dcasmr/Pie_charting-demo1.ipynb Secret

## Pie_charting-demo1.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Ebola.csv\",encoding ='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countries</th>\n",
       "      <th>Type</th>\n",
       "      <th>Number_of_cases</th>\n",
       "      <th>Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>comfirmed cases</td>\n",
       "      <td>3351</td>\n",
       "      <td>2083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>Probable cases</td>\n",
       "      <td>453</td>\n",
       "      <td>453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>comfirmed cases</td>\n",
       "      <td>8704</td>\n",
       "      <td>3589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>Probable cases</td>\n",
       "      <td>287</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>Suspected cases</td>\n",
       "      <td>5131</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Countries             Type  Number_of_cases  Death\n",
       "0        Guinea  comfirmed cases             3351   2083\n",
       "1        Guinea   Probable cases              453    453\n",
       "2  Sierra Leone  comfirmed cases             8704   3589\n",
       "3  Sierra Leone   Probable cases              287    208\n",
       "4  Sierra Leone  Suspected cases             5131    158"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Sierra = df[df['Countries']=='Sierra Leone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Guinea = df[df['Countries']=='Guinea']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countries</th>\n",
       "      <th>Type</th>\n",
       "      <th>Number_of_cases</th>\n",
       "      <th>Death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>comfirmed cases</td>\n",
       "      <td>8704</td>\n",
       "      <td>3589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>Probable cases</td>\n",
       "      <td>287</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>Suspected cases</td>\n",
       "      <td>5131</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Countries             Type  Number_of_cases  Death\n",
       "2  Sierra Leone  comfirmed cases             8704   3589\n",
       "3  Sierra Leone   Probable cases              287    208\n",
       "4  Sierra Leone  Suspected cases             5131    158"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sierra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = Sierra['Type']\n",
    "sizes =  Sierra['Death']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colors = [\"#66c2a5\", \"#a6d854\", \"#95a5a6\", \"#e74c3c\", \"#34495e\", \"#2ecc71\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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/b6L9z9WfX43m5Xa8l1fvZ8ZnO8vteKJiSehWcsdTUrnz/z7hZ0etQJdSLbVx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\nmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24a5d177438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(sizes, labels = labels, autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "plt.title('Pie chart of Ebola deaths in Sierra Leone \\n Year 2015')\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}