Pandas_NB1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.request import urlretrieve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('italy-covid-daywise.csv', <http.client.HTTPMessage at 0x7f510c05dd30>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urlretrieve('https://hub.jovian.ml/wp-content/uploads/2020/09/italy-covid-daywise.csv', \n",
    "            'italy-covid-daywise.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }</style><table id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >date</th>        <th class=\"col_heading level0 col1\" >new_cases</th>        <th class=\"col_heading level0 col2\" >new_deaths</th>        <th class=\"col_heading level0 col3\" >new_tests</th>    </tr></thead><tbody>\n",
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       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row0_col3\" class=\"data row0 col3\" >53541.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5level0_row1\" class=\"row_heading level0 row1\" >244</th>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row1_col0\" class=\"data row1 col0\" >2020-08-31</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row1_col1\" class=\"data row1 col1\" >1365.000000</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row1_col2\" class=\"data row1 col2\" >4.000000</td>\n",
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       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row2_col0\" class=\"data row2 col0\" >2020-09-01</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row2_col1\" class=\"data row2 col1\" >996.000000</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row2_col2\" class=\"data row2 col2\" >6.000000</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row2_col3\" class=\"data row2 col3\" >54395.000000</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5level0_row3\" class=\"row_heading level0 row3\" >246</th>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row3_col0\" class=\"data row3 col0\" >2020-09-02</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row3_col1\" class=\"data row3 col1\" >975.000000</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row3_col2\" class=\"data row3 col2\" >8.000000</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row3_col3\" class=\"data row3 col3\" >nan</td>\n",
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       "                        <th id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5level0_row4\" class=\"row_heading level0 row4\" >247</th>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row4_col0\" class=\"data row4 col0\" >2020-09-03</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row4_col1\" class=\"data row4 col1\" >1326.000000</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row4_col2\" class=\"data row4 col2\" >6.000000</td>\n",
       "                        <td id=\"T_0bb525b4_ffbf_11ea_8275_f1d18ad5dec5row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f48803198b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df means dataframe\n",
    "\n",
    "covid_df = pd.read_csv('italy-covid-daywise.csv')\n",
    "covid_df.tail().style.background_gradient(cmap = \"Greens\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(covid_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
       "    </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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'new_cases', 'new_deaths', 'new_tests'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_data_dict = {\n",
    "    'date' : ['2020-08-30', '2020-08-31', '2020-09-01',  '2020-09-02', '2020-09-03'],\n",
    "    'new_cases' : [1444, 1365, 996, 975, 1326],\n",
    "    'new_deaths' : [1, 4, 6, 8, 6],\n",
    "    'new_tests' : [53541, 42583, 54395, None, None]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1444, 1365, 996, 975, 1326]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data_dict['new_cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df['new_cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each column is represented using a data structure called Series, which is essentially a numpy array with some\n",
    "# extra methods and properties.\n",
    "\n",
    "type(covid_df['new_cases'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "975.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df['new_cases'][246]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas also provides the .at method to directly retrieve at a specific row & column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "975.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.at[246, 'new_cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.at[246, 'new_tests']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "975.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or \n",
    "\n",
    "covid_df.new_cases[246]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also pass a list of columns within the indexing notation [] to access a subset of the data frame with just the given columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  new_cases\n",
       "0  2019-12-31        0.0\n",
       "1  2020-01-01        0.0\n",
       "2  2020-01-02        0.0\n",
       "3  2020-01-03        0.0\n",
       "4  2020-01-04        0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases_df = covid_df[['date', 'new_cases']]\n",
    "cases_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
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       "      <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",
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       "      <th>2</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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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",
       "    </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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df_copy = covid_df.copy()\n",
    "covid_df_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, however, that the new data frame cases_df is simply a \"view\" of the original data frame covid_df i.e. they both point to the same data in the computer's memory, and changing any values inside one of them will also change the respective values in the other. Sharing data between data frames makes data manipulation in Pandas blazing fast, and you needn't worry about the overhead of copying thousands or millions of rows every time you want to crate a new data frame by operating on an existing one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          2020-08-30\n",
       "new_cases           1444\n",
       "new_deaths             1\n",
       "new_tests          53541\n",
       "Name: 243, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to access a specific row, we use .loc method\n",
    "\n",
    "covid_df.loc[243]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          2020-08-30\n",
       "new_cases           1444\n",
       "new_deaths             1\n",
       "new_tests          53541\n",
       "Name: 243, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.iloc[243]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(covid_df.at[0, 'new_tests'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
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       "      <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",
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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",
       "    </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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to find the first index that doesn't contain a NaN value using first_valid_index method of a series.\n",
    "\n",
    "covid_df.new_tests.first_valid_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_tests</th>\n",
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       "  </thead>\n",
       "  <tbody>\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",
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       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  new_cases  new_deaths  new_tests\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"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.loc[108:113]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>221.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23225.0</td>\n",
       "    </tr>\n",
       "    <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>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>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>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>34</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>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>177</th>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>577.0</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>29421.0</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",
       "175  2020-06-23      221.0        23.0    23225.0\n",
       "100  2020-04-09     3836.0       540.0        NaN\n",
       "4    2020-01-04        0.0         0.0        NaN\n",
       "134  2020-05-13     1402.0       172.0    37049.0\n",
       "79   2020-03-19     4207.0       473.0        NaN\n",
       "34   2020-02-03        0.0         0.0        NaN\n",
       "74   2020-03-14     2547.0       252.0        NaN\n",
       "124  2020-05-03     1900.0       474.0    27047.0\n",
       "177  2020-06-25      577.0       -31.0    29421.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get random sample from the dataframe\n",
    "\n",
    "covid_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to count NaN values in a column\n",
    "covid_df.new_tests.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'DataFrame' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-ec73d57843c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcovid_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovid_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_tests\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNaN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'DataFrame' object is not callable"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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