solved - 03 - Indexing and selecting data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indexing and selecting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "try:\n",
    "    import seaborn\n",
    "except ImportError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area    capital         country  population\n",
       "0   30510   Brussels         Belgium        11.3\n",
       "1  671308      Paris          France        64.3\n",
       "2  357050     Berlin         Germany        81.3\n",
       "3   41526  Amsterdam     Netherlands        16.9\n",
       "4  244820     London  United Kingdom        64.9"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# redefining the example objects\n",
    "\n",
    "# series\n",
    "population = pd.Series({'Germany': 81.3, 'Belgium': 11.3, 'France': 64.3, \n",
    "                        'United Kingdom': 64.9, 'Netherlands': 16.9})\n",
    "\n",
    "# dataframe\n",
    "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",
    "countries = pd.DataFrame(data)\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting the index to the country names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population\n",
       "country                                      \n",
       "Belgium          30510   Brussels        11.3\n",
       "France          671308      Paris        64.3\n",
       "Germany         357050     Berlin        81.3\n",
       "Netherlands      41526  Amsterdam        16.9\n",
       "United Kingdom  244820     London        64.9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = countries.set_index('country')\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some notes on selecting data\n",
    "\n",
    "One of pandas' basic features is the labeling of rows and columns, but this makes indexing also a bit more complex compared to numpy. We now have to distuinguish between:\n",
    "\n",
    "- selection by label\n",
    "- selection by position."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `data[]` provides some convenience shortcuts "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a DataFrame, basic indexing selects the columns.\n",
    "\n",
    "Selecting a single column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['area']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or multiple columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[['area', 'population']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But, slicing accesses the rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               area    capital  population\n",
       "country                                   \n",
       "France       671308      Paris        64.3\n",
       "Germany      357050     Berlin        81.3\n",
       "Netherlands   41526  Amsterdam        16.9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['France':'Netherlands']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "    <b>NOTE</b>: Unlike slicing in numpy, the end label is **included**.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So as a summary, `[]` provides the following convenience shortcuts:\n",
    "\n",
    "- Series: selecting a label: `s[label]`\n",
    "- DataFrame: selecting a single or multiple columns: `df['col']` or `df[['col1', 'col2']]`\n",
    "- DataFrame: slicing the rows: `df['row_label1':'row_label2']` or `df[mask]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Systematic indexing with `loc` and `iloc`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using `[]` like above, you can only select from one axis at once (rows or columns, not both). For more advanced indexing, you have some extra attributes:\n",
    "    \n",
    "* `loc`: selection by label\n",
    "* `iloc`: selection by position\n",
    "\n",
    "These methods index the different dimensions of the frame:\n",
    "\n",
    "* `df.loc[row_indexer, column_indexer]`\n",
    "* `df.iloc[row_indexer, column_indexer]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting a single element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "357050"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.loc['Germany', 'area']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the row or column indexer can also be a list, slice, boolean array, .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.loc['France':'Germany', ['area', 'population']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Selecting by position with `iloc` works similar as indexing numpy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>France</th>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          capital  population\n",
       "country                      \n",
       "Belgium  Brussels        11.3\n",
       "France      Paris        64.3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.iloc[0:2,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The different indexing methods can also be used to assign data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries2 = countries.copy()\n",
    "countries2.loc['Belgium':'Germany', 'population'] = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population\n",
       "country                                      \n",
       "Belgium          30510   Brussels        10.0\n",
       "France          671308      Paris        10.0\n",
       "Germany         357050     Berlin        10.0\n",
       "Netherlands      41526  Amsterdam        16.9\n",
       "United Kingdom  244820     London        64.9"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean indexing (filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often, you want to select rows based on a certain condition. This can be done with 'boolean indexing' (like a where clause in SQL). \n",
    "\n",
    "The indexer (or boolean mask) should be 1-dimensional and the same length as the thing being indexed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['area'] > 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area capital  population\n",
       "country                                   \n",
       "France          671308   Paris        64.3\n",
       "Germany         357050  Berlin        81.3\n",
       "United Kingdom  244820  London        64.9"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[countries['area'] > 100000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<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": 14,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "      <td>370.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "      <td>95.783158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density\n",
       "country                                                  \n",
       "Belgium          30510   Brussels        11.3  370.370370\n",
       "France          671308      Paris        64.3   95.783158\n",
       "Germany         357050     Berlin        81.3  227.699202\n",
       "Netherlands      41526  Amsterdam        16.9  406.973944\n",
       "United Kingdom  244820     London        64.9  265.092721"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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": 15,
     "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": 16,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "      <td>370.370370</td>\n",
       "      <td>1.355755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "      <td>95.783158</td>\n",
       "      <td>0.350618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "      <td>0.833502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "      <td>1.489744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density  density_ratio\n",
       "country                                                                 \n",
       "Belgium          30510   Brussels        11.3  370.370370       1.355755\n",
       "France          671308      Paris        64.3   95.783158       0.350618\n",
       "Germany         357050     Berlin        81.3  227.699202       0.833502\n",
       "Netherlands      41526  Amsterdam        16.9  406.973944       1.489744\n",
       "United Kingdom  244820     London        64.9  265.092721       0.970382"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "      <td>370.370370</td>\n",
       "      <td>1.355755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "      <td>95.783158</td>\n",
       "      <td>0.350618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "      <td>0.833502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "      <td>1.489744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density  density_ratio\n",
       "country                                                                 \n",
       "Belgium          30510   Brussels        11.3  370.370370       1.355755\n",
       "France          671308      Paris        64.3   95.783158       0.350618\n",
       "Germany         357050     Berlin        81.3  227.699202       0.833502\n",
       "Netherlands      41526  Amsterdam        16.9  406.973944       1.489744\n",
       "United Kingdom  244820  Cambridge        64.9  265.092721       0.970382"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.loc['United Kingdom', 'capital'] = 'Cambridge'\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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": 18,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "      <td>0.833502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density  density_ratio\n",
       "country                                                                 \n",
       "Germany         357050     Berlin        81.3  227.699202       0.833502\n",
       "United Kingdom  244820  Cambridge        64.9  265.092721       0.970382"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[(countries['density'] > 100) & (countries['density'] < 300)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Some other useful methods: `isin` and string methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `isin` method of Series is very useful to select rows that may contain certain values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = countries['capital']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s.isin?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Belgium           False\n",
       "France            False\n",
       "Germany            True\n",
       "Netherlands       False\n",
       "United Kingdom    False\n",
       "Name: capital, dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isin(['Berlin', 'London'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can then be used to filter the dataframe with boolean indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "      <td>0.833502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           area capital  population     density  density_ratio\n",
       "country                                                       \n",
       "Germany  357050  Berlin        81.3  227.699202       0.833502"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[countries['capital'].isin(['Berlin', 'London'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to select all data for which the capital starts with a 'B'. In Python, when having a string, we could use the `startswith` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Berlin'.startswith('B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In pandas, these are available on a Series through the `str` namespace:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Belgium            True\n",
       "France            False\n",
       "Germany            True\n",
       "Netherlands       False\n",
       "United Kingdom    False\n",
       "Name: capital, dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['capital'].str.startswith('B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For an overview of all string methods, see: http://pandas.pydata.org/pandas-docs/stable/api.html#string-handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Select all countries that have capital names with more than 7 characters\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "      <td>370.370370</td>\n",
       "      <td>1.355755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "      <td>1.489744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density  density_ratio\n",
       "country                                                                 \n",
       "Belgium          30510   Brussels        11.3  370.370370       1.355755\n",
       "Netherlands      41526  Amsterdam        16.9  406.973944       1.489744\n",
       "United Kingdom  244820  Cambridge        64.9  265.092721       0.970382"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[countries['capital'].str.len() > 7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Select all countries that have capital names that contain the character sequence 'am'\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "      <td>1.489744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density  density_ratio\n",
       "country                                                                 \n",
       "Netherlands      41526  Amsterdam        16.9  406.973944       1.489744\n",
       "United Kingdom  244820  Cambridge        64.9  265.092721       0.970382"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[countries['capital'].str.contains('am')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pitfall: chained indexing (and the 'SettingWithCopyWarning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries.loc['Belgium', 'capital'] = 'Ghent' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>30510</td>\n",
       "      <td>Ghent</td>\n",
       "      <td>11.3</td>\n",
       "      <td>370.370370</td>\n",
       "      <td>1.355755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "      <td>95.783158</td>\n",
       "      <td>0.350618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "      <td>0.833502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "      <td>1.489744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density  density_ratio\n",
       "country                                                                 \n",
       "Belgium          30510      Ghent        11.3  370.370370       1.355755\n",
       "France          671308      Paris        64.3   95.783158       0.350618\n",
       "Germany         357050     Berlin        81.3  227.699202       0.833502\n",
       "Netherlands      41526  Amsterdam        16.9  406.973944       1.489744\n",
       "United Kingdom  244820  Cambridge        64.9  265.092721       0.970382"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/massimo/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "countries['capital']['Belgium'] = 'Antwerp' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>30510</td>\n",
       "      <td>Antwerp</td>\n",
       "      <td>11.3</td>\n",
       "      <td>370.370370</td>\n",
       "      <td>1.355755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "      <td>95.783158</td>\n",
       "      <td>0.350618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "      <td>0.833502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "      <td>1.489744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density  density_ratio\n",
       "country                                                                 \n",
       "Belgium          30510    Antwerp        11.3  370.370370       1.355755\n",
       "France          671308      Paris        64.3   95.783158       0.350618\n",
       "Germany         357050     Berlin        81.3  227.699202       0.833502\n",
       "Netherlands      41526  Amsterdam        16.9  406.973944       1.489744\n",
       "United Kingdom  244820  Cambridge        64.9  265.092721       0.970382"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/massimo/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "countries[countries['capital'] == 'Antwerp']['capital'] = 'Brussels' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</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>30510</td>\n",
       "      <td>Antwerp</td>\n",
       "      <td>11.3</td>\n",
       "      <td>370.370370</td>\n",
       "      <td>1.355755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "      <td>95.783158</td>\n",
       "      <td>0.350618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "      <td>0.833502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "      <td>1.489744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "      <td>0.970382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density  density_ratio\n",
       "country                                                                 \n",
       "Belgium          30510    Antwerp        11.3  370.370370       1.355755\n",
       "France          671308      Paris        64.3   95.783158       0.350618\n",
       "Germany         357050     Berlin        81.3  227.699202       0.833502\n",
       "Netherlands      41526  Amsterdam        16.9  406.973944       1.489744\n",
       "United Kingdom  244820  Cambridge        64.9  265.092721       0.970382"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to avoid this?\n",
    "\n",
    "* Use `loc` instead of chained indexing if possible!\n",
    "* Or `copy` explicitly if you don't want to change the original data."
   ]
  }
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