DataVisualisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "62821c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d26f1370",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\n",
    "  \"./data/3_TwoNumOrdered.csv\", \n",
    "    sep=' '\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1fb55dc8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'value'], dtype='object')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the column\n",
    "data.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "4243cbde",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1822 entries, 0 to 1821\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   date    1822 non-null   object \n",
      " 1   value   1822 non-null   float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 28.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#check type of each column\n",
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "61144a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tranform the type pf date to datetime pandas (use pd.to_datetime)\n",
    "data['date']=pd.to_datetime(data['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a492c4f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1822 entries, 0 to 1821\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   date    1822 non-null   datetime64[ns]\n",
      " 1   value   1822 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 28.6 KB\n"
     ]
    }
   ],
   "source": [
    "# check again\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00866130",
   "metadata": {},
   "source": [
    "[check](https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.year.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "18976f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-04-28</td>\n",
       "      <td>135.98</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>147.49</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-04-30</td>\n",
       "      <td>146.93</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>139.89</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-02</td>\n",
       "      <td>125.60</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1817</th>\n",
       "      <td>2018-04-19</td>\n",
       "      <td>8298.69</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1818</th>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>8880.23</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1819</th>\n",
       "      <td>2018-04-21</td>\n",
       "      <td>8997.57</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1820</th>\n",
       "      <td>2018-04-22</td>\n",
       "      <td>9001.64</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>8958.55</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1822 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date    value  year  month  day\n",
       "0    2013-04-28   135.98  2013      4   28\n",
       "1    2013-04-29   147.49  2013      4   29\n",
       "2    2013-04-30   146.93  2013      4   30\n",
       "3    2013-05-01   139.89  2013      5    1\n",
       "4    2013-05-02   125.60  2013      5    2\n",
       "...         ...      ...   ...    ...  ...\n",
       "1817 2018-04-19  8298.69  2018      4   19\n",
       "1818 2018-04-20  8880.23  2018      4   20\n",
       "1819 2018-04-21  8997.57  2018      4   21\n",
       "1820 2018-04-22  9001.64  2018      4   22\n",
       "1821 2018-04-23  8958.55  2018      4   23\n",
       "\n",
       "[1822 rows x 5 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create year/ month/day column\n",
    "data['year']=data['date'].dt.year\n",
    "data['month']=data['date'].dt.month\n",
    "data['day']=data['date'].dt.day\n",
    "     \n",
    "data\n",
    "\n",
    "#hint data['date'].dt.year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d0e20811",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1822 entries, 0 to 1821\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   date    1822 non-null   datetime64[ns]\n",
      " 1   value   1822 non-null   float64       \n",
      " 2   year    1822 non-null   int64         \n",
      " 3   month   1822 non-null   int64         \n",
      " 4   day     1822 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(3)\n",
      "memory usage: 71.3 KB\n"
     ]
    }
   ],
   "source": [
    "#check again\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b6c4342c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2009b7a82e0>]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# line plot data['date']/ value\n",
    "import datetime\n",
    "# 1- create a figure using plt.subplots, fix figsize=(8,6)\n",
    "import matplotlib.pyplot as plt\n",
    "# 2-plot \n",
    "fig,ax=plt.subplots(figsize=(8,6)) \n",
    "ax.plot(data['date'],data['value'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "10334749",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3- set the x limits of the current axes between  (2015, 1, 26) and  (2017, 2, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "a18bc070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2009b836d00>]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(8,6)) \n",
    "ax.set_xlim([datetime.date(2015, 1, 26), datetime.date(2017, 2, 1)])\n",
    "ax.plot(data['date'],data['value'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db31153",
   "metadata": {},
   "source": [
    "# understand subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "5597099e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAFpCAYAAABaoss2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXEklEQVR4nO3dYYhd533n8e9vRzFs3DTO1pNuKsm7KihxVbCLM1W92e3WIbSRXIq2kBdSQk1MghBYpa8WC5ZNX+RVCYUSImcYjDB5E72JN1XDJNqF0hrW612NF1u2kpWZVVJrKoPHcfDSBKqV898X9yq+vrnSnJk5d6Rn5/uBwfd5znPP+evwh9+cc+8cp6qQJElt+Ce3ugBJktSdwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDVkzeBOcirJ60levsH2JPlykuUk55M80H+ZkiQJul1xPwUcuMn2g8De4c9R4KubL0uSJE2yZnBX1TPAmzdZcgj4Wg08B9yV5EN9FShJkt7Rx2fcO4HLI+OV4ZwkSerZjh72kQlzE5+jmuQog9vp3HnnnR+99957ezi8Wvf888+/UVWz09q/fadJptl39pxupI++6yO4V4DdI+NdwJVJC6tqAVgAmJubq6WlpR4Or9Yl+btp7t++0yTT7Dt7TjfSR9/1cav8DPDI8NvlDwJvVdVrPexXkiSNWfOKO8nXgYeAu5OsAH8KvAegquaBReBhYBn4CfDotIqVJGm7WzO4q+rIGtsLeKy3iiRJ0g355DRJkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSGdgjvJgSQXkywnOTFh+/uT/FWSF5NcSPJo/6VKkqQ1gzvJDHASOAjsA44k2Te27DHgu1V1P/AQ8OdJ7ui5VkmStr0uV9z7geWqulRVV4HTwKGxNQW8L0mAXwDeBK71WqkkSeoU3DuByyPjleHcqK8AvwZcAV4C/qSqfjq+oyRHkywlWVpdXd1gydL62HfaavacpqlLcGfCXI2NPwm8APwK8BvAV5L84s+9qWqhquaqam52dnadpUobY99pq9lzmqYuwb0C7B4Z72JwZT3qUeDpGlgGvg/c20+JkiTpui7BfQ7Ym2TP8Atnh4EzY2teBT4BkOSXgY8Al/osVJIkwY61FlTVtSTHgbPADHCqqi4kOTbcPg98EXgqyUsMbq0/XlVvTLFuSZK2pTWDG6CqFoHFsbn5kddXgN/rtzRJkjTOJ6dJktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ3pFNxJDiS5mGQ5yYkbrHkoyQtJLiT5237LlCRJADvWWpBkBjgJ/C6wApxLcqaqvjuy5i7gCeBAVb2a5INTqleSpG2tyxX3fmC5qi5V1VXgNHBobM2ngaer6lWAqnq93zIlSRJ0C+6dwOWR8cpwbtSHgQ8k+Zskzyd5ZNKOkhxNspRkaXV1dWMVS+tk32mr2XOapi7BnQlzNTbeAXwU+H3gk8B/TPLhn3tT1UJVzVXV3Ozs7LqLlTbCvtNWs+c0TWt+xs3gCnv3yHgXcGXCmjeq6sfAj5M8A9wPvNJLlZIkCeh2xX0O2JtkT5I7gMPAmbE1fwn8dpIdSd4L/BbwvX5LlSRJa15xV9W1JMeBs8AMcKqqLiQ5Ntw+X1XfS/Id4DzwU+DJqnp5moVLkrQddblVTlUtAotjc/Nj4y8BX+qvNEmSNM4np0mS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSGdgjvJgSQXkywnOXGTdb+Z5O0kn+qvREmSdN2awZ1kBjgJHAT2AUeS7LvBuj8DzvZdpCRJGuhyxb0fWK6qS1V1FTgNHJqw7o+BbwCv91ifJEka0SW4dwKXR8Yrw7mfSbIT+ENgvr/SJEnSuC7BnQlzNTb+C+Dxqnr7pjtKjiZZSrK0urrasURpc+w7bTV7TtPUJbhXgN0j413AlbE1c8DpJD8APgU8keTfje+oqhaqaq6q5mZnZzdWsbRO9p22mj2nadrRYc05YG+SPcDfA4eBT48uqKo9118neQr4VlV9s78yJUkSdAjuqrqW5DiDb4vPAKeq6kKSY8Ptfq4tSdIW6XLFTVUtAotjcxMDu6o+u/myJEnSJD45TZKkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqSKfgTnIgycUky0lOTNj+mSTnhz/PJrm//1IlSdKawZ1kBjgJHAT2AUeS7Btb9n3gd6rqPuCLwELfhUqSpG5X3PuB5aq6VFVXgdPAodEFVfVsVf1oOHwO2NVvmZIkCboF907g8sh4ZTh3I58Dvj1pQ5KjSZaSLK2urnavUtoE+05bzZ7TNHUJ7kyYq4kLk48zCO7HJ22vqoWqmququdnZ2e5VSptg32mr2XOaph0d1qwAu0fGu4Ar44uS3Ac8CRysqh/2U54kSRrV5Yr7HLA3yZ4kdwCHgTOjC5LcAzwN/FFVvdJ/mZIkCTpccVfVtSTHgbPADHCqqi4kOTbcPg98Afgl4IkkANeqam56ZUuStD11uVVOVS0Ci2Nz8yOvPw98vt/SJEnSOJ+cJklSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhnQK7iQHklxMspzkxITtSfLl4fbzSR7ov1RJkrRmcCeZAU4CB4F9wJEk+8aWHQT2Dn+OAl/tuU5JkkS3K+79wHJVXaqqq8Bp4NDYmkPA12rgOeCuJB/quVZJkra9LsG9E7g8Ml4Zzq13jSRJ2qQdHdZkwlxtYA1JjjK4lQ7wj0le7nD8abobeMMabnkNH5nmzu272+74t0sNU+s7e84abmLTfdcluFeA3SPjXcCVDayhqhaABYAkS1U1t65qe2YNt0cNSZamuX/77vY6/u1Uw7T2bc9Zw81q2Ow+utwqPwfsTbInyR3AYeDM2JozwCPDb5c/CLxVVa9ttjhJkvRua15xV9W1JMeBs8AMcKqqLiQ5Ntw+DywCDwPLwE+AR6dXsiRJ21eXW+VU1SKDcB6dmx95XcBj6zz2wjrXT4M1DNzqGrby+Lf63wq3voZbfXzYXjVsp3/rzVjDwKZryCBzJUlSC3zkqSRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGrJmcCc5leT1JC/fYHuSfDnJcpLzSR7ov0xJkgTdrrifAg7cZPtBYO/w5yjw1c2XJUmSJlkzuKvqGeDNmyw5BHytBp4D7kryob4KlCRJ7+jjM+6dwOWR8cpwTpIk9WxHD/vIhLmauDA5yuB2OnfeeedH77333h4Or9Y9//zzb1TV7LT2b99pkmn2nT2nG+mj7/oI7hVg98h4F3Bl0sKqWgAWAObm5mppaamHw6t1Sf5umvu37zTJNPvOntON9NF3fdwqPwM8Mvx2+YPAW1X1Wg/7lSRJY9a84k7ydeAh4O4kK8CfAu8BqKp5YBF4GFgGfgI8Oq1iJUna7tYM7qo6ssb2Ah7rrSJJknRDPjlNkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWpIp+BOciDJxSTLSU5M2P7+JH+V5MUkF5I82n+pkiRpzeBOMgOcBA4C+4AjSfaNLXsM+G5V3Q88BPx5kjt6rlWSpG2vyxX3fmC5qi5V1VXgNHBobE0B70sS4BeAN4FrvVYqSZI6BfdO4PLIeGU4N+orwK8BV4CXgD+pqp+O7yjJ0SRLSZZWV1c3WLK0Pvadtpo9p2nqEtyZMFdj408CLwC/AvwG8JUkv/hzb6paqKq5qpqbnZ1dZ6nSxth32mr2nKapS3CvALtHxrsYXFmPehR4ugaWge8D9/ZToiRJuq5LcJ8D9ibZM/zC2WHgzNiaV4FPACT5ZeAjwKU+C5UkSbBjrQVVdS3JceAsMAOcqqoLSY4Nt88DXwSeSvISg1vrj1fVG1OsW5KkbWnN4AaoqkVgcWxufuT1FeD3+i1NkiSN88lpkiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqSKfgTnIgycUky0lO3GDNQ0leSHIhyd/2W6YkSQLYsdaCJDPASeB3gRXgXJIzVfXdkTV3AU8AB6rq1SQfnFK9kiRta12uuPcDy1V1qaquAqeBQ2NrPg08XVWvAlTV6/2WKUmSoFtw7wQuj4xXhnOjPgx8IMnfJHk+ySN9FShJkt6x5q1yIBPmasJ+Pgp8AvinwH9L8lxVvfKuHSVHgaMA99xzz/qrlTbAvtNWs+c0TV2uuFeA3SPjXcCVCWu+U1U/rqo3gGeA+8d3VFULVTVXVXOzs7MbrVlaF/tOW82e0zR1Ce5zwN4ke5LcARwGzoyt+Uvgt5PsSPJe4LeA7/VbqiRJWvNWeVVdS3IcOAvMAKeq6kKSY8Pt81X1vSTfAc4DPwWerKqXp1m4JEnbUZfPuKmqRWBxbG5+bPwl4Ev9lSZJksb55DRJkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSGdgjvJgSQXkywnOXGTdb+Z5O0kn+qvREmSdN2awZ1kBjgJHAT2AUeS7LvBuj8DzvZdpCRJGuhyxb0fWK6qS1V1FTgNHJqw7o+BbwCv91ifJEka0SW4dwKXR8Yrw7mfSbIT+ENg/mY7SnI0yVKSpdXV1fXWKm2IfaetZs9pmroEdybM1dj4L4DHq+rtm+2oqhaqaq6q5mZnZzuWKG2OfaetZs9pmnZ0WLMC7B4Z7wKujK2ZA04nAbgbeDjJtar6Zh9FSpKkgS7BfQ7Ym2QP8PfAYeDTowuqas/110meAr5laEuS1L81g7uqriU5zuDb4jPAqaq6kOTYcPtNP9eWJEn96XLFTVUtAotjcxMDu6o+u/myJEnSJD45TZKkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqSKfgTnIgycUky0lOTNj+mSTnhz/PJrm//1IlSdKawZ1kBjgJHAT2AUeS7Btb9n3gd6rqPuCLwELfhUqSpG5X3PuB5aq6VFVXgdPAodEFVfVsVf1oOHwO2NVvmZIkCboF907g8sh4ZTh3I58Dvj1pQ5KjSZaSLK2urnavUtoE+05bzZ7TNHUJ7kyYq4kLk48zCO7HJ22vqoWqmququdnZ2e5VSptg32mr2XOaph0d1qwAu0fGu4Ar44uS3Ac8CRysqh/2U54kSRrV5Yr7HLA3yZ4kdwCHgTOjC5LcAzwN/FFVvdJ/mZIkCTpccVfVtSTHgbPADHCqqi4kOTbcPg98Afgl4IkkANeqam56ZUuStD11uVVOVS0Ci2Nz8yOvPw98vt/SJEnSOJ+cJklSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhnQK7iQHklxMspzkxITtSfLl4fbzSR7ov1RJkrRmcCeZAU4CB4F9wJEk+8aWHQT2Dn+OAl/tuU5JkkS3K+79wHJVXaqqq8Bp4NDYmkPA12rgOeCuJB/quVZJkra9LsG9E7g8Ml4Zzq13jSRJ2qQdHdZkwlxtYA1JjjK4lQ7wj0le7nD8abobeMMabnkNH5nmzu272+74t0sNU+s7e84abmLTfdcluFeA3SPjXcCVDayhqhaABYAkS1U1t65qe2YNt0cNSZamuX/77vY6/u1Uw7T2bc9Zw81q2Ow+utwqPwfsTbInyR3AYeDM2JozwCPDb5c/CLxVVa9ttjhJkvRua15xV9W1JMeBs8AMcKqqLiQ5Ntw+DywCDwPLwE+AR6dXsiRJ21eXW+VU1SKDcB6dmx95XcBj6zz2wjrXT4M1DNzqGrby+Lf63wq3voZbfXzYXjVsp3/rzVjDwKZryCBzJUlSC3zkqSRJDZlKcG/mEalrvben439meNzzSZ5Ncv/Ith8keSnJC5v59l+HGh5K8tbwOC8k+ULX9/ZYw78fOf7LSd5O8s+G2zZ9HpKcSvL6jf4Ups8+uNU917EG+w77bqPH2kQNU+07e+5nx9iyvqOqev1h8AW2/w38KnAH8CKwb2zNw8C3Gfz994PAf+/63p6O/zHgA8PXB68ffzj+AXD3FpyDh4BvbeS9fdUwtv4PgL/u+Tz8W+AB4OUbbO+lD251z9l39t127Tt7buv7rqqmcsW9mUekdnnvpo9fVc9W1Y+Gw+cY/N15nzbz7+jjHGxkP0eAr2/gODdUVc8Ab95kSV99cKt7rlMN9t1E9l3bfWfPDW1h300luDfziNQ+Hp263n18jsFvQdcV8J+TPJ/B0482omsN/yrJi0m+neTX1/nevmogyXuBA8A3Rqb7OA8brXG95+BW91zXGkbZd/bd/w99Z89111svdPpzsHXazCNSOz06tYfjDxYmH2fQyP9mZPpfV9WVJB8E/kuS/zX8TarvGv4n8C+q6h+SPAx8k8H/Xa2Pc9C1huv+APivVTX622If52GjNa73HNzqnutaw2ChfXedfdf9WJupYbBwOn1nz3XXWy9M44p7M49I7fTo1B6OT5L7gCeBQ1X1w+vzVXVl+N/Xgf/E4DbGeq1ZQ1X9n6r6h+HrReA9Se7uWn8fNYw4zNito57Ow0ZrXO85uNU917UG++7d7Lvux9pMDdPsO3uuu/56oTb5gfz4D4Or+EvAHt75oP3Xx9b8Pu/+kP5/dH1vT8e/h8FT3j42Nn8n8L6R188CB6Z0Dv457/wd/X7g1eH52PQ5WM+5BN7P4HOZO/s+D8P3/0tu/GWNXvrgVvecfWffbde+s+e2vu+qqv/gHhbyMPAKg2/K/Yfh3DHg2PB1gJPD7S8Bczd77xSO/yTwI+CF4c/ScP5XhyftReDCRo/fsYbjw2O8yOALIx+72XunUcNw/Fng9Nj7ejkPDH6zfQ34vwx+q/zctPrgVvecfWffbde+s+e2vu98cpokSQ3xyWmSJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5Kkhvw/eM3890J6DFUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a 2×3 grid of subplots\n",
    "fig,ax= plt.subplots(2,3,sharex='col',sharey='row',figsize=(8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f472f1fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 6 Axes>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        ax[i,j].text(0.5,0.5,str((i,j)),fontsize=18,ha='center')\n",
    "        \n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "4bc47db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\n",
    "  \"data/multivariate.csv\", \n",
    "    sep=';'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e04883f3",
   "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>Pop</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Mortality rate</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Infant mortality</th>\n",
       "      <th>Children per woman</th>\n",
       "      <th>Growth rate</th>\n",
       "      <th>Population aged 65+</th>\n",
       "      <th>Group</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Channel Islands</td>\n",
       "      <td>166</td>\n",
       "      <td>9.280</td>\n",
       "      <td>9.140</td>\n",
       "      <td>81.269</td>\n",
       "      <td>7.276</td>\n",
       "      <td>1.492</td>\n",
       "      <td>4.56</td>\n",
       "      <td>30</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>5733</td>\n",
       "      <td>10.707</td>\n",
       "      <td>9.620</td>\n",
       "      <td>80.839</td>\n",
       "      <td>3.495</td>\n",
       "      <td>1.758</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1132</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>1302</td>\n",
       "      <td>10.698</td>\n",
       "      <td>12.657</td>\n",
       "      <td>77.342</td>\n",
       "      <td>2.843</td>\n",
       "      <td>1.657</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>256</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>5556</td>\n",
       "      <td>10.672</td>\n",
       "      <td>9.865</td>\n",
       "      <td>81.576</td>\n",
       "      <td>1.839</td>\n",
       "      <td>1.764</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1212</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>337</td>\n",
       "      <td>12.836</td>\n",
       "      <td>6.467</td>\n",
       "      <td>83.220</td>\n",
       "      <td>1.752</td>\n",
       "      <td>1.893</td>\n",
       "      <td>7.83</td>\n",
       "      <td>50</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>333</td>\n",
       "      <td>18.300</td>\n",
       "      <td>3.500</td>\n",
       "      <td>74.900</td>\n",
       "      <td>13.100</td>\n",
       "      <td>2.570</td>\n",
       "      <td>16.50</td>\n",
       "      <td>1099</td>\n",
       "      <td>West</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>19482</td>\n",
       "      <td>21.200</td>\n",
       "      <td>5.500</td>\n",
       "      <td>70.600</td>\n",
       "      <td>14.800</td>\n",
       "      <td>2.750</td>\n",
       "      <td>33.10</td>\n",
       "      <td>848</td>\n",
       "      <td>West</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>81086</td>\n",
       "      <td>15.600</td>\n",
       "      <td>5.800</td>\n",
       "      <td>76.300</td>\n",
       "      <td>9.700</td>\n",
       "      <td>2.000</td>\n",
       "      <td>7.80</td>\n",
       "      <td>6672</td>\n",
       "      <td>West</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>9543</td>\n",
       "      <td>9.900</td>\n",
       "      <td>1.800</td>\n",
       "      <td>77.600</td>\n",
       "      <td>5.200</td>\n",
       "      <td>1.720</td>\n",
       "      <td>15.20</td>\n",
       "      <td>144</td>\n",
       "      <td>West</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>28758</td>\n",
       "      <td>30.300</td>\n",
       "      <td>6.600</td>\n",
       "      <td>64.600</td>\n",
       "      <td>48.100</td>\n",
       "      <td>3.730</td>\n",
       "      <td>22.20</td>\n",
       "      <td>870</td>\n",
       "      <td>West</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country    Pop  Birth rate  Mortality rate  Life expectancy  \\\n",
       "0         Channel Islands    166       9.280           9.140           81.269   \n",
       "1                 Denmark   5733      10.707           9.620           80.839   \n",
       "2                 Estonia   1302      10.698          12.657           77.342   \n",
       "3                 Finland   5556      10.672           9.865           81.576   \n",
       "4                 Iceland    337      12.836           6.467           83.220   \n",
       "..                    ...    ...         ...             ...              ...   \n",
       "196          Saudi Arabia    333      18.300           3.500           74.900   \n",
       "197  Syrian Arab Republic  19482      21.200           5.500           70.600   \n",
       "198                Turkey  81086      15.600           5.800           76.300   \n",
       "199  United Arab Emirates   9543       9.900           1.800           77.600   \n",
       "200                 Yemen  28758      30.300           6.600           64.600   \n",
       "\n",
       "     Infant mortality  Children per woman  Growth rate  Population aged 65+  \\\n",
       "0               7.276               1.492         4.56                   30   \n",
       "1               3.495               1.758         3.67                 1132   \n",
       "2               2.843               1.657        -2.63                  256   \n",
       "3               1.839               1.764         2.65                 1212   \n",
       "4               1.752               1.893         7.83                   50   \n",
       "..                ...                 ...          ...                  ...   \n",
       "196            13.100               2.570        16.50                 1099   \n",
       "197            14.800               2.750        33.10                  848   \n",
       "198             9.700               2.000         7.80                 6672   \n",
       "199             5.200               1.720        15.20                  144   \n",
       "200            48.100               3.730        22.20                  870   \n",
       "\n",
       "     Group Continent  \n",
       "0    North    Europe  \n",
       "1    North    Europe  \n",
       "2    North    Europe  \n",
       "3    North    Europe  \n",
       "4    North    Europe  \n",
       "..     ...       ...  \n",
       "196   West      Asia  \n",
       "197   West      Asia  \n",
       "198   West      Asia  \n",
       "199   West      Asia  \n",
       "200   West      Asia  \n",
       "\n",
       "[201 rows x 11 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "6d2287fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\hp\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Mortality rate', ylabel='Density'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the distribution of Birth rate and Mortality rate in the same plot \n",
    "import seaborn as sns\n",
    "sns.set_color_codes() \n",
    "sns.distplot(data['Birth rate'])\n",
    "sns.distplot(data['Mortality rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "4aa76f50",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Mortality rate', ylabel='Density'>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['Mortality rate'],kde=True, hist=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c6451c03",
   "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>Pop</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Mortality rate</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Infant mortality</th>\n",
       "      <th>Children per woman</th>\n",
       "      <th>Growth rate</th>\n",
       "      <th>Population aged 65+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pop</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042879</td>\n",
       "      <td>-0.005079</td>\n",
       "      <td>-0.006034</td>\n",
       "      <td>0.013773</td>\n",
       "      <td>-0.046170</td>\n",
       "      <td>-0.032978</td>\n",
       "      <td>0.751681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Birth rate</th>\n",
       "      <td>-0.042879</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043080</td>\n",
       "      <td>-0.839977</td>\n",
       "      <td>0.877932</td>\n",
       "      <td>0.977673</td>\n",
       "      <td>0.899490</td>\n",
       "      <td>-0.133978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mortality rate</th>\n",
       "      <td>-0.005079</td>\n",
       "      <td>-0.043080</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.294270</td>\n",
       "      <td>0.196863</td>\n",
       "      <td>0.008703</td>\n",
       "      <td>-0.313049</td>\n",
       "      <td>0.049057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life expectancy</th>\n",
       "      <td>-0.006034</td>\n",
       "      <td>-0.839977</td>\n",
       "      <td>-0.294270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.925539</td>\n",
       "      <td>-0.784815</td>\n",
       "      <td>-0.656512</td>\n",
       "      <td>0.079067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infant mortality</th>\n",
       "      <td>0.013773</td>\n",
       "      <td>0.877932</td>\n",
       "      <td>0.196863</td>\n",
       "      <td>-0.925539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843144</td>\n",
       "      <td>0.730304</td>\n",
       "      <td>-0.072616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children per woman</th>\n",
       "      <td>-0.046170</td>\n",
       "      <td>0.977673</td>\n",
       "      <td>0.008703</td>\n",
       "      <td>-0.784815</td>\n",
       "      <td>0.843144</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872341</td>\n",
       "      <td>-0.131308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Growth rate</th>\n",
       "      <td>-0.032978</td>\n",
       "      <td>0.899490</td>\n",
       "      <td>-0.313049</td>\n",
       "      <td>-0.656512</td>\n",
       "      <td>0.730304</td>\n",
       "      <td>0.872341</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Population aged 65+</th>\n",
       "      <td>0.751681</td>\n",
       "      <td>-0.133978</td>\n",
       "      <td>0.049057</td>\n",
       "      <td>0.079067</td>\n",
       "      <td>-0.072616</td>\n",
       "      <td>-0.131308</td>\n",
       "      <td>-0.117647</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pop  Birth rate  Mortality rate  Life expectancy  \\\n",
       "Pop                  1.000000   -0.042879       -0.005079        -0.006034   \n",
       "Birth rate          -0.042879    1.000000       -0.043080        -0.839977   \n",
       "Mortality rate      -0.005079   -0.043080        1.000000        -0.294270   \n",
       "Life expectancy     -0.006034   -0.839977       -0.294270         1.000000   \n",
       "Infant mortality     0.013773    0.877932        0.196863        -0.925539   \n",
       "Children per woman  -0.046170    0.977673        0.008703        -0.784815   \n",
       "Growth rate         -0.032978    0.899490       -0.313049        -0.656512   \n",
       "Population aged 65+  0.751681   -0.133978        0.049057         0.079067   \n",
       "\n",
       "                     Infant mortality  Children per woman  Growth rate  \\\n",
       "Pop                          0.013773           -0.046170    -0.032978   \n",
       "Birth rate                   0.877932            0.977673     0.899490   \n",
       "Mortality rate               0.196863            0.008703    -0.313049   \n",
       "Life expectancy             -0.925539           -0.784815    -0.656512   \n",
       "Infant mortality             1.000000            0.843144     0.730304   \n",
       "Children per woman           0.843144            1.000000     0.872341   \n",
       "Growth rate                  0.730304            0.872341     1.000000   \n",
       "Population aged 65+         -0.072616           -0.131308    -0.117647   \n",
       "\n",
       "                     Population aged 65+  \n",
       "Pop                             0.751681  \n",
       "Birth rate                     -0.133978  \n",
       "Mortality rate                  0.049057  \n",
       "Life expectancy                 0.079067  \n",
       "Infant mortality               -0.072616  \n",
       "Children per woman             -0.131308  \n",
       "Growth rate                    -0.117647  \n",
       "Population aged 65+             1.000000  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the correlation matrix \n",
    "mat=data.corr()\n",
    "mat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1aae894f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the heatmap using the correlationn matrix\n",
    "sns.set(rc = {'figure.figsize':(15,8)})\n",
    "sns.heatmap(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "d9b6426d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">amin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">amax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Mortality rate</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Mortality rate</th>\n",
       "      <th>Birth rate</th>\n",
       "      <th>Mortality rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>31.354386</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>10.8</td>\n",
       "      <td>2.400</td>\n",
       "      <td>48.4</td>\n",
       "      <td>14.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>18.131373</td>\n",
       "      <td>6.054902</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.600</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>10.381300</td>\n",
       "      <td>10.781825</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.467</td>\n",
       "      <td>13.8</td>\n",
       "      <td>16.187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>11.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.5</td>\n",
       "      <td>7.500</td>\n",
       "      <td>12.5</td>\n",
       "      <td>8.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>20.730769</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>12.8</td>\n",
       "      <td>4.700</td>\n",
       "      <td>27.7</td>\n",
       "      <td>7.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>16.497368</td>\n",
       "      <td>6.884211</td>\n",
       "      <td>9.6</td>\n",
       "      <td>3.000</td>\n",
       "      <td>25.6</td>\n",
       "      <td>10.900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean                      amin                      amax  \\\n",
       "              Birth rate Mortality rate Birth rate Mortality rate Birth rate   \n",
       "Continent                                                                      \n",
       "Africa         31.354386       8.500000       10.8          2.400       48.4   \n",
       "Asia           18.131373       6.054902        7.5          1.600       35.0   \n",
       "Europe         10.381300      10.781825        7.5          6.467       13.8   \n",
       "North America  11.500000       8.000000       10.5          7.500       12.5   \n",
       "Oceania        20.730769       6.200000       12.8          4.700       27.7   \n",
       "South America  16.497368       6.884211        9.6          3.000       25.6   \n",
       "\n",
       "                              \n",
       "              Mortality rate  \n",
       "Continent                     \n",
       "Africa                14.800  \n",
       "Asia                  11.500  \n",
       "Europe                16.187  \n",
       "North America          8.500  \n",
       "Oceania                7.600  \n",
       "South America         10.900  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using pivot_table , average,min,max( morality_rate, birth_rate ) for each continent  , comments \n",
    "import numpy as np\n",
    "table = pd.pivot_table(data, values=['Mortality rate', 'Birth rate'], index=['Continent'],\n",
    "                      aggfunc=[np.mean,np.min, np.max])\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "9e9168f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Local\\Temp/ipykernel_21748/45868392.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  data.groupby('Group')['Pop','Growth rate'].mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pop</th>\n",
       "      <th>Growth rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AusNZ</th>\n",
       "      <td>14807.000000</td>\n",
       "      <td>10.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caribean</th>\n",
       "      <td>2569.882353</td>\n",
       "      <td>4.623529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>19535.500000</td>\n",
       "      <td>12.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>60235.368421</td>\n",
       "      <td>12.394737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melanesia</th>\n",
       "      <td>2034.400000</td>\n",
       "      <td>15.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Micronesia</th>\n",
       "      <td>133.333333</td>\n",
       "      <td>11.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle</th>\n",
       "      <td>18264.777778</td>\n",
       "      <td>25.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North</th>\n",
       "      <td>16815.444444</td>\n",
       "      <td>8.515556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>182897.000000</td>\n",
       "      <td>8.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polynesia</th>\n",
       "      <td>199.333333</td>\n",
       "      <td>8.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>21388.000000</td>\n",
       "      <td>6.646667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South-Central</th>\n",
       "      <td>135035.714286</td>\n",
       "      <td>13.121429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South-East</th>\n",
       "      <td>59491.363636</td>\n",
       "      <td>12.327273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>18769.756098</td>\n",
       "      <td>16.773171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop  Growth rate\n",
       "Group                                    \n",
       "AusNZ           14807.000000    10.950000\n",
       "Caribean         2569.882353     4.623529\n",
       "Central         19535.500000    12.925000\n",
       "East            60235.368421    12.394737\n",
       "Melanesia        2034.400000    15.160000\n",
       "Micronesia        133.333333    11.766667\n",
       "Middle          18264.777778    25.777778\n",
       "North           16815.444444     8.515556\n",
       "North America  182897.000000     8.050000\n",
       "Polynesia         199.333333     8.033333\n",
       "South           21388.000000     6.646667\n",
       "South-Central  135035.714286    13.121429\n",
       "South-East      59491.363636    12.327273\n",
       "West            18769.756098    16.773171"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average pop and growth_rate  for each group in one code sentence. Use the groupby() method. \n",
    "data.groupby('Group')['Pop','Growth rate'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "bb67ed3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Pop</th>\n",
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       "      <th>Continent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Niger</td>\n",
       "      <td>22445</td>\n",
       "      <td>48.400</td>\n",
       "      <td>8.20</td>\n",
       "      <td>63.10</td>\n",
       "      <td>49.500</td>\n",
       "      <td>7.430</td>\n",
       "      <td>40.10</td>\n",
       "      <td>581</td>\n",
       "      <td>West</td>\n",
       "      <td>Africa</td>\n",
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       "      <th>197</th>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>19482</td>\n",
       "      <td>21.200</td>\n",
       "      <td>5.50</td>\n",
       "      <td>70.60</td>\n",
       "      <td>14.800</td>\n",
       "      <td>2.750</td>\n",
       "      <td>33.10</td>\n",
       "      <td>848</td>\n",
       "      <td>West</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>43021</td>\n",
       "      <td>41.200</td>\n",
       "      <td>8.50</td>\n",
       "      <td>61.10</td>\n",
       "      <td>55.600</td>\n",
       "      <td>5.410</td>\n",
       "      <td>32.20</td>\n",
       "      <td>1062</td>\n",
       "      <td>East</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>12329</td>\n",
       "      <td>41.800</td>\n",
       "      <td>10.40</td>\n",
       "      <td>58.20</td>\n",
       "      <td>70.300</td>\n",
       "      <td>5.620</td>\n",
       "      <td>32.00</td>\n",
       "      <td>319</td>\n",
       "      <td>East</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Chad</td>\n",
       "      <td>15444</td>\n",
       "      <td>43.400</td>\n",
       "      <td>13.10</td>\n",
       "      <td>52.70</td>\n",
       "      <td>85.900</td>\n",
       "      <td>5.740</td>\n",
       "      <td>31.30</td>\n",
       "      <td>383</td>\n",
       "      <td>Middle</td>\n",
       "      <td>Africa</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>1936</td>\n",
       "      <td>10.093</td>\n",
       "      <td>15.36</td>\n",
       "      <td>74.59</td>\n",
       "      <td>5.493</td>\n",
       "      <td>1.555</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>383</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>4417</td>\n",
       "      <td>10.600</td>\n",
       "      <td>15.60</td>\n",
       "      <td>71.30</td>\n",
       "      <td>8.300</td>\n",
       "      <td>1.560</td>\n",
       "      <td>-5.40</td>\n",
       "      <td>7283</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>6022</td>\n",
       "      <td>15.500</td>\n",
       "      <td>4.60</td>\n",
       "      <td>80.40</td>\n",
       "      <td>7.900</td>\n",
       "      <td>1.710</td>\n",
       "      <td>-6.20</td>\n",
       "      <td>530</td>\n",
       "      <td>West</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Romania</td>\n",
       "      <td>19105</td>\n",
       "      <td>8.900</td>\n",
       "      <td>13.70</td>\n",
       "      <td>75.20</td>\n",
       "      <td>8.300</td>\n",
       "      <td>1.540</td>\n",
       "      <td>-6.90</td>\n",
       "      <td>3575</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>6992</td>\n",
       "      <td>9.400</td>\n",
       "      <td>15.60</td>\n",
       "      <td>74.60</td>\n",
       "      <td>8.000</td>\n",
       "      <td>1.600</td>\n",
       "      <td>-7.60</td>\n",
       "      <td>1463</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country    Pop  Birth rate  Mortality rate  Life expectancy  \\\n",
       "94                  Niger  22445      48.400            8.20            63.10   \n",
       "197  Syrian Arab Republic  19482      21.200            5.50            70.60   \n",
       "58                 Uganda  43021      41.200            8.50            61.10   \n",
       "42                Burundi  12329      41.800           10.40            58.20   \n",
       "65                   Chad  15444      43.400           13.10            52.70   \n",
       "..                    ...    ...         ...             ...              ...   \n",
       "6                  Latvia   1936      10.093           15.36            74.59   \n",
       "20                Ukraine   4417      10.600           15.60            71.30   \n",
       "192               Lebanon   6022      15.500            4.60            80.40   \n",
       "17                Romania  19105       8.900           13.70            75.20   \n",
       "12               Bulgaria   6992       9.400           15.60            74.60   \n",
       "\n",
       "     Infant mortality  Children per woman  Growth rate  Population aged 65+  \\\n",
       "94             49.500               7.430        40.10                  581   \n",
       "197            14.800               2.750        33.10                  848   \n",
       "58             55.600               5.410        32.20                 1062   \n",
       "42             70.300               5.620        32.00                  319   \n",
       "65             85.900               5.740        31.30                  383   \n",
       "..                ...                 ...          ...                  ...   \n",
       "6               5.493               1.555        -4.36                  383   \n",
       "20              8.300               1.560        -5.40                 7283   \n",
       "192             7.900               1.710        -6.20                  530   \n",
       "17              8.300               1.540        -6.90                 3575   \n",
       "12              8.000               1.600        -7.60                 1463   \n",
       "\n",
       "      Group Continent  \n",
       "94     West    Africa  \n",
       "197    West      Asia  \n",
       "58     East    Africa  \n",
       "42     East    Africa  \n",
       "65   Middle    Africa  \n",
       "..      ...       ...  \n",
       "6     North    Europe  \n",
       "20     East    Europe  \n",
       "192    West      Asia  \n",
       "17     East    Europe  \n",
       "12     East    Europe  \n",
       "\n",
       "[201 rows x 11 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the DataFrame in descending order of the growth_rate\n",
    "data.sort_values(by=['Growth rate'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "7ac13e95",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Channel Islands</td>\n",
       "      <td>166</td>\n",
       "      <td>9.280</td>\n",
       "      <td>9.140</td>\n",
       "      <td>81.269</td>\n",
       "      <td>7.276</td>\n",
       "      <td>1.492</td>\n",
       "      <td>4.56</td>\n",
       "      <td>30</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>5733</td>\n",
       "      <td>10.707</td>\n",
       "      <td>9.620</td>\n",
       "      <td>80.839</td>\n",
       "      <td>3.495</td>\n",
       "      <td>1.758</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1132</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>1302</td>\n",
       "      <td>10.698</td>\n",
       "      <td>12.657</td>\n",
       "      <td>77.342</td>\n",
       "      <td>2.843</td>\n",
       "      <td>1.657</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>256</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>5556</td>\n",
       "      <td>10.672</td>\n",
       "      <td>9.865</td>\n",
       "      <td>81.576</td>\n",
       "      <td>1.839</td>\n",
       "      <td>1.764</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1212</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>1936</td>\n",
       "      <td>10.093</td>\n",
       "      <td>15.360</td>\n",
       "      <td>74.590</td>\n",
       "      <td>5.493</td>\n",
       "      <td>1.555</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>383</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>2817</td>\n",
       "      <td>10.459</td>\n",
       "      <td>16.187</td>\n",
       "      <td>73.831</td>\n",
       "      <td>3.076</td>\n",
       "      <td>1.641</td>\n",
       "      <td>-4.27</td>\n",
       "      <td>535</td>\n",
       "      <td>North</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>9429</td>\n",
       "      <td>11.600</td>\n",
       "      <td>14.600</td>\n",
       "      <td>71.800</td>\n",
       "      <td>3.500</td>\n",
       "      <td>1.650</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>1387</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>6992</td>\n",
       "      <td>9.400</td>\n",
       "      <td>15.600</td>\n",
       "      <td>74.600</td>\n",
       "      <td>8.000</td>\n",
       "      <td>1.600</td>\n",
       "      <td>-7.60</td>\n",
       "      <td>1463</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>10563</td>\n",
       "      <td>10.000</td>\n",
       "      <td>10.600</td>\n",
       "      <td>79.200</td>\n",
       "      <td>2.100</td>\n",
       "      <td>1.540</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2067</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>9754</td>\n",
       "      <td>9.300</td>\n",
       "      <td>13.500</td>\n",
       "      <td>75.700</td>\n",
       "      <td>4.200</td>\n",
       "      <td>1.410</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>1864</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Poland</td>\n",
       "      <td>38523</td>\n",
       "      <td>9.400</td>\n",
       "      <td>10.400</td>\n",
       "      <td>78.100</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.330</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>6686</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Republic of Moldova</td>\n",
       "      <td>4045</td>\n",
       "      <td>10.000</td>\n",
       "      <td>11.700</td>\n",
       "      <td>71.900</td>\n",
       "      <td>9.700</td>\n",
       "      <td>1.230</td>\n",
       "      <td>-2.70</td>\n",
       "      <td>464</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Romania</td>\n",
       "      <td>19105</td>\n",
       "      <td>8.900</td>\n",
       "      <td>13.700</td>\n",
       "      <td>75.200</td>\n",
       "      <td>8.300</td>\n",
       "      <td>1.540</td>\n",
       "      <td>-6.90</td>\n",
       "      <td>3575</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>143261</td>\n",
       "      <td>12.300</td>\n",
       "      <td>14.200</td>\n",
       "      <td>70.500</td>\n",
       "      <td>7.300</td>\n",
       "      <td>1.730</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>20676</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>5434</td>\n",
       "      <td>10.300</td>\n",
       "      <td>10.300</td>\n",
       "      <td>76.800</td>\n",
       "      <td>4.400</td>\n",
       "      <td>1.440</td>\n",
       "      <td>0.30</td>\n",
       "      <td>837</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>4417</td>\n",
       "      <td>10.600</td>\n",
       "      <td>15.600</td>\n",
       "      <td>71.300</td>\n",
       "      <td>8.300</td>\n",
       "      <td>1.560</td>\n",
       "      <td>-5.40</td>\n",
       "      <td>7283</td>\n",
       "      <td>East</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2919</td>\n",
       "      <td>13.800</td>\n",
       "      <td>7.800</td>\n",
       "      <td>78.500</td>\n",
       "      <td>12.500</td>\n",
       "      <td>1.780</td>\n",
       "      <td>2.70</td>\n",
       "      <td>393</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>3782</td>\n",
       "      <td>8.400</td>\n",
       "      <td>11.100</td>\n",
       "      <td>77.100</td>\n",
       "      <td>6.400</td>\n",
       "      <td>1.230</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>644</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4195</td>\n",
       "      <td>9.100</td>\n",
       "      <td>12.800</td>\n",
       "      <td>78.000</td>\n",
       "      <td>3.200</td>\n",
       "      <td>1.480</td>\n",
       "      <td>-3.70</td>\n",
       "      <td>844</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Greece</td>\n",
       "      <td>10872</td>\n",
       "      <td>7.900</td>\n",
       "      <td>11.300</td>\n",
       "      <td>81.700</td>\n",
       "      <td>2.300</td>\n",
       "      <td>1.300</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>2383</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Italy</td>\n",
       "      <td>59788</td>\n",
       "      <td>8.200</td>\n",
       "      <td>10.300</td>\n",
       "      <td>83.800</td>\n",
       "      <td>1.900</td>\n",
       "      <td>1.490</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>1397</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Malta</td>\n",
       "      <td>421</td>\n",
       "      <td>9.000</td>\n",
       "      <td>9.300</td>\n",
       "      <td>81.200</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.490</td>\n",
       "      <td>2.00</td>\n",
       "      <td>86</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>626</td>\n",
       "      <td>11.000</td>\n",
       "      <td>10.300</td>\n",
       "      <td>76.700</td>\n",
       "      <td>3.600</td>\n",
       "      <td>1.650</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>94</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>10229</td>\n",
       "      <td>7.500</td>\n",
       "      <td>10.700</td>\n",
       "      <td>81.900</td>\n",
       "      <td>2.600</td>\n",
       "      <td>1.240</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>2246</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>8743</td>\n",
       "      <td>10.200</td>\n",
       "      <td>13.100</td>\n",
       "      <td>75.400</td>\n",
       "      <td>8.200</td>\n",
       "      <td>1.600</td>\n",
       "      <td>-3.90</td>\n",
       "      <td>1626</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>2073</td>\n",
       "      <td>10.100</td>\n",
       "      <td>10.000</td>\n",
       "      <td>81.100</td>\n",
       "      <td>2.700</td>\n",
       "      <td>1.660</td>\n",
       "      <td>0.80</td>\n",
       "      <td>406</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Spain</td>\n",
       "      <td>46117</td>\n",
       "      <td>8.500</td>\n",
       "      <td>9.200</td>\n",
       "      <td>83.300</td>\n",
       "      <td>2.500</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.10</td>\n",
       "      <td>9048</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>TFYR Macedonia</td>\n",
       "      <td>2085</td>\n",
       "      <td>11.100</td>\n",
       "      <td>9.800</td>\n",
       "      <td>76.000</td>\n",
       "      <td>8.400</td>\n",
       "      <td>1.560</td>\n",
       "      <td>0.90</td>\n",
       "      <td>284</td>\n",
       "      <td>South</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Austria</td>\n",
       "      <td>8614</td>\n",
       "      <td>9.700</td>\n",
       "      <td>9.500</td>\n",
       "      <td>82.200</td>\n",
       "      <td>2.500</td>\n",
       "      <td>1.530</td>\n",
       "      <td>2.40</td>\n",
       "      <td>166</td>\n",
       "      <td>West</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>France</td>\n",
       "      <td>65206</td>\n",
       "      <td>11.900</td>\n",
       "      <td>9.100</td>\n",
       "      <td>82.900</td>\n",
       "      <td>2.800</td>\n",
       "      <td>1.990</td>\n",
       "      <td>4.10</td>\n",
       "      <td>13167</td>\n",
       "      <td>West</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Germany</td>\n",
       "      <td>80561</td>\n",
       "      <td>8.700</td>\n",
       "      <td>11.300</td>\n",
       "      <td>81.600</td>\n",
       "      <td>2.500</td>\n",
       "      <td>1.440</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>17746</td>\n",
       "      <td>West</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>17085</td>\n",
       "      <td>10.500</td>\n",
       "      <td>8.800</td>\n",
       "      <td>82.100</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.770</td>\n",
       "      <td>3.00</td>\n",
       "      <td>333</td>\n",
       "      <td>West</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>1285</td>\n",
       "      <td>10.800</td>\n",
       "      <td>8.000</td>\n",
       "      <td>75.100</td>\n",
       "      <td>10.100</td>\n",
       "      <td>1.430</td>\n",
       "      <td>2.70</td>\n",
       "      <td>140</td>\n",
       "      <td>East</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>98</td>\n",
       "      <td>15.500</td>\n",
       "      <td>7.700</td>\n",
       "      <td>73.800</td>\n",
       "      <td>8.100</td>\n",
       "      <td>2.210</td>\n",
       "      <td>4.60</td>\n",
       "      <td>7</td>\n",
       "      <td>East</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>105</td>\n",
       "      <td>10.000</td>\n",
       "      <td>9.100</td>\n",
       "      <td>76.100</td>\n",
       "      <td>13.600</td>\n",
       "      <td>1.620</td>\n",
       "      <td>2.60</td>\n",
       "      <td>14</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>286</td>\n",
       "      <td>11.800</td>\n",
       "      <td>10.900</td>\n",
       "      <td>76.300</td>\n",
       "      <td>7.900</td>\n",
       "      <td>1.800</td>\n",
       "      <td>2.30</td>\n",
       "      <td>44</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>11383</td>\n",
       "      <td>9.600</td>\n",
       "      <td>8.300</td>\n",
       "      <td>80.100</td>\n",
       "      <td>4.700</td>\n",
       "      <td>1.580</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>1745</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Grenada</td>\n",
       "      <td>108</td>\n",
       "      <td>18.100</td>\n",
       "      <td>7.100</td>\n",
       "      <td>74.000</td>\n",
       "      <td>8.400</td>\n",
       "      <td>2.070</td>\n",
       "      <td>4.80</td>\n",
       "      <td>8</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Guadeloupe</td>\n",
       "      <td>474</td>\n",
       "      <td>12.200</td>\n",
       "      <td>7.300</td>\n",
       "      <td>82.100</td>\n",
       "      <td>4.600</td>\n",
       "      <td>2.100</td>\n",
       "      <td>3.60</td>\n",
       "      <td>76</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Jamaica</td>\n",
       "      <td>2823</td>\n",
       "      <td>16.600</td>\n",
       "      <td>7.100</td>\n",
       "      <td>76.200</td>\n",
       "      <td>13.100</td>\n",
       "      <td>1.980</td>\n",
       "      <td>3.30</td>\n",
       "      <td>273</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Martinique</td>\n",
       "      <td>396</td>\n",
       "      <td>10.600</td>\n",
       "      <td>9.100</td>\n",
       "      <td>82.700</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.870</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>83</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>3678</td>\n",
       "      <td>11.300</td>\n",
       "      <td>8.000</td>\n",
       "      <td>80.300</td>\n",
       "      <td>5.300</td>\n",
       "      <td>1.580</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>566</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Saint Vincent and the Grenadines</td>\n",
       "      <td>110</td>\n",
       "      <td>15.000</td>\n",
       "      <td>7.400</td>\n",
       "      <td>73.400</td>\n",
       "      <td>15.100</td>\n",
       "      <td>1.890</td>\n",
       "      <td>2.70</td>\n",
       "      <td>9</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>1373</td>\n",
       "      <td>12.900</td>\n",
       "      <td>9.900</td>\n",
       "      <td>70.800</td>\n",
       "      <td>22.700</td>\n",
       "      <td>1.730</td>\n",
       "      <td>2.30</td>\n",
       "      <td>142</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>United States Virgin Islands</td>\n",
       "      <td>107</td>\n",
       "      <td>12.700</td>\n",
       "      <td>8.200</td>\n",
       "      <td>81.300</td>\n",
       "      <td>8.400</td>\n",
       "      <td>2.170</td>\n",
       "      <td>1.50</td>\n",
       "      <td>21</td>\n",
       "      <td>Caribean</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>6188</td>\n",
       "      <td>16.800</td>\n",
       "      <td>7.000</td>\n",
       "      <td>74.000</td>\n",
       "      <td>14.100</td>\n",
       "      <td>1.860</td>\n",
       "      <td>3.50</td>\n",
       "      <td>542</td>\n",
       "      <td>Central</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>347</td>\n",
       "      <td>13.800</td>\n",
       "      <td>9.400</td>\n",
       "      <td>77.800</td>\n",
       "      <td>10.900</td>\n",
       "      <td>1.980</td>\n",
       "      <td>3.70</td>\n",
       "      <td>512</td>\n",
       "      <td>South</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>907</td>\n",
       "      <td>18.600</td>\n",
       "      <td>7.300</td>\n",
       "      <td>70.700</td>\n",
       "      <td>13.100</td>\n",
       "      <td>2.470</td>\n",
       "      <td>4.90</td>\n",
       "      <td>58</td>\n",
       "      <td>Melanesia</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>China</td>\n",
       "      <td>1393686</td>\n",
       "      <td>11.300</td>\n",
       "      <td>7.400</td>\n",
       "      <td>76.600</td>\n",
       "      <td>9.300</td>\n",
       "      <td>1.590</td>\n",
       "      <td>3.70</td>\n",
       "      <td>15442</td>\n",
       "      <td>East</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Korea</td>\n",
       "      <td>25527</td>\n",
       "      <td>14.200</td>\n",
       "      <td>9.500</td>\n",
       "      <td>71.200</td>\n",
       "      <td>19.000</td>\n",
       "      <td>1.930</td>\n",
       "      <td>4.70</td>\n",
       "      <td>2314</td>\n",
       "      <td>East</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Japan</td>\n",
       "      <td>125738</td>\n",
       "      <td>8.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>84.100</td>\n",
       "      <td>1.900</td>\n",
       "      <td>1.470</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>3488</td>\n",
       "      <td>East</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>50897</td>\n",
       "      <td>9.000</td>\n",
       "      <td>6.100</td>\n",
       "      <td>82.900</td>\n",
       "      <td>2.100</td>\n",
       "      <td>1.340</td>\n",
       "      <td>3.70</td>\n",
       "      <td>7395</td>\n",
       "      <td>East</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>2341</td>\n",
       "      <td>7.500</td>\n",
       "      <td>7.500</td>\n",
       "      <td>80.400</td>\n",
       "      <td>3.400</td>\n",
       "      <td>1.030</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3318</td>\n",
       "      <td>East</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>68416</td>\n",
       "      <td>9.800</td>\n",
       "      <td>8.400</td>\n",
       "      <td>75.200</td>\n",
       "      <td>9.300</td>\n",
       "      <td>1.450</td>\n",
       "      <td>1.50</td>\n",
       "      <td>8118</td>\n",
       "      <td>South-East</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>20996</td>\n",
       "      <td>14.700</td>\n",
       "      <td>7.100</td>\n",
       "      <td>75.600</td>\n",
       "      <td>6.800</td>\n",
       "      <td>2.020</td>\n",
       "      <td>4.20</td>\n",
       "      <td>2186</td>\n",
       "      <td>South-Central</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>3035</td>\n",
       "      <td>12.400</td>\n",
       "      <td>9.600</td>\n",
       "      <td>75.400</td>\n",
       "      <td>11.200</td>\n",
       "      <td>1.500</td>\n",
       "      <td>0.90</td>\n",
       "      <td>356</td>\n",
       "      <td>West</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>3974</td>\n",
       "      <td>12.800</td>\n",
       "      <td>11.500</td>\n",
       "      <td>75.500</td>\n",
       "      <td>11.500</td>\n",
       "      <td>1.820</td>\n",
       "      <td>0.50</td>\n",
       "      <td>569</td>\n",
       "      <td>West</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>6022</td>\n",
       "      <td>15.500</td>\n",
       "      <td>4.600</td>\n",
       "      <td>80.400</td>\n",
       "      <td>7.900</td>\n",
       "      <td>1.710</td>\n",
       "      <td>-6.20</td>\n",
       "      <td>530</td>\n",
       "      <td>West</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Country      Pop  Birth rate  Mortality rate  \\\n",
       "0                     Channel Islands      166       9.280           9.140   \n",
       "1                             Denmark     5733      10.707           9.620   \n",
       "2                             Estonia     1302      10.698          12.657   \n",
       "3                             Finland     5556      10.672           9.865   \n",
       "6                              Latvia     1936      10.093          15.360   \n",
       "7                           Lithuania     2817      10.459          16.187   \n",
       "11                            Belarus     9429      11.600          14.600   \n",
       "12                           Bulgaria     6992       9.400          15.600   \n",
       "13                     Czech Republic    10563      10.000          10.600   \n",
       "14                            Hungary     9754       9.300          13.500   \n",
       "15                             Poland    38523       9.400          10.400   \n",
       "16                Republic of Moldova     4045      10.000          11.700   \n",
       "17                            Romania    19105       8.900          13.700   \n",
       "18                 Russian Federation   143261      12.300          14.200   \n",
       "19                           Slovakia     5434      10.300          10.300   \n",
       "20                            Ukraine     4417      10.600          15.600   \n",
       "21                            Albania     2919      13.800           7.800   \n",
       "22             Bosnia and Herzegovina     3782       8.400          11.100   \n",
       "23                            Croatia     4195       9.100          12.800   \n",
       "24                             Greece    10872       7.900          11.300   \n",
       "25                              Italy    59788       8.200          10.300   \n",
       "26                              Malta      421       9.000           9.300   \n",
       "27                         Montenegro      626      11.000          10.300   \n",
       "28                           Portugal    10229       7.500          10.700   \n",
       "29                             Serbia     8743      10.200          13.100   \n",
       "30                           Slovenia     2073      10.100          10.000   \n",
       "31                              Spain    46117       8.500           9.200   \n",
       "32                     TFYR Macedonia     2085      11.100           9.800   \n",
       "33                            Austria     8614       9.700           9.500   \n",
       "35                             France    65206      11.900           9.100   \n",
       "36                            Germany    80561       8.700          11.300   \n",
       "38                        Netherlands    17085      10.500           8.800   \n",
       "50                          Mauritius     1285      10.800           8.000   \n",
       "55                         Seychelles       98      15.500           7.700   \n",
       "100                             Aruba      105      10.000           9.100   \n",
       "102                          Barbados      286      11.800          10.900   \n",
       "103                              Cuba    11383       9.600           8.300   \n",
       "106                           Grenada      108      18.100           7.100   \n",
       "107                        Guadeloupe      474      12.200           7.300   \n",
       "109                           Jamaica     2823      16.600           7.100   \n",
       "110                        Martinique      396      10.600           9.100   \n",
       "111                       Puerto Rico     3678      11.300           8.000   \n",
       "113  Saint Vincent and the Grenadines      110      15.000           7.400   \n",
       "114               Trinidad and Tobago     1373      12.900           9.900   \n",
       "115      United States Virgin Islands      107      12.700           8.200   \n",
       "118                       El Salvador     6188      16.800           7.000   \n",
       "135                           Uruguay      347      13.800           9.400   \n",
       "139                              Fiji      907      18.600           7.300   \n",
       "150                             China  1393686      11.300           7.400   \n",
       "153                             Korea    25527      14.200           9.500   \n",
       "154                             Japan   125738       8.000          11.000   \n",
       "156                 Republic of Korea    50897       9.000           6.100   \n",
       "157                            Taiwan     2341       7.500           7.500   \n",
       "166                          Thailand    68416       9.800           8.400   \n",
       "179                         Sri Lanka    20996      14.700           7.100   \n",
       "183                           Armenia     3035      12.400           9.600   \n",
       "187                           Georgia     3974      12.800          11.500   \n",
       "192                           Lebanon     6022      15.500           4.600   \n",
       "\n",
       "     Life expectancy  Infant mortality  Children per woman  Growth rate  \\\n",
       "0             81.269             7.276               1.492         4.56   \n",
       "1             80.839             3.495               1.758         3.67   \n",
       "2             77.342             2.843               1.657        -2.63   \n",
       "3             81.576             1.839               1.764         2.65   \n",
       "6             74.590             5.493               1.555        -4.36   \n",
       "7             73.831             3.076               1.641        -4.27   \n",
       "11            71.800             3.500               1.650        -3.30   \n",
       "12            74.600             8.000               1.600        -7.60   \n",
       "13            79.200             2.100               1.540         0.70   \n",
       "14            75.700             4.200               1.410        -3.50   \n",
       "15            78.100             4.000               1.330        -1.20   \n",
       "16            71.900             9.700               1.230        -2.70   \n",
       "17            75.200             8.300               1.540        -6.90   \n",
       "18            70.500             7.300               1.730        -1.00   \n",
       "19            76.800             4.400               1.440         0.30   \n",
       "20            71.300             8.300               1.560        -5.40   \n",
       "21            78.500            12.500               1.780         2.70   \n",
       "22            77.100             6.400               1.230        -2.90   \n",
       "23            78.000             3.200               1.480        -3.70   \n",
       "24            81.700             2.300               1.300        -2.00   \n",
       "25            83.800             1.900               1.490        -0.20   \n",
       "26            81.200             4.000               1.490         2.00   \n",
       "27            76.700             3.600               1.650        -0.10   \n",
       "28            81.900             2.600               1.240        -3.40   \n",
       "29            75.400             8.200               1.600        -3.90   \n",
       "30            81.100             2.700               1.660         0.80   \n",
       "31            83.300             2.500               1.380         1.10   \n",
       "32            76.000             8.400               1.560         0.90   \n",
       "33            82.200             2.500               1.530         2.40   \n",
       "35            82.900             2.800               1.990         4.10   \n",
       "36            81.600             2.500               1.440        -1.00   \n",
       "38            82.100             3.000               1.770         3.00   \n",
       "50            75.100            10.100               1.430         2.70   \n",
       "55            73.800             8.100               2.210         4.60   \n",
       "100           76.100            13.600               1.620         2.60   \n",
       "102           76.300             7.900               1.800         2.30   \n",
       "103           80.100             4.700               1.580        -0.70   \n",
       "106           74.000             8.400               2.070         4.80   \n",
       "107           82.100             4.600               2.100         3.60   \n",
       "109           76.200            13.100               1.980         3.30   \n",
       "110           82.700             5.000               1.870        -1.10   \n",
       "111           80.300             5.300               1.580        -0.30   \n",
       "113           73.400            15.100               1.890         2.70   \n",
       "114           70.800            22.700               1.730         2.30   \n",
       "115           81.300             8.400               2.170         1.50   \n",
       "118           74.000            14.100               1.860         3.50   \n",
       "135           77.800            10.900               1.980         3.70   \n",
       "139           70.700            13.100               2.470         4.90   \n",
       "150           76.600             9.300               1.590         3.70   \n",
       "153           71.200            19.000               1.930         4.70   \n",
       "154           84.100             1.900               1.470        -2.60   \n",
       "156           82.900             2.100               1.340         3.70   \n",
       "157           80.400             3.400               1.030         0.10   \n",
       "166           75.200             9.300               1.450         1.50   \n",
       "179           75.600             6.800               2.020         4.20   \n",
       "183           75.400            11.200               1.500         0.90   \n",
       "187           75.500            11.500               1.820         0.50   \n",
       "192           80.400             7.900               1.710        -6.20   \n",
       "\n",
       "     Population aged 65+          Group      Continent  \n",
       "0                     30          North         Europe  \n",
       "1                   1132          North         Europe  \n",
       "2                    256          North         Europe  \n",
       "3                   1212          North         Europe  \n",
       "6                    383          North         Europe  \n",
       "7                    535          North         Europe  \n",
       "11                  1387           East         Europe  \n",
       "12                  1463           East         Europe  \n",
       "13                  2067           East         Europe  \n",
       "14                  1864           East         Europe  \n",
       "15                  6686           East         Europe  \n",
       "16                   464           East         Europe  \n",
       "17                  3575           East         Europe  \n",
       "18                 20676           East         Europe  \n",
       "19                   837           East         Europe  \n",
       "20                  7283           East         Europe  \n",
       "21                   393          South         Europe  \n",
       "22                   644          South         Europe  \n",
       "23                   844          South         Europe  \n",
       "24                  2383          South         Europe  \n",
       "25                  1397          South         Europe  \n",
       "26                    86          South         Europe  \n",
       "27                    94          South         Europe  \n",
       "28                  2246          South         Europe  \n",
       "29                  1626          South         Europe  \n",
       "30                   406          South         Europe  \n",
       "31                  9048          South         Europe  \n",
       "32                   284          South         Europe  \n",
       "33                   166           West         Europe  \n",
       "35                 13167           West         Europe  \n",
       "36                 17746           West         Europe  \n",
       "38                   333           West         Europe  \n",
       "50                   140           East         Africa  \n",
       "55                     7           East         Africa  \n",
       "100                   14       Caribean  South America  \n",
       "102                   44       Caribean  South America  \n",
       "103                 1745       Caribean  South America  \n",
       "106                    8       Caribean  South America  \n",
       "107                   76       Caribean  South America  \n",
       "109                  273       Caribean  South America  \n",
       "110                   83       Caribean  South America  \n",
       "111                  566       Caribean  South America  \n",
       "113                    9       Caribean  South America  \n",
       "114                  142       Caribean  South America  \n",
       "115                   21       Caribean  South America  \n",
       "118                  542        Central  South America  \n",
       "135                  512          South  South America  \n",
       "139                   58      Melanesia        Oceania  \n",
       "150                15442           East           Asia  \n",
       "153                 2314           East           Asia  \n",
       "154                 3488           East           Asia  \n",
       "156                 7395           East           Asia  \n",
       "157                 3318           East           Asia  \n",
       "166                 8118     South-East           Asia  \n",
       "179                 2186  South-Central           Asia  \n",
       "183                  356           West           Asia  \n",
       "187                  569           West           Asia  \n",
       "192                  530           West           Asia  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the countries where less than 5\n",
    "data[data['Growth rate']<5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ea8a8de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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