Day 28 - 50 days of Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "933ce8c8",
   "metadata": {},
   "source": [
    "## 1.\n",
    "\n",
    "#### Using pandas, import csv of the countries data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b930545d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>331002651</td>\n",
       "      <td>65298</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>9629091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>1439323776</td>\n",
       "      <td>16708</td>\n",
       "      <td>3.80%</td>\n",
       "      <td>9640011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japan</td>\n",
       "      <td>126476461</td>\n",
       "      <td>39058</td>\n",
       "      <td>2.80%</td>\n",
       "      <td>377975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>83783942</td>\n",
       "      <td>55803</td>\n",
       "      <td>3.20%</td>\n",
       "      <td>357022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>67886011</td>\n",
       "      <td>44177</td>\n",
       "      <td>4.80%</td>\n",
       "      <td>242500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Population  GDP_per_capita Unemployment_rate     Area\n",
       "0   United States   331002651           65298             6.10%  9629091\n",
       "1           China  1439323776           16708             3.80%  9640011\n",
       "2           Japan   126476461           39058             2.80%   377975\n",
       "3         Germany    83783942           55803             3.20%   357022\n",
       "4  United Kingdom    67886011           44177             4.80%   242500"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df = pd.read_csv(\"countries_population_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3421c883",
   "metadata": {},
   "source": [
    "## 2. \n",
    "\n",
    "#### You have decided to add another column to your DataFrame. The column will have capital cities of each country. Here is the data below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c14f2b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of the DataFrame\n",
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10e20b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Capital_City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>Washington, D.C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Tokyo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country      Capital_City\n",
       "0   United States  Washington, D.C.\n",
       "1           China           Beijing\n",
       "2           Japan             Tokyo\n",
       "3         Germany            Berlin\n",
       "4  United Kingdom            London"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading capital cities data\n",
    "df2 = pd.read_csv(\"Countries_data_capital_cities.csv\")\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd14d299",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Area</th>\n",
       "      <th>Capital_City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>331002651</td>\n",
       "      <td>65298</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>9629091</td>\n",
       "      <td>Washington, D.C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>1439323776</td>\n",
       "      <td>16708</td>\n",
       "      <td>3.80%</td>\n",
       "      <td>9640011</td>\n",
       "      <td>Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japan</td>\n",
       "      <td>126476461</td>\n",
       "      <td>39058</td>\n",
       "      <td>2.80%</td>\n",
       "      <td>377975</td>\n",
       "      <td>Tokyo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>83783942</td>\n",
       "      <td>55803</td>\n",
       "      <td>3.20%</td>\n",
       "      <td>357022</td>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>67886011</td>\n",
       "      <td>44177</td>\n",
       "      <td>4.80%</td>\n",
       "      <td>242500</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Population  GDP_per_capita Unemployment_rate     Area   \n",
       "0   United States   331002651           65298             6.10%  9629091  \\\n",
       "1           China  1439323776           16708             3.80%  9640011   \n",
       "2           Japan   126476461           39058             2.80%   377975   \n",
       "3         Germany    83783942           55803             3.20%   357022   \n",
       "4  United Kingdom    67886011           44177             4.80%   242500   \n",
       "\n",
       "       Capital_City  \n",
       "0  Washington, D.C.  \n",
       "1           Beijing  \n",
       "2             Tokyo  \n",
       "3            Berlin  \n",
       "4            London  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using merge to merge DataFrame on 'Country'\n",
    "merged_df = df_copy.merge(df2, on='Country')\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd306b46",
   "metadata": {},
   "source": [
    "## 3. \n",
    "\n",
    "#### Using Matplotlib, create a bar chart to visualize the distribution of population among different continents.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f8c87466",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "merged_df.sort_values(by = [\"Population\"], axis=0, \n",
    "                      inplace=True, \n",
    "                      ascending=False)    \n",
    "fig = plt.figure(figsize = (15, 8))\n",
    "plt.bar(merged_df.Country, merged_df.Population,\n",
    "        color ='blue',\n",
    "        width = 0.6)\n",
    " \n",
    "plt.xlabel(\"Country\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title(\"Countries Population\", fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7c9984",
   "metadata": {},
   "source": [
    "## 4. \n",
    "\n",
    "#### Using NumPy, calculate the correlation between the population and GDP per capita for each country and create a scatter plot to visualize the relationship between the two variables (population and GDP per capita) using matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fcd4efc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.49159759],\n",
       "       [-0.49159759,  1.        ]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "population = merged_df[\"Population\"]\n",
    "gdp_per_capita = merged_df[\"GDP_per_capita\"]\n",
    "correlation = np.corrcoef(population, gdp_per_capita)\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "054fb02f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+kvTq+0iSJEkHQs8CeFVtr6pnVtUzgWcD3wD+AjgbuKaqVgPXNO8BTgFWN4+zgA8AJHkScA7wHOB44Jyp0N6s89qu7U7u1feRJEmSDoS2hqC8GPiXqroTOBW4pKlfAqxvXp8KXFod1wPDSZ4MrAOurqr7qup+4Grg5GbZoVV1fVUVcGnXviRJkqRFqa0A/krgz5rXR1bVPc3rLwNHNq9XAXd1bXN3U5utfvc09e+S5KwkY0nGdu3atZDvIUmSJC1IzwN4kkcDPw38+d7LmjPX1esequqCqhqpqpEVK1b0+uMkSZKkGbVxBvwU4LNV9ZXm/Vea4SM0z/c29XHg6K7tjmpqs9WPmqYuSZIkLVptBPDT+M7wE4ArgamZTM4APt5VP72ZDeUE4IFmqMoocFKSw5qLL08CRptlX0tyQjP7yeld+5IkSZIWpUN6ufMkjwf+K/C6rvJ5wOVJzgTuBF7R1K8CXgLsoDNjymsAquq+JO8EbmzWe0dV3de8fj1wMTAEfLJ5SJIkSYtWOsOwDx4jIyM1NjbW7zYkSZK0hCW5qapGplvmnTAlSZKkFhnAJUmSpBYZwCVJkqQWGcAlSZKkFhnAJUmSpBYZwCVJkqQWGcAlSZKkFhnAJUmSpBYZwCVJkqQWGcAlSZKkFhnAJUmSpBYZwCVJkqQWGcAlSZKkFhnAJUmSpBYZwCVJkqQWHdLvBqSD3Zat42wa3c7OiUlWDg+xYd0a1q9d1e+2JElSjxjApT7asnWcjZu3Mbl7DwDjE5Ns3LwNwBAuSdIS5RAUqY82jW5/OHxPmdy9h02j2/vUkSRJ6jUDuNRHOycm51WXJEmDzwAu9dHK4aF51SVJ0uAzgEt9tGHdGoaWL3tEbWj5MjasW9OnjiRJUq95EabUR1MXWjoLiiRJBw8DuNRn69euMnBLknQQcQiKJEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktSingbwJMNJrkjyT0m+mOS5SZ6U5OoktzXPhzXrJsn5SXYkuSXJs7r2c0az/m1JzuiqPzvJtmab85Okl99HkiRJWqhenwH/A+Cvquq/AM8AvgicDVxTVauBa5r3AKcAq5vHWcAHAJI8CTgHeA5wPHDOVGhv1nlt13Yn9/j7SJIkSQvSswCe5InAjwMXAlTVt6pqAjgVuKRZ7RJgffP6VODS6rgeGE7yZGAdcHVV3VdV9wNXAyc3yw6tquurqoBLu/YlSZIkLUq9PAN+LLAL+JMkW5N8MMnjgSOr6p5mnS8DRzavVwF3dW1/d1ObrX73NHVJkiRp0eplAD8EeBbwgapaCzzId4abANCcua4e9gBAkrOSjCUZ27VrV68/TpIkSZpRLwP43cDdVXVD8/4KOoH8K83wEZrne5vl48DRXdsf1dRmqx81Tf27VNUFVTVSVSMrVqxY0JeSJEmSFqJnAbyqvgzclWRNU3ox8AXgSmBqJpMzgI83r68ETm9mQzkBeKAZqjIKnJTksObiy5OA0WbZ15Kc0Mx+cnrXvhatLVvHed5513Ls2Z/geeddy5at0/7OIEmSpCXqkB7v/5eBDyd5NHA78Bo6of/yJGcCdwKvaNa9CngJsAP4RrMuVXVfkncCNzbrvaOq7mtevx64GBgCPtk8Fq0tW8fZuHkbk7v3ADA+McnGzdsAWL/W4euSJEkHg3SGYR88RkZGamxsrC+f/bzzrmV8YvK76quGh/j02S/qQ0eSJEnqhSQ3VdXIdMu8E2aLdk4TvmerS5IkaekxgLdo5fDQvOqSJElaegzgLdqwbg1Dy5c9oja0fBkb1q2ZYQtJkiQtNb2+CFNdpi603DS6nZ0Tk6wcHmLDujVegClJknQQMYC3bP3aVQZuSZKkg5gBvI+2bB33bLgkSdJBxgDeJ84JLkmSdHDyIsw+2TS6/eHwPWVy9x42jW7vU0eSJElqgwG8T5wTXJIk6eBkAO8T5wSXJEk6OBnA+8Q5wSVJkg5OXoTZJ84JLkmSdHAygPdRG3OCO9WhJEnS4mIAX8Kc6lCSJGnxcQz4EuZUh5IkSYuPAXwJc6pDSZKkxccAvoQ51aEkSdLiYwBfwpzqUJIkafHxIswlzKkOJUmSFh8D+BLXxlSHkiRJmjuHoEiSJEktMoBLkiRJLTKAS5IkSS0ygEuSJEktMoBLkiRJLTKAS5IkSS1yGsIWbNk67lzckiRJAgzgPbdl6zgbN29jcvceAMYnJtm4eRuAIVySJOkg5BCUHts0uv3h8D1lcvceNo1u71NHkiRJ6icDeI/tnJicV12SJElLmwG8x1YOD82rLkmSpKXNAN5jG9atYWj5skfUhpYvY8O6NX3qSJIkSf3kRZg9NnWhpbOgSJIkCQzgrVi/dpWBW5IkSYBDUCRJkqRWGcAlSZKkFhnAJUmSpBYZwCVJkqQWGcAlSZKkFhnAJUmSpBb1NIAn+dck25LcnGSsqT0pydVJbmueD2vqSXJ+kh1JbknyrK79nNGsf1uSM7rqz272v6PZNr38PpIkSdJCtXEG/IVV9cyqGmnenw1cU1WrgWua9wCnAKubx1nAB6AT2IFzgOcAxwPnTIX2Zp3Xdm13cu+/jiRJkrT/+jEE5VTgkub1JcD6rvql1XE9MJzkycA64Oqquq+q7geuBk5ulh1aVddXVQGXdu1LkiRJWpR6HcAL+OskNyU5q6kdWVX3NK+/DBzZvF4F3NW17d1Nbbb63dPUJUmSpEWr17eif35VjSf5XuDqJP/UvbCqKkn1uAea8H8WwFOe8pRef5wkSZI0o32eAU+yOskVSb6Q5Papx1x2XlXjzfO9wF/QGcP9lWb4CM3zvc3q48DRXZsf1dRmqx81TX26Pi6oqpGqGlmxYsVcWpckSZJ6Yi5DUP6EzsWODwEvpDPW+k/3tVGSxyd5wtRr4CTg88CVwNRMJmcAH29eXwmc3syGcgLwQDNUZRQ4KclhzcWXJwGjzbKvJTmhmf3k9K59SZIkSYvSXIagDFXVNUlSVXcC5ya5CXjbPrY7EviLZmbAQ4CPVNVfJbkRuDzJmcCdwCua9a8CXgLsAL4BvAagqu5L8k7gxma9d1TVfc3r1wMXA0PAJ5uHJEmStGjNJYB/M8mjgNuSvJHOMI/v2ddGVXU78Ixp6l8FXjxNvYA3zLCvi4CLpqmPAT+8r176bcvWcTaNbmfnxCQrh4fYsG4N69d6vagkSdLBaC5DUN4EPA74FeDZwM/RGe6hOdiydZyNm7cxPjFJAeMTk2zcvI0tW6cdri5JkqQlbi4B/Jiq+vequruqXlNVLwOcSmSONo1uZ3L3nkfUJnfvYdPo9j51JEmSpH6aSwDfOMeaprFzYnJedUmSJC1tM44BT3IKnYsiVyU5v2vRoXRmRNEcrBweYnyasL1yeKgP3UiSJKnfZjsDvhMYA/4DuKnrcSWd28NrDjasW8PQ8mWPqA0tX8aGdWv61JEkSZL6acYz4FX1OeBzST5cVZ7x3k9Ts504C4okSZJg9iEol1fVK4Ct090uvqqe3tPOlpD1a1cZuCVJkgTMPg/4m5rnn2qjEXU4Z7gkSdLSNtsQlHua5zuTfB9wPFDAjVX15Zb6O6hMzRk+NW3h1JzhgCFckiRpidjnNIRJfhH4DPBS4OXA9Ul+odeNHYycM1ySJGnpm8ut6DcAa5tbyJPkcOAfmObW8FoY5wyXJEla+uZyI56vAl/vev/1pqYDbKa5wZ0zXJIkaemYSwDfAdyQ5Nwk5wDXA/+c5C1J3tLb9g4uzhkuSZK09M1lCMq/NI8pH2+en3Dg2zm4OWe4JEnS0peq75rie0kbGRmpsbGxfrchSZKkJSzJTVU1Mt2yfZ4BT7IC+A3gqcBjp+pV9aID1qEkSZJ0kJjLGPAPA/8EHAu8HfhX4MYe9iRJkiQtWXMJ4IdX1YXA7qr6m6r6BcCz35IkSdJ+mMtFmLub53uS/CSwE3hS71qSJEmSlq65BPDfTvJE4NeA/wMcCvxqT7uSJEmSlqh9BvCq+svm5QPAC3vbjvbHlq3jTl0oSZI0IGYcA55kU5LXTVN/XZLzetuW5mrL1nE2bt7G+MQkBYxPTLJx8za2bB3vd2uSJEmaxmwXYb4IuGCa+h8DP9WbdjRfm0a3M7l7zyNqk7v3sGl0e586kiRJ0mxmC+CPqWnu0lNV3wbSu5Y0HzsnJudVlyRJUn/NFsAnk6zeu9jUTHeLxMrhoXnVJUmS1F+zBfC3AZ9M8uokT2serwE+0SzTIrBh3RqGli97RG1o+TI2rFvTp44kSZI0mxlnQamqTyZZD2wAfrkpfx54WVVta6E3zcHUbCfOgiJJkjQYMs0w7yVtZGSkxsbG+t2GJEmSlrAkN1XVyHTL5nIjHi1izgEuSZI0WAzgA2xqDvCpaQin5gAHDOGSJEmL1GwXYZJkWRJvO79IOQe4JEnS4Jk1gFfVHuC0lnrRPDkHuCRJ0uCZyxCUTyf5Q+CjwINTxar6bM+60pysHB5ifJqw7RzgkiRJi9dcAvgzm+d3dNWKzq3q1Ucb1q15xBhwcA5wSZKkxW6fAbyqXthGI5o/5wCXJEkaPPsM4EmOBP4XsLKqTklyHPDcqrqw591pn9avXWXgliRJGiCzXoTZuBgYBVY27/8ZeHOP+pEkSZKWtLkE8COq6nLg2wBV9RCwZ/ZNJEmSJE1nLgH8wSSH07nwkiQnAA/0tCtJkiRpiZrLLChvAa4Evj/Jp4EVwMt72pUkSZK0RO3rTpgrmnXWAz8KvA54alXdMtcPaO6muTXJXzbvj01yQ5IdST6a5NFN/THN+x3N8mO69rGxqW9Psq6rfnJT25Hk7Hl8b0mSJKkvZgzgSX4RuBX4P8A24Pur6vNVtXuen/Em4Itd798NvKeqfgC4HzizqZ8J3N/U39OsRzPryiuBpwInA+9vQv0y4H3AKcBxwGnNupIkSdKiNdsZ8DfTOdv9XDpnvzfOd+dJjgJ+Evhg8z50buBzRbPKJXTOrgOc2rynWf7iZv1Tgcuq6ptVdQewAzi+eeyoqtur6lvAZc26kiRJ0qI1WwD/VlXtAqiq24HH7Mf+3wv8Bs0MKsDhwEQzkwrA3cDUJNargLuaz3uIzoWeh3fX99pmprokSZK0aM12EeZRSc6f6X1V/cpsO07yU8C9VXVTkhMX1OUCJTkLOAvgKU95Sj9bkSRJ0kFutgC+Ya/3N81z388DfjrJS4DHAocCfwAMJzmkOct9FDDerD8OHA3cneQQ4InAV7vqU7q3man+CFV1AXABwMjISM3zewjYsnXcW95LkiQdADMG8Kq6ZKZlc1FVG2nGjTdnwH+9qn42yZ/TmcbwMuAM4OPNJlc27/+xWX5tVVWSK4GPJPl9OnfjXA18BgiwOsmxdIL3K4FXLaRnTW/L1nE2bt7G5O7O/ZfGJybZuHkbgCFckiRpnuZyI54D7a3AW5LsoDPG+8KmfiFweFN/C3A2QFXdClwOfAH4K+ANVbWnOYP+RmCUziwrlzfr6gDbNLr94fA9ZXL3HjaNbu9TR5IkSYNrLjfiWbCqug64rnl9O50ZTPZe5z+An5lh+98Bfmea+lXAVQewVU1j58TkvOqSJEma2T7PgCc5oo1GtHitHB6aV12SJEkzm+1GPP9Pkl3AtiR3J/nRFvvSIrJh3RqGli97RG1o+TI2rFvTp44kSZIG12xnwH8H+LGqejLwMuBd7bSkxWb92lW866VPY9XwEAFWDQ/xrpc+zQswJUmS9sNsY8Afqqp/AqiqG5I8oaWetAitX7vKwC1JknQAzBbAvzfJW2Z6X1W/37u2JEmSpKVptgD+x8ATZnkvSZIkaZ5muxHP29tsRJIkSToYzDoNYZIXJvlYklubxxXNXS0lSZIk7YfZpiH8SeAi4C/p3OL9Z+nc9OaiJC9ppz1JkiRpaZltDPgGYH1Vfa6rdnOSMeD/4B0oJUmSpHmbbQjK9+0VvgGoqluAI3vXkiRJkrR0zXYG/MH9XKYe2LJ1nE2j29k5McnK4SE2rFvjvNySJEkDaLYA/v1JrpymHuA/96gfTWPL1nE2bt7G5O49AIxPTLJx8zYAQ7gkSdKAmS2AnzrLst870I1oZptGtz8cvqdM7t7DptHtBnBJkqQBM9s84H/TZiOa2c6JyXnVJUmStHjNNg3hqUne0PX+hiS3N4+Xt9OeAFYOD82rLkmSpMVrtllQfgPoHgP+GOBHgBOB/97DnrSXDevWMLR82SNqQ8uXsWHdmj51JEmSpP012xjwR1fVXV3v/76qvgp8Ncnje9yXukyN83YWFEmSpME3WwA/rPtNVb2x6+2K3rSjmaxfu8rALUmStATMNgTlhiSv3buY5HXAZ3rXkiRJkrR0zXYG/FeBLUleBXy2qT2bzljw9T3uS5IkSVqSZpuG8F7gR5O8CHhqU/5EVV3bSmeSJEnSEjTbGXAAmsBt6F5kvDW9JEnSYNpnANfi463pJUmSBtdsF2FqkZrt1vSSJEla3AzgA8hb00uSJA0uA/gA8tb0kiRJg8sAPoC8Nb0kSdLg8iLMAeSt6SVJkgaXAXxAeWt6SZKkweQQFEmSJKlFBnBJkiSpRQZwSZIkqUUGcEmSJKlFBnBJkiSpRc6Cssht2TrudIOSJElLiAF8EduydZyNm7cxuXsPAOMTk2zcvA3AEC5JkjSgHIKyiG0a3f5w+J4yuXsPm0a396kjSZIkLZQBfBHbOTE5r7okSZIWPwP4IrZyeGhedUmSJC1+PQvgSR6b5DNJPpfk1iRvb+rHJrkhyY4kH03y6Kb+mOb9jmb5MV372tjUtydZ11U/uantSHJ2r75Lv2xYt4ah5cseURtavowN69b0qSNJkiQtVC/PgH8TeFFVPQN4JnBykhOAdwPvqaofAO4HzmzWPxO4v6m/p1mPJMcBrwSeCpwMvD/JsiTLgPcBpwDHAac16y4Z69eu4l0vfRqrhocIsGp4iHe99GlegClJkjTAejYLSlUV8O/N2+XNo4AXAa9q6pcA5wIfAE5tXgNcAfxhkjT1y6rqm8AdSXYAxzfr7aiq2wGSXNas+4Vefad+WL92lYFbkiRpCenpGPDmTPXNwL3A1cC/ABNV9VCzyt3AVLpcBdwF0Cx/ADi8u77XNjPVJUmSpEWrpwG8qvZU1TOBo+ictf4vvfy8mSQ5K8lYkrFdu3b1owVJkiQJaGkWlKqaAD4FPBcYTjI19OUoYLx5PQ4cDdAsfyLw1e76XtvMVJ/u8y+oqpGqGlmxYsWB+EqSJEnSfunlLCgrkgw3r4eA/wp8kU4Qf3mz2hnAx5vXVzbvaZZf24wjvxJ4ZTNLyrHAauAzwI3A6mZWlUfTuVDzyl59H0mSJOlA6OWt6J8MXNLMVvIo4PKq+sskXwAuS/LbwFbgwmb9C4EPNRdZ3kcnUFNVtya5nM7FlQ8Bb6iqPQBJ3giMAsuAi6rq1h5+H0mSJGnB0jnJfPAYGRmpsbGxfrchSZKkJSzJTVU1Mt0y74QpSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS16JB+N6Dv2LJ1nE2j29k5McnK4SE2rFvD+rWr+t2WJEmSDiAD+CKxZes4GzdvY3L3HgDGJybZuHkbgCFckiRpCXEIyiKxaXT7w+F7yuTuPWwa3d6njiRJktQLBvBFYufE5LzqkiRJGkwG8EVi5fDQvOqSJEkaTAbwRWLDujUMLV/2iNrQ8mVsWLemTx1JkiSpF7wIc5GYutDSWVAkSZKWNgP4IrJ+7SoDtyRJ0hLnEBRJkiSpRQZwSZIkqUUGcEmSJKlFBnBJkiSpRQZwSZIkqUUGcEmSJKlFBnBJkiSpRQZwSZIkqUU9C+BJjk7yqSRfSHJrkjc19ScluTrJbc3zYU09Sc5PsiPJLUme1bWvM5r1b0tyRlf92Um2NducnyS9+j6SJEnSgdDLM+APAb9WVccBJwBvSHIccDZwTVWtBq5p3gOcAqxuHmcBH4BOYAfOAZ4DHA+cMxXam3Ve27XdyT38PpIkSdKC9SyAV9U9VfXZ5vXXgS8Cq4BTgUua1S4B1jevTwUurY7rgeEkTwbWAVdX1X1VdT9wNXBys+zQqrq+qgq4tGtfkiRJ0qLUyhjwJMcAa4EbgCOr6p5m0ZeBI5vXq4C7uja7u6nNVr97mrokSZK0aPU8gCf5HuBjwJur6mvdy5oz19VCD2clGUsytmvXrl5/nCRJkjSjngbwJMvphO8PV9XmpvyVZvgIzfO9TX0cOLpr86Oa2mz1o6apf5equqCqRqpqZMWKFQv7UpIkSdIC9HIWlAAXAl+sqt/vWnQlMDWTyRnAx7vqpzezoZwAPNAMVRkFTkpyWHPx5UnAaLPsa0lOaD7r9K59SZIkSYvSIT3c9/OAnwe2Jbm5qf0mcB5weZIzgTuBVzTLrgJeAuwAvgG8BqCq7kvyTuDGZr13VNV9zevXAxcDQ8Anm4ckSZK0aKUzDPvgMTIyUmNjY/1uQ5IkSUtYkpuqamS6Zd4JU5IkSWqRAVySJElqkQFckiRJapEBXJIkSWqRAVySJElqkQFckiRJapEBXJIkSWqRAVySJElqUS/vhClJkiT1xZat42wa3c7OiUlWDg+xYd0a1q9d1e+2AAO4JEmSlpgtW8fZuHkbk7v3ADA+McnGzdsAFkUIdwiKJEmSlpRNo9sfDt9TJnfvYdPo9j519EgGcEmSJC0pOycm51VvmwFckiRJS8rK4aF51dtmAJckSdKSsmHdGoaWL3tEbWj5MjasW9Onjh7JizAlSZK0pExdaOksKJIkSVJL1q9dtWgC994cgiJJkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS16JB+N6ClYcvWcTaNbmfnxCQrh4fYsG4N69eu6ndbkiRJi44BXAu2Zes4GzdvY3L3HgDGJybZuHkbgCFckiRpLw5B0YJtGt3+cPieMrl7D5tGt/epI0mSpMXLAK4F2zkxOa+6JEnSwcwArgVbOTw0r7okSdLBrGcBPMlFSe5N8vmu2pOSXJ3ktub5sKaeJOcn2ZHkliTP6trmjGb925Kc0VV/dpJtzTbnJ0mvvotmt2HdGoaWL3tEbWj5MjasW9OnjiRJkhavXp4Bvxg4ea/a2cA1VbUauKZ5D3AKsLp5nAV8ADqBHTgHeA5wPHDOVGhv1nlt13Z7f5Zasn7tKt710qexaniIAKuGh3jXS5/mBZiSJEnT6NksKFX1t0mO2at8KnBi8/oS4DrgrU390qoq4Pokw0me3Kx7dVXdB5DkauDkJNcBh1bV9U39UmA98MlefR/NPtXg+rWrDNySJElz0PY0hEdW1T3N6y8DRzavVwF3da13d1ObrX73NHX1iFMNSpIkHRh9uwizOdtdbXxWkrOSjCUZ27VrVxsfueQ41aAkSdKB0XYA/0oztITm+d6mPg4c3bXeUU1ttvpR09SnVVUXVNVIVY2sWLFiwV/iYORUg5IkSQdG2wH8SmBqJpMzgI931U9vZkM5AXigGaoyCpyU5LDm4suTgNFm2deSnNDMfnJ6177UA041KEmSdGD0chrCPwP+EViT5O4kZwLnAf81yW3ATzTvAa4Cbgd2AH8MvB6gufjyncCNzeMdUxdkNut8sNnmX/ACzJ5ajFMNbtk6zvPOu5Zjz/4EzzvvWrZsnfGPIJIkSYtGOkOxDx4jIyM1NjbW7zYG0myzoPSjl+6LQqHzC4HTH0qSpMUgyU1VNTLdsrZnQdEAW0xTDc52Uehi6VGSJGk63opeA8mLQiVJ0qAygGsgeVGoJEkaVAZwDaTFeFGoJEnSXDgGXANpapz3YrkoVJIkaa4M4BpYi+miUEmSpLlyCIokSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktQiA7gkSZLUIgO4JEmS1CIDuCRJktSiQ/rdgCRJkg4eW7aOs2l0OzsnJlk5PMSGdWtYv3ZVv9tqlQFckiRJrdiydZyNm7cxuXsPAOMTk2zcvA3goArhDkGRJElSKzaNbn84fE+Z3L2HTaPb+9RRfxjAJUmS1IqdE5Pzqi9VBnBJkiS1YuXw0LzqS5UBXJIkSa3YsG4NQ8uXPaI2tHwZG9at6VNH/eFFmJIkSWrF1IWWzoIiSZIktWT92lUHXeDem0NQJEmSpBYZwCVJkqQWDXwAT3Jyku1JdiQ5u9/9SJIkSbMZ6ACeZBnwPuAU4DjgtCTH9bcrSZIkaWYDHcCB44EdVXV7VX0LuAw4tc89SZIkSTMa9AC+Crir6/3dTU2SJElalAY9gM9JkrOSjCUZ27VrV7/bkSRJ0kFs0AP4OHB01/ujmtojVNUFVTVSVSMrVqxorTlJkiRpb4MewG8EVic5NsmjgVcCV/a5J0mSJGlGA30nzKp6KMkbgVFgGXBRVd3a57YkSZKkGQ10AAeoqquAq/rdhyRJkjQXgz4ERZIkSRooBnBJkiSpRQZwSZIkqUUGcEmSJKlFqap+99CqJLuAO1v4qCOAf2vhcw4mHtPe8LgeeB7TA89jeuB5TA88j+mBN8jH9D9V1bQ3oDnoAnhbkoxV1Ui/+1hKPKa94XE98DymB57H9MDzmB54HtMDb6keU4egSJIkSS0ygEuSJEktMoD3zgX9bmAJ8pj2hsf1wPOYHnge0wPPY3rgeUwPvCV5TB0DLkmSJLXIM+CSJElSiwzgC5Tk5CTbk+xIcvY0yx+T5KPN8huSHNOHNgfKHI7pW5J8IcktSa5J8p/60ecg2dcx7VrvZUkqyZK74vxAm8sxTfKK5r/VW5N8pO0eB80c/r//lCSfSrK1+f//S/rR5yBJclGSe5N8foblSXJ+c8xvSfKstnscNHM4pj/bHMttSf4hyTPa7nHQ7OuYdq33I0keSvLytnrrFQP4AiRZBrwPOAU4DjgtyXF7rXYmcH9V/QDwHuDd7XY5WOZ4TLcCI1X1dOAK4Hfb7XKwzPGYkuQJwJuAG9rtcPDM5ZgmWQ1sBJ5XVU8F3tx2n4Nkjv+d/hZweVWtBV4JvL/dLgfSxcDJsyw/BVjdPM4CPtBCT4PuYmY/pncAL6iqpwHvZImOYT7ALmb2Yzr1b8S7gb9uo6FeM4AvzPHAjqq6vaq+BVwGnLrXOqcClzSvrwBenCQt9jho9nlMq+pTVfWN5u31wFEt9zho5vLfKXR+ULwb+I82mxtQczmmrwXeV1X3A1TVvS33OGjmckwLOLR5/URgZ4v9DaSq+lvgvllWORW4tDquB4aTPLmd7gbTvo5pVf3D1P/v8WfUnMzhv1OAXwY+BiyJf0sN4AuzCrir6/3dTW3adarqIeAB4PBWuhtMczmm3c4EPtnTjgbfPo9p82fno6vqE202NsDm8t/pDwI/mOTTSa5PMuvZHc3pmJ4L/FySu4Gr6PxA1sLM999czY8/ow6AJKuA/8YS+gvNIf1uQNpfSX4OGAFe0O9eBlmSRwG/D7y6z60sNYfQ+bP+iXTOgP1tkqdV1UQ/mxpwpwEXV9X/TvJc4ENJfriqvt3vxqS9JXkhnQD+/H73sgS8F3hrVX17qQwiMIAvzDhwdNf7o5radOvcneQQOn82/Wo77Q2kuRxTkvwE8D/ojLP7Zku9Dap9HdMnAD8MXNf8w/Z9wJVJfrqqxlrrcrDM5b/Tu4Ebqmo3cEeSf6YTyG9sp8WBM5djeibNONGq+sckjwWOYIn8SbpP5vRvruYnydOBDwKnVJU/8xduBLis+Rl1BPCSJA9V1Za+drUADkFZmBuB1UmOTfJoOhcFXbnXOlcCZzSvXw5cW06+Ppt9HtMka4E/An7acbVzMusxraoHquqIqjqmqo6hM2bR8D27ufx/fwuds98kOYLOkJTbW+xx0MzlmH4JeDFAkh8CHgvsarXLpedK4PRmNpQTgAeq6p5+NzXIkjwF2Az8fFX9c7/7WQqq6tiun1FXAK8f5PANngFfkKp6KMkbgVFgGXBRVd2a5B3AWFVdCVxI58+kO+hcYPDK/nW8+M3xmG4Cvgf48+a34S9V1U/3relFbo7HVPMwx2M6CpyU5AvAHmCDZ8JmNsdj+mvAHyf5VToXZL7aExqzS/JndH4RPKIZO38OsBygqv4/OmPpXwLsAL4BvKY/nQ6OORzTt9G51uv9zc+oh6rKqV1nMYdjuuR4J0xJkiSpRQ5BkSRJklpkAJckSZJaZACXJEmSWmQAlyRJklpkAJckSZIaSS5Kcm+Sz89h3f+U5JoktyS5LslRc/kMA7gkDbAke5LcnOTzSf48yeMO8P6vSzLrFGpJ3tz9uUmuSjJ8IPuQpBZdTHPTrzn4PeDSqno68A7gXXPZyAAuSYNtsqqeWVU/DHwL+KU+9PBm4OEAXlUvqaqJPvQhSQtWVX9L594tD0vy/Un+KslNSf4uyX9pFh0HXNu8/hRw6lw+wwAuSUvH3wE/kORJSbY0fxK9vrktNknOTfKhJP+Y5LYkr23qJyb5y6mdJPnDJK/ee+dJPpBkLMmtSd7e1H4FWAl8Ksmnmtq/Nnf/JMlbmrPzn0/y5qZ2TJIvJvnjZl9/nWSop0dGkhbmAuCXq+rZwK8D72/qnwNe2rz+b8ATkhy+r50ZwCVpCUhyCHAKsA14O7C1+ZPobwKXdq36dOBFwHOBtyVZOY+P+R/NHf2eDrwgydOr6nxgJ/DCqnrhXj09m86dFZ8DnAC8NsnaZvFq4H1V9VRgAnjZfL6vJLUlyfcAP0rnDtw3A38EPLlZ/Ot0/j3cCrwAGKdz9+NZeSt6SRpsQ80PBOicAb8QuIEm0FbVtUkOT3Jos87Hq2oSmGzOWB9PJwDPxSuSnEXnZ8eT6fzp9ZZZ1n8+8BdV9SBAks3AjwFXAndU1VTfNwHHzLEHSWrbo4CJqnrm3guqaifNGfAmqL9sLkPwDOCSNNgm9/6hkGS29Wua9w/xyL+IPnbvjZIcS+dMz49U1f1JLp5uvXn4ZtfrPYBDUCQtSlX1tSR3JPmZqvrzdP6RfXpVfa4ZbndfVX0b2AhcNJd9OgRFkpaevwN+Fjrju4F/q6qvNctOTfLYZoziicCNwJ3AcUke08xe8uJp9nko8CDwQJIj6Qx3mfJ14Akz9LE+yeOSPJ7O+Mi/W9hXk6TeSvJnwD8Ca5LcneRMOv+mnpnkc8CtfOdiyxOB7Un+GTgS+J25fIZnwCVp6TkXuCjJLcA3gDO6lt1C50r9I4B3Nn8+JcnlwOeBO4Cte++wOdOzFfgn4C7g012LLwD+KsnO7nHgVfXZ5kz5Z5rSB6tqa5JjDsSXlKReqKrTZlj0XVMTVtUVwBXz/YxU7f3XSEnSUpTkXODfq+r3+t2LJB3MHIIiSZIktcgz4JIkSVKLPAMuSZIktcgALkmSJLXIAC5JkiS1yAAuSZIktcgALkmSJLXIAC5JkiS16P8H5hmXB3fewt0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize = (12, 8))\n",
    "plt.scatter(population, gdp_per_capita)\n",
    "plt.xlabel('Population')\n",
    "plt.ylabel('GDP Per Capita')\n",
    "plt.title(\"GDP Per Population\", fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf5c5b0",
   "metadata": {},
   "source": [
    "## 5.\n",
    "\n",
    "#### Using pandas return the top 3 countries with the lowest GDP. Assign this to a variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff2013b9",
   "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>Population</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Area</th>\n",
       "      <th>Capital_City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>India</td>\n",
       "      <td>1380004385</td>\n",
       "      <td>2252</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>3287263</td>\n",
       "      <td>New Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>212559417</td>\n",
       "      <td>8711</td>\n",
       "      <td>13.90%</td>\n",
       "      <td>8515767</td>\n",
       "      <td>Brasília</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>128932753</td>\n",
       "      <td>9877</td>\n",
       "      <td>3.60%</td>\n",
       "      <td>1964375</td>\n",
       "      <td>Mexico City</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Population  GDP_per_capita Unemployment_rate     Area Capital_City\n",
       "16   India  1380004385            2252             6.10%  3287263    New Delhi\n",
       "17  Brazil   212559417            8711            13.90%  8515767     Brasília\n",
       "10  Mexico   128932753            9877             3.60%  1964375  Mexico City"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_low_gdp_3 = merged_df.sort_values(by='GDP_per_capita', ascending=True, \n",
    "                                     inplace=False).head(3)\n",
    "df_low_gdp_3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c4550ff",
   "metadata": {},
   "source": [
    "## 6.\n",
    "\n",
    "#### Compare the unemployment rate of Switzerland, China and USA. Plot that on a bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2dafcbc0",
   "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>Population</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Area</th>\n",
       "      <th>Capital_City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>1439323776</td>\n",
       "      <td>16708</td>\n",
       "      <td>3.80%</td>\n",
       "      <td>9640011</td>\n",
       "      <td>Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>331002651</td>\n",
       "      <td>65298</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>9629091</td>\n",
       "      <td>Washington, D.C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>8654622</td>\n",
       "      <td>82792</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>41285</td>\n",
       "      <td>Bern</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Population  GDP_per_capita Unemployment_rate     Area   \n",
       "1           China  1439323776           16708             3.80%  9640011  \\\n",
       "0   United States   331002651           65298             6.10%  9629091   \n",
       "13    Switzerland     8654622           82792             2.50%    41285   \n",
       "\n",
       "        Capital_City  \n",
       "1            Beijing  \n",
       "0   Washington, D.C.  \n",
       "13              Bern  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the DataFrame for three countries\n",
    "countries = [\"Switzerland\", \"China\", \"United States\"]\n",
    "\n",
    "filtered_df = merged_df.query(\"Country==@countries\" )\n",
    "filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1ec40fd9",
   "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>Population</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Area</th>\n",
       "      <th>Capital_City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>1439323776</td>\n",
       "      <td>16708</td>\n",
       "      <td>3.8</td>\n",
       "      <td>9640011</td>\n",
       "      <td>Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>331002651</td>\n",
       "      <td>65298</td>\n",
       "      <td>6.1</td>\n",
       "      <td>9629091</td>\n",
       "      <td>Washington, D.C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>8654622</td>\n",
       "      <td>82792</td>\n",
       "      <td>2.5</td>\n",
       "      <td>41285</td>\n",
       "      <td>Bern</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Population  GDP_per_capita  Unemployment_rate     Area   \n",
       "1           China  1439323776           16708                3.8  9640011  \\\n",
       "0   United States   331002651           65298                6.1  9629091   \n",
       "13    Switzerland     8654622           82792                2.5    41285   \n",
       "\n",
       "        Capital_City  \n",
       "1            Beijing  \n",
       "0   Washington, D.C.  \n",
       "13              Bern  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cleaning the unemployment_rate column\n",
    "filtered_df[\"Unemployment_rate\"] = filtered_df[\"Unemployment_rate\"].str.replace(\"%\", \"\").astype(float)\n",
    "filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "045ee367",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "plt.bar(filtered_df.Country, filtered_df.Unemployment_rate)\n",
    "plt.xlabel(\"Countries\")\n",
    "plt.ylabel(\"Unemployment Rate\")\n",
    "plt.title(\"Unemployment Rate of Switzerland, USA and China\",fontsize=20)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}