53.30        NaN  Unknown   \n",
       "0      None       NaN      NaN   40.20   39.30        NaN  Unknown   \n",
       "0      None       NaN      NaN   52.50   47.50        NaN  Unknown   \n",
       "0      None       NaN      NaN   41.40   39.30        NaN  Unknown   \n",
       "0      None       NaN      NaN   37.00   36.10        NaN  Unknown   \n",
       "0      None       NaN      NaN   38.10   38.20        NaN      NaN   \n",
       "0      None       NaN      NaN   45.60   43.70        NaN      NaN   \n",
       "0      None       NaN      NaN   43.40   40.00        NaN      NaN   \n",
       "0      None       NaN      NaN   24.70   24.50       1.70      NaN   \n",
       "0      None       NaN      NaN   31.00   28.90        NaN      NaN   \n",
       "0      None       NaN      NaN   41.20   39.50        NaN      NaN   \n",
       "0      None       NaN      NaN   51.90   48.40        NaN      NaN   \n",
       "0      None       NaN      NaN   38.80   36.70        NaN      NaN   \n",
       "0      None       NaN      NaN   34.00   32.00        NaN      NaN   \n",
       "0      None       NaN      NaN   61.90   58.90        NaN      NaN   \n",
       "0      None       NaN      NaN   34.40   33.90       5.50      NaN   \n",
       "0      None       NaN      NaN   39.50   39.60       6.50      NaN   \n",
       "0      None       NaN      NaN   33.00   32.80       4.50      NaN   \n",
       "..      ...       ...      ...     ...     ...        ...      ...   \n",
       "28     None       NaN      NaN   55.27   51.13      18.97  Unknown   \n",
       "28     none       NaN      NaN   42.47   38.67       9.50  Unknown   \n",
       "28     none       NaN      NaN   52.93   47.37      17.79  Unknown   \n",
       "28     none       NaN      NaN   30.00   28.00       2.97  Unknown   \n",
       "28     none       NaN      NaN   44.67   41.07      10.99  Unknown   \n",
       "28     none       NaN      NaN   47.07   45.53      12.99  Unknown   \n",
       "28     none       NaN      NaN   70.53   62.53        NaN  Unknown   \n",
       "28     none       NaN      NaN   42.57   39.87       9.33  Unknown   \n",
       "28     none       NaN      NaN   33.50   31.50       4.70  Unknown   \n",
       "28     None       NaN      NaN   55.50   51.10      19.00  Unknown   \n",
       "28     None    KE8122      NaN   53.47   49.10      18.00  Unknown   \n",
       "28     None       NaN      NaN   68.67   64.60      35.42  Unknown   \n",
       "28     None       NaN      NaN   38.20   34.23       6.30  Unknown   \n",
       "28     none       NaN      NaN   38.20   33.50       5.96  Unknown   \n",
       "28     none       NaN      NaN   48.57   44.47      11.88  Unknown   \n",
       "28     none       NaN      NaN   46.03   42.81      11.32  Unknown   \n",
       "28  KEL0227       NaN      NaN   73.49   67.03      49.50  Unknown   \n",
       "28     none       NaN      NaN   65.00   57.00      28.44  Unknown   \n",
       "28     none       NaN      NaN   37.13   34.63       5.92  Unknown   \n",
       "28     none       NaN      NaN   43.33   41.00       9.52  Unknown   \n",
       "28     none       NaN      NaN   52.90   49.40      18.70  Unknown   \n",
       "28     none       NaN      NaN   37.00   34.03       5.55  Unknown   \n",
       "28     none       NaN      NaN   46.47   42.57      12.13  Unknown   \n",
       "28     none    KE6491      NaN   64.93   59.97      31.55  Unknown   \n",
       "28     None       NaN      NaN   48.03   45.87      13.07  Unknown   \n",
       "28     None       NaN      NaN   57.13   50.57      21.09  Unknown   \n",
       "28     None   KES0416      NaN   42.07   38.37       9.02  Unknown   \n",
       "28     None       NaN      NaN   57.20   52.30        NaN  Unknown   \n",
       "28     None       NaN      NaN   51.90   48.50        NaN  Unknown   \n",
       "28     none       NaN      NaN   34.60   31.20       4.29  Unknown   \n",
       "\n",
       "                                TurtleCharacteristics  \\\n",
       "0                              algae at rear of shell   \n",
       "0   multiple b's on front flippers&  a lot of alga...   \n",
       "0                                               clean   \n",
       "0   1 b 3 CS+ calcerous algae at rear end of shell...   \n",
       "0   very lively+ right eye is hanging out + swolle...   \n",
       "0                                     large chip 6 LM   \n",
       "0   green and whitish-pink calcerous algae growth ...   \n",
       "0   calcerous algae growth-especially rear end and...   \n",
       "0                                        clean shell    \n",
       "0   10 LMS has a small chip& right rear flipper is...   \n",
       "0                                              b 10RM   \n",
       "0   small scratches and saome flakings on the shel...   \n",
       "0   white calcerous algae on 5CS anmd supras& inde...   \n",
       "0                        pink calcerous algae 3+5 LLS   \n",
       "0                                      sm bs on shell   \n",
       "0                            sm bs on neck and flipps   \n",
       "0                                 Tip of LFF missing.   \n",
       "0   6 CS& 5 RLS. Brown algae on flippers& neck and...   \n",
       "0   Spaeared in the right side of the head . Brown...   \n",
       "0   A big spear gun injury in the left side of the...   \n",
       "0   Left eye is infected. 7 CS& 5 RLS. 2 huge remo...   \n",
       "0                                     Perfect turtle.   \n",
       "0   Spear gun injury in the joint between nuchal a...   \n",
       "0   Fibropapillomas tumours in both eyes and eye l...   \n",
       "0   Highly dehydrated and imaciated. Thick brown a...   \n",
       "0   Greenish algea cover on the rear of the shell....   \n",
       "0   Heavily infested with fibro virus on neck& sho...   \n",
       "0   Thick green algae toward rear shells& flaking ...   \n",
       "0   Thick green algae covering the carapace& shell...   \n",
       "0   3rd LmS is slightly deformed & V-notch in the ...   \n",
       "..                                                ...   \n",
       "28  Notch on the 18th RMS\\nThin green algae on car...   \n",
       "28  Tiny barnacles on shoulders, flippers and neck...   \n",
       "28  Barnacles on carapace \\nTiny barnacles on rear...   \n",
       "28  Patches of white calcareous algae on carapace ...   \n",
       "28  Patches of calcareous algae\\nPatches of barnac...   \n",
       "28  Small patch of fibro on eye\\nPatches of algae ...   \n",
       "28  Fibro on shoulders, neck and on both eyes\\nPat...   \n",
       "28  Calcareous algae and tiny barnacles on carapac...   \n",
       "28       Calcareous algae on carapace\\nclean plastron   \n",
       "28  Numerus BS around neck and shoulder, pink patc...   \n",
       "28   Wide gap between supras\\nGreen algae on carapace   \n",
       "28                                   Flaking carapace   \n",
       "28  Green algae on carapace+plastron\\nFew tiny BS ...   \n",
       "28  Green algae on carapace and plastron\\nA few ti...   \n",
       "28  Chip on the 5th CS\\nGreen algae on carapace an...   \n",
       "28              Notches on both supra\\nTag rip on LRF   \n",
       "28     Patch of calcareous algae on carapace \\n12 LMS   \n",
       "28                                   RRF half missing   \n",
       "28  White patches of calcareous algae on joints of...   \n",
       "28  Heavy flaking on the scutes \\nA few tiny barna...   \n",
       "28  Green algae on carapace\\nMissing tip of RRF\\nS...   \n",
       "28  Light pinkish calcareous algae on plastron\\nLi...   \n",
       "28                         algae on carapace \\n12 LMS   \n",
       "28  Green algae on carapace \\nSmall barnacles on s...   \n",
       "28     Tip of RFF missing\\nBruising on front flippers   \n",
       "28                 White calcareous algae on carapace   \n",
       "28  Calcareous + green algae on carapace\\nBarnacle...   \n",
       "28                                       Clean turtle   \n",
       "28  Green algae on carapace\\ntip of left supra mis...   \n",
       "28  Thick pink patches of calcareous algae on cara...   \n",
       "\n",
       "                                               Status  \\\n",
       "0                                            Released   \n",
       "0                                            Released   \n",
       "0                                            Released   \n",
       "0                                            Released   \n",
       "0                                            Released   \n",
       "0                                            Released   \n",
       "0                                            Released   \n",
       "0                                            Released   \n",
       "0                                            Released   \n",
       "0                                            Released   \n",
       "0                            2smb 3LLS& 1sm b R supra   \n",
       "0                                            Released   \n",
       "0                                            Released   \n",
       "0                             b scar 4RMS& 2LLS& 4LLS   \n",
       "0    sm patches of calcerous algae on shell and su...   \n",
       "0                                      b join 3/4 LLS   \n",
       "0                                            Released   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "0                                                 NaN   \n",
       "..                                                ...   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Admitted   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Admitted   \n",
       "28                                           Admitted   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "28                                           Released   \n",
       "\n",
       "                              ReleaseSite Date_TimeRelease  \n",
       "0                          ReleaseSite_50         22/12/00  \n",
       "0                          ReleaseSite_62         28/10/01  \n",
       "0                          ReleaseSite_50         01/11/01  \n",
       "0                          ReleaseSite_50         11/03/02  \n",
       "0                          ReleaseSite_62         08/08/02  \n",
       "0                           ReleaseSite_8         10/08/02  \n",
       "0                          ReleaseSite_62         21/10/02  \n",
       "0                          ReleaseSite_50         21/10/02  \n",
       "0                          ReleaseSite_50         22/10/02  \n",
       "0                          ReleaseSite_62              NaN  \n",
       "0                                Released   ReleaseSite_50  \n",
       "0                          ReleaseSite_62              NaN  \n",
       "0                          ReleaseSite_50              NaN  \n",
       "0     many sm bs on rear shell and supras         Released  \n",
       "0                                Released   ReleaseSite_50  \n",
       "0                                Released   ReleaseSite_62  \n",
       "0                          ReleaseSite_62              NaN  \n",
       "0                          ReleaseSite_62              NaN  \n",
       "0                          ReleaseSite_19              NaN  \n",
       "0                          ReleaseSite_19              NaN  \n",
       "0                          ReleaseSite_19              NaN  \n",
       "0                          ReleaseSite_18              NaN  \n",
       "0                          ReleaseSite_19              NaN  \n",
       "0                          ReleaseSite_19              NaN  \n",
       "0                          ReleaseSite_19              NaN  \n",
       "0                          ReleaseSite_62              NaN  \n",
       "0                          ReleaseSite_19              NaN  \n",
       "0                          ReleaseSite_62              NaN  \n",
       "0                          ReleaseSite_19              NaN  \n",
       "0                          ReleaseSite_62              NaN  \n",
       "..                                    ...              ...  \n",
       "28                         ReleaseSite_62         28/11/18  \n",
       "28                         ReleaseSite_62         30/11/18  \n",
       "28                          ReleaseSite_0              NaN  \n",
       "28                         ReleaseSite_68         02/12/18  \n",
       "28                         ReleaseSite_62         04/12/18  \n",
       "28                          ReleaseSite_0              NaN  \n",
       "28                          ReleaseSite_0              NaN  \n",
       "28                         ReleaseSite_68         07/12/18  \n",
       "28                         ReleaseSite_68         07/12/18  \n",
       "28                         ReleaseSite_68         09/12/18  \n",
       "28                         ReleaseSite_62         11/12/18  \n",
       "28                         ReleaseSite_62         11/12/18  \n",
       "28                         ReleaseSite_68         12/12/18  \n",
       "28                         ReleaseSite_68         12/12/18  \n",
       "28                         ReleaseSite_68         13/12/18  \n",
       "28                         ReleaseSite_62         13/12/18  \n",
       "28                         ReleaseSite_68         13/12/18  \n",
       "28                         ReleaseSite_62         14/12/18  \n",
       "28                         ReleaseSite_68         16/12/18  \n",
       "28                         ReleaseSite_68         16/12/18  \n",
       "28                         ReleaseSite_68         16/12/18  \n",
       "28                         ReleaseSite_68         16/12/18  \n",
       "28                         ReleaseSite_68         17/12/18  \n",
       "28                         ReleaseSite_68         17/12/18  \n",
       "28                         ReleaseSite_68         18/12/18  \n",
       "28                         ReleaseSite_68         18/12/18  \n",
       "28                         ReleaseSite_68         18/12/18  \n",
       "28                         ReleaseSite_68         24/12/18  \n",
       "28                         ReleaseSite_68         24/12/18  \n",
       "28                         ReleaseSite_37         28/12/18  \n",
       "\n",
       "[18062 rows x 23 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16343"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TurtleCharacteristics'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Date variables  manipulations  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Breakdown Date_TimeCaught to months, weeks etc \n",
    "df['Date_TimeCaught'] = pd.to_datetime(df['Date_TimeCaught'], format='%Y-%m-%d')\n",
    "df['year_caught'] = df['Date_TimeCaught'].dt.year\n",
    "df['month_caught'] = df['Date_TimeCaught'].dt.month\n",
    "df['week_of_year_caught'] = df['Date_TimeCaught'].dt.week\n",
    "df['day_caught'] = df['Date_TimeCaught'].dt.day\n",
    "df['day_of_week_caught'] = pd.DatetimeIndex(df['Date_TimeCaught']).dayofweek\n",
    "df[\"year_woy\"]=df.year_caught*100+df.week_of_year_caught\n",
    "df['day_of_year']= pd.DatetimeIndex(df['Date_TimeCaught']).dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rescue_ID</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>Researcher</th>\n",
       "      <th>ForagingGround</th>\n",
       "      <th>CaptureMethod</th>\n",
       "      <th>Fisher</th>\n",
       "      <th>LandingSite</th>\n",
       "      <th>Species</th>\n",
       "      <th>Tag_1</th>\n",
       "      <th>Tag_2</th>\n",
       "      <th>Lost_Tags</th>\n",
       "      <th>T_Number</th>\n",
       "      <th>CCL_cm</th>\n",
       "      <th>CCW_cm</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Sex</th>\n",
       "      <th>TurtleCharacteristics</th>\n",
       "      <th>Status</th>\n",
       "      <th>ReleaseSite</th>\n",
       "      <th>Date_TimeRelease</th>\n",
       "      <th>year_caught</th>\n",
       "      <th>month_caught</th>\n",
       "      <th>week_of_year_caught</th>\n",
       "      <th>day_caught</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>day_of_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2000_RE_0060</td>\n",
       "      <td>2000-12-22</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1072</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00147</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.70</td>\n",
       "      <td>62.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>algae at rear of shell</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>22/12/00</td>\n",
       "      <td>2000</td>\n",
       "      <td>12</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>200051</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0187</td>\n",
       "      <td>2001-10-28</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_520</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>W442</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.85</td>\n",
       "      <td>31.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>multiple b's on front flippers&amp;  a lot of alga...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>28/10/01</td>\n",
       "      <td>2001</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>200143</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0197</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1669</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE0376</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.80</td>\n",
       "      <td>49.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>clean</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>01/11/01</td>\n",
       "      <td>2001</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>200144</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0031</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>Researcher_32</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1798</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00302</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.50</td>\n",
       "      <td>59.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1 b 3 CS+ calcerous algae at rear end of shell...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>11/03/02</td>\n",
       "      <td>2002</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>200211</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0118</td>\n",
       "      <td>2002-08-08</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Beached</td>\n",
       "      <td>Fisher_1918</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0113</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.70</td>\n",
       "      <td>33.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>very lively+ right eye is hanging out + swolle...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>08/08/02</td>\n",
       "      <td>2002</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>200232</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CaptureSite    CaptureSiteCategory    Type     Rescue_ID Date_TimeCaught  \\\n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2000_RE_0060      2000-12-22   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0187      2001-10-28   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0197      2001-11-01   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0031      2002-03-11   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0118      2002-08-08   \n",
       "\n",
       "      Researcher ForagingGround CaptureMethod       Fisher  \\\n",
       "0  Researcher_25          Ocean           Net  Fisher_1072   \n",
       "0   Researcher_6          Ocean           Net   Fisher_520   \n",
       "0   Researcher_6          Ocean           Net  Fisher_1669   \n",
       "0  Researcher_32          Ocean           Net  Fisher_1798   \n",
       "0  Researcher_25          Ocean       Beached  Fisher_1918   \n",
       "\n",
       "                         LandingSite    Species           Tag_1 Tag_2  \\\n",
       "0  LandingSite_CaptureSiteCategory_2  Species_6         CC00147  None   \n",
       "0  LandingSite_CaptureSiteCategory_2  Species_6            W442  None   \n",
       "0  LandingSite_CaptureSiteCategory_2  Species_5          KE0376  None   \n",
       "0  LandingSite_CaptureSiteCategory_2  Species_6         CC00302  None   \n",
       "0  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0113  None   \n",
       "\n",
       "  Lost_Tags T_Number  CCL_cm  CCW_cm  Weight_Kg      Sex  \\\n",
       "0       NaN      NaN   64.70   62.60        NaN  Unknown   \n",
       "0       NaN      NaN   35.85   31.35        NaN  Unknown   \n",
       "0       NaN      NaN   51.80   49.20        NaN  Unknown   \n",
       "0       NaN      NaN   60.50   59.00        NaN  Unknown   \n",
       "0       NaN      NaN   34.70   33.00        NaN  Unknown   \n",
       "\n",
       "                               TurtleCharacteristics    Status  \\\n",
       "0                             algae at rear of shell  Released   \n",
       "0  multiple b's on front flippers&  a lot of alga...  Released   \n",
       "0                                              clean  Released   \n",
       "0  1 b 3 CS+ calcerous algae at rear end of shell...  Released   \n",
       "0  very lively+ right eye is hanging out + swolle...  Released   \n",
       "\n",
       "      ReleaseSite Date_TimeRelease  year_caught  month_caught  \\\n",
       "0  ReleaseSite_50         22/12/00         2000            12   \n",
       "0  ReleaseSite_62         28/10/01         2001            10   \n",
       "0  ReleaseSite_50         01/11/01         2001            11   \n",
       "0  ReleaseSite_50         11/03/02         2002             3   \n",
       "0  ReleaseSite_62         08/08/02         2002             8   \n",
       "\n",
       "   week_of_year_caught  day_caught  day_of_week_caught  year_woy  day_of_year  \n",
       "0                   51          22                   4    200051          357  \n",
       "0                   43          28                   6    200143          301  \n",
       "0                   44           1                   3    200144          305  \n",
       "0                   11          11                   0    200211           70  \n",
       "0                   32           8                   3    200232          220  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look for null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date_TimeRelease         6520\n",
       "Sex                      4330\n",
       "Status                   3633\n",
       "ReleaseSite                75\n",
       "TurtleCharacteristics      41\n",
       "day_of_year                 0\n",
       "Species                     0\n",
       "CaptureSiteCategory         0\n",
       "Type                        0\n",
       "Rescue_ID                   0\n",
       "Date_TimeCaught             0\n",
       "Researcher                  0\n",
       "ForagingGround              0\n",
       "CaptureMethod               0\n",
       "Fisher                      0\n",
       "LandingSite                 0\n",
       "CCL_cm                      0\n",
       "Tag_1                       0\n",
       "Tag_2                       0\n",
       "year_woy                    0\n",
       "CCW_cm                      0\n",
       "Weight_Kg                   0\n",
       "year_caught                 0\n",
       "month_caught                0\n",
       "week_of_year_caught         0\n",
       "day_caught                  0\n",
       "day_of_week_caught          0\n",
       "CaptureSite                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sort_values(ascending=False) ## Get the  NaNs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "# del df['T_Number']\n",
    "# del df['Lost_Tags']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# taking the median value becuase it outlier-resistant. Try with mean once  this is done  \n",
    "df['Weight_Kg'].fillna((df['Weight_Kg'].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CCL_cm</th>\n",
       "      <th>CCW_cm</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>year_caught</th>\n",
       "      <th>month_caught</th>\n",
       "      <th>week_of_year_caught</th>\n",
       "      <th>day_caught</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>day_of_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CaptureSite_0</th>\n",
       "      <td>39.900</td>\n",
       "      <td>38.200</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>201430.0</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_1</th>\n",
       "      <td>39.000</td>\n",
       "      <td>36.500</td>\n",
       "      <td>7.130</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201326.0</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_10</th>\n",
       "      <td>38.000</td>\n",
       "      <td>35.700</td>\n",
       "      <td>7.345</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201650.5</td>\n",
       "      <td>167.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_11</th>\n",
       "      <td>41.600</td>\n",
       "      <td>40.135</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200914.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_12</th>\n",
       "      <td>39.700</td>\n",
       "      <td>37.950</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201048.0</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_13</th>\n",
       "      <td>39.400</td>\n",
       "      <td>37.600</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201444.0</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_14</th>\n",
       "      <td>41.400</td>\n",
       "      <td>39.200</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>201313.0</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_15</th>\n",
       "      <td>36.500</td>\n",
       "      <td>34.000</td>\n",
       "      <td>5.785</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201503.0</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_16</th>\n",
       "      <td>42.600</td>\n",
       "      <td>40.700</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201117.0</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_17</th>\n",
       "      <td>36.900</td>\n",
       "      <td>34.300</td>\n",
       "      <td>5.800</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201428.0</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_18</th>\n",
       "      <td>42.200</td>\n",
       "      <td>41.700</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200938.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_19</th>\n",
       "      <td>39.600</td>\n",
       "      <td>37.735</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201443.0</td>\n",
       "      <td>244.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_2</th>\n",
       "      <td>39.050</td>\n",
       "      <td>36.500</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201112.0</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_20</th>\n",
       "      <td>47.500</td>\n",
       "      <td>43.300</td>\n",
       "      <td>11.680</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201318.0</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_21</th>\n",
       "      <td>46.400</td>\n",
       "      <td>43.300</td>\n",
       "      <td>7.975</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201207.0</td>\n",
       "      <td>168.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_22</th>\n",
       "      <td>37.415</td>\n",
       "      <td>35.000</td>\n",
       "      <td>6.025</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201505.5</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_23</th>\n",
       "      <td>44.100</td>\n",
       "      <td>41.975</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201033.0</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_24</th>\n",
       "      <td>37.700</td>\n",
       "      <td>35.300</td>\n",
       "      <td>7.060</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201545.0</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_25</th>\n",
       "      <td>43.500</td>\n",
       "      <td>41.500</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201047.0</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_26</th>\n",
       "      <td>41.700</td>\n",
       "      <td>39.700</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201143.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_27</th>\n",
       "      <td>44.300</td>\n",
       "      <td>42.000</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201148.0</td>\n",
       "      <td>172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_28</th>\n",
       "      <td>37.100</td>\n",
       "      <td>34.900</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201236.0</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_3</th>\n",
       "      <td>37.470</td>\n",
       "      <td>35.100</td>\n",
       "      <td>6.220</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201839.0</td>\n",
       "      <td>277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_4</th>\n",
       "      <td>39.450</td>\n",
       "      <td>37.950</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201303.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_5</th>\n",
       "      <td>44.500</td>\n",
       "      <td>41.850</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201051.0</td>\n",
       "      <td>168.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_6</th>\n",
       "      <td>38.550</td>\n",
       "      <td>35.885</td>\n",
       "      <td>6.760</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>26.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201518.0</td>\n",
       "      <td>181.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_7</th>\n",
       "      <td>46.950</td>\n",
       "      <td>43.550</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200925.0</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_8</th>\n",
       "      <td>40.450</td>\n",
       "      <td>37.200</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201246.5</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CaptureSite_9</th>\n",
       "      <td>39.200</td>\n",
       "      <td>36.500</td>\n",
       "      <td>7.500</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>201432.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                CCL_cm  CCW_cm  Weight_Kg  year_caught  month_caught  \\\n",
       "CaptureSite                                                            \n",
       "CaptureSite_0   39.900  38.200      7.500       2014.0           8.0   \n",
       "CaptureSite_1   39.000  36.500      7.130       2013.0           7.0   \n",
       "CaptureSite_10  38.000  35.700      7.345       2016.0           6.0   \n",
       "CaptureSite_11  41.600  40.135      7.500       2009.0           7.0   \n",
       "CaptureSite_12  39.700  37.950      7.500       2010.0           7.0   \n",
       "CaptureSite_13  39.400  37.600      7.500       2014.0           8.0   \n",
       "CaptureSite_14  41.400  39.200      7.500       2013.0           6.0   \n",
       "CaptureSite_15  36.500  34.000      5.785       2015.0           7.0   \n",
       "CaptureSite_16  42.600  40.700      7.500       2011.0           8.0   \n",
       "CaptureSite_17  36.900  34.300      5.800       2014.0           8.0   \n",
       "CaptureSite_18  42.200  41.700      7.500       2009.0           7.0   \n",
       "CaptureSite_19  39.600  37.735      7.500       2014.0           9.0   \n",
       "CaptureSite_2   39.050  36.500      7.500       2011.0           6.0   \n",
       "CaptureSite_20  47.500  43.300     11.680       2013.0           5.0   \n",
       "CaptureSite_21  46.400  43.300      7.975       2012.0           6.0   \n",
       "CaptureSite_22  37.415  35.000      6.025       2015.0           7.0   \n",
       "CaptureSite_23  44.100  41.975      7.500       2010.0           7.0   \n",
       "CaptureSite_24  37.700  35.300      7.060       2015.0           7.0   \n",
       "CaptureSite_25  43.500  41.500      7.500       2010.0           8.0   \n",
       "CaptureSite_26  41.700  39.700      7.500       2011.0           8.0   \n",
       "CaptureSite_27  44.300  42.000      7.500       2011.0           6.0   \n",
       "CaptureSite_28  37.100  34.900      7.500       2012.0           8.0   \n",
       "CaptureSite_3   37.470  35.100      6.220       2018.0          10.0   \n",
       "CaptureSite_4   39.450  37.950      7.500       2013.0           9.0   \n",
       "CaptureSite_5   44.500  41.850      7.500       2010.0           6.0   \n",
       "CaptureSite_6   38.550  35.885      6.760       2015.0           6.5   \n",
       "CaptureSite_7   46.950  43.550      7.500       2009.0           6.0   \n",
       "CaptureSite_8   40.450  37.200      7.500       2012.0           9.0   \n",
       "CaptureSite_9   39.200  36.500      7.500       2014.0           7.0   \n",
       "\n",
       "                week_of_year_caught  day_caught  day_of_week_caught  year_woy  \\\n",
       "CaptureSite                                                                     \n",
       "CaptureSite_0                  35.0        16.0                 2.0  201430.0   \n",
       "CaptureSite_1                  30.0        17.0                 3.0  201326.0   \n",
       "CaptureSite_10                 24.0        15.0                 3.0  201650.5   \n",
       "CaptureSite_11                 30.0        17.0                 3.0  200914.0   \n",
       "CaptureSite_12                 31.0        16.0                 3.0  201048.0   \n",
       "CaptureSite_13                 35.0        15.0                 3.0  201444.0   \n",
       "CaptureSite_14                 26.0        18.0                 4.0  201313.0   \n",
       "CaptureSite_15                 28.0        16.0                 3.0  201503.0   \n",
       "CaptureSite_16                 31.0        16.0                 3.0  201117.0   \n",
       "CaptureSite_17                 32.0        14.0                 3.0  201428.0   \n",
       "CaptureSite_18                 31.0        21.0                 2.0  200938.0   \n",
       "CaptureSite_19                 34.0        14.0                 3.0  201443.0   \n",
       "CaptureSite_2                  25.0        16.0                 3.0  201112.0   \n",
       "CaptureSite_20                 22.0        17.0                 3.0  201318.0   \n",
       "CaptureSite_21                 24.0        15.0                 3.0  201207.0   \n",
       "CaptureSite_22                 28.0        15.0                 3.0  201505.5   \n",
       "CaptureSite_23                 30.0        17.0                 3.0  201033.0   \n",
       "CaptureSite_24                 28.0        16.0                 3.0  201545.0   \n",
       "CaptureSite_25                 34.0        16.0                 3.0  201047.0   \n",
       "CaptureSite_26                 33.0        18.0                 3.0  201143.0   \n",
       "CaptureSite_27                 25.0        15.0                 3.0  201148.0   \n",
       "CaptureSite_28                 32.0        15.0                 3.0  201236.0   \n",
       "CaptureSite_3                  40.0        18.0                 3.0  201839.0   \n",
       "CaptureSite_4                  37.0        15.0                 3.0  201303.0   \n",
       "CaptureSite_5                  25.0        16.0                 3.0  201051.0   \n",
       "CaptureSite_6                  26.5        16.0                 3.0  201518.0   \n",
       "CaptureSite_7                  23.0        15.0                 3.0  200925.0   \n",
       "CaptureSite_8                  37.0        15.0                 3.0  201246.5   \n",
       "CaptureSite_9                  29.0        16.0                 3.0  201432.0   \n",
       "\n",
       "                day_of_year  \n",
       "CaptureSite                  \n",
       "CaptureSite_0         240.0  \n",
       "CaptureSite_1         206.0  \n",
       "CaptureSite_10        167.5  \n",
       "CaptureSite_11        204.0  \n",
       "CaptureSite_12        212.0  \n",
       "CaptureSite_13        240.0  \n",
       "CaptureSite_14        181.0  \n",
       "CaptureSite_15        197.0  \n",
       "CaptureSite_16        215.0  \n",
       "CaptureSite_17        224.0  \n",
       "CaptureSite_18        204.0  \n",
       "CaptureSite_19        244.0  \n",
       "CaptureSite_2         173.0  \n",
       "CaptureSite_20        151.0  \n",
       "CaptureSite_21        168.5  \n",
       "CaptureSite_22        192.0  \n",
       "CaptureSite_23        207.0  \n",
       "CaptureSite_24        197.0  \n",
       "CaptureSite_25        236.0  \n",
       "CaptureSite_26        230.0  \n",
       "CaptureSite_27        172.0  \n",
       "CaptureSite_28        224.0  \n",
       "CaptureSite_3         277.0  \n",
       "CaptureSite_4         253.0  \n",
       "CaptureSite_5         168.5  \n",
       "CaptureSite_6         181.5  \n",
       "CaptureSite_7         156.0  \n",
       "CaptureSite_8         256.0  \n",
       "CaptureSite_9         202.0  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by = df['CaptureSite']).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a better approach than filling null values with the whole dataset's median would be to fill \n",
    "df['CCL_cm'] = df['CCL_cm'].fillna(df.groupby(['CaptureSite'])['CCL_cm'].transform('median'))\n",
    "df['CCW_cm'] = df['CCW_cm'].fillna(df.groupby(['CaptureSite'])['CCW_cm'].transform('median'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_TimeRelease</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22/12/00</td>\n",
       "      <td>2000-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28/10/01</td>\n",
       "      <td>2001-10-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/01</td>\n",
       "      <td>2001-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/03/02</td>\n",
       "      <td>2002-03-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08/08/02</td>\n",
       "      <td>2002-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10/08/02</td>\n",
       "      <td>2002-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21/10/02</td>\n",
       "      <td>2002-10-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21/10/02</td>\n",
       "      <td>2002-10-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22/10/02</td>\n",
       "      <td>2002-10-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-06-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-09-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-09-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-09-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-09-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-09-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-01-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-02-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-02-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-06-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-07-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-08-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-08-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-09-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-04-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-09-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-10-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-10-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18032</th>\n",
       "      <td>28/11/18</td>\n",
       "      <td>2018-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18033</th>\n",
       "      <td>30/11/18</td>\n",
       "      <td>2018-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18034</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18035</th>\n",
       "      <td>02/12/18</td>\n",
       "      <td>2018-12-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18036</th>\n",
       "      <td>04/12/18</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18037</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18038</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18039</th>\n",
       "      <td>07/12/18</td>\n",
       "      <td>2018-12-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18040</th>\n",
       "      <td>07/12/18</td>\n",
       "      <td>2018-12-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18041</th>\n",
       "      <td>09/12/18</td>\n",
       "      <td>2018-12-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18042</th>\n",
       "      <td>11/12/18</td>\n",
       "      <td>2018-12-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18043</th>\n",
       "      <td>11/12/18</td>\n",
       "      <td>2018-12-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18044</th>\n",
       "      <td>12/12/18</td>\n",
       "      <td>2018-12-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18045</th>\n",
       "      <td>12/12/18</td>\n",
       "      <td>2018-12-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18046</th>\n",
       "      <td>13/12/18</td>\n",
       "      <td>2018-12-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18047</th>\n",
       "      <td>13/12/18</td>\n",
       "      <td>2018-12-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18048</th>\n",
       "      <td>13/12/18</td>\n",
       "      <td>2018-12-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18049</th>\n",
       "      <td>14/12/18</td>\n",
       "      <td>2018-12-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18050</th>\n",
       "      <td>16/12/18</td>\n",
       "      <td>2018-12-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18051</th>\n",
       "      <td>16/12/18</td>\n",
       "      <td>2018-12-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18052</th>\n",
       "      <td>16/12/18</td>\n",
       "      <td>2018-12-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18053</th>\n",
       "      <td>16/12/18</td>\n",
       "      <td>2018-12-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18054</th>\n",
       "      <td>17/12/18</td>\n",
       "      <td>2018-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18055</th>\n",
       "      <td>17/12/18</td>\n",
       "      <td>2018-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18056</th>\n",
       "      <td>18/12/18</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18057</th>\n",
       "      <td>18/12/18</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18058</th>\n",
       "      <td>18/12/18</td>\n",
       "      <td>2018-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18059</th>\n",
       "      <td>24/12/18</td>\n",
       "      <td>2018-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18060</th>\n",
       "      <td>24/12/18</td>\n",
       "      <td>2018-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18061</th>\n",
       "      <td>28/12/18</td>\n",
       "      <td>2018-12-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18062 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date_TimeRelease Date_TimeCaught\n",
       "0             22/12/00      2000-12-22\n",
       "1             28/10/01      2001-10-28\n",
       "2             01/11/01      2001-11-01\n",
       "3             11/03/02      2002-03-11\n",
       "4             08/08/02      2002-08-08\n",
       "5             10/08/02      2002-08-10\n",
       "6             21/10/02      2002-10-21\n",
       "7             21/10/02      2002-10-21\n",
       "8             22/10/02      2002-10-22\n",
       "9                  NaN      2003-06-09\n",
       "10                 NaN      2003-08-11\n",
       "11                 NaN      2003-09-02\n",
       "12                 NaN      2003-09-22\n",
       "13                 NaN      2003-09-22\n",
       "14                 NaN      2003-09-22\n",
       "15                 NaN      2003-09-26\n",
       "16                 NaN      2004-10-16\n",
       "17                 NaN      2008-01-23\n",
       "18                 NaN      2009-01-06\n",
       "19                 NaN      2009-02-09\n",
       "20                 NaN      2009-02-17\n",
       "21                 NaN      2009-06-25\n",
       "22                 NaN      2009-07-19\n",
       "23                 NaN      2009-08-04\n",
       "24                 NaN      2009-08-12\n",
       "25                 NaN      2009-09-23\n",
       "26                 NaN      2010-04-23\n",
       "27                 NaN      2010-09-22\n",
       "28                 NaN      2010-10-02\n",
       "29                 NaN      2010-10-28\n",
       "...                ...             ...\n",
       "18032         28/11/18      2018-11-28\n",
       "18033         30/11/18      2018-11-30\n",
       "18034              NaN      2018-11-30\n",
       "18035         02/12/18      2018-12-02\n",
       "18036         04/12/18      2018-12-04\n",
       "18037              NaN      2018-12-04\n",
       "18038              NaN      2018-12-04\n",
       "18039         07/12/18      2018-12-07\n",
       "18040         07/12/18      2018-12-07\n",
       "18041         09/12/18      2018-12-09\n",
       "18042         11/12/18      2018-12-11\n",
       "18043         11/12/18      2018-12-11\n",
       "18044         12/12/18      2018-12-12\n",
       "18045         12/12/18      2018-12-12\n",
       "18046         13/12/18      2018-12-13\n",
       "18047         13/12/18      2018-12-13\n",
       "18048         13/12/18      2018-12-13\n",
       "18049         14/12/18      2018-12-14\n",
       "18050         16/12/18      2018-12-15\n",
       "18051         16/12/18      2018-12-15\n",
       "18052         16/12/18      2018-12-16\n",
       "18053         16/12/18      2018-12-16\n",
       "18054         17/12/18      2018-12-17\n",
       "18055         17/12/18      2018-12-17\n",
       "18056         18/12/18      2018-12-18\n",
       "18057         18/12/18      2018-12-18\n",
       "18058         18/12/18      2018-12-18\n",
       "18059         24/12/18      2018-12-24\n",
       "18060         24/12/18      2018-12-24\n",
       "18061         28/12/18      2018-12-28\n",
       "\n",
       "[18062 rows x 2 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For null Date_TimeRelease, insert Date_TimeCaught values if variance is  not so high \\\n",
    "df[['Date_TimeRelease', 'Date_TimeCaught']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date_TimeRelease'] = pd.to_datetime(df['Date_TimeRelease'], errors='coerce') #convert Date_TimeRelease to date format \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Date_TimeRelease.fillna(df.Date_TimeCaught, inplace=True) #fill NaNs for Date_TimeRelease with Date_TimeCaught"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date_TimeRelease'].replace({'NaT': df[\"Date_TimeCaught\"]}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_of_year              0\n",
       "year_woy                 0\n",
       "CaptureSiteCategory      0\n",
       "Type                     0\n",
       "Rescue_ID                0\n",
       "Date_TimeCaught          0\n",
       "Researcher               0\n",
       "ForagingGround           0\n",
       "CaptureMethod            0\n",
       "Fisher                   0\n",
       "LandingSite              0\n",
       "Species                  0\n",
       "Tag_1                    0\n",
       "Tag_2                    0\n",
       "CCL_cm                   0\n",
       "CCW_cm                   0\n",
       "Weight_Kg                0\n",
       "Sex                      0\n",
       "TurtleCharacteristics    0\n",
       "Status                   0\n",
       "ReleaseSite              0\n",
       "Date_TimeRelease         0\n",
       "year_caught              0\n",
       "month_caught             0\n",
       "week_of_year_caught      0\n",
       "day_caught               0\n",
       "day_of_week_caught       0\n",
       "CaptureSite              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df1 = df.copy()\n",
    "df = df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2 = df.copy() #copy the dataframes for the future just in case\n",
    "# df3 = df.copy() #copy the dataframes for the future just in case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rescue_ID</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>Researcher</th>\n",
       "      <th>ForagingGround</th>\n",
       "      <th>CaptureMethod</th>\n",
       "      <th>Fisher</th>\n",
       "      <th>LandingSite</th>\n",
       "      <th>Species</th>\n",
       "      <th>Tag_1</th>\n",
       "      <th>Tag_2</th>\n",
       "      <th>CCL_cm</th>\n",
       "      <th>CCW_cm</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Sex</th>\n",
       "      <th>TurtleCharacteristics</th>\n",
       "      <th>Status</th>\n",
       "      <th>ReleaseSite</th>\n",
       "      <th>Date_TimeRelease</th>\n",
       "      <th>year_caught</th>\n",
       "      <th>month_caught</th>\n",
       "      <th>week_of_year_caught</th>\n",
       "      <th>day_caught</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>day_of_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2000_RE_0060</td>\n",
       "      <td>2000-12-22</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1072</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00147</td>\n",
       "      <td>None</td>\n",
       "      <td>64.70</td>\n",
       "      <td>62.60</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>algae at rear of shell</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>2000-12-22</td>\n",
       "      <td>2000</td>\n",
       "      <td>12</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>200051</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0187</td>\n",
       "      <td>2001-10-28</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_520</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>W442</td>\n",
       "      <td>None</td>\n",
       "      <td>35.85</td>\n",
       "      <td>31.35</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>multiple b's on front flippers&amp;  a lot of alga...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>2001-10-28</td>\n",
       "      <td>2001</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>200143</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0197</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1669</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE0376</td>\n",
       "      <td>None</td>\n",
       "      <td>51.80</td>\n",
       "      <td>49.20</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>clean</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>2001-01-11</td>\n",
       "      <td>2001</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>200144</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0031</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>Researcher_32</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1798</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00302</td>\n",
       "      <td>None</td>\n",
       "      <td>60.50</td>\n",
       "      <td>59.00</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1 b 3 CS+ calcerous algae at rear end of shell...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>2002-11-03</td>\n",
       "      <td>2002</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>200211</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0118</td>\n",
       "      <td>2002-08-08</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Beached</td>\n",
       "      <td>Fisher_1918</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0113</td>\n",
       "      <td>None</td>\n",
       "      <td>34.70</td>\n",
       "      <td>33.00</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>very lively+ right eye is hanging out + swolle...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>2002-08-08</td>\n",
       "      <td>2002</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>200232</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CaptureSite    CaptureSiteCategory    Type     Rescue_ID Date_TimeCaught  \\\n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2000_RE_0060      2000-12-22   \n",
       "1  CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0187      2001-10-28   \n",
       "2  CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0197      2001-11-01   \n",
       "3  CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0031      2002-03-11   \n",
       "4  CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0118      2002-08-08   \n",
       "\n",
       "      Researcher ForagingGround CaptureMethod       Fisher  \\\n",
       "0  Researcher_25          Ocean           Net  Fisher_1072   \n",
       "1   Researcher_6          Ocean           Net   Fisher_520   \n",
       "2   Researcher_6          Ocean           Net  Fisher_1669   \n",
       "3  Researcher_32          Ocean           Net  Fisher_1798   \n",
       "4  Researcher_25          Ocean       Beached  Fisher_1918   \n",
       "\n",
       "                         LandingSite    Species           Tag_1 Tag_2  CCL_cm  \\\n",
       "0  LandingSite_CaptureSiteCategory_2  Species_6         CC00147  None   64.70   \n",
       "1  LandingSite_CaptureSiteCategory_2  Species_6            W442  None   35.85   \n",
       "2  LandingSite_CaptureSiteCategory_2  Species_5          KE0376  None   51.80   \n",
       "3  LandingSite_CaptureSiteCategory_2  Species_6         CC00302  None   60.50   \n",
       "4  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0113  None   34.70   \n",
       "\n",
       "   CCW_cm  Weight_Kg      Sex  \\\n",
       "0   62.60        7.5  Unknown   \n",
       "1   31.35        7.5  Unknown   \n",
       "2   49.20        7.5  Unknown   \n",
       "3   59.00        7.5  Unknown   \n",
       "4   33.00        7.5  Unknown   \n",
       "\n",
       "                               TurtleCharacteristics    Status  \\\n",
       "0                             algae at rear of shell  Released   \n",
       "1  multiple b's on front flippers&  a lot of alga...  Released   \n",
       "2                                              clean  Released   \n",
       "3  1 b 3 CS+ calcerous algae at rear end of shell...  Released   \n",
       "4  very lively+ right eye is hanging out + swolle...  Released   \n",
       "\n",
       "      ReleaseSite Date_TimeRelease  year_caught  month_caught  \\\n",
       "0  ReleaseSite_50       2000-12-22         2000            12   \n",
       "1  ReleaseSite_62       2001-10-28         2001            10   \n",
       "2  ReleaseSite_50       2001-01-11         2001            11   \n",
       "3  ReleaseSite_50       2002-11-03         2002             3   \n",
       "4  ReleaseSite_62       2002-08-08         2002             8   \n",
       "\n",
       "   week_of_year_caught  day_caught  day_of_week_caught  year_woy  day_of_year  \n",
       "0                   51          22                   4    200051          357  \n",
       "1                   43          28                   6    200143          301  \n",
       "2                   44           1                   3    200144          305  \n",
       "3                   11          11                   0    200211           70  \n",
       "4                   32           8                   3    200232          220  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df1['Date_TimeRelease'] = pd.to_datetime(df1['Date_TimeRelease'])\n",
    "\n",
    "#df1['Date_TimeRelease'].str.contains('Release', na=False)\n",
    "#df1['Date_TimeRelease'] = df1.Date_TimeRelease.str.replace(r'(^.*notches .*$)', '')\n",
    "#df1['Date_TimeRelease'] = np.where((df1.Date_TimeRelease == ''),np.NaN,df1.Date_TimeRelease) #Worked \n",
    "\n",
    "                                                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[\"Date_TimeRelease\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown    17910\n",
       "Female       113\n",
       "Male          39\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sex'].fillna('Unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Sex']=='Not_Recorded', 'Sex']='Unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rescue_ID</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>Researcher</th>\n",
       "      <th>ForagingGround</th>\n",
       "      <th>CaptureMethod</th>\n",
       "      <th>Fisher</th>\n",
       "      <th>LandingSite</th>\n",
       "      <th>Species</th>\n",
       "      <th>Tag_1</th>\n",
       "      <th>Tag_2</th>\n",
       "      <th>CCL_cm</th>\n",
       "      <th>CCW_cm</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Sex</th>\n",
       "      <th>TurtleCharacteristics</th>\n",
       "      <th>Status</th>\n",
       "      <th>ReleaseSite</th>\n",
       "      <th>Date_TimeRelease</th>\n",
       "      <th>year_caught</th>\n",
       "      <th>month_caught</th>\n",
       "      <th>week_of_year_caught</th>\n",
       "      <th>day_caught</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>day_of_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2000_RE_0060</td>\n",
       "      <td>2000-12-22</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1072</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00147</td>\n",
       "      <td>None</td>\n",
       "      <td>64.70</td>\n",
       "      <td>62.60</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>algae at rear of shell</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>22/12/00</td>\n",
       "      <td>2000</td>\n",
       "      <td>12</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>200051</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0187</td>\n",
       "      <td>2001-10-28</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_520</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>W442</td>\n",
       "      <td>None</td>\n",
       "      <td>35.85</td>\n",
       "      <td>31.35</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>multiple b's on front flippers&amp;  a lot of alga...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>28/10/01</td>\n",
       "      <td>2001</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>200143</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0197</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1669</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE0376</td>\n",
       "      <td>None</td>\n",
       "      <td>51.80</td>\n",
       "      <td>49.20</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>clean</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>01/11/01</td>\n",
       "      <td>2001</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>200144</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0031</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>Researcher_32</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1798</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00302</td>\n",
       "      <td>None</td>\n",
       "      <td>60.50</td>\n",
       "      <td>59.00</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1 b 3 CS+ calcerous algae at rear end of shell...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>11/03/02</td>\n",
       "      <td>2002</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>200211</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0118</td>\n",
       "      <td>2002-08-08</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Beached</td>\n",
       "      <td>Fisher_1918</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0113</td>\n",
       "      <td>None</td>\n",
       "      <td>34.70</td>\n",
       "      <td>33.00</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>very lively+ right eye is hanging out + swolle...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>08/08/02</td>\n",
       "      <td>2002</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>200232</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CaptureSite    CaptureSiteCategory    Type     Rescue_ID Date_TimeCaught  \\\n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2000_RE_0060      2000-12-22   \n",
       "1  CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0187      2001-10-28   \n",
       "2  CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0197      2001-11-01   \n",
       "3  CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0031      2002-03-11   \n",
       "4  CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0118      2002-08-08   \n",
       "\n",
       "      Researcher ForagingGround CaptureMethod       Fisher  \\\n",
       "0  Researcher_25          Ocean           Net  Fisher_1072   \n",
       "1   Researcher_6          Ocean           Net   Fisher_520   \n",
       "2   Researcher_6          Ocean           Net  Fisher_1669   \n",
       "3  Researcher_32          Ocean           Net  Fisher_1798   \n",
       "4  Researcher_25          Ocean       Beached  Fisher_1918   \n",
       "\n",
       "                         LandingSite    Species           Tag_1 Tag_2  CCL_cm  \\\n",
       "0  LandingSite_CaptureSiteCategory_2  Species_6         CC00147  None   64.70   \n",
       "1  LandingSite_CaptureSiteCategory_2  Species_6            W442  None   35.85   \n",
       "2  LandingSite_CaptureSiteCategory_2  Species_5          KE0376  None   51.80   \n",
       "3  LandingSite_CaptureSiteCategory_2  Species_6         CC00302  None   60.50   \n",
       "4  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0113  None   34.70   \n",
       "\n",
       "   CCW_cm  Weight_Kg      Sex  \\\n",
       "0   62.60        7.5  Unknown   \n",
       "1   31.35        7.5  Unknown   \n",
       "2   49.20        7.5  Unknown   \n",
       "3   59.00        7.5  Unknown   \n",
       "4   33.00        7.5  Unknown   \n",
       "\n",
       "                               TurtleCharacteristics    Status  \\\n",
       "0                             algae at rear of shell  Released   \n",
       "1  multiple b's on front flippers&  a lot of alga...  Released   \n",
       "2                                              clean  Released   \n",
       "3  1 b 3 CS+ calcerous algae at rear end of shell...  Released   \n",
       "4  very lively+ right eye is hanging out + swolle...  Released   \n",
       "\n",
       "      ReleaseSite Date_TimeRelease  year_caught  month_caught  \\\n",
       "0  ReleaseSite_50         22/12/00         2000            12   \n",
       "1  ReleaseSite_62         28/10/01         2001            10   \n",
       "2  ReleaseSite_50         01/11/01         2001            11   \n",
       "3  ReleaseSite_50         11/03/02         2002             3   \n",
       "4  ReleaseSite_62         08/08/02         2002             8   \n",
       "\n",
       "   week_of_year_caught  day_caught  day_of_week_caught  year_woy  day_of_year  \n",
       "0                   51          22                   4    200051          357  \n",
       "1                   43          28                   6    200143          301  \n",
       "2                   44           1                   3    200144          305  \n",
       "3                   11          11                   0    200211           70  \n",
       "4                   32           8                   3    200232          220  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Status'].fillna('Not_Recorded', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ReleaseSite_62                                                            9951\n",
       "ReleaseSite_11                                                            2091\n",
       "ReleaseSite_18                                                            1603\n",
       "ReleaseSite_68                                                            1596\n",
       "ReleaseSite_50                                                             566\n",
       "ReleaseSite_19                                                             433\n",
       "ReleaseSite_0                                                              273\n",
       "ReleaseSite_8                                                              233\n",
       "Released                                                                   214\n",
       "ReleaseSite_73                                                             103\n",
       "ReleaseSite_75                                                              98\n",
       "ReleaseSite_17                                                              76\n",
       "Unknown                                                                     75\n",
       "ReleaseSite_56                                                              55\n",
       "ReleaseSite_7                                                               53\n",
       "ReleaseSite_66                                                              36\n",
       "ReleaseSite_23                                                              28\n",
       "ReleaseSite_28                                                              27\n",
       "ReleaseSite_32                                                              25\n",
       "ReleaseSite_52                                                              25\n",
       "ReleaseSite_5                                                               22\n",
       "ReleaseSite_35                                                              17\n",
       "ReleaseSite_9                                                               15\n",
       "ReleaseSite_70                                                              15\n",
       "ReleaseSite_60                                                              14\n",
       "ReleaseSite_45                                                              14\n",
       "ReleaseSite_47                                                              13\n",
       "ReleaseSite_31                                                              12\n",
       "ReleaseSite_12                                                              12\n",
       "ReleaseSite_78                                                              10\n",
       "                                                                          ... \n",
       " R supra has a chip and some bleeding                                        1\n",
       " notch join 10/11LMS                                                         1\n",
       " tiny barnacles on front flippers                                            1\n",
       " very emaciated and weak& sunken eyes& neck and plastron                     1\n",
       " net damage on top of LFF                                                    1\n",
       " notch on RRF                                                                1\n",
       " covered in green algae at rear.                                             1\n",
       " 'tooth marks' 2LLS& 2RLS& 2CS + 3 CS                                        1\n",
       " indent on LFF                                                               1\n",
       "ReleaseSite_54                                                               1\n",
       " tag rip on the LRF                                                          1\n",
       " tiny barnacles around neck and shoulders                                    1\n",
       "ReleaseSite_36                                                               1\n",
       " LFF under arm net wound +little bleeding                                    1\n",
       " 1b 9RMS& 2CS& 9LMS                                                          1\n",
       " brown and pink algae on algae                                               1\n",
       " Bs on plastron& jagged LFF                                                  1\n",
       "ReleaseSite_21                                                               1\n",
       " small barnacles around the neck                                             1\n",
       " sm notch RFF                                                                1\n",
       " thin green algae on the carapace                                            1\n",
       " chip on the third LLS and on the last LMS                                   1\n",
       " notch in 11th LMS                                                           1\n",
       " notch in middle of the inner edge of LFF                                    1\n",
       " b scar hole 2LLS & 4LLS                                                     1\n",
       " spots of white calcerous algae 9&11RMS and L supra& net damage tip LF       1\n",
       "ReleaseSite_34                                                               1\n",
       " small scar on the top of the left of the head                               1\n",
       " notch RRF                                                                   1\n",
       "ReleaseSite_41                                                               1\n",
       "Name: ReleaseSite, Length: 272, dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ReleaseSite'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ReleaseSite'].fillna('Unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TurtleCharacteristics'].fillna('Unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rescue_ID</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>Researcher</th>\n",
       "      <th>ForagingGround</th>\n",
       "      <th>CaptureMethod</th>\n",
       "      <th>Fisher</th>\n",
       "      <th>LandingSite</th>\n",
       "      <th>Species</th>\n",
       "      <th>Tag_1</th>\n",
       "      <th>Tag_2</th>\n",
       "      <th>CCL_cm</th>\n",
       "      <th>CCW_cm</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Sex</th>\n",
       "      <th>TurtleCharacteristics</th>\n",
       "      <th>Status</th>\n",
       "      <th>ReleaseSite</th>\n",
       "      <th>Date_TimeRelease</th>\n",
       "      <th>year_caught</th>\n",
       "      <th>month_caught</th>\n",
       "      <th>week_of_year_caught</th>\n",
       "      <th>day_caught</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>day_of_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18057</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1511</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_569</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1828</td>\n",
       "      <td>None</td>\n",
       "      <td>57.13</td>\n",
       "      <td>50.57</td>\n",
       "      <td>21.09</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>White calcareous algae on carapace</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>51</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>201851</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18058</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1514</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_125</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES0563</td>\n",
       "      <td>None</td>\n",
       "      <td>42.07</td>\n",
       "      <td>38.37</td>\n",
       "      <td>9.02</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Calcareous + green algae on carapace\\nBarnacle...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>51</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>201851</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18059</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1532</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1343</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1833</td>\n",
       "      <td>None</td>\n",
       "      <td>57.20</td>\n",
       "      <td>52.30</td>\n",
       "      <td>7.50</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Clean turtle</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>201852</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18060</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1533</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1551</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KES1831</td>\n",
       "      <td>None</td>\n",
       "      <td>51.90</td>\n",
       "      <td>48.50</td>\n",
       "      <td>7.50</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Green algae on carapace\\ntip of left supra mis...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_68</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>201852</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18061</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>CaptureSiteCategory_1</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2018_RE_1550</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>Researcher_30</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1551</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_1</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>KES1432</td>\n",
       "      <td>none</td>\n",
       "      <td>34.60</td>\n",
       "      <td>31.20</td>\n",
       "      <td>4.29</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Thick pink patches of calcareous algae on cara...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_37</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>52</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>201852</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CaptureSite    CaptureSiteCategory    Type     Rescue_ID  \\\n",
       "18057  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1511   \n",
       "18058  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1514   \n",
       "18059  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1532   \n",
       "18060  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1533   \n",
       "18061  CaptureSite_9  CaptureSiteCategory_1  Type_1  2018_RE_1550   \n",
       "\n",
       "      Date_TimeCaught     Researcher ForagingGround CaptureMethod  \\\n",
       "18057      2018-12-18  Researcher_30          Ocean           Net   \n",
       "18058      2018-12-18  Researcher_30          Ocean           Net   \n",
       "18059      2018-12-24  Researcher_30          Ocean           Net   \n",
       "18060      2018-12-24  Researcher_30          Ocean           Net   \n",
       "18061      2018-12-28  Researcher_30          Ocean           Net   \n",
       "\n",
       "            Fisher                        LandingSite    Species    Tag_1  \\\n",
       "18057   Fisher_569  LandingSite_CaptureSiteCategory_1  Species_5  KES1828   \n",
       "18058   Fisher_125  LandingSite_CaptureSiteCategory_1  Species_6  KES0563   \n",
       "18059  Fisher_1343  LandingSite_CaptureSiteCategory_1  Species_5  KES1833   \n",
       "18060  Fisher_1551  LandingSite_CaptureSiteCategory_1  Species_5  KES1831   \n",
       "18061  Fisher_1551  LandingSite_CaptureSiteCategory_1  Species_6  KES1432   \n",
       "\n",
       "      Tag_2  CCL_cm  CCW_cm  Weight_Kg      Sex  \\\n",
       "18057  None   57.13   50.57      21.09  Unknown   \n",
       "18058  None   42.07   38.37       9.02  Unknown   \n",
       "18059  None   57.20   52.30       7.50  Unknown   \n",
       "18060  None   51.90   48.50       7.50  Unknown   \n",
       "18061  none   34.60   31.20       4.29  Unknown   \n",
       "\n",
       "                                   TurtleCharacteristics    Status  \\\n",
       "18057                 White calcareous algae on carapace  Released   \n",
       "18058  Calcareous + green algae on carapace\\nBarnacle...  Released   \n",
       "18059                                       Clean turtle  Released   \n",
       "18060  Green algae on carapace\\ntip of left supra mis...  Released   \n",
       "18061  Thick pink patches of calcareous algae on cara...  Released   \n",
       "\n",
       "          ReleaseSite Date_TimeRelease  year_caught  month_caught  \\\n",
       "18057  ReleaseSite_68       2018-12-18         2018            12   \n",
       "18058  ReleaseSite_68       2018-12-18         2018            12   \n",
       "18059  ReleaseSite_68       2018-12-24         2018            12   \n",
       "18060  ReleaseSite_68       2018-12-24         2018            12   \n",
       "18061  ReleaseSite_37       2018-12-28         2018            12   \n",
       "\n",
       "       week_of_year_caught  day_caught  day_of_week_caught  year_woy  \\\n",
       "18057                   51          18                   1    201851   \n",
       "18058                   51          18                   1    201851   \n",
       "18059                   52          24                   0    201852   \n",
       "18060                   52          24                   0    201852   \n",
       "18061                   52          28                   4    201852   \n",
       "\n",
       "       day_of_year  \n",
       "18057          352  \n",
       "18058          352  \n",
       "18059          358  \n",
       "18060          358  \n",
       "18061          362  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split Id to Year woy and CaptureSite\n",
    "sample_submission[\"year_woy\"]=(sample_submission.ID.apply(lambda x: x.split(\"_\")[-1])).astype(int)\n",
    "sample_submission[\"CaptureSite\"]=sample_submission.ID.apply(lambda x: (\"_\").join(x.split(\"_\")[0:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Rescue_ID</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>Researcher</th>\n",
       "      <th>ForagingGround</th>\n",
       "      <th>CaptureMethod</th>\n",
       "      <th>Fisher</th>\n",
       "      <th>LandingSite</th>\n",
       "      <th>Species</th>\n",
       "      <th>Tag_1</th>\n",
       "      <th>Tag_2</th>\n",
       "      <th>CCL_cm</th>\n",
       "      <th>CCW_cm</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Sex</th>\n",
       "      <th>TurtleCharacteristics</th>\n",
       "      <th>Status</th>\n",
       "      <th>ReleaseSite</th>\n",
       "      <th>Date_TimeRelease</th>\n",
       "      <th>year_caught</th>\n",
       "      <th>month_caught</th>\n",
       "      <th>week_of_year_caught</th>\n",
       "      <th>day_caught</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>day_of_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2000_RE_0060</td>\n",
       "      <td>2000-12-22</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1072</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00147</td>\n",
       "      <td>None</td>\n",
       "      <td>64.70</td>\n",
       "      <td>62.60</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>algae at rear of shell</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>2000-12-22</td>\n",
       "      <td>2000</td>\n",
       "      <td>12</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>200051</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0187</td>\n",
       "      <td>2001-10-28</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_520</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>W442</td>\n",
       "      <td>None</td>\n",
       "      <td>35.85</td>\n",
       "      <td>31.35</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>multiple b's on front flippers&amp;  a lot of alga...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>2001-10-28</td>\n",
       "      <td>2001</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>200143</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001_RE_0197</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>Researcher_6</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1669</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>KE0376</td>\n",
       "      <td>None</td>\n",
       "      <td>51.80</td>\n",
       "      <td>49.20</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>clean</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>2001-01-11</td>\n",
       "      <td>2001</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>200144</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0031</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>Researcher_32</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Net</td>\n",
       "      <td>Fisher_1798</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_6</td>\n",
       "      <td>CC00302</td>\n",
       "      <td>None</td>\n",
       "      <td>60.50</td>\n",
       "      <td>59.00</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1 b 3 CS+ calcerous algae at rear end of shell...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_50</td>\n",
       "      <td>2002-11-03</td>\n",
       "      <td>2002</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>200211</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2002_RE_0118</td>\n",
       "      <td>2002-08-08</td>\n",
       "      <td>Researcher_25</td>\n",
       "      <td>Ocean</td>\n",
       "      <td>Beached</td>\n",
       "      <td>Fisher_1918</td>\n",
       "      <td>LandingSite_CaptureSiteCategory_2</td>\n",
       "      <td>Species_5</td>\n",
       "      <td>NotTagged_0113</td>\n",
       "      <td>None</td>\n",
       "      <td>34.70</td>\n",
       "      <td>33.00</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>very lively+ right eye is hanging out + swolle...</td>\n",
       "      <td>Released</td>\n",
       "      <td>ReleaseSite_62</td>\n",
       "      <td>2002-08-08</td>\n",
       "      <td>2002</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>200232</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CaptureSite    CaptureSiteCategory    Type     Rescue_ID Date_TimeCaught  \\\n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1  2000_RE_0060      2000-12-22   \n",
       "1  CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0187      2001-10-28   \n",
       "2  CaptureSite_0  CaptureSiteCategory_2  Type_1  2001_RE_0197      2001-11-01   \n",
       "3  CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0031      2002-03-11   \n",
       "4  CaptureSite_0  CaptureSiteCategory_2  Type_1  2002_RE_0118      2002-08-08   \n",
       "\n",
       "      Researcher ForagingGround CaptureMethod       Fisher  \\\n",
       "0  Researcher_25          Ocean           Net  Fisher_1072   \n",
       "1   Researcher_6          Ocean           Net   Fisher_520   \n",
       "2   Researcher_6          Ocean           Net  Fisher_1669   \n",
       "3  Researcher_32          Ocean           Net  Fisher_1798   \n",
       "4  Researcher_25          Ocean       Beached  Fisher_1918   \n",
       "\n",
       "                         LandingSite    Species           Tag_1 Tag_2  CCL_cm  \\\n",
       "0  LandingSite_CaptureSiteCategory_2  Species_6         CC00147  None   64.70   \n",
       "1  LandingSite_CaptureSiteCategory_2  Species_6            W442  None   35.85   \n",
       "2  LandingSite_CaptureSiteCategory_2  Species_5          KE0376  None   51.80   \n",
       "3  LandingSite_CaptureSiteCategory_2  Species_6         CC00302  None   60.50   \n",
       "4  LandingSite_CaptureSiteCategory_2  Species_5  NotTagged_0113  None   34.70   \n",
       "\n",
       "   CCW_cm  Weight_Kg      Sex  \\\n",
       "0   62.60        7.5  Unknown   \n",
       "1   31.35        7.5  Unknown   \n",
       "2   49.20        7.5  Unknown   \n",
       "3   59.00        7.5  Unknown   \n",
       "4   33.00        7.5  Unknown   \n",
       "\n",
       "                               TurtleCharacteristics    Status  \\\n",
       "0                             algae at rear of shell  Released   \n",
       "1  multiple b's on front flippers&  a lot of alga...  Released   \n",
       "2                                              clean  Released   \n",
       "3  1 b 3 CS+ calcerous algae at rear end of shell...  Released   \n",
       "4  very lively+ right eye is hanging out + swolle...  Released   \n",
       "\n",
       "      ReleaseSite Date_TimeRelease  year_caught  month_caught  \\\n",
       "0  ReleaseSite_50       2000-12-22         2000            12   \n",
       "1  ReleaseSite_62       2001-10-28         2001            10   \n",
       "2  ReleaseSite_50       2001-01-11         2001            11   \n",
       "3  ReleaseSite_50       2002-11-03         2002             3   \n",
       "4  ReleaseSite_62       2002-08-08         2002             8   \n",
       "\n",
       "   week_of_year_caught  day_caught  day_of_week_caught  year_woy  day_of_year  \n",
       "0                   51          22                   4    200051          357  \n",
       "1                   43          28                   6    200143          301  \n",
       "2                   44           1                   3    200144          305  \n",
       "3                   11          11                   0    200211           70  \n",
       "4                   32           8                   3    200232          220  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>year_woy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>200051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CaptureSite_1</td>\n",
       "      <td>200123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CaptureSite_10</td>\n",
       "      <td>200050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CaptureSite_11</td>\n",
       "      <td>199828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CaptureSite_12</td>\n",
       "      <td>200107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CaptureSite  year_woy\n",
       "0   CaptureSite_0    200051\n",
       "1   CaptureSite_1    200123\n",
       "2  CaptureSite_10    200050\n",
       "3  CaptureSite_11    199828\n",
       "4  CaptureSite_12    200107"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys=pd.concat([df[[\"year_woy\",\"CaptureSite\"]],sample_submission[[\"year_woy\",\"CaptureSite\"]]])\n",
    "# keys.sort_values([\"CaptureSite\",\"year_woy\"],inplace=True)\n",
    "CaptureSite_min_year_woy=keys.groupby(\"CaptureSite\").year_woy.min().rename(\"year_woy\").reset_index()\n",
    "CaptureSite_min_year_woy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "range_year_woy=pd.DataFrame()\n",
    "range_year_woy[\"Date_TimeCaught\"]=pd.date_range(start=df.Date_TimeCaught.min(),end=datetime(2019,10,31))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7863</th>\n",
       "      <td>2019-10-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7864</th>\n",
       "      <td>2019-10-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7865</th>\n",
       "      <td>2019-10-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7866</th>\n",
       "      <td>2019-10-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7867</th>\n",
       "      <td>2019-10-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date_TimeCaught\n",
       "7863      2019-10-27\n",
       "7864      2019-10-28\n",
       "7865      2019-10-29\n",
       "7866      2019-10-30\n",
       "7867      2019-10-31"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range_year_woy.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998-04-17</td>\n",
       "      <td>1998</td>\n",
       "      <td>16</td>\n",
       "      <td>199816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1998-04-20</td>\n",
       "      <td>1998</td>\n",
       "      <td>17</td>\n",
       "      <td>199817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1998-04-27</td>\n",
       "      <td>1998</td>\n",
       "      <td>18</td>\n",
       "      <td>199818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1998-05-04</td>\n",
       "      <td>1998</td>\n",
       "      <td>19</td>\n",
       "      <td>199819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1998-05-11</td>\n",
       "      <td>1998</td>\n",
       "      <td>20</td>\n",
       "      <td>199820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1998-05-18</td>\n",
       "      <td>1998</td>\n",
       "      <td>21</td>\n",
       "      <td>199821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1998-05-25</td>\n",
       "      <td>1998</td>\n",
       "      <td>22</td>\n",
       "      <td>199822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1998-06-01</td>\n",
       "      <td>1998</td>\n",
       "      <td>23</td>\n",
       "      <td>199823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1998-06-08</td>\n",
       "      <td>1998</td>\n",
       "      <td>24</td>\n",
       "      <td>199824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1998-06-15</td>\n",
       "      <td>1998</td>\n",
       "      <td>25</td>\n",
       "      <td>199825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1998-06-22</td>\n",
       "      <td>1998</td>\n",
       "      <td>26</td>\n",
       "      <td>199826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1998-06-29</td>\n",
       "      <td>1998</td>\n",
       "      <td>27</td>\n",
       "      <td>199827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1998-07-06</td>\n",
       "      <td>1998</td>\n",
       "      <td>28</td>\n",
       "      <td>199828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1998-07-13</td>\n",
       "      <td>1998</td>\n",
       "      <td>29</td>\n",
       "      <td>199829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1998-07-20</td>\n",
       "      <td>1998</td>\n",
       "      <td>30</td>\n",
       "      <td>199830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1998-07-27</td>\n",
       "      <td>1998</td>\n",
       "      <td>31</td>\n",
       "      <td>199831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1998-08-03</td>\n",
       "      <td>1998</td>\n",
       "      <td>32</td>\n",
       "      <td>199832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1998-08-10</td>\n",
       "      <td>1998</td>\n",
       "      <td>33</td>\n",
       "      <td>199833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1998-08-17</td>\n",
       "      <td>1998</td>\n",
       "      <td>34</td>\n",
       "      <td>199834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1998-08-24</td>\n",
       "      <td>1998</td>\n",
       "      <td>35</td>\n",
       "      <td>199835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1998-08-31</td>\n",
       "      <td>1998</td>\n",
       "      <td>36</td>\n",
       "      <td>199836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1998-09-07</td>\n",
       "      <td>1998</td>\n",
       "      <td>37</td>\n",
       "      <td>199837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>1998-09-14</td>\n",
       "      <td>1998</td>\n",
       "      <td>38</td>\n",
       "      <td>199838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>1998-09-21</td>\n",
       "      <td>1998</td>\n",
       "      <td>39</td>\n",
       "      <td>199839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1998-09-28</td>\n",
       "      <td>1998</td>\n",
       "      <td>40</td>\n",
       "      <td>199840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>1998-10-05</td>\n",
       "      <td>1998</td>\n",
       "      <td>41</td>\n",
       "      <td>199841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1998-10-12</td>\n",
       "      <td>1998</td>\n",
       "      <td>42</td>\n",
       "      <td>199842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>1998-10-19</td>\n",
       "      <td>1998</td>\n",
       "      <td>43</td>\n",
       "      <td>199843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1998-10-26</td>\n",
       "      <td>1998</td>\n",
       "      <td>44</td>\n",
       "      <td>199844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>1998-11-02</td>\n",
       "      <td>1998</td>\n",
       "      <td>45</td>\n",
       "      <td>199845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>2019-04-08</td>\n",
       "      <td>2019</td>\n",
       "      <td>15</td>\n",
       "      <td>201915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7668</th>\n",
       "      <td>2019-04-15</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>201916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>2019-04-22</td>\n",
       "      <td>2019</td>\n",
       "      <td>17</td>\n",
       "      <td>201917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7682</th>\n",
       "      <td>2019-04-29</td>\n",
       "      <td>2019</td>\n",
       "      <td>18</td>\n",
       "      <td>201918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7689</th>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>2019</td>\n",
       "      <td>19</td>\n",
       "      <td>201919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7696</th>\n",
       "      <td>2019-05-13</td>\n",
       "      <td>2019</td>\n",
       "      <td>20</td>\n",
       "      <td>201920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7703</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>201921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7710</th>\n",
       "      <td>2019-05-27</td>\n",
       "      <td>2019</td>\n",
       "      <td>22</td>\n",
       "      <td>201922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7717</th>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>201923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7724</th>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>2019</td>\n",
       "      <td>24</td>\n",
       "      <td>201924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7731</th>\n",
       "      <td>2019-06-17</td>\n",
       "      <td>2019</td>\n",
       "      <td>25</td>\n",
       "      <td>201925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7738</th>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>2019</td>\n",
       "      <td>26</td>\n",
       "      <td>201926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7745</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>27</td>\n",
       "      <td>201927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7752</th>\n",
       "      <td>2019-07-08</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "      <td>201928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7759</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>201929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7766</th>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>201930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7773</th>\n",
       "      <td>2019-07-29</td>\n",
       "      <td>2019</td>\n",
       "      <td>31</td>\n",
       "      <td>201931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7780</th>\n",
       "      <td>2019-08-05</td>\n",
       "      <td>2019</td>\n",
       "      <td>32</td>\n",
       "      <td>201932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7787</th>\n",
       "      <td>2019-08-12</td>\n",
       "      <td>2019</td>\n",
       "      <td>33</td>\n",
       "      <td>201933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7794</th>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>2019</td>\n",
       "      <td>34</td>\n",
       "      <td>201934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7801</th>\n",
       "      <td>2019-08-26</td>\n",
       "      <td>2019</td>\n",
       "      <td>35</td>\n",
       "      <td>201935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7808</th>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019</td>\n",
       "      <td>36</td>\n",
       "      <td>201936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7815</th>\n",
       "      <td>2019-09-09</td>\n",
       "      <td>2019</td>\n",
       "      <td>37</td>\n",
       "      <td>201937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7822</th>\n",
       "      <td>2019-09-16</td>\n",
       "      <td>2019</td>\n",
       "      <td>38</td>\n",
       "      <td>201938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7829</th>\n",
       "      <td>2019-09-23</td>\n",
       "      <td>2019</td>\n",
       "      <td>39</td>\n",
       "      <td>201939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7836</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>201940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7843</th>\n",
       "      <td>2019-10-07</td>\n",
       "      <td>2019</td>\n",
       "      <td>41</td>\n",
       "      <td>201941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7850</th>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>2019</td>\n",
       "      <td>42</td>\n",
       "      <td>201942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7857</th>\n",
       "      <td>2019-10-21</td>\n",
       "      <td>2019</td>\n",
       "      <td>43</td>\n",
       "      <td>201943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7864</th>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>44</td>\n",
       "      <td>201944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1129 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date_TimeCaught  year  week_of_year  year_woy\n",
       "0         1998-04-17  1998            16    199816\n",
       "3         1998-04-20  1998            17    199817\n",
       "10        1998-04-27  1998            18    199818\n",
       "17        1998-05-04  1998            19    199819\n",
       "24        1998-05-11  1998            20    199820\n",
       "31        1998-05-18  1998            21    199821\n",
       "38        1998-05-25  1998            22    199822\n",
       "45        1998-06-01  1998            23    199823\n",
       "52        1998-06-08  1998            24    199824\n",
       "59        1998-06-15  1998            25    199825\n",
       "66        1998-06-22  1998            26    199826\n",
       "73        1998-06-29  1998            27    199827\n",
       "80        1998-07-06  1998            28    199828\n",
       "87        1998-07-13  1998            29    199829\n",
       "94        1998-07-20  1998            30    199830\n",
       "101       1998-07-27  1998            31    199831\n",
       "108       1998-08-03  1998            32    199832\n",
       "115       1998-08-10  1998            33    199833\n",
       "122       1998-08-17  1998            34    199834\n",
       "129       1998-08-24  1998            35    199835\n",
       "136       1998-08-31  1998            36    199836\n",
       "143       1998-09-07  1998            37    199837\n",
       "150       1998-09-14  1998            38    199838\n",
       "157       1998-09-21  1998            39    199839\n",
       "164       1998-09-28  1998            40    199840\n",
       "171       1998-10-05  1998            41    199841\n",
       "178       1998-10-12  1998            42    199842\n",
       "185       1998-10-19  1998            43    199843\n",
       "192       1998-10-26  1998            44    199844\n",
       "199       1998-11-02  1998            45    199845\n",
       "...              ...   ...           ...       ...\n",
       "7661      2019-04-08  2019            15    201915\n",
       "7668      2019-04-15  2019            16    201916\n",
       "7675      2019-04-22  2019            17    201917\n",
       "7682      2019-04-29  2019            18    201918\n",
       "7689      2019-05-06  2019            19    201919\n",
       "7696      2019-05-13  2019            20    201920\n",
       "7703      2019-05-20  2019            21    201921\n",
       "7710      2019-05-27  2019            22    201922\n",
       "7717      2019-06-03  2019            23    201923\n",
       "7724      2019-06-10  2019            24    201924\n",
       "7731      2019-06-17  2019            25    201925\n",
       "7738      2019-06-24  2019            26    201926\n",
       "7745      2019-07-01  2019            27    201927\n",
       "7752      2019-07-08  2019            28    201928\n",
       "7759      2019-07-15  2019            29    201929\n",
       "7766      2019-07-22  2019            30    201930\n",
       "7773      2019-07-29  2019            31    201931\n",
       "7780      2019-08-05  2019            32    201932\n",
       "7787      2019-08-12  2019            33    201933\n",
       "7794      2019-08-19  2019            34    201934\n",
       "7801      2019-08-26  2019            35    201935\n",
       "7808      2019-09-02  2019            36    201936\n",
       "7815      2019-09-09  2019            37    201937\n",
       "7822      2019-09-16  2019            38    201938\n",
       "7829      2019-09-23  2019            39    201939\n",
       "7836      2019-09-30  2019            40    201940\n",
       "7843      2019-10-07  2019            41    201941\n",
       "7850      2019-10-14  2019            42    201942\n",
       "7857      2019-10-21  2019            43    201943\n",
       "7864      2019-10-28  2019            44    201944\n",
       "\n",
       "[1129 rows x 4 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range_year_woy[\"year\"]=range_year_woy.Date_TimeCaught.dt.year\n",
    "range_year_woy[\"week_of_year\"]=range_year_woy.Date_TimeCaught.dt.weekofyear\n",
    "range_year_woy[\"year_woy\"]=range_year_woy.year*100+range_year_woy.week_of_year\n",
    "range_year_woy.drop_duplicates([\"year_woy\"],inplace=True)\n",
    "range_year_woy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wanda\\.conda\\envs\\deepml37\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "28048"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data=[]\n",
    "for site , year_woy in zip(CaptureSite_min_year_woy.CaptureSite.values,CaptureSite_min_year_woy.year_woy.values) :\n",
    "\n",
    "    one_site_df=range_year_woy[range_year_woy.year_woy>=year_woy]\n",
    "    one_site_df[\"CaptureSite\"]=site\n",
    "    final_data.append(one_site_df)\n",
    "final_data=pd.concat(final_data)\n",
    "len(final_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "Target=df.groupby([\"year_woy\",\"CaptureSite\"]).CaptureSite.count().rename(\"Capture_Number\").reset_index()\n",
    "final_data=final_data.merge(Target,on=[\"year_woy\",\"CaptureSite\"],how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data.Capture_Number.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>Capture_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>200701</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>2007-01-08</td>\n",
       "      <td>2007</td>\n",
       "      <td>2</td>\n",
       "      <td>200702</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>2007-01-15</td>\n",
       "      <td>2007</td>\n",
       "      <td>3</td>\n",
       "      <td>200703</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>2007-01-22</td>\n",
       "      <td>2007</td>\n",
       "      <td>4</td>\n",
       "      <td>200704</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>2007-01-29</td>\n",
       "      <td>2007</td>\n",
       "      <td>5</td>\n",
       "      <td>200705</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>2007-02-05</td>\n",
       "      <td>2007</td>\n",
       "      <td>6</td>\n",
       "      <td>200706</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>2007-02-12</td>\n",
       "      <td>2007</td>\n",
       "      <td>7</td>\n",
       "      <td>200707</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2007-02-19</td>\n",
       "      <td>2007</td>\n",
       "      <td>8</td>\n",
       "      <td>200708</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2007-02-26</td>\n",
       "      <td>2007</td>\n",
       "      <td>9</td>\n",
       "      <td>200709</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>2007-03-05</td>\n",
       "      <td>2007</td>\n",
       "      <td>10</td>\n",
       "      <td>200710</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>2007-03-12</td>\n",
       "      <td>2007</td>\n",
       "      <td>11</td>\n",
       "      <td>200711</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>2007-03-19</td>\n",
       "      <td>2007</td>\n",
       "      <td>12</td>\n",
       "      <td>200712</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>2007-03-26</td>\n",
       "      <td>2007</td>\n",
       "      <td>13</td>\n",
       "      <td>200713</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>2007-04-02</td>\n",
       "      <td>2007</td>\n",
       "      <td>14</td>\n",
       "      <td>200714</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>2007-04-09</td>\n",
       "      <td>2007</td>\n",
       "      <td>15</td>\n",
       "      <td>200715</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>2007-04-16</td>\n",
       "      <td>2007</td>\n",
       "      <td>16</td>\n",
       "      <td>200716</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>2007-04-23</td>\n",
       "      <td>2007</td>\n",
       "      <td>17</td>\n",
       "      <td>200717</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>2007-04-30</td>\n",
       "      <td>2007</td>\n",
       "      <td>18</td>\n",
       "      <td>200718</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>2007-05-07</td>\n",
       "      <td>2007</td>\n",
       "      <td>19</td>\n",
       "      <td>200719</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2007-05-14</td>\n",
       "      <td>2007</td>\n",
       "      <td>20</td>\n",
       "      <td>200720</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2007-05-21</td>\n",
       "      <td>2007</td>\n",
       "      <td>21</td>\n",
       "      <td>200721</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2007-05-28</td>\n",
       "      <td>2007</td>\n",
       "      <td>22</td>\n",
       "      <td>200722</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2007-06-04</td>\n",
       "      <td>2007</td>\n",
       "      <td>23</td>\n",
       "      <td>200723</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2007-06-11</td>\n",
       "      <td>2007</td>\n",
       "      <td>24</td>\n",
       "      <td>200724</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2007-06-18</td>\n",
       "      <td>2007</td>\n",
       "      <td>25</td>\n",
       "      <td>200725</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>2007-06-25</td>\n",
       "      <td>2007</td>\n",
       "      <td>26</td>\n",
       "      <td>200726</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>2007</td>\n",
       "      <td>27</td>\n",
       "      <td>200727</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>2007-07-09</td>\n",
       "      <td>2007</td>\n",
       "      <td>28</td>\n",
       "      <td>200728</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>2007-07-16</td>\n",
       "      <td>2007</td>\n",
       "      <td>29</td>\n",
       "      <td>200729</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>2007-07-23</td>\n",
       "      <td>2007</td>\n",
       "      <td>30</td>\n",
       "      <td>200730</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27503</th>\n",
       "      <td>2009-06-08</td>\n",
       "      <td>2009</td>\n",
       "      <td>24</td>\n",
       "      <td>200924</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27504</th>\n",
       "      <td>2009-06-15</td>\n",
       "      <td>2009</td>\n",
       "      <td>25</td>\n",
       "      <td>200925</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27505</th>\n",
       "      <td>2009-06-22</td>\n",
       "      <td>2009</td>\n",
       "      <td>26</td>\n",
       "      <td>200926</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27506</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>2009</td>\n",
       "      <td>27</td>\n",
       "      <td>200927</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27507</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>2009</td>\n",
       "      <td>28</td>\n",
       "      <td>200928</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27508</th>\n",
       "      <td>2009-07-13</td>\n",
       "      <td>2009</td>\n",
       "      <td>29</td>\n",
       "      <td>200929</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27509</th>\n",
       "      <td>2009-07-20</td>\n",
       "      <td>2009</td>\n",
       "      <td>30</td>\n",
       "      <td>200930</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27510</th>\n",
       "      <td>2009-07-27</td>\n",
       "      <td>2009</td>\n",
       "      <td>31</td>\n",
       "      <td>200931</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27511</th>\n",
       "      <td>2009-08-03</td>\n",
       "      <td>2009</td>\n",
       "      <td>32</td>\n",
       "      <td>200932</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27512</th>\n",
       "      <td>2009-08-10</td>\n",
       "      <td>2009</td>\n",
       "      <td>33</td>\n",
       "      <td>200933</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27513</th>\n",
       "      <td>2009-08-17</td>\n",
       "      <td>2009</td>\n",
       "      <td>34</td>\n",
       "      <td>200934</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27514</th>\n",
       "      <td>2009-08-24</td>\n",
       "      <td>2009</td>\n",
       "      <td>35</td>\n",
       "      <td>200935</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27515</th>\n",
       "      <td>2009-08-31</td>\n",
       "      <td>2009</td>\n",
       "      <td>36</td>\n",
       "      <td>200936</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27516</th>\n",
       "      <td>2009-09-07</td>\n",
       "      <td>2009</td>\n",
       "      <td>37</td>\n",
       "      <td>200937</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27517</th>\n",
       "      <td>2009-09-14</td>\n",
       "      <td>2009</td>\n",
       "      <td>38</td>\n",
       "      <td>200938</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27518</th>\n",
       "      <td>2009-09-21</td>\n",
       "      <td>2009</td>\n",
       "      <td>39</td>\n",
       "      <td>200939</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27519</th>\n",
       "      <td>2009-09-28</td>\n",
       "      <td>2009</td>\n",
       "      <td>40</td>\n",
       "      <td>200940</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27520</th>\n",
       "      <td>2009-10-05</td>\n",
       "      <td>2009</td>\n",
       "      <td>41</td>\n",
       "      <td>200941</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27521</th>\n",
       "      <td>2009-10-12</td>\n",
       "      <td>2009</td>\n",
       "      <td>42</td>\n",
       "      <td>200942</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27522</th>\n",
       "      <td>2009-10-19</td>\n",
       "      <td>2009</td>\n",
       "      <td>43</td>\n",
       "      <td>200943</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27523</th>\n",
       "      <td>2009-10-26</td>\n",
       "      <td>2009</td>\n",
       "      <td>44</td>\n",
       "      <td>200944</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27524</th>\n",
       "      <td>2009-11-02</td>\n",
       "      <td>2009</td>\n",
       "      <td>45</td>\n",
       "      <td>200945</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27525</th>\n",
       "      <td>2009-11-09</td>\n",
       "      <td>2009</td>\n",
       "      <td>46</td>\n",
       "      <td>200946</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27526</th>\n",
       "      <td>2009-11-16</td>\n",
       "      <td>2009</td>\n",
       "      <td>47</td>\n",
       "      <td>200947</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27527</th>\n",
       "      <td>2009-11-23</td>\n",
       "      <td>2009</td>\n",
       "      <td>48</td>\n",
       "      <td>200948</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27528</th>\n",
       "      <td>2009-11-30</td>\n",
       "      <td>2009</td>\n",
       "      <td>49</td>\n",
       "      <td>200949</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27529</th>\n",
       "      <td>2009-12-07</td>\n",
       "      <td>2009</td>\n",
       "      <td>50</td>\n",
       "      <td>200950</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27530</th>\n",
       "      <td>2009-12-14</td>\n",
       "      <td>2009</td>\n",
       "      <td>51</td>\n",
       "      <td>200951</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27531</th>\n",
       "      <td>2009-12-21</td>\n",
       "      <td>2009</td>\n",
       "      <td>52</td>\n",
       "      <td>200952</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27532</th>\n",
       "      <td>2009-12-28</td>\n",
       "      <td>2009</td>\n",
       "      <td>53</td>\n",
       "      <td>200953</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3027 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date_TimeCaught  year  week_of_year  year_woy    CaptureSite  \\\n",
       "316        2007-01-01  2007             1    200701  CaptureSite_0   \n",
       "317        2007-01-08  2007             2    200702  CaptureSite_0   \n",
       "318        2007-01-15  2007             3    200703  CaptureSite_0   \n",
       "319        2007-01-22  2007             4    200704  CaptureSite_0   \n",
       "320        2007-01-29  2007             5    200705  CaptureSite_0   \n",
       "321        2007-02-05  2007             6    200706  CaptureSite_0   \n",
       "322        2007-02-12  2007             7    200707  CaptureSite_0   \n",
       "323        2007-02-19  2007             8    200708  CaptureSite_0   \n",
       "324        2007-02-26  2007             9    200709  CaptureSite_0   \n",
       "325        2007-03-05  2007            10    200710  CaptureSite_0   \n",
       "326        2007-03-12  2007            11    200711  CaptureSite_0   \n",
       "327        2007-03-19  2007            12    200712  CaptureSite_0   \n",
       "328        2007-03-26  2007            13    200713  CaptureSite_0   \n",
       "329        2007-04-02  2007            14    200714  CaptureSite_0   \n",
       "330        2007-04-09  2007            15    200715  CaptureSite_0   \n",
       "331        2007-04-16  2007            16    200716  CaptureSite_0   \n",
       "332        2007-04-23  2007            17    200717  CaptureSite_0   \n",
       "333        2007-04-30  2007            18    200718  CaptureSite_0   \n",
       "334        2007-05-07  2007            19    200719  CaptureSite_0   \n",
       "335        2007-05-14  2007            20    200720  CaptureSite_0   \n",
       "336        2007-05-21  2007            21    200721  CaptureSite_0   \n",
       "337        2007-05-28  2007            22    200722  CaptureSite_0   \n",
       "338        2007-06-04  2007            23    200723  CaptureSite_0   \n",
       "339        2007-06-11  2007            24    200724  CaptureSite_0   \n",
       "340        2007-06-18  2007            25    200725  CaptureSite_0   \n",
       "341        2007-06-25  2007            26    200726  CaptureSite_0   \n",
       "342        2007-07-02  2007            27    200727  CaptureSite_0   \n",
       "343        2007-07-09  2007            28    200728  CaptureSite_0   \n",
       "344        2007-07-16  2007            29    200729  CaptureSite_0   \n",
       "345        2007-07-23  2007            30    200730  CaptureSite_0   \n",
       "...               ...   ...           ...       ...            ...   \n",
       "27503      2009-06-08  2009            24    200924  CaptureSite_9   \n",
       "27504      2009-06-15  2009            25    200925  CaptureSite_9   \n",
       "27505      2009-06-22  2009            26    200926  CaptureSite_9   \n",
       "27506      2009-06-29  2009            27    200927  CaptureSite_9   \n",
       "27507      2009-07-06  2009            28    200928  CaptureSite_9   \n",
       "27508      2009-07-13  2009            29    200929  CaptureSite_9   \n",
       "27509      2009-07-20  2009            30    200930  CaptureSite_9   \n",
       "27510      2009-07-27  2009            31    200931  CaptureSite_9   \n",
       "27511      2009-08-03  2009            32    200932  CaptureSite_9   \n",
       "27512      2009-08-10  2009            33    200933  CaptureSite_9   \n",
       "27513      2009-08-17  2009            34    200934  CaptureSite_9   \n",
       "27514      2009-08-24  2009            35    200935  CaptureSite_9   \n",
       "27515      2009-08-31  2009            36    200936  CaptureSite_9   \n",
       "27516      2009-09-07  2009            37    200937  CaptureSite_9   \n",
       "27517      2009-09-14  2009            38    200938  CaptureSite_9   \n",
       "27518      2009-09-21  2009            39    200939  CaptureSite_9   \n",
       "27519      2009-09-28  2009            40    200940  CaptureSite_9   \n",
       "27520      2009-10-05  2009            41    200941  CaptureSite_9   \n",
       "27521      2009-10-12  2009            42    200942  CaptureSite_9   \n",
       "27522      2009-10-19  2009            43    200943  CaptureSite_9   \n",
       "27523      2009-10-26  2009            44    200944  CaptureSite_9   \n",
       "27524      2009-11-02  2009            45    200945  CaptureSite_9   \n",
       "27525      2009-11-09  2009            46    200946  CaptureSite_9   \n",
       "27526      2009-11-16  2009            47    200947  CaptureSite_9   \n",
       "27527      2009-11-23  2009            48    200948  CaptureSite_9   \n",
       "27528      2009-11-30  2009            49    200949  CaptureSite_9   \n",
       "27529      2009-12-07  2009            50    200950  CaptureSite_9   \n",
       "27530      2009-12-14  2009            51    200951  CaptureSite_9   \n",
       "27531      2009-12-21  2009            52    200952  CaptureSite_9   \n",
       "27532      2009-12-28  2009            53    200953  CaptureSite_9   \n",
       "\n",
       "       Capture_Number  \n",
       "316               0.0  \n",
       "317               0.0  \n",
       "318               0.0  \n",
       "319               0.0  \n",
       "320               0.0  \n",
       "321               0.0  \n",
       "322               0.0  \n",
       "323               0.0  \n",
       "324               0.0  \n",
       "325               0.0  \n",
       "326               0.0  \n",
       "327               0.0  \n",
       "328               0.0  \n",
       "329               0.0  \n",
       "330               0.0  \n",
       "331               0.0  \n",
       "332               0.0  \n",
       "333               0.0  \n",
       "334               0.0  \n",
       "335               0.0  \n",
       "336               0.0  \n",
       "337               0.0  \n",
       "338               0.0  \n",
       "339               0.0  \n",
       "340               0.0  \n",
       "341               0.0  \n",
       "342               0.0  \n",
       "343               0.0  \n",
       "344               0.0  \n",
       "345               0.0  \n",
       "...               ...  \n",
       "27503             0.0  \n",
       "27504             0.0  \n",
       "27505             0.0  \n",
       "27506             0.0  \n",
       "27507             0.0  \n",
       "27508             0.0  \n",
       "27509             0.0  \n",
       "27510             0.0  \n",
       "27511             1.0  \n",
       "27512             0.0  \n",
       "27513             2.0  \n",
       "27514             1.0  \n",
       "27515             0.0  \n",
       "27516             1.0  \n",
       "27517             1.0  \n",
       "27518             1.0  \n",
       "27519             0.0  \n",
       "27520             1.0  \n",
       "27521             0.0  \n",
       "27522             0.0  \n",
       "27523             4.0  \n",
       "27524             0.0  \n",
       "27525             1.0  \n",
       "27526             1.0  \n",
       "27527             0.0  \n",
       "27528             0.0  \n",
       "27529             7.0  \n",
       "27530             0.0  \n",
       "27531             3.0  \n",
       "27532             4.0  \n",
       "\n",
       "[3027 rows x 6 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data[(final_data.year==2007)|(final_data.year==2009)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=final_data[final_data.year<2019]\n",
    "test=final_data[final_data.year==2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>Capture_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>201901</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>201902</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>201903</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>201904</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>201905</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date_TimeCaught  year  week_of_year  year_woy    CaptureSite  \\\n",
       "944      2019-01-01  2019             1    201901  CaptureSite_0   \n",
       "945      2019-01-07  2019             2    201902  CaptureSite_0   \n",
       "946      2019-01-14  2019             3    201903  CaptureSite_0   \n",
       "947      2019-01-21  2019             4    201904  CaptureSite_0   \n",
       "948      2019-01-28  2019             5    201905  CaptureSite_0   \n",
       "\n",
       "     Capture_Number  \n",
       "944             0.0  \n",
       "945             0.0  \n",
       "946             0.0  \n",
       "947             0.0  \n",
       "948             0.0  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    1276\n",
       "Name: Capture_Number, dtype: int64"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "test['Capture_Number'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv(\"final_data.csv\",index=False)\n",
    "test.to_csv(\"submission_data.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Piecing everything together "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>Capture_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26767</th>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>2018</td>\n",
       "      <td>48</td>\n",
       "      <td>201848</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26768</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>2018</td>\n",
       "      <td>49</td>\n",
       "      <td>201849</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26769</th>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>2018</td>\n",
       "      <td>50</td>\n",
       "      <td>201850</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26770</th>\n",
       "      <td>2018-12-17</td>\n",
       "      <td>2018</td>\n",
       "      <td>51</td>\n",
       "      <td>201851</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26771</th>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>2018</td>\n",
       "      <td>52</td>\n",
       "      <td>201852</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date_TimeCaught  year  week_of_year  year_woy    CaptureSite  \\\n",
       "26767      2018-11-26  2018            48    201848  CaptureSite_9   \n",
       "26768      2018-12-03  2018            49    201849  CaptureSite_9   \n",
       "26769      2018-12-10  2018            50    201850  CaptureSite_9   \n",
       "26770      2018-12-17  2018            51    201851  CaptureSite_9   \n",
       "26771      2018-12-24  2018            52    201852  CaptureSite_9   \n",
       "\n",
       "       Capture_Number  \n",
       "26767             6.0  \n",
       "26768             6.0  \n",
       "26769            12.0  \n",
       "26770             5.0  \n",
       "26771             3.0  "
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dates = pd.read_csv('final_data.csv')\n",
    "df_dates.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>Capture_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>201901</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>201902</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>201903</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>201904</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>201905</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date_TimeCaught  year  week_of_year  year_woy    CaptureSite  Capture_Number\n",
       "0      2019-01-01  2019             1    201901  CaptureSite_0             0.0\n",
       "1      2019-01-07  2019             2    201902  CaptureSite_0             0.0\n",
       "2      2019-01-14  2019             3    201903  CaptureSite_0             0.0\n",
       "3      2019-01-21  2019             4    201904  CaptureSite_0             0.0\n",
       "4      2019-01-28  2019             5    201905  CaptureSite_0             0.0"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submission = pd.read_csv('submission_data.csv')\n",
    "df_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dates['Date_TimeCaught'] = pd.to_datetime(df_dates['Date_TimeCaught'], format='%Y-%m-%d')\n",
    "df_submission['Date_TimeCaught'] = pd.to_datetime(df_submission['Date_TimeCaught'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dates['day_of_year']= pd.DatetimeIndex(df_dates['Date_TimeCaught']).dayofyear\n",
    "df_submission['day_of_year']= pd.DatetimeIndex(df_submission['Date_TimeCaught']).dayofyear\n",
    "df_dates['month'] = df_dates['Date_TimeCaught'].dt.month\n",
    "df_submission['month'] = df_submission['Date_TimeCaught'].dt.month\n",
    "df_dates['day_of_week_caught'] = pd.DatetimeIndex(df_dates['Date_TimeCaught']).dayofweek\n",
    "df_submission['day_of_week_caught'] = pd.DatetimeIndex(df_submission['Date_TimeCaught']).dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dates['day_of_month'] = pd.DatetimeIndex(df_dates['Date_TimeCaught']).day\n",
    "df_submission['day_of_month'] = pd.DatetimeIndex(df_submission['Date_TimeCaught']).day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>Capture_Number</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>day_of_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>201901</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>201902</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>201903</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>201904</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>201905</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-02-04</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>201906</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-02-11</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>201907</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-02-18</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>201908</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-02-25</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>201909</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>201910</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-03-11</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>201911</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>201912</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-03-25</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "      <td>201913</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>14</td>\n",
       "      <td>201914</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-04-08</td>\n",
       "      <td>2019</td>\n",
       "      <td>15</td>\n",
       "      <td>201915</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-04-15</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>201916</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-04-22</td>\n",
       "      <td>2019</td>\n",
       "      <td>17</td>\n",
       "      <td>201917</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-04-29</td>\n",
       "      <td>2019</td>\n",
       "      <td>18</td>\n",
       "      <td>201918</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>2019</td>\n",
       "      <td>19</td>\n",
       "      <td>201919</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-05-13</td>\n",
       "      <td>2019</td>\n",
       "      <td>20</td>\n",
       "      <td>201920</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>201921</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-05-27</td>\n",
       "      <td>2019</td>\n",
       "      <td>22</td>\n",
       "      <td>201922</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>201923</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>2019</td>\n",
       "      <td>24</td>\n",
       "      <td>201924</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-06-17</td>\n",
       "      <td>2019</td>\n",
       "      <td>25</td>\n",
       "      <td>201925</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>2019</td>\n",
       "      <td>26</td>\n",
       "      <td>201926</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>175</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>27</td>\n",
       "      <td>201927</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-07-08</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "      <td>201928</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>189</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>201929</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>201930</td>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>203</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>2019-04-08</td>\n",
       "      <td>2019</td>\n",
       "      <td>15</td>\n",
       "      <td>201915</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>2019-04-15</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>201916</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>2019-04-22</td>\n",
       "      <td>2019</td>\n",
       "      <td>17</td>\n",
       "      <td>201917</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>2019-04-29</td>\n",
       "      <td>2019</td>\n",
       "      <td>18</td>\n",
       "      <td>201918</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>2019</td>\n",
       "      <td>19</td>\n",
       "      <td>201919</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>2019-05-13</td>\n",
       "      <td>2019</td>\n",
       "      <td>20</td>\n",
       "      <td>201920</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>2019</td>\n",
       "      <td>21</td>\n",
       "      <td>201921</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>2019-05-27</td>\n",
       "      <td>2019</td>\n",
       "      <td>22</td>\n",
       "      <td>201922</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>201923</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>2019</td>\n",
       "      <td>24</td>\n",
       "      <td>201924</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2019-06-17</td>\n",
       "      <td>2019</td>\n",
       "      <td>25</td>\n",
       "      <td>201925</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>2019</td>\n",
       "      <td>26</td>\n",
       "      <td>201926</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>175</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>27</td>\n",
       "      <td>201927</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>2019-07-08</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "      <td>201928</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>189</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>201929</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>2019-07-22</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>201930</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>203</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>2019-07-29</td>\n",
       "      <td>2019</td>\n",
       "      <td>31</td>\n",
       "      <td>201931</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>210</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>2019-08-05</td>\n",
       "      <td>2019</td>\n",
       "      <td>32</td>\n",
       "      <td>201932</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>217</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1264</th>\n",
       "      <td>2019-08-12</td>\n",
       "      <td>2019</td>\n",
       "      <td>33</td>\n",
       "      <td>201933</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>224</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>2019</td>\n",
       "      <td>34</td>\n",
       "      <td>201934</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>231</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>2019-08-26</td>\n",
       "      <td>2019</td>\n",
       "      <td>35</td>\n",
       "      <td>201935</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019</td>\n",
       "      <td>36</td>\n",
       "      <td>201936</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>2019-09-09</td>\n",
       "      <td>2019</td>\n",
       "      <td>37</td>\n",
       "      <td>201937</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>2019-09-16</td>\n",
       "      <td>2019</td>\n",
       "      <td>38</td>\n",
       "      <td>201938</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>259</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>2019-09-23</td>\n",
       "      <td>2019</td>\n",
       "      <td>39</td>\n",
       "      <td>201939</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>201940</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>273</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>2019-10-07</td>\n",
       "      <td>2019</td>\n",
       "      <td>41</td>\n",
       "      <td>201941</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>280</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>2019</td>\n",
       "      <td>42</td>\n",
       "      <td>201942</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>287</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>2019-10-21</td>\n",
       "      <td>2019</td>\n",
       "      <td>43</td>\n",
       "      <td>201943</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>294</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>44</td>\n",
       "      <td>201944</td>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>301</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date_TimeCaught  year  week_of_year  year_woy    CaptureSite  \\\n",
       "0         2019-01-01  2019             1    201901  CaptureSite_0   \n",
       "1         2019-01-07  2019             2    201902  CaptureSite_0   \n",
       "2         2019-01-14  2019             3    201903  CaptureSite_0   \n",
       "3         2019-01-21  2019             4    201904  CaptureSite_0   \n",
       "4         2019-01-28  2019             5    201905  CaptureSite_0   \n",
       "5         2019-02-04  2019             6    201906  CaptureSite_0   \n",
       "6         2019-02-11  2019             7    201907  CaptureSite_0   \n",
       "7         2019-02-18  2019             8    201908  CaptureSite_0   \n",
       "8         2019-02-25  2019             9    201909  CaptureSite_0   \n",
       "9         2019-03-04  2019            10    201910  CaptureSite_0   \n",
       "10        2019-03-11  2019            11    201911  CaptureSite_0   \n",
       "11        2019-03-18  2019            12    201912  CaptureSite_0   \n",
       "12        2019-03-25  2019            13    201913  CaptureSite_0   \n",
       "13        2019-04-01  2019            14    201914  CaptureSite_0   \n",
       "14        2019-04-08  2019            15    201915  CaptureSite_0   \n",
       "15        2019-04-15  2019            16    201916  CaptureSite_0   \n",
       "16        2019-04-22  2019            17    201917  CaptureSite_0   \n",
       "17        2019-04-29  2019            18    201918  CaptureSite_0   \n",
       "18        2019-05-06  2019            19    201919  CaptureSite_0   \n",
       "19        2019-05-13  2019            20    201920  CaptureSite_0   \n",
       "20        2019-05-20  2019            21    201921  CaptureSite_0   \n",
       "21        2019-05-27  2019            22    201922  CaptureSite_0   \n",
       "22        2019-06-03  2019            23    201923  CaptureSite_0   \n",
       "23        2019-06-10  2019            24    201924  CaptureSite_0   \n",
       "24        2019-06-17  2019            25    201925  CaptureSite_0   \n",
       "25        2019-06-24  2019            26    201926  CaptureSite_0   \n",
       "26        2019-07-01  2019            27    201927  CaptureSite_0   \n",
       "27        2019-07-08  2019            28    201928  CaptureSite_0   \n",
       "28        2019-07-15  2019            29    201929  CaptureSite_0   \n",
       "29        2019-07-22  2019            30    201930  CaptureSite_0   \n",
       "...              ...   ...           ...       ...            ...   \n",
       "1246      2019-04-08  2019            15    201915  CaptureSite_9   \n",
       "1247      2019-04-15  2019            16    201916  CaptureSite_9   \n",
       "1248      2019-04-22  2019            17    201917  CaptureSite_9   \n",
       "1249      2019-04-29  2019            18    201918  CaptureSite_9   \n",
       "1250      2019-05-06  2019            19    201919  CaptureSite_9   \n",
       "1251      2019-05-13  2019            20    201920  CaptureSite_9   \n",
       "1252      2019-05-20  2019            21    201921  CaptureSite_9   \n",
       "1253      2019-05-27  2019            22    201922  CaptureSite_9   \n",
       "1254      2019-06-03  2019            23    201923  CaptureSite_9   \n",
       "1255      2019-06-10  2019            24    201924  CaptureSite_9   \n",
       "1256      2019-06-17  2019            25    201925  CaptureSite_9   \n",
       "1257      2019-06-24  2019            26    201926  CaptureSite_9   \n",
       "1258      2019-07-01  2019            27    201927  CaptureSite_9   \n",
       "1259      2019-07-08  2019            28    201928  CaptureSite_9   \n",
       "1260      2019-07-15  2019            29    201929  CaptureSite_9   \n",
       "1261      2019-07-22  2019            30    201930  CaptureSite_9   \n",
       "1262      2019-07-29  2019            31    201931  CaptureSite_9   \n",
       "1263      2019-08-05  2019            32    201932  CaptureSite_9   \n",
       "1264      2019-08-12  2019            33    201933  CaptureSite_9   \n",
       "1265      2019-08-19  2019            34    201934  CaptureSite_9   \n",
       "1266      2019-08-26  2019            35    201935  CaptureSite_9   \n",
       "1267      2019-09-02  2019            36    201936  CaptureSite_9   \n",
       "1268      2019-09-09  2019            37    201937  CaptureSite_9   \n",
       "1269      2019-09-16  2019            38    201938  CaptureSite_9   \n",
       "1270      2019-09-23  2019            39    201939  CaptureSite_9   \n",
       "1271      2019-09-30  2019            40    201940  CaptureSite_9   \n",
       "1272      2019-10-07  2019            41    201941  CaptureSite_9   \n",
       "1273      2019-10-14  2019            42    201942  CaptureSite_9   \n",
       "1274      2019-10-21  2019            43    201943  CaptureSite_9   \n",
       "1275      2019-10-28  2019            44    201944  CaptureSite_9   \n",
       "\n",
       "      Capture_Number  day_of_year  month  day_of_week_caught  day_of_month  \n",
       "0                0.0            1      1                   1             1  \n",
       "1                0.0            7      1                   0             7  \n",
       "2                0.0           14      1                   0            14  \n",
       "3                0.0           21      1                   0            21  \n",
       "4                0.0           28      1                   0            28  \n",
       "5                0.0           35      2                   0             4  \n",
       "6                0.0           42      2                   0            11  \n",
       "7                0.0           49      2                   0            18  \n",
       "8                0.0           56      2                   0            25  \n",
       "9                0.0           63      3                   0             4  \n",
       "10               0.0           70      3                   0            11  \n",
       "11               0.0           77      3                   0            18  \n",
       "12               0.0           84      3                   0            25  \n",
       "13               0.0           91      4                   0             1  \n",
       "14               0.0           98      4                   0             8  \n",
       "15               0.0          105      4                   0            15  \n",
       "16               0.0          112      4                   0            22  \n",
       "17               0.0          119      4                   0            29  \n",
       "18               0.0          126      5                   0             6  \n",
       "19               0.0          133      5                   0            13  \n",
       "20               0.0          140      5                   0            20  \n",
       "21               0.0          147      5                   0            27  \n",
       "22               0.0          154      6                   0             3  \n",
       "23               0.0          161      6                   0            10  \n",
       "24               0.0          168      6                   0            17  \n",
       "25               0.0          175      6                   0            24  \n",
       "26               0.0          182      7                   0             1  \n",
       "27               0.0          189      7                   0             8  \n",
       "28               0.0          196      7                   0            15  \n",
       "29               0.0          203      7                   0            22  \n",
       "...              ...          ...    ...                 ...           ...  \n",
       "1246             0.0           98      4                   0             8  \n",
       "1247             0.0          105      4                   0            15  \n",
       "1248             0.0          112      4                   0            22  \n",
       "1249             0.0          119      4                   0            29  \n",
       "1250             0.0          126      5                   0             6  \n",
       "1251             0.0          133      5                   0            13  \n",
       "1252             0.0          140      5                   0            20  \n",
       "1253             0.0          147      5                   0            27  \n",
       "1254             0.0          154      6                   0             3  \n",
       "1255             0.0          161      6                   0            10  \n",
       "1256             0.0          168      6                   0            17  \n",
       "1257             0.0          175      6                   0            24  \n",
       "1258             0.0          182      7                   0             1  \n",
       "1259             0.0          189      7                   0             8  \n",
       "1260             0.0          196      7                   0            15  \n",
       "1261             0.0          203      7                   0            22  \n",
       "1262             0.0          210      7                   0            29  \n",
       "1263             0.0          217      8                   0             5  \n",
       "1264             0.0          224      8                   0            12  \n",
       "1265             0.0          231      8                   0            19  \n",
       "1266             0.0          238      8                   0            26  \n",
       "1267             0.0          245      9                   0             2  \n",
       "1268             0.0          252      9                   0             9  \n",
       "1269             0.0          259      9                   0            16  \n",
       "1270             0.0          266      9                   0            23  \n",
       "1271             0.0          273      9                   0            30  \n",
       "1272             0.0          280     10                   0             7  \n",
       "1273             0.0          287     10                   0            14  \n",
       "1274             0.0          294     10                   0            21  \n",
       "1275             0.0          301     10                   0            28  \n",
       "\n",
       "[1276 rows x 10 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add some more factors i.e. Site Category and Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>Capture_Number</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>day_of_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>2000</td>\n",
       "      <td>52</td>\n",
       "      <td>200052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2000-12-18</td>\n",
       "      <td>2000</td>\n",
       "      <td>51</td>\n",
       "      <td>200051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>353</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>200101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001-01-08</td>\n",
       "      <td>2001</td>\n",
       "      <td>2</td>\n",
       "      <td>200102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2001-01-15</td>\n",
       "      <td>2001</td>\n",
       "      <td>3</td>\n",
       "      <td>200103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CaptureSite    CaptureSiteCategory    Type Date_TimeCaught  year  \\\n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1      2000-01-01  2000   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1      2000-12-18  2000   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1      2001-01-01  2001   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1      2001-01-08  2001   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1      2001-01-15  2001   \n",
       "\n",
       "   week_of_year  year_woy  Capture_Number  day_of_year  month  \\\n",
       "0            52    200052             0.0            1      1   \n",
       "0            51    200051             1.0          353     12   \n",
       "0             1    200101             0.0            1      1   \n",
       "0             2    200102             0.0            8      1   \n",
       "0             3    200103             0.0           15      1   \n",
       "\n",
       "   day_of_week_caught  day_of_month  \n",
       "0                   5             1  \n",
       "0                   0            18  \n",
       "0                   0             1  \n",
       "0                   0             8  \n",
       "0                   0            15  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dates = pd.merge(categ, df_dates, on='CaptureSite', right_index=True)\n",
    "df_dates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>Capture_Number</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>day_of_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>201901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>201902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>201903</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>201904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>CaptureSiteCategory_2</td>\n",
       "      <td>Type_1</td>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>201905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CaptureSite    CaptureSiteCategory    Type Date_TimeCaught  year  \\\n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1      2019-01-01  2019   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1      2019-01-07  2019   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1      2019-01-14  2019   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1      2019-01-21  2019   \n",
       "0  CaptureSite_0  CaptureSiteCategory_2  Type_1      2019-01-28  2019   \n",
       "\n",
       "   week_of_year  year_woy  Capture_Number  day_of_year  month  \\\n",
       "0             1    201901             0.0            1      1   \n",
       "0             2    201902             0.0            7      1   \n",
       "0             3    201903             0.0           14      1   \n",
       "0             4    201904             0.0           21      1   \n",
       "0             5    201905             0.0           28      1   \n",
       "\n",
       "   day_of_week_caught  day_of_month  \n",
       "0                   1             1  \n",
       "0                   0             7  \n",
       "0                   0            14  \n",
       "0                   0            21  \n",
       "0                   0            28  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submission = pd.merge(categ, df_submission, on='CaptureSite', right_index=True)\n",
    "df_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform those categories and types to numerical values\n",
    "\n",
    "df_dates[\"CaptureSiteCategory\"] = df_dates[\"CaptureSiteCategory\"].str.split(pat = \"_\", expand=True)[1]\n",
    "df_dates[\"Type\"] = df_dates[\"Type\"].str.split(pat = \"_\", expand=True)[1]\n",
    "df_submission[\"CaptureSiteCategory\"] = df_submission[\"CaptureSiteCategory\"].str.split(pat = \"_\", expand=True)[1]\n",
    "df_submission[\"Type\"] = df_submission[\"Type\"].str.split(pat = \"_\", expand=True)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>Capture_Number</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>day_of_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>201901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>201902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>201903</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>201904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>201905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CaptureSite CaptureSiteCategory Type Date_TimeCaught  year  week_of_year  \\\n",
       "0  CaptureSite_0                   2    1      2019-01-01  2019             1   \n",
       "0  CaptureSite_0                   2    1      2019-01-07  2019             2   \n",
       "0  CaptureSite_0                   2    1      2019-01-14  2019             3   \n",
       "0  CaptureSite_0                   2    1      2019-01-21  2019             4   \n",
       "0  CaptureSite_0                   2    1      2019-01-28  2019             5   \n",
       "\n",
       "   year_woy  Capture_Number  day_of_year  month  day_of_week_caught  \\\n",
       "0    201901             0.0            1      1                   1   \n",
       "0    201902             0.0            7      1                   0   \n",
       "0    201903             0.0           14      1                   0   \n",
       "0    201904             0.0           21      1                   0   \n",
       "0    201905             0.0           28      1                   0   \n",
       "\n",
       "   day_of_month  \n",
       "0             1  \n",
       "0             7  \n",
       "0            14  \n",
       "0            21  \n",
       "0            28  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dates[\"CaptureSite_id\"] = df_dates[\"CaptureSite\"].str.split(pat = \"_\", expand=True)[1]\n",
    "df_submission[\"CaptureSite_id\"] = df_submission[\"CaptureSite\"].str.split(pat = \"_\", expand=True)[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>Capture_Number</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>CaptureSite_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>201901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>201902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>201903</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>201904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>201905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CaptureSite CaptureSiteCategory Type Date_TimeCaught  year  week_of_year  \\\n",
       "0  CaptureSite_0                   2    1      2019-01-01  2019             1   \n",
       "0  CaptureSite_0                   2    1      2019-01-07  2019             2   \n",
       "0  CaptureSite_0                   2    1      2019-01-14  2019             3   \n",
       "0  CaptureSite_0                   2    1      2019-01-21  2019             4   \n",
       "0  CaptureSite_0                   2    1      2019-01-28  2019             5   \n",
       "\n",
       "   year_woy  Capture_Number  day_of_year  month  day_of_week_caught  \\\n",
       "0    201901             0.0            1      1                   1   \n",
       "0    201902             0.0            7      1                   0   \n",
       "0    201903             0.0           14      1                   0   \n",
       "0    201904             0.0           21      1                   0   \n",
       "0    201905             0.0           28      1                   0   \n",
       "\n",
       "   day_of_month CaptureSite_id  \n",
       "0             1              0  \n",
       "0             7              0  \n",
       "0            14              0  \n",
       "0            21              0  \n",
       "0            28              0  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick look at the numbers in training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'No. of  Captured Turtles')"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_dates['Capture_Number'].plot(kind='hist')\n",
    "plt.xlabel('No. of  Captured Turtles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Capture_Number        1.000000\n",
       "year_woy              0.190068\n",
       "year                  0.188593\n",
       "month                 0.051607\n",
       "day_of_year           0.051572\n",
       "week_of_year          0.048549\n",
       "day_of_month         -0.000615\n",
       "day_of_week_caught   -0.009141\n",
       "Name: Capture_Number, dtype: float64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dates.corr()[\"Capture_Number\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d0cf51f5f8>"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dates.plot(x='year', y='Capture_Number', kind='scatter', alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d0cee1b320>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dates.plot(x='year_woy', y='Capture_Number', kind='scatter', alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick run through the modelling bit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>Capture_Number</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>CaptureSite_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>2018</td>\n",
       "      <td>48</td>\n",
       "      <td>201848</td>\n",
       "      <td>6.0</td>\n",
       "      <td>330</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>2018</td>\n",
       "      <td>49</td>\n",
       "      <td>201849</td>\n",
       "      <td>6.0</td>\n",
       "      <td>337</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>2018</td>\n",
       "      <td>50</td>\n",
       "      <td>201850</td>\n",
       "      <td>12.0</td>\n",
       "      <td>344</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-12-17</td>\n",
       "      <td>2018</td>\n",
       "      <td>51</td>\n",
       "      <td>201851</td>\n",
       "      <td>5.0</td>\n",
       "      <td>351</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>2018</td>\n",
       "      <td>52</td>\n",
       "      <td>201852</td>\n",
       "      <td>3.0</td>\n",
       "      <td>358</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CaptureSite CaptureSiteCategory Type Date_TimeCaught  year  \\\n",
       "28  CaptureSite_9                   1    1      2018-11-26  2018   \n",
       "28  CaptureSite_9                   1    1      2018-12-03  2018   \n",
       "28  CaptureSite_9                   1    1      2018-12-10  2018   \n",
       "28  CaptureSite_9                   1    1      2018-12-17  2018   \n",
       "28  CaptureSite_9                   1    1      2018-12-24  2018   \n",
       "\n",
       "    week_of_year  year_woy  Capture_Number  day_of_year  month  \\\n",
       "28            48    201848             6.0          330     11   \n",
       "28            49    201849             6.0          337     12   \n",
       "28            50    201850            12.0          344     12   \n",
       "28            51    201851             5.0          351     12   \n",
       "28            52    201852             3.0          358     12   \n",
       "\n",
       "    day_of_week_caught  day_of_month CaptureSite_id  \n",
       "28                   0            26              9  \n",
       "28                   0             3              9  \n",
       "28                   0            10              9  \n",
       "28                   0            17              9  \n",
       "28                   0            24              9  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dates.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CaptureSiteCategory', 'Type', 'year', 'week_of_year', 'year_woy',\n",
       "       'day_of_year', 'month', 'day_of_week_caught', 'day_of_month',\n",
       "       'CaptureSite_id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_cols = df_dates.drop(['Capture_Number','Date_TimeCaught', 'CaptureSite'], axis=1).columns\n",
    "remove_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "ycol = 'Capture_Number'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Simple  regression model \n",
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "lm.fit(df_dates[remove_cols], df_dates[ycol])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.5127492752484795\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "print(\"RMSE:\", sqrt(mean_squared_error(df_dates[ycol], lm.predict(df_dates[remove_cols]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2d0d1f905f8>"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d0cf639550>]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'True Value')"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Value')"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_dates[ycol], lm.predict(df_dates[remove_cols]), alpha=0.3)\n",
    "plt.plot(df_dates[ycol], df_dates[ycol]) # A straight line - we want to be close to this\n",
    "plt.xlabel('True Value')\n",
    "plt.ylabel('Predicted Value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model didnt  perform well. True and  predicted values are wide apart. They should be  as close as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=100) # Good model for  regression tasks\n",
    "rf.fit(df_dates[remove_cols], df_dates[ycol])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.46112704113415715\n"
     ]
    }
   ],
   "source": [
    "print(\"RMSE:\", sqrt(mean_squared_error(df_dates[ycol], rf.predict(df_dates[remove_cols]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2d0d4cb2358>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d0d4c975f8>]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'True Value')"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Value')"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_dates[ycol], rf.predict(df_dates[remove_cols]), alpha=0.3)\n",
    "plt.plot(df_dates[ycol], df_dates[ycol]) \n",
    "plt.xlabel('True Value')\n",
    "plt.ylabel('Predicted Value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite close to each other thus interesting results. Predicted values and  actual values are actually close "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x720 with 0 Axes>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importances')"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = remove_cols\n",
    "importances = rf.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "# fig, ax = plt.subplots()    \n",
    "plt.figure(figsize=(25,10))\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
    "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "for i, v in enumerate(importances[indices]):\n",
    "    plt.text(v, i, \" \"+str(v), color='blue', va='center', fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Avoiding  Overfitting  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24094, 13) (2678, 13)\n",
      "(24094, 13) (2678, 13)\n",
      "(24095, 13) (2677, 13)\n",
      "(24095, 13) (2677, 13)\n",
      "(24095, 13) (2677, 13)\n",
      "(24095, 13) (2677, 13)\n",
      "(24095, 13) (2677, 13)\n",
      "(24095, 13) (2677, 13)\n",
      "(24095, 13) (2677, 13)\n",
      "(24095, 13) (2677, 13)\n"
     ]
    }
   ],
   "source": [
    "##Using Kfolds validation \n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "# Demonstrating splits\n",
    "kf = KFold(n_splits=10, shuffle=False)\n",
    "for train, test in kf.split(df_dates):\n",
    "  print(df_dates.iloc[train].shape, df_dates.iloc[test].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2372834260288208\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.161725446028112\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2058778966715435\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2254776233389353\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2033137225304271\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1445834335943896\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.239042386929577\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2764530931711195\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.242449455704868\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3452826585714688\n",
      "Average score in 10-fold CV: 1.2281489142569262\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=10, shuffle=True)\n",
    "\n",
    "scores = []\n",
    "for train, test in kf.split(df_dates):\n",
    "  rf = RandomForestRegressor(n_estimators=100)\n",
    "  rf.fit(df_dates[remove_cols].iloc[train], df_dates[ycol].iloc[train])\n",
    "  rmse = sqrt(mean_squared_error(df_dates[ycol].iloc[test], rf.predict(df_dates[remove_cols].iloc[test])))\n",
    "  scores.append(rmse)\n",
    "  print(rmse)\n",
    "\n",
    "print(\"Average score in 10-fold CV:\", np.mean(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average score in 10-fold CV: 1.3236876653186502 for max depth: 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average score in 10-fold CV: 1.2571083054461747 for max depth: 6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average score in 10-fold CV: 1.2082578452522204 for max depth: 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average score in 10-fold CV: 1.193162110162519 for max depth: 9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average score in 10-fold CV: 1.1897261428182493 for max depth: 10\n"
     ]
    }
   ],
   "source": [
    "### Some more fine tuning  \n",
    "for md in [5, 6, 8, 9, 10]:\n",
    "    scores = []\n",
    "    for train, test in kf.split(df_dates):\n",
    "        rf = RandomForestRegressor(n_estimators=100, max_depth=md)\n",
    "        rf.fit(df_dates[remove_cols].iloc[train], df_dates[ycol].iloc[train])\n",
    "        rmse = sqrt(mean_squared_error(df_dates[ycol].iloc[test], rf.predict(df_dates[remove_cols].iloc[test])))\n",
    "        scores.append(rmse)\n",
    "    print(\"Average score in 10-fold CV:\", np.mean(scores), \"for max depth:\", md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=100, max_depth=9)\n",
    "rf.fit(df_dates[remove_cols], df_dates[ycol])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_submission['Capture_Number'] = rf.predict(df_submission[remove_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_submission['ID'] = df_submission[\"CaptureSite\"]+ \"_\" + df_submission[\"year_woy\"].map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_submission['Capture_Number'] = df_submission['Capture_Number'].apply(np.round).astype(int) ## Capture numbers are e rounded here. THere is nothing like 0.7 turtles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaptureSite</th>\n",
       "      <th>CaptureSiteCategory</th>\n",
       "      <th>Type</th>\n",
       "      <th>Date_TimeCaught</th>\n",
       "      <th>year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year_woy</th>\n",
       "      <th>Capture_Number</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week_caught</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>CaptureSite_id</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>201901</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CaptureSite_0_201901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>201902</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>CaptureSite_0_201902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>201903</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>CaptureSite_0_201903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>201904</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>CaptureSite_0_201904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>201905</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>CaptureSite_0_201905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CaptureSite CaptureSiteCategory Type Date_TimeCaught  year  week_of_year  \\\n",
       "0  CaptureSite_0                   2    1      2019-01-01  2019             1   \n",
       "0  CaptureSite_0                   2    1      2019-01-07  2019             2   \n",
       "0  CaptureSite_0                   2    1      2019-01-14  2019             3   \n",
       "0  CaptureSite_0                   2    1      2019-01-21  2019             4   \n",
       "0  CaptureSite_0                   2    1      2019-01-28  2019             5   \n",
       "\n",
       "   year_woy  Capture_Number  day_of_year  month  day_of_week_caught  \\\n",
       "0    201901               1            1      1                   1   \n",
       "0    201902               1            7      1                   0   \n",
       "0    201903               1           14      1                   0   \n",
       "0    201904               1           21      1                   0   \n",
       "0    201905               1           28      1                   0   \n",
       "\n",
       "   day_of_month CaptureSite_id                    ID  \n",
       "0             1              0  CaptureSite_0_201901  \n",
       "0             7              0  CaptureSite_0_201902  \n",
       "0            14              0  CaptureSite_0_201903  \n",
       "0            21              0  CaptureSite_0_201904  \n",
       "0            28              0  CaptureSite_0_201905  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_submission[['ID', 'Capture_Number']].to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Capture_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201901</td>\n",
       "      <td>1</td>\n",
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       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201902</td>\n",
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       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201904</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201905</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201906</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201907</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201908</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201909</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201910</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201911</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201912</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201913</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201914</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201915</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201916</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201917</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201918</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201919</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201921</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201922</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201923</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201924</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201925</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201926</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201927</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201928</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201929</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CaptureSite_0_201930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201915</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201916</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201917</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201918</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201919</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201920</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201921</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201922</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201923</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201924</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201925</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201926</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201927</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201928</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201929</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201930</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201931</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201932</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201933</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201934</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201935</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201936</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201937</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201938</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201939</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201940</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201941</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201942</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201943</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CaptureSite_9_201944</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1276 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID  Capture_Number\n",
       "0   CaptureSite_0_201901               1\n",
       "0   CaptureSite_0_201902               1\n",
       "0   CaptureSite_0_201903               1\n",
       "0   CaptureSite_0_201904               1\n",
       "0   CaptureSite_0_201905               1\n",
       "0   CaptureSite_0_201906               1\n",
       "0   CaptureSite_0_201907               1\n",
       "0   CaptureSite_0_201908               1\n",
       "0   CaptureSite_0_201909               1\n",
       "0   CaptureSite_0_201910               1\n",
       "0   CaptureSite_0_201911               1\n",
       "0   CaptureSite_0_201912               1\n",
       "0   CaptureSite_0_201913               1\n",
       "0   CaptureSite_0_201914               1\n",
       "0   CaptureSite_0_201915               1\n",
       "0   CaptureSite_0_201916               1\n",
       "0   CaptureSite_0_201917               1\n",
       "0   CaptureSite_0_201918               1\n",
       "0   CaptureSite_0_201919               1\n",
       "0   CaptureSite_0_201920               1\n",
       "0   CaptureSite_0_201921               1\n",
       "0   CaptureSite_0_201922               1\n",
       "0   CaptureSite_0_201923               1\n",
       "0   CaptureSite_0_201924               1\n",
       "0   CaptureSite_0_201925               1\n",
       "0   CaptureSite_0_201926               1\n",
       "0   CaptureSite_0_201927               1\n",
       "0   CaptureSite_0_201928               1\n",
       "0   CaptureSite_0_201929               1\n",
       "0   CaptureSite_0_201930               1\n",
       "..                   ...             ...\n",
       "28  CaptureSite_9_201915               5\n",
       "28  CaptureSite_9_201916               5\n",
       "28  CaptureSite_9_201917               5\n",
       "28  CaptureSite_9_201918               5\n",
       "28  CaptureSite_9_201919               5\n",
       "28  CaptureSite_9_201920               4\n",
       "28  CaptureSite_9_201921               4\n",
       "28  CaptureSite_9_201922               4\n",
       "28  CaptureSite_9_201923               4\n",
       "28  CaptureSite_9_201924               4\n",
       "28  CaptureSite_9_201925               4\n",
       "28  CaptureSite_9_201926               4\n",
       "28  CaptureSite_9_201927               4\n",
       "28  CaptureSite_9_201928               4\n",
       "28  CaptureSite_9_201929               4\n",
       "28  CaptureSite_9_201930               4\n",
       "28  CaptureSite_9_201931               4\n",
       "28  CaptureSite_9_201932               4\n",
       "28  CaptureSite_9_201933               4\n",
       "28  CaptureSite_9_201934               4\n",
       "28  CaptureSite_9_201935               4\n",
       "28  CaptureSite_9_201936               4\n",
       "28  CaptureSite_9_201937               4\n",
       "28  CaptureSite_9_201938               4\n",
       "28  CaptureSite_9_201939               4\n",
       "28  CaptureSite_9_201940               4\n",
       "28  CaptureSite_9_201941               5\n",
       "28  CaptureSite_9_201942               4\n",
       "28  CaptureSite_9_201943               4\n",
       "28  CaptureSite_9_201944               4\n",
       "\n",
       "[1276 rows x 2 columns]"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submission[['ID', 'Capture_Number']]"
   ]
  }
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