Pandas_NB2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Data from the data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }</style><table id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4dd\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >date</th>        <th class=\"col_heading level0 col1\" >new_cases</th>        <th class=\"col_heading level0 col2\" >new_deaths</th>        <th class=\"col_heading level0 col3\" >new_tests</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddlevel0_row0\" class=\"row_heading level0 row0\" >243</th>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow0_col0\" class=\"data row0 col0\" >2020-08-30</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow0_col1\" class=\"data row0 col1\" >1444.000000</td>\n",
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       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow0_col3\" class=\"data row0 col3\" >53541.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddlevel0_row1\" class=\"row_heading level0 row1\" >244</th>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow1_col0\" class=\"data row1 col0\" >2020-08-31</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow1_col1\" class=\"data row1 col1\" >1365.000000</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow1_col2\" class=\"data row1 col2\" >4.000000</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow1_col3\" class=\"data row1 col3\" >42583.000000</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddlevel0_row2\" class=\"row_heading level0 row2\" >245</th>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow2_col0\" class=\"data row2 col0\" >2020-09-01</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow2_col1\" class=\"data row2 col1\" >996.000000</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow2_col2\" class=\"data row2 col2\" >6.000000</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow2_col3\" class=\"data row2 col3\" >54395.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddlevel0_row3\" class=\"row_heading level0 row3\" >246</th>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow3_col0\" class=\"data row3 col0\" >2020-09-02</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow3_col1\" class=\"data row3 col1\" >975.000000</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow3_col2\" class=\"data row3 col2\" >8.000000</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow3_col3\" class=\"data row3 col3\" >nan</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddlevel0_row4\" class=\"row_heading level0 row4\" >247</th>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow4_col0\" class=\"data row4 col0\" >2020-09-03</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow4_col1\" class=\"data row4 col1\" >1326.000000</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow4_col2\" class=\"data row4 col2\" >6.000000</td>\n",
       "                        <td id=\"T_fe3fbe50_fc9c_11ea_98db_55d2441bb4ddrow4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "            </tr>\n",
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      ],
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       "<pandas.io.formats.style.Styler at 0x7f4e2844c6a0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df = pd.read_csv('italy-covid-daywise.csv')\n",
    "covid_df.tail().style.background_gradient(cmap = 'Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_cases = covid_df.new_cases.sum()\n",
    "total_deaths = covid_df.new_deaths.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Cases are 271515.0 and Deaths are 35497.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Number of Cases are {} and Deaths are {}\".format(total_cases, total_deaths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Death Rate is 13.07 %\n"
     ]
    }
   ],
   "source": [
    "death_rate = total_deaths / total_cases\n",
    "print(\"Death Rate is {:.2f} %\".format(death_rate * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5214766.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_tests = 935310 + covid_df.new_tests.sum()\n",
    "total_tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.206657403227681\n"
     ]
    }
   ],
   "source": [
    "positive_rate = total_cases / total_tests\n",
    "print(positive_rate * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want only want to look at the days which had more than 1000 reported cases. We can use a boolean expression to chech which rows satisfy this criterion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "243     True\n",
       "244     True\n",
       "245    False\n",
       "246    False\n",
       "247     True\n",
       "Name: new_cases, Length: 248, dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_cases = covid_df.new_cases > 1000\n",
    "high_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>1797.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>2313.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>2651.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>64294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  new_cases  new_deaths  new_tests\n",
       "68   2020-03-08     1247.0        36.0        NaN\n",
       "69   2020-03-09     1492.0       133.0        NaN\n",
       "70   2020-03-10     1797.0        98.0        NaN\n",
       "72   2020-03-12     2313.0       196.0        NaN\n",
       "73   2020-03-13     2651.0       189.0        NaN\n",
       "..          ...        ...         ...        ...\n",
       "241  2020-08-28     1409.0         5.0    65135.0\n",
       "242  2020-08-29     1460.0         9.0    64294.0\n",
       "243  2020-08-30     1444.0         1.0    53541.0\n",
       "244  2020-08-31     1365.0         4.0    42583.0\n",
       "247  2020-09-03     1326.0         6.0        NaN\n",
       "\n",
       "[72 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df[high_cases]       # rows with boolean value True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>1797.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>2313.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>2651.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>64294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  new_cases  new_deaths  new_tests\n",
       "68   2020-03-08     1247.0        36.0        NaN\n",
       "69   2020-03-09     1492.0       133.0        NaN\n",
       "70   2020-03-10     1797.0        98.0        NaN\n",
       "72   2020-03-12     2313.0       196.0        NaN\n",
       "73   2020-03-13     2651.0       189.0        NaN\n",
       "..          ...        ...         ...        ...\n",
       "241  2020-08-28     1409.0         5.0    65135.0\n",
       "242  2020-08-29     1460.0         9.0    64294.0\n",
       "243  2020-08-30     1444.0         1.0    53541.0\n",
       "244  2020-08-31     1365.0         4.0    42583.0\n",
       "247  2020-09-03     1326.0         6.0        NaN\n",
       "\n",
       "[72 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or\n",
    "\n",
    "high_cases_df = covid_df[covid_df.new_cases > 1000]\n",
    "high_cases_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data frame contains 72 rows, but only the first 5 & last 5 rows are displayed by default with Jupyter, for brevity. To view, all the rows, we can modify some display options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
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       "      <th>68</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>36.0</td>\n",
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       "      <th>69</th>\n",
       "      <td>2020-03-09</td>\n",
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       "      <td>133.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>70</th>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>1797.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>72</th>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>2313.0</td>\n",
       "      <td>196.0</td>\n",
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       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>2651.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>2547.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>3497.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>3526.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>4207.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>5322.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>5986.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>6557.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>4789.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>5249.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>5210.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>6153.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>5959.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>5974.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>5217.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>4050.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>4053.0</td>\n",
       "      <td>839.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>4782.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>4585.0</td>\n",
       "      <td>764.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>4805.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>4316.0</td>\n",
       "      <td>527.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>3599.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2020-04-08</td>\n",
       "      <td>3039.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>3836.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>4204.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>3951.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2020-04-12</td>\n",
       "      <td>4694.0</td>\n",
       "      <td>619.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2020-04-13</td>\n",
       "      <td>4092.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>564.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>2972.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>2667.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2020-04-17</td>\n",
       "      <td>3786.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2020-04-18</td>\n",
       "      <td>3493.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2020-04-19</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>7841.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>2256.0</td>\n",
       "      <td>454.0</td>\n",
       "      <td>28095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>2729.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>44248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>3370.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>37083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>2646.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>95273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2020-04-25</td>\n",
       "      <td>3021.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>38676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>2357.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>24113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>2324.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>26678.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2020-04-28</td>\n",
       "      <td>1739.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>37554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>2091.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>38589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2086.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>41441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>43732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>31231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>27047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>1389.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>22999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>32211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>37771.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>13665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>45428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>1327.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>36091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>1083.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>31384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2020-05-13</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>37049.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2020-08-23</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>47463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2020-08-24</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>57640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>64294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  new_cases  new_deaths  new_tests\n",
       "68   2020-03-08     1247.0        36.0        NaN\n",
       "69   2020-03-09     1492.0       133.0        NaN\n",
       "70   2020-03-10     1797.0        98.0        NaN\n",
       "72   2020-03-12     2313.0       196.0        NaN\n",
       "73   2020-03-13     2651.0       189.0        NaN\n",
       "74   2020-03-14     2547.0       252.0        NaN\n",
       "75   2020-03-15     3497.0       173.0        NaN\n",
       "76   2020-03-16     2823.0       370.0        NaN\n",
       "77   2020-03-17     4000.0       347.0        NaN\n",
       "78   2020-03-18     3526.0       347.0        NaN\n",
       "79   2020-03-19     4207.0       473.0        NaN\n",
       "80   2020-03-20     5322.0       429.0        NaN\n",
       "81   2020-03-21     5986.0       625.0        NaN\n",
       "82   2020-03-22     6557.0       795.0        NaN\n",
       "83   2020-03-23     5560.0       649.0        NaN\n",
       "84   2020-03-24     4789.0       601.0        NaN\n",
       "85   2020-03-25     5249.0       743.0        NaN\n",
       "86   2020-03-26     5210.0       685.0        NaN\n",
       "87   2020-03-27     6153.0       660.0        NaN\n",
       "88   2020-03-28     5959.0       971.0        NaN\n",
       "89   2020-03-29     5974.0       887.0        NaN\n",
       "90   2020-03-30     5217.0       758.0        NaN\n",
       "91   2020-03-31     4050.0       810.0        NaN\n",
       "92   2020-04-01     4053.0       839.0        NaN\n",
       "93   2020-04-02     4782.0       727.0        NaN\n",
       "94   2020-04-03     4668.0       760.0        NaN\n",
       "95   2020-04-04     4585.0       764.0        NaN\n",
       "96   2020-04-05     4805.0       681.0        NaN\n",
       "97   2020-04-06     4316.0       527.0        NaN\n",
       "98   2020-04-07     3599.0       636.0        NaN\n",
       "99   2020-04-08     3039.0       604.0        NaN\n",
       "100  2020-04-09     3836.0       540.0        NaN\n",
       "101  2020-04-10     4204.0       612.0        NaN\n",
       "102  2020-04-11     3951.0       570.0        NaN\n",
       "103  2020-04-12     4694.0       619.0        NaN\n",
       "104  2020-04-13     4092.0       431.0        NaN\n",
       "105  2020-04-14     3153.0       564.0        NaN\n",
       "106  2020-04-15     2972.0       604.0        NaN\n",
       "107  2020-04-16     2667.0       578.0        NaN\n",
       "108  2020-04-17     3786.0       525.0        NaN\n",
       "109  2020-04-18     3493.0       575.0        NaN\n",
       "110  2020-04-19     3491.0       480.0        NaN\n",
       "111  2020-04-20     3047.0       433.0     7841.0\n",
       "112  2020-04-21     2256.0       454.0    28095.0\n",
       "113  2020-04-22     2729.0       534.0    44248.0\n",
       "114  2020-04-23     3370.0       437.0    37083.0\n",
       "115  2020-04-24     2646.0       464.0    95273.0\n",
       "116  2020-04-25     3021.0       420.0    38676.0\n",
       "117  2020-04-26     2357.0       415.0    24113.0\n",
       "118  2020-04-27     2324.0       260.0    26678.0\n",
       "119  2020-04-28     1739.0       333.0    37554.0\n",
       "120  2020-04-29     2091.0       382.0    38589.0\n",
       "121  2020-04-30     2086.0       323.0    41441.0\n",
       "122  2020-05-01     1872.0       285.0    43732.0\n",
       "123  2020-05-02     1965.0       269.0    31231.0\n",
       "124  2020-05-03     1900.0       474.0    27047.0\n",
       "125  2020-05-04     1389.0       174.0    22999.0\n",
       "126  2020-05-05     1221.0       195.0    32211.0\n",
       "127  2020-05-06     1075.0       236.0    37771.0\n",
       "128  2020-05-07     1444.0       369.0    13665.0\n",
       "129  2020-05-08     1401.0       274.0    45428.0\n",
       "130  2020-05-09     1327.0       243.0    36091.0\n",
       "131  2020-05-10     1083.0       194.0    31384.0\n",
       "134  2020-05-13     1402.0       172.0    37049.0\n",
       "236  2020-08-23     1071.0         3.0    47463.0\n",
       "237  2020-08-24     1209.0         7.0    33358.0\n",
       "240  2020-08-27     1366.0        13.0    57640.0\n",
       "241  2020-08-28     1409.0         5.0    65135.0\n",
       "242  2020-08-29     1460.0         9.0    64294.0\n",
       "243  2020-08-30     1444.0         1.0    53541.0\n",
       "244  2020-08-31     1365.0         4.0    42583.0\n",
       "247  2020-09-03     1326.0         6.0        NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "with pd.option_context('display.max_row', 100):\n",
    "    display(covid_df[covid_df.new_cases > 1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We cannot use style attribute with high_cases because high_cases is a Series data structure.\n",
    "But we can use style attribute with high_cases_df as it is a dataframe not a Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "            background-color:  #fff3ed;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow66_col3 {\n",
       "            background-color:  #f4503a;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow67_col1 {\n",
       "            background-color:  #ffebe2;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow67_col2 {\n",
       "            background-color:  #fff4ef;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow67_col3 {\n",
       "            background-color:  #e63328;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow68_col1 {\n",
       "            background-color:  #fee9df;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow68_col2 {\n",
       "            background-color:  #fff4ee;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow68_col3 {\n",
       "            background-color:  #e93529;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow69_col1 {\n",
       "            background-color:  #feeae0;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow69_col2 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow69_col3 {\n",
       "            background-color:  #f96245;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow70_col1 {\n",
       "            background-color:  #ffece4;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow70_col2 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow70_col3 {\n",
       "            background-color:  #fc8b6b;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow71_col1 {\n",
       "            background-color:  #ffeee6;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow71_col2 {\n",
       "            background-color:  #fff4ef;\n",
       "            color:  #000000;\n",
       "        }    #T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow71_col3 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4dd\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >date</th>        <th class=\"col_heading level0 col1\" >new_cases</th>        <th class=\"col_heading level0 col2\" >new_deaths</th>        <th class=\"col_heading level0 col3\" >new_tests</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row0\" class=\"row_heading level0 row0\" >68</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow0_col0\" class=\"data row0 col0\" >2020-03-08</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow0_col1\" class=\"data row0 col1\" >1247.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow0_col2\" class=\"data row0 col2\" >36.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row1\" class=\"row_heading level0 row1\" >69</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow1_col0\" class=\"data row1 col0\" >2020-03-09</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow1_col1\" class=\"data row1 col1\" >1492.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow1_col2\" class=\"data row1 col2\" >133.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row2\" class=\"row_heading level0 row2\" >70</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow2_col0\" class=\"data row2 col0\" >2020-03-10</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow2_col1\" class=\"data row2 col1\" >1797.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow2_col2\" class=\"data row2 col2\" >98.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow2_col3\" class=\"data row2 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row3\" class=\"row_heading level0 row3\" >72</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow3_col0\" class=\"data row3 col0\" >2020-03-12</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow3_col1\" class=\"data row3 col1\" >2313.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow3_col2\" class=\"data row3 col2\" >196.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row4\" class=\"row_heading level0 row4\" >73</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow4_col0\" class=\"data row4 col0\" >2020-03-13</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow4_col1\" class=\"data row4 col1\" >2651.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow4_col2\" class=\"data row4 col2\" >189.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row5\" class=\"row_heading level0 row5\" >74</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow5_col0\" class=\"data row5 col0\" >2020-03-14</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow5_col1\" class=\"data row5 col1\" >2547.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow5_col2\" class=\"data row5 col2\" >252.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row6\" class=\"row_heading level0 row6\" >75</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow6_col0\" class=\"data row6 col0\" >2020-03-15</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow6_col1\" class=\"data row6 col1\" >3497.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow6_col2\" class=\"data row6 col2\" >173.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row7\" class=\"row_heading level0 row7\" >76</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow7_col0\" class=\"data row7 col0\" >2020-03-16</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow7_col1\" class=\"data row7 col1\" >2823.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow7_col2\" class=\"data row7 col2\" >370.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow7_col3\" class=\"data row7 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row8\" class=\"row_heading level0 row8\" >77</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow8_col0\" class=\"data row8 col0\" >2020-03-17</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow8_col1\" class=\"data row8 col1\" >4000.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow8_col2\" class=\"data row8 col2\" >347.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row9\" class=\"row_heading level0 row9\" >78</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow9_col0\" class=\"data row9 col0\" >2020-03-18</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow9_col1\" class=\"data row9 col1\" >3526.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow9_col2\" class=\"data row9 col2\" >347.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow9_col3\" class=\"data row9 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row10\" class=\"row_heading level0 row10\" >79</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow10_col0\" class=\"data row10 col0\" >2020-03-19</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow10_col1\" class=\"data row10 col1\" >4207.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow10_col2\" class=\"data row10 col2\" >473.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row11\" class=\"row_heading level0 row11\" >80</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow11_col0\" class=\"data row11 col0\" >2020-03-20</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow11_col1\" class=\"data row11 col1\" >5322.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow11_col2\" class=\"data row11 col2\" >429.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow11_col3\" class=\"data row11 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row12\" class=\"row_heading level0 row12\" >81</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow12_col0\" class=\"data row12 col0\" >2020-03-21</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow12_col1\" class=\"data row12 col1\" >5986.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow12_col2\" class=\"data row12 col2\" >625.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow12_col3\" class=\"data row12 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row13\" class=\"row_heading level0 row13\" >82</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow13_col0\" class=\"data row13 col0\" >2020-03-22</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow13_col1\" class=\"data row13 col1\" >6557.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow13_col2\" class=\"data row13 col2\" >795.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row14\" class=\"row_heading level0 row14\" >83</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow14_col0\" class=\"data row14 col0\" >2020-03-23</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow14_col1\" class=\"data row14 col1\" >5560.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow14_col2\" class=\"data row14 col2\" >649.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row15\" class=\"row_heading level0 row15\" >84</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow15_col0\" class=\"data row15 col0\" >2020-03-24</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow15_col1\" class=\"data row15 col1\" >4789.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow15_col2\" class=\"data row15 col2\" >601.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row16\" class=\"row_heading level0 row16\" >85</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow16_col0\" class=\"data row16 col0\" >2020-03-25</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow16_col1\" class=\"data row16 col1\" >5249.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow16_col2\" class=\"data row16 col2\" >743.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row17\" class=\"row_heading level0 row17\" >86</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow17_col0\" class=\"data row17 col0\" >2020-03-26</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow17_col1\" class=\"data row17 col1\" >5210.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow17_col2\" class=\"data row17 col2\" >685.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row18\" class=\"row_heading level0 row18\" >87</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow18_col0\" class=\"data row18 col0\" >2020-03-27</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow18_col1\" class=\"data row18 col1\" >6153.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow18_col2\" class=\"data row18 col2\" >660.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row19\" class=\"row_heading level0 row19\" >88</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow19_col0\" class=\"data row19 col0\" >2020-03-28</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow19_col1\" class=\"data row19 col1\" >5959.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow19_col2\" class=\"data row19 col2\" >971.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow19_col3\" class=\"data row19 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row20\" class=\"row_heading level0 row20\" >89</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow20_col0\" class=\"data row20 col0\" >2020-03-29</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow20_col1\" class=\"data row20 col1\" >5974.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow20_col2\" class=\"data row20 col2\" >887.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow20_col3\" class=\"data row20 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row21\" class=\"row_heading level0 row21\" >90</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow21_col0\" class=\"data row21 col0\" >2020-03-30</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow21_col1\" class=\"data row21 col1\" >5217.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow21_col2\" class=\"data row21 col2\" >758.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow21_col3\" class=\"data row21 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row22\" class=\"row_heading level0 row22\" >91</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow22_col0\" class=\"data row22 col0\" >2020-03-31</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow22_col1\" class=\"data row22 col1\" >4050.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow22_col2\" class=\"data row22 col2\" >810.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow22_col3\" class=\"data row22 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row23\" class=\"row_heading level0 row23\" >92</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow23_col0\" class=\"data row23 col0\" >2020-04-01</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow23_col1\" class=\"data row23 col1\" >4053.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow23_col2\" class=\"data row23 col2\" >839.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow23_col3\" class=\"data row23 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row24\" class=\"row_heading level0 row24\" >93</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow24_col0\" class=\"data row24 col0\" >2020-04-02</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow24_col1\" class=\"data row24 col1\" >4782.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow24_col2\" class=\"data row24 col2\" >727.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow24_col3\" class=\"data row24 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row25\" class=\"row_heading level0 row25\" >94</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow25_col0\" class=\"data row25 col0\" >2020-04-03</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow25_col1\" class=\"data row25 col1\" >4668.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow25_col2\" class=\"data row25 col2\" >760.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow25_col3\" class=\"data row25 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row26\" class=\"row_heading level0 row26\" >95</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow26_col0\" class=\"data row26 col0\" >2020-04-04</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow26_col1\" class=\"data row26 col1\" >4585.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow26_col2\" class=\"data row26 col2\" >764.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow26_col3\" class=\"data row26 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row27\" class=\"row_heading level0 row27\" >96</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow27_col0\" class=\"data row27 col0\" >2020-04-05</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow27_col1\" class=\"data row27 col1\" >4805.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow27_col2\" class=\"data row27 col2\" >681.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow27_col3\" class=\"data row27 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row28\" class=\"row_heading level0 row28\" >97</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow28_col0\" class=\"data row28 col0\" >2020-04-06</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow28_col1\" class=\"data row28 col1\" >4316.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow28_col2\" class=\"data row28 col2\" >527.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow28_col3\" class=\"data row28 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row29\" class=\"row_heading level0 row29\" >98</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow29_col0\" class=\"data row29 col0\" >2020-04-07</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow29_col1\" class=\"data row29 col1\" >3599.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow29_col2\" class=\"data row29 col2\" >636.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow29_col3\" class=\"data row29 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row30\" class=\"row_heading level0 row30\" >99</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow30_col0\" class=\"data row30 col0\" >2020-04-08</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow30_col1\" class=\"data row30 col1\" >3039.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow30_col2\" class=\"data row30 col2\" >604.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow30_col3\" class=\"data row30 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row31\" class=\"row_heading level0 row31\" >100</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow31_col0\" class=\"data row31 col0\" >2020-04-09</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow31_col1\" class=\"data row31 col1\" >3836.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow31_col2\" class=\"data row31 col2\" >540.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow31_col3\" class=\"data row31 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row32\" class=\"row_heading level0 row32\" >101</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow32_col0\" class=\"data row32 col0\" >2020-04-10</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow32_col1\" class=\"data row32 col1\" >4204.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow32_col2\" class=\"data row32 col2\" >612.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow32_col3\" class=\"data row32 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row33\" class=\"row_heading level0 row33\" >102</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow33_col0\" class=\"data row33 col0\" >2020-04-11</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow33_col1\" class=\"data row33 col1\" >3951.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow33_col2\" class=\"data row33 col2\" >570.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow33_col3\" class=\"data row33 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row34\" class=\"row_heading level0 row34\" >103</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow34_col0\" class=\"data row34 col0\" >2020-04-12</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow34_col1\" class=\"data row34 col1\" >4694.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow34_col2\" class=\"data row34 col2\" >619.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow34_col3\" class=\"data row34 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row35\" class=\"row_heading level0 row35\" >104</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow35_col0\" class=\"data row35 col0\" >2020-04-13</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow35_col1\" class=\"data row35 col1\" >4092.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow35_col2\" class=\"data row35 col2\" >431.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow35_col3\" class=\"data row35 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row36\" class=\"row_heading level0 row36\" >105</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow36_col0\" class=\"data row36 col0\" >2020-04-14</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow36_col1\" class=\"data row36 col1\" >3153.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow36_col2\" class=\"data row36 col2\" >564.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow36_col3\" class=\"data row36 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row37\" class=\"row_heading level0 row37\" >106</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow37_col0\" class=\"data row37 col0\" >2020-04-15</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow37_col1\" class=\"data row37 col1\" >2972.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow37_col2\" class=\"data row37 col2\" >604.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow37_col3\" class=\"data row37 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row38\" class=\"row_heading level0 row38\" >107</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow38_col0\" class=\"data row38 col0\" >2020-04-16</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow38_col1\" class=\"data row38 col1\" >2667.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow38_col2\" class=\"data row38 col2\" >578.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow38_col3\" class=\"data row38 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row39\" class=\"row_heading level0 row39\" >108</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow39_col0\" class=\"data row39 col0\" >2020-04-17</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow39_col1\" class=\"data row39 col1\" >3786.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow39_col2\" class=\"data row39 col2\" >525.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow39_col3\" class=\"data row39 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row40\" class=\"row_heading level0 row40\" >109</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow40_col0\" class=\"data row40 col0\" >2020-04-18</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow40_col1\" class=\"data row40 col1\" >3493.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow40_col2\" class=\"data row40 col2\" >575.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow40_col3\" class=\"data row40 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row41\" class=\"row_heading level0 row41\" >110</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow41_col0\" class=\"data row41 col0\" >2020-04-19</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow41_col1\" class=\"data row41 col1\" >3491.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow41_col2\" class=\"data row41 col2\" >480.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow41_col3\" class=\"data row41 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row42\" class=\"row_heading level0 row42\" >111</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow42_col0\" class=\"data row42 col0\" >2020-04-20</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow42_col1\" class=\"data row42 col1\" >3047.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow42_col2\" class=\"data row42 col2\" >433.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow42_col3\" class=\"data row42 col3\" >7841.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row43\" class=\"row_heading level0 row43\" >112</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow43_col0\" class=\"data row43 col0\" >2020-04-21</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow43_col1\" class=\"data row43 col1\" >2256.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow43_col2\" class=\"data row43 col2\" >454.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow43_col3\" class=\"data row43 col3\" >28095.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row44\" class=\"row_heading level0 row44\" >113</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow44_col0\" class=\"data row44 col0\" >2020-04-22</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow44_col1\" class=\"data row44 col1\" >2729.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow44_col2\" class=\"data row44 col2\" >534.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow44_col3\" class=\"data row44 col3\" >44248.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row45\" class=\"row_heading level0 row45\" >114</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow45_col0\" class=\"data row45 col0\" >2020-04-23</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow45_col1\" class=\"data row45 col1\" >3370.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow45_col2\" class=\"data row45 col2\" >437.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow45_col3\" class=\"data row45 col3\" >37083.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row46\" class=\"row_heading level0 row46\" >115</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow46_col0\" class=\"data row46 col0\" >2020-04-24</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow46_col1\" class=\"data row46 col1\" >2646.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow46_col2\" class=\"data row46 col2\" >464.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow46_col3\" class=\"data row46 col3\" >95273.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row47\" class=\"row_heading level0 row47\" >116</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow47_col0\" class=\"data row47 col0\" >2020-04-25</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow47_col1\" class=\"data row47 col1\" >3021.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow47_col2\" class=\"data row47 col2\" >420.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow47_col3\" class=\"data row47 col3\" >38676.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row48\" class=\"row_heading level0 row48\" >117</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow48_col0\" class=\"data row48 col0\" >2020-04-26</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow48_col1\" class=\"data row48 col1\" >2357.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow48_col2\" class=\"data row48 col2\" >415.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow48_col3\" class=\"data row48 col3\" >24113.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row49\" class=\"row_heading level0 row49\" >118</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow49_col0\" class=\"data row49 col0\" >2020-04-27</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow49_col1\" class=\"data row49 col1\" >2324.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow49_col2\" class=\"data row49 col2\" >260.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow49_col3\" class=\"data row49 col3\" >26678.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row50\" class=\"row_heading level0 row50\" >119</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow50_col0\" class=\"data row50 col0\" >2020-04-28</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow50_col1\" class=\"data row50 col1\" >1739.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow50_col2\" class=\"data row50 col2\" >333.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow50_col3\" class=\"data row50 col3\" >37554.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row51\" class=\"row_heading level0 row51\" >120</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow51_col0\" class=\"data row51 col0\" >2020-04-29</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow51_col1\" class=\"data row51 col1\" >2091.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow51_col2\" class=\"data row51 col2\" >382.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow51_col3\" class=\"data row51 col3\" >38589.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row52\" class=\"row_heading level0 row52\" >121</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow52_col0\" class=\"data row52 col0\" >2020-04-30</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow52_col1\" class=\"data row52 col1\" >2086.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow52_col2\" class=\"data row52 col2\" >323.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow52_col3\" class=\"data row52 col3\" >41441.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row53\" class=\"row_heading level0 row53\" >122</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow53_col0\" class=\"data row53 col0\" >2020-05-01</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow53_col1\" class=\"data row53 col1\" >1872.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow53_col2\" class=\"data row53 col2\" >285.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow53_col3\" class=\"data row53 col3\" >43732.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row54\" class=\"row_heading level0 row54\" >123</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow54_col0\" class=\"data row54 col0\" >2020-05-02</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow54_col1\" class=\"data row54 col1\" >1965.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow54_col2\" class=\"data row54 col2\" >269.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow54_col3\" class=\"data row54 col3\" >31231.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row55\" class=\"row_heading level0 row55\" >124</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow55_col0\" class=\"data row55 col0\" >2020-05-03</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow55_col1\" class=\"data row55 col1\" >1900.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow55_col2\" class=\"data row55 col2\" >474.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow55_col3\" class=\"data row55 col3\" >27047.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row56\" class=\"row_heading level0 row56\" >125</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow56_col0\" class=\"data row56 col0\" >2020-05-04</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow56_col1\" class=\"data row56 col1\" >1389.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow56_col2\" class=\"data row56 col2\" >174.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow56_col3\" class=\"data row56 col3\" >22999.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row57\" class=\"row_heading level0 row57\" >126</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow57_col0\" class=\"data row57 col0\" >2020-05-05</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow57_col1\" class=\"data row57 col1\" >1221.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow57_col2\" class=\"data row57 col2\" >195.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow57_col3\" class=\"data row57 col3\" >32211.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row58\" class=\"row_heading level0 row58\" >127</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow58_col0\" class=\"data row58 col0\" >2020-05-06</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow58_col1\" class=\"data row58 col1\" >1075.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow58_col2\" class=\"data row58 col2\" >236.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow58_col3\" class=\"data row58 col3\" >37771.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row59\" class=\"row_heading level0 row59\" >128</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow59_col0\" class=\"data row59 col0\" >2020-05-07</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow59_col1\" class=\"data row59 col1\" >1444.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow59_col2\" class=\"data row59 col2\" >369.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow59_col3\" class=\"data row59 col3\" >13665.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row60\" class=\"row_heading level0 row60\" >129</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow60_col0\" class=\"data row60 col0\" >2020-05-08</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow60_col1\" class=\"data row60 col1\" >1401.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow60_col2\" class=\"data row60 col2\" >274.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow60_col3\" class=\"data row60 col3\" >45428.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row61\" class=\"row_heading level0 row61\" >130</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow61_col0\" class=\"data row61 col0\" >2020-05-09</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow61_col1\" class=\"data row61 col1\" >1327.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow61_col2\" class=\"data row61 col2\" >243.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow61_col3\" class=\"data row61 col3\" >36091.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row62\" class=\"row_heading level0 row62\" >131</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow62_col0\" class=\"data row62 col0\" >2020-05-10</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow62_col1\" class=\"data row62 col1\" >1083.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow62_col2\" class=\"data row62 col2\" >194.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow62_col3\" class=\"data row62 col3\" >31384.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row63\" class=\"row_heading level0 row63\" >134</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow63_col0\" class=\"data row63 col0\" >2020-05-13</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow63_col1\" class=\"data row63 col1\" >1402.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow63_col2\" class=\"data row63 col2\" >172.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow63_col3\" class=\"data row63 col3\" >37049.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row64\" class=\"row_heading level0 row64\" >236</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow64_col0\" class=\"data row64 col0\" >2020-08-23</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow64_col1\" class=\"data row64 col1\" >1071.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow64_col2\" class=\"data row64 col2\" >3.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow64_col3\" class=\"data row64 col3\" >47463.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row65\" class=\"row_heading level0 row65\" >237</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow65_col0\" class=\"data row65 col0\" >2020-08-24</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow65_col1\" class=\"data row65 col1\" >1209.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow65_col2\" class=\"data row65 col2\" >7.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow65_col3\" class=\"data row65 col3\" >33358.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row66\" class=\"row_heading level0 row66\" >240</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow66_col0\" class=\"data row66 col0\" >2020-08-27</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow66_col1\" class=\"data row66 col1\" >1366.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow66_col2\" class=\"data row66 col2\" >13.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow66_col3\" class=\"data row66 col3\" >57640.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row67\" class=\"row_heading level0 row67\" >241</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow67_col0\" class=\"data row67 col0\" >2020-08-28</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow67_col1\" class=\"data row67 col1\" >1409.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow67_col2\" class=\"data row67 col2\" >5.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow67_col3\" class=\"data row67 col3\" >65135.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row68\" class=\"row_heading level0 row68\" >242</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow68_col0\" class=\"data row68 col0\" >2020-08-29</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow68_col1\" class=\"data row68 col1\" >1460.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow68_col2\" class=\"data row68 col2\" >9.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow68_col3\" class=\"data row68 col3\" >64294.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row69\" class=\"row_heading level0 row69\" >243</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow69_col0\" class=\"data row69 col0\" >2020-08-30</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow69_col1\" class=\"data row69 col1\" >1444.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow69_col2\" class=\"data row69 col2\" >1.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow69_col3\" class=\"data row69 col3\" >53541.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row70\" class=\"row_heading level0 row70\" >244</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow70_col0\" class=\"data row70 col0\" >2020-08-31</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow70_col1\" class=\"data row70 col1\" >1365.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow70_col2\" class=\"data row70 col2\" >4.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow70_col3\" class=\"data row70 col3\" >42583.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddlevel0_row71\" class=\"row_heading level0 row71\" >247</th>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow71_col0\" class=\"data row71 col0\" >2020-09-03</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow71_col1\" class=\"data row71 col1\" >1326.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow71_col2\" class=\"data row71 col2\" >6.000000</td>\n",
       "                        <td id=\"T_d0753cea_fc9f_11ea_98db_55d2441bb4ddrow71_col3\" class=\"data row71 col3\" >nan</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f4dfccdc850>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_cases_df.head(200).style.background_gradient(cmap = 'Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05206657403227681"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>positive_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  new_cases  new_deaths  new_tests  positive_rate\n",
       "0  2019-12-31        0.0         0.0        NaN            NaN\n",
       "1  2020-01-01        0.0         0.0        NaN            NaN\n",
       "2  2020-01-02        0.0         0.0        NaN            NaN\n",
       "3  2020-01-03        0.0         0.0        NaN            NaN\n",
       "4  2020-01-04        0.0         0.0        NaN            NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here, we are adding a column 'positive_rate' in the covid_df data frame.\n",
    "\n",
    "covid_df[\"positive_rate\"] = covid_df.new_cases / covid_df.new_tests\n",
    "covid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df.drop(columns = ['positive_rate'], inplace = True)\n",
    "\n",
    "# inplace = True  is used to replace the existing dataframe i.e. covid_df after removing the column positive_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>996.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>975.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  new_cases  new_deaths  new_tests\n",
       "0    2019-12-31        0.0         0.0        NaN\n",
       "1    2020-01-01        0.0         0.0        NaN\n",
       "2    2020-01-02        0.0         0.0        NaN\n",
       "3    2020-01-03        0.0         0.0        NaN\n",
       "4    2020-01-04        0.0         0.0        NaN\n",
       "..          ...        ...         ...        ...\n",
       "243  2020-08-30     1444.0         1.0    53541.0\n",
       "244  2020-08-31     1365.0         4.0    42583.0\n",
       "245  2020-09-01      996.0         6.0    54395.0\n",
       "246  2020-09-02      975.0         8.0        NaN\n",
       "247  2020-09-03     1326.0         6.0        NaN\n",
       "\n",
       "[248 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting rows using column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>6557.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>6153.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>5986.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>5974.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>5959.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>5322.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>5249.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>5217.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>5210.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  new_cases  new_deaths  new_tests\n",
       "82  2020-03-22     6557.0       795.0        NaN\n",
       "87  2020-03-27     6153.0       660.0        NaN\n",
       "81  2020-03-21     5986.0       625.0        NaN\n",
       "89  2020-03-29     5974.0       887.0        NaN\n",
       "88  2020-03-28     5959.0       971.0        NaN\n",
       "83  2020-03-23     5560.0       649.0        NaN\n",
       "80  2020-03-20     5322.0       429.0        NaN\n",
       "85  2020-03-25     5249.0       743.0        NaN\n",
       "90  2020-03-30     5217.0       758.0        NaN\n",
       "86  2020-03-26     5210.0       685.0        NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.sort_values('new_cases', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>5959.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>5974.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>4053.0</td>\n",
       "      <td>839.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>4050.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>6557.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>4585.0</td>\n",
       "      <td>764.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>5217.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>5249.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>4782.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  new_cases  new_deaths  new_tests\n",
       "88  2020-03-28     5959.0       971.0        NaN\n",
       "89  2020-03-29     5974.0       887.0        NaN\n",
       "92  2020-04-01     4053.0       839.0        NaN\n",
       "91  2020-03-31     4050.0       810.0        NaN\n",
       "82  2020-03-22     6557.0       795.0        NaN\n",
       "95  2020-04-04     4585.0       764.0        NaN\n",
       "94  2020-04-03     4668.0       760.0        NaN\n",
       "90  2020-03-30     5217.0       758.0        NaN\n",
       "85  2020-03-25     5249.0       743.0        NaN\n",
       "93  2020-04-02     4782.0       727.0        NaN"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.sort_values('new_deaths', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that daily deaths hit a peak just about week after the peak in the daily new cases.\n",
    "\n",
    "Let's also look at the days with the least number of cases. We might expect to see the first few days of the year in this list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>-148.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>29875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  new_cases  new_deaths  new_tests\n",
       "172  2020-06-20     -148.0        47.0    29875.0\n",
       "0    2019-12-31        0.0         0.0        NaN\n",
       "29   2020-01-29        0.0         0.0        NaN\n",
       "30   2020-01-30        0.0         0.0        NaN\n",
       "32   2020-02-01        0.0         0.0        NaN\n",
       "33   2020-02-02        0.0         0.0        NaN\n",
       "34   2020-02-03        0.0         0.0        NaN\n",
       "36   2020-02-05        0.0         0.0        NaN\n",
       "37   2020-02-06        0.0         0.0        NaN\n",
       "38   2020-02-07        0.0         0.0        NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.sort_values(\"new_cases\").head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>301.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>210.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>328.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>32921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>331.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>28570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>-148.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>29875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>264.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>24581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>224.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>221.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23225.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  new_cases  new_deaths  new_tests\n",
       "168  2020-06-16      301.0        26.0    27762.0\n",
       "169  2020-06-17      210.0        34.0    33957.0\n",
       "170  2020-06-18      328.0        43.0    32921.0\n",
       "171  2020-06-19      331.0        66.0    28570.0\n",
       "172  2020-06-20     -148.0        47.0    29875.0\n",
       "173  2020-06-21      264.0        49.0    24581.0\n",
       "174  2020-06-22      224.0        24.0    16152.0\n",
       "175  2020-06-23      221.0        23.0    23225.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.loc[168:175]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this was indeed a data entry error, we can use one of the following approaches for dealing with the missing or faulty value:\n",
    "\n",
    "    Replace it with 0.\n",
    "    Repalce it with the average of the entire column\n",
    "    Replace it with the average of the values on the previous & next date\n",
    "    Discard the row entirely\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>328.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>32921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>331.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>28570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>297.5</td>\n",
       "      <td>47.0</td>\n",
       "      <td>29875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>264.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>24581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>224.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16152.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  new_cases  new_deaths  new_tests\n",
       "170  2020-06-18      328.0        43.0    32921.0\n",
       "171  2020-06-19      331.0        66.0    28570.0\n",
       "172  2020-06-20      297.5        47.0    29875.0\n",
       "173  2020-06-21      264.0        49.0    24581.0\n",
       "174  2020-06-22      224.0        24.0    16152.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.at[172, 'new_cases'] = (covid_df.at[171, 'new_cases'] + covid_df.at[173, 'new_cases']) / 2\n",
    "covid_df.loc[170:174]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2019-12-31\n",
       "1      2020-01-01\n",
       "2      2020-01-02\n",
       "3      2020-01-03\n",
       "4      2020-01-04\n",
       "          ...    \n",
       "243    2020-08-30\n",
       "244    2020-08-31\n",
       "245    2020-09-01\n",
       "246    2020-09-02\n",
       "247    2020-09-03\n",
       "Name: date, Length: 248, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2019-12-31\n",
       "1     2020-01-01\n",
       "2     2020-01-02\n",
       "3     2020-01-03\n",
       "4     2020-01-04\n",
       "         ...    \n",
       "243   2020-08-30\n",
       "244   2020-08-31\n",
       "245   2020-09-01\n",
       "246   2020-09-02\n",
       "247   2020-09-03\n",
       "Name: date, Length: 248, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice that the dtype is object. This means that pandas don't know that this column contains dates.\n",
    "# So, we'll change this column dtype to datetime:\n",
    "\n",
    "covid_df['date'] = pd.to_datetime(covid_df.date)\n",
    "covid_df.date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that it now has the datatype datetime64. We can now extract different parts of the data into separate columns, using the DatetimeIndex class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df['year'] = pd.DatetimeIndex(covid_df.date).year\n",
    "covid_df['month'] = pd.DatetimeIndex(covid_df.date).month\n",
    "covid_df['day'] = pd.DatetimeIndex(covid_df.date).day\n",
    "covid_df['weekday'] = pd.DatetimeIndex(covid_df.date).weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53541.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42583.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>996.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54395.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>975.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  new_cases  new_deaths  new_tests  year  month  day  weekday\n",
       "0   2019-12-31        0.0         0.0        NaN  2019     12   31        1\n",
       "1   2020-01-01        0.0         0.0        NaN  2020      1    1        2\n",
       "2   2020-01-02        0.0         0.0        NaN  2020      1    2        3\n",
       "3   2020-01-03        0.0         0.0        NaN  2020      1    3        4\n",
       "4   2020-01-04        0.0         0.0        NaN  2020      1    4        5\n",
       "..         ...        ...         ...        ...   ...    ...  ...      ...\n",
       "243 2020-08-30     1444.0         1.0    53541.0  2020      8   30        6\n",
       "244 2020-08-31     1365.0         4.0    42583.0  2020      8   31        0\n",
       "245 2020-09-01      996.0         6.0    54395.0  2020      9    1        1\n",
       "246 2020-09-02      975.0         8.0        NaN  2020      9    2        2\n",
       "247 2020-09-03     1326.0         6.0        NaN  2020      9    3        3\n",
       "\n",
       "[248 rows x 8 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
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       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>43732.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2020-05-02</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>31231.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>27047.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>1389.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>22999.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>32211.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>37771.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>13665.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>45428.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>1327.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>36091.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>1083.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>31384.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>802.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>25823.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2020-05-12</td>\n",
       "      <td>744.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>39620.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2020-05-13</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>37049.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2020-05-14</td>\n",
       "      <td>888.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>41131.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>992.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>39027.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2020-05-16</td>\n",
       "      <td>789.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>40657.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2020-05-17</td>\n",
       "      <td>875.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>33505.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2020-05-18</td>\n",
       "      <td>675.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>26101.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2020-05-19</td>\n",
       "      <td>451.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>40226.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2020-05-20</td>\n",
       "      <td>813.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>38617.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>665.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>40644.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2020-05-22</td>\n",
       "      <td>642.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>42987.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2020-05-23</td>\n",
       "      <td>652.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>42579.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>669.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>34206.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2020-05-25</td>\n",
       "      <td>531.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20676.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>300.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>33944.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>397.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37299.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>584.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>39838.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>593.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>38233.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>516.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>36051.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>416.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>28948.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  new_cases  new_deaths  new_tests  year  month  day  weekday\n",
       "122 2020-05-01     1872.0       285.0    43732.0  2020      5    1        4\n",
       "123 2020-05-02     1965.0       269.0    31231.0  2020      5    2        5\n",
       "124 2020-05-03     1900.0       474.0    27047.0  2020      5    3        6\n",
       "125 2020-05-04     1389.0       174.0    22999.0  2020      5    4        0\n",
       "126 2020-05-05     1221.0       195.0    32211.0  2020      5    5        1\n",
       "127 2020-05-06     1075.0       236.0    37771.0  2020      5    6        2\n",
       "128 2020-05-07     1444.0       369.0    13665.0  2020      5    7        3\n",
       "129 2020-05-08     1401.0       274.0    45428.0  2020      5    8        4\n",
       "130 2020-05-09     1327.0       243.0    36091.0  2020      5    9        5\n",
       "131 2020-05-10     1083.0       194.0    31384.0  2020      5   10        6\n",
       "132 2020-05-11      802.0       165.0    25823.0  2020      5   11        0\n",
       "133 2020-05-12      744.0       179.0    39620.0  2020      5   12        1\n",
       "134 2020-05-13     1402.0       172.0    37049.0  2020      5   13        2\n",
       "135 2020-05-14      888.0       195.0    41131.0  2020      5   14        3\n",
       "136 2020-05-15      992.0       262.0    39027.0  2020      5   15        4\n",
       "137 2020-05-16      789.0       242.0    40657.0  2020      5   16        5\n",
       "138 2020-05-17      875.0       153.0    33505.0  2020      5   17        6\n",
       "139 2020-05-18      675.0       145.0    26101.0  2020      5   18        0\n",
       "140 2020-05-19      451.0        99.0    40226.0  2020      5   19        1\n",
       "141 2020-05-20      813.0       162.0    38617.0  2020      5   20        2\n",
       "142 2020-05-21      665.0       161.0    40644.0  2020      5   21        3\n",
       "143 2020-05-22      642.0       156.0    42987.0  2020      5   22        4\n",
       "144 2020-05-23      652.0       130.0    42579.0  2020      5   23        5\n",
       "145 2020-05-24      669.0       119.0    34206.0  2020      5   24        6\n",
       "146 2020-05-25      531.0        50.0    20676.0  2020      5   25        0\n",
       "147 2020-05-26      300.0        92.0    33944.0  2020      5   26        1\n",
       "148 2020-05-27      397.0        78.0    37299.0  2020      5   27        2\n",
       "149 2020-05-28      584.0       117.0    39838.0  2020      5   28        3\n",
       "150 2020-05-29      593.0        70.0    38233.0  2020      5   29        4\n",
       "151 2020-05-30      516.0        87.0    36051.0  2020      5   30        5\n",
       "152 2020-05-31      416.0       111.0    28948.0  2020      5   31        6"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df_may = covid_df[covid_df.month == 5]\n",
    "covid_df_may"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1872.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>43732.0</td>\n",
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       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1965.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>31231.0</td>\n",
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       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1900.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>27047.0</td>\n",
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       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1389.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>22999.0</td>\n",
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       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1221.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>32211.0</td>\n",
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       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1075.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>37771.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1444.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>13665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1401.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>45428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1327.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>36091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1083.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>31384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>802.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>25823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>744.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>39620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1402.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>37049.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>888.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>41131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>992.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>39027.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>789.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>40657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>875.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>33505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>675.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>26101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>451.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>40226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>813.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>38617.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>665.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>40644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>642.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>42987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>652.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>42579.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>669.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>34206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>531.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>300.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>33944.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>397.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>584.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>39838.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>593.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>38233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>516.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>36051.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>416.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>28948.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     new_cases  new_deaths  new_tests\n",
       "122     1872.0       285.0    43732.0\n",
       "123     1965.0       269.0    31231.0\n",
       "124     1900.0       474.0    27047.0\n",
       "125     1389.0       174.0    22999.0\n",
       "126     1221.0       195.0    32211.0\n",
       "127     1075.0       236.0    37771.0\n",
       "128     1444.0       369.0    13665.0\n",
       "129     1401.0       274.0    45428.0\n",
       "130     1327.0       243.0    36091.0\n",
       "131     1083.0       194.0    31384.0\n",
       "132      802.0       165.0    25823.0\n",
       "133      744.0       179.0    39620.0\n",
       "134     1402.0       172.0    37049.0\n",
       "135      888.0       195.0    41131.0\n",
       "136      992.0       262.0    39027.0\n",
       "137      789.0       242.0    40657.0\n",
       "138      875.0       153.0    33505.0\n",
       "139      675.0       145.0    26101.0\n",
       "140      451.0        99.0    40226.0\n",
       "141      813.0       162.0    38617.0\n",
       "142      665.0       161.0    40644.0\n",
       "143      642.0       156.0    42987.0\n",
       "144      652.0       130.0    42579.0\n",
       "145      669.0       119.0    34206.0\n",
       "146      531.0        50.0    20676.0\n",
       "147      300.0        92.0    33944.0\n",
       "148      397.0        78.0    37299.0\n",
       "149      584.0       117.0    39838.0\n",
       "150      593.0        70.0    38233.0\n",
       "151      516.0        87.0    36051.0\n",
       "152      416.0       111.0    28948.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df_may_metrics = covid_df_may[[\"new_cases\", \"new_deaths\", \"new_tests\"]]\n",
    "covid_df_may_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "new_cases       29073.0\n",
       "new_deaths       5658.0\n",
       "new_tests     1078720.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_may_totals = covid_df_may_metrics.sum()\n",
    "covid_may_totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(covid_may_totals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "new_cases       29073.0\n",
       "new_deaths       5658.0\n",
       "new_tests     1078720.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The operations above can also be combined into a single statement\n",
    "\n",
    "covid_df[covid_df.month == 5][[\"new_cases\", \"new_deaths\", \"new_tests\"]].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1189.7142857142858"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df[covid_df.weekday == 4].new_cases.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping and aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f4df85cb6a0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_month = covid_df.groupby('month')\n",
    "covid_month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to aggregate after grouping. Let's aggregate using sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>885.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100851.0</td>\n",
       "      <td>11570.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101852.0</td>\n",
       "      <td>16091.0</td>\n",
       "      <td>419591.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>5658.0</td>\n",
       "      <td>1078720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8217.5</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>830354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6722.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>797692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21060.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>1098704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3297.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>54395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       new_cases  new_deaths  new_tests\n",
       "month                                  \n",
       "1            3.0         0.0        0.0\n",
       "2          885.0        21.0        0.0\n",
       "3       100851.0     11570.0        0.0\n",
       "4       101852.0     16091.0   419591.0\n",
       "5        29073.0      5658.0  1078720.0\n",
       "6         8217.5      1404.0   830354.0\n",
       "7         6722.0       388.0   797692.0\n",
       "8        21060.0       345.0  1098704.0\n",
       "9         3297.0        20.0    54395.0\n",
       "12           0.0         0.0        0.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_month_df = covid_df.groupby('month')[['new_cases', 'new_deaths', 'new_tests']].sum()\n",
    "covid_month_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1109.142857</td>\n",
       "      <td>124.800000</td>\n",
       "      <td>19826.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>918.638889</td>\n",
       "      <td>129.944444</td>\n",
       "      <td>31166.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>927.305556</td>\n",
       "      <td>144.750000</td>\n",
       "      <td>34665.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1095.666667</td>\n",
       "      <td>141.472222</td>\n",
       "      <td>34488.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1189.714286</td>\n",
       "      <td>144.885714</td>\n",
       "      <td>39225.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1198.528571</td>\n",
       "      <td>157.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1247.257143</td>\n",
       "      <td>159.457143</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           new_cases  new_deaths     new_tests\n",
       "weekday                                       \n",
       "0        1109.142857  124.800000  19826.700000\n",
       "1         918.638889  129.944444  31166.900000\n",
       "2         927.305556  144.750000  34665.210526\n",
       "3        1095.666667  141.472222  34488.578947\n",
       "4        1189.714286  144.885714  39225.684211\n",
       "5        1198.528571  157.000000  34589.473684\n",
       "6        1247.257143  159.457143  28588.105263"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_month_mean_df = covid_df.groupby('weekday')[['new_cases', 'new_deaths', 'new_tests']].mean()\n",
    "covid_month_mean_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df['total_cases'] = covid_df.new_cases.cumsum()\n",
    "covid_df['total_deaths'] = covid_df.new_deaths.cumsum()\n",
    "covid_df['total_tests'] = covid_df.new_tests.cumsum() + 935310"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_tests</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Italy</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53541.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>267298.5</td>\n",
       "      <td>35473.0</td>\n",
       "      <td>5117788.0</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42583.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>268663.5</td>\n",
       "      <td>35477.0</td>\n",
       "      <td>5160371.0</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>996.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54395.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>269659.5</td>\n",
       "      <td>35483.0</td>\n",
       "      <td>5214766.0</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>975.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>270634.5</td>\n",
       "      <td>35491.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>271960.5</td>\n",
       "      <td>35497.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  new_cases  new_deaths  new_tests  year  month  day  weekday  \\\n",
       "0   2019-12-31        0.0         0.0        NaN  2019     12   31        1   \n",
       "1   2020-01-01        0.0         0.0        NaN  2020      1    1        2   \n",
       "2   2020-01-02        0.0         0.0        NaN  2020      1    2        3   \n",
       "3   2020-01-03        0.0         0.0        NaN  2020      1    3        4   \n",
       "4   2020-01-04        0.0         0.0        NaN  2020      1    4        5   \n",
       "..         ...        ...         ...        ...   ...    ...  ...      ...   \n",
       "243 2020-08-30     1444.0         1.0    53541.0  2020      8   30        6   \n",
       "244 2020-08-31     1365.0         4.0    42583.0  2020      8   31        0   \n",
       "245 2020-09-01      996.0         6.0    54395.0  2020      9    1        1   \n",
       "246 2020-09-02      975.0         8.0        NaN  2020      9    2        2   \n",
       "247 2020-09-03     1326.0         6.0        NaN  2020      9    3        3   \n",
       "\n",
       "     total_cases  total_deaths  total_tests location  \n",
       "0            0.0           0.0          NaN    Italy  \n",
       "1            0.0           0.0          NaN    Italy  \n",
       "2            0.0           0.0          NaN    Italy  \n",
       "3            0.0           0.0          NaN    Italy  \n",
       "4            0.0           0.0          NaN    Italy  \n",
       "..           ...           ...          ...      ...  \n",
       "243     267298.5       35473.0    5117788.0    Italy  \n",
       "244     268663.5       35477.0    5160371.0    Italy  \n",
       "245     269659.5       35483.0    5214766.0    Italy  \n",
       "246     270634.5       35491.0          NaN    Italy  \n",
       "247     271960.5       35497.0          NaN    Italy  \n",
       "\n",
       "[248 rows x 12 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging data from multiple sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('location.csv', <http.client.HTTPMessage at 0x7f4dfc6d9790>)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from urllib.request import urlretrieve\n",
    "urlretrieve(\"https://hub.jovian.ml/wp-content/uploads/2020/09/locations.csv\", \"location.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>location</th>\n",
       "      <th>continent</th>\n",
       "      <th>population</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3.892834e+07</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1803.987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2.877800e+06</td>\n",
       "      <td>78.57</td>\n",
       "      <td>2.890</td>\n",
       "      <td>11803.431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>4.385104e+07</td>\n",
       "      <td>76.88</td>\n",
       "      <td>1.900</td>\n",
       "      <td>13913.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>7.726500e+04</td>\n",
       "      <td>83.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>3.286627e+07</td>\n",
       "      <td>61.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5819.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2.982597e+07</td>\n",
       "      <td>66.12</td>\n",
       "      <td>0.700</td>\n",
       "      <td>1479.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1.838396e+07</td>\n",
       "      <td>63.89</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3689.251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1.486293e+07</td>\n",
       "      <td>61.49</td>\n",
       "      <td>1.700</td>\n",
       "      <td>1899.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>World</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.794799e+09</td>\n",
       "      <td>72.58</td>\n",
       "      <td>2.705</td>\n",
       "      <td>15469.207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>International</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>212 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          location continent    population  life_expectancy  \\\n",
       "0      Afghanistan      Asia  3.892834e+07            64.83   \n",
       "1          Albania    Europe  2.877800e+06            78.57   \n",
       "2          Algeria    Africa  4.385104e+07            76.88   \n",
       "3          Andorra    Europe  7.726500e+04            83.73   \n",
       "4           Angola    Africa  3.286627e+07            61.15   \n",
       "..             ...       ...           ...              ...   \n",
       "207          Yemen      Asia  2.982597e+07            66.12   \n",
       "208         Zambia    Africa  1.838396e+07            63.89   \n",
       "209       Zimbabwe    Africa  1.486293e+07            61.49   \n",
       "210          World       NaN  7.794799e+09            72.58   \n",
       "211  International       NaN           NaN              NaN   \n",
       "\n",
       "     hospital_beds_per_thousand  gdp_per_capita  \n",
       "0                         0.500        1803.987  \n",
       "1                         2.890       11803.431  \n",
       "2                         1.900       13913.839  \n",
       "3                           NaN             NaN  \n",
       "4                           NaN        5819.495  \n",
       "..                          ...             ...  \n",
       "207                       0.700        1479.147  \n",
       "208                       2.000        3689.251  \n",
       "209                       1.700        1899.775  \n",
       "210                       2.705       15469.207  \n",
       "211                         NaN             NaN  \n",
       "\n",
       "[212 rows x 6 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_df = pd.read_csv(\"location.csv\")\n",
    "location_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>location</th>\n",
       "      <th>continent</th>\n",
       "      <th>population</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   location continent  population  life_expectancy  \\\n",
       "97    Italy    Europe  60461828.0            83.51   \n",
       "\n",
       "    hospital_beds_per_thousand  gdp_per_capita  \n",
       "97                        3.18       35220.084  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_df[location_df.location == \"Italy\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_tests</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53541.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>267298.5</td>\n",
       "      <td>35473.0</td>\n",
       "      <td>5117788.0</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42583.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>268663.5</td>\n",
       "      <td>35477.0</td>\n",
       "      <td>5160371.0</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>996.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54395.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>269659.5</td>\n",
       "      <td>35483.0</td>\n",
       "      <td>5214766.0</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>975.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>270634.5</td>\n",
       "      <td>35491.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>271960.5</td>\n",
       "      <td>35497.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  new_cases  new_deaths  new_tests  year  month  day  weekday  \\\n",
       "0   2019-12-31        0.0         0.0        NaN  2019     12   31        1   \n",
       "1   2020-01-01        0.0         0.0        NaN  2020      1    1        2   \n",
       "2   2020-01-02        0.0         0.0        NaN  2020      1    2        3   \n",
       "3   2020-01-03        0.0         0.0        NaN  2020      1    3        4   \n",
       "4   2020-01-04        0.0         0.0        NaN  2020      1    4        5   \n",
       "..         ...        ...         ...        ...   ...    ...  ...      ...   \n",
       "243 2020-08-30     1444.0         1.0    53541.0  2020      8   30        6   \n",
       "244 2020-08-31     1365.0         4.0    42583.0  2020      8   31        0   \n",
       "245 2020-09-01      996.0         6.0    54395.0  2020      9    1        1   \n",
       "246 2020-09-02      975.0         8.0        NaN  2020      9    2        2   \n",
       "247 2020-09-03     1326.0         6.0        NaN  2020      9    3        3   \n",
       "\n",
       "     total_cases  total_deaths  total_tests location  \n",
       "0            0.0           0.0          NaN    Italy  \n",
       "1            0.0           0.0          NaN    Italy  \n",
       "2            0.0           0.0          NaN    Italy  \n",
       "3            0.0           0.0          NaN    Italy  \n",
       "4            0.0           0.0          NaN    Italy  \n",
       "..           ...           ...          ...      ...  \n",
       "243     267298.5       35473.0    5117788.0    Italy  \n",
       "244     268663.5       35477.0    5160371.0    Italy  \n",
       "245     269659.5       35483.0    5214766.0    Italy  \n",
       "246     270634.5       35491.0          NaN    Italy  \n",
       "247     271960.5       35497.0          NaN    Italy  \n",
       "\n",
       "[248 rows x 12 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df['location'] = 'Italy'\n",
    "covid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_tests</th>\n",
       "      <th>location</th>\n",
       "      <th>continent</th>\n",
       "      <th>population</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53541.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>267298.5</td>\n",
       "      <td>35473.0</td>\n",
       "      <td>5117788.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42583.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>268663.5</td>\n",
       "      <td>35477.0</td>\n",
       "      <td>5160371.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>996.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54395.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>269659.5</td>\n",
       "      <td>35483.0</td>\n",
       "      <td>5214766.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>975.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>270634.5</td>\n",
       "      <td>35491.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>271960.5</td>\n",
       "      <td>35497.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  new_cases  new_deaths  new_tests  year  month  day  weekday  \\\n",
       "0   2019-12-31        0.0         0.0        NaN  2019     12   31        1   \n",
       "1   2020-01-01        0.0         0.0        NaN  2020      1    1        2   \n",
       "2   2020-01-02        0.0         0.0        NaN  2020      1    2        3   \n",
       "3   2020-01-03        0.0         0.0        NaN  2020      1    3        4   \n",
       "4   2020-01-04        0.0         0.0        NaN  2020      1    4        5   \n",
       "..         ...        ...         ...        ...   ...    ...  ...      ...   \n",
       "243 2020-08-30     1444.0         1.0    53541.0  2020      8   30        6   \n",
       "244 2020-08-31     1365.0         4.0    42583.0  2020      8   31        0   \n",
       "245 2020-09-01      996.0         6.0    54395.0  2020      9    1        1   \n",
       "246 2020-09-02      975.0         8.0        NaN  2020      9    2        2   \n",
       "247 2020-09-03     1326.0         6.0        NaN  2020      9    3        3   \n",
       "\n",
       "     total_cases  total_deaths  total_tests location continent  population  \\\n",
       "0            0.0           0.0          NaN    Italy    Europe  60461828.0   \n",
       "1            0.0           0.0          NaN    Italy    Europe  60461828.0   \n",
       "2            0.0           0.0          NaN    Italy    Europe  60461828.0   \n",
       "3            0.0           0.0          NaN    Italy    Europe  60461828.0   \n",
       "4            0.0           0.0          NaN    Italy    Europe  60461828.0   \n",
       "..           ...           ...          ...      ...       ...         ...   \n",
       "243     267298.5       35473.0    5117788.0    Italy    Europe  60461828.0   \n",
       "244     268663.5       35477.0    5160371.0    Italy    Europe  60461828.0   \n",
       "245     269659.5       35483.0    5214766.0    Italy    Europe  60461828.0   \n",
       "246     270634.5       35491.0          NaN    Italy    Europe  60461828.0   \n",
       "247     271960.5       35497.0          NaN    Italy    Europe  60461828.0   \n",
       "\n",
       "     life_expectancy  hospital_beds_per_thousand  gdp_per_capita  \n",
       "0              83.51                        3.18       35220.084  \n",
       "1              83.51                        3.18       35220.084  \n",
       "2              83.51                        3.18       35220.084  \n",
       "3              83.51                        3.18       35220.084  \n",
       "4              83.51                        3.18       35220.084  \n",
       "..               ...                         ...             ...  \n",
       "243            83.51                        3.18       35220.084  \n",
       "244            83.51                        3.18       35220.084  \n",
       "245            83.51                        3.18       35220.084  \n",
       "246            83.51                        3.18       35220.084  \n",
       "247            83.51                        3.18       35220.084  \n",
       "\n",
       "[248 rows x 17 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = covid_df.merge(location_df, on = \"location\")\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df['cases_per_thousand'] = merged_df.total_cases * 1e3 / merged_df.population\n",
    "merged_df['deaths_per_thousand'] = merged_df.total_deaths * 1e3 / merged_df.population\n",
    "merged_df['tests_per_thousand'] = merged_df.total_tests * 1e3 / merged_df.population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
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       "      <th>population</th>\n",
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       "      <th>gdp_per_capita</th>\n",
       "      <th>cases_per_thousand</th>\n",
       "      <th>deaths_per_thousand</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2020-01-01</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>60461828.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
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       "      <td>2020-01-04</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53541.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>267298.5</td>\n",
       "      <td>35473.0</td>\n",
       "      <td>5117788.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>4.420946</td>\n",
       "      <td>0.586701</td>\n",
       "      <td>84.644943</td>\n",
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       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42583.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>268663.5</td>\n",
       "      <td>35477.0</td>\n",
       "      <td>5160371.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>4.443523</td>\n",
       "      <td>0.586767</td>\n",
       "      <td>85.349239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>996.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54395.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>269659.5</td>\n",
       "      <td>35483.0</td>\n",
       "      <td>5214766.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>4.459996</td>\n",
       "      <td>0.586866</td>\n",
       "      <td>86.248897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>975.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>270634.5</td>\n",
       "      <td>35491.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>4.476122</td>\n",
       "      <td>0.586998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>271960.5</td>\n",
       "      <td>35497.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>4.498053</td>\n",
       "      <td>0.587098</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  new_cases  new_deaths  new_tests  year  month  day  weekday  \\\n",
       "0   2019-12-31        0.0         0.0        NaN  2019     12   31        1   \n",
       "1   2020-01-01        0.0         0.0        NaN  2020      1    1        2   \n",
       "2   2020-01-02        0.0         0.0        NaN  2020      1    2        3   \n",
       "3   2020-01-03        0.0         0.0        NaN  2020      1    3        4   \n",
       "4   2020-01-04        0.0         0.0        NaN  2020      1    4        5   \n",
       "..         ...        ...         ...        ...   ...    ...  ...      ...   \n",
       "243 2020-08-30     1444.0         1.0    53541.0  2020      8   30        6   \n",
       "244 2020-08-31     1365.0         4.0    42583.0  2020      8   31        0   \n",
       "245 2020-09-01      996.0         6.0    54395.0  2020      9    1        1   \n",
       "246 2020-09-02      975.0         8.0        NaN  2020      9    2        2   \n",
       "247 2020-09-03     1326.0         6.0        NaN  2020      9    3        3   \n",
       "\n",
       "     total_cases  total_deaths  total_tests location continent  population  \\\n",
       "0            0.0           0.0          NaN    Italy    Europe  60461828.0   \n",
       "1            0.0           0.0          NaN    Italy    Europe  60461828.0   \n",
       "2            0.0           0.0          NaN    Italy    Europe  60461828.0   \n",
       "3            0.0           0.0          NaN    Italy    Europe  60461828.0   \n",
       "4            0.0           0.0          NaN    Italy    Europe  60461828.0   \n",
       "..           ...           ...          ...      ...       ...         ...   \n",
       "243     267298.5       35473.0    5117788.0    Italy    Europe  60461828.0   \n",
       "244     268663.5       35477.0    5160371.0    Italy    Europe  60461828.0   \n",
       "245     269659.5       35483.0    5214766.0    Italy    Europe  60461828.0   \n",
       "246     270634.5       35491.0          NaN    Italy    Europe  60461828.0   \n",
       "247     271960.5       35497.0          NaN    Italy    Europe  60461828.0   \n",
       "\n",
       "     life_expectancy  hospital_beds_per_thousand  gdp_per_capita  \\\n",
       "0              83.51                        3.18       35220.084   \n",
       "1              83.51                        3.18       35220.084   \n",
       "2              83.51                        3.18       35220.084   \n",
       "3              83.51                        3.18       35220.084   \n",
       "4              83.51                        3.18       35220.084   \n",
       "..               ...                         ...             ...   \n",
       "243            83.51                        3.18       35220.084   \n",
       "244            83.51                        3.18       35220.084   \n",
       "245            83.51                        3.18       35220.084   \n",
       "246            83.51                        3.18       35220.084   \n",
       "247            83.51                        3.18       35220.084   \n",
       "\n",
       "     cases_per_thousand  deaths_per_thousand  tests_per_thousand  \n",
       "0              0.000000             0.000000                 NaN  \n",
       "1              0.000000             0.000000                 NaN  \n",
       "2              0.000000             0.000000                 NaN  \n",
       "3              0.000000             0.000000                 NaN  \n",
       "4              0.000000             0.000000                 NaN  \n",
       "..                  ...                  ...                 ...  \n",
       "243            4.420946             0.586701           84.644943  \n",
       "244            4.443523             0.586767           85.349239  \n",
       "245            4.459996             0.586866           86.248897  \n",
       "246            4.476122             0.586998                 NaN  \n",
       "247            4.498053             0.587098                 NaN  \n",
       "\n",
       "[248 rows x 20 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing data back to files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df = merged_df[[\n",
    "    'date',\n",
    "    'new_cases',\n",
    "    'total_cases',\n",
    "    'new_deaths',\n",
    "    'total_deaths',\n",
    "    'new_tests',\n",
    "    'total_tests',\n",
    "    'cases_per_thousand',\n",
    "    'deaths_per_thousand',\n",
    "    'tests_per_thousand',\n",
    "    'location',\n",
    "    'continent'\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\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>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>total_tests</th>\n",
       "      <th>cases_per_thousand</th>\n",
       "      <th>deaths_per_thousand</th>\n",
       "      <th>tests_per_thousand</th>\n",
       "      <th>location</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>267298.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35473.0</td>\n",
       "      <td>53541.0</td>\n",
       "      <td>5117788.0</td>\n",
       "      <td>4.420946</td>\n",
       "      <td>0.586701</td>\n",
       "      <td>84.644943</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>268663.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35477.0</td>\n",
       "      <td>42583.0</td>\n",
       "      <td>5160371.0</td>\n",
       "      <td>4.443523</td>\n",
       "      <td>0.586767</td>\n",
       "      <td>85.349239</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>996.0</td>\n",
       "      <td>269659.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35483.0</td>\n",
       "      <td>54395.0</td>\n",
       "      <td>5214766.0</td>\n",
       "      <td>4.459996</td>\n",
       "      <td>0.586866</td>\n",
       "      <td>86.248897</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>975.0</td>\n",
       "      <td>270634.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35491.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.476122</td>\n",
       "      <td>0.586998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>271960.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35497.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.498053</td>\n",
       "      <td>0.587098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  new_cases  total_cases  new_deaths  total_deaths  new_tests  \\\n",
       "0   2019-12-31        0.0          0.0         0.0           0.0        NaN   \n",
       "1   2020-01-01        0.0          0.0         0.0           0.0        NaN   \n",
       "2   2020-01-02        0.0          0.0         0.0           0.0        NaN   \n",
       "3   2020-01-03        0.0          0.0         0.0           0.0        NaN   \n",
       "4   2020-01-04        0.0          0.0         0.0           0.0        NaN   \n",
       "..         ...        ...          ...         ...           ...        ...   \n",
       "243 2020-08-30     1444.0     267298.5         1.0       35473.0    53541.0   \n",
       "244 2020-08-31     1365.0     268663.5         4.0       35477.0    42583.0   \n",
       "245 2020-09-01      996.0     269659.5         6.0       35483.0    54395.0   \n",
       "246 2020-09-02      975.0     270634.5         8.0       35491.0        NaN   \n",
       "247 2020-09-03     1326.0     271960.5         6.0       35497.0        NaN   \n",
       "\n",
       "     total_tests  cases_per_thousand  deaths_per_thousand  tests_per_thousand  \\\n",
       "0            NaN            0.000000             0.000000                 NaN   \n",
       "1            NaN            0.000000             0.000000                 NaN   \n",
       "2            NaN            0.000000             0.000000                 NaN   \n",
       "3            NaN            0.000000             0.000000                 NaN   \n",
       "4            NaN            0.000000             0.000000                 NaN   \n",
       "..           ...                 ...                  ...                 ...   \n",
       "243    5117788.0            4.420946             0.586701           84.644943   \n",
       "244    5160371.0            4.443523             0.586767           85.349239   \n",
       "245    5214766.0            4.459996             0.586866           86.248897   \n",
       "246          NaN            4.476122             0.586998                 NaN   \n",
       "247          NaN            4.498053             0.587098                 NaN   \n",
       "\n",
       "    location continent  \n",
       "0      Italy    Europe  \n",
       "1      Italy    Europe  \n",
       "2      Italy    Europe  \n",
       "3      Italy    Europe  \n",
       "4      Italy    Europe  \n",
       "..       ...       ...  \n",
       "243    Italy    Europe  \n",
       "244    Italy    Europe  \n",
       "245    Italy    Europe  \n",
       "246    Italy    Europe  \n",
       "247    Italy    Europe  \n",
       "\n",
       "[248 rows x 12 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df.to_csv('results.csv', index = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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