Assignment_plotting_covid_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Corona virus\n",
    "\n",
    "Er zijn verschillende websites die visualisaties maken van het corona virus. Een bekende is \n",
    "\n",
    "https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6\n",
    "\n",
    "Maar hoe wordt die gemaakt? In dit notebook kijken we naar verschillende visualisaties van het virus. Data kun je vinden op \n",
    "\n",
    "https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Opdracht plotjes\n",
    "\n",
    "maak een barplot van 4 landen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-03-21T10:13:08</td>\n",
       "      <td>67800</td>\n",
       "      <td>3139</td>\n",
       "      <td>58946</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-03-21T17:43:03</td>\n",
       "      <td>53578</td>\n",
       "      <td>4825</td>\n",
       "      <td>6072</td>\n",
       "      <td>41.8719</td>\n",
       "      <td>12.5674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-03-21T13:13:30</td>\n",
       "      <td>25374</td>\n",
       "      <td>1375</td>\n",
       "      <td>2125</td>\n",
       "      <td>40.4637</td>\n",
       "      <td>-3.7492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2020-03-21T20:43:02</td>\n",
       "      <td>22213</td>\n",
       "      <td>84</td>\n",
       "      <td>233</td>\n",
       "      <td>51.1657</td>\n",
       "      <td>10.4515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region          Last Update  Confirmed  Deaths  \\\n",
       "0          Hubei          China  2020-03-21T10:13:08      67800    3139   \n",
       "1            NaN          Italy  2020-03-21T17:43:03      53578    4825   \n",
       "2            NaN          Spain  2020-03-21T13:13:30      25374    1375   \n",
       "3            NaN        Germany  2020-03-21T20:43:02      22213      84   \n",
       "\n",
       "   Recovered  Latitude  Longitude  \n",
       "0      58946   30.9756   112.2707  \n",
       "1       6072   41.8719    12.5674  \n",
       "2       2125   40.4637    -3.7492  \n",
       "3        233   51.1657    10.4515  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#allereerst maken we de data zo klein dat ze maar vier landen bevatten\n",
    "data = data[0:4]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "# bepaal voor de x de landen, voor de hoogte nemen we de confirmed kolom\n",
    "plt.figure()\n",
    "plt.ylabel('# bevestigd Coronagevallen')\n",
    "plt.bar(x = data['Country/Region'], height = data['Confirmed'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time series\n",
    "Laten we eens naar de time series data kijken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Allereerst laden we de data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>9/26/21</th>\n",
       "      <th>9/27/21</th>\n",
       "      <th>9/28/21</th>\n",
       "      <th>9/29/21</th>\n",
       "      <th>9/30/21</th>\n",
       "      <th>10/1/21</th>\n",
       "      <th>10/2/21</th>\n",
       "      <th>10/3/21</th>\n",
       "      <th>10/4/21</th>\n",
       "      <th>10/5/21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>154960</td>\n",
       "      <td>155072</td>\n",
       "      <td>155093</td>\n",
       "      <td>155128</td>\n",
       "      <td>155174</td>\n",
       "      <td>155191</td>\n",
       "      <td>155191</td>\n",
       "      <td>155191</td>\n",
       "      <td>155287</td>\n",
       "      <td>155309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>167893</td>\n",
       "      <td>168188</td>\n",
       "      <td>168782</td>\n",
       "      <td>169462</td>\n",
       "      <td>170131</td>\n",
       "      <td>170778</td>\n",
       "      <td>171327</td>\n",
       "      <td>171794</td>\n",
       "      <td>171794</td>\n",
       "      <td>172618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>202722</td>\n",
       "      <td>202877</td>\n",
       "      <td>203045</td>\n",
       "      <td>203198</td>\n",
       "      <td>203359</td>\n",
       "      <td>203517</td>\n",
       "      <td>203657</td>\n",
       "      <td>203789</td>\n",
       "      <td>203915</td>\n",
       "      <td>204046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15167</td>\n",
       "      <td>15189</td>\n",
       "      <td>15192</td>\n",
       "      <td>15209</td>\n",
       "      <td>15222</td>\n",
       "      <td>15222</td>\n",
       "      <td>15222</td>\n",
       "      <td>15222</td>\n",
       "      <td>15267</td>\n",
       "      <td>15271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>55121</td>\n",
       "      <td>55583</td>\n",
       "      <td>56040</td>\n",
       "      <td>56583</td>\n",
       "      <td>56583</td>\n",
       "      <td>58076</td>\n",
       "      <td>58603</td>\n",
       "      <td>58943</td>\n",
       "      <td>58943</td>\n",
       "      <td>59895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 627 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  9/26/21  9/27/21  9/28/21  \\\n",
       "0        0        0        0        0  ...   154960   155072   155093   \n",
       "1        0        0        0        0  ...   167893   168188   168782   \n",
       "2        0        0        0        0  ...   202722   202877   203045   \n",
       "3        0        0        0        0  ...    15167    15189    15192   \n",
       "4        0        0        0        0  ...    55121    55583    56040   \n",
       "\n",
       "   9/29/21  9/30/21  10/1/21  10/2/21  10/3/21  10/4/21  10/5/21  \n",
       "0   155128   155174   155191   155191   155191   155287   155309  \n",
       "1   169462   170131   170778   171327   171794   171794   172618  \n",
       "2   203198   203359   203517   203657   203789   203915   204046  \n",
       "3    15209    15222    15222    15222    15222    15267    15271  \n",
       "4    56583    56583    58076    58603    58943    58943    59895  \n",
       "\n",
       "[5 rows x 627 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'\n",
    "data_t = pd.read_csv(url)\n",
    "data_t.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
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       "      <th>10/4/21</th>\n",
       "      <th>10/5/21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
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       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>155287</td>\n",
       "      <td>155309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>169462</td>\n",
       "      <td>170131</td>\n",
       "      <td>170778</td>\n",
       "      <td>171327</td>\n",
       "      <td>171794</td>\n",
       "      <td>171794</td>\n",
       "      <td>172618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>203517</td>\n",
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       "      <td>203789</td>\n",
       "      <td>203915</td>\n",
       "      <td>204046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>15222</td>\n",
       "      <td>15267</td>\n",
       "      <td>15271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>56583</td>\n",
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       "      <td>58943</td>\n",
       "      <td>58943</td>\n",
       "      <td>59895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 623 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  9/26/21  9/27/21  9/28/21  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...   154960   155072   155093   \n",
       "Albania               0        0        0  ...   167893   168188   168782   \n",
       "Algeria               0        0        0  ...   202722   202877   203045   \n",
       "Andorra               0        0        0  ...    15167    15189    15192   \n",
       "Angola                0        0        0  ...    55121    55583    56040   \n",
       "\n",
       "                9/29/21  9/30/21  10/1/21  10/2/21  10/3/21  10/4/21  10/5/21  \n",
       "Country/Region                                                                 \n",
       "Afghanistan      155128   155174   155191   155191   155191   155287   155309  \n",
       "Albania          169462   170131   170778   171327   171794   171794   172618  \n",
       "Algeria          203198   203359   203517   203657   203789   203915   204046  \n",
       "Andorra           15209    15222    15222    15222    15222    15267    15271  \n",
       "Angola            56583    56583    58076    58603    58943    58943    59895  \n",
       "\n",
       "[5 rows x 623 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def reformat(data_t):\n",
    "    data_t.index = data_t['Country/Region']\n",
    "    data_t = data_t.drop('Province/State', axis = 1)\n",
    "    data_t = data_t.drop('Country/Region', axis = 1)\n",
    "    data_t = data_t.drop('Lat', axis = 1)\n",
    "    data_t = data_t.drop('Long', axis = 1)\n",
    "    return data_t\n",
    "\n",
    "data_t = reformat(data_t)\n",
    "data_t.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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1777399, 1788435, 1798570, 1807444, 1814143, 1821040, 1827273, 1833668, 1838937, 1843521, 1847372, 1851329, 1854784, 1859199, 1862586, 1865511, 1867815, 1869909, 1872093, 1874856, 1878214, 1881121, 1883513, 1885805, 1887831, 1889992, 1892104, 1894667, 1897344, 1899647, 1901900, 1904128, 1906434, 1909211, 1911699, 1913908, 1916316, 1918769, 1921159, 1923596, 1926424, 1929018, 1931833, 1934283, 1936572, 1938743, 1941055, 1943882, 1946655, 1949195, 1951886, 1954233, 1956348, 1958804, 1961585, 1963947, 1966634, 1968832, 1968832, 1972653, 1974662, 1977016, 1979114, 1981252, 1983252, 1984915, 1986241, 1987905, 1989805, 1991628, 1993309, 1994901, 1996498, 1997885, 1999592, 2001347, 2003050, 2004763, 2006421, 2008024, 2009648, 2011530]]\n"
     ]
    }
   ],
   "source": [
    "#check zit Nederland erin?\n",
    "nl = data_t.loc[r\"Netherlands\"].values.tolist()\n",
    "print(nl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "#Nederland zit er twee keer in, Nederland en curacao\n",
    "#check zit Nederland erin? Ik wil alleen Nederland\n",
    "nl = data_t.loc[r\"Netherlands\"].values.tolist()[0]\n",
    "print(nl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
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       "      <th>9/26/21</th>\n",
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       "      <th>10/3/21</th>\n",
       "      <th>10/4/21</th>\n",
       "      <th>10/5/21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
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       "      <td>2019</td>\n",
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       "      <td>2068</td>\n",
       "      <td>2083</td>\n",
       "      <td>2096</td>\n",
       "      <td>2116</td>\n",
       "      <td>2126</td>\n",
       "      <td>2141</td>\n",
       "      <td>2157</td>\n",
       "      <td>2168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>2011530</td>\n",
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       "</table>\n",
       "<p>5 rows × 623 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Netherlands           0        0        0        0        0        0        0   \n",
       "Netherlands           0        0        0        0        0        0        0   \n",
       "Netherlands           0        0        0        0        0        0        0   \n",
       "Netherlands           0        0        0        0        0        0        0   \n",
       "Netherlands           0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  9/26/21  9/27/21  9/28/21  \\\n",
       "Country/Region                             ...                              \n",
       "Netherlands           0        0        0  ...    15416    15432    15448   \n",
       "Netherlands           0        0        0  ...     2019     2045     2068   \n",
       "Netherlands           0        0        0  ...    16373    16383    16428   \n",
       "Netherlands           0        0        0  ...     4213     4235     4263   \n",
       "Netherlands           0        0        0  ...  1996498  1997885  1999592   \n",
       "\n",
       "                9/29/21  9/30/21  10/1/21  10/2/21  10/3/21  10/4/21  10/5/21  \n",
       "Country/Region                                                                 \n",
       "Netherlands       15472    15500    15514    15533    15536    15543    15560  \n",
       "Netherlands        2083     2096     2116     2126     2141     2157     2168  \n",
       "Netherlands       16465    16500    16556    16592    16609    16618    16650  \n",
       "Netherlands        4263     4283     4291     4307     4321     4342     4342  \n",
       "Netherlands     2001347  2003050  2004763  2006421  2008024  2009648  2011530  \n",
       "\n",
       "[5 rows x 623 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlr = data_t.loc[r\"Netherlands\"]\n",
    "nlr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "# dan laden we x en y\n",
    "nl = data_t.loc[r\"Netherlands\"].values.tolist()[4]\n",
    "it = data_t.loc[r\"Italy\"].values.tolist()\n",
    "x = pd.date_range('1/22/2020', periods = len(nl), freq ='d') \n",
    "plt.plot(x, nl, label=\"Nederland\")\n",
    "plt.plot(x, it, label=\"Italië\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Opdracht\n",
    "1. Maak de labels van de time series mooier. Plot ook Sweden of andere landen. Maak een zwarte achtergrond met dikke lijnen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "623"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Opdracht uitwerking\n",
    "\n",
    "Om de opdracht goed uit te kunnen voeren ga ik eerst op internet en in de documentatie zoeken hoe ik dit kan doen. Ik vind het volgende\n",
    "\n",
    "voor dikke lijnen kan ik gewoon een optie meegegeven in de plot functie. Ik had gegoogled op 'change line thickness in matplotlib plot' en vond toen deze pagina\n",
    "\n",
    "    https://pythonprogramming.net/graphing-matplotlib-python-part-3-colors-line-thickness/\n",
    "\n",
    "ik kies ervoor om `plt.plot(x, nl, label=\"Nederland\", linewidth=5)` te gebruiken\n",
    "\n",
    "Voor de date time thicks moet ik wat langer zoeken. Ik google op keywords zoals display datetime format x label matplotlib en vind een sites zoals\n",
    "\n",
    "    https://stackoverflow.com/questions/5498510/creating-graph-with-date-and-time-in-axis-labels-with-matplotlib\n",
    "    https://matplotlib.org/3.2.0/gallery/text_labels_and_annotations/date.html\n",
    "    \n",
    "Ik probeer wat uit en ik kom uiteindelijk op de volgende code\n",
    "    \n",
    "    import matplotlib.dates as mdates\n",
    "    days = mdates.WeekdayLocator()\n",
    "    daysFmt = mdates.DateFormatter('%m-%d')\n",
    "\n",
    "    ax = plt.gca() \n",
    "    ax.xaxis.set_major_locator(days)\n",
    "    ax.xaxis.set_major_formatter(daysFmt)\n",
    "\n",
    "om de achtergrond zwart te maken vind ik verschillende oplossingen. zie \n",
    "    \n",
    "    https://stackoverflow.com/questions/14088687/how-to-change-plot-background-color\n",
    "\n",
    "Ik kies ervoor om de `ax.patch.set_facecolor('black')` te gebruiken omdat ik toch al een ax gebruikt heb\n",
    "\n",
    "Mijn oplossing ziet er als volgt uit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from matplotlib.ticker import NullFormatter\n",
    "from matplotlib.dates import DateFormatter\n",
    "#dan laden we x en y\n",
    "\n",
    "#x-waarden moeten datetime waarden zijn van 1/22/2020 tot en met 3/17/2030\n",
    "x = pd.date_range('1/22/2020', periods = len(nl), freq ='d') \n",
    "\n",
    "#y-waarden zijn de rijen per land aan eenvolgende data\n",
    "nl = data_t.loc[r\"Netherlands\"].values.tolist()[4]\n",
    "it = data_t.loc[r\"Italy\"].values.tolist()\n",
    "sw = data_t.loc[r\"Sweden\"].values.tolist()\n",
    "ir = data_t.loc[r\"Iran\"].values.tolist()\n",
    "be = data_t.loc[r\"Belgium\"].values.tolist()\n",
    "sp = data_t.loc[r\"Spain\"].values.tolist()\n",
    "ch = data_t.loc[r\"China\"].values.tolist()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "#plot de landen met dikke lijnen\n",
    "plt.plot(x, nl, label=\"Nederland\", linewidth=5)\n",
    "#plt.plot(x, it, label=\"Italië\", linewidth=5)\n",
    "#plt.plot(x, ir, label=\"Iran\", linewidth=5)\n",
    "plt.plot(x, sw, label=\"Zweden\", linewidth=5)\n",
    "#plt.plot(x, be, label=\"België\", linewidth=5)\n",
    "#plt.plot(x, sp, label=\"Spain\", linewidth=5)\n",
    "plt.legend()\n",
    "plt.title('Covid gevallen')\n",
    "\n",
    "# dit heb ik opgezocht op internet. \n",
    "# dit is een trucje om de data in een mooi formaat van de x-as labels te krijgen\n",
    "import matplotlib.dates as mdates\n",
    "months = mdates.MonthLocator()\n",
    "mt = mdates.DateFormatter('%m')\n",
    "ax = plt.gca() \n",
    "ax.xaxis.set_major_locator(months)\n",
    "ax.xaxis.set_major_formatter(mt)\n",
    "#einde trucje voor mooie x-as\n",
    "\n",
    "#zet nu achtergrond op zwart\n",
    "ax.patch.set_facecolor('black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Challenge\n",
    "Maak een plot van confirmed, death en recovered. De data staat op https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data\n",
    "\n",
    "Deze opdracht kan op verschillende manieren. Dat kan een barplot zijn van alle landen, een timeseries van enkele landen etc. Het is maar net hoe je de onderzoeksvraag wilt beantwoorden. Er is geen 1 antwoord maar meerdere antwoorden op deze vraag. Ik besluit om een barplot te maken met totalen per categorie en % Daarvoor heb ik een sommatie nodig van alle landen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "#sommatie van alle rijen uit de dataframe\n",
    "confirmed = data['Confirmed'].sum()\n",
    "deaths = data['Deaths'].sum()\n",
    "recovered = data['Recovered'].sum()\n",
    "#laad x en y\n",
    "x =['Confirmed', 'Deaths', 'Recovered']\n",
    "y =[confirmed, deaths, recovered]\n",
    "#maak plotjes met kleuren en titel\n",
    "bars = plt.bar(x=x, height=y)\n",
    "bars[0].set_color('yellow')\n",
    "bars[1].set_color('red')\n",
    "bars[2].set_color('green')\n",
    "plt.title('Corona counts world wide')\n",
    "total = confirmed + deaths + recovered\n",
    "# plot percentages \n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.gca().text(bar.get_x() + bar.get_width()/2, bar.get_height() - 500, str(int(height/total*100)) + '%', \n",
    "                 ha='center', color='black', fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}