{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import basic libs & set default parameters for plot\n",
    "\n",
    "import numpy as np; np.random.seed(sum(map(ord, 'calmap')))\n",
    "import pandas as pd\n",
    "import calmap\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (20,3)\n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import 'Daily' Press Briefing Data\n",
    "\n",
    "full_df=pd.read_csv('Full_Press_Briefing_Data.csv')\n",
    "full_df['D']=pd.to_datetime(full_df[['Year', 'Month', 'Day']])\n",
    "full_df['Is_Briefing_Present']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a full calendar date range for 2013-2018. \n",
    "\n",
    "full_Calendar=pd.date_range(start='2013-01-01',end='2018-12-31',freq='D')\n",
    "calendar_df=pd.DataFrame()\n",
    "calendar_df=calendar_df.assign(D=full_Calendar)\n",
    "\n",
    "# Merge with Daily Briefing Data. When Daily_Briefing is not present, set it to zero\n",
    "final_df=calendar_df.merge(full_df[['D','Is_Briefing_Present']],on='D',how='left')\n",
    "final_df.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'2013')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot first year for obama\n",
    "\n",
    "obama_first_year_2013=final_df[final_df.D<'2014-01-01']\n",
    "obama_first_year_2013.set_index('D',inplace=True)\n",
    "events_2013 = pd.Series(obama_first_year_2013['Is_Briefing_Present'])\n",
    "ax=calmap.yearplot(events_2013, year=2013, cmap=plt.cm.Blues, fillcolor=u'whitesmoke', linewidth=1, linecolor=None, daylabels=['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], dayticks=True, monthlabels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], monthticks=True)\n",
    "\n",
    "font = {'family': 'sans-serif',\n",
    "        'color':  '#05266d',\n",
    "        'weight': 'bold',\n",
    "        'size': 42,\n",
    "        }\n",
    "\n",
    "ax.set_ylabel('2013',fontdict=font)\n",
    "#ax.set_label_position('top')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'2014')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot second year for obama\n",
    "\n",
    "obama_second_year_2014=final_df[(final_df.D>='2014-01-01') & (final_df.D<'2015-01-01')]\n",
    "obama_second_year_2014.set_index('D',inplace=True)\n",
    "events_2014 = pd.Series(obama_second_year_2014['Is_Briefing_Present'])\n",
    "ax=calmap.yearplot(events_2014, year=2014, cmap=plt.cm.Blues, fillcolor=u'whitesmoke', linewidth=1, linecolor=None, daylabels=['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], dayticks=True, monthlabels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], monthticks=True)\n",
    "ax.set_ylabel('2014',fontdict=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'2017')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot first year for Trump\n",
    "\n",
    "trump_first_year_2017=final_df[(final_df.D>='2017-01-01') & (final_df.D<'2018-01-01')]\n",
    "trump_first_year_2017.set_index('D',inplace=True)\n",
    "events_2017 = pd.Series(trump_first_year_2017['Is_Briefing_Present'])\n",
    "ax=calmap.yearplot(events_2017, year=2017, cmap='OrRd', fillcolor=u'whitesmoke', linewidth=1, linecolor=None, daylabels=['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], dayticks=True, monthlabels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], monthticks=True)\n",
    "\n",
    "font = {'family': 'sans-serif',\n",
    "        'color':  '#870801',\n",
    "        'weight': 'bold',\n",
    "        'size': 42,\n",
    "        }\n",
    "\n",
    "ax.set_ylabel('2017',fontdict=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'2018')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot second year for Trump\n",
    "\n",
    "trump_second_year_2018=final_df[final_df.D>='2018-01-01']\n",
    "trump_second_year_2018.set_index('D',inplace=True)\n",
    "events_2018 = pd.Series(trump_second_year_2018['Is_Briefing_Present'])\n",
    "ax=calmap.yearplot(events_2018, year=2018, cmap='OrRd', fillcolor=u'whitesmoke', linewidth=1, linecolor=None, daylabels=['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], dayticks=True, monthlabels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], monthticks=True)\n",
    "\n",
    "ax.set_ylabel('2018',fontdict=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}