Pollution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing Jupyter notebook from BeginningAnalysis.ipynb\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient: \n",
      " 156.8\n",
      "R-squared: 0.84\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient: \n",
      " 0.68\n",
      "R-squared: 0.00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient: \n",
      " -2.46\n",
      "R-squared: 0.01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient: \n",
      " 11.44\n",
      "R-squared: 0.09\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                 -5.008690\n",
      "p-value                         0.000021\n",
      "#Lags Used                      0.000000\n",
      "Number of Observations Used    23.000000\n",
      "dtype: float64\n",
      "Best ARIMA(0, 1, 0) MSE=1422.000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                 -5.556892\n",
      "p-value                         0.000002\n",
      "#Lags Used                      0.000000\n",
      "Number of Observations Used    45.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                -6.564338e+00\n",
      "p-value                        8.230672e-09\n",
      "#Lags Used                     1.000000e+01\n",
      "Number of Observations Used    2.900000e+01\n",
      "dtype: float64\n",
      "Best ARIMA(2, 1, 0) MSE=3698.317\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/robin/.local/lib/python3.6/site-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.25.7) or chardet (3.0.4) doesn't match a supported version!\n",
      "  RequestsDependencyWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, date, time, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats,optimize\n",
    "from sklearn import linear_model\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "\n",
    "\n",
    "import import_ipynb\n",
    "import BeginningAnalysis\n",
    "import requests\n",
    "import json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Hour</th>\n",
       "      <th>barometricPressure</th>\n",
       "      <th>precipitationLastHour</th>\n",
       "      <th>relativeHumidity</th>\n",
       "      <th>temperature</th>\n",
       "      <th>time</th>\n",
       "      <th>PEF(l/min)</th>\n",
       "      <th>FEV1(l)</th>\n",
       "      <th>unixDate</th>\n",
       "      <th>unixTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-23</th>\n",
       "      <td>19</td>\n",
       "      <td>102030.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.412102</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2020-02-23 19:51:00</td>\n",
       "      <td>476</td>\n",
       "      <td>2.78</td>\n",
       "      <td>18315.0</td>\n",
       "      <td>70320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23</th>\n",
       "      <td>19</td>\n",
       "      <td>102030.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.061975</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2020-02-23 19:27:00</td>\n",
       "      <td>476</td>\n",
       "      <td>2.78</td>\n",
       "      <td>18315.0</td>\n",
       "      <td>70320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>5</td>\n",
       "      <td>101970.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.888442</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2020-02-24 05:51:00</td>\n",
       "      <td>294</td>\n",
       "      <td>1.62</td>\n",
       "      <td>18316.0</td>\n",
       "      <td>18780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>15</td>\n",
       "      <td>101830.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.763303</td>\n",
       "      <td>9.4</td>\n",
       "      <td>2020-02-24 15:51:00</td>\n",
       "      <td>384</td>\n",
       "      <td>2.49</td>\n",
       "      <td>18316.0</td>\n",
       "      <td>56220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>4</td>\n",
       "      <td>101420.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.909044</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2020-02-25 04:51:00</td>\n",
       "      <td>382</td>\n",
       "      <td>2.93</td>\n",
       "      <td>18317.0</td>\n",
       "      <td>15240.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Hour  barometricPressure  precipitationLastHour  relativeHumidity  \\\n",
       "Date                                                                            \n",
       "2020-02-23    19            102030.0                    0.0         30.412102   \n",
       "2020-02-23    19            102030.0                    0.0         33.061975   \n",
       "2020-02-24     5            101970.0                    0.0         72.888442   \n",
       "2020-02-24    15            101830.0                    0.0         60.763303   \n",
       "2020-02-25     4            101420.0                    0.0         88.909044   \n",
       "\n",
       "            temperature                time  PEF(l/min)  FEV1(l)  unixDate  \\\n",
       "Date                                                                         \n",
       "2020-02-23         10.6 2020-02-23 19:51:00         476     2.78   18315.0   \n",
       "2020-02-23         10.6 2020-02-23 19:27:00         476     2.78   18315.0   \n",
       "2020-02-24          3.3 2020-02-24 05:51:00         294     1.62   18316.0   \n",
       "2020-02-24          9.4 2020-02-24 15:51:00         384     2.49   18316.0   \n",
       "2020-02-25          6.7 2020-02-25 04:51:00         382     2.93   18317.0   \n",
       "\n",
       "            unixTime  \n",
       "Date                  \n",
       "2020-02-23   70320.0  \n",
       "2020-02-23   70320.0  \n",
       "2020-02-24   18780.0  \n",
       "2020-02-24   56220.0  \n",
       "2020-02-25   15240.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_spiro = pd.read_csv('weather_spiro.csv',parse_dates=['time'])\n",
    "weather_spiro.set_index('Date',inplace=True)\n",
    "weather_spiro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PEF(l/min)</th>\n",
       "      <th>FEV1(l)</th>\n",
       "      <th>unixDate</th>\n",
       "      <th>unixTime</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2020-02-21</th>\n",
       "      <td>219</td>\n",
       "      <td>1.72</td>\n",
       "      <td>18313.0</td>\n",
       "      <td>36720.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>253</td>\n",
       "      <td>1.78</td>\n",
       "      <td>18313.0</td>\n",
       "      <td>42300.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>388</td>\n",
       "      <td>2.54</td>\n",
       "      <td>18314.0</td>\n",
       "      <td>12660.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>415</td>\n",
       "      <td>2.89</td>\n",
       "      <td>18314.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23</th>\n",
       "      <td>476</td>\n",
       "      <td>2.78</td>\n",
       "      <td>18315.0</td>\n",
       "      <td>70320.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            PEF(l/min)  FEV1(l)  unixDate  unixTime  Hour\n",
       "Date                                                     \n",
       "2020-02-21         219     1.72   18313.0   36720.0    10\n",
       "2020-02-21         253     1.78   18313.0   42300.0    11\n",
       "2020-02-22         388     2.54   18314.0   12660.0     3\n",
       "2020-02-22         415     2.89   18314.0     480.0     0\n",
       "2020-02-23         476     2.78   18315.0   70320.0    19"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_spiro = pd.read_csv('raw_spiro.csv',parse_dates=['Date'])\n",
    "raw_spiro.set_index(['Date'],inplace=True)\n",
    "raw_spiro = raw_spiro.drop(['Time'], axis=1)\n",
    "raw_spiro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Environment does not have key: AIR_NOW_TOKEN\n"
     ]
    }
   ],
   "source": [
    "#https://docs.airnowapi.org/ is good but is currently down - using local data\n",
    "TOKEN = %env AIR_NOW_TOKEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shape mismatch: value array of shape (2,) could not be broadcast to indexing result of shape (2,3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-388b0e80366d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#date.strftime('%Y-%m-%d')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpollution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraw_spiro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpollution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mraw_spiro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;31m#pollution[raw_spiro.index[2]] = 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#df = pandas.DataFrame(columns=['a','b','c','d'], index=['x','y','z'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/robin/.local/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    192\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_setitem_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_with_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_has_valid_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/robin/.local/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer\u001b[0;34m(self, indexer, value)\u001b[0m\n\u001b[1;32m    637\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m             self.obj._data = self.obj._data.setitem(indexer=indexer,\n\u001b[0;32m--> 639\u001b[0;31m                                                     value=value)\n\u001b[0m\u001b[1;32m    640\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_update_cacher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/robin/.local/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36msetitem\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   3439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3440\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3441\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'setitem'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3443\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mputmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/robin/.local/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m   3327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3328\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mgr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3329\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3330\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/robin/.local/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36msetitem\u001b[0;34m(self, indexer, value, mgr)\u001b[0m\n\u001b[1;32m    899\u001b[0m         \u001b[0;31m# set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 901\u001b[0;31m             \u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    902\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m         \u001b[0;31m# coerce and try to infer the dtypes of the result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: shape mismatch: value array of shape (2,) could not be broadcast to indexing result of shape (2,3)"
     ]
    }
   ],
   "source": [
    "#date.strftime('%Y-%m-%d')\n",
    "pollution = pd.DataFrame(index=raw_spiro.index,columns=['a','b','c'])\n",
    "pollution.loc[raw_spiro.index[0]] = pd.Series([1, 5, 2])\n",
    "#pollution[raw_spiro.index[2]] = 2\n",
    "#df = pandas.DataFrame(columns=['a','b','c','d'], index=['x','y','z'])\n",
    "#df.loc['y'] = pandas.Series({'a':1, 'b':5, 'c':2, 'd':3})\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>OZONE</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [OZONE, PM2.5, PM10]\n",
       "Index: []"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(columns=['OZONE','PM2.5','PM10'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>OZONE</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [OZONE, PM2.5, PM10]\n",
       "Index: []"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_daily_pollution(date,zip_code,factors,distance=5):\n",
    "    r = requests.get('http://www.airnowapi.org/aq/observation/zipCode/historical/?format=application/json'\n",
    "                     +'&zipCode=' + str(zip_code) + '&date=' + date.strftime('%Y-%m-%d') + 'T00-0000'\n",
    "                     +'&distance=' + str(distance) + '&API_KEY=' + TOKEN)\n",
    "    return json.loads(r.text)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-02-21', '2020-02-21', '2020-02-22', '2020-02-22',\n",
       "               '2020-02-23', '2020-02-24', '2020-02-24', '2020-02-25',\n",
       "               '2020-02-25', '2020-02-26'],\n",
       "              dtype='datetime64[ns]', name='Date', freq=None)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_spiro.index[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>OZONE</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23</th>\n",
       "      <td>43</td>\n",
       "      <td>49</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           OZONE PM2.5 PM10\n",
       "Date                       \n",
       "2020-02-21    38    26   11\n",
       "2020-02-21    38    26   11\n",
       "2020-02-22    39    39   18\n",
       "2020-02-22    39    39   18\n",
       "2020-02-23    43    49   21"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pollution = pd.DataFrame(index=raw_spiro.index)\n",
    "\n",
    "zip_code = 11432\n",
    "distance = 5\n",
    "factors = ['OZONE','PM2.5','PM10']\n",
    "results = pd.DataFrame(columns=factors,index=raw_spiro.index)\n",
    "for day in raw_spiro.index:\n",
    "    daily_pollution = get_daily_pollution(day,zip_code,factors)\n",
    "    df = pd.DataFrame(columns=factors)\n",
    "    for item in daily_pollution:\n",
    "        \n",
    "        for factor in factors:\n",
    "            if item['ParameterName'] == factor:\n",
    "                results[factor][day] = item['AQI']\n",
    "        #df['Date'] = pd.to_datetime(item['DateObserved'])\n",
    "    #print(df.head())\n",
    "    #results = results.append(df, ignore_index=True)\n",
    "\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>OZONE</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           OZONE PM2.5 PM10\n",
       "Date                       \n",
       "2020-02-21    10   NaN  NaN\n",
       "2020-02-21    10   NaN  NaN\n",
       "2020-02-22   NaN   NaN  NaN\n",
       "2020-02-22   NaN   NaN  NaN\n",
       "2020-02-23   NaN   NaN  NaN\n",
       "2020-02-24   NaN   NaN  NaN\n",
       "2020-02-24   NaN   NaN  NaN\n",
       "2020-02-25   NaN   NaN  NaN\n",
       "2020-02-25   NaN   NaN  NaN\n",
       "2020-02-26   NaN   NaN  NaN\n",
       "2020-02-26   NaN   NaN  NaN\n",
       "2020-02-27   NaN   NaN  NaN\n",
       "2020-02-27   NaN   NaN  NaN\n",
       "2020-02-27   NaN   NaN  NaN\n",
       "2020-02-28   NaN   NaN  NaN\n",
       "2020-02-29   NaN   NaN  NaN\n",
       "2020-02-29   NaN   NaN  NaN\n",
       "2020-02-29   NaN   NaN  NaN\n",
       "2020-03-01   NaN   NaN  NaN\n",
       "2020-03-02   NaN   NaN  NaN\n",
       "2020-03-02   NaN   NaN  NaN\n",
       "2020-03-02   NaN   NaN  NaN\n",
       "2020-03-02   NaN   NaN  NaN\n",
       "2020-03-03   NaN   NaN  NaN\n",
       "2020-03-03   NaN   NaN  NaN\n",
       "2020-03-03   NaN   NaN  NaN\n",
       "2020-03-03   NaN   NaN  NaN\n",
       "2020-03-03   NaN   NaN  NaN\n",
       "2020-03-03   NaN   NaN  NaN\n",
       "2020-03-03   NaN   NaN  NaN\n",
       "...          ...   ...  ...\n",
       "2020-04-02   NaN   NaN  NaN\n",
       "2020-04-02   NaN   NaN  NaN\n",
       "2020-04-02   NaN   NaN  NaN\n",
       "2020-04-01   NaN   NaN  NaN\n",
       "2020-04-01   NaN   NaN  NaN\n",
       "2020-04-02   NaN   NaN  NaN\n",
       "2020-04-02   NaN   NaN  NaN\n",
       "2020-04-02   NaN   NaN  NaN\n",
       "2020-04-02   NaN   NaN  NaN\n",
       "2020-04-02   NaN   NaN  NaN\n",
       "2020-04-02   NaN   NaN  NaN\n",
       "2020-04-03   NaN   NaN  NaN\n",
       "2020-04-03   NaN   NaN  NaN\n",
       "2020-04-03   NaN   NaN  NaN\n",
       "2020-04-03   NaN   NaN  NaN\n",
       "2020-04-03   NaN   NaN  NaN\n",
       "2020-04-04   NaN   NaN  NaN\n",
       "2020-04-04   NaN   NaN  NaN\n",
       "2020-04-04   NaN   NaN  NaN\n",
       "2020-04-04   NaN   NaN  NaN\n",
       "2020-04-05   NaN   NaN  NaN\n",
       "2020-04-05   NaN   NaN  NaN\n",
       "2020-04-05   NaN   NaN  NaN\n",
       "2020-04-05   NaN   NaN  NaN\n",
       "2020-04-06   NaN   NaN  NaN\n",
       "2020-04-06   NaN   NaN  NaN\n",
       "2020-04-06   NaN   NaN  NaN\n",
       "2020-04-06   NaN   NaN  NaN\n",
       "2020-04-06   NaN   NaN  NaN\n",
       "2020-04-06   NaN   NaN  NaN\n",
       "\n",
       "[235 rows x 3 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(columns=factors,index=raw_spiro.index)\n",
    "df['OZONE'][raw_spiro.index[0]]=10\n",
    "\n",
    "#df['OZONE'] = 35\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pollution = pd.read_csv('pollution.csv',parse_dates=['date'])\n",
    "pollution = pollution.rename(columns={\"date\": \"Date\"})\n",
    "pollution.set_index('Date',inplace=True)\n",
    "pollution = pollution.replace(' ','0')\n",
    "pollution = pollution.astype('int64')"
   ]
  },
  {
   "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>pm25</th>\n",
       "      <th>o3</th>\n",
       "      <th>no2</th>\n",
       "      <th>co</th>\n",
       "      <th>PEF(l/min)</th>\n",
       "      <th>FEV1(l)</th>\n",
       "      <th>unixDate</th>\n",
       "      <th>unixTime</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2020-02-21</th>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>219</td>\n",
       "      <td>1.72</td>\n",
       "      <td>18313.0</td>\n",
       "      <td>36720.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>253</td>\n",
       "      <td>1.78</td>\n",
       "      <td>18313.0</td>\n",
       "      <td>42300.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>388</td>\n",
       "      <td>2.54</td>\n",
       "      <td>18314.0</td>\n",
       "      <td>12660.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>415</td>\n",
       "      <td>2.89</td>\n",
       "      <td>18314.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23</th>\n",
       "      <td>36</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>476</td>\n",
       "      <td>2.78</td>\n",
       "      <td>18315.0</td>\n",
       "      <td>70320.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pm25   o3   no2   co  PEF(l/min)  FEV1(l)  unixDate  unixTime  \\\n",
       "Date                                                                         \n",
       "2020-02-21     15   27    12    2         219     1.72   18313.0   36720.0   \n",
       "2020-02-21     15   27    12    2         253     1.78   18313.0   42300.0   \n",
       "2020-02-22     16   26    22    3         388     2.54   18314.0   12660.0   \n",
       "2020-02-22     16   26    22    3         415     2.89   18314.0     480.0   \n",
       "2020-02-23     36   29     0    0         476     2.78   18315.0   70320.0   \n",
       "\n",
       "            Hour  \n",
       "Date              \n",
       "2020-02-21    10  \n",
       "2020-02-21    11  \n",
       "2020-02-22     3  \n",
       "2020-02-22     0  \n",
       "2020-02-23    19  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spiro_pollution = pollution.join(raw_spiro,how='inner')\n",
    "spiro_pollution.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>PEF(l/min)</td>    <th>  R-squared:         </th> <td>   0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 08 Apr 2020</td> <th>  Prob (F-statistic):</th>  <td>0.0242</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>05:31:08</td>     <th>  Log-Likelihood:    </th> <td> -1378.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   235</td>      <th>  AIC:               </th> <td>   2767.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   230</td>      <th>  BIC:               </th> <td>   2785.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  418.8710</td> <td>   69.636</td> <td>    6.015</td> <td> 0.000</td> <td>  281.665</td> <td>  556.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> pm25</th> <td>    0.0642</td> <td>    0.542</td> <td>    0.118</td> <td> 0.906</td> <td>   -1.004</td> <td>    1.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> o3</th>   <td>    0.5872</td> <td>    2.017</td> <td>    0.291</td> <td> 0.771</td> <td>   -3.388</td> <td>    4.562</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> no2</th>  <td>    4.4702</td> <td>    2.094</td> <td>    2.135</td> <td> 0.034</td> <td>    0.345</td> <td>    8.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> co</th>   <td>  -51.1645</td> <td>   17.442</td> <td>   -2.933</td> <td> 0.004</td> <td>  -85.532</td> <td>  -16.797</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 3.018</td> <th>  Durbin-Watson:     </th> <td>   1.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.221</td> <th>  Jarque-Bera (JB):  </th> <td>   2.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.091</td> <th>  Prob(JB):          </th> <td>   0.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.550</td> <th>  Cond. No.          </th> <td>    473.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             PEF(l/min)   R-squared:                       0.047\n",
       "Model:                            OLS   Adj. R-squared:                  0.031\n",
       "Method:                 Least Squares   F-statistic:                     2.862\n",
       "Date:                Wed, 08 Apr 2020   Prob (F-statistic):             0.0242\n",
       "Time:                        05:31:08   Log-Likelihood:                -1378.6\n",
       "No. Observations:                 235   AIC:                             2767.\n",
       "Df Residuals:                     230   BIC:                             2785.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        418.8710     69.636      6.015      0.000     281.665     556.077\n",
       " pm25          0.0642      0.542      0.118      0.906      -1.004       1.132\n",
       " o3            0.5872      2.017      0.291      0.771      -3.388       4.562\n",
       " no2           4.4702      2.094      2.135      0.034       0.345       8.595\n",
       " co          -51.1645     17.442     -2.933      0.004     -85.532     -16.797\n",
       "==============================================================================\n",
       "Omnibus:                        3.018   Durbin-Watson:                   1.649\n",
       "Prob(Omnibus):                  0.221   Jarque-Bera (JB):                2.307\n",
       "Skew:                           0.091   Prob(JB):                        0.316\n",
       "Kurtosis:                       2.550   Cond. No.                         473.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = spiro_pollution['PEF(l/min)']\n",
    "X = spiro_pollution[[' pm25',' o3',' no2',' co']]\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "mod = sm.OLS(Y,X)\n",
    "fit = mod.fit()\n",
    "summary = fit.summary()\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient: \n",
      " -1.3\n",
      "R-squared: 0.01\n"
     ]
    }
   ],
   "source": [
    "lin_reg_plot(spiro_pollution[' no2'],spiro_pollution['PEF(l/min)'])"
   ]
  }
 ],
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