4.Iteration-inexing-filter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Iteration, Indexing and Filters\n",
    "In this notebook we will learn indexing ```DataFrame``` with ```loc``` and ```iloc``` functionality and ```slicing``` operation. Some Data ```Filtering``` techniques will also be conducted.\n",
    "\n",
    "Read more about these functionality from [Pydata documentation for indexing](https://pandas.pydata.org/docs/user_guide/groupby.html)[1].\n",
    "Some parts of this notebook are taken from [EuroScipy 2016 Pandas Tutorial by Joris Van den Bossche and Nicholas Devenish](https://github.com/jorisvandenbossche/pandas-tutorial)[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = pd.read_csv('data/titanic.csv')\n",
    "titanic = titanic.set_index(\"Name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "\n",
       "                                                    SibSp  Parch     Ticket  \\\n",
       "Name                                                                          \n",
       "Braund, Mr. Owen Harris                                 1      0  A/5 21171   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   PC 17599   \n",
       "\n",
       "                                                       Fare Cabin Embarked  \n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                              7.2500   NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...  71.2833   C85        C  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Lets create a small sample of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = titanic[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Iteration of rows implementing   ```iterrows```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Braund, Mr. Owen Harris [1, 0, 3, 'male', 22.0, 1, 0, 'A/5 21171', 7.25, nan, 'S']\n",
      "Cumings, Mrs. John Bradley (Florence Briggs Thayer) [2, 1, 1, 'female', 38.0, 1, 0, 'PC 17599', 71.2833, 'C85', 'C']\n",
      "Heikkinen, Miss. Laina [3, 1, 3, 'female', 26.0, 0, 0, 'STON/O2. 3101282', 7.925, nan, 'S']\n",
      "Futrelle, Mrs. Jacques Heath (Lily May Peel) [4, 1, 1, 'female', 35.0, 1, 0, '113803', 53.1, 'C123', 'S']\n",
      "Allen, Mr. William Henry [5, 0, 3, 'male', 35.0, 0, 0, '373450', 8.05, nan, 'S']\n"
     ]
    }
   ],
   "source": [
    "for index,row in sample.iterrows():\n",
    "    print(index,list(row))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can select specific columns by passing column names in ```row()``` input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Braund, Mr. Owen Harris male 22.0\n",
      "Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0\n",
      "Heikkinen, Miss. Laina female 26.0\n",
      "Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0\n",
      "Allen, Mr. William Henry male 35.0\n"
     ]
    }
   ],
   "source": [
    "for index,row in sample.iterrows():\n",
    "    print(index,row['Sex'],row['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Iteration of rows with  ```iteritems```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Braund, Mr. Owen Harris [1, 0, 3, 'male', 22.0, 1, 0, 'A/5 21171', 7.25, nan, 'S']\n",
      "Cumings, Mrs. John Bradley (Florence Briggs Thayer) [2, 1, 1, 'female', 38.0, 1, 0, 'PC 17599', 71.2833, 'C85', 'C']\n",
      "Heikkinen, Miss. Laina [3, 1, 3, 'female', 26.0, 0, 0, 'STON/O2. 3101282', 7.925, nan, 'S']\n",
      "Futrelle, Mrs. Jacques Heath (Lily May Peel) [4, 1, 1, 'female', 35.0, 1, 0, '113803', 53.1, 'C123', 'S']\n",
      "Allen, Mr. William Henry [5, 0, 3, 'male', 35.0, 0, 0, '373450', 8.05, nan, 'S']\n"
     ]
    }
   ],
   "source": [
    "for index,row in sample.T.iteritems():\n",
    "    print(index,list(row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId 1 2 3\n",
      "Survived 0 1 1\n",
      "Pclass 3 1 3\n",
      "Sex male female female\n",
      "Age 22.0 38.0 26.0\n",
      "SibSp 1 1 0\n",
      "Parch 0 0 0\n",
      "Ticket A/5 21171 PC 17599 STON/O2. 3101282\n",
      "Fare 7.25 71.2833 7.925\n",
      "Cabin nan C85 nan\n",
      "Embarked S C S\n"
     ]
    }
   ],
   "source": [
    "for index,row in sample.iteritems():\n",
    "    print(index,row[0],row[1],row[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.  Indexing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source: [Using iloc, loc, & ix to select rows and columns in Pandas DataFrames](https://www.shanelynn.ie/select-pandas-dataframe-rows-and-columns-using-iloc-loc-and-ix/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ```loc``` and ```iloc``` :\n",
    "\n",
    "The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.\n",
    "\n",
    "The Pandas loc indexer can be used with DataFrames for two different use cases:\n",
    "\n",
    "  - a.) Selecting rows by label/index\n",
    "  - b.) Selecting rows with a boolean / conditional lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "\n",
       "                                                    SibSp  Parch     Ticket  \\\n",
       "Name                                                                          \n",
       "Braund, Mr. Owen Harris                                 1      0  A/5 21171   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   PC 17599   \n",
       "\n",
       "                                                       Fare Cabin Embarked  \n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                              7.2500   NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...  71.2833   C85        C  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.iloc[0:2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    2\n",
       "Survived       1\n",
       "Pclass         1\n",
       "Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.iloc[1,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></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>Braund, Mr. Owen Harris</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allen, Mr. William Henry</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Survived  Pclass     Sex  \\\n",
       "Name                                                                           \n",
       "Braund, Mr. Owen Harris                                    0       3    male   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...         1       1  female   \n",
       "Heikkinen, Miss. Laina                                     1       3  female   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)               1       1  female   \n",
       "Allen, Mr. William Henry                                   0       3    male   \n",
       "\n",
       "                                                     Age  SibSp  Parch  \\\n",
       "Name                                                                     \n",
       "Braund, Mr. Owen Harris                             22.0      1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...  38.0      1      0   \n",
       "Heikkinen, Miss. Laina                              26.0      0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        35.0      1      0   \n",
       "Allen, Mr. William Henry                            35.0      0      0   \n",
       "\n",
       "                                                              Ticket  \n",
       "Name                                                                  \n",
       "Braund, Mr. Owen Harris                                    A/5 21171  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  \n",
       "Allen, Mr. William Henry                                      373450  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.loc[:,'Survived': 'Ticket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId            1\n",
       "Survived               0\n",
       "Pclass                 3\n",
       "Sex                 male\n",
       "Age                   22\n",
       "SibSp                  1\n",
       "Parch                  0\n",
       "Ticket         A/5 21171\n",
       "Fare                7.25\n",
       "Cabin                NaN\n",
       "Embarked               S\n",
       "Name: Braund, Mr. Owen Harris, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.loc['Braund, Mr. Owen Harris',:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4 - Data Filters:\n",
    "\n",
    "Data Filters can be performed by either selecting specific set of column names or by seting ```boolean filters```."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dictionary to DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>area</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>11.3</td>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>64.3</td>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>81.3</td>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>16.9</td>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>64.9</td>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  population    area    capital\n",
       "0         Belgium        11.3   30510   Brussels\n",
       "1          France        64.3  671308      Paris\n",
       "2         Germany        81.3  357050     Berlin\n",
       "3     Netherlands        16.9   41526  Amsterdam\n",
       "4  United Kingdom        64.9  244820     London"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n",
    "        'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n",
    "        'area': [30510, 671308, 357050, 41526, 244820],\n",
    "        'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']}\n",
    "\n",
    "\n",
    "\n",
    "countries = pd.DataFrame(data)\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To set the ```country``` as index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>population</th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>11.3</td>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>64.3</td>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>81.3</td>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>16.9</td>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.9</td>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                population    area    capital\n",
       "country                                      \n",
       "Belgium               11.3   30510   Brussels\n",
       "France                64.3  671308      Paris\n",
       "Germany               81.3  357050     Berlin\n",
       "Netherlands           16.9   41526  Amsterdam\n",
       "United Kingdom        64.9  244820     London"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = countries.set_index('country')\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To find the ```area``` of each country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Belgium            30510\n",
       "France            671308\n",
       "Germany           357050\n",
       "Netherlands        41526\n",
       "United Kingdom    244820\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['area']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To create a ```list``` instead of pandas ```Series``` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Area = list(countries['area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[30510, 671308, 357050, 41526, 244820]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To create a ```sub-DataFrame``` by choosing ```area``` and ```population```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>30510</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area  population\n",
       "country                           \n",
       "Belgium          30510        11.3\n",
       "France          671308        64.3\n",
       "Germany         357050        81.3\n",
       "Netherlands      41526        16.9\n",
       "United Kingdom  244820        64.9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[['area', 'population']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To observe slice of data by ```index``` names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>population</th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>64.3</td>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>81.3</td>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>16.9</td>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             population    area    capital\n",
       "country                                   \n",
       "France             64.3  671308      Paris\n",
       "Germany            81.3  357050     Berlin\n",
       "Netherlands        16.9   41526  Amsterdam"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['France':'Netherlands']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To observe the data at specific row identified by ```index``` name(s) and spefic column identified by ```column```name(s):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "357050"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.loc['Germany', 'area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           area  population\n",
       "country                    \n",
       "France   671308        64.3\n",
       "Germany  357050        81.3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.loc['France':'Germany', ['area', 'population']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To set ```boolean``` selection for ```subDataFrame```:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Belgium           False\n",
       "France             True\n",
       "Germany            True\n",
       "Netherlands       False\n",
       "United Kingdom     True\n",
       "Name: area, dtype: bool"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['area'] > 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>64.3</td>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>81.3</td>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.9</td>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                population    area capital\n",
       "country                                   \n",
       "France                64.3  671308   Paris\n",
       "Germany               81.3  357050  Berlin\n",
       "United Kingdom        64.9  244820  London"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[countries['area'] > 100000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Exercises:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Add a column `density` with the population density (note: population column is expressed in millions)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>11.3</td>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>370.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>64.3</td>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>95.783158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>81.3</td>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>227.699202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>16.9</td>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>406.973944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.9</td>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>265.092721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                population    area    capital     density\n",
       "country                                                  \n",
       "Belgium               11.3   30510   Brussels  370.370370\n",
       "France                64.3  671308      Paris   95.783158\n",
       "Germany               81.3  357050     Berlin  227.699202\n",
       "Netherlands           16.9   41526  Amsterdam  406.973944\n",
       "United Kingdom        64.9  244820     London  265.092721"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['density'] = countries['population']*1000000 / countries['area']\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Select the capital and the population column of those countries where the density is larger than 300\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>capital</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               capital  population\n",
       "country                           \n",
       "Belgium       Brussels        11.3\n",
       "Netherlands  Amsterdam        16.9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.loc[countries['density'] > 300, ['capital', 'population']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Add a column 'density_ratio' with the ratio of the density to the mean density\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>density</th>\n",
       "      <th>density_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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>Belgium</th>\n",
       "      <td>11.3</td>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>370.370370</td>\n",
       "      <td>1.355755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>64.3</td>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>95.783158</td>\n",
       "      <td>0.350618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>81.3</td>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>227.699202</td>\n",
       "      <td>0.833502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>16.9</td>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>406.973944</td>\n",
       "      <td>1.489744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.9</td>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                population    area    capital     density  density_ratio\n",
       "country                                                                 \n",
       "Belgium               11.3   30510   Brussels  370.370370       1.355755\n",
       "France                64.3  671308      Paris   95.783158       0.350618\n",
       "Germany               81.3  357050     Berlin  227.699202       0.833502\n",
       "Netherlands           16.9   41526  Amsterdam  406.973944       1.489744\n",
       "United Kingdom        64.9  244820     London  265.092721       0.970382"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['density_ratio'] = countries['density'] / countries['density'].mean()\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Change the capital of the UK to Cambridge\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>density</th>\n",
       "      <th>density_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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>Belgium</th>\n",
       "      <td>11.3</td>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>370.370370</td>\n",
       "      <td>1.355755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>64.3</td>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>95.783158</td>\n",
       "      <td>0.350618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>81.3</td>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>227.699202</td>\n",
       "      <td>0.833502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>16.9</td>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>406.973944</td>\n",
       "      <td>1.489744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.9</td>\n",
       "      <td>244820</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                population    area    capital     density  density_ratio\n",
       "country                                                                 \n",
       "Belgium               11.3   30510   Brussels  370.370370       1.355755\n",
       "France                64.3  671308      Paris   95.783158       0.350618\n",
       "Germany               81.3  357050     Berlin  227.699202       0.833502\n",
       "Netherlands           16.9   41526  Amsterdam  406.973944       1.489744\n",
       "United Kingdom        64.9  244820  Cambridge  265.092721       0.970382"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.loc['United Kingdom', 'capital'] = 'Cambridge'\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Select all countries whose population density is between 100 and 300 people/km²\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>density</th>\n",
       "      <th>density_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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>Germany</th>\n",
       "      <td>81.3</td>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>227.699202</td>\n",
       "      <td>0.833502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>64.9</td>\n",
       "      <td>244820</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                population    area    capital     density  density_ratio\n",
       "country                                                                 \n",
       "Germany               81.3  357050     Berlin  227.699202       0.833502\n",
       "United Kingdom        64.9  244820  Cambridge  265.092721       0.970382"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[(countries['density'] > 100) & (countries['density'] < 300)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References:\n",
    "1. [Pydata documentation: Group by: split-apply-combine](https://pandas.pydata.org/docs/user_guide/groupby.html)\n",
    "2. [EuroScipy 2016 Pandas Tutorial by Joris Van den Bossche and Nicholas Devenish](https://github.com/jorisvandenbossche/pandas-tutorial)"
   ]
  }
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