gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    \"country\": [\"Brazil\", \"Russia\", \"India\", \"China\", \"South Africa\"],\n",
    "    \"capital\": [\"Brasilia\", \"Moscow\", \"New Dehli\", \"Beijing\", \"Pretoria\"],\n",
    "    \"area\": [8.516, 17.10, 3.286, 9.597, 1.221],\n",
    "    \"population\": [200.4, 143.5, 1252, 1357, 52.98],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>8.516</td>\n",
       "      <td>200.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2</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>3</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>4</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",
       "0        Brazil   Brasilia   8.516      200.40\n",
       "1        Russia     Moscow  17.100      143.50\n",
       "2         India  New Dehli   3.286     1252.00\n",
       "3         China    Beijing   9.597     1357.00\n",
       "4  South Africa   Pretoria   1.221       52.98"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<table border=\"1\" class=\"dataframe\">\n",
      "  <thead>\n",
      "    <tr style=\"text-align: right;\">\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",
      "      <td>Brazil</td>\n",
      "      <td>Brasilia</td>\n",
      "      <td>8.516</td>\n",
      "      <td>200.40</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>Russia</td>\n",
      "      <td>Moscow</td>\n",
      "      <td>17.100</td>\n",
      "      <td>143.50</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>India</td>\n",
      "      <td>New Dehli</td>\n",
      "      <td>3.286</td>\n",
      "      <td>1252.00</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>China</td>\n",
      "      <td>Beijing</td>\n",
      "      <td>9.597</td>\n",
      "      <td>1357.00</td>\n",
      "    </tr>\n",
      "    <tr>\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"
     ]
    }
   ],
   "source": [
    "table = df.to_html(index=False)\n",
    "print(table)"
   ]
  }
 ],
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