how-to-get-column-names-Pandas-python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Get Column Names\n",
    "This is the example notebook for the post about [how to get Pandas column names](https://www.marsja.se/how-to-get-the-column-names-from-a-pandas-dataframe-print-and-list/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>region</th>\n",
       "      <th>tfr</th>\n",
       "      <th>contraception</th>\n",
       "      <th>educationMale</th>\n",
       "      <th>educationFemale</th>\n",
       "      <th>lifeMale</th>\n",
       "      <th>lifeFemale</th>\n",
       "      <th>infantMortality</th>\n",
       "      <th>GDPperCapita</th>\n",
       "      <th>economicActivityMale</th>\n",
       "      <th>economicActivityFemale</th>\n",
       "      <th>illiteracyMale</th>\n",
       "      <th>illiteracyFemale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>6.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2848.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>52.800</td>\n",
       "      <td>85.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>863.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>3.81</td>\n",
       "      <td>52.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>9.9</td>\n",
       "      <td>67.5</td>\n",
       "      <td>70.3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>76.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>26.100</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American.Samoa</th>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.8</td>\n",
       "      <td>42.4</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                region   tfr  contraception  educationMale  educationFemale  \\\n",
       "Afghanistan       Asia  6.90            NaN            NaN              NaN   \n",
       "Albania         Europe  2.60            NaN            NaN              NaN   \n",
       "Algeria         Africa  3.81           52.0           11.1              9.9   \n",
       "American.Samoa    Asia   NaN            NaN            NaN              NaN   \n",
       "Andorra         Europe   NaN            NaN            NaN              NaN   \n",
       "\n",
       "                lifeMale  lifeFemale  infantMortality  GDPperCapita  \\\n",
       "Afghanistan         45.0        46.0            154.0        2848.0   \n",
       "Albania             68.0        74.0             32.0         863.0   \n",
       "Algeria             67.5        70.3             44.0        1531.0   \n",
       "American.Samoa      68.0        73.0             11.0           NaN   \n",
       "Andorra              NaN         NaN              NaN           NaN   \n",
       "\n",
       "                economicActivityMale  economicActivityFemale  illiteracyMale  \\\n",
       "Afghanistan                     87.5                     7.2          52.800   \n",
       "Albania                          NaN                     NaN             NaN   \n",
       "Algeria                         76.4                     7.8          26.100   \n",
       "American.Samoa                  58.8                    42.4           0.264   \n",
       "Andorra                          NaN                     NaN             NaN   \n",
       "\n",
       "                illiteracyFemale  \n",
       "Afghanistan                85.00  \n",
       "Albania                      NaN  \n",
       "Algeria                    51.00  \n",
       "American.Samoa              0.36  \n",
       "Andorra                      NaN  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/carData/UN98.csv',\n",
    "                index_col=0)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['region', 'tfr', 'contraception', 'educationMale', 'educationFemale',\n",
      "       'lifeMale', 'lifeFemale', 'infantMortality', 'GDPperCapita',\n",
      "       'economicActivityMale', 'economicActivityFemale', 'illiteracyMale',\n",
      "       'illiteracyFemale'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['region', 'tfr', 'contraception', 'educationMale', 'educationFemale',\n",
      "       'lifeMale', 'lifeFemale', 'infantMortality', 'GDPperCapita',\n",
      "       'economicActivityMale', 'economicActivityFemale', 'illiteracyMale',\n",
      "       'illiteracyFemale'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region\n",
      "tfr\n",
      "contraception\n",
      "educationMale\n",
      "educationFemale\n",
      "lifeMale\n",
      "lifeFemale\n",
      "infantMortality\n",
      "GDPperCapita\n",
      "economicActivityMale\n",
      "economicActivityFemale\n",
      "illiteracyMale\n",
      "illiteracyFemale\n"
     ]
    }
   ],
   "source": [
    "for col_name in df.columns: \n",
    "    print(col_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['region', 'tfr', 'contraception', 'educationMale', 'educationFemale', 'lifeMale', 'lifeFemale', 'infantMortality', 'GDPperCapita', 'economicActivityMale', 'economicActivityFemale', 'illiteracyMale', 'illiteracyFemale']\n"
     ]
    }
   ],
   "source": [
    "print(list(df.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['region', 'tfr', 'contraception', 'educationMale', 'educationFemale', 'lifeMale', 'lifeFemale', 'infantMortality', 'GDPperCapita', 'economicActivityMale', 'economicActivityFemale', 'illiteracyMale', 'illiteracyFemale']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GDPperCapita',\n",
       " 'contraception',\n",
       " 'economicActivityFemale',\n",
       " 'economicActivityMale',\n",
       " 'educationFemale',\n",
       " 'educationMale',\n",
       " 'illiteracyFemale',\n",
       " 'illiteracyMale',\n",
       " 'infantMortality',\n",
       " 'lifeFemale',\n",
       " 'lifeMale',\n",
       " 'region',\n",
       " 'tfr']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print Values using Column Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.9  2.6  3.81  nan  nan 6.69  nan 2.62 1.7  1.89 1.42 2.3  1.95 2.97\n",
      " 3.14 1.73 1.4  1.62 3.66 5.83 5.89 4.36 1.4  4.45 2.17 2.7  1.45 6.57\n",
      " 6.28 4.5  5.3  1.61 3.56 4.95 5.51 2.44 1.8  2.69 5.51 5.87 3.5  2.95\n",
      " 1.6  1.55 2.31 1.4  6.24 1.82 5.39  nan 2.8  4.32 3.1  3.4  3.09 5.51\n",
      " 5.34 1.3  7.   2.76 1.83 1.63  nan 2.85 5.4  5.2  8.   1.9  1.3  5.28\n",
      " 1.38  nan 2.1  3.04 4.9  6.61 5.42 2.32 4.6  4.3  1.32 1.4  2.19 3.07\n",
      " 2.63 4.77 5.25 1.8  2.75 1.19 5.1  2.44 1.48 5.13 2.3  4.85 3.8  2.1\n",
      " 1.65 2.77 3.21 6.69 1.4  2.75 4.86 6.33 5.92 1.45 1.5  1.76 1.6  1.9\n",
      " 5.65 6.69 3.24 6.8  6.6  2.1  4.49 2.   5.03 2.28 2.75 5.6  1.8   nan\n",
      " 3.27 3.1  6.06 3.3  4.9  4.95 1.55 2.1  2.53 2.02 3.85 7.1  5.97 5.11\n",
      " 1.88 7.2  5.02 3.   2.63 4.65 4.17 2.98 3.62 1.65 1.48 2.1  3.77 2.1\n",
      " 1.4  1.35 6.   2.63 3.82 3.8   nan  nan 5.9  5.62 2.59 6.06 1.79 1.5\n",
      " 1.3  4.98 7.   3.81 1.22 2.1  3.86 4.61 2.39 4.46 1.8  1.46 4.   3.93\n",
      " 5.48 1.74 6.08 4.02 2.1  2.92 2.5  3.58  nan 7.1  1.38 3.46 1.72 1.96\n",
      " 2.25 3.48 4.36 2.98 2.97 3.03 3.98 7.6  1.8  5.49 4.68]\n"
     ]
    }
   ],
   "source": [
    "print(df['tfr'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get multiple Columns"
   ]
  },
  {
   "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",
       "    }\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>tfr</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>6.90</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2.60</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>3.81</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American.Samoa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western.Sahara</th>\n",
       "      <td>3.98</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>7.60</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yugoslavia</th>\n",
       "      <td>1.80</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>5.49</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>4.68</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tfr  region\n",
       "Afghanistan     6.90    Asia\n",
       "Albania         2.60  Europe\n",
       "Algeria         3.81  Africa\n",
       "American.Samoa   NaN    Asia\n",
       "Andorra          NaN  Europe\n",
       "...              ...     ...\n",
       "Western.Sahara  3.98  Africa\n",
       "Yemen           7.60    Asia\n",
       "Yugoslavia      1.80  Europe\n",
       "Zambia          5.49  Africa\n",
       "Zimbabwe        4.68  Africa\n",
       "\n",
       "[207 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['tfr', 'region']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rename Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>region</th>\n",
       "      <th>TFR</th>\n",
       "      <th>contraception</th>\n",
       "      <th>educationMale</th>\n",
       "      <th>educationFemale</th>\n",
       "      <th>lifeMale</th>\n",
       "      <th>lifeFemale</th>\n",
       "      <th>infantMortality</th>\n",
       "      <th>GDPperCapita</th>\n",
       "      <th>economicActivityMale</th>\n",
       "      <th>economicActivityFemale</th>\n",
       "      <th>illiteracyMale</th>\n",
       "      <th>illiteracyFemale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>6.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2848.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>52.800</td>\n",
       "      <td>85.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>863.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>3.81</td>\n",
       "      <td>52.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>9.9</td>\n",
       "      <td>67.5</td>\n",
       "      <td>70.3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>76.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>26.100</td>\n",
       "      <td>51.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American.Samoa</th>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.8</td>\n",
       "      <td>42.4</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western.Sahara</th>\n",
       "      <td>Africa</td>\n",
       "      <td>3.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.8</td>\n",
       "      <td>63.1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>Asia</td>\n",
       "      <td>7.60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.4</td>\n",
       "      <td>58.4</td>\n",
       "      <td>80.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>80.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>32.406</td>\n",
       "      <td>69.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yugoslavia</th>\n",
       "      <td>Europe</td>\n",
       "      <td>1.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.8</td>\n",
       "      <td>75.3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.782</td>\n",
       "      <td>9.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>Africa</td>\n",
       "      <td>5.49</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>42.2</td>\n",
       "      <td>43.7</td>\n",
       "      <td>103.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.400</td>\n",
       "      <td>28.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>Africa</td>\n",
       "      <td>4.68</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.6</td>\n",
       "      <td>49.4</td>\n",
       "      <td>68.0</td>\n",
       "      <td>786.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>46.7</td>\n",
       "      <td>9.600</td>\n",
       "      <td>20.100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                region   TFR  contraception  educationMale  educationFemale  \\\n",
       "Afghanistan       Asia  6.90            NaN            NaN              NaN   \n",
       "Albania         Europe  2.60            NaN            NaN              NaN   \n",
       "Algeria         Africa  3.81           52.0           11.1              9.9   \n",
       "American.Samoa    Asia   NaN            NaN            NaN              NaN   \n",
       "Andorra         Europe   NaN            NaN            NaN              NaN   \n",
       "...                ...   ...            ...            ...              ...   \n",
       "Western.Sahara  Africa  3.98            NaN            NaN              NaN   \n",
       "Yemen             Asia  7.60            7.0            NaN              NaN   \n",
       "Yugoslavia      Europe  1.80            NaN            NaN              NaN   \n",
       "Zambia          Africa  5.49           25.0            7.9              6.8   \n",
       "Zimbabwe        Africa  4.68           48.0            NaN              NaN   \n",
       "\n",
       "                lifeMale  lifeFemale  infantMortality  GDPperCapita  \\\n",
       "Afghanistan         45.0        46.0            154.0        2848.0   \n",
       "Albania             68.0        74.0             32.0         863.0   \n",
       "Algeria             67.5        70.3             44.0        1531.0   \n",
       "American.Samoa      68.0        73.0             11.0           NaN   \n",
       "Andorra              NaN         NaN              NaN           NaN   \n",
       "...                  ...         ...              ...           ...   \n",
       "Western.Sahara      59.8        63.1             64.0           NaN   \n",
       "Yemen               57.4        58.4             80.0         732.0   \n",
       "Yugoslavia          69.8        75.3             19.0        1487.0   \n",
       "Zambia              42.2        43.7            103.0         382.0   \n",
       "Zimbabwe            47.6        49.4             68.0         786.0   \n",
       "\n",
       "                economicActivityMale  economicActivityFemale  illiteracyMale  \\\n",
       "Afghanistan                     87.5                     7.2          52.800   \n",
       "Albania                          NaN                     NaN             NaN   \n",
       "Algeria                         76.4                     7.8          26.100   \n",
       "American.Samoa                  58.8                    42.4           0.264   \n",
       "Andorra                          NaN                     NaN             NaN   \n",
       "...                              ...                     ...             ...   \n",
       "Western.Sahara                   NaN                     NaN             NaN   \n",
       "Yemen                           80.6                     1.9          32.406   \n",
       "Yugoslavia                       NaN                     NaN           1.782   \n",
       "Zambia                           NaN                     NaN          14.400   \n",
       "Zimbabwe                        77.7                    46.7           9.600   \n",
       "\n",
       "                illiteracyFemale  \n",
       "Afghanistan               85.000  \n",
       "Albania                      NaN  \n",
       "Algeria                   51.000  \n",
       "American.Samoa             0.360  \n",
       "Andorra                      NaN  \n",
       "...                          ...  \n",
       "Western.Sahara               NaN  \n",
       "Yemen                     69.552  \n",
       "Yugoslavia                 9.072  \n",
       "Zambia                    28.700  \n",
       "Zimbabwe                  20.100  \n",
       "\n",
       "[207 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'tfr': 'TFR'})"
   ]
  }
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