{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hosting my notebooks on [Binder](http://docs.mybinder.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration of a [dataset](https://data.hdx.rwlabs.org/dataset/ebola-outbreaks-before-2014) on Ebola outbreaks before 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "source: Humanitarian Data Exchange ([HDX](http://data.hdx.rwlabs.org/))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from bokeh.charts import Histogram, Bar, BoxPlot, Scatter\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.io import output_notebook, output_file\n",
    "from bokeh.palettes import Spectral11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Loading the dataset to a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ebola_df = pd.read_csv(\"data/out/ebola_outbreaks_before_2014-geometry_fixed.csv\", encoding=\"utf-8\", index_col=False)\n",
    "\n",
    "ebola_data = ebola_df.drop(ebola_df.columns[[0, 1, 2, 9]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_code_iso_2_digits</th>\n",
       "      <th>country_name</th>\n",
       "      <th>duration_days</th>\n",
       "      <th>ebola_subtype</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_datetime</th>\n",
       "      <th>geometry</th>\n",
       "      <th>geometry_geojson</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>reported_number_of_deaths_among_cases</th>\n",
       "      <th>reported_number_of_human_cases</th>\n",
       "      <th>reported_of_deaths_among_cases</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_datetime</th>\n",
       "      <th>year_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RU</td>\n",
       "      <td>Russia</td>\n",
       "      <td>365</td>\n",
       "      <td>Zaire virus</td>\n",
       "      <td>2004-12-31T00:00:00Z</td>\n",
       "      <td>2004-12-31T00:00:00Z</td>\n",
       "      <td>(POLYGON ((132.448985 42.845404, 132.44988 42....</td>\n",
       "      <td>{u'type': u'MultiPolygon', u'coordinates': [[[...</td>\n",
       "      <td>64.686314</td>\n",
       "      <td>97.745306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-01-01T00:00:00Z</td>\n",
       "      <td>2004-01-01T00:00:00Z</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PH</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>365</td>\n",
       "      <td>Reston virus</td>\n",
       "      <td>1996-12-31T00:00:00Z</td>\n",
       "      <td>1996-12-31T00:00:00Z</td>\n",
       "      <td>(POLYGON ((119.849783 4.796861, 119.833995 4.7...</td>\n",
       "      <td>{u'type': u'MultiPolygon', u'coordinates': [[[...</td>\n",
       "      <td>12.750349</td>\n",
       "      <td>122.731210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1996-01-01T00:00:00Z</td>\n",
       "      <td>1996-01-01T00:00:00Z</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>364</td>\n",
       "      <td>Reston virus</td>\n",
       "      <td>1990-12-31T00:00:00Z</td>\n",
       "      <td>1990-12-31T00:00:00Z</td>\n",
       "      <td>(POLYGON ((-155.606519 20.137956, -155.586363 ...</td>\n",
       "      <td>{u'type': u'MultiPolygon', u'coordinates': [[[...</td>\n",
       "      <td>39.783730</td>\n",
       "      <td>-100.445882</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1990-01-01T00:00:00Z</td>\n",
       "      <td>1990-01-01T00:00:00Z</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code_iso_2_digits country_name  duration_days ebola_subtype  \\\n",
       "0                        RU       Russia            365   Zaire virus   \n",
       "1                        PH  Philippines            365  Reston virus   \n",
       "2                        US          USA            364  Reston virus   \n",
       "\n",
       "               end_date          end_datetime  \\\n",
       "0  2004-12-31T00:00:00Z  2004-12-31T00:00:00Z   \n",
       "1  1996-12-31T00:00:00Z  1996-12-31T00:00:00Z   \n",
       "2  1990-12-31T00:00:00Z  1990-12-31T00:00:00Z   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  (POLYGON ((132.448985 42.845404, 132.44988 42....   \n",
       "1  (POLYGON ((119.849783 4.796861, 119.833995 4.7...   \n",
       "2  (POLYGON ((-155.606519 20.137956, -155.586363 ...   \n",
       "\n",
       "                                    geometry_geojson   latitude   longitude  \\\n",
       "0  {u'type': u'MultiPolygon', u'coordinates': [[[...  64.686314   97.745306   \n",
       "1  {u'type': u'MultiPolygon', u'coordinates': [[[...  12.750349  122.731210   \n",
       "2  {u'type': u'MultiPolygon', u'coordinates': [[[...  39.783730 -100.445882   \n",
       "\n",
       "   reported_number_of_deaths_among_cases  reported_number_of_human_cases  \\\n",
       "0                                      1                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               4   \n",
       "\n",
       "   reported_of_deaths_among_cases            start_date        start_datetime  \\\n",
       "0                               1  2004-01-01T00:00:00Z  2004-01-01T00:00:00Z   \n",
       "1                               0  1996-01-01T00:00:00Z  1996-01-01T00:00:00Z   \n",
       "2                               0  1990-01-01T00:00:00Z  1990-01-01T00:00:00Z   \n",
       "\n",
       "  year_s  \n",
       "0   2004  \n",
       "1   1996  \n",
       "2   1990  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ebola outbreaks per country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Handle non-ASCII character\n",
    "ebola_data = ebola_data.replace(u\"C\\xf4te d'Ivoire (Ivory Coast)\", \n",
    "                                u\"Cote d'Ivoire (Ivory Coast)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### General description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country_name\n",
      "Cote d'Ivoire (Ivory Coast)         1\n",
      "England                             1\n",
      "Italy                               1\n",
      "South Africa                        1\n",
      "Russia                              2\n",
      "Philippines                         3\n",
      "Sudan (South Sudan)                 3\n",
      "USA                                 3\n",
      "Gabon                               4\n",
      "Uganda                              5\n",
      "Democratic Republic of the Congo    9\n",
      "Name: country_name, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    11.000000\n",
       "mean      3.000000\n",
       "std       2.408319\n",
       "min       1.000000\n",
       "25%       1.000000\n",
       "50%       3.000000\n",
       "75%       3.500000\n",
       "max       9.000000\n",
       "Name: country_name, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outbrks_data = ebola_data.groupby([\"country_name\"])[\"country_name\"].count().order()\n",
    "\n",
    "print outbrks_data\n",
    "\n",
    "outbrks_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Prior to 2014, there has been Ebola outbreaks in **11** countries.\n",
    "\n",
    "* Those countries have had between **1** and **9** outbreaks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of outbreaks per country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  proportions of outbreaks (%)\n",
      "country_name                                                  \n",
      "Cote d'Ivoire (Ivory Coast)                           3.030303\n",
      "England                                               3.030303\n",
      "Italy                                                 3.030303\n",
      "South Africa                                          3.030303\n",
      "Russia                                                6.060606\n",
      "Philippines                                           9.090909\n",
      "Sudan (South Sudan)                                   9.090909\n",
      "USA                                                   9.090909\n",
      "Gabon                                                12.121212\n",
      "Uganda                                               15.151515\n",
      "Democratic Republic of the Congo                     27.272727\n"
     ]
    },
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     "metadata": {},
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     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[6]</strong>&gt;</code></p>"
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       "<bokeh.io._CommsHandle at 0x7f1d00b10190>"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "total_outbrks = ebola_data.groupby([\"country_name\"])[\"country_name\"].count().sum()\n",
    "outbrks_percent = outbrks_data.mul(100).truediv(total_outbrks)\n",
    "\n",
    "data = outbrks_percent.to_frame(name='proportions of outbreaks (%)')\n",
    "\n",
    "print data\n",
    "\n",
    "bar_outbrks = Bar(data, values='proportions of outbreaks (%)', color='navy',\n",
    "                   title=\"Proportion of ebola outbreaks per country in %\")\n",
    "\n",
    "output_notebook()\n",
    "output_file(\"outbreaks.html\", title=\"Proportion of ebola outbreaks per country in %\")\n",
    "\n",
    "show(bar_outbrks)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "* Almost one third of the outbreaks before 2014 happened in DRC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of ebola victims per country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reported_number_of_human_cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cote d'Ivoire (Ivory Coast)</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Democratic Republic of the Congo</th>\n",
       "      <td>1201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabon</th>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan (South Sudan)</th>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  reported_number_of_human_cases\n",
       "country_name                                                    \n",
       "Cote d'Ivoire (Ivory Coast)                                    1\n",
       "Democratic Republic of the Congo                            1201\n",
       "England                                                        1\n",
       "Gabon                                                        214\n",
       "Italy                                                          0\n",
       "Philippines                                                    9\n",
       "Russia                                                         2\n",
       "South Africa                                                   2\n",
       "Sudan (South Sudan)                                          335\n",
       "USA                                                            4\n",
       "Uganda                                                       592"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola_data[[\"country_name\", \"reported_number_of_human_cases\"]].groupby([\"country_name\"]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_ebola_victims = ebola_data[[\"country_name\", \"reported_number_of_human_cases\", \n",
    "                             \"reported_number_of_deaths_among_cases\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
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       "            var render_items = [{\"docid\":\"7d2db8d8-3493-4622-a5ab-d409599a946e\",\"elementid\":\"66f8b92d-1c22-4231-a2a8-b580f3dd0945\",\"modelid\":\"b54cb84a-7ef1-4cd9-9084-c738af7d0391\",\"notebook_comms_target\":\"0091db37-c82d-4d88-96b9-f4d52e7576a1\"}];\n",
       "            \n",
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       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[10]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7f1d00a4bbd0>"
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     "execution_count": 10,
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   ],
   "source": [
    "box_ev = BoxPlot(df_ebola_victims, values=\"reported_number_of_human_cases\",\n",
    "                 label='country_name', title=\"Number of ebola victims per country\",\n",
    "                 outliers=False, whisker_color='country_name')\n",
    "\n",
    "output_notebook()\n",
    "output_file(\"victims.html\", title=\"Number of ebola victims per country\")\n",
    "\n",
    "show(box_ev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reported_number_of_deaths_among_cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cote d'Ivoire (Ivory Coast)</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Democratic Republic of the Congo</th>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabon</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan (South Sudan)</th>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  reported_number_of_deaths_among_cases\n",
       "country_name                                                           \n",
       "Cote d'Ivoire (Ivory Coast)                                           0\n",
       "Democratic Republic of the Congo                                    946\n",
       "England                                                               0\n",
       "Gabon                                                               150\n",
       "Italy                                                                 0\n",
       "Philippines                                                           0\n",
       "Russia                                                                2\n",
       "South Africa                                                          1\n",
       "Sudan (South Sudan)                                                 180\n",
       "USA                                                                   0\n",
       "Uganda                                                              269"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola_data[[\"country_name\", \"reported_number_of_deaths_among_cases\"]].groupby([\"country_name\"]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   },
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      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[12]</strong>&gt;</code></p>"
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       "<bokeh.io._CommsHandle at 0x7f1d00021950>"
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    "box_ed = BoxPlot(df_ebola_victims, values=\"reported_number_of_deaths_among_cases\", \n",
    "                 label='country_name', title=\"Number of deaths from ebola per country\",\n",
    "                 outliers=False, whisker_color='country_name')\n",
    "\n",
    "output_notebook()\n",
    "output_file(\"deaths.html\", title=\"Number of deaths from ebola per country\")\n",
    "\n",
    "show(box_ed)"
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    "* The number of victims (cases and deaths) tends to vary in one country depending on the outbreak\n",
    "* The number of victims seem to correlate with the number of outbreak except when looking at The USA and South Sudan.\n",
    "\n",
    "The number of victims could be due to the country's health system or the moment in time when the outbreak occurred.\n",
    "\n",
    "=> It be interested to plot the number of victims vs the number of outbreaks, a value from WHO representing countries health system score and to look at the number of victims over time"
   ]
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    "I'd like to look at: \n",
    "* number of human cases vs virus subtype\n",
    "* number of human deaths vs virus subtype\n",
    "* number of outbreaks vs number of human cases\n",
    "* number of outbreaks vs average length of outbreaks\n",
    "* number of human cases vs average length of outbreaks\n",
    "* look at the timeline of global outbreaks and outbreaks per affected country\n",
    "* look at the global state of the most frequently affected countries"
   ]
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   "source": [
    "### Virus subtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
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    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reported_number_of_human_cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ebola_subtype</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bundibugyo virus</th>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reston virus</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan virus</th>\n",
       "      <td>779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taï Forest virus</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zaire virus</th>\n",
       "      <td>1383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "                  reported_number_of_human_cases\n",
       "ebola_subtype                                   \n",
       "Bundibugyo virus                             185\n",
       "Reston virus                                  13\n",
       "Sudan virus                                  779\n",
       "Taï Forest virus                               1\n",
       "Zaire virus                                 1383"
      ]
     },
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   ],
   "source": [
    "cases_by_subtype = ebola_data[[\"ebola_subtype\", \"reported_number_of_human_cases\"]].groupby([\"ebola_subtype\"]).sum()\n",
    "cases_by_subtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reported_number_of_human_cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ebola_subtype</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bundibugyo virus</th>\n",
       "      <td>7.835663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reston virus</th>\n",
       "      <td>0.550614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan virus</th>\n",
       "      <td>32.994494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taï Forest virus</th>\n",
       "      <td>0.042355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zaire virus</th>\n",
       "      <td>58.576874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  reported_number_of_human_cases\n",
       "ebola_subtype                                   \n",
       "Bundibugyo virus                        7.835663\n",
       "Reston virus                            0.550614\n",
       "Sudan virus                            32.994494\n",
       "Taï Forest virus                        0.042355\n",
       "Zaire virus                            58.576874"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_cases_by_subtype = cases_by_subtype.sum()\n",
    "cases_subtype_percent = cases_by_subtype.mul(100).truediv(total_cases_by_subtype)\n",
    "\n",
    "cases_subtype_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reported_number_of_deaths_among_cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ebola_subtype</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bundibugyo virus</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reston virus</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan virus</th>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taï Forest virus</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zaire virus</th>\n",
       "      <td>1086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  reported_number_of_deaths_among_cases\n",
       "ebola_subtype                                          \n",
       "Bundibugyo virus                                     50\n",
       "Reston virus                                          0\n",
       "Sudan virus                                         412\n",
       "Taï Forest virus                                      0\n",
       "Zaire virus                                        1086"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deaths_by_subtype = ebola_data[[\"ebola_subtype\", \"reported_number_of_deaths_among_cases\"]].groupby([\"ebola_subtype\"]).sum()\n",
    "deaths_by_subtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reported_number_of_deaths_among_cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ebola_subtype</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bundibugyo virus</th>\n",
       "      <td>3.229974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reston virus</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan virus</th>\n",
       "      <td>26.614987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taï Forest virus</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zaire virus</th>\n",
       "      <td>70.155039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  reported_number_of_deaths_among_cases\n",
       "ebola_subtype                                          \n",
       "Bundibugyo virus                               3.229974\n",
       "Reston virus                                   0.000000\n",
       "Sudan virus                                   26.614987\n",
       "Taï Forest virus                               0.000000\n",
       "Zaire virus                                   70.155039"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_deaths_by_subtype = deaths_by_subtype.sum()\n",
    "deaths_subtype_percent = deaths_by_subtype.mul(100).truediv(total_deaths_by_subtype)\n",
    "\n",
    "deaths_subtype_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outbreaks by virus subtype (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ebola_subtype</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bundibugyo virus</th>\n",
       "      <td>6.060606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reston virus</th>\n",
       "      <td>21.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan virus</th>\n",
       "      <td>24.242424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taï Forest virus</th>\n",
       "      <td>3.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zaire virus</th>\n",
       "      <td>45.454545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Outbreaks by virus subtype (%)\n",
       "ebola_subtype                                   \n",
       "Bundibugyo virus                        6.060606\n",
       "Reston virus                           21.212121\n",
       "Sudan virus                            24.242424\n",
       "Taï Forest virus                        3.030303\n",
       "Zaire virus                            45.454545"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outbrks_by_subtype = ebola_data.groupby([\"ebola_subtype\"])[\"ebola_subtype\"].count()\n",
    "total_outbrks_by_subtype = outbrks_by_subtype.sum()\n",
    "outbrks_subtype_percent = outbrks_by_subtype.mul(100).truediv(total_outbrks_by_subtype)\n",
    "\n",
    "data_percent = outbrks_subtype_percent.to_frame(name='Outbreaks by virus subtype (%)')\n",
    "data_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_subtypes_proportions = {\"Ebola subtypes\" : [\"Bundibugyo virus\", \"Reston virus\", \"Sudan virus\", \"Taï Forest virus\", \"Zaire virus\"],\n",
    "                            \"Outbreaks by virus subtype (%)\" : [6.060606, 21.212121, 24.242424, 3.030303, 45.454545],\n",
    "                            \"Reported human cases by virus subtype (%)\" : [7.835663, 0.550614, 32.994494, 0.042355, 58.576874],\n",
    "                            \"Reported deaths among cases by virus subtype (%)\" : [3.229974, 0.0, 26.614987, 0.0, 70.155039]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_subtypes_proportions = pd.DataFrame(all_subtypes_proportions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ebola subtypes</th>\n",
       "      <th>Outbreaks by virus subtype (%)</th>\n",
       "      <th>Reported deaths among cases by virus subtype (%)</th>\n",
       "      <th>Reported human cases by virus subtype (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bundibugyo virus</td>\n",
       "      <td>6.060606</td>\n",
       "      <td>3.229974</td>\n",
       "      <td>7.835663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Reston virus</td>\n",
       "      <td>21.212121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.550614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sudan virus</td>\n",
       "      <td>24.242424</td>\n",
       "      <td>26.614987</td>\n",
       "      <td>32.994494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taï Forest virus</td>\n",
       "      <td>3.030303</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Zaire virus</td>\n",
       "      <td>45.454545</td>\n",
       "      <td>70.155039</td>\n",
       "      <td>58.576874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ebola subtypes  Outbreaks by virus subtype (%)  \\\n",
       "0  Bundibugyo virus                        6.060606   \n",
       "1      Reston virus                       21.212121   \n",
       "2       Sudan virus                       24.242424   \n",
       "3  Taï Forest virus                        3.030303   \n",
       "4       Zaire virus                       45.454545   \n",
       "\n",
       "   Reported deaths among cases by virus subtype (%)  \\\n",
       "0                                          3.229974   \n",
       "1                                          0.000000   \n",
       "2                                         26.614987   \n",
       "3                                          0.000000   \n",
       "4                                         70.155039   \n",
       "\n",
       "   Reported human cases by virus subtype (%)  \n",
       "0                                   7.835663  \n",
       "1                                   0.550614  \n",
       "2                                  32.994494  \n",
       "3                                   0.042355  \n",
       "4                                  58.576874  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_subtypes_proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1cfa2fbe50>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rXLhwAcOGDROCJ7HdutbW1sI5nTp1CmPGjIG7uzteeOEFre127tyJAwcOYNu2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1cfa2ab3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_subtypes_proportions.plot(kind=\"bar\", x=\"Ebola subtypes\", figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Out of the 5 virus subtypes: \n",
    "* 2 are responsible for low proportions of outbreaks, human cases and deaths (Taï Forest virus and Bundibugyo virus)\n",
    "* 2 are responsible for high proportions of outbreaks, human cases and deaths (Zaïre virus and Sudan virus)\n",
    "* 1 is responsible for a high proportion of outbreaks, but low proportion of human cases or deaths (Reston virus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Zaïre virus accounts for 70% of reported death cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "outbreaks_dates_by_country = ebola_data[['country_name', 'start_date', 'start_date', ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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