01beach_nourishment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beach Nourishment Projects\n",
    "\n",
    "Initial exploration, cleaning and visualizations of the Beach Nourishment project data.  Creates new excel file beach_data as well as several visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt  \n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID State         Location  Year Completed Primary Funding Source  \\\n",
      "0   5    AL  Sand Island Bar            1987                Federal   \n",
      "1   8    AL      Perdido Key            1986                Federal   \n",
      "2  11    AL  Sand Island Bar            1988                Federal   \n",
      "3  17    AL      Gulf Shores            2001                  Local   \n",
      "4  20    AL  Sand Island Bar            1988                Federal   \n",
      "\n",
      "      Justification  Length (ft)  Volume (CY)  Total Cost  Year Completed CPI  \\\n",
      "0     Demonstration         6000       464000           0               114.0   \n",
      "1        Navigation            0       660000     1406977               110.0   \n",
      "2     Demonstration          500         1400       60000               118.0   \n",
      "3  Shore Protection        16000      1800000     6000000               177.0   \n",
      "4     Demonstration        10560      2000000     2000000               118.0   \n",
      "\n",
      "   Adjusted Cost (2022)  Total Cost/CY  Adjusted Cost/CY    CY/Foot  \\\n",
      "0                  0.00           0.00              0.00      77.33   \n",
      "1            3743837.89           2.13              5.67  660000.00   \n",
      "2             148830.51          42.86            106.31       2.80   \n",
      "3            9922033.90           3.33              5.51     112.50   \n",
      "4            4961016.95           1.00              2.48     189.39   \n",
      "\n",
      "   Current CPI Other Info                                     Source Comment  \n",
      "0        292.7        NaN  http://www.wes.army.mil/el/dots/budm/facts/san...  \n",
      "1        292.7        NaN                                                138  \n",
      "2        292.7        NaN       http://www.southalabama.edu/cesrp/hbeach.htm  \n",
      "3        292.7        NaN  http://www.weartv.com/news/Stories/may/0503/05...  \n",
      "4        292.7        NaN  http://www.weartv.com/news/Stories/may/0503/05...  \n"
     ]
    }
   ],
   "source": [
    "#set column names\n",
    "\n",
    "column_names = ['ID', 'State', 'Location', 'Year Completed', 'Primary Funding Source', 'Justification', 'Length (ft)', 'Volume (CY)', 'Total Cost',\t'Year Completed CPI', 'Adjusted Cost (2022)', 'Total Cost/CY', 'Adjusted Cost/CY', 'CY/Foot', 'Current CPI', 'Other Info', 'Source Comment']\n",
    "\n",
    "beach = pd.read_excel('data/beach_nourishment_PSDS.xlsx', names = column_names, header=0)\n",
    "\n",
    "\n",
    "   \n",
    "print(beach.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the region column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ID State                              Location  Year Completed  \\\n",
      "0        5    AL                       Sand Island Bar            1987   \n",
      "1        8    AL                           Perdido Key            1986   \n",
      "2       11    AL                       Sand Island Bar            1988   \n",
      "3       17    AL                           Gulf Shores            2001   \n",
      "4       20    AL                       Sand Island Bar            1988   \n",
      "...    ...   ...                                   ...             ...   \n",
      "2454  6810    CA    Goleta Beach, Santa Barbara County            2017   \n",
      "2455  6811    CA    Goleta Beach, Santa Barbara County            2010   \n",
      "2456  6812    CA          Newport Beach, Orange County            2009   \n",
      "2457  6813    CA  Surfside/Sunset Beach, Orange County            2009   \n",
      "2458  6814    CA            Morro Bay, San Luis Obispo            2017   \n",
      "\n",
      "     Primary Funding Source     Justification  Length (ft)  Volume (CY)  \\\n",
      "0                   Federal     Demonstration         6000       464000   \n",
      "1                   Federal        Navigation            0       660000   \n",
      "2                   Federal     Demonstration          500         1400   \n",
      "3                     Local  Shore Protection        16000      1800000   \n",
      "4                   Federal     Demonstration        10560      2000000   \n",
      "...                     ...               ...          ...          ...   \n",
      "2454                  Local  Shore Protection            0         6000   \n",
      "2455                  Local  Shore Protection         4200        50000   \n",
      "2456                  Local  Shore Protection            0       120000   \n",
      "2457                Federal  Shore Protection            0      1750000   \n",
      "2458                Federal  Shore Protection            0       994000   \n",
      "\n",
      "      Total Cost  Year Completed CPI  Adjusted Cost (2022)  Total Cost/CY  \\\n",
      "0              0               114.0                  0.00           0.00   \n",
      "1        1406977               110.0            3743837.89           2.13   \n",
      "2          60000               118.0             148830.51          42.86   \n",
      "3        6000000               177.0            9922033.90           3.33   \n",
      "4        2000000               118.0            4961016.95           1.00   \n",
      "...          ...                 ...                   ...            ...   \n",
      "2454       65000               245.1              77623.42          10.83   \n",
      "2455     1600000               218.1            2147271.89          32.00   \n",
      "2456           0               214.5                  0.00           0.00   \n",
      "2457    12000000               214.5           16374825.17           6.86   \n",
      "2458           0               245.1                  0.00           0.00   \n",
      "\n",
      "      Adjusted Cost/CY     CY/Foot  Current CPI Other Info  \\\n",
      "0                 0.00       77.33        292.7        NaN   \n",
      "1                 5.67   660000.00        292.7        NaN   \n",
      "2               106.31        2.80        292.7        NaN   \n",
      "3                 5.51      112.50        292.7        NaN   \n",
      "4                 2.48      189.39        292.7        NaN   \n",
      "...                ...         ...          ...        ...   \n",
      "2454             12.94     6000.00        292.7        NaN   \n",
      "2455             42.95       11.90        292.7        NaN   \n",
      "2456              0.00   120000.00        292.7        NaN   \n",
      "2457              9.36  1750000.00        292.7        NaN   \n",
      "2458              0.00   994000.00        292.7        NaN   \n",
      "\n",
      "                                         Source Comment      Region  \n",
      "0     http://www.wes.army.mil/el/dots/budm/facts/san...  Gulf Coast  \n",
      "1                                                   138  Gulf Coast  \n",
      "2          http://www.southalabama.edu/cesrp/hbeach.htm  Gulf Coast  \n",
      "3     http://www.weartv.com/news/Stories/may/0503/05...  Gulf Coast  \n",
      "4     http://www.weartv.com/news/Stories/may/0503/05...  Gulf Coast  \n",
      "...                                                 ...         ...  \n",
      "2454  https://www.noozhawk.com/county_moving_sand_go...  West Coast  \n",
      "2455  https://coastalcare.org/2010/07/goleta-beach-c...  West Coast  \n",
      "2456  https://www.surfline.com/templates/article_htm...  West Coast  \n",
      "2457  https://www.spl.usace.army.mil/Portals/17/docs...  West Coast  \n",
      "2458  https://scholarworks.calstate.edu/downloads/rv...  West Coast  \n",
      "\n",
      "[2459 rows x 18 columns]\n"
     ]
    }
   ],
   "source": [
    "from categorizeregion import add_region_column\n",
    "\n",
    "beach = add_region_column(beach)\n",
    "print(beach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2459 entries, 0 to 2458\n",
      "Data columns (total 18 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   ID                      2459 non-null   int64  \n",
      " 1   State                   2459 non-null   object \n",
      " 2   Location                2459 non-null   object \n",
      " 3   Year Completed          2459 non-null   int64  \n",
      " 4   Primary Funding Source  2457 non-null   object \n",
      " 5   Justification           2459 non-null   object \n",
      " 6   Length (ft)             2459 non-null   int64  \n",
      " 7   Volume (CY)             2459 non-null   int64  \n",
      " 8   Total Cost              2459 non-null   int64  \n",
      " 9   Year Completed CPI      2459 non-null   float64\n",
      " 10  Adjusted Cost (2022)    2459 non-null   float64\n",
      " 11  Total Cost/CY           2459 non-null   float64\n",
      " 12  Adjusted Cost/CY        2459 non-null   float64\n",
      " 13  CY/Foot                 2459 non-null   float64\n",
      " 14  Current CPI             2459 non-null   float64\n",
      " 15  Other Info              178 non-null    object \n",
      " 16  Source Comment          1025 non-null   object \n",
      " 17  Region                  2459 non-null   object \n",
      "dtypes: float64(6), int64(5), object(7)\n",
      "memory usage: 345.9+ KB\n"
     ]
    }
   ],
   "source": [
    "beach.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year Completed</th>\n",
       "      <th>Length (ft)</th>\n",
       "      <th>Volume (CY)</th>\n",
       "      <th>Total Cost</th>\n",
       "      <th>Year Completed CPI</th>\n",
       "      <th>Adjusted Cost (2022)</th>\n",
       "      <th>Total Cost/CY</th>\n",
       "      <th>Adjusted Cost/CY</th>\n",
       "      <th>CY/Foot</th>\n",
       "      <th>Current CPI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2459.000000</td>\n",
       "      <td>2459.000000</td>\n",
       "      <td>2459.000000</td>\n",
       "      <td>2.459000e+03</td>\n",
       "      <td>2.459000e+03</td>\n",
       "      <td>2459.000000</td>\n",
       "      <td>2.459000e+03</td>\n",
       "      <td>2.459000e+03</td>\n",
       "      <td>2.459000e+03</td>\n",
       "      <td>2.459000e+03</td>\n",
       "      <td>2459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3670.630744</td>\n",
       "      <td>1990.987393</td>\n",
       "      <td>7022.884913</td>\n",
       "      <td>6.381578e+05</td>\n",
       "      <td>4.083488e+06</td>\n",
       "      <td>140.888825</td>\n",
       "      <td>6.277340e+06</td>\n",
       "      <td>1.045617e+05</td>\n",
       "      <td>2.046339e+05</td>\n",
       "      <td>2.139504e+05</td>\n",
       "      <td>292.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2080.600537</td>\n",
       "      <td>20.741685</td>\n",
       "      <td>12700.530217</td>\n",
       "      <td>1.298209e+06</td>\n",
       "      <td>1.279058e+07</td>\n",
       "      <td>80.422742</td>\n",
       "      <td>1.635226e+07</td>\n",
       "      <td>1.228604e+06</td>\n",
       "      <td>2.296069e+06</td>\n",
       "      <td>9.670660e+05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1923.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>292.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1857.500000</td>\n",
       "      <td>1975.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>55.450000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.954500e+01</td>\n",
       "      <td>292.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3707.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>2333.000000</td>\n",
       "      <td>2.352520e+05</td>\n",
       "      <td>2.090300e+05</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>8.698481e+05</td>\n",
       "      <td>1.420000e+00</td>\n",
       "      <td>6.420000e+00</td>\n",
       "      <td>9.933000e+01</td>\n",
       "      <td>292.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5553.500000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>8448.000000</td>\n",
       "      <td>7.011330e+05</td>\n",
       "      <td>2.894885e+06</td>\n",
       "      <td>207.300000</td>\n",
       "      <td>6.130253e+06</td>\n",
       "      <td>8.565000e+00</td>\n",
       "      <td>1.544500e+01</td>\n",
       "      <td>9.700000e+04</td>\n",
       "      <td>292.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6814.000000</td>\n",
       "      <td>2024.000000</td>\n",
       "      <td>141000.000000</td>\n",
       "      <td>2.600930e+07</td>\n",
       "      <td>2.200000e+08</td>\n",
       "      <td>292.700000</td>\n",
       "      <td>2.861956e+08</td>\n",
       "      <td>4.351273e+07</td>\n",
       "      <td>6.152742e+07</td>\n",
       "      <td>2.600930e+07</td>\n",
       "      <td>292.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID  Year Completed    Length (ft)   Volume (CY)    Total Cost  \\\n",
       "count  2459.000000     2459.000000    2459.000000  2.459000e+03  2.459000e+03   \n",
       "mean   3670.630744     1990.987393    7022.884913  6.381578e+05  4.083488e+06   \n",
       "std    2080.600537       20.741685   12700.530217  1.298209e+06  1.279058e+07   \n",
       "min       5.000000     1923.000000       0.000000  0.000000e+00  0.000000e+00   \n",
       "25%    1857.500000     1975.500000       0.000000  7.500000e+04  0.000000e+00   \n",
       "50%    3707.000000     1995.000000    2333.000000  2.352520e+05  2.090300e+05   \n",
       "75%    5553.500000     2007.000000    8448.000000  7.011330e+05  2.894885e+06   \n",
       "max    6814.000000     2024.000000  141000.000000  2.600930e+07  2.200000e+08   \n",
       "\n",
       "       Year Completed CPI  Adjusted Cost (2022)  Total Cost/CY  \\\n",
       "count         2459.000000          2.459000e+03   2.459000e+03   \n",
       "mean           140.888825          6.277340e+06   1.045617e+05   \n",
       "std             80.422742          1.635226e+07   1.228604e+06   \n",
       "min             13.000000          0.000000e+00   0.000000e+00   \n",
       "25%             55.450000          0.000000e+00   0.000000e+00   \n",
       "50%            152.000000          8.698481e+05   1.420000e+00   \n",
       "75%            207.300000          6.130253e+06   8.565000e+00   \n",
       "max            292.700000          2.861956e+08   4.351273e+07   \n",
       "\n",
       "       Adjusted Cost/CY       CY/Foot  Current CPI  \n",
       "count      2.459000e+03  2.459000e+03       2459.0  \n",
       "mean       2.046339e+05  2.139504e+05        292.7  \n",
       "std        2.296069e+06  9.670660e+05          0.0  \n",
       "min        0.000000e+00  0.000000e+00        292.7  \n",
       "25%        0.000000e+00  2.954500e+01        292.7  \n",
       "50%        6.420000e+00  9.933000e+01        292.7  \n",
       "75%        1.544500e+01  9.700000e+04        292.7  \n",
       "max        6.152742e+07  2.600930e+07        292.7  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beach.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                           0\n",
      "State                        0\n",
      "Location                     0\n",
      "Year Completed               0\n",
      "Primary Funding Source       2\n",
      "Justification                0\n",
      "Length (ft)                  0\n",
      "Volume (CY)                  0\n",
      "Total Cost                   0\n",
      "Year Completed CPI           0\n",
      "Adjusted Cost (2022)         0\n",
      "Total Cost/CY                0\n",
      "Adjusted Cost/CY             0\n",
      "CY/Foot                      0\n",
      "Current CPI                  0\n",
      "Other Info                2281\n",
      "Source Comment            1434\n",
      "Region                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(beach.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Follow up later \n",
    "--- \n",
    "###### look at the reasons to identify whether emergencies (for example) should be grouped together\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Justification  Counts\n",
      "0                      Bypass       2\n",
      "1               Demonstration       4\n",
      "2       Ecosystem Restoration      47\n",
      "3                   Emergency     263\n",
      "4              Emergency Dune      30\n",
      "5   Environmental Restoration       4\n",
      "6              FEMA Emergency      27\n",
      "7                       MsCIP       3\n",
      "8                  Navigation     581\n",
      "9                 Restoration      20\n",
      "10                Section 111      26\n",
      "11           Shore Protection     777\n",
      "12                    Unknown     675\n"
     ]
    }
   ],
   "source": [
    "group_just = beach.groupby(['Justification'])\n",
    "just_counts = group_just.size().reset_index(name='Counts')\n",
    "print(just_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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    "fig = px.pie(just_counts, values='Counts', names='Justification', title='Beach Projects Justification')\n",
    "fig.show()\n"
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     "text": [
      "  Primary Funding Source  Counts\n",
      "0                Federal    1451\n",
      "1                  Local     241\n",
      "2                  Other       1\n",
      "3                Private      45\n",
      "4                  State     366\n",
      "5                Unknown     353\n"
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    "group_funding = beach.groupby(['Primary Funding Source'])\n",
    "fund_counts = group_funding.size().reset_index(name='Counts')\n",
    "print(fund_counts)"
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    "fig.show()"
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      "                Length (ft)  Volume (CY)  Adjusted Cost (2022)\n",
      "Year Completed                                                \n",
      "1923                   8750      1700000          4.860111e+06\n",
      "1926                   2500       850000          2.230244e+06\n",
      "1927                   1056       500000          1.721765e+06\n",
      "1930                      0      5200000          1.798549e+07\n",
      "1933                      0       606000          0.000000e+00\n",
      "...                     ...          ...                   ...\n",
      "2020                 531236     25340067          5.626332e+08\n",
      "2021                 446650     25619252          3.945162e+08\n",
      "2022                 474984     29261205          3.781656e+08\n",
      "2023                 180167     10415952          2.154468e+08\n",
      "2024                  15239      5014000          8.154300e+07\n",
      "\n",
      "[95 rows x 3 columns]\n"
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    "length_by_year = beach.groupby('Year Completed').agg({'Length (ft)': 'sum', 'Volume (CY)': 'sum', 'Adjusted Cost (2022)': 'sum' })\n",
    "print(length_by_year)\n",
    "\n"
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    "fig = px.scatter(length_by_year, \n",
    "                 x='Volume (CY)', \n",
    "                 y='Adjusted Cost (2022)', \n",
    "                 hover_name=length_by_year.index,\n",
    "                 title='Yearly Volume vs. Adjusted Cost',\n",
    "                 labels={'Volume (CY)': 'Volume (Cubic Yards)', 'Adjusted Cost (2022)': 'Adjusted Cost (2022 USD)'})\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig = px.scatter(length_by_year, x='Volume (CY)', y='Adjusted Cost (2022)', trendline=\"ols\", hover_name=length_by_year.index)\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#group by decades\n",
    "beach['Decade'] = (beach['Year Completed'] // 10) * 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "        Length (ft)  Volume (CY)  Adjusted Cost (2022)\n",
      "Decade                                                \n",
      "1920          12306      3050000          8.812120e+06\n",
      "1930          31152     57278679          9.058872e+07\n",
      "1940         118904    124673873          7.262977e+07\n",
      "1950         346908     55528220          2.899058e+08\n",
      "1960         845385    116927693          4.578632e+08\n",
      "1970         959789    121067482          8.465630e+08\n",
      "1980        1298632    168510796          1.138752e+09\n",
      "1990        2629012    229384195          2.085555e+09\n",
      "2000        4358623    245757025          2.804483e+09\n",
      "2010        5020287    351401609          6.008521e+09\n",
      "2020        1648276     95650476          1.632305e+09\n"
     ]
    }
   ],
   "source": [
    "decade_data = beach.groupby('Decade').agg({\n",
    "    'Length (ft)': 'sum',\n",
    "    'Volume (CY)': 'sum',\n",
    "    'Adjusted Cost (2022)': 'sum'\n",
    "})\n",
    "print(decade_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Create a scatter plot with decade_data\n",
    "fig = px.scatter(decade_data, x='Volume (CY)', y='Adjusted Cost (2022)', \n",
    "                 text=decade_data.index, size='Volume (CY)', \n",
    "                 title='Volume of Sand vs. Cost by Decades',\n",
    "                 labels={'Volume (CY)': 'Volume of Sand (Cubic Yards)', 'Adjusted Cost (2022)': 'Adjusted Cost (2022 USD)'})\n",
    "\n",
    "fig.update_traces(textposition='top center')\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "    State          Justification  Counts\n",
      "0      AL          Demonstration       3\n",
      "1      AL  Ecosystem Restoration       1\n",
      "2      AL              Emergency       5\n",
      "3      AL         FEMA Emergency       1\n",
      "4      AL             Navigation       2\n",
      "..    ...                    ...     ...\n",
      "99     VA       Shore Protection      58\n",
      "100    VA                Unknown      11\n",
      "101    WA             Navigation       1\n",
      "102    WA       Shore Protection       3\n",
      "103    WA                Unknown       9\n",
      "\n",
      "[104 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "grouped_data = beach.groupby(['State', 'Justification'])\n",
    "counts = grouped_data.size().reset_index(name='Counts')\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def identify_outliers(column):\n",
    "#    Q1 = column.quantile(0.25)\n",
    "#    Q3 = column.quantile(0.75)\n",
    "#    IQR = Q3 - Q1\n",
    "#    lower_bound = Q1 - 1.5 * IQR\n",
    "#    upper_bound = Q3 + 1.5 * IQR\n",
    "    \n",
    "    # Return a boolean Series where True means the value is NOT an outlier\n",
    " #   return column.between(lower_bound, upper_bound, inclusive='both')\n",
    "\n",
    "# Automatically select numeric columns to check\n",
    "#columns_to_check = beach.select_dtypes(include=[np.number]).columns\n",
    "# Apply the outlier detection function to each specified column and store the result\n",
    "#mask = pd.concat([identify_outliers(beach[col]) for col in columns_to_check], axis=1).all(axis=1)\n",
    "\n",
    "#print(mask)\n",
    "#mask.value_counts()\n",
    "# Filter the DataFrame to only include rows where all specified columns are NOT outliers\n",
    "#df_filtered = beach[mask]\n",
    "\n",
    "#\n",
    "# print(df_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_beach_data = beach.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ID State                              Location  Year Completed  \\\n",
      "0        5    AL                       Sand Island Bar            1987   \n",
      "1        8    AL                           Perdido Key            1986   \n",
      "2       11    AL                       Sand Island Bar            1988   \n",
      "3       17    AL                           Gulf Shores            2001   \n",
      "4       20    AL                       Sand Island Bar            1988   \n",
      "...    ...   ...                                   ...             ...   \n",
      "2454  6810    CA    Goleta Beach, Santa Barbara County            2017   \n",
      "2455  6811    CA    Goleta Beach, Santa Barbara County            2010   \n",
      "2456  6812    CA          Newport Beach, Orange County            2009   \n",
      "2457  6813    CA  Surfside/Sunset Beach, Orange County            2009   \n",
      "2458  6814    CA            Morro Bay, San Luis Obispo            2017   \n",
      "\n",
      "     Primary Funding Source     Justification  Length (ft)  Volume (CY)  \\\n",
      "0                   Federal     Demonstration         6000       464000   \n",
      "1                   Federal        Navigation            0       660000   \n",
      "2                   Federal     Demonstration          500         1400   \n",
      "3                     Local  Shore Protection        16000      1800000   \n",
      "4                   Federal     Demonstration        10560      2000000   \n",
      "...                     ...               ...          ...          ...   \n",
      "2454                  Local  Shore Protection            0         6000   \n",
      "2455                  Local  Shore Protection         4200        50000   \n",
      "2456                  Local  Shore Protection            0       120000   \n",
      "2457                Federal  Shore Protection            0      1750000   \n",
      "2458                Federal  Shore Protection            0       994000   \n",
      "\n",
      "      Total Cost  Year Completed CPI  Adjusted Cost (2022)  Total Cost/CY  \\\n",
      "0              0               114.0                  0.00           0.00   \n",
      "1        1406977               110.0            3743837.89           2.13   \n",
      "2          60000               118.0             148830.51          42.86   \n",
      "3        6000000               177.0            9922033.90           3.33   \n",
      "4        2000000               118.0            4961016.95           1.00   \n",
      "...          ...                 ...                   ...            ...   \n",
      "2454       65000               245.1              77623.42          10.83   \n",
      "2455     1600000               218.1            2147271.89          32.00   \n",
      "2456           0               214.5                  0.00           0.00   \n",
      "2457    12000000               214.5           16374825.17           6.86   \n",
      "2458           0               245.1                  0.00           0.00   \n",
      "\n",
      "      Adjusted Cost/CY     CY/Foot  Current CPI Other Info  \\\n",
      "0                 0.00       77.33        292.7        NaN   \n",
      "1                 5.67   660000.00        292.7        NaN   \n",
      "2               106.31        2.80        292.7        NaN   \n",
      "3                 5.51      112.50        292.7        NaN   \n",
      "4                 2.48      189.39        292.7        NaN   \n",
      "...                ...         ...          ...        ...   \n",
      "2454             12.94     6000.00        292.7        NaN   \n",
      "2455             42.95       11.90        292.7        NaN   \n",
      "2456              0.00   120000.00        292.7        NaN   \n",
      "2457              9.36  1750000.00        292.7        NaN   \n",
      "2458              0.00   994000.00        292.7        NaN   \n",
      "\n",
      "                                         Source Comment      Region  Decade  \n",
      "0     http://www.wes.army.mil/el/dots/budm/facts/san...  Gulf Coast    1980  \n",
      "1                                                   138  Gulf Coast    1980  \n",
      "2          http://www.southalabama.edu/cesrp/hbeach.htm  Gulf Coast    1980  \n",
      "3     http://www.weartv.com/news/Stories/may/0503/05...  Gulf Coast    2000  \n",
      "4     http://www.weartv.com/news/Stories/may/0503/05...  Gulf Coast    1980  \n",
      "...                                                 ...         ...     ...  \n",
      "2454  https://www.noozhawk.com/county_moving_sand_go...  West Coast    2010  \n",
      "2455  https://coastalcare.org/2010/07/goleta-beach-c...  West Coast    2010  \n",
      "2456  https://www.surfline.com/templates/article_htm...  West Coast    2000  \n",
      "2457  https://www.spl.usace.army.mil/Portals/17/docs...  West Coast    2000  \n",
      "2458  https://scholarworks.calstate.edu/downloads/rv...  West Coast    2010  \n",
      "\n",
      "[2459 rows x 19 columns]\n"
     ]
    }
   ],
   "source": [
    "print(clean_beach_data)"
   ]
  },
  {
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   "execution_count": 44,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "State\n",
      "AL     15\n",
      "CA    366\n",
      "CT     37\n",
      "DE    120\n",
      "FL    680\n",
      "GA     14\n",
      "LA     46\n",
      "MA     55\n",
      "MD     37\n",
      "ME     19\n",
      "MS     25\n",
      "NC    284\n",
      "NH      7\n",
      "NJ    354\n",
      "NY    128\n",
      "RI     14\n",
      "SC     94\n",
      "TX     76\n",
      "VA     75\n",
      "WA     13\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "group_by_state = clean_beach_data.groupby('State')\n",
    "\n",
    "count_per_state = group_by_state.size()\n",
    "print(count_per_state)\n"
   ]
  },
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    "fig.show()\n"
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  {
   "cell_type": "code",
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   "source": [
    "totals_by_year = clean_beach_data.groupby('Year Completed')['Adjusted Cost (2022)'].sum().reset_index()\n"
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  {
   "cell_type": "code",
   "execution_count": 47,
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Convert the 'Year Completed' column to string if it's not already, to ensure consistent tick labeling\n",
    "totals_by_year['Year Completed'] = totals_by_year['Year Completed'].astype(str)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(totals_by_year['Year Completed'], totals_by_year['Adjusted Cost (2022)'], marker='o', linestyle='-')\n",
    "plt.title('Adjusted Cost by Year')\n",
    "plt.xlabel('Year Completed')\n",
    "plt.ylabel('Adjusted Cost (2022) in $')\n",
    "\n",
    "# Adjust the x-axis to show ticks you want. Example: Every 5 years.\n",
    "# This assumes 'Year Completed' are already sorted.\n",
    "years = totals_by_year['Year Completed']\n",
    "# Option: Show every 5th year in the plot or adjust according to your dataset size and readability.\n",
    "plt.xticks(ticks=years[::5], labels=years[::5], rotation=45)\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "beach.to_excel('data/beach_data.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_ana",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}