pandas_01_basics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a15f02ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "38c79df3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apples</th>\n",
       "      <th>Bananas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017 Sales</th>\n",
       "      <td>34</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018 Sales</th>\n",
       "      <td>41</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Apples  Bananas\n",
       "2017 Sales      34       21\n",
       "2018 Sales      41       34"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating DataFrames\n",
    "fruit_sales = pd.DataFrame({\"Apples\":[34, 41], \"Bananas\":[21, 34]}, index=[\"2017 Sales\", \"2018 Sales\"])\n",
    "fruit_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cd7af0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Flour     4 cups\n",
       "Milk       1 cup\n",
       "Eggs     2 large\n",
       "Spam       1 can\n",
       "Name: Dinner, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating Series\n",
    "ingredients = pd.Series([\"4 cups\", \"1 cup\", \"2 large\", \"1 can\"], index=[\"Flour\", \"Milk\", \"Eggs\", \"Spam\"], name=\"Dinner\")\n",
    "ingredients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f7bee3af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'country': ['Brazil', 'Russia', 'India', 'China', 'South Africa'],\n",
       " 'capital': ['Brasilia', 'Moscow', 'New Dehli', 'Beijing', 'Pretoria'],\n",
       " 'area': [8.156, 17.1, 3.286, 9.597, 1.221],\n",
       " 'population': [200.4, 143.5, 1252, 1357, 52.98]}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {\n",
    "    'country': [\"Brazil\", \"Russia\", \"India\", \"China\", \"South Africa\"],\n",
    "    'capital': [\"Brasilia\", \"Moscow\", \"New Dehli\", \"Beijing\", \"Pretoria\"],\n",
    "    'area': [8.156, 17.10, 3.286, 9.597, 1.221],\n",
    "    'population': [200.4, 143.5, 1252, 1357, 52.98]\n",
    "}\n",
    "dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5304a6a3",
   "metadata": {},
   "source": [
    "## Create from dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "caed9fc9",
   "metadata": {
    "scrolled": true
   },
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.100</td>\n",
       "      <td>143.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>India</td>\n",
       "      <td>New Dehli</td>\n",
       "      <td>3.286</td>\n",
       "      <td>1252.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Pretoria</td>\n",
       "      <td>1.221</td>\n",
       "      <td>52.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country    capital    area  population\n",
       "BR        Brazil   Brasilia   8.156      200.40\n",
       "RU        Russia     Moscow  17.100      143.50\n",
       "IN         India  New Dehli   3.286     1252.00\n",
       "CH         China    Beijing   9.597     1357.00\n",
       "SA  South Africa   Pretoria   1.221       52.98"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brics = pd.DataFrame(dict)\n",
    "#Set the row indexes\n",
    "brics.index = ['BR', 'RU', 'IN', 'CH', 'SA'] \n",
    "brics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ba032207",
   "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></th>\n",
       "      <th>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <th>Brazil</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <th>Russia</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.100</td>\n",
       "      <td>143.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <th>India</th>\n",
       "      <td>India</td>\n",
       "      <td>New Dehli</td>\n",
       "      <td>3.286</td>\n",
       "      <td>1252.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <th>China</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <th>South Africa</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Pretoria</td>\n",
       "      <td>1.221</td>\n",
       "      <td>52.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country    capital    area  population\n",
       "   country                                                  \n",
       "BR Brazil              Brazil   Brasilia   8.156      200.40\n",
       "RU Russia              Russia     Moscow  17.100      143.50\n",
       "IN India                India  New Dehli   3.286     1252.00\n",
       "CH China                China    Beijing   9.597     1357.00\n",
       "SA South Africa  South Africa   Pretoria   1.221       52.98"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the index to Country column, \n",
    "# drop, don't drop the new indexed column from DataFrame\n",
    "# Inplace, instead of returning new DataFrame, modify the existing one inplace\n",
    "# Append, append the new column with the current index\n",
    "\n",
    "brics.set_index('country', drop=False, inplace=True, append=True)\n",
    "brics\n",
    "#help(brics.set_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a1e6f441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.100</td>\n",
       "      <td>143.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>India</td>\n",
       "      <td>New Dehli</td>\n",
       "      <td>3.286</td>\n",
       "      <td>1252.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Pretoria</td>\n",
       "      <td>1.221</td>\n",
       "      <td>52.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country    capital    area  population\n",
       "BR        Brazil   Brasilia   8.156      200.40\n",
       "RU        Russia     Moscow  17.100      143.50\n",
       "IN         India  New Dehli   3.286     1252.00\n",
       "CH         China    Beijing   9.597     1357.00\n",
       "SA  South Africa   Pretoria   1.221       52.98"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reset the index back again\n",
    "brics.index = ['BR', 'RU', 'IN', 'CH', 'SA']\n",
    "brics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4637dc32",
   "metadata": {},
   "source": [
    "## Write DataFrame to a File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fa06f6ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "brics.to_csv(\"Data\\\\brics_from_program.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4aee35",
   "metadata": {},
   "source": [
    "## Read DataFrame from a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1052a311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.100</td>\n",
       "      <td>143.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>India</td>\n",
       "      <td>New Dehli</td>\n",
       "      <td>3.286</td>\n",
       "      <td>1252.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Pretoria</td>\n",
       "      <td>1.221</td>\n",
       "      <td>52.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country    capital    area  population\n",
       "BR        Brazil   Brasilia   8.156      200.40\n",
       "RU        Russia     Moscow  17.100      143.50\n",
       "IN         India  New Dehli   3.286     1252.00\n",
       "CH         China    Beijing   9.597     1357.00\n",
       "SA  South Africa   Pretoria   1.221       52.98"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load from CSV file, first col is index for rows, first row is name of columns\n",
    "brics = pd.read_csv(\"Data\\\\brics.csv\", index_col=0)\n",
    "brics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e39d6a6d",
   "metadata": {},
   "source": [
    "## Accessing values in the form of Series, DataFrame using indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a3558481",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BR          Brazil\n",
      "RU          Russia\n",
      "IN           India\n",
      "CH           China\n",
      "SA    South Africa\n",
      "Name: country, dtype: object\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "#Accessing a column using square bracket [] return the values of that colums in a Series. This will return a Series.\n",
    "\n",
    "c1 = brics['country']\n",
    "print(c1.head())\n",
    "print(type(c1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bf2da353",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country\n",
      "BR        Brazil\n",
      "RU        Russia\n",
      "IN         India\n",
      "CH         China\n",
      "SA  South Africa\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "#If you want to get DataFrame returned use 2 square brackets [[]]. This will return a DataFrame.\n",
    "\n",
    "c1 = brics[['country']]\n",
    "print(c1.head())\n",
    "print(type(c1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abfbeddb",
   "metadata": {},
   "source": [
    "## Returning 2 or more columns from DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9b05d9fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.head of          country    area\n",
      "BR        Brazil   8.156\n",
      "RU        Russia  17.100\n",
      "IN         India   3.286\n",
      "CH         China   9.597\n",
      "SA  South Africa   1.221>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "c1 = brics[['country', 'area']]\n",
    "print(c1.head)\n",
    "print(type(c1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fa2378",
   "metadata": {},
   "source": [
    "## Selecting Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "19cf84c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "   country    capital    area  population\n",
      "RU  Russia     Moscow  17.100       143.5\n",
      "IN   India  New Dehli   3.286      1252.0\n"
     ]
    }
   ],
   "source": [
    "r1 = brics[1:3]\n",
    "print(type(r1))\n",
    "print(r1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4641b1dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country    capital   area  population\n",
      "IN         India  New Dehli  3.286     1252.00\n",
      "CH         China    Beijing  9.597     1357.00\n",
      "SA  South Africa   Pretoria  1.221       52.98\n"
     ]
    }
   ],
   "source": [
    "r1 = brics[2:]\n",
    "print(r1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76ad2169",
   "metadata": {},
   "source": [
    "## Slicing using Rows i.e. loc and iloc\n",
    "### loc returns based on labels of the rows\n",
    "### iloc return based on position of the rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "da274a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "country       Russia\n",
       "capital       Moscow\n",
       "area            17.1\n",
       "population     143.5\n",
       "Name: RU, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get Row use loc function\n",
    "r1 = brics.loc['RU']\n",
    "print(type(r1))\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a511b280",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.1</td>\n",
       "      <td>143.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country capital  area  population\n",
       "RU  Russia  Moscow  17.1       143.5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use 2 square brackets to get Row data is DataFrame\n",
    "r1 = brics.loc[['RU']]\n",
    "print(type(r1))\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa86436b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.100</td>\n",
       "      <td>143.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  capital    area  population\n",
       "RU  Russia   Moscow  17.100       143.5\n",
       "CH   China  Beijing   9.597      1357.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#returns multiple rows based on index or row labels\n",
    "r1 = brics.loc[['RU', 'CH']]\n",
    "print(type(r1))\n",
    "r1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424d370c",
   "metadata": {},
   "source": [
    "## You can also return the selected rows and selected columns only. Starting with Rows first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "91d1d240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>country</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>17.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>9.597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country    area\n",
       "RU  Russia  17.100\n",
       "CH   China   9.597"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1 = brics.loc[['RU', 'CH'], [\"country\", \"area\"]]\n",
    "print(type(r1))\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "208ac196",
   "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>country</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>8.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>17.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>India</td>\n",
       "      <td>3.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>9.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>1.221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country    area\n",
       "BR        Brazil   8.156\n",
       "RU        Russia  17.100\n",
       "IN         India   3.286\n",
       "CH         China   9.597\n",
       "SA  South Africa   1.221"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All rows but only selected columns\n",
    "r1= brics.loc[:,[\"country\", \"area\"]]\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1aaf8485",
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>India</td>\n",
       "      <td>New Dehli</td>\n",
       "      <td>3.286</td>\n",
       "      <td>1252.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Pretoria</td>\n",
       "      <td>1.221</td>\n",
       "      <td>52.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country    capital   area  population\n",
       "IN         India  New Dehli  3.286     1252.00\n",
       "SA  South Africa   Pretoria  1.221       52.98"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selected rows all columns\n",
    "r1= brics.loc[['IN', 'SA'],:]\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f25d52e8",
   "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>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>3.286</td>\n",
       "      <td>1252.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>1.221</td>\n",
       "      <td>52.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area  population\n",
       "IN  3.286     1252.00\n",
       "SA  1.221       52.98"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selected rows sekected columns\n",
    "r1= brics.loc[['IN', 'SA'],['area', 'population']]\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a9b49db1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>India</td>\n",
       "      <td>New Dehli</td>\n",
       "      <td>3.286</td>\n",
       "      <td>1252.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Pretoria</td>\n",
       "      <td>1.221</td>\n",
       "      <td>52.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country    capital   area  population\n",
       "IN         India  New Dehli  3.286     1252.00\n",
       "CH         China    Beijing  9.597     1357.00\n",
       "SA  South Africa   Pretoria  1.221       52.98"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can also get multiple rows based on rows location\n",
    "r1 = brics.iloc[-3:]\n",
    "print(type(r1))\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a1a6fbb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.100</td>\n",
       "      <td>143.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>India</td>\n",
       "      <td>New Dehli</td>\n",
       "      <td>3.286</td>\n",
       "      <td>1252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country    capital    area  population\n",
       "BR  Brazil   Brasilia   8.156       200.4\n",
       "RU  Russia     Moscow  17.100       143.5\n",
       "IN   India  New Dehli   3.286      1252.0\n",
       "CH   China    Beijing   9.597      1357.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can also get multiple rows based on rows location\n",
    "r1 = brics.iloc[0:-1]\n",
    "print(type(r1))\n",
    "r1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c0ac504",
   "metadata": {},
   "source": [
    "## Filtering DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2dbc31a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BR     True\n",
       "RU     True\n",
       "IN    False\n",
       "CH     True\n",
       "SA    False\n",
       "Name: area, dtype: bool"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_huge = brics.area > 8\n",
    "is_huge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "29770084",
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.100</td>\n",
       "      <td>143.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country   capital    area  population\n",
       "BR  Brazil  Brasilia   8.156       200.4\n",
       "RU  Russia    Moscow  17.100       143.5\n",
       "CH   China   Beijing   9.597      1357.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huge_countries = brics[is_huge]\n",
    "huge_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "92a8f204",
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.100</td>\n",
       "      <td>143.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country   capital    area  population\n",
       "BR  Brazil  Brasilia   8.156       200.4\n",
       "RU  Russia    Moscow  17.100       143.5\n",
       "CH   China   Beijing   9.597      1357.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huge_countries = brics[brics.area > 8]\n",
    "huge_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d6ec006a",
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country   capital   area  population\n",
       "BR  Brazil  Brasilia  8.156       200.4\n",
       "CH   China   Beijing  9.597      1357.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huge_countries = brics[np.logical_and(brics[\"area\"] > 8, brics[\"area\"] < 10)]\n",
    "huge_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "74962e64",
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country   capital   area  population\n",
       "BR  Brazil  Brasilia  8.156       200.4\n",
       "CH   China   Beijing  9.597      1357.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Same thing can be done with a logical operator\n",
    "\n",
    "huge_countries = brics[(brics.area > 8) & (brics.area < 10)]\n",
    "huge_countries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c74c6adf",
   "metadata": {},
   "source": [
    "## IsIn method for selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "36f718e8",
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country   capital   area  population\n",
       "BR  Brazil  Brasilia  8.156       200.4\n",
       "CH   China   Beijing  9.597      1357.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brics.loc[brics.country.isin(['Brazil', 'China'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49e7fdf",
   "metadata": {},
   "source": [
    "## Notnull based selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e7cb4dbb",
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>200.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.100</td>\n",
       "      <td>143.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>India</td>\n",
       "      <td>New Dehli</td>\n",
       "      <td>3.286</td>\n",
       "      <td>1252.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>1357.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Pretoria</td>\n",
       "      <td>1.221</td>\n",
       "      <td>52.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country    capital    area  population\n",
       "BR        Brazil   Brasilia   8.156      200.40\n",
       "RU        Russia     Moscow  17.100      143.50\n",
       "IN         India  New Dehli   3.286     1252.00\n",
       "CH         China    Beijing   9.597     1357.00\n",
       "SA  South Africa   Pretoria   1.221       52.98"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brics.loc[brics.country.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5727d58",
   "metadata": {},
   "source": [
    "## Looping a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5b372fa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country\n",
      "capital\n",
      "area\n",
      "population\n"
     ]
    }
   ],
   "source": [
    "#only prints column names\n",
    "for val in brics:\n",
    "    print(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e1e20ea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "BR\n",
      "<class 'pandas.core.series.Series'>\n",
      "country         Brazil\n",
      "capital       Brasilia\n",
      "area             8.156\n",
      "population       200.4\n",
      "Name: BR, dtype: object\n",
      "<class 'str'>\n",
      "RU\n",
      "<class 'pandas.core.series.Series'>\n",
      "country       Russia\n",
      "capital       Moscow\n",
      "area            17.1\n",
      "population     143.5\n",
      "Name: RU, dtype: object\n",
      "<class 'str'>\n",
      "IN\n",
      "<class 'pandas.core.series.Series'>\n",
      "country           India\n",
      "capital       New Dehli\n",
      "area              3.286\n",
      "population       1252.0\n",
      "Name: IN, dtype: object\n",
      "<class 'str'>\n",
      "CH\n",
      "<class 'pandas.core.series.Series'>\n",
      "country         China\n",
      "capital       Beijing\n",
      "area            9.597\n",
      "population     1357.0\n",
      "Name: CH, dtype: object\n",
      "<class 'str'>\n",
      "SA\n",
      "<class 'pandas.core.series.Series'>\n",
      "country       South Africa\n",
      "capital           Pretoria\n",
      "area                 1.221\n",
      "population           52.98\n",
      "Name: SA, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# You need to explicitly mention that you need to iterate over rows.\n",
    "\n",
    "for lab, row in brics.iterrows():\n",
    "    print(type(lab), lab, type(row), row, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e0939a19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country    capital    area  population  name_length\n",
      "BR        Brazil   Brasilia   8.156      200.40          6.0\n",
      "RU        Russia     Moscow  17.100      143.50          6.0\n",
      "IN         India  New Dehli   3.286     1252.00          5.0\n",
      "CH         China    Beijing   9.597     1357.00          5.0\n",
      "SA  South Africa   Pretoria   1.221       52.98         12.0\n"
     ]
    }
   ],
   "source": [
    "# Add new columns to DataFrame using for loop\n",
    "for lab, row in brics.iterrows():\n",
    "    brics.loc[lab, 'name_length'] = len(row[\"country\"])\n",
    "\n",
    "print(brics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fc16b846",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country    capital    area  population  name_length\n",
      "BR        Brazil   Brasilia   8.156      200.40            6\n",
      "RU        Russia     Moscow  17.100      143.50            6\n",
      "IN         India  New Dehli   3.286     1252.00            5\n",
      "CH         China    Beijing   9.597     1357.00            5\n",
      "SA  South Africa   Pretoria   1.221       52.98           12\n"
     ]
    }
   ],
   "source": [
    "# We can do the above using apply\n",
    "# In this example we are selecting a column name_length in brics DataFrame, which doesn't exists, so it'll be created.\n",
    "# Then we are selecting the country columns from it and then we are applying the len function on each row of it.\n",
    "\n",
    "brics[\"name_length\"] = brics[\"country\"].apply(len)\n",
    "print(brics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d7d190",
   "metadata": {},
   "source": [
    "# Summary Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d913fa9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5.000000\n",
       "mean      7.872000\n",
       "std       6.194341\n",
       "min       1.221000\n",
       "25%       3.286000\n",
       "50%       8.156000\n",
       "75%       9.597000\n",
       "max      17.100000\n",
       "Name: area, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brics.area.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8c959f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          5\n",
       "unique         5\n",
       "top       Brazil\n",
       "freq           1\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brics.country.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "df7f5617",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean 7.872, Mode 0     1.221\n",
      "1     3.286\n",
      "2     8.156\n",
      "3     9.597\n",
      "4    17.100\n",
      "dtype: float64, Std 6.19434140970612\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean {}, Mode {}, Std {}\".format(brics.area.mean(), brics.area.mode(), brics.area.std()),  sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4187bc74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6  5 12]\n",
      "6     2\n",
      "5     2\n",
      "12    1\n",
      "Name: name_length, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(brics.name_length.unique())\n",
    "print(brics.name_length.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b40189e",
   "metadata": {},
   "source": [
    "# Maps and Apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b6fa92e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "601.176\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BR   -400.776\n",
       "RU   -457.676\n",
       "IN    650.824\n",
       "CH    755.824\n",
       "SA   -548.196\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The below map method takes a function which accepts 1 parameter which is the value \n",
    "# of the column from each row on which it's called upon like in this case it is population column \n",
    "# The method than returns the how far the population is from it's mean\n",
    "\n",
    "population_mean = brics.population.mean()\n",
    "print(population_mean)\n",
    "brics.population.map(lambda p: p - population_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "70438f85",
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "      <th>name_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.156</td>\n",
       "      <td>-400.776</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>17.100</td>\n",
       "      <td>-457.676</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>India</td>\n",
       "      <td>New Dehli</td>\n",
       "      <td>3.286</td>\n",
       "      <td>650.824</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>9.597</td>\n",
       "      <td>755.824</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Pretoria</td>\n",
       "      <td>1.221</td>\n",
       "      <td>-548.196</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country    capital    area  population  name_length\n",
       "BR        Brazil   Brasilia   8.156    -400.776            6\n",
       "RU        Russia     Moscow  17.100    -457.676            6\n",
       "IN         India  New Dehli   3.286     650.824            5\n",
       "CH         China    Beijing   9.597     755.824            5\n",
       "SA  South Africa   Pretoria   1.221    -548.196           12"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applies a method on each row of the DataFrame or Series.\n",
    "population_mean = brics.population.mean()\n",
    "def remean_population(row):\n",
    "    row.population = row.population - population_mean\n",
    "    return row\n",
    "\n",
    "brics.apply(remean_population, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12b6cbad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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