lesson4.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "17039a0c-37dd-4ce5-bf30-a1ab7c78ccb0",
   "metadata": {},
   "source": [
    "# LESSON 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b801cd6-70c8-4526-8960-d6a6e398bc48",
   "metadata": {},
   "source": [
    "## Reading csv files using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "144764f4-97b1-4ce7-94d9-fa6094534339",
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.request import urlretrieve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ddcb34b-da1f-4efe-9c10-cb9414f92cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('italy-covid-daywise.csv', <http.client.HTTPMessage at 0x210fbc63cc8>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "italy_covid_url = 'https://gist.githubusercontent.com/aakashns/f6a004fa20c84fec53262f9a8bfee775/raw/f309558b1cf5103424cef58e2ecb8704dcd4d74c/italy-covid-daywise.csv'\n",
    "\n",
    "urlretrieve(italy_covid_url, 'italy-covid-daywise.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8fd2d944-a4aa-42ec-95a5-8449aa213c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas --upgrade --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79614aee-2e68-478b-8076-4cf93a021081",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cc5e916a-f289-4d9d-a232-7fbaecbab943",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df = pd.read_csv('italy-covid-daywise.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ccf591cb-8b3b-423f-b6f9-b297acb29d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(covid_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5867ee83-c12b-45f1-a9df-14500134d6ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bdd7aa8f-cee9-4250-9460-97902d2214f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 248 entries, 0 to 247\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   date        248 non-null    object \n",
      " 1   new_cases   248 non-null    float64\n",
      " 2   new_deaths  248 non-null    float64\n",
      " 3   new_tests   135 non-null    float64\n",
      "dtypes: float64(3), object(1)\n",
      "memory usage: 7.9+ KB\n"
     ]
    }
   ],
   "source": [
    "covid_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0248215f-26de-41b6-93ff-b1aa95d74a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>135.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1094.818548</td>\n",
       "      <td>143.133065</td>\n",
       "      <td>31699.674074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1554.508002</td>\n",
       "      <td>227.105538</td>\n",
       "      <td>11622.209757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-148.000000</td>\n",
       "      <td>-31.000000</td>\n",
       "      <td>7841.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>123.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25259.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>342.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>29545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1371.750000</td>\n",
       "      <td>175.250000</td>\n",
       "      <td>37711.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6557.000000</td>\n",
       "      <td>971.000000</td>\n",
       "      <td>95273.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         new_cases  new_deaths     new_tests\n",
       "count   248.000000  248.000000    135.000000\n",
       "mean   1094.818548  143.133065  31699.674074\n",
       "std    1554.508002  227.105538  11622.209757\n",
       "min    -148.000000  -31.000000   7841.000000\n",
       "25%     123.000000    3.000000  25259.000000\n",
       "50%     342.000000   17.000000  29545.000000\n",
       "75%    1371.750000  175.250000  37711.000000\n",
       "max    6557.000000  971.000000  95273.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "855e5856-83c5-497e-9532-55d051daa0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'new_cases', 'new_deaths', 'new_tests'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37c4df1b-36d4-4f38-a6d3-5faf0f740f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6eb2546-335d-4bc9-84ea-aa4badf1f950",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install the library \n",
    "!pip install jovian --upgrade --quiet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a378837-ab8d-4b5d-98fa-7b38a3f943bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "if (window.IPython && IPython.notebook.kernel) IPython.notebook.kernel.execute('jovian.utils.jupyter.get_notebook_name_saved = lambda: \"' + IPython.notebook.notebook_name + '\"')"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c557a7ad-854b-4587-8d1f-7815db6e1d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Committed successfully! https://jovian.ai/thehimanshutiwari/lesson4\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://jovian.ai/thehimanshutiwari/lesson4'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jovian.commit(filename='lesson4.ipynb', envirnment=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "501572d8-62b9-46a4-b4f1-6205b4c474dd",
   "metadata": {},
   "source": [
    "## Retrieving data from a data frame\n",
    "\n",
    "The first thing you might want to do is retrieve data from this data frame, e.g., the counts of a specific day or the list of values in a particular column. To do this, it might help to understand the internal representation of data in a data frame. Conceptually, you can think of a dataframe as a dictionary of lists: keys are column names, and values are lists/arrays containing data for the respective columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2155131f-3583-4b53-a6f8-459f40478641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.0\n",
       "1         0.0\n",
       "2         0.0\n",
       "3         0.0\n",
       "4         0.0\n",
       "        ...  \n",
       "243    1444.0\n",
       "244    1365.0\n",
       "245     996.0\n",
       "246     975.0\n",
       "247    1326.0\n",
       "Name: new_cases, Length: 248, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df['new_cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "31a0b0a9-bfd2-407b-8563-a45449d10969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(covid_df['new_cases'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ef75d5-eb11-448d-a7fb-86ff7f5a45dd",
   "metadata": {},
   "source": [
    "Each column is represented using a data structure called `Series`, which is essentially a numpy array with some extra methods and properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "000ad85a-9f35-4f67-8876-e4e58b1f4e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "975.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df['new_cases'][246]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0fe19770-5bb1-42fb-ae50-0f38f844a748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "975.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.at[246, 'new_cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "27c674ad-9df0-46aa-94b5-f89f2cdb4e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0         0.0\n",
       "1         0.0\n",
       "2         0.0\n",
       "3         0.0\n",
       "4         0.0\n",
       "        ...  \n",
       "243    1444.0\n",
       "244    1365.0\n",
       "245     996.0\n",
       "246     975.0\n",
       "247    1326.0\n",
       "Name: new_cases, Length: 248, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.new_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cb26e1a7-6cc1-4505-b249-cfdf6ea608ae",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>1444.0</td>\n",
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       "    <tr>\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  new_cases  new_deaths  new_tests\n",
       "138  2020-05-17      875.0       153.0    33505.0\n",
       "139  2020-05-18      675.0       145.0    26101.0\n",
       "140  2020-05-19      451.0        99.0    40226.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.loc[138:140]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "25297148-1e1d-45cc-a54b-88eb45568574",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e42de821-0b06-42cb-8663-ed546f32bb20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Updating notebook \"thehimanshutiwari/lesson4\" on https://jovian.ai/\u001b[0m\n",
      "[jovian] Committed successfully! https://jovian.ai/thehimanshutiwari/lesson4\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://jovian.ai/thehimanshutiwari/lesson4'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jovian.commit(filename='lesson4.ipynb', envirnment=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85be335d-6c42-4067-81d9-4871b9b998e9",
   "metadata": {},
   "source": [
    "## Analyzing data from data frames\n",
    "\n",
    "Let's try to answer some questions about our data.\n",
    "\n",
    "**Q: What are the total number of reported cases and deaths related to Covid-19 in Italy?**\n",
    "\n",
    "Similar to Numpy arrays, a Pandas series supports the `sum` method to answer these questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "287ea379-212a-45d1-8e1d-137c8d122de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_cases = covid_df.new_cases.sum()\n",
    "total_deaths = covid_df.new_deaths.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3749ef15-e2a9-4fba-a328-0ac2208eaa7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of reported cases is 271515 and the number of reported deaths is 35497.\n"
     ]
    }
   ],
   "source": [
    "print('The number of reported cases is {} and the number of reported deaths is {}.'.format(int(total_cases), int(total_deaths)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea4424f5-ee7c-463c-ba7d-9c64abc82111",
   "metadata": {},
   "source": [
    "**Q: What is the overall death rate (ratio of reported deaths to reported cases)?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "042391cd-d1d4-43a2-9634-32bfa18839b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "death_rate = covid_df.new_deaths.sum() / covid_df.new_cases.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5e6c2f8c-417d-4f5b-b2f4-3626755585fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The overall reported death rate in Italy is 13.07 %.\n"
     ]
    }
   ],
   "source": [
    "print(\"The overall reported death rate in Italy is {:.2f} %.\".format(death_rate*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec3f68f-6e14-4dde-8b45-4c79a531da3a",
   "metadata": {},
   "source": [
    "**Q: What is the overall number of tests conducted? A total of 935310 tests were conducted before daily test numbers were reported.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e8c326ce-d06c-4554-a4e7-667e2d9600e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_tests = 935310\n",
    "total_tests = initial_tests + covid_df.new_tests.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2bf473dc-be1d-4cb2-af80-487815131979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5214766.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_tests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f19d4e7a-4769-4de4-af26-f27cb1b9a1ed",
   "metadata": {},
   "source": [
    "**Q: What fraction of tests returned a positive result?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "26eb75e2-bd51-4f40-b1d2-17d4377fe376",
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_rate = total_cases / total_tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a852bb35-eb8c-468d-b3ad-22dfa097ad8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.21% of tests in Italy led to a positive diagnosis.\n"
     ]
    }
   ],
   "source": [
    "print('{:.2f}% of tests in Italy led to a positive diagnosis.'.format(positive_rate*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2c3964fb-29a9-434a-8a37-d73856101f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5c90d2f1-e155-417c-bf36-2adb960f8e51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Updating notebook \"thehimanshutiwari/lesson4\" on https://jovian.ai/\u001b[0m\n",
      "[jovian] Committed successfully! https://jovian.ai/thehimanshutiwari/lesson4\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://jovian.ai/thehimanshutiwari/lesson4'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jovian.commit(filename='lesson4.ipynb', envirnment=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb3234e-abe2-453a-9bfc-fc898d6851a7",
   "metadata": {},
   "source": [
    "## Querying and sorting rows\n",
    "\n",
    "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 check which rows satisfy this criterion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5a539a6e-9892-48f7-bea2-ac87d6c6f41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_new_cases = covid_df.new_cases > 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4a4111e2-cfff-4b64-9924-d53b158d7379",
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_new_cases"
   ]
  },
  {
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   "execution_count": 56,
   "id": "42a68f8d-4863-47c0-b9ef-e2e90c3545db",
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       "           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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df[high_new_cases]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1d721c5b-3c9c-4a5c-bec3-65dc7ca91bb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_cases_df = covid_df[covid_df.new_cases > 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "004bf766-6501-421d-ba30-2dc5ad6a2d7a",
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       "           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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_cases_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6621edf9-1802-40da-af5e-502212a50bf6",
   "metadata": {},
   "outputs": [
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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>74</th>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>2547.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <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",
       "    </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>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_rows', 100):\n",
    "    display(covid_df[covid_df.new_cases > 1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "014638af-c92f-485a-9832-fdc0425acd66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05206657403227681"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "39e9b608-6d65-48fb-a623-11e000932b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_ratio_df = covid_df[covid_df.new_cases / covid_df.new_tests > positive_rate]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "304c3685-52b1-404f-8f2f-ddf87dc24523",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>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>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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  new_cases  new_deaths  new_tests\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",
       "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",
       "120  2020-04-29     2091.0       382.0    38589.0\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",
       "128  2020-05-07     1444.0       369.0    13665.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_ratio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "35260e26-a3c3-42e4-b178-83e1300dc375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1           NaN\n",
       "2           NaN\n",
       "3           NaN\n",
       "4           NaN\n",
       "         ...   \n",
       "243    0.026970\n",
       "244    0.032055\n",
       "245    0.018311\n",
       "246         NaN\n",
       "247         NaN\n",
       "Length: 248, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.new_cases / covid_df.new_tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b180f956-176c-4708-a745-fff21396ffc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df['positive_rate'] = covid_df.new_cases / covid_df.new_tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b177640f-d2f7-45d5-8136-74270e670da4",
   "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",
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       "      <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",
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       "      <td>2020-01-01</td>\n",
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       "      <td>NaN</td>\n",
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       "      <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2020-08-30</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>53541.0</td>\n",
       "      <td>0.026970</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>0.032055</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>0.018311</td>\n",
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       "    <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>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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 5 columns</p>\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\n",
       "..          ...        ...         ...        ...            ...\n",
       "243  2020-08-30     1444.0         1.0    53541.0       0.026970\n",
       "244  2020-08-31     1365.0         4.0    42583.0       0.032055\n",
       "245  2020-09-01      996.0         6.0    54395.0       0.018311\n",
       "246  2020-09-02      975.0         8.0        NaN            NaN\n",
       "247  2020-09-03     1326.0         6.0        NaN            NaN\n",
       "\n",
       "[248 rows x 5 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c7944fc1-83cc-4c20-a6c1-ecdc9b6f6c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df.drop(columns=['positive_rate'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ec2097-2e58-4334-bc69-5c25a7a948c4",
   "metadata": {},
   "source": [
    "### Sorting rows using column values\n",
    "\n",
    "The rows can also be sorted by a specific column using `.sort_values`. Let's sort to identify the days with the highest number of cases, then chain it with the `head` method to list just the first ten results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "081a8469-6c0e-4bc8-9143-ed7e18b98dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>89</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>5974.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>5217.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>86</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>5210.0</td>\n",
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      "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": 67,
     "metadata": {},
     "output_type": "execute_result"
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       "           date  new_cases  new_deaths  new_tests\n",
       "172  2020-06-20     -148.0        47.0    29875.0\n",
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       "           date  new_cases  new_deaths  new_tests\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",
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       "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"
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     "execution_count": 70,
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    "covid_df.loc[169:175]"
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  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c9aa4f2d-18d5-44fb-b3a3-5b8950faa902",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df.at[172, 'new_cases'] = (covid_df.at[171, 'new_cases'] + covid_df.at[173, 'new_cases'])/2"
   ]
  },
  {
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   "execution_count": 72,
   "id": "9084db41-6828-4b9b-b6e0-0a0af04f83b6",
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       "           date  new_cases  new_deaths  new_tests\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      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\n",
       "175  2020-06-23      221.0        23.0    23225.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
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    }
   ],
   "source": [
    "covid_df.loc[169:175]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "04106e85-6013-49d1-b533-7fcad9146de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e5ae00f2-b3e3-4137-88ca-730528bb33e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Updating notebook \"thehimanshutiwari/lesson4\" on https://jovian.ai/\u001b[0m\n",
      "[jovian] Committed successfully! https://jovian.ai/thehimanshutiwari/lesson4\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://jovian.ai/thehimanshutiwari/lesson4'"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jovian.commit(filename = 'lesson3.ipynb',environment=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa9499e-9e12-4268-bc2e-a4dce748e189",
   "metadata": {},
   "source": [
    "## Working with dates\n",
    "\n",
    "While we've looked at overall numbers for the cases, tests, positive rate, etc., it would also be useful to study these numbers on a month-by-month basis. The `date` column might come in handy here, as Pandas provides many utilities for working with dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "beb10b82-17a4-4251-8866-eb3784fe0e39",
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f6d3a0f5-5cc6-47da-a795-7249af83fbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df['date'] = pd.to_datetime(covid_df.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "63fa203d-2532-4f35-8553-d51feae4a33c",
   "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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "11df9966-51f4-497b-a037-5c44270a4992",
   "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": 79,
   "id": "91599203-e72c-4788-bb0f-edb953a60ae9",
   "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",
       "      <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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b4ab3088-edc2-4ece-ac61-420f9cacc9ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Query the rows for May\n",
    "covid_df_may = covid_df[covid_df.month == 5]\n",
    "\n",
    "# Extract the subset of columns to be aggregated\n",
    "covid_df_may_metrics = covid_df_may[['new_cases', 'new_deaths', 'new_tests']]\n",
    "\n",
    "# Get the column-wise sum\n",
    "covid_may_totals = covid_df_may_metrics.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a6218519-d537-43a2-998c-4700ca5ed0e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "new_cases       29073.0\n",
       "new_deaths       5658.0\n",
       "new_tests     1078720.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_may_totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8aefbefc-9bff-4173-85c4-21c2a6b26ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "new_cases       29073.0\n",
       "new_deaths       5658.0\n",
       "new_tests     1078720.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df[covid_df.month ==5] [['new_cases','new_deaths','new_tests']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f1b4b315-6e8d-4e80-9d87-7c7f72c589f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1096.6149193548388"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall average\n",
    "covid_df.new_cases.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5473cf7b-a390-450c-ad6a-4a89387b355d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1247.2571428571428"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average for Sundays\n",
    "covid_df[covid_df.weekday == 6].new_cases.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e3a5f88e-6ac0-4af3-9a9c-825e1437052b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "87552152-31a7-457e-9345-91ea32e6aef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Updating notebook \"thehimanshutiwari/lesson4\" on https://jovian.ai/\u001b[0m\n",
      "[jovian] Committed successfully! https://jovian.ai/thehimanshutiwari/lesson4\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://jovian.ai/thehimanshutiwari/lesson4'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jovian.commit(filename = 'lesson3.ipynb',environment=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bbbc64d-41de-4fc7-918c-5a93bcc8d205",
   "metadata": {},
   "source": [
    "## Grouping and aggregation\n",
    "\n",
    "As a next step, we might want to summarize the day-wise data and create a new dataframe with month-wise data. We can use the `groupby` function to create a group for each month, select the columns we wish to aggregate, and aggregate them using the `sum` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "70a54634-eb8a-47cc-b4ca-c96ba401681e",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_month_df = covid_df.groupby('month')[['new_cases', 'new_deaths', 'new_tests']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "857fbfc8-3be2-48d4-a599-b84f9af30f11",
   "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",
       "      <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",
       "    <tr>\n",
       "      <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",
       "      <td>29073.0</td>\n",
       "      <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": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_month_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bfc2c5c3-25a5-4f58-9eb8-cb4832563de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_month_mean_df = covid_df.groupby('weekday')[['new_cases', 'new_deaths', 'new_tests']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e7a63faf-b09d-4fdc-9934-cf5028d12509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <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",
       "      <td>34589.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1247.257143</td>\n",
       "      <td>159.457143</td>\n",
       "      <td>28588.105263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</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": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_month_mean_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0a166b93-959a-40c0-8b05-f1d2cb048a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df['total_cases'] = covid_df.new_cases.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "eccd356a-9476-4cc4-a84e-d0dc6250b9df",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df['total_deaths'] = covid_df.new_deaths.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fce87db5-7933-42eb-986c-b2ac0981a528",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df['total_tests'] = covid_df.new_tests.cumsum() + initial_tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0c7e99f3-50fc-4d0e-94c8-f633670dfd64",
   "metadata": {},
   "outputs": [
    {
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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",
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       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>1365.0</td>\n",
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       "      <td>42583.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
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       "      <td>268663.5</td>\n",
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       "      <td>2020-09-01</td>\n",
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       "      <td>6.0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>975.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>9</td>\n",
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       "          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  \n",
       "0            0.0           0.0          NaN  \n",
       "1            0.0           0.0          NaN  \n",
       "2            0.0           0.0          NaN  \n",
       "3            0.0           0.0          NaN  \n",
       "4            0.0           0.0          NaN  \n",
       "..           ...           ...          ...  \n",
       "243     267298.5       35473.0    5117788.0  \n",
       "244     268663.5       35477.0    5160371.0  \n",
       "245     269659.5       35483.0    5214766.0  \n",
       "246     270634.5       35491.0          NaN  \n",
       "247     271960.5       35497.0          NaN  \n",
       "\n",
       "[248 rows x 11 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e46e69-d984-4191-82bc-c9d5fe4d288d",
   "metadata": {},
   "source": [
    "## Merging data from multiple sources\n",
    "\n",
    "To determine other metrics like test per million, cases per million, etc., we require some more information about the country, viz. its population. Let's download another file `locations.csv` that contains health-related information for many countries, including Italy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "66fb8723-ef8b-4a61-8a3d-e7c16f7ed32e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('locations.csv', <http.client.HTTPMessage at 0x210847fc048>)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urlretrieve('https://gist.githubusercontent.com/aakashns/8684589ef4f266116cdce023377fc9c8/raw/99ce3826b2a9d1e6d0bde7e9e559fc8b6e9ac88b/locations.csv', \n",
    "            'locations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f4920764-fcb2-4993-889a-93443fbd9bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "locations_df = pd.read_csv('locations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a86adc5c-25dc-44a4-bb17-876815c44e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
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       "      <td>61.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5819.495</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "    <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",
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       "    <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": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "7ba8264f-8061-4fcd-ae24-4bf17a9f3b0e",
   "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>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",
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      "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": 100,
     "metadata": {},
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   ],
   "source": [
    "locations_df[locations_df.location == \"Italy\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c5c8e1da-1187-4c14-9ac8-79441d13b068",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df['location'] = \"Italy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1463877d-9709-4b02-85d5-8c3c20f1afce",
   "metadata": {},
   "outputs": [
    {
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       "      <th>location</th>\n",
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       "      <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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4fe56274-6d4e-47dc-9b9e-f1c951acfd4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = covid_df.merge(locations_df, on=\"location\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8215ab97-a3e6-4218-b2d7-1b1671647414",
   "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",
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       "      <th>gdp_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>60461828.0</td>\n",
       "      <td>83.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35220.084</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2020-01-03</td>\n",
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       "      <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",
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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",
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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",
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       "    <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",
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       "      <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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f8607a66-efd8-4b60-9e28-780ffad84c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df['cases_per_million'] = merged_df.total_cases * 1e6 / merged_df.population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ed9beb4d-1bf3-400d-896f-4253df7232d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df['deaths_per_million'] = merged_df.total_deaths * 1e6 / merged_df.population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "2c32d747-8016-4dd4-8ac5-fbf895446fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df['tests_per_million'] = merged_df.total_tests * 1e6 / merged_df.population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a07d45c7-16a1-4e80-8d75-0582bd59d6b8",
   "metadata": {},
   "outputs": [
    {
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       "          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",
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       "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_million  deaths_per_million  tests_per_million  \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        4420.946386          586.700753       84644.943252  \n",
       "244        4443.522614          586.766910       85349.238862  \n",
       "245        4459.995818          586.866146       86248.897403  \n",
       "246        4476.121695          586.998461                NaN  \n",
       "247        4498.052887          587.097697                NaN  \n",
       "\n",
       "[248 rows x 20 columns]"
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     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "merged_df"
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  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b05b3281-94f4-49bd-91ca-d5b3829de0b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Updating notebook \"thehimanshutiwari/lesson4\" on https://jovian.ai/\u001b[0m\n",
      "[jovian] Committed successfully! https://jovian.ai/thehimanshutiwari/lesson4\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://jovian.ai/thehimanshutiwari/lesson4'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jovian.commit(filename = 'lesson3.ipynb',environment=None)"
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  {
   "cell_type": "markdown",
   "id": "cc2d563d-3221-4c9e-bff6-c16260d335b1",
   "metadata": {},
   "source": [
    "## Writing data back to files\n",
    "\n",
    "After completing your analysis and adding new columns, you should write the results back to a file. Otherwise, the data will be lost when the Jupyter notebook shuts down. Before writing to file, let us first create a data frame containing just the columns we wish to record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6b0561e5-4437-4cc2-b5be-205e78fd10f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df = merged_df[['date',\n",
    "                       'new_cases', \n",
    "                       'total_cases', \n",
    "                       'new_deaths', \n",
    "                       'total_deaths', \n",
    "                       'new_tests', \n",
    "                       'total_tests', \n",
    "                       'cases_per_million', \n",
    "                       'deaths_per_million', \n",
    "                       'tests_per_million']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "890599b0-3de0-43bf-8663-8a8c9e463b59",
   "metadata": {},
   "outputs": [
    {
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       "    <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",
       "    </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",
       "    </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",
       "    </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",
       "    </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>4420.946386</td>\n",
       "      <td>586.700753</td>\n",
       "      <td>84644.943252</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>4443.522614</td>\n",
       "      <td>586.766910</td>\n",
       "      <td>85349.238862</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>4459.995818</td>\n",
       "      <td>586.866146</td>\n",
       "      <td>86248.897403</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>4476.121695</td>\n",
       "      <td>586.998461</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>271960.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35497.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4498.052887</td>\n",
       "      <td>587.097697</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 10 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_million  deaths_per_million  tests_per_million  \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        4420.946386          586.700753       84644.943252  \n",
       "244    5160371.0        4443.522614          586.766910       85349.238862  \n",
       "245    5214766.0        4459.995818          586.866146       86248.897403  \n",
       "246          NaN        4476.121695          586.998461                NaN  \n",
       "247          NaN        4498.052887          587.097697                NaN  \n",
       "\n",
       "[248 rows x 10 columns]"
      ]
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      "[jovian] Updating notebook \"thehimanshutiwari/lesson4\" on https://jovian.ai/\u001b[0m\n",
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      "[jovian] Updating notebook \"thehimanshutiwari/lesson4\" on https://jovian.ai/\u001b[0m\n",
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      "[jovian] Updating notebook \"thehimanshutiwari/lesson4\" on https://jovian.ai/\u001b[0m\n",
      "[jovian] Committed successfully! https://jovian.ai/thehimanshutiwari/lesson4\u001b[0m\n"
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