Research_Question_2/Figure-9_Source-contribution-Delhi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "coordinate-brass",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from pandas import json_normalize\n",
    "import pickle\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import datetime, timedelta\n",
    "import re\n",
    "\n",
    "\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.dates import DateFormatter\n",
    "from collections import Counter\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline  \n",
    "\n",
    "pd.set_option('max_colwidth', 200)\n",
    "%config Completer.use_jedi = False\n",
    "#from helper import toi_add_city_column, hindu_add_city_column, filter_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c53a0c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def latexify(fig_width=None, fig_height=None, columns=2):\n",
    "    \"\"\"Set up matplotlib's RC params for LaTeX plotting.\n",
    "    Call this before plotting a figure.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    fig_width : float, optional, inches\n",
    "    fig_height : float,  optional, inches\n",
    "    columns : {1, 2}\n",
    "    \"\"\"\n",
    "\n",
    "    # code adapted from http://www.scipy.org/Cookbook/Matplotlib/LaTeX_Examples\n",
    "\n",
    "    # Width and max height in inches for IEEE journals taken from\n",
    "    # computer.org/cms/Computer.org/Journal%20templates/transactions_art_guide.pdf\n",
    "\n",
    "    assert(columns in [1,2])\n",
    "\n",
    "    if fig_width is None:\n",
    "        fig_width = 3.32 if columns==1 else 6.75 # width in inches\n",
    "\n",
    "    if fig_height is None:\n",
    "        golden_mean = (sqrt(5)-1.0)/2.0    # Aesthetic ratio\n",
    "        fig_height = fig_width*golden_mean # height in inches\n",
    "\n",
    "    MAX_HEIGHT_INCHES = 8.0\n",
    "    if fig_height > MAX_HEIGHT_INCHES:\n",
    "        print(\"WARNING: fig_height too large:\" + fig_height + \n",
    "              \"so will reduce to\" + MAX_HEIGHT_INCHES + \"inches.\")\n",
    "        fig_height = MAX_HEIGHT_INCHES\n",
    "\n",
    "    params = {'backend': 'ps',\n",
    "              'text.latex.preamble': ['\\\\usepackage{gensymb}'],\n",
    "              'axes.labelsize': 8, # fontsize for x and y labels (was 10)\n",
    "              'axes.titlesize': 8,\n",
    "              'font.size': 8, # was 10\n",
    "              'legend.fontsize': 8, # was 10\n",
    "              'xtick.labelsize': 8,\n",
    "              'ytick.labelsize': 8,\n",
    "              'text.usetex': True,\n",
    "              'figure.figsize': [fig_width,fig_height],\n",
    "              'font.family': 'serif'\n",
    "    }\n",
    "\n",
    "    matplotlib.rcParams.update(params)\n",
    "\n",
    "\n",
    "def format_axes(ax,twinx=False,SPINE_COLOR=\"gray\"):\n",
    "    \n",
    "    if twinx:\n",
    "        for spine in ['top']:\n",
    "            ax.spines[spine].set_visible(False)\n",
    "\n",
    "        for spine in ['left', 'bottom', 'right']:\n",
    "            ax.spines[spine].set_color(SPINE_COLOR)\n",
    "            ax.spines[spine].set_linewidth(0.5)\n",
    "        \n",
    "    else:\n",
    "        for spine in ['top', 'right']:\n",
    "            ax.spines[spine].set_visible(False)\n",
    "\n",
    "        for spine in ['left', 'bottom']:\n",
    "            ax.spines[spine].set_color(SPINE_COLOR)\n",
    "            ax.spines[spine].set_linewidth(0.5)\n",
    "\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "\n",
    "    for axis in [ax.xaxis, ax.yaxis]:\n",
    "        axis.set_tick_params(direction='out', color=SPINE_COLOR)\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "modern-affairs",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>media</th>\n",
       "      <th>date</th>\n",
       "      <th>url</th>\n",
       "      <th>heading</th>\n",
       "      <th>content</th>\n",
       "      <th>other.author</th>\n",
       "      <th>other.top_image</th>\n",
       "      <th>other.category</th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>district</th>\n",
       "      <th>matches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15738</th>\n",
       "      <td>Hindu</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>https://www.thehindu.com/children/tickle-in-his-throat/article27412689.ece</td>\n",
       "      <td>Tickle in his throat</td>\n",
       "      <td>Slo and Gau came across Jack in the middle of a coughing fit. What, they wondered, could have brought it on?\\n\\n“Wheez! wheez! cough! splutter...,” went the jackal. He could barely breathe.\\n\\nThe...</td>\n",
       "      <td>NimiKurian</td>\n",
       "      <td>https://www.thehindu.com/children/571qnu/article27412683.ece/ALTERNATES/LANDSCAPE_615/Illus1jpg</td>\n",
       "      <td>children</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'smog': 1.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3048</th>\n",
       "      <td>TOI</td>\n",
       "      <td>2015-12-17</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/noida/Foul-air-leads-to-30-rise-in-bronchial-diseases/articleshow/50211620.cms</td>\n",
       "      <td>Foul air leads to 30% rise in bronchial diseases</td>\n",
       "      <td>\\n\\nNoida: As the quality of city air reaches hazardous levels, it has started showing its effects on the health of Noida residents. Hospitals in the city are reporting a sudden surge in respirato...</td>\n",
       "      <td>Sharmila Bhowmick / TNN /  Dec 17, 2015, 04:19 IST</td>\n",
       "      <td>https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>noida</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>uttar pradesh</td>\n",
       "      <td>gautam buddha nagar</td>\n",
       "      <td>{'air quality': 3.0, 'pm 2.5': 1.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16616</th>\n",
       "      <td>Hindu</td>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>https://www.thehindu.com/sci-tech/science/study-finds-heat-wave-like-conditions-in-cars/article31543108.ece</td>\n",
       "      <td>Study finds ‘heat wave-like’ conditions in cars</td>\n",
       "      <td>They may lead to ailments such as heat stress, heat strokes and fluctuation in blood pressure\\n\\nThe ambient temperature inside cars could shoot up to as much as 65°C and induce conditions akin to...</td>\n",
       "      <td>JacobKoshy</td>\n",
       "      <td>https://www.thehindu.com/sci-tech/science/xp3ub8/article31543107.ece/ALTERNATES/LANDSCAPE_615/IN09CAR</td>\n",
       "      <td>science</td>\n",
       "      <td>jacobkoshy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'carbon monoxide': 1.0}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       media       date  \\\n",
       "15738  Hindu 2019-06-03   \n",
       "3048     TOI 2015-12-17   \n",
       "16616  Hindu 2020-05-09   \n",
       "\n",
       "                                                                                                                           url  \\\n",
       "15738                                               https://www.thehindu.com/children/tickle-in-his-throat/article27412689.ece   \n",
       "3048   http://timesofindia.indiatimes.com//city/noida/Foul-air-leads-to-30-rise-in-bronchial-diseases/articleshow/50211620.cms   \n",
       "16616              https://www.thehindu.com/sci-tech/science/study-finds-heat-wave-like-conditions-in-cars/article31543108.ece   \n",
       "\n",
       "                                                heading  \\\n",
       "15738                              Tickle in his throat   \n",
       "3048   Foul air leads to 30% rise in bronchial diseases   \n",
       "16616   Study finds ‘heat wave-like’ conditions in cars   \n",
       "\n",
       "                                                                                                                                                                                                       content  \\\n",
       "15738  Slo and Gau came across Jack in the middle of a coughing fit. What, they wondered, could have brought it on?\\n\\n“Wheez! wheez! cough! splutter...,” went the jackal. He could barely breathe.\\n\\nThe...   \n",
       "3048   \\n\\nNoida: As the quality of city air reaches hazardous levels, it has started showing its effects on the health of Noida residents. Hospitals in the city are reporting a sudden surge in respirato...   \n",
       "16616  They may lead to ailments such as heat stress, heat strokes and fluctuation in blood pressure\\n\\nThe ambient temperature inside cars could shoot up to as much as 65°C and induce conditions akin to...   \n",
       "\n",
       "                                             other.author  \\\n",
       "15738                                          NimiKurian   \n",
       "3048   Sharmila Bhowmick / TNN /  Dec 17, 2015, 04:19 IST   \n",
       "16616                                          JacobKoshy   \n",
       "\n",
       "                                                                                                                                other.top_image  \\\n",
       "15738                                           https://www.thehindu.com/children/571qnu/article27412683.ece/ALTERNATES/LANDSCAPE_615/Illus1jpg   \n",
       "3048   https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg   \n",
       "16616                                     https://www.thehindu.com/sci-tech/science/xp3ub8/article31543107.ece/ALTERNATES/LANDSCAPE_615/IN09CAR   \n",
       "\n",
       "      other.category        city    year          state             district  \\\n",
       "15738       children         NaN     NaN            NaN                  NaN   \n",
       "3048             NaN       noida  2015.0  uttar pradesh  gautam buddha nagar   \n",
       "16616        science  jacobkoshy     NaN            NaN                  NaN   \n",
       "\n",
       "                                   matches  \n",
       "15738                        {'smog': 1.0}  \n",
       "3048   {'air quality': 3.0, 'pm 2.5': 1.0}  \n",
       "16616             {'carbon monoxide': 1.0}  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_articles = pd.read_csv(\"../dataset/News_articles_dataset/News_articles_dataset.csv.gz\")\n",
    "df_articles[\"date\"] = pd.to_datetime(df_articles[\"date\"])\n",
    "df_articles.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df767e55",
   "metadata": {},
   "source": [
    "##  Sources"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23687123",
   "metadata": {},
   "source": [
    "### India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6bb3cf82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !conda install openpyxl -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "749cb3e8",
   "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>ST_CEN_CD</th>\n",
       "      <th>state_name</th>\n",
       "      <th>DUST</th>\n",
       "      <th>WINDUST</th>\n",
       "      <th>WASTE</th>\n",
       "      <th>RESI</th>\n",
       "      <th>TRANS</th>\n",
       "      <th>POWER</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>BIOB</th>\n",
       "      <th>AGR</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "      <td>7.602899</td>\n",
       "      <td>8.557988</td>\n",
       "      <td>5.262316</td>\n",
       "      <td>30.723745</td>\n",
       "      <td>11.050788</td>\n",
       "      <td>7.252404</td>\n",
       "      <td>14.124743</td>\n",
       "      <td>1.338199</td>\n",
       "      <td>10.391891</td>\n",
       "      <td>3.695359</td>\n",
       "      <td>JK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>7.897604</td>\n",
       "      <td>6.376178</td>\n",
       "      <td>4.443600</td>\n",
       "      <td>28.651499</td>\n",
       "      <td>11.402736</td>\n",
       "      <td>8.669375</td>\n",
       "      <td>13.665820</td>\n",
       "      <td>2.481085</td>\n",
       "      <td>12.525487</td>\n",
       "      <td>3.886562</td>\n",
       "      <td>HP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>10.388898</td>\n",
       "      <td>4.192453</td>\n",
       "      <td>4.311272</td>\n",
       "      <td>31.647284</td>\n",
       "      <td>10.676158</td>\n",
       "      <td>8.458820</td>\n",
       "      <td>13.173587</td>\n",
       "      <td>3.333891</td>\n",
       "      <td>10.430349</td>\n",
       "      <td>3.387287</td>\n",
       "      <td>PB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>11.801000</td>\n",
       "      <td>4.184000</td>\n",
       "      <td>4.050000</td>\n",
       "      <td>26.584000</td>\n",
       "      <td>10.048000</td>\n",
       "      <td>10.473000</td>\n",
       "      <td>14.715000</td>\n",
       "      <td>2.494000</td>\n",
       "      <td>12.180000</td>\n",
       "      <td>3.473000</td>\n",
       "      <td>CH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>8.838814</td>\n",
       "      <td>4.485787</td>\n",
       "      <td>4.038621</td>\n",
       "      <td>30.151010</td>\n",
       "      <td>10.948515</td>\n",
       "      <td>10.288065</td>\n",
       "      <td>12.633006</td>\n",
       "      <td>4.610603</td>\n",
       "      <td>10.897680</td>\n",
       "      <td>3.108411</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ST_CEN_CD        state_name       DUST   WINDUST     WASTE       RESI  \\\n",
       "0        1.0   Jammu & Kashmir   7.602899  8.557988  5.262316  30.723745   \n",
       "1        2.0  Himachal Pradesh   7.897604  6.376178  4.443600  28.651499   \n",
       "2        3.0            Punjab  10.388898  4.192453  4.311272  31.647284   \n",
       "3        4.0        Chandigarh  11.801000  4.184000  4.050000  26.584000   \n",
       "4        5.0       Uttarakhand   8.838814  4.485787  4.038621  30.151010   \n",
       "\n",
       "       TRANS      POWER      INDUS      BIOB        AGR     OTHER Unnamed: 12  \n",
       "0  11.050788   7.252404  14.124743  1.338199  10.391891  3.695359          JK  \n",
       "1  11.402736   8.669375  13.665820  2.481085  12.525487  3.886562          HP  \n",
       "2  10.676158   8.458820  13.173587  3.333891  10.430349  3.387287          PB  \n",
       "3  10.048000  10.473000  14.715000  2.494000  12.180000  3.473000          CH  \n",
       "4  10.948515  10.288065  12.633006  4.610603  10.897680  3.108411          UK  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data source: http://urbanemissions.info/wp-content/uploads/misc/india_wustl_extracts_pmsa.xlsx\n",
    "df_sources_india = pd.read_excel(\"Source_contribution_pm25_india.xlsx\",sheet_name=\"final_bystate\")\n",
    "df_sources_india.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "742379b2",
   "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>Source code</th>\n",
       "      <th>Contriution_Delhi</th>\n",
       "      <th>Source_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DUST</td>\n",
       "      <td>11.8</td>\n",
       "      <td>Dust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WINDUST</td>\n",
       "      <td>2.7</td>\n",
       "      <td>Dust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WASTE</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Open Burning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RESI</td>\n",
       "      <td>32.3</td>\n",
       "      <td>Residential Biomass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRANS</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Vehicular Emissions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POWER</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Industry and Energy production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>16.7</td>\n",
       "      <td>Industry and Energy production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BIOB</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Open Burning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AGR</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OTHER</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Source code  Contriution_Delhi                     Source_name\n",
       "0        DUST               11.8                            Dust\n",
       "1     WINDUST                2.7                            Dust\n",
       "2       WASTE                3.1                    Open Burning\n",
       "3        RESI               32.3             Residential Biomass\n",
       "4       TRANS                8.0             Vehicular Emissions\n",
       "5       POWER               11.0  Industry and Energy production\n",
       "6       INDUS               16.7  Industry and Energy production\n",
       "7        BIOB                2.4                    Open Burning\n",
       "8         AGR                8.4                             NaN\n",
       "9       OTHER                3.5                             NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This sheet is created by us to aggregate primary sources of air pollution\n",
    "#Category in original study mentioned as Source_code\n",
    "#Aggregated name of source mentioned as Source_name\n",
    "df_sources_delhi = pd.read_excel(\"Source_contribution_pm25_delhi.xlsx\", sheet_name=\"Aggregated_sources\").iloc[:,1:-1]\n",
    "df_sources_delhi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16e4608f",
   "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>source</th>\n",
       "      <th>perc_contribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Residential Biomass</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industry and Energy production</td>\n",
       "      <td>27.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dust</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vehicular Emissions</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Open Burning</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           source  perc_contribution\n",
       "3             Residential Biomass               32.3\n",
       "1  Industry and Energy production               27.7\n",
       "0                            Dust               14.5\n",
       "4             Vehicular Emissions                8.0\n",
       "2                    Open Burning                5.5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_source_contr_del = df_sources_delhi.groupby(\"Source_name\").sum().reset_index() #aggregated source and its contribution\n",
    "df_source_contr_del.columns = [\"source\",\"perc_contribution\"]\n",
    "df_source_contr_del.sort_values(by=\"perc_contribution\",inplace=True,ascending=False) #sort\n",
    "df_source_contr_del"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "955113af",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# sources_contribution_del = {\n",
    "#     \"Residential biomass\":23.8,\n",
    "#     \"Coal\": 9.1, #+ 2.9 + 6.3,      #total coal+ industrical coal + powerplants coal\n",
    "#     \"Transportation\":3.6, # Transportation+ Distributed diesel\n",
    "#     \"Open burning\":18.8,\n",
    "#     \"Brick production\":2.4,\n",
    "#     \"Dust\":38.1 #+ 13.9 #Anthropogenic dust + total dust\n",
    "# }\n",
    "\n",
    "# sources_contribution_del = {\n",
    "#     \"Residential biomass\":32.3,\n",
    "#     \"Industry and Energy production\": 27.7,\n",
    "#     \"Transportation\":8, \n",
    "#     \"Open burning\":5.5,\n",
    "#     \"Dust\":14.5 #+ 13.9 \n",
    "# }\n",
    "\n",
    "\n",
    "# df_source_contr_del = pd.DataFrame(sources_contribution_del.items(),columns=[\"source\",\"perc_contribution\"]).nlargest(10,\"perc_contribution\")\n",
    "# df_source_contr_del"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "96014545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "#print(total_articles)\n",
    "#ax.set_title(f\"Causes - {total_articles} articles\")\n",
    "ax.bar(df_source_contr_del[\"source\"],df_source_contr_del[\"perc_contribution\"],color=\"#f4921a\")\n",
    "ax.tick_params(axis=\"x\",rotation=80)\n",
    "# ax.tick_params(axis=\"y\",labelsize=15)\n",
    "ax.set_ylabel(\"Percentage\")\n",
    "#ax.grid(axis=\"y\")\n",
    "#ax.legend(fontsize=12)\n",
    "format_axes(ax)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "440d6500",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sources_HEI_queries = {\n",
    "    \"Residential Biomass\":[\"biomass fuels\",\"wood burning\",\"burning wood\",\n",
    "                           \"firewood\",\"cooking\",\"household air pollution\"], \n",
    "    \n",
    " \n",
    "    \n",
    "    \"Vehicular Emissions\": [\"vehicles?\",\"petrol\",\"diesel\",\"vehicular emissions\"],\n",
    "    \n",
    "    \n",
    "    \"Open Burning\":[\"open burning\",\"garbage burning\",\"burning garbage\",\"burning waste\",\"waste burning\",\n",
    "                    \"burning organic waste\",\"crop burning\",\"stubble\",\"burning crops?\",\"burning paddy\",\n",
    "                    \"paddy burning\",\"crop residue\"],\n",
    "    \n",
    "    \n",
    "    \n",
    "    \"Industry and Energy production\":[\"energy sector\",\"energy production\",\"coal\",\"power plant\",\"industry\",\n",
    "                                      \"industrial pollution\",\"polluting industries\",\"industrial emissions\",\n",
    "                                      \"fossil fuel\"] ,\n",
    "    \n",
    "    \"Dust\":[\"[\\s,)(-]dust[-\\s,)(.]\",\"construction\",\"demolition\"],\n",
    "    \n",
    "}\n",
    "\n",
    "#\"Brick production\":[\"[\\s,)(-]brick[\\s,)(-s.]\"],\n",
    "#\"Coal\":[\"[\\s,)(-]coal[-\\s,)(.]\"],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19756ba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_regex_string_from_single_word(keyword,space=False,dostrip=True):\n",
    "    \n",
    "    #to check empty string before the keyword\n",
    "    re_str = \"\"\n",
    "    space = \"\"\n",
    "    if space:\n",
    "        space = \"\\\\b\"\n",
    "        re_str+=space\n",
    "    keyword = keyword.lower()\n",
    "    if dostrip:\n",
    "        keyword = keyword.strip()\n",
    "    words = keyword.split(\" \")\n",
    "    n = len(words)\n",
    "    for ind,word in enumerate(words):\n",
    "        re_str+=word\n",
    "        if ind+1 < n:\n",
    "            #to accept white space between two word\n",
    "            re_str+=\"\\\\s\"\n",
    "    \n",
    "    #to check empty string after the keyword\n",
    "    re_str+= space\n",
    "    return re_str\n",
    "\n",
    "def get_matches(text,queries,print_re=False,print_matches=False,dostrip=True):\n",
    "    '''\n",
    "    regex serach from text using queris\n",
    "    returns list of keyword matches\n",
    "    '''\n",
    "    re_str = \"\"\n",
    "    for each in queries:\n",
    "        re_str = re_str + get_regex_string_from_single_word(each,dostrip) + \"|\"\n",
    "    re_str = re_str[:-1]\n",
    "    if print_re:\n",
    "        print(re_str)\n",
    "    if print_matches:\n",
    "        print(re.findall(re_str,text,flags=re.I))\n",
    "    return re.findall(re_str,text,flags=re.I)\n",
    "\n",
    "def get_frequency_of_keyword(df,keyword_dict,column=\"content\",dostrip=True):\n",
    "    '''\n",
    "    frequency of keyword from given df using given queries(keyword_dict)\n",
    "    '''\n",
    "    keyword_freq = {}\n",
    "    for key in keyword_dict:\n",
    "        df[key] = df[column].apply(get_matches,queries=keyword_dict[key],dostrip=dostrip)\n",
    "        temp_multi = df[df[key].apply(lambda x:len(x)>0)]\n",
    "\n",
    "        print(f\"{key}-{len(temp_multi)}\")\n",
    "        keyword_freq[key] = len(temp_multi)\n",
    "        \n",
    "    #transpose & et frquency\n",
    "    freq_df = pd.DataFrame(keyword_freq,index=range(len(keyword_freq))).T[0]\n",
    "    freq_df = freq_df.reset_index()\n",
    "    freq_df.columns = [\"keyword\",\"frequency\"]\n",
    "    freq_df = freq_df.sort_values(by=\"frequency\",ascending=False)\n",
    "    return freq_df\n",
    "\n",
    "\n",
    "def filter_cities(which_df,allowed_cities):\n",
    "    '''\n",
    "    gives only those rows of dataframe which \"city\" columns\n",
    "    contains given cities\n",
    "    allowed cities can be one city or list of cities\n",
    "    '''\n",
    "    if type(allowed_cities) == str:\n",
    "        allowed_cities = [allowed_cities]\n",
    "    #remove NA values in city\n",
    "    which_df = which_df.dropna(subset=[\"city\"])\n",
    "\n",
    "    #city can be list or str\n",
    "    filtered_df = which_df[which_df[\"city\"].apply(lambda city: True in [each.lower().strip() in city for each in allowed_cities] if type(city) == str or type(city) == list else False)]\n",
    "    return filtered_df\n",
    "\n",
    "def get_count_of_total_related_articles(df,which_dict,column = \"heading_and_content\"):\n",
    "    '''\n",
    "    total count of related articles of given topic\n",
    "    '''\n",
    "    queries = []\n",
    "    for key in which_dict:\n",
    "        queries.extend(which_dict[key])\n",
    "    new_col = \"causes\"\n",
    "    df[new_col] = df[column].apply(get_matches,queries=queries)\n",
    "    temp_multi = df[df[new_col].apply(lambda x:len(x)>0)]\n",
    "    return len(temp_multi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "83574856",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5296\n",
      "Residential Biomass-71\n",
      "Vehicular Emissions-1625\n",
      "Open Burning-1242\n",
      "Industry and Energy production-754\n",
      "Dust-1285\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>keyword</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vehicular Emissions</td>\n",
       "      <td>1625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dust</td>\n",
       "      <td>1285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Open Burning</td>\n",
       "      <td>1242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Industry and Energy production</td>\n",
       "      <td>754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Residential Biomass</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          keyword  frequency\n",
       "1             Vehicular Emissions       1625\n",
       "4                            Dust       1285\n",
       "2                    Open Burning       1242\n",
       "3  Industry and Energy production        754\n",
       "0             Residential Biomass         71"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get df of frequency of each topic\n",
    "#sources_freq = h.get_frequency_of_keyword(df_articles,sources_HEI_queries,\"heading_and_content\")\n",
    "df_del = filter_cities(df_articles,[\"delhi\",\"new delhi\",\"newdelhi\"])\n",
    "print(len(df_del))\n",
    "sources_freq_del = get_frequency_of_keyword(df_del,sources_HEI_queries,\"content\")\n",
    "sources_freq_del"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1c79f1dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3285\n"
     ]
    }
   ],
   "source": [
    "total_articles = get_count_of_total_related_articles(df_del,sources_HEI_queries,column=\"content\")\n",
    "print(total_articles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d739fd55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0221224751522924"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3115/5201\n",
    "69/3119"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6cd6bd12",
   "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>source</th>\n",
       "      <th>perc_contribution</th>\n",
       "      <th>keyword</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Residential Biomass</td>\n",
       "      <td>32.3</td>\n",
       "      <td>Residential Biomass</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industry and Energy production</td>\n",
       "      <td>27.7</td>\n",
       "      <td>Industry and Energy production</td>\n",
       "      <td>754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dust</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Dust</td>\n",
       "      <td>1285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vehicular Emissions</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Vehicular Emissions</td>\n",
       "      <td>1625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Open Burning</td>\n",
       "      <td>5.5</td>\n",
       "      <td>Open Burning</td>\n",
       "      <td>1242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           source  perc_contribution  \\\n",
       "0             Residential Biomass               32.3   \n",
       "1  Industry and Energy production               27.7   \n",
       "2                            Dust               14.5   \n",
       "3             Vehicular Emissions                8.0   \n",
       "4                    Open Burning                5.5   \n",
       "\n",
       "                          keyword  frequency  \n",
       "0             Residential Biomass         71  \n",
       "1  Industry and Energy production        754  \n",
       "2                            Dust       1285  \n",
       "3             Vehicular Emissions       1625  \n",
       "4                    Open Burning       1242  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merged_df = df_source_contr.merge(sources_freq_del,left_on=\"source\",right_on=\"keyword\")\n",
    "merged_df_del = df_source_contr_del.merge(sources_freq_del,left_on=\"source\",right_on=\"keyword\")\n",
    "merged_df_del"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "55697036",
   "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>source</th>\n",
       "      <th>perc_contribution</th>\n",
       "      <th>keyword</th>\n",
       "      <th>frequency</th>\n",
       "      <th>perc_articles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Residential Biomass</td>\n",
       "      <td>32.3</td>\n",
       "      <td>Residential Biomass</td>\n",
       "      <td>71</td>\n",
       "      <td>2.161339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industry and Energy production</td>\n",
       "      <td>27.7</td>\n",
       "      <td>Industry and Energy production</td>\n",
       "      <td>754</td>\n",
       "      <td>22.952816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dust</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Dust</td>\n",
       "      <td>1285</td>\n",
       "      <td>39.117199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vehicular Emissions</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Vehicular Emissions</td>\n",
       "      <td>1625</td>\n",
       "      <td>49.467275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Open Burning</td>\n",
       "      <td>5.5</td>\n",
       "      <td>Open Burning</td>\n",
       "      <td>1242</td>\n",
       "      <td>37.808219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           source  perc_contribution  \\\n",
       "0             Residential Biomass               32.3   \n",
       "1  Industry and Energy production               27.7   \n",
       "2                            Dust               14.5   \n",
       "3             Vehicular Emissions                8.0   \n",
       "4                    Open Burning                5.5   \n",
       "\n",
       "                          keyword  frequency  perc_articles  \n",
       "0             Residential Biomass         71       2.161339  \n",
       "1  Industry and Energy production        754      22.952816  \n",
       "2                            Dust       1285      39.117199  \n",
       "3             Vehicular Emissions       1625      49.467275  \n",
       "4                    Open Burning       1242      37.808219  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_del[\"perc_articles\"] = merged_df_del[\"frequency\"].apply(lambda x:100*x/total_articles)\n",
    "merged_df_del"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2d781929",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged_df_del.replace({\n",
    "#     \"Residential biomass\" : \"Residential\\nBiomass\",\n",
    "#     \"Open burning\": \"Open\\nBurning\",\n",
    "#     \"Brick production\": \"Brick\\nProduction\",\n",
    "#     \"Transportation\": \"Vehicular\\nEmissions\"\n",
    "# }, inplace=True)\n",
    "\n",
    "merged_df_del.replace({\n",
    "    \"Residential biomass\" : \"Residential\\nBiomass\",\n",
    "    \"Residential Biomass\" : \"Residential\\nBiomass\",\n",
    "    \"Open burning\": \"Open\\nBurning\",\n",
    "    \"Open Burning\": \"Open\\nBurning\",\n",
    "    \"Industry and Energy production\": \"Industry\\nand Energy\\nproduction\",\n",
    "    \"Transportation\": \"Vehicular\\nEmissions\",\n",
    "    \"Vehicular Emissions\":'Vehicular\\nEmissions'\n",
    "}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "caa4a1d4",
   "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>source</th>\n",
       "      <th>perc_contribution</th>\n",
       "      <th>keyword</th>\n",
       "      <th>frequency</th>\n",
       "      <th>perc_articles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Residential\\nBiomass</td>\n",
       "      <td>32.3</td>\n",
       "      <td>Residential\\nBiomass</td>\n",
       "      <td>71</td>\n",
       "      <td>2.161339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industry\\nand Energy\\nproduction</td>\n",
       "      <td>27.7</td>\n",
       "      <td>Industry\\nand Energy\\nproduction</td>\n",
       "      <td>754</td>\n",
       "      <td>22.952816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dust</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Dust</td>\n",
       "      <td>1285</td>\n",
       "      <td>39.117199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vehicular\\nEmissions</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Vehicular\\nEmissions</td>\n",
       "      <td>1625</td>\n",
       "      <td>49.467275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Open\\nBurning</td>\n",
       "      <td>5.5</td>\n",
       "      <td>Open\\nBurning</td>\n",
       "      <td>1242</td>\n",
       "      <td>37.808219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             source  perc_contribution  \\\n",
       "0              Residential\\nBiomass               32.3   \n",
       "1  Industry\\nand Energy\\nproduction               27.7   \n",
       "2                              Dust               14.5   \n",
       "3              Vehicular\\nEmissions                8.0   \n",
       "4                     Open\\nBurning                5.5   \n",
       "\n",
       "                            keyword  frequency  perc_articles  \n",
       "0              Residential\\nBiomass         71       2.161339  \n",
       "1  Industry\\nand Energy\\nproduction        754      22.952816  \n",
       "2                              Dust       1285      39.117199  \n",
       "3              Vehicular\\nEmissions       1625      49.467275  \n",
       "4                     Open\\nBurning       1242      37.808219  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merged_df_del.replace({\"Residential\\nBiomass\":\"RB\",\"Open\\nBurning\":\"OB\",\n",
    "#                       \"Vehicular\\nEmissions\":\"VE\",\"Brick\\nProduction\":\"BP\"},inplace=True)\n",
    "merged_df_del"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "20509197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 201.6x108 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get count of releted articles\n",
    "#total_articles = h.get_count_of_total_related_articles(df,sources)\n",
    "#print(total_articles)\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "latexify(columns=1,fig_height=1.5,fig_width=2.8)\n",
    "\n",
    "which_merged_df = merged_df_del\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "n_sources = len(which_merged_df)\n",
    "width = 0.4\n",
    "x = np.arange(0,n_sources,1)\n",
    "\n",
    "source_color = \"tab:red\"\n",
    "article_color = \"tab:green\"\n",
    "\n",
    "#sources contrinbution\n",
    "ax.tick_params(axis=\"x\",rotation=90)\n",
    "plt1 = ax.bar(x,which_merged_df[\"perc_contribution\"],color=source_color,label=\"Source contribution\",width=width)\n",
    "ax.set_ylabel(\"Source contrubution\\nto PM$_{2.5}$(\\%)\")\n",
    "ax.set_xlabel(\"Sources\")\n",
    "\n",
    "#news articles\n",
    "plt2 = ax2.bar(x+width,which_merged_df[\"frequency\"],label=\"News articles\",width=width,color=article_color)\n",
    "ax2.set_ylabel(\"News Articles\")\n",
    "\n",
    "ax.set_xticks(x+width/2)\n",
    "\n",
    "ax.set_xticklabels(which_merged_df[\"source\"])\n",
    "\n",
    "format_axes(ax)\n",
    "plts = plt1+plt2\n",
    "labels = [plt1.get_label(),plt2.get_label()]\n",
    "ax.legend(plts,labels,bbox_to_anchor=(2,1))\n",
    "\n",
    "leg = ax.get_legend()\n",
    "leg.legendHandles[0].set_color(source_color)\n",
    "leg.legendHandles[1].set_color(article_color)\n",
    "\n",
    "for ax in [ax,ax2]:\n",
    "    for spine in ['top']:\n",
    "        ax.spines[spine].set_visible(False)\n",
    "\n",
    "    for spine in ['left', 'bottom', 'right']:\n",
    "        ax.spines[spine].set_color(\"gray\")\n",
    "        ax.spines[spine].set_linewidth(0.5)\n",
    "    \n",
    "ax.spines[\"left\"].set_color(source_color)\n",
    "ax.spines[\"right\"].set_color(article_color)\n",
    "ax.spines[\"left\"].set_linewidth(1.3)\n",
    "ax.spines[\"right\"].set_linewidth(1.3)\n",
    "\n",
    "fig.show()\n",
    "#fig.savefig(\"plots/latex/sources_barplot.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1b5a0903",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"../figures/fig9_source_contriution_delhi_revised.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "daed67a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.1.3'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34410baf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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