week-2/2-pandas-dataframes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'Population': [35.467, 63.951, 80.94 , 60.665, 127.061, 64.511, 318.523],\n",
    "    'GDP': [\n",
    "        1785387,\n",
    "        2833687,\n",
    "        3874437,\n",
    "        2167744,\n",
    "        4602367,\n",
    "        2950039,\n",
    "        17348075\n",
    "    ],\n",
    "    'Surface Area': [\n",
    "        9984670,\n",
    "        640679,\n",
    "        357114,\n",
    "        301336,\n",
    "        377930,\n",
    "        242495,\n",
    "        9525067\n",
    "    ],\n",
    "    'HDI': [\n",
    "        0.913,\n",
    "        0.888,\n",
    "        0.916,\n",
    "        0.873,\n",
    "        0.891,\n",
    "        0.907,\n",
    "        0.915\n",
    "    ],\n",
    "    'Continent': [\n",
    "        'America',\n",
    "        'Europe',\n",
    "        'Europe',\n",
    "        'Europe',\n",
    "        'Asia',\n",
    "        'Europe',\n",
    "        'America'\n",
    "    ]\n",
    "}, columns=['Population', 'GDP', 'Surface Area', 'HDI', 'Continent'])"
   ]
  },
  {
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Population       GDP  Surface Area    HDI Continent\n",
       "0      35.467   1785387       9984670  0.913   America\n",
       "1      63.951   2833687        640679  0.888    Europe\n",
       "2      80.940   3874437        357114  0.916    Europe\n",
       "3      60.665   2167744        301336  0.873    Europe\n",
       "4     127.061   4602367        377930  0.891      Asia\n",
       "5      64.511   2950039        242495  0.907    Europe\n",
       "6     318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = [\n",
    "    'Canada',\n",
    "    'France',\n",
    "    'Germany',\n",
    "    'Italy',\n",
    "    'Japan',\n",
    "    'United Kingdom',\n",
    "    'United States',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "Canada              35.467   1785387       9984670  0.913   America\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Population', 'GDP', 'Surface Area', 'HDI', 'Continent'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Canada', 'France', 'Germany', 'Italy', 'Japan', 'United Kingdom',\n",
       "       'United States'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 7 entries, Canada to United States\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Population    7 non-null      float64\n",
      " 1   GDP           7 non-null      int64  \n",
      " 2   Surface Area  7 non-null      int64  \n",
      " 3   HDI           7 non-null      float64\n",
      " 4   Continent     7 non-null      object \n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 336.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>107.302571</td>\n",
       "      <td>5.080248e+06</td>\n",
       "      <td>3.061327e+06</td>\n",
       "      <td>0.900429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>97.249970</td>\n",
       "      <td>5.494020e+06</td>\n",
       "      <td>4.576187e+06</td>\n",
       "      <td>0.016592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>35.467000</td>\n",
       "      <td>1.785387e+06</td>\n",
       "      <td>2.424950e+05</td>\n",
       "      <td>0.873000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>62.308000</td>\n",
       "      <td>2.500716e+06</td>\n",
       "      <td>3.292250e+05</td>\n",
       "      <td>0.889500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>64.511000</td>\n",
       "      <td>2.950039e+06</td>\n",
       "      <td>3.779300e+05</td>\n",
       "      <td>0.907000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>104.000500</td>\n",
       "      <td>4.238402e+06</td>\n",
       "      <td>5.082873e+06</td>\n",
       "      <td>0.914000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>318.523000</td>\n",
       "      <td>1.734808e+07</td>\n",
       "      <td>9.984670e+06</td>\n",
       "      <td>0.916000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Population           GDP  Surface Area       HDI\n",
       "count    7.000000  7.000000e+00  7.000000e+00  7.000000\n",
       "mean   107.302571  5.080248e+06  3.061327e+06  0.900429\n",
       "std     97.249970  5.494020e+06  4.576187e+06  0.016592\n",
       "min     35.467000  1.785387e+06  2.424950e+05  0.873000\n",
       "25%     62.308000  2.500716e+06  3.292250e+05  0.889500\n",
       "50%     64.511000  2.950039e+06  3.779300e+05  0.907000\n",
       "75%    104.000500  4.238402e+06  5.082873e+06  0.914000\n",
       "max    318.523000  1.734808e+07  9.984670e+06  0.916000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 7 entries, Canada to United States\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Population    7 non-null      float64\n",
      " 1   GDP           7 non-null      int64  \n",
      " 2   Surface Area  7 non-null      int64  \n",
      " 3   HDI           7 non-null      float64\n",
      " 4   Continent     7 non-null      object \n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 336.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population       35.467\n",
       "GDP             1785387\n",
       "Surface Area    9984670\n",
       "HDI               0.913\n",
       "Continent       America\n",
       "Name: Canada, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"Canada\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population       35.467\n",
       "GDP             1785387\n",
       "Surface Area    9984670\n",
       "HDI               0.913\n",
       "Continent       America\n",
       "Name: Canada, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Canada            America\n",
       "France             Europe\n",
       "Germany            Europe\n",
       "Italy              Europe\n",
       "Japan                Asia\n",
       "United Kingdom     Europe\n",
       "United States     America\n",
       "Name: Continent, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Continent\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Continent\n",
       "Canada           America\n",
       "France            Europe\n",
       "Germany           Europe\n",
       "Italy             Europe\n",
       "Japan               Asia\n",
       "United Kingdom    Europe\n",
       "United States    America"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Continent\"].to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>America</td>\n",
       "      <td>1785387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2833687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>Europe</td>\n",
       "      <td>3874437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2167744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>4602367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2950039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>America</td>\n",
       "      <td>17348075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Continent       GDP\n",
       "Canada           America   1785387\n",
       "France            Europe   2833687\n",
       "Germany           Europe   3874437\n",
       "Italy             Europe   2167744\n",
       "Japan               Asia   4602367\n",
       "United Kingdom    Europe   2950039\n",
       "United States    America  17348075"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Continent\", \"GDP\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "         Population      GDP  Surface Area    HDI Continent\n",
       "France       63.951  2833687        640679  0.888    Europe\n",
       "Germany      80.940  3874437        357114  0.916    Europe"
      ]
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     "execution_count": 23,
     "metadata": {},
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   "source": [
    "df[1:3]"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.873</td>\n",
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      "text/plain": [
       "         Population      GDP  Surface Area    HDI Continent\n",
       "France       63.951  2833687        640679  0.888    Europe\n",
       "Germany      80.940  3874437        357114  0.916    Europe\n",
       "Italy        60.665  2167744        301336  0.873    Europe"
      ]
     },
     "execution_count": 24,
     "metadata": {},
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   "source": [
    "df.loc['France': 'Italy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df.loc['France': 'Italy', 'GDP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France     2833687\n",
       "Germany    3874437\n",
       "Italy      2167744\n",
       "Name: GDP, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "a"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
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     "execution_count": 28,
     "metadata": {},
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   "source": [
    "type(a)"
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   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
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     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "a.dtypes"
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   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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     "data": {
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      "text/plain": [
       "             GDP  Population\n",
       "France   2833687      63.951\n",
       "Germany  3874437      80.940\n",
       "Italy    2167744      60.665"
      ]
     },
     "execution_count": 35,
     "metadata": {},
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   "source": [
    "df.loc['France': 'Italy', ['GDP', 'Population']]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Canada            False\n",
       "France            False\n",
       "Germany            True\n",
       "Italy             False\n",
       "Japan              True\n",
       "United Kingdom    False\n",
       "United States      True\n",
       "Name: Population, dtype: bool"
      ]
     },
     "execution_count": 36,
     "metadata": {},
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   "source": [
    "df['Population'] > 70"
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   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "               Population       GDP  Surface Area    HDI Continent\n",
       "Germany            80.940   3874437        357114  0.916    Europe\n",
       "Japan             127.061   4602367        377930  0.891      Asia\n",
       "United States     318.523  17348075       9525067  0.915   America"
      ]
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     "metadata": {},
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   "source": [
    "df.loc[df['Population'] > 70]"
   ]
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   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"Canada\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "Japan              127.061   4602367        377930  0.891      Asia\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"Canada\") "
   ]
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  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent\n",
       "France              63.951   2833687        640679  0.888    Europe\n",
       "Germany             80.940   3874437        357114  0.916    Europe\n",
       "Italy               60.665   2167744        301336  0.873    Europe\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe\n",
       "United States      318.523  17348075       9525067  0.915   America"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Canada', 'Japan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Canada</th>\n",
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       "      <th>Germany</th>\n",
       "      <td>3874437</td>\n",
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       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>Europe</td>\n",
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       "    <tr>\n",
       "      <th>Japan</th>\n",
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       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     GDP  Surface Area Continent\n",
       "Canada           1785387       9984670   America\n",
       "France           2833687        640679    Europe\n",
       "Germany          3874437        357114    Europe\n",
       "Italy            2167744        301336    Europe\n",
       "Japan            4602367        377930      Asia\n",
       "United Kingdom   2950039        242495    Europe\n",
       "United States   17348075       9525067   America"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=[\"Population\", \"HDI\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1785387</td>\n",
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       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2833687</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
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       "    <tr>\n",
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      "text/plain": [
       "                     GDP  Surface Area Continent\n",
       "Canada           1785387       9984670   America\n",
       "France           2833687        640679    Europe\n",
       "Germany          3874437        357114    Europe\n",
       "Italy            2167744        301336    Europe\n",
       "Japan            4602367        377930      Asia\n",
       "United Kingdom   2950039        242495    Europe\n",
       "United States   17348075       9525067   America"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Population', 'HDI'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>Canada</th>\n",
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       "    <tr>\n",
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       "      <td>2833687</td>\n",
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       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>3874437</td>\n",
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       "      <th>Italy</th>\n",
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       "    <tr>\n",
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      "text/plain": [
       "                     GDP  Surface Area Continent\n",
       "Canada           1785387       9984670   America\n",
       "France           2833687        640679    Europe\n",
       "Germany          3874437        357114    Europe\n",
       "Italy            2167744        301336    Europe\n",
       "Japan            4602367        377930      Asia\n",
       "United Kingdom   2950039        242495    Europe\n",
       "United States   17348075       9525067   America"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Population', 'HDI'], axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
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       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
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       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
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       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP\n",
       "Canada              35.467   1785387\n",
       "France              63.951   2833687\n",
       "Germany             80.940   3874437\n",
       "Italy               60.665   2167744\n",
       "Japan              127.061   4602367\n",
       "United Kingdom      64.511   2950039\n",
       "United States      318.523  17348075"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Population', 'GDP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.35467</td>\n",
       "      <td>17853.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.63951</td>\n",
       "      <td>28336.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>0.80940</td>\n",
       "      <td>38744.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>0.60665</td>\n",
       "      <td>21677.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.27061</td>\n",
       "      <td>46023.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>0.64511</td>\n",
       "      <td>29500.39</td>\n",
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       "      <th>United States</th>\n",
       "      <td>3.18523</td>\n",
       "      <td>173480.75</td>\n",
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      ],
      "text/plain": [
       "                Population        GDP\n",
       "Canada             0.35467   17853.87\n",
       "France             0.63951   28336.87\n",
       "Germany            0.80940   38744.37\n",
       "Italy              0.60665   21677.44\n",
       "Japan              1.27061   46023.67\n",
       "United Kingdom     0.64511   29500.39\n",
       "United States      3.18523  173480.75"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Population', 'GDP']] / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "langs = pd.Series(\n",
    "    ['French', 'German', 'Italian'],\n",
    "    index=['France', 'Germany', 'Italy'],\n",
    "    name='Language'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France      French\n",
       "Germany     German\n",
       "Italy      Italian\n",
       "Name: Language, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "langs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"language\"] = langs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italian</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent Language  \\\n",
       "Canada              35.467   1785387       9984670  0.913   America      NaN   \n",
       "France              63.951   2833687        640679  0.888    Europe   French   \n",
       "Germany             80.940   3874437        357114  0.916    Europe   German   \n",
       "Italy               60.665   2167744        301336  0.873    Europe  Italian   \n",
       "Japan              127.061   4602367        377930  0.891      Asia      NaN   \n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe      NaN   \n",
       "United States      318.523  17348075       9525067  0.915   America      NaN   \n",
       "\n",
       "               language  \n",
       "Canada              NaN  \n",
       "France           French  \n",
       "Germany          German  \n",
       "Italy           Italian  \n",
       "Japan               NaN  \n",
       "United Kingdom      NaN  \n",
       "United States       NaN  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Language\"] = langs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"language\"], axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387</td>\n",
       "      <td>9984670</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687</td>\n",
       "      <td>640679</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.940</td>\n",
       "      <td>3874437</td>\n",
       "      <td>357114</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "      <td>German</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744</td>\n",
       "      <td>301336</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367</td>\n",
       "      <td>377930</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039</td>\n",
       "      <td>242495</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075</td>\n",
       "      <td>9525067</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population       GDP  Surface Area    HDI Continent Language\n",
       "Canada              35.467   1785387       9984670  0.913   America      NaN\n",
       "France              63.951   2833687        640679  0.888    Europe   French\n",
       "Germany             80.940   3874437        357114  0.916    Europe   German\n",
       "Italy               60.665   2167744        301336  0.873    Europe  Italian\n",
       "Japan              127.061   4602367        377930  0.891      Asia      NaN\n",
       "United Kingdom      64.511   2950039        242495  0.907    Europe      NaN\n",
       "United States      318.523  17348075       9525067  0.915   America      NaN"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_row = pd.Series({\n",
    "    'Population': 83.000,\n",
    "    'Continent': 'Europe',\n",
    "    'Language': 'Türkçe'\n",
    "}, name=\"Türkiye\"\n",
    ")   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population      83.0\n",
       "Continent     Europe\n",
       "Language      Türkçe\n",
       "Name: Türkiye, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Surface Area</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35.467</td>\n",
       "      <td>1785387.0</td>\n",
       "      <td>9984670.0</td>\n",
       "      <td>0.913</td>\n",
       "      <td>America</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>63.951</td>\n",
       "      <td>2833687.0</td>\n",
       "      <td>640679.0</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Europe</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>80.94</td>\n",
       "      <td>3874437.0</td>\n",
       "      <td>357114.0</td>\n",
       "      <td>0.916</td>\n",
       "      <td>Europe</td>\n",
       "      <td>German</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>60.665</td>\n",
       "      <td>2167744.0</td>\n",
       "      <td>301336.0</td>\n",
       "      <td>0.873</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>127.061</td>\n",
       "      <td>4602367.0</td>\n",
       "      <td>377930.0</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.511</td>\n",
       "      <td>2950039.0</td>\n",
       "      <td>242495.0</td>\n",
       "      <td>0.907</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>318.523</td>\n",
       "      <td>17348075.0</td>\n",
       "      <td>9525067.0</td>\n",
       "      <td>0.915</td>\n",
       "      <td>America</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Türkiye</th>\n",
       "      <td>83.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Türkçe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Population         GDP  Surface Area    HDI Continent Language\n",
       "Canada             35.467   1785387.0     9984670.0  0.913   America      NaN\n",
       "France             63.951   2833687.0      640679.0  0.888    Europe   French\n",
       "Germany             80.94   3874437.0      357114.0  0.916    Europe   German\n",
       "Italy              60.665   2167744.0      301336.0  0.873    Europe  Italian\n",
       "Japan             127.061   4602367.0      377930.0  0.891      Asia      NaN\n",
       "United Kingdom     64.511   2950039.0      242495.0  0.907    Europe      NaN\n",
       "United States     318.523  17348075.0     9525067.0  0.915   America      NaN\n",
       "Türkiye              83.0         NaN           NaN    NaN    Europe   Türkçe"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df, new_row.to_frame().T], ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use concat instead of append. append method is deprecated."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
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