Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
Created on Cognitive Class Labs
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Pie Charts, Box Plots, Scatter Plots, and Bubble Plots</font></h1>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Introduction\n",
"\n",
"In this lab session, we continue exploring the Matplotlib library. More specificatlly, we will learn how to create pie charts, box plots, scatter plots, and bubble charts."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with *p*andas](#0)<br>\n",
"2. [Downloading and Prepping Data](#2)<br>\n",
"3. [Visualizing Data using Matplotlib](#4) <br>\n",
"4. [Pie Charts](#6) <br>\n",
"5. [Box Plots](#8) <br>\n",
"6. [Scatter Plots](#10) <br>\n",
"7. [Bubble Plots](#12) <br> \n",
"</div>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
"\n",
"Toolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).\n",
"\n",
"Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website.\n",
"\n",
"The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Downloading and Prepping Data <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Import primary modules."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Download the dataset and read it into a *pandas* dataframe."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data downloaded and read into a dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2\n",
" )\n",
"\n",
"print('Data downloaded and read into a dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's take a look at the first five items in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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>Type</th>\n",
" <th>Coverage</th>\n",
" <th>OdName</th>\n",
" <th>AREA</th>\n",
" <th>AreaName</th>\n",
" <th>REG</th>\n",
" <th>RegName</th>\n",
" <th>DEV</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Afghanistan</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>5501</td>\n",
" <td>Southern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
" <td>2652</td>\n",
" <td>2111</td>\n",
" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Albania</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Algeria</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>...</td>\n",
" <td>3616</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>American Samoa</td>\n",
" <td>909</td>\n",
" <td>Oceania</td>\n",
" <td>957</td>\n",
" <td>Polynesia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Andorra</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n",
"1 Immigrants Foreigners Albania 908 Europe 925 \n",
"2 Immigrants Foreigners Algeria 903 Africa 912 \n",
"3 Immigrants Foreigners American Samoa 909 Oceania 957 \n",
"4 Immigrants Foreigners Andorra 908 Europe 925 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n",
"1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n",
"2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n",
"3 Polynesia 902 Developing regions 0 ... 0 0 1 \n",
"4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"0 2652 2111 1746 1758 2203 2635 2004 \n",
"1 702 560 716 561 539 620 603 \n",
"2 3623 4005 5393 4752 4325 3774 4331 \n",
"3 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's find out how many entries there are in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(195, 43)\n"
]
}
],
"source": [
"# print the dimensions of the dataframe\n",
"print(df_can.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* for a detailed description of this preprocessing."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n"
]
}
],
"source": [
"# clean up the dataset to remove unnecessary columns (eg. REG) \n",
"df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
"\n",
"# let's rename the columns so that they make sense\n",
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
"\n",
"# for sake of consistency, let's also make all column labels of type string\n",
"df_can.columns = list(map(str, df_can.columns))\n",
"\n",
"# set the country name as index - useful for quickly looking up countries using .loc method\n",
"df_can.set_index('Country', inplace=True)\n",
"\n",
"# add total column\n",
"df_can['Total'] = df_can.sum(axis=1)\n",
"\n",
"# years that we will be using in this lesson - useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"print('data dimensions:', df_can.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Visualizing Data using Matplotlib<a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Import `Matplotlib`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.0.2\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"\n",
"mpl.style.use('ggplot') # optional: for ggplot-like style\n",
"\n",
"# check for latest version of Matplotlib\n",
"print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Pie Charts <a id=\"6\"></a>\n",
"\n",
"A `pie chart` is a circualr graphic that displays numeric proportions by dividing a circle (or pie) into proportional slices. You are most likely already familiar with pie charts as it is widely used in business and media. We can create pie charts in Matplotlib by passing in the `kind=pie` keyword.\n",
"\n",
"Let's use a pie chart to explore the proportion (percentage) of new immigrants grouped by continents for the entire time period from 1980 to 2013. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Gather data. \n",
"\n",
"We will use *pandas* `groupby` method to summarize the immigration data by `Continent`. The general process of `groupby` involves the following steps:\n",
"\n",
"1. **Split:** Splitting the data into groups based on some criteria.\n",
"2. **Apply:** Applying a function to each group independently:\n",
" .sum()\n",
" .count()\n",
" .mean() \n",
" .std() \n",
" .aggregate()\n",
" .apply()\n",
" .etc..\n",
"3. **Combine:** Combining the results into a data structure."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://ibm.box.com/shared/static/tkfhxqkehfzpclco8f0eazhie33uxj9j.png\" height=400 align=\"center\">"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
]
},
{
"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>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Continent</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <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",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Africa</th>\n",
" <td>3951</td>\n",
" <td>4363</td>\n",
" <td>3819</td>\n",
" <td>2671</td>\n",
" <td>2639</td>\n",
" <td>2650</td>\n",
" <td>3782</td>\n",
" <td>7494</td>\n",
" <td>7552</td>\n",
" <td>9894</td>\n",
" <td>...</td>\n",
" <td>27523</td>\n",
" <td>29188</td>\n",
" <td>28284</td>\n",
" <td>29890</td>\n",
" <td>34534</td>\n",
" <td>40892</td>\n",
" <td>35441</td>\n",
" <td>38083</td>\n",
" <td>38543</td>\n",
" <td>618948</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia</th>\n",
" <td>31025</td>\n",
" <td>34314</td>\n",
" <td>30214</td>\n",
" <td>24696</td>\n",
" <td>27274</td>\n",
" <td>23850</td>\n",
" <td>28739</td>\n",
" <td>43203</td>\n",
" <td>47454</td>\n",
" <td>60256</td>\n",
" <td>...</td>\n",
" <td>159253</td>\n",
" <td>149054</td>\n",
" <td>133459</td>\n",
" <td>139894</td>\n",
" <td>141434</td>\n",
" <td>163845</td>\n",
" <td>146894</td>\n",
" <td>152218</td>\n",
" <td>155075</td>\n",
" <td>3317794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Europe</th>\n",
" <td>39760</td>\n",
" <td>44802</td>\n",
" <td>42720</td>\n",
" <td>24638</td>\n",
" <td>22287</td>\n",
" <td>20844</td>\n",
" <td>24370</td>\n",
" <td>46698</td>\n",
" <td>54726</td>\n",
" <td>60893</td>\n",
" <td>...</td>\n",
" <td>35955</td>\n",
" <td>33053</td>\n",
" <td>33495</td>\n",
" <td>34692</td>\n",
" <td>35078</td>\n",
" <td>33425</td>\n",
" <td>26778</td>\n",
" <td>29177</td>\n",
" <td>28691</td>\n",
" <td>1410947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Latin America and the Caribbean</th>\n",
" <td>13081</td>\n",
" <td>15215</td>\n",
" <td>16769</td>\n",
" <td>15427</td>\n",
" <td>13678</td>\n",
" <td>15171</td>\n",
" <td>21179</td>\n",
" <td>28471</td>\n",
" <td>21924</td>\n",
" <td>25060</td>\n",
" <td>...</td>\n",
" <td>24747</td>\n",
" <td>24676</td>\n",
" <td>26011</td>\n",
" <td>26547</td>\n",
" <td>26867</td>\n",
" <td>28818</td>\n",
" <td>27856</td>\n",
" <td>27173</td>\n",
" <td>24950</td>\n",
" <td>765148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Northern America</th>\n",
" <td>9378</td>\n",
" <td>10030</td>\n",
" <td>9074</td>\n",
" <td>7100</td>\n",
" <td>6661</td>\n",
" <td>6543</td>\n",
" <td>7074</td>\n",
" <td>7705</td>\n",
" <td>6469</td>\n",
" <td>6790</td>\n",
" <td>...</td>\n",
" <td>8394</td>\n",
" <td>9613</td>\n",
" <td>9463</td>\n",
" <td>10190</td>\n",
" <td>8995</td>\n",
" <td>8142</td>\n",
" <td>7677</td>\n",
" <td>7892</td>\n",
" <td>8503</td>\n",
" <td>241142</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 \\\n",
"Continent \n",
"Africa 3951 4363 3819 2671 2639 2650 \n",
"Asia 31025 34314 30214 24696 27274 23850 \n",
"Europe 39760 44802 42720 24638 22287 20844 \n",
"Latin America and the Caribbean 13081 15215 16769 15427 13678 15171 \n",
"Northern America 9378 10030 9074 7100 6661 6543 \n",
"\n",
" 1986 1987 1988 1989 ... 2005 \\\n",
"Continent ... \n",
"Africa 3782 7494 7552 9894 ... 27523 \n",
"Asia 28739 43203 47454 60256 ... 159253 \n",
"Europe 24370 46698 54726 60893 ... 35955 \n",
"Latin America and the Caribbean 21179 28471 21924 25060 ... 24747 \n",
"Northern America 7074 7705 6469 6790 ... 8394 \n",
"\n",
" 2006 2007 2008 2009 2010 \\\n",
"Continent \n",
"Africa 29188 28284 29890 34534 40892 \n",
"Asia 149054 133459 139894 141434 163845 \n",
"Europe 33053 33495 34692 35078 33425 \n",
"Latin America and the Caribbean 24676 26011 26547 26867 28818 \n",
"Northern America 9613 9463 10190 8995 8142 \n",
"\n",
" 2011 2012 2013 Total \n",
"Continent \n",
"Africa 35441 38083 38543 618948 \n",
"Asia 146894 152218 155075 3317794 \n",
"Europe 26778 29177 28691 1410947 \n",
"Latin America and the Caribbean 27856 27173 24950 765148 \n",
"Northern America 7677 7892 8503 241142 \n",
"\n",
"[5 rows x 35 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# group countries by continents and apply sum() function \n",
"df_continents = df_can.groupby('Continent', axis=0).sum()\n",
"\n",
"# note: the output of the groupby method is a `groupby' object. \n",
"# we can not use it further until we apply a function (eg .sum())\n",
"print(type(df_can.groupby('Continent', axis=0)))\n",
"\n",
"df_continents.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
"- `autopct` - is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
"- `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
"- `shadow` - Draws a shadow beneath the pie (to give a 3D feel)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 360x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# autopct create %, start angle represent starting point\n",
"df_continents['Total'].plot(kind='pie',\n",
" figsize=(5, 6),\n",
" autopct='%1.1f%%', # add in percentages\n",
" startangle=90, # start angle 90° (Africa)\n",
" shadow=True, # add shadow \n",
" )\n",
"\n",
"plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
"plt.axis('equal') # Sets the pie chart to look like a circle.\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
"\n",
"* Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
"* Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
"* Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
"* **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
"explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
"\n",
"df_continents['Total'].plot(kind='pie',\n",
" figsize=(15, 6),\n",
" autopct='%1.1f%%', \n",
" startangle=90, \n",
" shadow=True, \n",
" labels=None, # turn off labels on pie chart\n",
" pctdistance=1.12, # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
" colors=colors_list, # add custom colors\n",
" explode=explode_list # 'explode' lowest 3 continents\n",
" )\n",
"\n",
"# scale the title up by 12% to match pctdistance\n",
"plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
"\n",
"plt.axis('equal') \n",
"\n",
"# add legend\n",
"plt.legend(labels=df_continents.index, loc='upper left') \n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
"\n",
"**Note**: You might need to play with the explore values in order to fix any overlapping slice values."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_2013 = df_continents[['2013']]\n",
"df_2013.head()\n",
"\n",
"colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
"explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
"\n",
"df_2013['2013'].plot(kind='pie',\n",
" figsize=(15, 6),\n",
" autopct='%1.1f%%', \n",
" startangle=90, \n",
" shadow=True, \n",
" labels=None, # turn off labels on pie chart\n",
" pctdistance=1.12, # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
" colors=colors_list, # add custom colors\n",
" explode=explode_list # 'explode' lowest 3 continents\n",
" )\n",
"\n",
"# scale the title up by 12% to match pctdistance\n",
"plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
"\n",
"plt.axis('equal') \n",
"\n",
"# add legend\n",
"plt.legend(labels=df_continents.index, loc='upper left') \n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"explode_list = [0.1, 0, 0, 0, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
"-->\n",
"\n",
"<!--\n",
"df_continents['2013'].plot(kind='pie',\n",
" figsize=(15, 6),\n",
" autopct='%1.1f%%', \n",
" startangle=90, \n",
" shadow=True, \n",
" labels=None, # turn off labels on pie chart\n",
" pctdistance=1.12, # the ratio between the pie center and start of text label\n",
" explode=explode_list # 'explode' lowest 3 continents\n",
" )\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # scale the title up by 12% to match pctdistance\n",
"plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
"plt.axis('equal') \n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # add legend\n",
"plt.legend(labels=df_continents.index, loc='upper left') \n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # show plot\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Box Plots <a id=\"8\"></a>\n",
"\n",
"A `box plot` is a way of statistically representing the *distribution* of the data through five main dimensions: \n",
"\n",
"- **Minimun:** Smallest number in the dataset.\n",
"- **First quartile:** Middle number between the `minimum` and the `median`.\n",
"- **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
"- **Third quartile:** Middle number between `median` and `maximum`.\n",
"- **Maximum:** Highest number in the dataset."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<img src=\"https://ibm.box.com/shared/static/9nkxsfihu8mgt1go2kfasf61sywlu123.png\" width=440, align=\"center\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"To make a `box plot`, we can use `kind=box` in `plot` method invoked on a *pandas* series or dataframe.\n",
"\n",
"Let's plot the box plot for the Japanese immigrants between 1980 - 2013."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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>Country</th>\n",
" <th>Japan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>246</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country Japan\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# to get a dataframe, place extra square brackets around 'Japan'.\n",
"df_japan = df_can.loc[['Japan'], years].transpose()\n",
"df_japan.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot by passing in `kind='box'`."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgEAAAF2CAYAAAD3KUuFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xm8JFV58PHfcUYRVGS5gsyAAQXccN9IoohiUFHBGDkQFRAJSBT3N8YEXyWYEJMYlNe4ZFgEFIXHJTJG3EAJoIKicUcRFWEWBkc2YRCYod4/zrnS03OXmrm3u+9M/b6fT39u96nTVU/1ra566pxT1alpGiRJUvfca9QBSJKk0TAJkCSpo0wCJEnqKJMASZI6yiRAkqSOMgmQJKmjTALUWkrp6pTS20e4/J1SSheklG5LKXlt60YkpXR6Sun8UccxCCml+Sml01JKv00pNSmlvUcdk9SWScAcUXeSTc/j5pTSN1NK+406tg2VUtpxlneKfw9sBzwe2GGSZe5dl7njLC1zk5BSOi6ldNUIQ3gDcOAIl7+OlNL5KaXTZ2FWfwG8DHgRZbv8xizMc8ZSSo9OKX0ypfTzlNLdKaVTJqn3VymlH6aUVqWUrqnbyr366jw7pXRx3S/dkFL6SkrpyX11HpBSOrkmQ7ellL6QUnrYLK3LASml81JK19U4f5xSekNKKfXV2yGlFCmlW+rj7JTSdn11jq3rcstk+4qU0ntSSj9NKd1a1/kbKaUXzMa6zDUmAXPLxZSdyA7AnsB3gc/O1hdpE7Ab8K2maX7eNM11ow5G7TVNc3PTNDfOZB4ppXullObNVkyzaDdgadM032ia5rqmae7sr5BSus8I4toCuAY4Hvj+RBVSSkcC7wfeA+wBHAMcDbyrp85DgP8Gvgc8BdgLuBn4Ukrpfj2z+yiwD/BS4OlAAr6SUtp8FtZlb+CbwEtqnP8OvBt4a0+c96px7gL8GbAvsDtlH9qbLGwGLAb+aYrl/Rh4LfA44GnARcC5KaUnzcK6zC1N0/iYAw/gdOD8vrIHAA3w531l/wn8Bvg9cDmwb8/0DNwJPLWn7NBa9wlTLL+hnK19GrgNWAa8ua/O1cDb1yOWpu9x9RTLX995nT7JfPau03esrxNwMvAL4Hbgl8AJwGY97zkOuIpyNvfLuvzzgV166uwCfKZ+LquAHwKH9C37QuAU4P8C1wE31P/r/frqHUzZof6+fqYn9tah7EC/DvyuPr4PPLdn+vZ1vr+p078O7DXN9nUccNUE65yBn9d1+iywJWVH+7M6708BD+zfToHXAUuAW+s635ty8Pg1cCOwCLjPZNs35QTkhLoOtwJnA28EVk8Q40HAT4HVlAPAE4EvANfX934beN4E2+rxwEn1/7CCcqCb1xNP/za1d53293U7uKPG9yVg80k+1wuZYBuv5adSDqbLgd+03M53rvN5WV3uqrruzwQWAudRvp8/AZ6xHvuXC4FTJii/BPhwX9mb6jLuV1+/uMb0gJ46j6llj6uvd6+ve9dl6/oZvnJA+8z3A9/peb1vjeHhPWWP7v3fTrWvaLG8G4E3DGJdRvkYeQA+6j9i3Z3kfYA31x3FH/WUf7Lu4J4LPLLu5O4EHtFTZ/ygt2X9cv4OeN00y2/qzvJ19T1voOx0X9JT52rWTgKmjAV4Qp3vS4AHAw+aYvnTzevBlGbWs+rzB04yn7W+2JSDzT9Ssvmdgf0pO+V/6HnPcXWndwnlTOcpwGWUg2+qdR5DOTN4LPCw+jmtBp7VM58LgZuA9wKPAJ5XX/cu65V1Z3II8FDKWdUPgI/W6fPq/+FEyhnmbsCfU3f4wOaUA8CngScDuwLHUna2j5zi8z2OdZOA24DP13V6JuXA9GXKgeZxwDMoB89/6dtObwbOqP+n/Snb6HnAmcCjgBdSEq6/nmL7fjPlAH5IXcc31/XuTwJWAf9DaRnbnXIQ3Rs4rC5r9/r/vRPYvW9bvRF4W53/QfX/dXid/kDK2d05lO3pwZTv3EuAWyhN+w+hdD29kcmTgG0oycWv6NnG67bwO+DDNc7HtNzOd6Zsv7+gHHx3B/6LknyeX7eF3ev//1rg3i33LxcycRJwOfC+vrKjawzPrK93qv+Ht1CSvc2B91EStM1qncPreszrm9fFEy13lvaZZwJf6nn9D8AvJ6h3LT37rcn2FVMsZ37dTu8CnjiIdRnlY+QB+Kj/iLKTXE3ZMd4K3F3/5p46u9aNdr++934XOK3n9eaU5qwA/hf4bIvlN9QDUU/Zx4FLel5fPf5lahMLsCOTZOF972m7XhPuyPreM+0Xm3Km8/Oe18fV9+zaUzZ+ZvOcKeZzLnByX3w/6KvzYeCbfZ/h0X119qrL2ro+Jv3MKEnEEmB+X/lX6duZ900/jnWTgNXAWE/ZB4A19CRrlIPU5X3b6fWsfZb/eWAla7eunAt8qu99vUnAUuBdfTGezbpJwN3AQ1psv98Hju37nBf31fki8Ime1+fT16JUt40raXlwneiz7dkWrgTutT7bOfckAW/smf6UWvaWnrLxBHuPljFeyMRJwLsoydefUlrNHklpBWqAv+yptyela2F1/Z/8FHhoz/S/B5ZNMP9PAp9v+1mux2e+N+Wg/KKeskXANyao+23gA5PMY9J9BSWZvbV+J24EXjjb6zEXHo4JmFsuo5x5PJ7S5Hk8cEZK6bl1+qPq34v63ncRpdkLgKZpbqec+byEMpDuVS2X/82+11/vWWa/VrG0NJvzWkdK6ciU0mUppRUppVuBfwb+qK/ab5qm+cPAuaZprqQc2B5V57FFSunddUDSDXU++00wn+/1vV5Kab4npfSgWv/EOuDo1jqfL9S6uzal3/wUSn/rF1JKb0spPbxnfk+hnHHe1DePZ1DOeNfH0qZpVva8vg64rmma3/SVbbf227iiWbvf+zrgZ03T3DHN+wBIKW0JLAAu7ZvUv/0BrGia5pq+9z8opfTBOnDrprr+j2Y9/hdTCMrZ7q/rYN1DUkoPmOY9k/lO0zR397xen+28tw9/fPzLDyYom/AzXg//SFnnr1EOqhcDH6vT1gDUgXUfofSjP42SMFwBnNfys2kmm1C/T+Pb8Y/bBJxS2pPSdXVc0zSfa/OeqWKYwtco++I9Ka2rZ6aUnrgB85nT5o86AK3l9t4DEfC9lNI+lObeL03xvsS6G/nT69+tKDuKGzYgnjR9lVaxbKgZzyuldCDlDPdtlGblWyij1KcaFNS7/HH/BhxAaRL9KaUp/d8pzcq9+geFNdwzAHf87xsoO5h+SwCapjkypXQSpY/zz4B3pZSOaZrmP+s8rqA0C/db1WKdet01QawTlfWfLGzo+8alnjrTuW2CstMpTfVvpTTD305pRegffDfV/2JCTdMsTSk9AngW8GzK+I5/SSk9rWmaa1vEO13sE5loO+/9PJspymZ0IlcTt6NTSsdQkssVlG0OSpcElMGCNE1zzB8CTulgytnxQZSkdTkwllKa1zTNmp5FbE9pEZnMfpSkC9bdhtZRrzT6HPDPTdOc0Dd5OfCcCd62PfckTa01TXMbpcsD4NsppcdRtrmD13dec5ktAXPfasooXyhN/FCaj3s9o2caKaVHU/qUX005yzw7pbRZi2Xt2ff6jykHnIm0iWV8JzzdiO5W67WB9gL+t2maE5um+U7TND+nNLn2e1DvVRgppd2Bbbln/fcCzmqa5pymab5PGTi2+/oE0jTNCkr/5MObprlqgsfve+r+qMb8fMoAs6PqpMspYwlumeD9y9YnnlFpmuZmSh/3H/dN6t/+JrMX8MGmaRY3TfNDys7/oRsQyp1MsG02TXNH0zRfbJrmrZSxIFtQ+udnapDb+Yw0TbO6aZolTdPcRRmU+CtKVyLA/ShdAL3uro/xhO7rlIP5s8crpJS2orQcXDLFcn/ds/3+eqoY6yV651FaAPoTgPEYdkkp7dbznkdSxjRMGsN6uBflyoJNii0Bc8t9UkoPrs/vRxk89FzgnQBN0/wipfRJ4IMppVdTRmL/NWXE9MsAUkr3pZwVLW6a5tSU0mcozaLvoQxmm8oL6xnBlyiD2g5ikqy3TSyU5vRbgX1rU98dzQSXibWc14b6GXBESukA4EeUfr6XTFBvFfCRlNKbKDu291OuADi/Zz4HpJQ+XdfpzZQm7RXrGc+xwKkppZsoTZp3Ufphn980zatTSrsCR1LOdq6ty3gGpd8YysDINwGfTykdSznL2p6y872iaZrPrmc8o/LvwD+klH4KfAt4AfeM7p7Oz4CXp5QuoRzEj2f6RHMivwKeVZO/m+vjUMrO/luUQZ37UAYj/mQD5r+WAW/n66iXJY53Qdwf2Cal9HjgzqZpflLr7Epp3v8mZT2PoHzvX9TTlbEYeFNK6d2UboH7UFrWGuArdd2uTCmdC3wopXQE5bM8gdIFc84srMuBlG3/3cBZPfvJNT3dV+dTvicfSym9jvI9/gCl2+l/eub1EMqAzl1r0aNSSmPANU3T3JBS2h54DSXhuI7SmvqXlG3hL2a6LnPOqAcl+CgP1r1kaRXl7OD/sPbgoi255xKjO1j3EqMPUc5Sey/rejrlYLP/FMtvKKOgP1uXvRz4m746V7P21QFTxlLrHErZ2d7F1JcItpnXhUw/MPDZdV0eXF/fu873BkpXwMcpzZtNz3uOozT7vaKu4x2UgXYP66mzEyU5uq1+Nv9AOUO/cKr4gLf3rzflrPKb9XO+hZKkvaNO24FyKeKSGscySn9k7/9z2/p/Xko5m11KGUE+1SWgxzHBJYItYn0bsKRvO+2/lPWU3s+hln2YtQeVrvU+yoH2n7knUTybMrjsd1PFWMsfQ7lS5Pb6/3oNfYP86NtWJ4qT0npwUV1+Qxko9pI67xvr/+dHwBHTbHMTfZbrbAstv78711ie3lO2zgBbStP9dANXx+fV/7i6p87uNYbb6ufwNSa43JRy8PsWZXu9gfL9eHpfnQfUz/iG+tl9kZ7BtjN5sO6lmOusS8/355OUKzNuoSQg2/XVOX2Seb2yTt+aMrB1GeX7dR0l2XnebKzLXHuMX/6kjkvlNryHNE3zsWkrz2EppZdRLh3arFm7b3Kq9xwHvKJpml2nq6vBSSmdRrnufNO7IYs0R9kdoE1CvSvZ+PX7X22bAGg0UkoLKIMbv0YZhf4iSqvRMVO9T9LscmCgNhUHUZpxb6Hc7ERz2xrKVRqXUAagHUq5udCHRxqV1DF2B0iS1FG2BEiS1FEmAZIkdVQXBgba3yFJ6qJp7/rahSSAZcs2ihupSZucsbExVq5cOX1FSbNqwYIFrerZHSBJUkeZBEiS1FEmAZIkdZRJgCRJHWUSIElSR5kESJLUUSYBkiR1lEmAJEkdZRIgSVJHmQRIktRRJgGSJHWUSYAkSR3ViR8QkrT+Fi5cOOoQAFi6dOmoQ5A2WSYBkiY0GwffNUfuz7yTF89CNJIGwe4ASZI6yiRAkqSOMgmQJKmjTAIkSeookwBJkjrKJECSpI4yCZAkqaNMAiRJ6iiTAEmSOsokQJKkjjIJkCSpo0wCJEnqKJMASZI6yiRAkqSOMgmQJKmjTAIkSeookwBJkjrKJECSpI6aP4yF5JxPA14IXB8Re9SyfwNeBNwJ/AI4PCJuqtP+DjgCWAO8PiK+VMufB5wEzANOiYh3DyN+SZI2RcNqCTgdeF5f2VeAPSLiscCVwN8B5JwfBRwMPLq+54M553k553nAB4DnA48C/rLWlSRJG2AoSUBEXATc0Ff25YhYXV9eCuxYnx8AnB0Rd0TEr4CrgKfWx1UR8cuIuBM4u9aVJEkbYCjdAS28CjinPl9ISQrGLallANf2lT9topnlnI8CjgKICMbGxmY1WEntrAC/f9IcNvIkIOd8LLAaOKsWpQmqNUzcatFMNM+IWAQsGq+zcuXKmYYpaQP5/ZOGb8GCBa3qjTQJyDkfRhkwuE9EjB/QlwA79VTbEVhWn09WLkmS1tPIkoA60v9vgWdGxKqeSYuBj+ecTwQWALsB36K0EOyWc94FWEoZPPiy4UYtSdKmIzXNhC3qsyrn/Algb2CM0k34TsrVAJsBv63VLo2Io2v9YynjBFYDb4yIL9Ty/YD3US4RPC0i/qnF4ptly2wwkEZhzZH7M+/kxaMOQ+qc2h0wUff6WoaSBIyYSYA0IiYB0mi0TQK8Y6AkSR1lEiBJUkeZBEiS1FEmAZIkdZRJgCRJHWUSIElSR5kESJLUUSYBkiR1lEmAJEkdZRIgSVJHmQRIktRRJgGSJHWUSYAkSR1lEiBJUkeZBEiS1FEmAZIkdZRJgCRJHWUSIElSR5kESJLUUSYBkiR1lEmAJEkdZRIgSVJHmQRIktRRJgGSJHWUSYAkSR1lEiBJUkeZBEiS1FGpaZpRxzBozbJly0YdgzRUa97wMlh166jDmBu2uD/zTvr4qKOQhmrBggUAabp68wcfiqShW3Ur805ePOooGBsbY+XKlSONYc2R+490+dJcZneAJEkdZRIgSVJHmQRIktRRJgGSJHWUSYAkSR1lEiBJUkeZBEiS1FEmAZIkdZRJgCRJHWUSIElSR5kESJLUUSYBkiR1lEmAJEkdZRIgSVJHmQRIktRRJgGSJHWUSYAkSR01fxgLyTmfBrwQuD4i9qhl2wDnADsDVwM5Im7MOSfgJGA/YBXwyoj4bn3PYcDb62z/MSLOGEb8kiRtiobVEnA68Ly+srcBF0TEbsAF9TXA84Hd6uMo4EPwh6ThncDTgKcC78w5bz3wyCVJ2kQNJQmIiIuAG/qKDwDGz+TPAF7cU35mRDQRcSmwVc55B+C5wFci4oaIuBH4CusmFpIkqaWhdAdMYvuIWA4QEctzztvV8oXAtT31ltSyycrXkXM+itKKQEQwNjY2y6FLc9sKmBPb/fz580cex1z5LKS5aJRJwGTSBGXNFOXriIhFwKLxOitXrpyl0KSNx1zY7sfGxuZEHHMhBmmYFixY0KreKK8OWFGb+al/r6/lS4CdeurtCCybolySJG2AUSYBi4HD6vPDgHN7yg/NOaec857AzbXb4EvAvjnnreuAwH1rmSRJ2gAb1B2Qc94cWBMRd7as/wlgb2As57yEMsr/3UDknI8ArgEOrNXPo1weeBXlEsHDASLihpzzu4Bv13rHR0T/YENJktRSapoJu9XXknN+DxAR8a2c8wuAT1H64w+KiM8NOMaZapYts9dA3bLmyP2Zd/LiUYcxJ8YEzJXPQhqmOiZgorF0a2nbHfBy4Ef1+TuAVwD7AydsSHCSJGn02nYHbBERq3LO2wIPjYhPA+Sc/2hwoUmSpEFqmwRcmXN+ObAr5SY95JzHgNsHFZgkSRqstknAayj3878TOKKWPRf48iCCkiRJg9c2Cbg2Iv6ktyAizso5XzCAmCRJ0hC0HRh45STlP5mtQCRJ0nC1TQLWucwg57wlcPfshiNJkoZlyu6AnPO1lPsBbJ5zvqZv8rbAJwYVmCRJGqzpxgS8gtIKcB5wSE95A6yIiJ8NKjBJkjRYUyYBEfE/UC4HjIhVwwlJkiQNQ9urA1bnnI8CHg/cv3dCRBw661FJkqSBa5sEnAk8FvgcsGJw4UiSpGFpmwQ8F9glIm4aZDCSJGl42l4ieA2w2SADkSRJw7U+3QHn5pxPoq87ICK+OutRSZKkgWubBBxT//b/dHADPHT2wpEkScPSKgmIiF0GHYgkSRqutmMCJEnSJqZVS0D9nYDjgGcCY/T8lkBEPGQgkUmSpIFq2xLwQeCJwPHANsDrKFcMvHdAcUmSpAFrmwTsC/xFRJwLrKl/D2Lt3xOQJEkbkbZXB9wLuLk+vzXnvBWwHNh1IFFJmpHznnMmnDMX7u01B2J4zpm8aNQxSHNU2yTg+5TxABcAFwMfAG4FrhxQXJJmYL/zD2XeyYtHHQZjY2OsXLlypDGsOXJ/OGj0n4U0F7XtDjgSuLo+fz1wO7AV4I8HSZK0kZq2JSDnPA94JfBPABHxG+CvBhuWJEkatGlbAiJiDfBa4K7BhyNJkoalbXfAGcDRgwxEkiQNV9uBgU8FXpdzfitwLeU3AwCIiL0GEZgkSRqstknAyfUhSZI2EW1/QOiMQQciSZKGq+1vB7xqkkl3AEuASyPijlmLSpIkDVzb7oBDgT8GVlAO+jsC2wOXAzsD5JwPiIjLBxCjJEkagLZJwI+Bz0TE/xsvyDkfAzwCeDpwLPB+SqIgSZI2Am0vEXwZ8B99ZR8CXh4RDfBvwKNmMzBJkjRYbZOAFbDOb3C8ALi+Pr8v3kxIkqSNStvugNcDn8w5/4hyn4CdgD2AA+v0p1G6AyRJ0kai7SWCX845Pwx4PrAAOA/4fET8dnw68OWBRSlJkmZd25YAImIl8NEBxiJJkoZo0iQg5/zFiHhefX4xPbcK7uVtgyVJ2jhN1RJwZs/zUwYdiCRJGq5Jk4CI+HjPc28bLEnSJqb1mICc8zOAJwD37y2PiBNmOyhJkjR4bX874P1ABi4Gbu+ZNOE4AUmSNPe1bQl4ObBHRCwbZDCSJGl42t4x8FrKLwZKkqRNRNuWgCOAk3POn6DcQvgPIuKimQSQc34T8FeUroUfAocDOwBnA9sA3wUOiYg7c86bUa5aeBLwW+CgiLh6JsuXJKmr2rYEPIlyt8APAWf1PD42k4XnnBdSbkn85IjYA5gHHAz8C/DeiNgNuJGShFD/3hgRuwLvrfUkSdIGaNsScALwoog4f0AxbJ5zvgvYAlgOPJvyy4UAZwDHURKQA+pzgE8B/5FzTvWXDCVJ0npo2xJwGzCjZv+JRMRS4D3ANZSD/83Ad4CbImJ1rbYEWFifL6SMT6BOvxnYdrbjkiSpC9q2BLwDeF/O+Xju+flgACLi7g1deM55a8rZ/S7ATcAnKd0O/cbP9NMU03rnexRwVI2PsbGxDQ1R2iitgDmx3c+fP3/kccyVz0Kai9omAafVv6/uKUuUA/C8GSz/OcCvIuI3ADnnzwB/AmyVc55fz/Z3BMYvTVxC+RnjJTnn+cADgRv6ZxoRi4BF9WWzcuXKGYQobZzmwnY/NjY2J+KYCzFIw7RgwYJW9domAbtseChTugbYM+e8BeUmRPsAlwNfA15KuULgMODcWn9xff3NOv2rjgeQJGnDtEoCIuLXg1h4RFyWc/4U5TLA1cD/Us7gPw+cnXP+x1p2an3LqcBHc85XUVoADh5EXJIkdUHb2wY/kHIp30S/HbDvTAKIiHcC7+wr/iXw1Anq/h44cCbLkyRJRdvugE9S+v7/i7V/O0CSJG2k2iYBewLbRsRdgwxGkiQNT9v7BFwCPHKQgUiSpOFq2xLwSuC8nPNlrPvbAcfPdlCSJGnw2iYB/0S5Pv9qYMueci/PkyRpI9U2CTgY2D0ilg8yGEmSNDxtxwT8EnBQoCRJm5C2LQEfBRbnnN/PumMCvjrrUUmSpIFrmwS8tv49oa+8AR46e+FIkqRhaXvb4EH9doAkSRqRtmMCJEnSJmbKloCc88VMcxlgROw1qxFJkqShmK474JShRCFJkoZuyiQgIs4YViCSJGm4HBMgSVJHmQRIktRRJgGSJHXUpElAzvnSnufvHE44kiRpWKZqCdg953zf+vwtwwhGkiQNz1RXB5wLXJlzvhrYPOd80USVvE+AJEkbp0mTgIg4POf8dGBn4CnAqcMKSpIkDd509wm4BLgk53wf7xkgSdKmpe0PCJ2Wc34WcAiwEFgKfMyfEZYkaePV6hLBnPNfAecA1wGfAZYDH885HznA2CRJ0gC1agkA3gr8WUR8f7wg53wO8Gng5EEEJkmSBqvtzYK2BX7SV/YzYJvZDUeSJA1L2yTgEuDEnPMWADnn+wH/BnxjUIFJkqTBapsEHA08Frg557wCuAl4HPDqQQUmSZIGq+3VAcuBZ+acdwQWAMsiYslAI5MkSQPVdmAgAPXA78FfkqRNgL8iKElSR5kESJLUUdN2B+Sc7wXsDVwSEXcOPCJJkjQU07YERMTdwLkmAJIkbVradgdclHPec6CRSJKkoWp7dcCvgS/knM8FrgWa8QkR8Y5BBCZJkgarbRKwOfDZ+nzHAcUiSZKGqO3Ngg4fdCCSJGm4Wt8sKOf8SOClwPYRcUzO+eHAZhHxg4FFJ0mSBqbVwMCc84HARcBC4NBa/ADgxAHFJUmSBqzt1QHHA38WEUcDa2rZ9yk/IiRJkjZCbZOA7SgHfbjnyoCm57kkSdrItE0CvgMc0ld2MPCt2Q1HkiQNS9uBga8HvpxzPgK4X875S8DuwL4Di0ySJA1Uq5aAiPgp8AjgA8DbgY8Aj4mInw8wNkmSNECtf0UwIlYBXwcuBC6OiFsHFZQkSRq8Vt0BOeeHAGcBewI3AlvnnC8DXh4Rv55JADnnrYBTgD0oAw1fBfwMOAfYGbgayBFxY845AScB+wGrgFdGxHdnsnxJkrqqbUvAGZTBgVtFxHbA1sC3a/lMnQR8MSIeQbnk8ArgbcAFEbEbcEF9DfB8YLf6OAr40CwsX5KkTmqbBDwJ+JuIuA2gdgX8bS3fYDnnLYG9gFPrfO+MiJuAA7gnwTgDeHF9fgBwZkQ0EXEpsFXOeYeZxCBJUle1vTrgUuCplDEB454MfHOGy38o8BvgIznnx1FaG95AuTXxcoCIWJ5z3q7WX0j5FcNxS2rZ8t6Z5pyPorQUEBGMjY3NMExp47IC5sR2P3/+/JHHMVc+C2kumjQJyDkf3/PyF8B5OefPUw7CO1H65T8+C8t/IvC6iLgs53wS9zT9TyRNULbODYsiYhGwaHz6ypUrZximtPGZC9v92NjYnIhjLsQgDdOCBQta1ZuqO2Cnnsd9gc8Ad1DuHngH8F+1fCaWAEsi4rL6+lOUpGDFeDN//Xt9T/2det6/I7BshjFIktRJk7YEDOPngyPiupzztTnnh0fEz4B9gJ/Ux2HAu+vfc+tbFgPH5JzPBp4G3DzebSBJktbP+vyU8BbArsD9e8sj4hszjOF1wFk55/sAvwQOp7RQRL1D4TXAgbXueZRuiKsolwgOPFGRJGlT1fY+AYcC/wHcCdzeM6kBHjKTACLie5RBhv32maBuA7x2JsuTJElF25aAfwX+IiK+MshgJEnS8LS9T8CdlNsFS5KkTUTbJOCsSJBQAAAIo0lEQVT/AifmnL3YVpKkTUTb7oArgeOB1+Scx8sS0ETEvEEEJkmSBqttEvBR4EzKj/rcPk1dSZK0EWibBGwLvKOOzpckSZuAtmMCPgIcMshAJEnScLVtCXgq5U59x1J+j+MPImKvWY9KkiQNXNsk4OT6kCRJm4hWSUBEnDHoQCRJ0nC1vW3wqyabFhGnzV44kiRpWNp2B/QPCnww8DDg64BJgCRJG6G23QHP6i+rrQOPnPWIJEnSULS9RHAipwNHzFIckiRpyNqOCehPFrYAXgHcNOsRSZoVa47cf9QhrH098ahscf9RRyDNWW3HBKwG+u8WuBQ4cnbDkTQb5p28eNQhACURmSuxSFpX2yRgl77Xt0XEytkORpIkDU/bgYG/HnQgkiRpuKZMAnLOX2PdboBeTUTsM7shSZKkYZiuJeBjk5QvBF5PGSAoSZI2QlMmARFxau/rnPO2wN9RBgSeAxw/uNAkSdIgtb1EcEvgb4BjgP8GnhgRvxhkYJIkabCmGxOwOfBG4C3AhcDTI+LHQ4hLkiQN2HQtAb8C5gH/ClwObJ9z3r63QkR8dUCxSZKkAZouCfg95eqAv55kegM8dFYjkiRJQzHdwMCdhxSHJEkaspn8gJAkSdqImQRIktRRJgGSJHWUSYAkSR1lEiBJUkeZBEiS1FEmAZIkdZRJgCRJHWUSIElSR5kESJLUUSYBkiR1lEmAJEkdZRIgSVJHmQRIktRRJgGSJHWUSYAkSR1lEiBJUkeZBEiS1FEmAZIkddT8UQcAkHOeB1wOLI2IF+acdwHOBrYBvgscEhF35pw3A84EngT8FjgoIq4eUdiSJG3U5kpLwBuAK3pe/wvw3ojYDbgROKKWHwHcGBG7Au+t9SRJ0gYYeRKQc94ReAFwSn2dgGcDn6pVzgBeXJ8fUF9Tp+9T60uSpPU08iQAeB/wVuDu+npb4KaIWF1fLwEW1ucLgWsB6vSba31JkrSeRjomIOf8QuD6iPhOznnvWjzRmX3TYlrvfI8CjgKICMbGxmYhWknrawX4/ZPmsFEPDPxTYP+c837AfYEtKS0DW+Wc59ez/R2BZbX+EmAnYEnOeT7wQOCG/plGxCJgUX3ZrFy5crBrIWlSfv+k4VuwYEGreiPtDoiIv4uIHSNiZ+Bg4KsR8XLga8BLa7XDgHPr88X1NXX6VyNinZYASZI0vbkwJmAifwu8Oed8FaXP/9RafiqwbS1/M/C2EcUnSdJGLzXNJn8i3Sxbtmz6WpJm3Zoj92feyYtHHYbUObU7YNqr5+ZqS4AkSRowkwBJkjrKJECSpI4yCZAkqaNMAiRJ6iiTAEmSOsokQJKkjjIJkCSpo0wCJEnqKJMASZI6yiRAkqSOMgmQJKmjTAIkSeookwBJkjrKJECSpI4yCZAkqaNMAiRJ6iiTAEmSOsokQJKkjjIJkCSpo0wCJEnqKJMASZI6yiRAkqSOMgmQJKmjTAIkSeookwBJkjrKJECSpI4yCZAkqaNMAiRJ6iiTAEmSOsokQJKkjjIJkCSpo0wCJEnqKJMASZI6yiRAkqSOMgmQJKmjTAIkSeookwBJkjrKJECSpI4yCZAkqaNMAiRJ6iiTAEmSOsokQJKkjjIJkCSpo0wCJEnqKJMASZI6av4oF55z3gk4E3gwcDewKCJOyjlvA5wD7AxcDeSIuDHnnICTgP2AVcArI+K7o4hdkqSN3ahbAlYDb4mIRwJ7Aq/NOT8KeBtwQUTsBlxQXwM8H9itPo4CPjT8kCVJ2jSMNAmIiOXjZ/IR8TvgCmAhcABwRq12BvDi+vwA4MyIaCLiUmCrnPMOQw5bkqRNwki7A3rlnHcGngBcBmwfEcuhJAo55+1qtYXAtT1vW1LLlvfN6yhKSwERwdjY2GCDlzShFeD3T5rD5kQSkHO+P/Bp4I0RcUvOebKqaYKypr8gIhYBi8anr1y5clbilLpk4cKFszOjzTab0duXLl06O3FIHbJgwYJW9UaeBOSc701JAM6KiM/U4hU55x1qK8AOwPW1fAmwU8/bdwSWDS9aqTtm4+A7NjaGSbg0d4366oAEnApcEREn9kxaDBwGvLv+Pben/Jic89nA04Cbx7sNJEnS+hl1S8CfAocAP8w5f6+W/T3l4B855yOAa4AD67TzKJcHXkW5RPDw4YYrSdKmIzXNOl3qm5pm2TJ7DKRRsDtAGo06JmCicXRrGfV9AiRJ0oiYBEiS1FEmAZIkdZRJgCRJHWUSIElSR5kESJLUUSYBkiR1lEmAJEkdZRIgSVJHmQRIktRRnbht8KgDkCRpBKa9bfCof0BoGKb9ECQNRs758oh48qjjkDQxuwMkSeookwBJkjrKJEDSIC0adQCSJteFgYGSJGkCtgRIktRRJgGSWsk53zrqGCTNLpMASZI6qgv3CZA0S3LO9wfOBbYG7g28PSLOzTnvDHwRuAx4AnAlcGhErMo5vwN4EbA58A3g1RHR5JwvrPWfBWwFHBERFw95laROsyVA0vr4PfDnEfFEysH733PO4zfkejiwKCIeC9wCvKaW/0dEPCUi9qAkAi/smd/8iHgq8EbgnUNZA0l/YEuApPWRgBNyznsBdwMLge3rtGsj4uv1+ceA1wPvAZ6Vc34rsAWwDfBj4HO13mfq3+8AOw88eklrsSVA0vp4OfAg4EkR8XhgBXDfOq3/euMm53xf4IPASyPiMcDJPfUB7qh/1+BJiTR0JgGS1scDgesj4q6c87OAP+qZ9pCc8x/X538JXMI9B/yVdTzBS4cXqqTpmARImlbOeT7lrP0s4Mk558sprQI/7al2BXBYzvkHlGb/D0XETZSz/x8CnwW+PdTAJU3JOwZKmlbO+XHAyXUQ30TTdwb+uw7+k7SRsCVA0pRyzkcDnwDePupYJM0uWwIkSeooWwIkSeookwBJkjrKJECSpI4yCZAkqaNMAiRJ6iiTAEmSOur/A+fv54wzFHNhAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_japan.plot(kind='box', figsize=(8, 6))\n",
"\n",
"plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
"plt.ylabel('Number of Immigrants')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can immediately make a few key observations from the plot above:\n",
"1. The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and median number of immigrants is around 900 (median).\n",
"2. 25% of the years for period 1980 - 2013 had an annual immigrant count of ~500 or fewer (First quartile).\n",
"2. 75% of the years for period 1980 - 2013 had an annual immigrant count of ~1100 or fewer (Third quartile).\n",
"\n",
"We can view the actual numbers by calling the `describe()` method on the dataframe."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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>Country</th>\n",
" <th>Japan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>34.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>814.911765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>337.219771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>198.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>529.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>902.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1079.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1284.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country Japan\n",
"count 34.000000\n",
"mean 814.911765\n",
"std 337.219771\n",
"min 198.000000\n",
"25% 529.000000\n",
"50% 902.000000\n",
"75% 1079.000000\n",
"max 1284.000000"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_japan.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's analyize these two countries further using box plots.\n",
"\n",
"**Question:** Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the dataset for China and India and call the dataframe **df_CI**."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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>Country</th>\n",
" <th>China</th>\n",
" <th>India</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>5123</td>\n",
" <td>8880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>6682</td>\n",
" <td>8670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>3308</td>\n",
" <td>8147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>1863</td>\n",
" <td>7338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>1527</td>\n",
" <td>5704</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country China India\n",
"1980 5123 8880\n",
"1981 6682 8670\n",
"1982 3308 8147\n",
"1983 1863 7338\n",
"1984 1527 5704"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI = df_can.loc[['China','India'], years].transpose()\n",
"df_CI.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
"df_CI.head()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's view the percentages associated with both countries using the `describe()` method."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"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>Country</th>\n",
" <th>China</th>\n",
" <th>India</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>34.000000</td>\n",
" <td>34.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>19410.647059</td>\n",
" <td>20350.117647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>13568.230790</td>\n",
" <td>10007.342579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1527.000000</td>\n",
" <td>4211.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5512.750000</td>\n",
" <td>10637.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>19945.000000</td>\n",
" <td>20235.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>31568.500000</td>\n",
" <td>28699.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>42584.000000</td>\n",
" <td>36210.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country China India\n",
"count 34.000000 34.000000\n",
"mean 19410.647059 20350.117647\n",
"std 13568.230790 10007.342579\n",
"min 1527.000000 4211.000000\n",
"25% 5512.750000 10637.750000\n",
"50% 19945.000000 20235.000000\n",
"75% 31568.500000 28699.500000\n",
"max 42584.000000 36210.000000"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.describe()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot data."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},