no2_data_pull.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a4db27fa-c90d-4498-ad7d-5cb4c1fcdae9",
   "metadata": {},
   "source": [
    "# **NO2 AQ monitoring station data analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "975a534a-7183-4942-b43a-cde0b6d6eba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using pandas 1.3.3\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import xgboost as xg\n",
    "import numpy as np\n",
    "import useful_functions\n",
    "from useful_functions import *\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from cartopy.io import shapereader\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from datetime import datetime\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8be50b0-c47d-465b-a49c-400cf0ae3248",
   "metadata": {},
   "source": [
    " ### Load in EPA data and format it - Source: https://aqs.epa.gov/aqsweb/airdata/download_files.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e908ffb-792e-4fba-a6d8-419e00345163",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Code</th>\n",
       "      <th>County_Code</th>\n",
       "      <th>Site_Num</th>\n",
       "      <th>Parameter_Code</th>\n",
       "      <th>POC</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Datum</th>\n",
       "      <th>Parameter_Name</th>\n",
       "      <th>Sample_Duration</th>\n",
       "      <th>...</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Method_Code</th>\n",
       "      <th>Method_Name</th>\n",
       "      <th>Local_Site_Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>State_Name</th>\n",
       "      <th>County_Name</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>CBSA_Name</th>\n",
       "      <th>Date_of_Last_Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen_dioxide_(NO2)</td>\n",
       "      <td>1_HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...</td>\n",
       "      <td>North_Birmingham</td>\n",
       "      <td>NO._B'HAM,SOU_R.R.,_3009_28TH_ST._NO.</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>Birmingham-Hoover,_AL</td>\n",
       "      <td>6/12/2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen_dioxide_(NO2)</td>\n",
       "      <td>1_HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...</td>\n",
       "      <td>North_Birmingham</td>\n",
       "      <td>NO._B'HAM,SOU_R.R.,_3009_28TH_ST._NO.</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>Birmingham-Hoover,_AL</td>\n",
       "      <td>6/12/2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen_dioxide_(NO2)</td>\n",
       "      <td>1_HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...</td>\n",
       "      <td>North_Birmingham</td>\n",
       "      <td>NO._B'HAM,SOU_R.R.,_3009_28TH_ST._NO.</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>Birmingham-Hoover,_AL</td>\n",
       "      <td>6/12/2020</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen_dioxide_(NO2)</td>\n",
       "      <td>1_HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...</td>\n",
       "      <td>North_Birmingham</td>\n",
       "      <td>NO._B'HAM,SOU_R.R.,_3009_28TH_ST._NO.</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>Birmingham-Hoover,_AL</td>\n",
       "      <td>6/12/2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen_dioxide_(NO2)</td>\n",
       "      <td>1_HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...</td>\n",
       "      <td>North_Birmingham</td>\n",
       "      <td>NO._B'HAM,SOU_R.R.,_3009_28TH_ST._NO.</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>Birmingham-Hoover,_AL</td>\n",
       "      <td>6/12/2020</td>\n",
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       "<p>5 rows × 29 columns</p>\n",
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      "text/plain": [
       "   State_Code  County_Code  Site_Num  Parameter_Code  POC   Latitude  \\\n",
       "0           1           73        23           42602    1  33.553056   \n",
       "1           1           73        23           42602    1  33.553056   \n",
       "2           1           73        23           42602    1  33.553056   \n",
       "3           1           73        23           42602    1  33.553056   \n",
       "4           1           73        23           42602    1  33.553056   \n",
       "\n",
       "   Longitude  Datum          Parameter_Name Sample_Duration  ... AQI  \\\n",
       "0    -86.815  WGS84  Nitrogen_dioxide_(NO2)          1_HOUR  ...   8   \n",
       "1    -86.815  WGS84  Nitrogen_dioxide_(NO2)          1_HOUR  ...  16   \n",
       "2    -86.815  WGS84  Nitrogen_dioxide_(NO2)          1_HOUR  ...  12   \n",
       "3    -86.815  WGS84  Nitrogen_dioxide_(NO2)          1_HOUR  ...  17   \n",
       "4    -86.815  WGS84  Nitrogen_dioxide_(NO2)          1_HOUR  ...  30   \n",
       "\n",
       "  Method_Code                                        Method_Name  \\\n",
       "0         200  Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...   \n",
       "1         200  Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...   \n",
       "2         200  Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...   \n",
       "3         200  Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...   \n",
       "4         200  Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...   \n",
       "\n",
       "    Local_Site_Name                                Address  State_Name  \\\n",
       "0  North_Birmingham  NO._B'HAM,SOU_R.R.,_3009_28TH_ST._NO.     Alabama   \n",
       "1  North_Birmingham  NO._B'HAM,SOU_R.R.,_3009_28TH_ST._NO.     Alabama   \n",
       "2  North_Birmingham  NO._B'HAM,SOU_R.R.,_3009_28TH_ST._NO.     Alabama   \n",
       "3  North_Birmingham  NO._B'HAM,SOU_R.R.,_3009_28TH_ST._NO.     Alabama   \n",
       "4  North_Birmingham  NO._B'HAM,SOU_R.R.,_3009_28TH_ST._NO.     Alabama   \n",
       "\n",
       "   County_Name   City_Name              CBSA_Name  Date_of_Last_Change  \n",
       "0    Jefferson  Birmingham  Birmingham-Hoover,_AL            6/12/2020  \n",
       "1    Jefferson  Birmingham  Birmingham-Hoover,_AL            6/12/2020  \n",
       "2    Jefferson  Birmingham  Birmingham-Hoover,_AL            6/12/2020  \n",
       "3    Jefferson  Birmingham  Birmingham-Hoover,_AL            6/12/2020  \n",
       "4    Jefferson  Birmingham  Birmingham-Hoover,_AL            6/12/2020  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in AQ data from EPA and format to remove spaces\n",
    "in_df = pd.read_csv(r'C:\\Users\\xavie\\Documents\\data_science_scripts\\daily_us_no2_data.csv')\n",
    "\n",
    "for col in list(in_df.columns):\n",
    "        if in_df[col].dtypes == object:\n",
    "            in_df[col].replace(' ', '_', regex=True, inplace=True)\n",
    "        if ' ' in str(col):\n",
    "            new = str(col).replace(' ', '_')\n",
    "            in_df.rename(columns = {str(col):new}, inplace = True)\n",
    "            \n",
    "\n",
    "in_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "057ae61a-2c50-4ee7-8e1b-dd7353265523",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns: Index(['State_Code', 'County_Code', 'Site_Num', 'Parameter_Code', 'POC',\n",
      "       'Latitude', 'Longitude', 'Datum', 'Parameter_Name', 'Sample_Duration',\n",
      "       'Pollutant_Standard', 'Date_Local', 'Units_of_Measure', 'Event_Type',\n",
      "       'Observation_Count', 'Observation_Percent', 'Arithmetic_Mean',\n",
      "       '1st_Max_Value', '1st_Max_Hour', 'AQI', 'Method_Code', 'Method_Name',\n",
      "       'Local_Site_Name', 'Address', 'State_Name', 'County_Name', 'City_Name',\n",
      "       'CBSA_Name', 'Date_of_Last_Change'],\n",
      "      dtype='object')\n",
      "Methods used: ['Teledyne-API_Model_200EUP_or_T200UP_-_Photolytic-Chemiluminescence'\n",
      " 'Instrumental_-_Chemiluminescence_Thermo_Electron_42C-TL,_42i-TL'\n",
      " 'INSTRUMENTAL_-_GAS_PHASE_CHEMILUMINESCENCE'\n",
      " 'INSTRUMENTAL_-_CHEMILUMINESCENCE'\n",
      " 'Teledyne_Model_T500U_-_Cavity_Attenuated_Phase_Shift_Spectroscopy'\n",
      " 'Instrumental_-_Chemiluminescence_Teledyne_API_200_EU/501'\n",
      " 'Instrumental_-_Chemiluminescence'\n",
      " 'INSTRUMENTAL_-_GAS-PHASE_CHEMILUMINESCENCE'\n",
      " 'Teledyne-API_Model_T200P_Chemiluminescence_-_Photolytic-Chemiluminescence']\n",
      "Monitoring sites: 455\n"
     ]
    }
   ],
   "source": [
    "# print column names\n",
    "print('Columns: %s' % in_df.columns)\n",
    "\n",
    "# find unique methods used for relevant columns\n",
    "print('Methods used: %s' % in_df.Method_Name.unique())\n",
    "    \n",
    "# print total number of sites\n",
    "print('Monitoring sites: %s' % len(list(in_df.Latitude.unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73d41241-b950-4dc2-8c01-eebb430b8c77",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>State_Code</th>\n",
       "      <th>Site_Num</th>\n",
       "      <th>Parameter_Code</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
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       "      <th>mean_no2</th>\n",
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       "      <th>State_Name</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
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       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen_dioxide_(NO2)</td>\n",
       "      <td>NO2_1-hour</td>\n",
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       "      <td>Alabama</td>\n",
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       "      <td>WGS84</td>\n",
       "      <td>Nitrogen_dioxide_(NO2)</td>\n",
       "      <td>NO2_1-hour</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>Parts_per_billion</td>\n",
       "      <td>11</td>\n",
       "      <td>46</td>\n",
       "      <td>6.263636</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>42602</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen_dioxide_(NO2)</td>\n",
       "      <td>NO2_1-hour</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>Parts_per_billion</td>\n",
       "      <td>21</td>\n",
       "      <td>88</td>\n",
       "      <td>4.957143</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>6/12/2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen_dioxide_(NO2)</td>\n",
       "      <td>NO2_1-hour</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>Parts_per_billion</td>\n",
       "      <td>24</td>\n",
       "      <td>100</td>\n",
       "      <td>5.891667</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>6/12/2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen_dioxide_(NO2)</td>\n",
       "      <td>NO2_1-hour</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>Parts_per_billion</td>\n",
       "      <td>24</td>\n",
       "      <td>100</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>6/12/2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State_Code  Site_Num  Parameter_Code        lat    long  Datum  \\\n",
       "0           1        23           42602  33.553056 -86.815  WGS84   \n",
       "1           1        23           42602  33.553056 -86.815  WGS84   \n",
       "2           1        23           42602  33.553056 -86.815  WGS84   \n",
       "3           1        23           42602  33.553056 -86.815  WGS84   \n",
       "4           1        23           42602  33.553056 -86.815  WGS84   \n",
       "\n",
       "           Parameter_Name Pollutant_Standard Date_Local   Units_of_Measure  \\\n",
       "0  Nitrogen_dioxide_(NO2)         NO2_1-hour   1/1/2019  Parts_per_billion   \n",
       "1  Nitrogen_dioxide_(NO2)         NO2_1-hour   1/8/2019  Parts_per_billion   \n",
       "2  Nitrogen_dioxide_(NO2)         NO2_1-hour   1/9/2019  Parts_per_billion   \n",
       "3  Nitrogen_dioxide_(NO2)         NO2_1-hour  1/10/2019  Parts_per_billion   \n",
       "4  Nitrogen_dioxide_(NO2)         NO2_1-hour  1/11/2019  Parts_per_billion   \n",
       "\n",
       "   Observation_Count  Observation_Percent   mean_no2  Method_Code  \\\n",
       "0                 21                   88   4.314286          200   \n",
       "1                 11                   46   6.263636          200   \n",
       "2                 21                   88   4.957143          200   \n",
       "3                 24                  100   5.891667          200   \n",
       "4                 24                  100  14.500000          200   \n",
       "\n",
       "                                         Method_Name State_Name County_Name  \\\n",
       "0  Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...    Alabama   Jefferson   \n",
       "1  Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...    Alabama   Jefferson   \n",
       "2  Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...    Alabama   Jefferson   \n",
       "3  Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...    Alabama   Jefferson   \n",
       "4  Teledyne-API_Model_200EUP_or_T200UP_-_Photolyt...    Alabama   Jefferson   \n",
       "\n",
       "    City_Name Date_of_Last_Change  \n",
       "0  Birmingham           6/12/2020  \n",
       "1  Birmingham           6/12/2020  \n",
       "2  Birmingham           6/12/2020  \n",
       "3  Birmingham           6/12/2020  \n",
       "4  Birmingham           6/12/2020  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop excess columns\n",
    "drop_cols = ['Address', 'Sample_Duration', 'Local_Site_Name', 'CBSA_Name', 'County_Code', 'POC', '1st_Max_Value', '1st_Max_Hour', 'AQI', 'Event_Type']\n",
    "df = in_df.drop(drop_cols, axis=1)\n",
    "df.rename(columns = {'Arithmetic_Mean':'mean_no2', 'Latitude':'lat', 'Longitude': 'long'}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a6a377-ed2a-443a-9720-7d4a9e9452b4",
   "metadata": {},
   "source": [
    "### Visualize day % observation values and apply quality threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fddddc7c-ba1d-4c57-80b5-518abd90c4df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observation percent: mean= 95.63, STD= 9.43\n",
      "3 STDs less than mean: 67.34\n",
      "Total observations: 155489\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e94c21bcd0814d31912864cd824f24f2",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize distribution of observation %, and find decent threshold for inclusion (Keller et al., 2021 used 75%)\n",
    "%matplotlib widget\n",
    "plot = sns.displot(data=in_df, x=\"Observation_Percent\", binwidth=1)\n",
    "plot.set(xlim=(50, None))\n",
    "\n",
    "# find mean and std of observation %, could roughly -3 STDs be a good cut off\n",
    "obs_percents = in_df['Observation_Percent'].to_numpy()\n",
    "mean, std = np.round(np.nanmean(obs_percents),2), np.round(np.nanstd(obs_percents),2)\n",
    "three_stdless = mean - (3 * std)\n",
    "print('Observation percent: mean= %s, STD= %s' % (mean, std))\n",
    "print('3 STDs less than mean: %s' % three_stdless)\n",
    "\n",
    "# calculate number of rows so we can see how many are removed\n",
    "row_num = df.shape[0]\n",
    "print('Total observations: %s' % row_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6568295b-c164-40d8-b845-b90bb6945308",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New # of day mean NO2 values: 152301\n",
      "Rows removed by 70 percent threshold: 3188\n"
     ]
    }
   ],
   "source": [
    "# remove days with not enough observations\n",
    "thresh = 70\n",
    "df_v2 = df.loc[df['Observation_Percent'] >= thresh]\n",
    "row_num2 = df_v2.shape[0]\n",
    "print('New # of day mean NO2 values: %s' %row_num2)\n",
    "print('Rows removed by %s percent threshold: %s' % (thresh, row_num - row_num2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59cc57de-b4d6-46cc-bdfc-20cdc0d2b250",
   "metadata": {},
   "source": [
    "## **Visualize NO2 data**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "878503cb-ac4c-4092-9f2e-1443ba11242d",
   "metadata": {},
   "source": [
    "### All observed NO2 concentrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e34e3b1d-45b0-47b5-b508-f4e03c6d4581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1100005a640>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "601cde32e80642df853af2d6f016df73",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize aq data (all observations)\n",
    "%matplotlib widget\n",
    "plot = sns.displot(data=df_v2, x=\"mean_no2\", binwidth=1)\n",
    "plot.set(xlabel='Mean daily NO2 concentration (ppb)')\n",
    "plot.set(ylabel='Observations')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b541027c-1293-486c-ad11-7f25c89bb6ef",
   "metadata": {},
   "source": [
    "### Explore methods and method codes. Plot NO2 stratified by method codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6df9fad6-dad3-4b81-a62b-9562589ef365",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of methods codes: 12\n",
      "# of method names: 9\n",
      "The most methods any code is assocaited w/ is 1\n",
      "The most codes any name is assocaited w/ is 3\n",
      "[(1, 'Teledyne-API_Model_200EUP_or_T200UP_-_Photolytic-Chemiluminescence'), (1, 'Instrumental_-_Chemiluminescence_Thermo_Electron_42C-TL,_42i-TL'), (1, 'INSTRUMENTAL_-_GAS_PHASE_CHEMILUMINESCENCE'), (3, 'INSTRUMENTAL_-_CHEMILUMINESCENCE'), (1, 'Teledyne_Model_T500U_-_Cavity_Attenuated_Phase_Shift_Spectroscopy'), (1, 'Instrumental_-_Chemiluminescence_Teledyne_API_200_EU/501'), (2, 'Instrumental_-_Chemiluminescence'), (1, 'INSTRUMENTAL_-_GAS-PHASE_CHEMILUMINESCENCE'), (1, 'Teledyne-API_Model_T200P_Chemiluminescence_-_Photolytic-Chemiluminescence')]\n"
     ]
    }
   ],
   "source": [
    "# make sure method codes and methods are aligned\n",
    "methods_dict = {}\n",
    "codes, methods = df_v2.Method_Code.unique(), df_v2.Method_Name.unique()\n",
    "print('# of methods codes: %s' % len(codes))\n",
    "print('# of method names: %s' %  len(methods))\n",
    "\n",
    "assos1 = []\n",
    "for i, key in enumerate(codes):\n",
    "    sub = df_v2.loc[df_v2['Method_Code'] == key]\n",
    "    assos1.append(len(sub.Method_Name.unique()))\n",
    "\n",
    "    if key == codes[-1]:\n",
    "        m = np.max(assos1)\n",
    "        print('The most methods any code is assocaited w/ is %s' % m)\n",
    "        \n",
    "\n",
    "assos2 = []\n",
    "for i, name in enumerate(methods):\n",
    "    sub = df_v2.loc[df_v2['Method_Name'] == name]\n",
    "    assos2.append(len(sub.Method_Code.unique()))\n",
    "    \n",
    "    if name == methods[-1]:\n",
    "        m = np.max(assos2)\n",
    "        print('The most codes any name is assocaited w/ is %s' % m)\n",
    "        \n",
    "        if m > 1:\n",
    "            print(list(zip(assos2, methods)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43af1ee3-7c24-4019-ae27-a9b81eb835af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Mean daily NO2 concentration (ppb)')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d6c2c5f594645ae8d7c1d118f14108f",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize no2 by method codes\n",
    "%matplotlib widget\n",
    "plot = sns.kdeplot(data=df_v2, x=\"mean_no2\", hue='Method_Code', multiple=\"stack\")\n",
    "sns.set_style()\n",
    "plot.set(xlabel='Mean daily NO2 concentration (ppb)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9beaeac-0615-47d6-a21b-12a581bb7f4f",
   "metadata": {},
   "source": [
    "### Create analysis ready dataframe and explore month dependent trends in NO2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08f727a1-a6bf-4cfa-9bea-6c6fce5e2b1d",
   "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>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Date_Local</th>\n",
       "      <th>mean_no2</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>counter</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>4.314286</td>\n",
       "      <td>2019</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>2</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>4.957143</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>5.891667</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-12</td>\n",
       "      <td>7.254167</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    long State_Name Date_Local   mean_no2  year  month  \\\n",
       "0  33.553056 -86.815    Alabama 2019-01-01   4.314286  2019      1   \n",
       "2  33.553056 -86.815    Alabama 2019-01-09   4.957143  2019      1   \n",
       "3  33.553056 -86.815    Alabama 2019-01-10   5.891667  2019      1   \n",
       "4  33.553056 -86.815    Alabama 2019-01-11  14.500000  2019      1   \n",
       "5  33.553056 -86.815    Alabama 2019-01-12   7.254167  2019      1   \n",
       "\n",
       "   dayofmonth  dayofyear  counter  weekend  \n",
       "0           1          1        1        0  \n",
       "2           9          9        1        0  \n",
       "3          10         10        1        0  \n",
       "4          11         11        1        0  \n",
       "5          12         12        1        1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a dataframe with just pertinent datat and create new month, day of the week, weekend/weekday columns\n",
    "keep_list = ['lat', 'long', 'State_Name', 'Date_Local', 'mean_no2']\n",
    "data = df_v2[keep_list]\n",
    "\n",
    "data['Date_Local'] =  pd.to_datetime(data['Date_Local'])\n",
    "data['year'] = data['Date_Local'].dt.year\n",
    "data['month'] = data['Date_Local'].dt.month\n",
    "data['dayofmonth'] = data['Date_Local'].dt.day\n",
    "data['dayofyear'] = data['Date_Local'].dt.dayofyear\n",
    "data['counter'] = 1\n",
    "\n",
    "# create column coding days as either weekdays or weekends (1=weekend, 0=not weekend)\n",
    "data['dayname'] = data['Date_Local'].dt.day_name()\n",
    "data['weekend'] = np.where((data['dayname'] == 'Saturday') | (data['dayname'] == 'Sunday') , 1, 0)\n",
    "data.drop(['dayname'], axis=1, inplace=True)\n",
    "\n",
    "# drop Alaska, Hawaii, and Puerto Rico (| is or in pandas)\n",
    "data.drop(data[(data.State_Name == 'Alaska') | (data.State_Name == 'Hawaii') | (data.State_Name == 'Puerto_Rico')].index, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c7df93cd-cef1-4690-9fe1-dd8a576f60e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11010ffcc10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05fbf488abc74a5989554d176b860cb1",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize no2 by month using a filled density plot\n",
    "%matplotlib widget\n",
    "plt.subplot(121)\n",
    "plot = sns.kdeplot(data=data, x=\"mean_no2\", hue='month', multiple=\"fill\", common_norm=False)\n",
    "sns.set_style()\n",
    "sns.set_style('white', rc={'axes.facecolor': 'white','patch.edgecolor': 'black','patch.force_edgecolor': True, 'axes.edgecolor': 'black'})\n",
    "plot.set(xlabel='Daily NO2 concentration (ppb)')\n",
    "plot.legend(range(1,13), loc='center right', bbox_to_anchor=(1.4,0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee720514-bcf6-42f4-a27d-97e95fe5c5d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11000015640>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76b40473e7b44677880e651666c5501a",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make pretty denisty plots!\n",
    "%matplotlib widget\n",
    "# Initialize the FacetGrid object\n",
    "sns.set_style()\n",
    "sns.set_theme(style=\"white\", rc={\"axes.facecolor\": (0, 0, 0, 0)})\n",
    "pal = sns.cubehelix_palette(13, rot=-.25, light=.7)\n",
    "g = sns.FacetGrid(data, row=\"month\", hue=\"month\", aspect=15, height=.5, palette=pal)\n",
    "\n",
    "# Draw the densities in a few steps\n",
    "g.map(sns.kdeplot, \"mean_no2\", bw_adjust=.5, clip_on=False,fill=True, alpha=1, linewidth=1.5)\n",
    "g.map(sns.kdeplot, \"mean_no2\", clip_on=False, color=\"w\", lw=2, bw_adjust=.5)\n",
    "\n",
    "# passing color=None to refline() uses the hue mapping\n",
    "g.refline(y=0, linewidth=2, linestyle=\"-\", color=None, clip_on=False)\n",
    "\n",
    "# Set the subplots to overlap\n",
    "g.figure.subplots_adjust(hspace=-.25)\n",
    "\n",
    "\n",
    "# Define and use a simple function to label the plot in axes coordinates\n",
    "def label(x, color, label):\n",
    "    ax = plt.gca()\n",
    "    ax.text(0.95, .2, label, fontweight=\"bold\", color=color,\n",
    "            ha=\"left\", va=\"center\", transform=ax.transAxes)\n",
    "\n",
    "\n",
    "g.map(label, \"month\")\n",
    "\n",
    "# Remove axes details that don't play well with overlap\n",
    "g.set_titles(\"\")\n",
    "g.set(xlabel='Daily NO2 concentrations (ppb)')\n",
    "g.set(yticks=[], ylabel=\"\")\n",
    "g.set(xlim=(0, 40))\n",
    "g.despine(bottom=True, left=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91960a7a-2fef-4c69-984a-cd2263d646d3",
   "metadata": {},
   "source": [
    "## **Verify data quality**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e7c4ab3-27bb-4c4a-8fe6-7f05d35c5afb",
   "metadata": {},
   "source": [
    "### Check for station daily NO2 observation duplicates and/or incorrect month-day mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "94d990a6-50fe-4a7c-bc27-e4b2e41d4c4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Months/day relationships are correct\n"
     ]
    }
   ],
   "source": [
    "# check that each month is associated w/ the correct # of days\n",
    "assos = []\n",
    "correct = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n",
    "for i, m in enumerate(data.month.unique()):\n",
    "    sub = data.loc[data['month'] == m]\n",
    "    assos.append(len(sub.dayofyear.unique()))\n",
    "if assos == correct:\n",
    "    print('Months/day relationships are correct')\n",
    "else:\n",
    "    print('Error! Debug!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b373dc68-63bd-4dbf-8d01-ebe4ee513e4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum amount of data points per day (if above 1 we need to filter): 2\n",
      "# of unique lat/long w/ duplicate day values: 8\n"
     ]
    },
    {
     "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>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Date_Local</th>\n",
       "      <th>mean_no2</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>counter</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>4.314286</td>\n",
       "      <td>2019</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>2</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>4.957143</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>5.891667</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-12</td>\n",
       "      <td>7.254167</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    long State_Name Date_Local   mean_no2  year  month  \\\n",
       "0  33.553056 -86.815    Alabama 2019-01-01   4.314286  2019      1   \n",
       "2  33.553056 -86.815    Alabama 2019-01-09   4.957143  2019      1   \n",
       "3  33.553056 -86.815    Alabama 2019-01-10   5.891667  2019      1   \n",
       "4  33.553056 -86.815    Alabama 2019-01-11  14.500000  2019      1   \n",
       "5  33.553056 -86.815    Alabama 2019-01-12   7.254167  2019      1   \n",
       "\n",
       "   dayofmonth  dayofyear  counter  weekend  \n",
       "0           1          1        1        0  \n",
       "2           9          9        1        0  \n",
       "3          10         10        1        0  \n",
       "4          11         11        1        0  \n",
       "5          12         12        1        1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for stations w/ duplicate day averaged NO2 values\n",
    "st_days = data.groupby(['lat', 'long', 'dayofyear']).sum() #group by unique lat/long dayofyear combos\n",
    "print('Maximum amount of data points per day (if above 1 we need to filter): %s' % st_days['counter'].max())\n",
    "dups = st_days.loc[st_days['counter'] == 2]\n",
    "\n",
    "# make a dataframe of only lat/longs with duplicae values\n",
    "z = dups.groupby(['lat', 'long']).std()\n",
    "print('# of unique lat/long w/ duplicate day values: %s' % z.shape[0])\n",
    "\n",
    "# drop duplicates if desired\n",
    "drop_dups = True\n",
    "if drop_dups == True:\n",
    "    data.drop_duplicates(['lat', 'long', 'dayofyear'], keep='first', inplace=True)\n",
    "    \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2065f7a-84fd-46ef-ac29-931f44c68b65",
   "metadata": {},
   "source": [
    "### Calculate the average amount of days w/ valid data for each month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90ac44f4-e71f-46c4-8902-5d5f284b4e47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>month</th>\n",
       "      <th>mean_no2</th>\n",
       "      <th>year</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>counter</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.732878</td>\n",
       "      <td>-80.16175</td>\n",
       "      <td>1</td>\n",
       "      <td>167.002379</td>\n",
       "      <td>58551</td>\n",
       "      <td>461</td>\n",
       "      <td>461</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.732878</td>\n",
       "      <td>-80.16175</td>\n",
       "      <td>2</td>\n",
       "      <td>55.282500</td>\n",
       "      <td>44418</td>\n",
       "      <td>341</td>\n",
       "      <td>1023</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.732878</td>\n",
       "      <td>-80.16175</td>\n",
       "      <td>3</td>\n",
       "      <td>137.195529</td>\n",
       "      <td>60570</td>\n",
       "      <td>477</td>\n",
       "      <td>2247</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.732878</td>\n",
       "      <td>-80.16175</td>\n",
       "      <td>4</td>\n",
       "      <td>70.350480</td>\n",
       "      <td>60570</td>\n",
       "      <td>465</td>\n",
       "      <td>3165</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.732878</td>\n",
       "      <td>-80.16175</td>\n",
       "      <td>5</td>\n",
       "      <td>50.323572</td>\n",
       "      <td>58551</td>\n",
       "      <td>493</td>\n",
       "      <td>3973</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat      long  month    mean_no2   year  dayofmonth  dayofyear  \\\n",
       "0  25.732878 -80.16175      1  167.002379  58551         461        461   \n",
       "1  25.732878 -80.16175      2   55.282500  44418         341       1023   \n",
       "2  25.732878 -80.16175      3  137.195529  60570         477       2247   \n",
       "3  25.732878 -80.16175      4   70.350480  60570         465       3165   \n",
       "4  25.732878 -80.16175      5   50.323572  58551         493       3973   \n",
       "\n",
       "   counter  weekend  \n",
       "0       29        8  \n",
       "1       22        8  \n",
       "2       30       10  \n",
       "3       30        8  \n",
       "4       29        8  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_months = data.groupby(['lat', 'long', 'month']).sum()\n",
    "st_months.reset_index(inplace=True)\n",
    "st_months.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c9010bf-f269-43f6-9fdc-18143c92e22a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean valid data days for month 1: 29.5635103926097/31\n",
      "Standard deviation for month 1: 4.192400323394821\n",
      "The discrepancy is -1.4364896073902997, and 0.3426413263481226 standard deviations\n",
      "\n",
      "Mean valid data days for month 2: 26.702764976958527/28\n",
      "Standard deviation for month 2: 3.660304148057222\n",
      "The discrepancy is -1.2972350230414733, and 0.3544063472785468 standard deviations\n",
      "\n",
      "Mean valid data days for month 3: 29.701149425287355/31\n",
      "Standard deviation for month 3: 3.57422105501127\n",
      "The discrepancy is -1.2988505747126453, and 0.36339402480201377 standard deviations\n",
      "\n",
      "Mean valid data days for month 4: 28.49074074074074/30\n",
      "Standard deviation for month 4: 3.7761320852662488\n",
      "The discrepancy is -1.5092592592592595, and 0.3996839160229863 standard deviations\n",
      "\n",
      "Mean valid data days for month 5: 29.41570438799076/31\n",
      "Standard deviation for month 5: 3.8974063769237683\n",
      "The discrepancy is -1.5842956120092389, and 0.4064999794195767 standard deviations\n",
      "\n",
      "Mean valid data days for month 6: 28.4088785046729/30\n",
      "Standard deviation for month 6: 3.6299140915924846\n",
      "The discrepancy is -1.5911214953271013, and 0.43833585456262364 standard deviations\n",
      "\n",
      "Mean valid data days for month 7: 29.014018691588785/31\n",
      "Standard deviation for month 7: 4.695871095488569\n",
      "The discrepancy is -1.9859813084112155, and 0.4229207463380315 standard deviations\n",
      "\n",
      "Mean valid data days for month 8: 28.889400921658986/31\n",
      "Standard deviation for month 8: 5.085601624265951\n",
      "The discrepancy is -2.1105990783410142, and 0.4150146303773165 standard deviations\n",
      "\n",
      "Mean valid data days for month 9: 28.022935779816514/30\n",
      "Standard deviation for month 9: 4.346719379353612\n",
      "The discrepancy is -1.977064220183486, and 0.45484054700524273 standard deviations\n",
      "\n",
      "Mean valid data days for month 10: 29.26096997690531/31\n",
      "Standard deviation for month 10: 4.208072741220554\n",
      "The discrepancy is -1.7390300230946885, and 0.41326044724937 standard deviations\n",
      "\n",
      "Mean valid data days for month 11: 28.56279069767442/30\n",
      "Standard deviation for month 11: 3.726649674090904\n",
      "The discrepancy is -1.4372093023255808, and 0.38565720634209605 standard deviations\n",
      "\n",
      "Mean valid data days for month 12: 29.703016241299302/31\n",
      "Standard deviation for month 12: 3.4155323116454324\n",
      "The discrepancy is -1.2969837587006978, and 0.3797310756740802 standard deviations\n",
      "\n"
     ]
    }
   ],
   "source": [
    "m_list = [] # will store a list for each month that has the # of valid days for all stations\n",
    "\n",
    "for i, month in enumerate(range(1, 13)):\n",
    "    sub = st_months.loc[st_months['month'] == month]\n",
    "    m_list.append(sub['counter'].to_numpy())\n",
    "\n",
    "# find average monthly coverage and compare to days in the month\n",
    "for i, month in enumerate(m_list):\n",
    "    c = correct[i]\n",
    "    mean = np.nanmean(month)\n",
    "    std = np.nanstd(month)\n",
    "    dis = c - mean\n",
    "    dis_z = dis / std # discrepancy Z score\n",
    "    print('Mean valid data days for month %s: %s/%s' % (i + 1, mean, c))\n",
    "    print('Standard deviation for month %s: %s' %  (i + 1, std))\n",
    "    print('The discrepancy is -%s, and %s standard deviations\\n' % (dis, dis_z))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a33124e2-bf91-457d-9028-7a142d0dfc54",
   "metadata": {},
   "source": [
    "### Map NO2 stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "50646699-bde2-4929-b9fb-3818017e03e2",
   "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>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>obsversations</th>\n",
       "      <th>weekend</th>\n",
       "      <th>station_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.732878</td>\n",
       "      <td>-80.161750</td>\n",
       "      <td>274</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.785472</td>\n",
       "      <td>-80.284206</td>\n",
       "      <td>327</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.798709</td>\n",
       "      <td>-80.210048</td>\n",
       "      <td>266</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.087567</td>\n",
       "      <td>-80.111152</td>\n",
       "      <td>344</td>\n",
       "      <td>102</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.132677</td>\n",
       "      <td>-80.169817</td>\n",
       "      <td>335</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat       long  obsversations  weekend  station_id\n",
       "0  25.732878 -80.161750            274       82           0\n",
       "1  25.785472 -80.284206            327       96           1\n",
       "2  25.798709 -80.210048            266       80           2\n",
       "3  26.087567 -80.111152            344      102           3\n",
       "4  26.132677 -80.169817            335      100           4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a dataframe storing unique latlong pairs for plotting on contigous USA map\n",
    "stations = data.groupby(['lat', 'long']).sum()\n",
    "rows = stations.shape[0]\n",
    "stations['station_id'] = range(0, rows)\n",
    "stations.rename(columns= {'counter':'obsversations'}, inplace=True)\n",
    "stations.drop(['month', 'year', 'dayofmonth', 'dayofyear','mean_no2'], axis=1, inplace=True)\n",
    "stations.reset_index(inplace=True)\n",
    "stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e858ef0b-43b1-4445-a53a-6957efe03fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a function that adds our mapping preferences\n",
    "def cartography(ax):\n",
    "    \"\"\"\"Add geographic features (may not work unless on cartopy 0.20.0)\"\"\"\n",
    "    ax.add_feature(cfeature.LAND)\n",
    "    ax.add_feature(cfeature.OCEAN)\n",
    "    ax.add_feature(cfeature.LAKES)\n",
    "    ax.add_feature(cfeature.COASTLINE)\n",
    "    ax.add_feature(cfeature.BORDERS)\n",
    "    ax.add_feature(cfeature.STATES)\n",
    "    ax.gridlines(proj, draw_labels=True, alpha=0, linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "05df861c-77f8-49e5-a734-3a25161ab667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of stations: 450\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8284f6c490714248ac3229286fab7d0b",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "proj = ccrs.PlateCarree()\n",
    "ax = plt.axes(projection=proj)\n",
    "\n",
    "im = ax.scatter(stations['long'], stations['lat'], c=stations['obsversations'], s=8, cmap=cm.bwr)\n",
    "cartography(ax)\n",
    "\n",
    "cbar = plt.colorbar(im, location='bottom', label='2019 valid data days', fraction=0.046)\n",
    "print('# of stations: %s' % stations.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c147b3-afbc-4d60-aa1e-33d72003002b",
   "metadata": {},
   "source": [
    "### Visualize AQ observations thru time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "84e1c1e9-4292-4cef-902a-37be32278520",
   "metadata": {},
   "outputs": [],
   "source": [
    "# store a dataframe for each day in a dictionary\n",
    "days_dict = {}\n",
    "labels = []\n",
    "for day in range(1, 366):\n",
    "    day_df = data.loc[data['dayofyear'] == day]\n",
    "    date_format = datetime.strptime(str(2019) + \"-\" + str(day), \"%Y-%j\").strftime(\"%m-%d-%Y\")\n",
    "    days_dict[day] = day_df\n",
    "    labels.append(date_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c650c639-86cc-433d-998a-30e84483ae07",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "from mpl_toolkits.axes_grid.inset_locator import inset_axes\n",
    "\n",
    "# build new animation frames if necessary \n",
    "build_new_frames = False\n",
    "out_folder = r'C:\\Users\\xavie\\Documents\\data_science_scripts\\animation1'\n",
    "norm_max = 30\n",
    "\n",
    "frames = []\n",
    "\n",
    "if build_new_frames:\n",
    "    for t in range(1, 366):\n",
    "        norm = plt.Normalize(vmin=0, vmax=norm_max)\n",
    "        df = days_dict[t]\n",
    "        proj = ccrs.PlateCarree()\n",
    "        fig = plt.gcf()\n",
    "        fig.bbox_inches='tight'\n",
    "\n",
    "        ax = plt.axes(projection=proj)\n",
    "        bounds = [-125.0011, -66.9326, 49.5904, 24.9493]\n",
    "        im = ax.scatter(df['long'], df['lat'], c=df['mean_no2'], s=8, cmap=cm.bwr, vmin=0, vmax=norm_max)\n",
    "        ax.set_extent(bounds, crs=ccrs.PlateCarree())\n",
    "        ax.set_title(labels[t - 1]) # add date to the plot\n",
    "        cartography(ax)\n",
    "\n",
    "        axins = inset_axes(ax, width=\"90%\", height=\"10%\", loc='lower center', borderpad=-4)\n",
    "\n",
    "        cbar = plt.colorbar(im, label='NO2 concentration (ppb)', orientation='horizontal', cax=axins, fraction=0.046, norm=norm)\n",
    "        cbar.set_ticks(np.arange(0, norm_max + 10, 10))\n",
    "\n",
    "        out_name = out_folder + '\\\\frame%s.png' % t\n",
    "\n",
    "        fig.savefig(out_name)\n",
    "        frames.append(out_name)\n",
    "        fig.clear()\n",
    "        \n",
    "else:\n",
    "    for t in range(1, 366):\n",
    "        out_name = out_folder + '\\\\frame%s.png' % t\n",
    "        frames.append(out_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aa53675c-bb31-4273-b58b-660ed5144097",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "import moviepy\n",
    "from moviepy.editor import ImageSequenceClip, VideoFileClip\n",
    "\n",
    "build_animation = False\n",
    "file = out_folder + \"\\\\no2_animation.gif\"\n",
    "\n",
    "if build_animation:\n",
    "    animation = ImageSequenceClip(frames, fps=20)\n",
    "    animation.ipython_display(fps=20, loop=True, autoplay=True)\n",
    "    animation.write_gif(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ea96c8-4fa3-4c2e-add9-446b706eca86",
   "metadata": {},
   "source": [
    "### Make unique monitoring station IDs and print to a XGBoost ready csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8f3724d1-d514-4854-9d51-a1bb26c65572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "450 unique staion Ids created\n",
      "455 unique staion Ids created\n"
     ]
    },
    {
     "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>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Date_Local</th>\n",
       "      <th>mean_no2</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>counter</th>\n",
       "      <th>weekend</th>\n",
       "      <th>station_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>4.314286</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>4.957143</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>5.891667</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019-01-12</td>\n",
       "      <td>7.254167</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    long State_Name Date_Local   mean_no2  year  month  \\\n",
       "0  33.553056 -86.815    Alabama 2019-01-01   4.314286  2019      1   \n",
       "2  33.553056 -86.815    Alabama 2019-01-09   4.957143  2019      1   \n",
       "3  33.553056 -86.815    Alabama 2019-01-10   5.891667  2019      1   \n",
       "4  33.553056 -86.815    Alabama 2019-01-11  14.500000  2019      1   \n",
       "5  33.553056 -86.815    Alabama 2019-01-12   7.254167  2019      1   \n",
       "\n",
       "   dayofmonth  dayofyear  counter  weekend  station_id  \n",
       "0           1          1        1        0           1  \n",
       "2           9          9        1        0           1  \n",
       "3          10         10        1        0           1  \n",
       "4          11         11        1        0           1  \n",
       "5          12         12        1        1           1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define output folder\n",
    "out_folder = r'C:\\Users\\xavie\\Documents\\data_science_scripts'\n",
    "\n",
    "# generate a unique location identifier and make a new column\n",
    "in_dfs = [data, df_v2]\n",
    "for df in in_dfs:\n",
    "    df['locations'] = list(zip(df.lat, df.long))\n",
    "    locations = df['locations'].unique()\n",
    "    id_dict = {}\n",
    "    for i, loc in enumerate(locations):\n",
    "        sid = i + 1\n",
    "        id_dict[loc] = sid\n",
    "\n",
    "    def form(row, id_dict):\n",
    "        in_tup = (row['lat'], row['long'])\n",
    "        out = id_dict[in_tup]\n",
    "        return out\n",
    "\n",
    "    df['station_id'] = df.apply(lambda row: form(row, id_dict), axis=1)\n",
    "    print(str(len(df['station_id'].unique())) + ' unique staion Ids created')\n",
    "    df.drop(['locations'],axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b8ac5959-9693-4182-9be3-d7574f454df8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data saved to csv files in @ C:\\Users\\xavie\\Documents\\data_science_scripts\n"
     ]
    }
   ],
   "source": [
    "data.to_csv(out_folder + '\\\\clean_no2_daily_2019.csv') # cleaned data, obs% threshold applied, only relevant columns\n",
    "df_v2.to_csv(out_folder + '\\\\allcols_no2_daily_2019.csv') # obs% threshold applied, more original columns (i.e., methods)\n",
    "print('Data saved to csv files in @ %s' % out_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc64ccba-81aa-47e4-b34d-602c5bd79f9d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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