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bigdataease/DV0101EN-2-2-1-Area-Plots-Histograms-and-Bar-Charts-py-v2.0.ipynb

Created March 2, 2020 10:00
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DV0101EN-2-2-1-Area-Plots-Histograms-and-Bar-Charts-py-v2.0.ipynb
{
"cells": [
{
"cell_type": "markdown",
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"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>Area Plots, Histograms, and Bar Plots</font></h1>"
]
},
{
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"source": [
"## Introduction\n",
"\n",
"In this lab, we will continue exploring the Matplotlib library and will learn how to create additional plots, namely area plots, histograms, and bar charts."
]
},
{
"cell_type": "markdown",
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},
"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with *pandas*](#0)<br>\n",
"2. [Downloading and Prepping Data](#2)<br>\n",
"3. [Visualizing Data using Matplotlib](#4) <br>\n",
"4. [Area Plots](#6) <br>\n",
"5. [Histograms](#8) <br>\n",
"6. [Bar Charts](#10) <br>\n",
"</div>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"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 that we are exploring 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. For this lesson, we will focus on the Canadian Immigration data."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
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},
"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. The first thing we'll do is import two key data analysis modules: *pandas* and **Numpy**."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
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"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"Solving environment: done\n",
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 4.5.11\n",
" latest version: 4.8.2\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base -c defaults conda\n",
"\n",
"\n",
"\n",
"## Package Plan ##\n",
"\n",
" environment location: /home/jupyterlab/conda/envs/python\n",
"\n",
" added / updated specs: \n",
" - xlrd\n",
"\n",
"\n",
"The following packages will be downloaded:\n",
"\n",
" package | build\n",
" ---------------------------|-----------------\n",
" numpy-base-1.15.4 | py36h81de0dd_0 4.2 MB anaconda\n",
" numpy-1.15.4 | py36h1d66e8a_0 35 KB anaconda\n",
" openssl-1.1.1 | h7b6447c_0 5.0 MB anaconda\n",
" mkl_fft-1.0.6 | py36h7dd41cf_0 150 KB anaconda\n",
" certifi-2019.11.28 | py36_0 156 KB anaconda\n",
" blas-1.0 | mkl 6 KB anaconda\n",
" scipy-1.1.0 | py36hfa4b5c9_1 18.0 MB anaconda\n",
" xlrd-1.2.0 | py_0 108 KB anaconda\n",
" mkl_random-1.0.1 | py36h4414c95_1 373 KB anaconda\n",
" scikit-learn-0.20.1 | py36h4989274_0 5.7 MB anaconda\n",
" ------------------------------------------------------------\n",
" Total: 33.7 MB\n",
"\n",
"The following packages will be UPDATED:\n",
"\n",
" certifi: 2019.9.11-py36_0 conda-forge --> 2019.11.28-py36_0 anaconda\n",
" mkl_fft: 1.0.4-py37h4414c95_1 --> 1.0.6-py36h7dd41cf_0 anaconda\n",
" mkl_random: 1.0.1-py37h4414c95_1 --> 1.0.1-py36h4414c95_1 anaconda\n",
" numpy-base: 1.15.1-py37h81de0dd_0 --> 1.15.4-py36h81de0dd_0 anaconda\n",
" openssl: 1.1.1d-h516909a_0 conda-forge --> 1.1.1-h7b6447c_0 anaconda\n",
" xlrd: 1.1.0-py37_1 --> 1.2.0-py_0 anaconda\n",
"\n",
"The following packages will be DOWNGRADED:\n",
"\n",
" blas: 1.1-openblas conda-forge --> 1.0-mkl anaconda\n",
" numpy: 1.16.2-py36_blas_openblash1522bff_0 conda-forge [blas_openblas] --> 1.15.4-py36h1d66e8a_0 anaconda\n",
" scikit-learn: 0.20.1-py36_blas_openblashebff5e3_1200 conda-forge [blas_openblas] --> 0.20.1-py36h4989274_0 anaconda\n",
" scipy: 1.2.1-py36_blas_openblash1522bff_0 conda-forge [blas_openblas] --> 1.1.0-py36hfa4b5c9_1 anaconda\n",
"\n",
"\n",
"Downloading and Extracting Packages\n",
"numpy-base-1.15.4 | 4.2 MB | ##################################### | 100% \n",
"numpy-1.15.4 | 35 KB | ##################################### | 100% \n",
"openssl-1.1.1 | 5.0 MB | ##################################### | 100% \n",
"mkl_fft-1.0.6 | 150 KB | ##################################### | 100% \n",
"certifi-2019.11.28 | 156 KB | ##################################### | 100% \n",
"blas-1.0 | 6 KB | ##################################### | 100% \n",
"scipy-1.1.0 | 18.0 MB | ##################################### | 100% \n",
"xlrd-1.2.0 | 108 KB | ##################################### | 100% \n",
"mkl_random-1.0.1 | 373 KB | ##################################### | 100% \n",
"scikit-learn-0.20.1 | 5.7 MB | ##################################### | 100% \n",
"Preparing transaction: done\n",
"Verifying transaction: done\n",
"Executing transaction: done\n"
]
}
],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library\n"
]
},
{
"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": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"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://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.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": {
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}
},
"source": [
"Let's take a look at the first five items in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
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"outputs": [
{
"data": {
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" <thead>\n",
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" <th>0</th>\n",
" <td>Immigrants</td>\n",
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" <td>Afghanistan</td>\n",
" <td>935</td>\n",
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" <td>5501</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>Immigrants</td>\n",
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" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
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" <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",
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" <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",
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"<p>5 rows × 43 columns</p>\n",
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"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": 7,
"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": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"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` lab for the rational and detailed description of the changes."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### 1. Clean up the dataset to remove columns that are not informative to us for visualization (eg. Type, AREA, REG)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
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"new_sheet": false,
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},
"outputs": [
{
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" <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>American Samoa</td>\n",
" <td>Oceania</td>\n",
" <td>Polynesia</td>\n",
" <td>Developing regions</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>...</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>Andorra</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</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>...</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 × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" OdName AreaName RegName DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"2 Algeria Africa Northern Africa Developing regions 80 67 \n",
"3 American Samoa Oceania Polynesia Developing regions 0 1 \n",
"4 Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"2 71 69 63 44 ... 3616 3626 4807 3623 4005 5393 4752 \n",
"3 0 0 0 0 ... 0 0 1 0 0 0 0 \n",
"4 0 0 0 0 ... 0 0 1 1 0 0 0 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"2 4325 3774 4331 \n",
"3 0 0 0 \n",
"4 0 1 1 \n",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
"\n",
"# let's view the first five elements and see how the dataframe was changed\n",
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how the columns Type, Coverage, AREA, REG, and DEV got removed from the dataframe."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### 2. Rename some of the columns so that they make sense."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</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>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
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" <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>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</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>Albania</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
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" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
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" <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>Algeria</td>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</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>American Samoa</td>\n",
" <td>Oceania</td>\n",
" <td>Polynesia</td>\n",
" <td>Developing regions</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>...</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>Andorra</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</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>...</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 × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Country Continent Region DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"2 Algeria Africa Northern Africa Developing regions 80 67 \n",
"3 American Samoa Oceania Polynesia Developing regions 0 1 \n",
"4 Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2004 2005 2006 2007 2008 2009 2010 \\\n",
"0 39 47 71 340 ... 2978 3436 3009 2652 2111 1746 1758 \n",
"1 0 0 0 0 ... 1450 1223 856 702 560 716 561 \n",
"2 71 69 63 44 ... 3616 3626 4807 3623 4005 5393 4752 \n",
"3 0 0 0 0 ... 0 0 1 0 0 0 0 \n",
"4 0 0 0 0 ... 0 0 1 1 0 0 0 \n",
"\n",
" 2011 2012 2013 \n",
"0 2203 2635 2004 \n",
"1 539 620 603 \n",
"2 4325 3774 4331 \n",
"3 0 0 0 \n",
"4 0 1 1 \n",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
"\n",
"# let's view the first five elements and see how the dataframe was changed\n",
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how the column names now make much more sense, even to an outsider."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### 3. For consistency, ensure that all column labels of type string."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# let's examine the types of the column labels\n",
"all(isinstance(column, str) for column in df_can.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how the above line of code returned *False* when we tested if all the column labels are of type **string**. So let's change them all to **string** type."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.columns = list(map(str, df_can.columns))\n",
"\n",
"# let's check the column labels types now\n",
"all(isinstance(column, str) for column in df_can.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### 4. Set the country name as index - useful for quickly looking up countries using .loc method."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"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>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</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>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
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" <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",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</td>\n",
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" <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>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</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>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>Algeria</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>69</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>American Samoa</th>\n",
" <td>Oceania</td>\n",
" <td>Polynesia</td>\n",
" <td>Developing regions</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>...</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>Andorra</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</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>2</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 × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 \n",
"American Samoa Oceania Polynesia Developing regions 0 1 \n",
"Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 1986 ... 2004 2005 2006 2007 \\\n",
"Country ... \n",
"Afghanistan 39 47 71 340 496 ... 2978 3436 3009 2652 \n",
"Albania 0 0 0 0 1 ... 1450 1223 856 702 \n",
"Algeria 71 69 63 44 69 ... 3616 3626 4807 3623 \n",
"American Samoa 0 0 0 0 0 ... 0 0 1 0 \n",
"Andorra 0 0 0 0 2 ... 0 0 1 1 \n",
"\n",
" 2008 2009 2010 2011 2012 2013 \n",
"Country \n",
"Afghanistan 2111 1746 1758 2203 2635 2004 \n",
"Albania 560 716 561 539 620 603 \n",
"Algeria 4005 5393 4752 4325 3774 4331 \n",
"American Samoa 0 0 0 0 0 0 \n",
"Andorra 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 37 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.set_index('Country', inplace=True)\n",
"\n",
"# let's view the first five elements and see how the dataframe was changed\n",
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how the country names now serve as indices."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### 5. Add total column."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</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>...</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>Country</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>Afghanistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>47</td>\n",
" <td>71</td>\n",
" <td>340</td>\n",
" <td>496</td>\n",
" <td>...</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",
" <td>117278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</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>1</td>\n",
" <td>...</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",
" <td>31398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>69</td>\n",
" <td>...</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",
" <td>138878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>American Samoa</th>\n",
" <td>Oceania</td>\n",
" <td>Polynesia</td>\n",
" <td>Developing regions</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>...</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",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Andorra</th>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</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>2</td>\n",
" <td>...</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",
" <td>30</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent Region DevName 1980 1981 \\\n",
"Country \n",
"Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"Albania Europe Southern Europe Developed regions 1 0 \n",
"Algeria Africa Northern Africa Developing regions 80 67 \n",
"American Samoa Oceania Polynesia Developing regions 0 1 \n",
"Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 1986 ... 2005 2006 2007 2008 \\\n",
"Country ... \n",
"Afghanistan 39 47 71 340 496 ... 3436 3009 2652 2111 \n",
"Albania 0 0 0 0 1 ... 1223 856 702 560 \n",
"Algeria 71 69 63 44 69 ... 3626 4807 3623 4005 \n",
"American Samoa 0 0 0 0 0 ... 0 1 0 0 \n",
"Andorra 0 0 0 0 2 ... 0 1 1 0 \n",
"\n",
" 2009 2010 2011 2012 2013 Total \n",
"Country \n",
"Afghanistan 1746 1758 2203 2635 2004 117278 \n",
"Albania 716 561 539 620 603 31398 \n",
"Algeria 5393 4752 4325 3774 4331 138878 \n",
"American Samoa 0 0 0 0 0 12 \n",
"Andorra 0 0 0 1 1 30 \n",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can['Total'] = df_can.sum(axis=1)\n",
"\n",
"# let's view the first five elements and see how the dataframe was changed\n",
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now the dataframe has an extra column that presents the total number of immigrants from each country in the dataset from 1980 - 2013. So if we print the dimension of the data, we get:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n"
]
}
],
"source": [
"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": [
"So now our dataframe has 38 columns instead of 37 columns that we had before."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"['1980',\n",
" '1981',\n",
" '1982',\n",
" '1983',\n",
" '1984',\n",
" '1985',\n",
" '1986',\n",
" '1987',\n",
" '1988',\n",
" '1989',\n",
" '1990',\n",
" '1991',\n",
" '1992',\n",
" '1993',\n",
" '1994',\n",
" '1995',\n",
" '1996',\n",
" '1997',\n",
" '1998',\n",
" '1999',\n",
" '2000',\n",
" '2001',\n",
" '2002',\n",
" '2003',\n",
" '2004',\n",
" '2005',\n",
" '2006',\n",
" '2007',\n",
" '2008',\n",
" '2009',\n",
" '2010',\n",
" '2011',\n",
" '2012',\n",
" '2013']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# finally, let's create a list of years from 1980 - 2013\n",
"# this will come in handy when we start plotting the data\n",
"years = list(map(str, range(1980, 2014)))\n",
"\n",
"years"
]
},
{
"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` and **Numpy**."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.1.1\n"
]
}
],
"source": [
"# use the inline backend to generate the plots within the browser\n",
"%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": [
"# Area Plots<a id=\"6\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"In the last module, we created a line plot that visualized the top 5 countries that contribued the most immigrants to Canada from 1980 to 2013. With a little modification to the code, we can visualize this plot as a cumulative plot, also knows as a **Stacked Line Plot** or **Area plot**."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"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>India</th>\n",
" <th>China</th>\n",
" <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
" <th>Philippines</th>\n",
" <th>Pakistan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>8880</td>\n",
" <td>5123</td>\n",
" <td>22045</td>\n",
" <td>6051</td>\n",
" <td>978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>8670</td>\n",
" <td>6682</td>\n",
" <td>24796</td>\n",
" <td>5921</td>\n",
" <td>972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>8147</td>\n",
" <td>3308</td>\n",
" <td>20620</td>\n",
" <td>5249</td>\n",
" <td>1201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>7338</td>\n",
" <td>1863</td>\n",
" <td>10015</td>\n",
" <td>4562</td>\n",
" <td>900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>5704</td>\n",
" <td>1527</td>\n",
" <td>10170</td>\n",
" <td>3801</td>\n",
" <td>668</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country India China United Kingdom of Great Britain and Northern Ireland \\\n",
"1980 8880 5123 22045 \n",
"1981 8670 6682 24796 \n",
"1982 8147 3308 20620 \n",
"1983 7338 1863 10015 \n",
"1984 5704 1527 10170 \n",
"\n",
"Country Philippines Pakistan \n",
"1980 6051 978 \n",
"1981 5921 972 \n",
"1982 5249 1201 \n",
"1983 4562 900 \n",
"1984 3801 668 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.sort_values(['Total'], ascending=False, axis=0, inplace=True)\n",
"\n",
"# get the top 5 entries\n",
"df_top5 = df_can.head()\n",
"\n",
"# transpose the dataframe\n",
"df_top5 = df_top5[years].transpose() \n",
"\n",
"df_top5.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Area plots are stacked by default. And to produce a stacked area plot, each column must be either all positive or all negative values (any NaN values will defaulted to 0). To produce an unstacked plot, pass `stacked=False`. "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n",
"df_top5.plot(kind='area', \n",
" stacked=False,\n",
" figsize=(20, 10), # pass a tuple (x, y) size\n",
" )\n",
"\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The unstacked plot has a default transparency (alpha value) at 0.5. We can modify this value by passing in the `alpha` parameter."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_top5.plot(kind='area', \n",
" alpha=0.25, # 0-1, default value a= 0.5\n",
" stacked=False,\n",
" figsize=(20, 10),\n",
" )\n",
"\n",
"plt.title('Immigration Trend of Top 5 Countries')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Two types of plotting\n",
"\n",
"As we discussed in the video lectures, there are two styles/options of ploting with `matplotlib`. Plotting using the Artist layer and plotting using the scripting layer.\n",
"\n",
"**Option 1: Scripting layer (procedural method) - using matplotlib.pyplot as 'plt' **\n",
"\n",
"You can use `plt` i.e. `matplotlib.pyplot` and add more elements by calling different methods procedurally; for example, `plt.title(...)` to add title or `plt.xlabel(...)` to add label to the x-axis.\n",
"```python\n",
" # Option 1: This is what we have been using so far\n",
" df_top5.plot(kind='area', alpha=0.35, figsize=(20, 10)) \n",
" plt.title('Immigration trend of top 5 countries')\n",
" plt.ylabel('Number of immigrants')\n",
" plt.xlabel('Years')\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Option 2: Artist layer (Object oriented method) - using an `Axes` instance from Matplotlib (preferred) **\n",
"\n",
"You can use an `Axes` instance of your current plot and store it in a variable (eg. `ax`). You can add more elements by calling methods with a little change in syntax (by adding \"*set_*\" to the previous methods). For example, use `ax.set_title()` instead of `plt.title()` to add title, or `ax.set_xlabel()` instead of `plt.xlabel()` to add label to the x-axis. \n",
"\n",
"This option sometimes is more transparent and flexible to use for advanced plots (in particular when having multiple plots, as you will see later). \n",
"\n",
"In this course, we will stick to the **scripting layer**, except for some advanced visualizations where we will need to use the **artist layer** to manipulate advanced aspects of the plots."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0, 'Years')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# option 2: preferred option with more flexibility\n",
"ax = df_top5.plot(kind='area', alpha=0.35, figsize=(20, 10))\n",
"\n",
"ax.set_title('Immigration Trend of Top 5 Countries')\n",
"ax.set_ylabel('Number of Immigrants')\n",
"ax.set_xlabel('Years')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question**: Use the scripting layer to create a stacked area plot of the 5 countries that contributed the least to immigration to Canada **from** 1980 to 2013. Use a transparency value of 0.45."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # get the 5 countries with the least contribution\n",
"df_least5 = df_can.tail(5)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # transpose the dataframe\n",
"df_least5 = df_least5[years].transpose() \n",
"df_least5.head()\n",
"-->\n",
"\n",
"<!--\n",
"df_least5.index = df_least5.index.map(int) # let's change the index values of df_least5 to type integer for plotting\n",
"df_least5.plot(kind='area', alpha=0.45, figsize=(20, 10)) \n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Immigration Trend of 5 Countries with Least Contribution to Immigration')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.xlabel('Years')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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}
},
"source": [
"**Question**: Use the artist layer to create an unstacked area plot of the 5 countries that contributed the least to immigration to Canada **from** 1980 to 2013. Use a transparency value of 0.55."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\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",
"\\\\ # get the 5 countries with the least contribution\n",
"df_least5 = df_can.tail(5)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # transpose the dataframe\n",
"df_least5 = df_least5[years].transpose() \n",
"df_least5.head()\n",
"-->\n",
"\n",
"<!--\n",
"df_least5.index = df_least5.index.map(int) # let's change the index values of df_least5 to type integer for plotting\n",
"-->\n",
"\n",
"<!--\n",
"ax = df_least5.plot(kind='area', alpha=0.55, stacked=False, figsize=(20, 10))\n",
"-->\n",
"\n",
"<!--\n",
"ax.set_title('Immigration Trend of 5 Countries with Least Contribution to Immigration')\n",
"ax.set_ylabel('Number of Immigrants')\n",
"ax.set_xlabel('Years')\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Histograms<a id=\"8\"></a>\n",
"\n",
"A histogram is a way of representing the *frequency* distribution of numeric dataset. The way it works is it partitions the x-axis into *bins*, assigns each data point in our dataset to a bin, and then counts the number of data points that have been assigned to each bin. So the y-axis is the frequency or the number of data points in each bin. Note that we can change the bin size and usually one needs to tweak it so that the distribution is displayed nicely."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question:** What is the frequency distribution of the number (population) of new immigrants from the various countries to Canada in 2013?"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Before we proceed with creating the histogram plot, let's first examine the data split into intervals. To do this, we will us **Numpy**'s `histrogram` method to get the bin ranges and frequency counts as follows:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
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},
"outputs": [
{
"data": {
"text/plain": [
"Country\n",
"India 33087\n",
"China 34129\n",
"United Kingdom of Great Britain and Northern Ireland 5827\n",
"Philippines 29544\n",
"Pakistan 12603\n",
"Name: 2013, dtype: int64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# let's quickly view the 2013 data\n",
"df_can['2013'].head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
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},
"new_sheet": false,
"run_control": {
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[178 11 1 2 0 0 0 0 1 2]\n",
"[ 0. 3412.9 6825.8 10238.7 13651.6 17064.5 20477.4 23890.3 27303.2\n",
" 30716.1 34129. ]\n"
]
}
],
"source": [
"# np.histogram returns 2 values\n",
"count, bin_edges = np.histogram(df_can['2013'])\n",
"\n",
"print(count) # frequency count\n",
"print(bin_edges) # bin ranges, default = 10 bins"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"By default, the `histrogram` method breaks up the dataset into 10 bins. The figure below summarizes the bin ranges and the frequency distribution of immigration in 2013. We can see that in 2013:\n",
"* 178 countries contributed between 0 to 3412.9 immigrants \n",
"* 11 countries contributed between 3412.9 to 6825.8 immigrants\n",
"* 1 country contributed between 6285.8 to 10238.7 immigrants, and so on..\n",
"\n",
"<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod2Fig1-Histogram.JPG\" align=\"center\" width=800>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily graph this distribution by passing `kind=hist` to `plot()`."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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},
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}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_can['2013'].plot(kind='hist', figsize=(8, 5))\n",
"\n",
"plt.title('Histogram of Immigration from 195 Countries in 2013') # add a title to the histogram\n",
"plt.ylabel('Number of Countries') # add y-label\n",
"plt.xlabel('Number of Immigrants') # add x-label\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"In the above plot, the x-axis represents the population range of immigrants in intervals of 3412.9. The y-axis represents the number of countries that contributed to the aforementioned population. \n",
"\n",
"Notice that the x-axis labels do not match with the bin size. This can be fixed by passing in a `xticks` keyword that contains the list of the bin sizes, as follows:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# 'bin_edges' is a list of bin intervals\n",
"count, bin_edges = np.histogram(df_can['2013'])\n",
"\n",
"df_can['2013'].plot(kind='hist', figsize=(8, 5), xticks=bin_edges)\n",
"\n",
"plt.title('Histogram of Immigration from 195 countries in 2013') # add a title to the histogram\n",
"plt.ylabel('Number of Countries') # add y-label\n",
"plt.xlabel('Number of Immigrants') # add x-label\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"*Side Note:* We could use `df_can['2013'].plot.hist()`, instead. In fact, throughout this lesson, using `some_data.plot(kind='type_plot', ...)` is equivalent to `some_data.plot.type_plot(...)`. That is, passing the type of the plot as argument or method behaves the same. \n",
"\n",
"See the *pandas* documentation for more info http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can also plot multiple histograms on the same plot. For example, let's try to answer the following questions using a histogram.\n",
"\n",
"**Question**: What is the immigration distribution for Denmark, Norway, and Sweden for years 1980 - 2013?"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"<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>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",
" <tr>\n",
" <th>Country</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",
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" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Denmark</th>\n",
" <td>272</td>\n",
" <td>293</td>\n",
" <td>299</td>\n",
" <td>106</td>\n",
" <td>93</td>\n",
" <td>73</td>\n",
" <td>93</td>\n",
" <td>109</td>\n",
" <td>129</td>\n",
" <td>129</td>\n",
" <td>...</td>\n",
" <td>89</td>\n",
" <td>62</td>\n",
" <td>101</td>\n",
" <td>97</td>\n",
" <td>108</td>\n",
" <td>81</td>\n",
" <td>92</td>\n",
" <td>93</td>\n",
" <td>94</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Norway</th>\n",
" <td>116</td>\n",
" <td>77</td>\n",
" <td>106</td>\n",
" <td>51</td>\n",
" <td>31</td>\n",
" <td>54</td>\n",
" <td>56</td>\n",
" <td>80</td>\n",
" <td>73</td>\n",
" <td>76</td>\n",
" <td>...</td>\n",
" <td>73</td>\n",
" <td>57</td>\n",
" <td>53</td>\n",
" <td>73</td>\n",
" <td>66</td>\n",
" <td>75</td>\n",
" <td>46</td>\n",
" <td>49</td>\n",
" <td>53</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sweden</th>\n",
" <td>281</td>\n",
" <td>308</td>\n",
" <td>222</td>\n",
" <td>176</td>\n",
" <td>128</td>\n",
" <td>158</td>\n",
" <td>187</td>\n",
" <td>198</td>\n",
" <td>171</td>\n",
" <td>182</td>\n",
" <td>...</td>\n",
" <td>129</td>\n",
" <td>205</td>\n",
" <td>139</td>\n",
" <td>193</td>\n",
" <td>165</td>\n",
" <td>167</td>\n",
" <td>159</td>\n",
" <td>134</td>\n",
" <td>140</td>\n",
" <td>140</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... \\\n",
"Country ... \n",
"Denmark 272 293 299 106 93 73 93 109 129 129 ... \n",
"Norway 116 77 106 51 31 54 56 80 73 76 ... \n",
"Sweden 281 308 222 176 128 158 187 198 171 182 ... \n",
"\n",
" 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n",
"Country \n",
"Denmark 89 62 101 97 108 81 92 93 94 81 \n",
"Norway 73 57 53 73 66 75 46 49 53 59 \n",
"Sweden 129 205 139 193 165 167 159 134 140 140 \n",
"\n",
"[3 rows x 34 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# let's quickly view the dataset \n",
"df_can.loc[['Denmark', 'Norway', 'Sweden'], years]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fdfeb32dd68>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# generate histogram\n",
"df_can.loc[['Denmark', 'Norway', 'Sweden'], years].plot.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"That does not look right! \n",
"\n",
"Don't worry, you'll often come across situations like this when creating plots. The solution often lies in how the underlying dataset is structured.\n",
"\n",
"Instead of plotting the population frequency distribution of the population for the 3 countries, *pandas* instead plotted the population frequency distribution for the `years`.\n",
"\n",
"This can be easily fixed by first transposing the dataset, and then plotting as shown below.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"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>Denmark</th>\n",
" <th>Norway</th>\n",
" <th>Sweden</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>272</td>\n",
" <td>116</td>\n",
" <td>281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>293</td>\n",
" <td>77</td>\n",
" <td>308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>299</td>\n",
" <td>106</td>\n",
" <td>222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>106</td>\n",
" <td>51</td>\n",
" <td>176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>93</td>\n",
" <td>31</td>\n",
" <td>128</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country Denmark Norway Sweden\n",
"1980 272 116 281\n",
"1981 293 77 308\n",
"1982 299 106 222\n",
"1983 106 51 176\n",
"1984 93 31 128"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# transpose dataframe\n",
"df_t = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
"df_t.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# generate histogram\n",
"df_t.plot(kind='hist', figsize=(10, 6))\n",
"\n",
"plt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\n",
"plt.ylabel('Number of Years')\n",
"plt.xlabel('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": [
"Let's make a few modifications to improve the impact and aesthetics of the previous plot:\n",
"* increase the bin size to 15 by passing in `bins` parameter\n",
"* set transparency to 60% by passing in `alpha` paramemter\n",
"* label the x-axis by passing in `x-label` paramater\n",
"* change the colors of the plots by passing in `color` parameter"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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x9zRtTP2ksxdwcxAE44IgeA9/gtk8fWbh9LfjW9pOwXcXNnVf6gW8FQTBdUEQvBEEwUx8F1hTvY9/IjRd5ueoZoT/bwR1rWEv42+V+A3+vqiqcJkX41t5Uq1nUfbnVBn1TC0wfIJz14z1ifO4SI8j9nojmyAIloRJ21r4e9oeS+u5aovv/k6XKrPUsf0ysHvYK5IyAH/sZbbeppb5U8Z2yJm4heeFO/HJd58gCLI9EfoysE/GsH2Bz4IgmJdr3hGlcrLyhkYqhha3JjPG9MRXZs/gK6ct8VegqUeCP8PvAAONMWOApWHX25XAtcaYmfh+573wjzCfARAEwUxjzBPAP40xp+L7zf8IrEvjV4af4a/wzjTGXIuvyK6KMN2qanBdEuw+/BNXo40xI/BdD9ey4h6AhnwEtDfGnIS/ut4D+H3GOLPD/w8wxryEb/HN1gxeDOV7P/4kM8YYcz6+8roG360ypkDLbG2M2QRfgVTiT9LDwmUOAwiC4EdjzF/xj8ADTMTXJdsDOwVBkJm85FtejrUgCK4xxiwDHjfGHBYEwZOrGlAQBD+E++uNGV9Nwndl3W+MOQPfXTQC/2DELRFmPQm4F9+q8mbasGvwFxFz0sb9CDg63K9L8cdRtguUO/BJ61b4h2+a6iPgJGPMgfh7zfbHP1jQVNcD04wxV+Bbybri69sGGWMewSddU/APP3XEP5T0M/5JypRJ+Cf/PwyC4Ou0YUOB/6a6h6Psz0EQfGKMGceKc8NX+BbHdVILK4LjIqVF6w1jTDt8S2/KL40xOwKLgiD4PBxnAL7ldwa+xexSfN2e3mL8GDDMGPMa/uKkE/5J03fx9yim1m0E/ngajm9p+yf+AYrZNEPYSPMAvq4/CPgmrAsBfkw7T1yPbxW7An9sdsdfSJ2bNq+1WNG9vjbQLiyTZanbJowxJ+N7MVIJfzf8Q1pvUb8LeyVJbXH7Dt8q9jj+SZg78Sf8y6CuP38Y/sBaEI4HvqK8GL+zfIC/Or4wCIL0d8CciK+MnsafsL/AH4AN3gMSXs0dg8/+38cfKOex8hVEvkRZl8QJW7MG4lsTpuFvYL4B/8BCY9vgP/jm9r/id/wj8F2m6eNMw59cb8VXvjflmF3s5RsEwU/49/osxVdkL+JP4PsGhXsn31H4Y+Yz/EluMD5x3iXjnpnL8BXVyfh71F4KP88pUFx18nmsBUHw/4A/4O9LOhDAGDPSrNqb6e/A10fp8w/wJ4EP8UnFNHw3y4D08mzAJHzy9WJai80kfEKQ2cV7Ir5Ofw1/EhxP/XuwUjEtAP6DvxhaqWvX+Jeojmwgpn/hT1h34U8yPfAPAzRJEARv4Pe3I/DH64WknfwaMB7fwjEW3yX5EL4lvXeQdi8h/r62zHLKWnYR9+ff4e9N+w/+WPwC/6RpU+dTUDHUGwewotsafB38Fj45S1kXX4/PwG+3L4DdgiD4Im2cv+KfVk7VuWPwx82gVKtcmDz1xyeBr+DfM/kM/oGM5uqEbwVshy+3BWn/zkuNFJ5DDsJfsLyDzzuGB0GQfm9aB1aUyc74lt+3WHFvLfjWxOH4+11T9dg4fN3QYF1mfL0iuYRNpx/inwJq9GpQ8s8Ysym+4jsgCIInYg5HVnPGmNHAJkEQ5KMLtSiFrRpTgyA4M2P45vjXRuwZBEHWricRiVciu0oLyRjTC3+fxFv4ZvBz8V0xd8cX1ZrF+J9M+QLfrbkp/n1en+GvrEQKxhhTgr8No2/csRSC8b9+cSD+vq8js4yyPzBaSZtI8VLitrJS/P0Sv8LfMzEd/3LNBvucJa82xL/lvSP+CaCXgcGBf42CSMGEXRQd446jgL7CP219dhAEszK/DLuORaSIqatUREREJCGS+nCCiIiIyBpHiZuIiIhIQiTxHjf17YqIiEiS5O1l/ElM3Jg/f35B519ZWUlVVZRXLBWW4lAcxRyD4lAcxR6D4lAcxRBHhw4d8jo/dZWKiIiIJIQSNxEREZGEUOImIiIikhCJvMdNRERE8icIAqqrq6mtrcWYvN1Hn9NXX33F0qXxv1M9X3EEQUBJSQkVFRUFLz8lbiIiImu46upqWrVqRVlZy6QFZWVllJaWtsiyWiqOmpoaqqurad26dV7ml4u6SkVERNZwtbW1LZa0ra7Kysqora0t+HKUuImIiKzhWqJ7dE3QEuWoxE1EREQK5uuvv+b000+nZ8+e9OnTh2OPPZZZs2blbf5Tpkxh2rRpeZtfsVO7qIiIiBREEAScdNJJDB48mFtuuQWA6dOns3DhQjbddNO8LOOVV16hbdu27Lrrrit9V1NTs9p1Aa9eayMiIiJF4+WXX6ZVq1Ycd9xxdcO6detGaWkpI0eO5Pnnn8cYw1lnncWBBx7IlClTuPXWWxk9ejQAw4cPZ4cdduDwww+nR48eDB48mIkTJ1JTU8O//vUvysvLuffeeyktLeWRRx7h8ssv54EHHmD99ddn+vTpdO3alWeffZZx48ax4YYbUltby5577skTTzxBu3bt4iqWZlHiJiIiIgXx0Ucfsf322680/Mknn+T9999n4sSJLFq0iIEDB7Lbbrs1Or927doxYcIE7r77bm699VauueYajj32WNq2bctpp50GwAMPPMCnn37KmDFjKC0tZb311mPs2LGccsop/Pe//2W77bZLbNIGusdNREREWtjUqVM56KCDKC0tpX379uy222688847jU633377AbDDDjswd+7cnOPtv//+da/5OPzww3n44YcBePDBB7HW5mEN4qPETURERApiq6224r333ltpeBAEWccvKyur913my3HLy8sBKC0tZfny5TmX26ZNm7q/O3bsSPv27XnppZd466232GuvvZq0DsVGXaXSqJuvn0R1dXXcYfCHYQPjDkFERJpgjz324G9/+xv33XcfRx99NABvv/0266+/PuPGjWPw4MF8++23TJ06lREjRvDzzz/z8ccfs3TpUpYuXcpLL72U9aGDdG3btuXHH39scJwjjzySs846i0MPPbQoXvzbHGpxExERkYIwxnD77bczefJkevbsSd++fbn22ms55JBD2HbbbRkwYADWWoYPH85GG21Ex44dGTRoEP3792fo0KF069at0WUMGDCA8ePHM2DAAKZOnZp1nL333pvFixdz+OGH53sVW5zJ1VxZxIL58+cXdAGVlZVUVVUVdBlJisPd+27RtLgVQ3kUw3YphhgUh+Io9hgUR/Q4lixZUq97sdDKysqoqalpseW98847jBw5kkcffbSgcWQrxw4dOgDk7c286ioVERGR1dZNN93E6NGjuemmm+IOJS/UVSoiIiKrraFDh/Laa6/RvXv3uEPJCyVuIiIiIgmhxE1EREQkIZS4iYiIiCSEEjcRERGRhNBTpSIiIhK7zp07s80221BTU0NpaSmDBw/mlFNOoaSkONqYttxyS2bOnBl3GErcREREpL71Jt+f1/l91+uoRsepqKhg4sSJAFRVVXHGGWfwww8/cN555+U1lqYKgiDnT3TFoTjSWBEREZFQZWUlV199NXfddRdBELB8+XIuu+wyBg4cSP/+/bn33nsBmDJlCocddhinnHIKvXr1YujQoXVJVo8ePbjyyisZNGgQ++23H++99x5HHXUUPXv2ZPTo0QAsXrwYay377LMP/fr1Y8KECQDMnTuX3r17M2zYMPbZZx/SX/y/aNEiBg0axLPPPtvCpeKpxU1ERESKzqabbkoQBFRVVTFhwgTWWWcdnnrqKZYuXcpBBx1E7969AZg+fTqTJk1ik0024cADD2TatGl172zr0KEDTzzxBJdccgnnnnsujz32GEuXLqVv374cd9xxlJeXc8cdd7DOOuvUJWR77703ALNmzeK6667jyiuvrItp4cKFnHjiiVxwwQX06tWr5QsFJW4iIiJSpFKtZy+++CIzZszgySefBOCHH35g9uzZtGrVih133DH1s1J07dqVuXPn1iVuqSRs2223ZcmSJay99tqsvfbalJeX891337Huuuty1VVXMXXqVIwxfPnllyxcuBCATp06sfPOO9fFUlNTw+GHH84VV1zB7rvv3mJlkEmJm4iIiBSdzz77jJKSEiorKwG4/PLL6dOnT71xpkyZwlprrVX3ubS0tN5vj5aXlwP+x+7TxyspKWH58uU88sgjfPPNNzz99NO0atWKHj16sHTpUoCVfnO0tLSU7bffnhdeeCHWxE33uImIiEhR+eabb7jwwgs58cQTMcbQu3dvRo8ezc8//wz4bswlS5Y0eznff/89lZWVtGrVipdffpl58+blHNcYw3XXXcesWbNi/d1TtbiJiIhI7KqrqxkwYEDd60AOO+wwhgwZAsBRRx3F3Llz2XfffQmCgHbt2nHnnXc2e5mHHnooxxxzDPvttx9du3blV7/6VYPjl5aWcvPNN3PCCSew9tprc8IJJzQ7hqYyxfSIa0RB+tMdhVBZWUlVVVVBl5GkONy971JdXR13GPxh2MCiKI9i2C7FEIPiUBzFHoPiiB7HkiVLVuoaLKSysrJ6XZpxyXcc2coxvP/O5GsZ6ioVERERSQglbiIiIiIJocRNREREJCFa5OEEa+2dwP7A1865buGwvwODgGXALOBE59y3LRGPiIiISBK1VIvb3cC+GcMmAt2cczsAHwPDWigWERERkURqkcTNOTcZWJQx7BnnXOpRjleBTi0Ri4iIiEhSFct73H4HjMn1pbV2CDAEwDlX9xblQikrKyv4MpIUhykxVFRUxB1G0ZRHMcRRDDEoDsVR7DEojuhxfPXVV5SVtWxKkLm8jTfemNNOO42//OUvANx8880sXryY888/v0XjaI7y8vLC5ygFnXsE1trhQA1wX65xnHOjgFHhx6DQ78Ip9vfttLSgNiiK97jV1NQURXkUw3YphhgUh+Io9hgUR/Q4li5dSmlpad3n0be9ltflHndK93qfs70/rby8nCeffJIzzjiDdu3aUVtbS21tbeT3rNXU1DQ5Ccv3e9yWLl26Uvmmfkc1X2J9qtRaezz+oYWjnXOJexOwiIiI5EdpaSlHH300o0aNWum7efPmYa2lf//+WGv54osvADjnnHMYOXIkhx12GFdccQX9+vXju+++IwgCunbtykMPPQTAmWeeyeTJk5k7dy4HH3ww++yzD/vssw/Tpk2r+37ChAl1yxs6dCjPPPNMC6x108WWuFlr9wX+BBzgnGv+D46JiIhIop1wwgk8+uijfP/99/WGDx8+nMMOO4xnn32WQw45hBEjRtR99+mnnzJmzBguueQSdtllF6ZNm8ZHH33Epptuymuv+ZbDN998k5133pnKykoeeOABJkyYwC233MLw4cMB/5NaY8b4O7a+//57Xn/9dfbaa68WWuumaZHEzVr7APAKsLW1dp619iTgJmAdYKK19m1r7a0tEYuIiIgUp3XWWYfDDjuMO+64o97wN954g4MPPhjwvy+aSsgA9t9//7pu3h49ejB16lReffVVjjvuOGbMmMGCBQvYYIMNaNu2LT///DPnn38+/fr149RTT+Xjjz8GYPfdd2fOnDlUVVXx2GOPMXDgwBa/5y+qFonKOXdklsF3ZBkmIiIia7CTTz6Zfffdl8MPPzznOMas+OnP9N8G7dGjB3fffTedOnXiT3/6E08//TRPPvkk3bv7e+xuu+022rdvz8SJE6mtrWXzzTevm/bQQw9l7NixjBs3jmuvvbYAa5Yf+uUEERERKRobbLABgwYN4oEHHqgbtssuu/D4448DMHbs2LpELFPHjh1ZtGgRs2fPZtNNN6V79+7ceuut9OjRA/DdoBtttBElJSU88sgjLF++vG5aay233347AFtvvXWhVq/ZlLiJiIhIUTn11FNZtGjF618vu+wyxowZQ//+/XnkkUe49NJLc06700471bWkde/enS+//JJdd90VgOOPP56HH36Y/fffn08//bRea1379u3ZcsstsdYWaK3yozg7cEVERCQ2ma/vaAkzZ86s+7t9+/bMmjWr7nPnzp3rnhBNd8MNN6w07B//+Efd37vuuivz5s2r+7z55pvz7LPP1n0eMWJE3etAfvrpJ2bPns1BBx3UvBUpMLW4iYiIyBpt8uTJ9OrVixNPPJF111037nAapHlFkxUAACAASURBVBY3ERERWaP16tWr7p1uxU4tbiIiIiIJocRNREREJCGUuImIiIgkhBI3ERERkYRQ4iYiIiJF4cYbb6Rv377079+fAQMG8Oabb+Zt3ltuuWXe5hUnPVUqIiIi9Yz6/Jm8zm/IL/dudJzXX3+dZ599lvHjx1NeXs6iRYtYtmxZXuNYHajFTURERGL39ddf065dO8rLywFo164dCxYs4OSTTwZgwoQJbLHFFixbtozq6mp23313AObMmcPRRx/Nvvvuy8EHH8wnn3wCwOeff86gQYMYOHAgV199db1l3XLLLQwcOJA+ffpwzTXXADB37lx69+7N+eefT9++fTnyyCP56aefWmr1I1PiJiIiIrHr3bs38+fPZ4899mDYsGG88sorbL/99kyfPh2AqVOnsvXWW/POO+/w1ltvsdNOOwFwwQUXcNlllzF+/HhGjBjBsGHDALj44os57rjjeOqpp9hoo43qlvPiiy8ye/ZsnnzySSZNmsS7777Lq6++CsDs2bM5/vjjef7551l33XV56qmnWrgUGqeuUhEREYld27ZtGT9+PFOnTmXKlCmcfvrpDBs2jC5dujBz5kzefvtthgwZwquvvsry5cvp3r07ixcv5o033uDUU0+tm0+qe3XatGncdtttABx66KFcccUVgE/cXnzxRfbee2+MMSxevJjZs2fTsWNHOnfuTLdu3QDYYYcdmDt3bguXQuOUuImIiEhRKC0tpWfPnvTs2ZNtttmGhx56iB49ejBp0iTKysrYc889Oeecc6itrWXEiBHU1tay7rrrMnHixKzzM8asNCwIAoYOHcqxxx5LWVlZ3W+Vzp07t66bNhVLdXV1YVa0GdRVKiIiIrH75JNP+PTTT+s+v//++3Tq1IkePXpw++23s/POO7Phhhvyv//9j08++YStt96addZZh86dO/PEE08APil7//33Af8D848//jgAY8eOrZtvnz59GDNmDIsXLwZgwYIFVFVVtdRqNpta3ERERCR2S5Ys4aKLLuL777+nrKyMLl26cPXVV9O6dWuqqqrYbbfdANhuu+34+uuv61rTbrrpJoYNG8aNN95ITU0NBx54IF27duXSSy/ljDPO4I477mDgwIF1y+nduzczZ87kgAMOAKBNmzb84x//oLS0tOVXehWYIAjijqGpgvnz5xd0AZWVlUWRfRdLHO7ed4uiufgPwwYWRXkUw3YphhgUh+Io9hgUR/Q4lixZQps2bVosjvQuyjjlO45s5dihQweAlftsV5Fa3IrYzddPKoqEqaKiIu4QACh9ahTrFUF5cMhZcUcgIiJrKN3jJiIiIpIQStxEREREEkKJm4iIyBougfe7F6WWKEclbiIiImu4kpKSonhYIMlqamooKSl8WqWHE0RERNZwFRUVVFdXs3Tp0qwvrc238vJyli5dWvDltFQcQRBQUlLSIg/zKXETERFZwxljaN26dYstr9hfj1LM1FUqIiIikhBK3EREREQSQombiIiISEIocRMRERFJCCVuIiIiIgmhxE1EREQkIZS4iYiIiCSEEjcRERGRhFDiJiIiIpIQStxEREREEkKJm4iIiEhCKHETERERSQglbiIiIiIJUdYSC7HW3gnsD3ztnOsWDmsHjAG6AHMA65z7X0vEIyIiIpJELdXidjewb8awC4HnnHNbAs+Fn0VEREQkhxZJ3Jxzk4FFGYMPBO4J/74HOKglYhERERFJqjjvcdvYObcAIPx/oxhjERERESl6LXKPW3NZa4cAQwCcc1RWVhZ0eWVlZQVfRhSmxFBRURF3GEUTR0lJSVHEYYpg/yiWfVRxKI5ijkFxKI6kxNEUcSZuX1lrf+GcW2Ct/QXwda4RnXOjgFHhx6CqqqqggVVWVlLoZUQR1AZUV1fHHQYVFRVFEUdtbW1RxNGqpib2/aNY9lHFoTiKOQbFoTiKIY4OHTrkdX5xdpWOA44P/z4eeDzGWERERESKXku9DuQBoA9Qaa2dB1wCXAU4a+1JwOfA4JaIRURERCSpWiRxc84dmeOrfi2xfBEREZHVgX45QURERCQhlLiJiIiIJIQSNxEREZGEUOImIiIikhBK3EREREQSQombiIiISEIocRMRERFJCCVuIiIiIgmhxE1EREQkIZS4iYiIiCSEEjcRERGRhFDiJiIiIpIQStxEREREEkKJm4iIiEhCKHETERERSQglbiIiIiIJocRNREREJCGUuImIiIgkhBI3ERERkYQoizsAEZHmuu6DsVRXV8cdBn+uPCruEERkNacWNxEREZGEUOImIiIikhCRukqtte2AaufcEmttCXA0sBx4wDkXFDJAEREREfGitrg9BWwT/n05MBwYBlxTiKBEREREZGVRE7etgbfCv48F9gH6AEcWICYRERERySJq4rYcaGWt7Qb84Jz7DFgErF2wyERERESknqivA3kGeBCoDP8H2A5YUIigRERERGRlURO3k4ATgZ+Bu8JhGwGXFiIoEREREVlZo4mbtbYUeAL4P+fc0tRw59zzhQxMREREROpr9B4359xyYMsWiEVEREREGhD14YRLgJuttR0LGYyIiIiI5Bb1Hrc7w/9PsNamhhkgcM6V5j0qEREREVlJ1MRNXaUiIiIiMYuUuDnnZhU6EBERERFpWNQWN6y1A4He+He5mdRw59zvChCXiIiIiGSI9HCCtfYi4G6gDf5nrhYDg4AlBYtMREREROqJ+lTpyUA/59yZwLLw/0FAp4JFJiIiIiL1RE3c2jnn3gv/XmatbeWcexXoW6C4RERERCRD1MRtlrV2u/Dv94FTrLVHAt8WJiwRERERyRT14YSL8b9N+gHwZ+ABYG3g9OYGYK09F98VGwDvASc656qbO18RERGR1U3U14E8kfb3K0CXfCw8/CWGs4DtnHM/WWsdcAT+QQgRERERSdOU14FsCRwG/MI5d1b4udw5Nz0PMbS21v6Mf2p1fjPnJyIiIrJaivo6kEOAKcAWwAnh4PWB65uzcOfcF8A1wOfAAuA759wzzZmniIiIyOoqaovb5cDezrm3rLWHhsPeBnZszsKttRsABwKb4R90eMhae4xz7t8Z4w0BhgA456isrGzOYhtVVlZW8GVEYUoMFRUVcYdBydwPaR0EcYdBSUl5UZSHKYL944YZj1Eb1MYaA8AFm9jYywKgZGFJUewbxVJ3FEMcxRCD4lAcSYmjKaImbhvjEzXwDxGk/98c/YHZzrmFANbasUBPoF7i5pwbBYxKLbeqqioPi86tsrKSQi8jiqA2oLo6/uc0WgcBy5cvjzsMamtri6I8WtXUxL5/1AbFURY1RVAWoPLIVAx1WDHEoDgURzHE0aFDh7zOL2ri9iZwFHBf2rDBwGvNXP7nwG7W2jbAT0A/4PVmzlNERERktRT1PW5nA3+z1j4HtLXWPglcCfyhOQt3zk0FHsYnhu+F8YxqcCIRERGRNVTU14F8YK3dBjgAeBaYC4xzzn3f3ACcc5cAlzR3PiIiIiKruwYTN2vtFcBfnHPLnHM/Ave3TFgiIiIikqmxrtLdgLettT1aIhgRERERya3BxM051w+4FviPtfZaa238z9uLiIiIrKEavcfNOXeHtfYJ/Cs6PrbWzsn4vleBYhMRERGRNFFfB3IosDNwB/B+4cIRERERkVwaezjhV8CdQFugn3Pu7YbGFxEREZHCaezhhNeBCUB3JW0iIiIi8Wqsq7Snc+6DFolERERERBrU2FOlStpEREREikTUn7wSERERkZgpcRMRERFJiJyJm7X21bS/9VuiIiIiIjFrqMVtq7RfSvhjSwQjIiIiIrk19FTp46z4pYTW1trJ2UbSLyeIiIiItIyciZtz7kRr7R5AF2BX/K8miIiIiEhMGnyPm3PuJeAla+1azrl7WigmEREREcki0m+VOufutNb2BY4FOgJfAP92zk0qZHAiIiIiskKk14FYa08GxgBfAmOBBcD91tpTChibiIiIiKSJ1OIGXAAMcM69kxpgrR0DPALcVojARERERKS+qC/g3RDI/Pmrj4B2+Q1HRERERHKJmri9BFxnrW0DYK1tC/wdmFKowERERESkvqiJ22nADsB31tqvgG+BXwOnFiowEREREakv6lOlC4De1tpOQAdgvnNuXkEjExEREZF6oj6cAECYrClhExEREYlB1K5SEREREYmZEjcRERGRhGi0q9RaWwL0AV5yzi0reEQiIiIiklWjLW7OuVrgcSVtIiIiIvGK2lU62Vq7W0EjEREREZEGRX2q9DPgaWvt48BcIEh94Zy7uBCBiYiIiEh9URO31sBj4d+dChSLiIiIiDQg6gt4Tyx0ICIiIiLSsMgv4LXWbgscBmzsnBtqrd0aKHfOvVuw6ERERESkTqSHE6y1g4HJQEfguHDwOsB1BYpLRERERDJEfar0UmCAc+40YHk47B38D82LiIiISAuImrhthE/UYMUTpUHa3yIiIiJSYFETtzeAYzOGHQG8lt9wRERERCSXqA8nnAU8Y609CWhrrZ0AbAXsXbDIRERERKSeSC1uzrkPgW2AfwIXAXcB2zvnZhYwNhERERFJE/l1IM65Jdbal4HZwHzn3I/5CMBauz5wO9ANf8/c75xzr+Rj3iIiIiKrk6ivA/mltfa/wBzgSWCOtfYla+2meYjhRmC8c24b/FOqM/IwTxEREZHVTtSHE+7BP6CwvnNuI2ADYFo4fJVZa9cFegF3ADjnljnnvm3OPEVERERWV1G7SncG9nbO/QzgnPvRWvsn4JtmLn9zYCFwl7X21/jk8Gzn3OL0kay1Q4Ah4bKprKxs5mIbVlZWVvBlRGFKDBUVFXGHgTGG0tLSuMOgpKSkKMqjZPztbFxbG28Mm21UFGVRLMdKycLi2DdumPEYtUG8+wbABZvY2LdLsewbikNxJCGOpoiauL0KdAdeThu2C9Dce9HKgN8AZzrnplprbwQuBEakj+ScGwWMCj8GVVVVzVxswyorKyn0MqIIagOqq6vjDoPWQcDy5csbH7HAamtri6I8KioqYo+jNiiOsqipqSmKY6VYyqMY9g0oju1SLPWo4lAcccfRoUOHvM4vZ+Jmrb007eMs4Clr7ZPAXKAzMBC4v5nLnwfMc85NDT8/jE/cRERERCRDQ/e4dU77VwGMBZbif0VhKfBoOHyVOee+BOaGP1gP0A/4oDnzFBEREVld5Wxxc86d2EIxnAncZ61dC/gUaKnlioiIiCRK5Pe4WWvbAL8C1k4f7pyb0pwAnHNv4++XExEREZEGRErcrLXHATcBy4Cf0r4KgF8WIC4RERERyRC1xe1q4FDn3MRCBiMiIiIiuUV9Ae8y4IUCxiEiIiIijYiauI0ArrPWJustdSIiIiKrkahdpR8DlwK/t9amhhkgcM7F/0p9ERERkTVA1MTtXmA0MIb6DyeIiIiISAuJmrhtCFzsnAsKGYyIiIiI5Bb1Hre7gGMLGYiIiIiINCxqi1t3YKi1djjwVfoXzrleeY9KRERERFYSNXG7LfwnIiIiIjGJlLg55+4pdCAiIiIi0rCoP3n1u1zfOefuzF84IiIiIpJL1K7SzAcTNgG2AF4GlLiJiIiItICoXaV9M4eFrXDb5j0iEREREckq6utAsrkbOClPcYiIiIhII6Le45aZ4LUBjgG+zXtEIiIiIpJV1HvcaoDMX034Ajglv+GIiIiISC5RE7fNMj4vds5V5TsYEREREckt6sMJnxU6kGJy8/WTqK6ujjsMKioq4g5BREREikiDiZu19nlW7iJNFzjn+uU3JBERERHJprEWt3/nGN4ROAv/kIKIiIiItIAGEzfn3B3pn621GwLD8A8ljAEuLVxoIiIiIpIu6utA1gXOB4YC/wF+45ybVcjARERERKS+xu5xaw2cA/wReAHYwzn3fgvEJSIiIiIZGmtxmw2UAlcDrwMbW2s3Th/BOTepQLGJiIiISJrGErdq/FOlp+f4PgA2z2tEIiIiIpJVYw8ndGmhOERERESkEc35kXkRERERaUFK3EREREQSQombiIiISEIocRMRERFJCCVuIiIiIgmhxE1EREQkIZS4iYiIiCSEEjcRERGRhFDiJiIiIpIQStxEREREEkKJm4iIiEhCKHETERERSYgGf2S+pVhrS4HXgS+cc/vHHY+IiIhIMSqWFrezgRlxByEiIiJSzGJP3Ky1nYD/A26POxYRERGRYlYMXaU3ABcA6+QawVo7BBgC4JyjsrKyoAGZEkNFRUVBl5GoOIyhtLQ07jAoKSkpivIohjhKTPwxANww4zFqg9q4wyia8iiWOMrKygpeTyYhBsWhOJISR1PEmrhZa/cHvnbOvWGt7ZNrPOfcKGBU+DGoqqoqaFxBbUB1dXVBlxFFRUVFUcTROghYvnx53GFQW1tbFOVRDNulNlBZKI7campqKHQ92ZjKysrYY1AciqMY4ujQoUNe5xd3V+lvgQOstXOAB4G9rLX/jjckERERkeIUa4ubc24YMAwgbHE7zzl3TJwxiYiIiBSruFvcRERERCSiYng4AQDn3AvACzGHISIiIlK01OImIiIikhBK3EREREQSQombiIiISEIocRMRERFJCCVuIiIiIgmhxE1EREQkIZS4iYiIiCSEEjcRERGRhFDiJiIiIpIQStxEREREEkKJm4iIiEhCKHETERERSQglbiIiIiIJocRNREREJCGUuImIiIgkhBI3ERERkYRQ4iYiIiKSEErcRERERBKiLO4ARKK6aeLPLF8edxRw9r5xRyDSsOs+GEt1dXWsMfy58qhYly+yulKLm4iIiEhCKHETERERSQglbiIiIiIJocRNREREJCGUuImIiIgkhBI3ERERkYRQ4iYiIiKSEErcRERERBJCiZuIiIhIQihxExEREUkIJW4iIiIiCaHETURERCQhlLiJiIiIJIQSNxEREZGEUOImIiIikhBK3EREREQSQombiIiISEIocRMRERFJCCVuIiIiIgmhxE1EREQkIcriXLi1tjMwGtgEqAVGOedujDMmERERkWIVd4tbDfBH59y2wG7AGdba7WKOSURERKQoxZq4OecWOOfeDP/+AZgBdIwzJhEREZFiFWtXaTprbRdgJ2Bqlu+GAEMAnHNUVlYWNBZTYqioqCjoMqIomfshrYMg7jAwxlBaWhp3GEUTxz+frSEI4o2jpO2HtCmCfYNNuxbHsWJKFEeRxVFWVlbwulpxJDeOVuNvZ+Pa2rjDwBzw+6Ioj6YoisTNWrs28AhwjnPu+8zvnXOjgFHhx6Cqqqqg8QS1AdXV1QVdRhStg4Dly5fHHQalpaWKo9jiCAJq4o4BKAlqi+JYqaioUBxFFkdNTQ2FrqujqKysVBxFGMfGtcVRd7Rqgf20Q4cOeZ1f3Pe4Ya1thU/a7nPOjY07HhEREZFiFWviZq01wB3ADOfcdXHGIiIiIlLs4u4q/S1wLPCetfbtcNifnXNPxRiTiIiISFGKNXFzzr0EmDhjEBEREUmK2O9xExEREZFolLiJiIiIJIQSNxEREZGEUOImIiIikhBK3EREREQSQombiIiISEIocRMRERFJCCVuIiIiIgmhxE1EREQkIZS4iYiIiCSEEjcRERGRhFDiJiIiIpIQStxEREREEkKJm4iIiEhCKHETERERSQglbiIiIiIJocRNREREJCGUuImIiIgkRFncAYgkzRtdFxEEQawxbE9prMtPKfl8BmstXx53GLBZt7gjAFQe6a77YCzV1dVxh8GfK4+KO4SicsP4K6gpgn20rLSUmvL44zgv7gBWgVrcRERERBJCiZuIiIhIQihxExEREUkIJW4iIiIiCaHETURERCQhlLiJiIiIJIQSNxEREZGEUOImIiIikhBK3EREREQSQombiIiISEIocRMRERFJCCVuIiIiIgmhxE1EREQkIZS4iYiIiCSEEjcRERGRhFDiJiIiIpIQStxEREREEkKJm4iIiEhCKHETERERSYiyuAOw1u4L3AiUArc7566KOSQRERGRohRri5u1thT4J7AfsB1wpLV2uzhjEhERESlWcXeVdgc+cc596pxbBjwIHBhzTCIiIiJFKe7ErSMwN+3zvHCYiIiIiGSI+x43k2VYkDnAWjsEGALgnKNDhw4FDeqcPxV2/iIi0nIKfc6IqhjiuOCAv8QdQtEphu3SFHG3uM0DOqd97gTMzxzJOTfKObeLc24XfLJX0H/W2jdaYjmKQ3EkOQbFoTiKPQbFoTiKKI68ibvFbRqwpbV2M+AL4AjgqHhDEhERESlOsba4OedqgKHABGCGH+TejzMmERERkWIVd4sbzrmngKfijiPDqLgDCCmO+hTHCsUQAyiOTIpjhWKIARRHJsVRX7HEEZkJgpWeBRARERGRIhT3wwkiIiIiElHsXaUtyVpbAUwGyvHr/rBz7hJr7WBgJLAt0N0593qO6fP281zhr0a8DnzhnNvfWvtr4FZgbWAOcLRz7vss080BfgCWAzXhk7arzFq7PnA70A3/KpbfAecAW4ejrA9865zbMcu0ZwOn4J+Yuc05d8MqxnAucHK4/PeAE51z1dbaM/H3QNYATzrnLsgxfb2ybMJy7wT2B752znULh7UDxgBd8NvBOuf+lzbNL4EPgJHOuWuyzDPSdowQx9+BQcAyYBa+TL611nZnRdO+CeN4NMs8L8O/zLoW+Bo4wTm30hPbjcWR9t15wN+B9s65KmvthsDDwK7A3c65oTnmuWNYHhX47fh759xrqxJHrv3BWjsMOAl/TJzlnJtQqPKw1o4hy7FhrW2FP45+g69bRjvnrswyz6zTNzGGrPuYtfZo4Py0yXcAfuOceztjnln3rUbKojMwGtgEX4ajnHM35qo7rbVd8PctfxTO4lXn3Gl5KI9ccTS4fSMct5HOARHiGImvExeGo/7ZOfeUtXYAcBWwFr7cz3fOTcox76j1Xq4Ycu2jUY/ZfJVF1mPfWrsW8C9gl3D8s51zL2SZ70iylGUDceQ6v2et05twzDZ4TojDmtbithTYyzn3a2BHYF9r7W7AdOAQ/EbPqgA/z3U2vmJLuR240Dm3PfAo9SvgTH2dczs2N2kL3QiMd85tA/wamOGcOzyc/47AI8DYzImstd3wB1X3cLr9rbVbNnXh1tqOwFnALuGJqRQ4wlrbF18R7+Cc6wqsVNmmySzLqO4G9s0YdiHwnHNuS+C58HO664GnG5hnU7ZjQ3FMBLo553YAPgaGhcOn48tqx3Caf1lrs12A/d05t0M43n+Ai1cxjlTFPAD4PG1wNTACOK+ReV4N/CWM4+Lwc5PjyLU/hMfgEUDXcJqbw2M1U17Ko4FjYzBQHm73nYFTw+SFiNNHjoEc+5hz7r60eR8LzMlM2kK59q2G1AB/dM5tC+wGnBGWfUN156xUPNmStjDmppZHrjga276NHbeNngMixgFwfdp6pxKNKmBQuM2OB+7NNtMm1ntZY2igTKMes/kqi1zH/ikAYVkMAK611ubKRbKVZS65zu+56vRIx2wD08dmjUrcnHOBc+7H8GOr8F/gnJvhnPuogUkhjz/PZa3tBPwfvgJO2ZoVB8pE4NBVmXcT41gX6AXcAeCcW5Z+5W2tNYAFHsgy+bb4q+glzj8d/CJw8CqGUga0DhOQNvh3+Z0OXOWcWxrG9nWOdchWlpE45yYDizIGHwjcE/59D3BQ2rIOAj4FGnryucnbMVsczrlnwnIFeBX/jkPSyhv8lWzWm1Rd/Va+trnGayyO0PXABenzcM4tds69hD8ZNCQA1g3/Xo8s72mMGEeu/eFA4EHn3FLn3GzgE/yxmjnPfJZHtmMjANqG+3BrfKtKzpbWRo6txmKIso8dmWveufatRuJY4Jx7M/z7B/yFUseIdWejmlAeueLIuX2jHLdNXY9ccTQw/ltuRQvg+0CFtbY8y6iR6r0oMWSWadRjNo9lkevY3w6fAKXW71t861uz5Dq/k7tOj3rM5jwnxGWN6iqFupazN4BfAf90zk2NOGm2n+fqsYph3IA/Ea6TNmw6cADwOP5KoHOW6cDvbM9YawPgX8655jwRszm+GfqusPvlDXyz9eLw+z2Br5xzM7NMOx24Imx+/wkYiO+ubBLn3BfW2mvwrTk/Ac84556x1l4N7GmtvQJf0ZznnJuWZRbZyrI5NnbOLQhjW2Ct3QjAWtsW+BP+CrGhK9ao27EpfodvqieMpQdwJ7ApcGzaSbiesOyOA74D+q7Kgq21B+C7oN+x1q7KLM4BJoTbuATouSozAbYi+/7QEZ98pOT82bx8lEeazGPjYXwFvwB/8XGucy5r0pdj+qaIso8dTrQLy3r7VhRhq8ROQGN152bW2rfwJ8OLnHP/bWDcJpdHZhzZtm8TjttVlhHHb4Gh1trj8PXhH93K3WqHAm+lkrMMufbzpsSQ0px9bJVkxJHr2H8HONBa+yB+3905/D/bLRSNlWXm8lc6v1trs9bpRD9mc00fmzWqxQ3AObc8bLrtBHQPu/yiyPbm4yY/kmutTd2v8kbGV7/DNzG/gU9CluWYxW+dc7/Bd9meYa3t1dQY0pTh+/dvcc7tBCymfjNwQ1ftM4C/4a/4x+MPxqwJREOstRvgD57NgA74K6Bjwtg2wDe9nw+48AoyfdpcZVkIf8E32//YyHhRt2Mk1trh+HK9LzXMOTc17EbZFRgW3tuxEufccOdc53DarPezNLLsNsBwonUr5nI6vkLsDJxL2Lq7CnLtD5GPy+aWR4bMY6M7/h67Dvh9+Y/W2s2bMH1TNLiPhYn9Eufc9IZmkm3faoy1dm1899s5ruF7NxcAvwzrlT8A94ct/Lk0qTyyxZFj+0Y9bldJljhuAbbAd9UtAK7NGL8rvt48NccsG633IsSQ0px9rMmyxJHr2L8Tf4H1Ov7CewrZzx0NlmU2TTy/N/WYLRprXOKWEnYJvkCWe3pyiPTzXBH8FjjA+ocMHgT2stb+2zn3oXNuqZs8PAAACnBJREFUb+fczviDbVaOuOeH/3+Nv79lpW6hJpgHzEtrdXwYn8gRNh8fQgNX4865O5xzv3HO9cJ356zKlV1/YLZzbqFz7mf8/Rg9w9jGhs3fr+FvYq3MmDZrWa5CDOm+stb+AiD8P9VV0QO4OlzWOcCfrbUrnfyjbscorLXH429MP9o5t1IyEibPi/EPljTkflat630LfIX2TrjenYA3rbWbNGEex7PiHpuHWPX9Ndf+sCrH5aqWB5Dz2DgKf6/oz+Gx+TI5un+iHFsNibCPHUEjJ+zG9q0c07TCn5jvc841eC9a2HX9Tfj3G2GMW+WYb5PKI0Ic6ds30nG7KrLF4Zz7KkweaoHbSNvfw9s6HgWOc87lqhei1HsNxhAOb9Y+1lQ54sh67Dvnapxz5zp/39qB+AcoVjp3NFSWjck4v+eq06Mes7mmj80albhZa9tb/xQl1trW+KThw4iT1/08l/VPxRwBjGtqDM65Yc65Ts65LuE8JjnnjknrkisBLsI/jZMZf1tr7Tqpv4G98d0mq8Q59yUw11qbegKpH/7JKwjLxjk3L9f0aTH/El9JrMrV3efAbtbaNuGVZT/8PRKPAXuF898K/zRWVUb8WctyFWJINw5f4RD+/3i4rD2dc13CZd0A/NU5d1PmxFG2YxTWP8H8J+AA59yStOGbhZUy1tpN8fc7zckyffqDIgcQfT+v45x7zzm3Udp6z8M/pfhlE2YzH+gd/r0Xq5bcQ+79YRz+YZZy6386b0uydLnkozzSZDs2PsdfOJjw2Nzt/7d377FX13Ucx58g2EW0TS4pidpFl7Ho6lxFyeZEY7Ww4pUEJIo0F1Zby6YYkTgzbbqhwNpyXmZKvoewFiyVWlmpuRUyA7VpIMNI8F4WGMivP96fQ98O55zf+f1+wOHE67H9tsOX7+X9/Zzb53xu7xbX6PW91Uqr11jZNoX8IdPs+IavrV6uOYhsMXk8Iq5vY/+RpduK0opxEjnOrJG2y6NZHM2e33bft33VIo5jK7udQ/l8Lt87q4DLIuKBFqfu9XOvtxiKAb3G+qJFHA3f++Wz/ojy+ExydYTHqNOsLFvE0ez7veFnOu2/Z5sd3zGH2hi3Y4HbygfKYDLF1kpJ5wA3AiOBVZLWRsRZkkaTy35Miohd5ZfaveTMx5tj36bnmippTnm8HLgFoBoD8FZghXKs0RDgzoi4Z4DX/SpwR6mMbgDOL9v3+tVeFwvA3coxbjuBOdGPKdKRYxCWAWvI5vJHyOUueoCbJa0ju4LOi4ieBjH0m6SlwARghKRngPnklP2QNIt8Y09p4zw3AT+MnDbf8HnsRxyXkdPaV5fnu7acwnjgUkk7yV/jX4mI5xvE8f1SId8NbAIazurrLY6IaNq1WVoxjgIOVw4AnxgRj9XFMRtYWCqbO4Av97M8bqbB6wFYLynIHxy7yNfh6/u5PBq1aC0mn+t1ZPftLRHxaIM4aHJ8X8piWIvX2CfIVvQNdeepxrCIxq+tVj5GzlT9k6TaTNW55Tx7fXaWOBZI2kV2R10UZfzQQMqjRRyz+vH87omj2XdAP+KYqlwGo4f8QVXrEr2YHHc1T9K8sm1iRGyrK49mr/O2Y4icfdmwTNt5z+7Dsmj23h9Fjn3bTeYon1GJr1oW1zYpy2aafb8/ROPP9Hbfs33+TtjfnDnBzMzMrEscUl2lZmZmZt3MFTczMzOzLuGKm5mZmVmXcMXNzMzMrEu44mZmZmbWJQ615UDMrIMk3UouV/HtDlx7ELncwmTgyYgYyOLVA41lGrnUw8ROxWBm3ckVN7NDWFnX6U3AO6LkqJV0ITA9IiZ0MLT9YTyZs/K4+G8+3j0kzQQujIjx+zuQiLiDPqSa2pckTQB+HBG9Jpc3s4OPu0rNbAjw9U4H0Ve1lfn74ATg6UaVtm5Sy5xhZocmfwCY2Q+Ab0laUnL87SHpRGAjMDQidpVtvyZbbG4qrVSzyTRT55M5a6eTeSmvJFfWvyQibqucdoSk1WSKmTVk7sZN5dzvJldt/xDwHDAvIqL8363AdrICdjrwGeAXdfGOJlNAjS+xXBMRPyqrni8Ghkp6FbguIua3KpTSGrmYXNn9nWQaqbnAreX8DwNTIuKlSjldACwAhpHZL/5IpgM6vpTZxeXcM6m07kmaWO77GLIlbixwe4MyPg9YIukWMn/j+8iV5e8ls0a8XIl9EfClUl73lGMPA34OvKGUA+RzdRywpDzeTuac/Ear8jGzznCLm5n9gUzI/M1+Hn8a8CgwnEzw/RPgVDLFz3RgkaRhlf2nkZW6EcBaSpdhyRe4upxjFDCVrKSMrRz7ReAq4Ejgdw1iWUrmVB0NfB74nqQzSqqqi4CHImJYb5W2is+R3asnA58mKz1zS+yDga81KIuTgC+QuTEvJ3Mmjs1b1Ol1+yNpBLCMrOgNB/4MfLTBeTeQ5XIVmaLn6nKfpwBjgO/Wn5pMsv12YBwws7Q2fhLYUsphWERsARYCCyPiKLKSGm2VjpkdcG5xMzOA7wAPSFrYj2M3RkQtt+5dZGVlQUS8Btwn6d9kJa6Wx3BVRPym7H858IqkMWRl5enauYA1ku4mK2C1vMA/rSTp3lENopxjPPCpiNgBrC05B2cAv+zHfQHcGBFby/l/C2yLiEfKv1cAZ9Ttf2W59n2S/gksjYhtleM/ANxfd8wkYH1ELC/73cDelegtEXFjebwLeKr8ATwn6Xoyj2nVDaVShqSfAe9vcZ87gXdJGlFy3/6+xb5m1kGuuJkZEbFO0krgUuDxPh6+tfJ4ezlf/bZqi9vmynVflfQi2XJ0AnCapGp37RDg9kbHNjAaeDEi/lHZtgn4cDs30UT9fbS6r/7sDxl3tUx6SkL5qv+5b0mjgBuAj5Otj4OBl+qOebby+F/lOs3MIrt4n5C0EbgiIla22N/MOsQVNzOrmU+OObuusq02kP/NwN/L42MGeJ0xtQelC/VoYAtZObk/Is5scWxPi//bAhwt6chK5e144K8DjHd/+xs5xgzYs2xJ/YzP+vu+umwbFxEvSJpMjmlrx15lGBFPAlMlDQY+CyyTNLzbJ3KY/T/yGDczAyAingLuojJuKyKeIys+0yUdJukCcgzUQEySNF7S4eRYt4cjYjOwEjhZ0gxJQ8vfqZJOaTP+zcCDwNWS3ihpHNmS1JFlN/pgFfBeSZPLjNE59F45PhJ4FXhZ0tuAS/pwva3AcElvqW2QNF3SyIjYDdRaPF/vwznN7ABxxc3MqhYAR9Rtm01WDF4gB9k/OMBr3Em27r1Izh6dBlBaySYC55KtZ88C15AzU9s1FTixHL8CmB8RqwcY735VxpRNAa4ly/g95ISR11ocdgXwQeAVsuK3vA/Xe4KcxLFB0stlJu7ZwPoy03QhcG4Zq2dmB5lBPT2teh7MzOxAKt2VzwDTIuJXnY7HzA4uHuNmZtZhks4i14XbTrZuDsIzO82sAXeVmpl13keAvwDPk+vFTY6I7Z0NycwORu4qNTMzM+sSbnEzMzMz6xKuuJmZmZl1CVfczMzMzLqEK25mZmZmXcIVNzMzM7Mu4YqbmZmZWZf4D8poBo+MVZM1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# let's get the x-tick values\n",
"count, bin_edges = np.histogram(df_t, 15)\n",
"\n",
"# un-stacked histogram\n",
"df_t.plot(kind ='hist', \n",
" figsize=(10, 6),\n",
" bins=15,\n",
" alpha=0.6,\n",
" xticks=bin_edges,\n",
" color=['coral', 'darkslateblue', 'mediumseagreen']\n",
" )\n",
"\n",
"plt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\n",
"plt.ylabel('Number of Years')\n",
"plt.xlabel('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": [
"Tip:\n",
"For a full listing of colors available in Matplotlib, run the following code in your python shell:\n",
"```python\n",
"import matplotlib\n",
"for name, hex in matplotlib.colors.cnames.items():\n",
" print(name, hex)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"If we do no want the plots to overlap each other, we can stack them using the `stacked` paramemter. Let's also adjust the min and max x-axis labels to remove the extra gap on the edges of the plot. We can pass a tuple (min,max) using the `xlim` paramater, as show below."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"count, bin_edges = np.histogram(df_t, 15)\n",
"xmin = bin_edges[0] - 10 # first bin value is 31.0, adding buffer of 10 for aesthetic purposes \n",
"xmax = bin_edges[-1] + 10 # last bin value is 308.0, adding buffer of 10 for aesthetic purposes\n",
"\n",
"# stacked Histogram\n",
"df_t.plot(kind='hist',\n",
" figsize=(10, 6), \n",
" bins=15,\n",
" xticks=bin_edges,\n",
" color=['coral', 'darkslateblue', 'mediumseagreen'],\n",
" stacked=True,\n",
" xlim=(xmin, xmax)\n",
" )\n",
"\n",
"plt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\n",
"plt.ylabel('Number of Years')\n",
"plt.xlabel('Number of Immigrants') \n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question**: Use the scripting layer to display the immigration distribution for Greece, Albania, and Bulgaria for years 1980 - 2013? Use an overlapping plot with 15 bins and a transparency value of 0.35."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\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",
"\\\\ # create a dataframe of the countries of interest (cof)\n",
"df_cof = df_can.loc[['Greece', 'Albania', 'Bulgaria'], years]\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # transpose the dataframe\n",
"df_cof = df_cof.transpose() \n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # let's get the x-tick values\n",
"count, bin_edges = np.histogram(df_cof, 15)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # Un-stacked Histogram\n",
"df_cof.plot(kind ='hist',\n",
" figsize=(10, 6),\n",
" bins=15,\n",
" alpha=0.35,\n",
" xticks=bin_edges,\n",
" color=['coral', 'darkslateblue', 'mediumseagreen']\n",
" )\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Histogram of Immigration from Greece, Albania, and Bulgaria from 1980 - 2013')\n",
"plt.ylabel('Number of Years')\n",
"plt.xlabel('Number of Immigrants')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Bar Charts (Dataframe) <a id=\"10\"></a>\n",
"\n",
"A bar plot is a way of representing data where the *length* of the bars represents the magnitude/size of the feature/variable. Bar graphs usually represent numerical and categorical variables grouped in intervals. \n",
"\n",
"To create a bar plot, we can pass one of two arguments via `kind` parameter in `plot()`:\n",
"\n",
"* `kind=bar` creates a *vertical* bar plot\n",
"* `kind=barh` creates a *horizontal* bar plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Vertical bar plot**\n",
"\n",
"In vertical bar graphs, the x-axis is used for labelling, and the length of bars on the y-axis corresponds to the magnitude of the variable being measured. Vertical bar graphs are particuarly useful in analyzing time series data. One disadvantage is that they lack space for text labelling at the foot of each bar. \n",
"\n",
"**Let's start off by analyzing the effect of Iceland's Financial Crisis:**\n",
"\n",
"The 2008 - 2011 Icelandic Financial Crisis was a major economic and political event in Iceland. Relative to the size of its economy, Iceland's systemic banking collapse was the largest experienced by any country in economic history. The crisis led to a severe economic depression in 2008 - 2011 and significant political unrest.\n",
"\n",
"**Question:** Let's compare the number of Icelandic immigrants (country = 'Iceland') to Canada from year 1980 to 2013. "
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"1980 17\n",
"1981 33\n",
"1982 10\n",
"1983 9\n",
"1984 13\n",
"Name: Iceland, dtype: object"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# step 1: get the data\n",
"df_iceland = df_can.loc['Iceland', years]\n",
"df_iceland.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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RbnyIituohhR4FtUX+xFTxVjPP5oqUbiZKlF5Dutfpn8IsK6vzERxvo7qi/P2+v29O9WVZb+p230N1RAeO06zv723jucO5jBERU99B1Od2v5d3caf13VtXy+/Z73P/K5+7z9YvyYrp9ln1ruIgKpv4ZfrbayiSu5Poh6uo15nY6r+kTfWj+PZcIiKR3BnR+uVVEnxevv5JO1cycQd8ze4KpD1h164gUmGqOgr8+be16SedyRwxTRxHcWGQ0CUrP+//f+oEvm19Wv3YfquTqY6EvavVInarVQX1HyI+krcvradV69zdf36Lpkmxj2ohqgZ/+GyU5PXaYJ6xl/zKdtbr/s07vzMuRR4TcO6evfLQ6kSrt9RJY8XUe3bW0/VXh/tPor6zZPUUUVR/BnVl/IBZVn+Z8vhSJJqJmFSxxTVfSyvpDrq9GdUp3O3pRpC4rapykqShsc+YVL3bE11+uveVP1Zvk01LpIJmCSNEI+ESZIktcAhKiRJklpgEiZJktSChdgnzPOnkiRpIZnwLi4LMQnjqquumnTZ2NgYa9bMbUzNudbRdnljGEx5YxidGLrQhlGIoQttGIUYutAGYxhM+SZ1LFu2bNJlno6UJElqgUmYJElSC0zCJEmSWmASJkmS1AKTMEmSpBaYhEmSJLXAJEySJKkFJmGSJEktMAmTJElqgUmYJElSC0zCJEmSWmASJkmS1AKTMEmSpBaYhEmSJLVgadsBSJIkLRS3H37AetOr+5YvOeGsxnV5JEySJKkFJmGSJEktMAmTJElqwVD6hEXEg4DTe2bdD3gL8PF6/k7ASiAy8/phxCRJktSmoRwJy8yLMnPXzNwV2B34A3AGcCRwTmbuDJxTT0uSJHVeG6cj9wV+lZmXAgcCp9TzTwEOaiEeSZKkoWsjCXs28Kn6+baZuQqg/rtNC/FIkiQNXVGW5dA2FhEbAVcBD8vM1RFxQ2Zu0bP8+szccoJyK4AVAJm5+9q1ayfdxtKlS1m3bt2c4pxrHW2XN4bBlDeG0YmhC20YhRi60IZRiKELbTCG2Zdf/YzHTLl82zPOW296o402Aigm3P6Mtjx3TwF+lJnjY5utjojtM3NVRGwPXDNRocw8Hji+nizXrFkz6QbGxsaYankTc62j7fLGMJjyxjA6MXShDaMQQxfaMAoxdKENxjCY8hPpr2/ZsmWTrjvs05HP4c5TkQBnAQfXzw8GzhxyPJIkSa0YWhIWEZsA+wGf65l9DLBfRFxcLztmWPFIkiS1aWinIzPzD8DWffOupbpaUpIkaVFxxHxJkqQWmIRJkiS1wCRMkiSpBSZhkiRJLTAJkyRJaoFJmCRJUgtMwiRJklpgEiZJktQCkzBJkqQWmIRJkiS1wCRMkiSpBSZhkiRJLTAJkyRJaoFJmCRJUgtMwiRJklpgEiZJktQCkzBJkqQWmIRJkiS1wCRMkiSpBSZhkiRJLTAJkyRJaoFJmCRJUgtMwiRJklpgEiZJktQCkzBJkqQWmIRJkiS1wCRMkiSpBSZhkiRJLTAJkyRJaoFJmCRJUgtMwiRJklpgEiZJktQCkzBJkqQWmIRJkiS1wCRMkiSpBUuHtaGI2AI4EXg4UAIvAi4CTgd2AlYCkZnXDysmSZKktgzzSNhxwJcy88HALsAFwJHAOZm5M3BOPS1JktR5Q0nCIuIewOOBkwAyc21m3gAcCJxSr3YKcNAw4pEkSWpbUZblvG8kInYFjgfOpzoK9kPglcCVmblFz3rXZ+aWE5RfAawAyMzd165dO+m2li5dyrp16+YU71zraLu8MQymvDGMTgxdaMMoxNCFNoxCDF1ogzHMvvzqZzxmyuXbnnHeetMbbbQRQDHh9me05dlbCjwKeHlmfjcijmMGpx4z83iqJA6gXLNmzaTrjo2NMdXyJuZaR9vljWEw5Y1hdGLoQhtGIYYutGEUYuhCG4xhMOUn0l/fsmXLJl13WH3CrgCuyMzv1tOfoUrKVkfE9gD132uGFI8kSVKrhpKEZebVwOUR8aB61r5UpybPAg6u5x0MnDmMeCRJkto2tCEqgJcDp0bERsCvgUOpksCMiMOAy4DlQ4xHkiSpNUNLwjLzJ8AeEyzad1gxSJIkjQpHzJckSWqBSZgkSVILTMIkSZJaYBImSZLUApMwSZKkFpiESZIktcAkTJIkqQUmYZIkSS0wCZMkSWqBSZgkSVILTMIkSZJaYBImSZLUApMwSZKkFpiESZIktcAkTJIkqQUmYZIkSS0wCZMkSWqBSZgkSVILTMIkSZJaYBImSZLUApMwSZKkFpiESZIktcAkTJIkqQUmYZIkSS1Y2mSliHgN8LXM/ElE7AUksA54Xmb+73wGKEmS1EVNj4S9GvhN/fydwLHA24H3zkdQkiRJXdc0CbtnZt4YEZsDuwDvz8yTgAfNX2iSJEnd1eh0JHB5RDwGeBjwjcy8PSLuAdw+f6FJkiR1V9Mk7LXAZ4C1wDPreU8HvjcfQUmSJHVdoyQsM78ILOub/WmqDvqSJEmaoUZ9wiLiuv55mflH4KqBRyRJkrQINO2Yf9f+GRFxV2DJYMORJElaHKY8HRkR3wRK4O4R8Y2+xTsA581XYJIkSV02XZ+wE4EC2BM4qWd+CawGvjZPcUmSJHXalElYZp4CEBHfycwL57KhiFgJ3EQ1rMW6zNwjIrYCTgd2AlYCkZnXz2U7kiRJC0HTqyMvjIgnA7sCm/Ute8sMtvfEzFzTM30kcE5mHhMRR9bTr59BfZIkSQtS03tHfgAI4H+AP/QsKue4/QOBfernpwDnYhImSZIWgaaDtT4H2DUzL5/Dtkrg7IgogX/PzOOBbTNzFUBmroqIbeZQvyRJ0oLRNAm7Frhhjtt6bGZeVSdaX4mIxn3MImIFsAIgMxkbG5t03aVLl065vIm51tF2eWMYTHljGJ0YutCGUYihC20YhRi60AZjmH351dMsn0l9RVlOf0YxIl4CPA14Z//2M/PXjbd2Z31HAb8HDgf2qY+CbQ+cm5nT3RS8vOqqyceIHRsbY82aNZMub2KudbRd3hgGU94YRieGLrRhFGLoQhtGIYYutMEYZl/+9sMPmHL5khPOWm962bJlUI00sYGmg7V+mOpekd8GLul5XNykcERsGhGbjz8Hngz8HDgLOLhe7WDgzIbxSJIkLWhNr45smqxNZlvgjIgY3+ZpmfmliPg+kBFxGHAZsHyO25EkSVoQmvYJm5P6lOUuE8y/Fth3GDFIkiSNkqZDVCwFXgo8ARij59xmZj5+fkKTJEnqrqanGf8NeAnwDWB34LPANnjbIkmSpFlpmoT9DfCUzDyO6pZDxwEHAU+ct8gkSZI6rGkStgkwPlDrLRGxSX0vyd3mJyxJkqRua9ox/wJgT+B7wA+AoyLid8CV8xWYJElSlzVNwl4JrKufv4Zq3LDNqUexlyRJ0sxMm4RFxBLgEcCpAJl5MfCX8xyXJElSp03bJywzbweOzczbhhCPJEnSotC0Y/5/RsRfz2skkiRJi0jTPmF3Bz4TEf9LdZXkn+76nZkvnI/AJEmSuqxpEvbz+iFJkqQBaHoD73+e70AkSZIWk6b3jnzSJItuA67IzEsHF5IkSVL3NT0deRKwrH5+LbB1/fwaYLuI+Bnw7Hr4CkmSJE2j6dWRJwHvA7bIzGXAFsBxwEfq598HPjQvEUqSJHVQ0yTslcAbMvMWgPrvm4BXZebNwBHAHvMToiRJUvc0TcJuprp3ZK/dgT/Uz+8YWESSJEmLQNM+YW8Bzo6Is6jGCdsB+Gvg5fXyfYHPDD48SZKkbmo6RMXHI+IHwDOpOuj/EviLzDy/Xv4F4AvzFqUkSVLHND0SRp1wnT+PsUiSJC0akyZhEXF8Zq6on3+CnlsV9fK2RZIkSTM31ZGw3/Q8v2S+A5EkSVpMJk3CMvOdPc+9bZEkSdIANe4TFhE7AY8ENuudn5mnDTgmSZKkzmt678g3UA1T8Qvglp5FJWASJkmSNENNj4QdAew+PiSFJEmS5qbpiPnXAivnMQ5JkqRFpemRsFcBx0fEe4Frehdk5mUDj0qSJKnjmiZhGwFPBp7bN78Elgw0IkmSpEWgaRL2IeCNwH+wfsd8SZKkBeH2ww9Yb3p13/IlJ5w1vGBonoQtBT6WmbfPZzCSJEmLRdOO+e8GjoyIYj6DkSRJWiyaHgl7BbAd8MaIuLZ3QWbeZ+BRSZIkdVzTJOz58xqFJEnSItMoCcvMrw9iYxGxBPgBcGVmPj0itgJOB3aiGocsMvP6QWxLkiRplDW9bdFS4DnAbmx478gVM9jeK4ELgHvU00cC52TmMRFxZD39+hnUJ0mStCA17Zj/SaoE6Q6qKzp7H41ExA7A04ATe2YfCJxSPz8FOKhpfZIkSQtZ0z5h+wM7ZuZNc9jWe4HXAZv3zNs2M1cBZOaqiNhmDvVLkiQtGE2TsPOBrYBZJWER8XTgmsz8YUTsM4vyK4AVAJnJ2NjYpOsuXbp0yuVNzLWOtssbw2DKG8PoxNCFNoxCDF1owyjE0IU2LNYYpjt916SuQdQxbiZXR54YEWf3bz8zP96g/GOBAyLiqcDdgXtExCeB1RGxfX0UbHv67kvZs43jgePryXLNmjWTbmhsbIypljcx1zraLm8MgylvDKMTQxfaMAoxdKENoxBDF9pgDBMbRF39dSxbtmzSdZsmYYcAjwO2ZP3bFpXAtElYZr4BeANAfSTsHzLz+RHxr8DBwDH13zMbxiNJkrSgNU3CXgnslpkXDHj7xwAZEYcBlwHLB1y/JEnSSGqahK2mSpLmLDPPBc6tn18L7DuIeiVJkhaSpknYvwGnRsQx9PXbysxfDzwqSZKkjmuahH2w/ntA3/wSWDK4cCRJkhaHprctajqoqyRJkhpoeiRMkiSpVbcfvv4Juf4xu5accNbwghmASZOwiPhSZu5fP/8m1anHDWTm4+cpNkmSpM6a6khY7/hfJ066liRJkmZs0iQsM0/reX7KZOtJkiRp5uxwL0mS1AKTMEmSpBaYhEmSJLVg0iQsIr7T8/yfhhOOJEnS4jDVkbAHRsTd6+dHDCMYSZKkxWKqISrOBH4ZESuBjSPiGxOt5DhhkiRJMzfVEBWHRsTewE7AnsBJwwpKkiSp66a8bVFmfgv4VkRs5FhhkiRJg9P0Bt4fjYgnAi8A7g1cCXwyM782n8FJkiR1VaMhKiLixcDpwNXA54BVwGkRcfg8xiZJktRZjY6EAa8D9svMn47PiIjTgc8CJ8xHYJIkSV3WdLDWrYHz++ZdBGw12HAkSZIWh6ZJ2LeAYyNiE4CI2BT4V+C8+QpMkiSpy5omYX8HPBK4MSJWAzcAuwAvma/AJEmSuqzp1ZGrgCdExA7AMuCqzLxiXiOTJEnqsKYd8wGoEy+TL0mSpDlqejpSkiRJA2QSJkmS1IJpT0dGxF2AfYBvZebaeY9oyG4//IAN5q3um15ywlnDCUaSJC0a0x4Jy8w7gDO7mIBJkiS1penpyG9ExF7zGokkSdIi0vTqyEuB/46IM4HLgXJ8QWa+ZT4CkyRJ6rKmSdjGwOfr5zvMUyySJEmLRtPBWg+d70AkSZIWk8aDtUbEQ4BnAdtm5ssi4kHA3TLzZ/MWnSRJUkc16pgfEcuBbwD3Bl5Yz94cOHae4pIkSeq0pldHvhXYLzP/Dri9nvdTqpt4S5IkaYaano7chirpgjuvjCx7nk8pIu5OdSTtbvU2P5OZ/xQRWwGnAzsBK4HIzOsbxiRJkrRgNT0S9kPgBX3zng18r2H524AnZeYuwK7A/vW4Y0cC52TmzsA59bQkSVLnNT0S9grg7Ig4DNg0Ir4MPBB4cpPCmVkCv68n71o/SuBAqlsiAZwCnAu8vmFMkiRJC1ajI2GZeSHwYOCDwJuBjwGPyMyLm24oIpZExE+Aa4CvZOZ3qa60XFVvYxXVaU9JkqTOK8qyUbcuACLi3sAy4KrMvHI2G4yILYAzgJdT3RR8i55l12fmlhOUWQGsAMjM3deunfw2lkuXLmXdunWN41n9jMdMu862Z5zXuL7ZxDDo8sYwmPLGMDoxdKENoxBDF9owCjF0oQ1txDCI79vp6pjv8rOpY6ONNgIoJlq30enIiLgPcCqwF3A9sGVEfBd4XmZe2qSOceRPgjIAABcKSURBVJl5Q0ScC+wPrI6I7TNzVURsT3WUbKIyxwPH15PlmjVrJq1/bGyMqZbPxkzrm2sMg2iDMXSjDcYwmPLGMJjyxjCY8sYwubnW13b5iepYtmzZpOs27Zh/ClXn/C0ycxtgS+D79fxpRcS96iNgRMTGwF8CFwJnAQfXqx0MnNkwHkmSpAWtaRK2O/DazLwZIDN/T9WBfveG5bcH/icifkaVvH0lM78AHAPsFxEXA/vV05IkSZ3X9OrI7wCPBr7dM28P4H+bFK5vbbTbBPOvBfZtGIMkSVJnTJqERcRbeyZ/BXwxIv4LuBzYEXgqcNr8hidJktRNUx0J27Fv+nP1322oBl89A7j7fAQlSZLUdZMmYZl56DADkSRJWkya9gkjIjYBHgBs1js/M2c2iJYkSZIajxP2QuADwFrglp5FJXCfeYhLkiSp05oeCXsX8MzM/Mp8BiNJkrRYNB0nbC3VzbUlSZI0AE2TsH8Ejo2IsfkMRpIkabFoejryl8BbgZdGxPi8Aigzc8l8BCZJktRlTZOwTwAfB05n/Y75kiRJmoWmSdjWwFsys5zPYCRJkhaLpn3CPga8YD4DkSRJWkyaHgl7NPCyiHgTsLp3QWY+fuBRSZJGwu2HH7De9Oq+5UtOOGt4wUgd0zQJO6F+SJIkaQAaJWGZecp8ByJJkrSYNL1t0YsmW5aZHx1cOJIkSYtD09OR/Z3ytwPuD3wbMAmTJEmaoaanI5/YP68+OvaQgUckSZK0CDQdomIiJwOHDSgOSZKkRaVpn7D+ZG0T4PnADQOPSJIkaRFo2idsHdA/Wv6VwOGDDUeSJGlxaJqE3bdv+ubMXDPoYCRJkhaLph3zL53vQCRJkhaTKZOwiPgfNjwN2avMzH0HG5IkSVL3TXck7JOTzL838AqqDvqSJEmaoSmTsMw8qXc6IrYG3kDVIf904K3zF5okSXPnTcg1qpoOUXEP4LXAy4AvAI/KzF/NZ2CSJEldNl2fsI2BVwFHAOcCe2fmL4YQlyRJUqdNdyTsN8AS4F3AD4BtI2Lb3hUy82vzFJskSVJnTZeE3Up1deTfT7K8BO430IgkSZIWgek65u80pDgkSZIWlbncwFuSJEmzZBImSZLUApMwSZKkFjS9gfecRMSOwMeB7YA7gOMz87iI2Ipq0NedgJVAZOb1w4hJkiSpTcM6ErYOOCIzHwLsBfy/iHgocCRwTmbuDJxTT0uSJHXeUJKwzFyVmT+qn98EXEB1/8kDgVPq1U4BDhpGPJIkSW0bep+wiNgJ2A34LrBtZq6CKlEDthl2PJIkSW0YSp+wcRGxGfBZ4FWZ+buIaFpuBbACIDMZGxubdN2lS5dOubxf/41cJzKT+mYTw6DLG8NgyhvD6MSwENuw+hmP2XBe3/S2Z5w3rzEMovx0n5HD/nycTR1daEMXYhjE9+1c38tB7AuD3J+GloRFxF2pErBTM/Nz9ezVEbF9Zq6KiO2BayYqm5nHA8fXk+WaNWsm3c7Y2BhTLZ+NmdY31xgG0QZj6EYbjGEw5Uclhn4L8bOl37DbMKg6ei3ENnQlhn5zra/t8hPVsWzZsknXHcrpyIgogJOACzLz2J5FZwEH188PBs4cRjySJEltG9aRsMcCLwD+LyJ+Us97I3AMkBFxGHAZsHxI8UiSJLVqKElYZn4LKCZZvO8wYpAkSRoljpgvSZLUApMwSZKkFpiESZIktcAkTJIkqQUmYZIkSS0wCZMkSWqBSZgkSVILTMIkSZJaMNQbeM+H2w8/YL3p/htrLjnhrOEFI40A/ye6w/ey4uugrvJImCRJUgtMwiRJklpgEiZJktQCkzBJkqQWmIRJkiS1wCRMkiSpBSZhkiRJLTAJkyRJasGCH6xV0mD1D4wJDo4ptW0UBqwdhRi6xiNhkiRJLTAJkyRJaoFJmCRJUgvsEyapk+y/ImnUeSRMkiSpBSZhkiRJLTAJkyRJaoF9wjrC/i+SpKn4PTF6PBImSZLUApMwSZKkFpiESZIktcAkTJIkqQUmYZIkSS0wCZMkSWqBSZgkSVILTMIkSZJaMJTBWiPio8DTgWsy8+H1vK2A04GdgJVAZOb1w4hH82OuAwE6kGClC6+DbdA4X0dpcsM6EnYysH/fvCOBczJzZ+CcelqSJGlRGEoSlpnfAK7rm30gcEr9/BTgoGHEIkmSNAra7BO2bWauAqj/btNiLJIkSUO1IG7gHRErgBUAmcnY2NiflvX3L+jXu+5EpivfqI5nPGbKOrc947wGW7nT0qVLp93mBjFMs3ym9bURwyi0YdB1LMTXcSD/E3Ms38/XcXbl+y3E13EUYpjuMx7m/3O+E6/jNOVHIYZhvI692kzCVkfE9pm5KiK2B66ZbMXMPB44vp4s16xZ03gjM1l3vuqYafmxsbGBxL3QY5hr+UG0Ya51dOF1HIUYfB0HU97XcXRiGPR7sVhfx1ErP1Edy5Ytm3TdNk9HngUcXD8/GDizxVgkSZKGalhDVHwK2AcYi4grgH8CjgEyIg4DLgOWDyMWSZKkUTCUJCwznzPJon2Hsf1R5zg60vr8nxgMX0dptDliviRJUgtMwiRJklpgEiZJktQCkzBJkqQWmIRJkiS1wCRMkiSpBSZhkiRJLTAJkyRJasGCuIG31MR0A1OCg1NKkkaHR8IkSZJaYBImSZLUApMwSZKkFtgnTIA3+pUkadg8EiZJktQCkzBJkqQWmIRJkiS1wCRMkiSpBSZhkiRJLTAJkyRJaoFJmCRJUgtMwiRJklpgEiZJktQCkzBJkqQWmIRJkiS1wCRMkiSpBd7AW+rhjcwlScPikTBJkqQWmIRJkiS1wCRMkiSpBSZhkiRJLbBjvjRAduyXNBE/GzQRj4RJkiS1wCRMkiSpBSZhkiRJLWi9T1hE7A8cBywBTszMY1oOSZIkad61eiQsIpYAHwSeAjwUeE5EPLTNmCRJkoah7dORjwYuycxfZ+Za4D+AA1uOSZIkad61nYTdG7i8Z/qKep4kSVKnFWVZtrbxiFgO/FVmvriefgHw6Mx8ed96K4AVAJm5+9ADlSRJmr1iopltHwm7AtixZ3oH4Kr+lTLz+MzcIzP3oGrIpI+I+OF068x3HW2XN4butMEYutOGUYihC20YhRi60AZjGHobJtT21ZHfB3aOiPsCVwLPBp7bbkiSJEnzr9UjYZm5DngZ8GXggmpW/qLNmCRJkoah7SNhZOYXgS8OsMrjR6COtssbw2DKG8PoxNCFNoxCDF1owyjE0IU2GMNgys+pjlY75kuSJC1WbXfMlyRJWpRMwiRJklpgEiZJktQCkzBJkqQWtH51pCRpfkTEXwEHUd0OrqQaDPvMzPzSAOp+S2a+tWEMOwDnZObKnvkvysyPTlO2AJZTxf4Z4ElU9xe+EPhIZt4xy9i/lplParjuWGau6Zl+PtV9j38OnJCZ017dFhHPAL6emddFxL2A9wC7AecDR2TmFdOUPxb4bGZ+u0nME5Tfimo4qKuAk4A3An9BNTTUOzLz+ob1PBF4JtUg6+uAi4ETM/OShuXdH/ss+CRsob+p9XoDf2MX24dMXcecP2gW+4dMvZ77Ywf2x4h4L/BA4ONUdyeBar94RUQ8JTNfOeNGre/FwJT7Y0S8A9gb+BHwxoh4b2a+v178MmC6/fGDwDbARlT74N2A/wSeCjwImLYNEfGzvlkF8MDx+Zn5yGmqOBt4VF3Xm4HHAacBTwceArx6uhiAt2fmQ+vnHwC+Q7U//CXwMWC/acq/AHh8vS+fDnwqM3/cYLvjPgn8H7A78Pz6+b/U2z2Z6rWdUkQcA2wLnANsB/wG+BXw6Yh4R2Z+epry7o8TWNBJWEfeVJjjG+uHzJ/M6YPGD5k/cX/swP4IPDUzHzhBvacDv6RZAvO7SRYVwMbTlQf+GtgtM9dFxFHAaRFxv8x8NVPcyqXH4zLzERFxV+BqYPvMXBsRpwFN34+VwO+AtwG31Nv9Zh1bE71x/k0d0811DD9qWMeSnucPyMy/rZ+fHBGvalD+iszcIyJ2prqzzCcjYgnwKap985fTlF+WmU+tf2BdkZn71PO/GRE/adiGp2XmIwAi4j+ofuS8NiI+Q/V6uj/OwoJOwujGmwpzf2NX4ocMzP2Dxg+ZivtjN/bHWyPi0Zn5vb75ewK3Ntg+wA3Anpm5un9BRFzeoPzS+s4oZOYNEfHXwPER8WmqJH8642X/GBHfz8y19fS6iLi9SQMy84D6yOjxwLsz86yI+GNmXtqkPLBxROxG1Yd6SWbe3BNToxiAcyPircA76+cHZebn6yOdNzYoX9bbvBg4Gjg6Ih4JPIdqsPMHTFP+LhGxJbA5sFlE7JSZKyNia5q9DwB3RMRWmXkdsIz6fywzr6/38em4P05goXfMvzUiHj3B/Jm+qTtn5j36HpsDqxqUX+9NpfqiuccM3lToeWOB9d5YYNo3NjMPAD5L9SGzS30K6o+ZeWnDD5qNI2K3iNidvg+ZJtuvnRsRb42IjevnB8GfTqfM6EMmM4/OzIcBAdyd5ndUGP+g2ZH6g6aOoekHzR31KSTo+5ChWQLj/oj7Y4+298dDgPdHxPkRcXb9uAB4f72siY8DfzbJstMalP9VRDxhfCIzb8/Mw4CLqI5qTufqiNisLrv/+MyI2A5Y26D8+HbPAJ4C7BMRZ9H8fwGq/7tjgXcD10XE9nUMW1P/rzTwMuAOqnYvBz4XETcBh1MddZ3OBu93Zv4sM9+QmdMlYFAlfxdS3a/5RcCJEfEV4GfAe5s1gXcAP46Is4FvUSWD1EeLf9qg/CG4P25goR8JOwT4cERszp2nf3ak+hV+SMM6xt/UDTJrZvCmZubXoXpTgcMi4m1UfTmauDoiNsvM38/2jc3MM+p/jqMj4sXM7kMG6g+ZzFw1iw+ZN1HtzACvjoibqU5jzfpDhupD4g0NYxj/oIE7P2ig+uf65wblxz9kLgIeDPw9zPhDxv0R98faRPtjCTyUIeyPmfkj4M/r9+3eVG26IjOvbhg/mfnmKZa9vkEVyyerNyI+3GD7T5lk0U1Up6cbq5P510TELlR985qWe+Iki24AHt+wjj8CRwFHRcQ9qX4sXds0BqpT8rOWmZ+KiASK+qjNmcCuwJWZ2eTHHZl5ep243Q+4pP6RR2b+Fnhug/LujxPoxG2L5vKmDmDbGwNk5i0TLLt3Zl45h7o3BTbNzGtmWG4X4C8y8yOz3XZdzxLgbpn5hxmWm/GHzPiX/kxjnKCeJdz5QbOUGX7Q1Ece1vuQmUUM7o/rl3N/bGl/rE8TPZr1LxT5Xja4uGFQdbRd3hhGpw1T1PvgzLxw+jXnr462yi/4JCwi7gP8LqvzuzsBewAXZOYv5ljHhZn582GUH4UYutCGEYphD3quZpvNP+Zc6+hCDF1oQ5sxRMSTgQ9RXVE5nnzvQNV/6KWZefZ819F2eWMYnTZMU/dlmXmf2ZYfRB1tlV/QSVhEHAm8BLiN6nz9PwDfBvYCTsrMY6coPpA6uhBDF9owCjHUfQ3eQ3WaYve67JbAH4EXZOa0HUfnWkcXYuhCG0Yhhqj62zwle4YpqeffF/hiZk7bB2audbRd3hhGqg3vm2RRARycmfdoEMOc6mi7/EQWesf8F1D1r3gs8G9UV1EdRnW49EVDqqMLMXShDaMQw3upPqT+kmqIhT9m5mOBt1ONE9XEXOvoQgxdaMMoxLCUO/sm9roSuGuD8oOoo+3yxjCY8oOo41Cqsf5+2Pf4Ac07tc+1jrbLb2Chd8y/PTNviYi1VJfCXwtVB8yoOmQPo44uxNCFNoxCDEuy6qQKcBn1VTyZ+ZWoxhAbRh1diKELbRiFGD4KfD+q4S3Gj5rtSDXkRtNEcq51tF3eGAZTfhB1fB/4eWae178gquF0mphrHW2X38BCT8J+FNXYQZtSDWh4SkR8iWqE7/OHVEcXYuhCG0Yhhh9ExEl12QOBcwEiYhPWH7dqPuvoQgxdaEPrMWTmO6O6Cu4AqqsBC6ojGc/LzEb/U3Oto+3yxjA6bQCexSRD9WTmfZvEMIA62i6/gYWehL2Y9W+v8udUg9ddRDXq9zDq6EIMXWjDKMTwEqpxfx4DfJU7R6cvgb9q2Ia51tGFGLrQhpGIof5ybPojZl7qaLu8MQym/FzryGqQ1zmZax1tl5/Igu6YL0maWFRDc7yB6l6m96pnXwOcCRyTDYa8mGsdbZc3hu60YRRiGEQb+i3oI2FRjVz7OqpBKHeg6hj3K+DDmXnKMOroQgxdaMMoxNBT/m+o+kqMl/9IZp48wzbMqo4uxNCFNoxIDAl8Ddgn67HqohrD7hCqWx5Nd//MQdTRdnljGP02HDyAGJrW0Xb5DSz0qyNPBX5NdWj+n4H3UV3h9qSobmQ8jDq6EEMX2jAKMYyX37+v/BNn0YbZ1tGFGLrQhlGIYafM/JfsGSw4M6/OzGOApuMZzbWOtssbw+i34V8GEEPTOtouv4EFfSSM6gU5uX5+bFQ31Dw6Ig6lOm/9xiHU0YUYutCGUYihC20YhRi60IZRiOHSiHgdcErWNzyOiG2pjlw0udnxIOpou7wxdKcNoxDDINqwnoV+JOzmiNgbIKq7oV8HkJl30OwGt4OoowsxdKENoxBDF9owCjF0oQ2jEMPfAlsDX4+I6yPiOqorLLcCmo77Mtc62i5vDN1pwyjEMIg2rGehHwn7O6qb4j6QagC1FwFEdYPbplfUzbWOLsTQhTaMQgxdaMMoxNCFNrQeQ2ZeHxEfA74CfCd77oUZEfsDX5rvOtoubwzdacMoxDCINmygLMtOPpYvX35o23V0IYYutGEUYuhCG0Yhhi60YVgxLF++/BXLly+/aPny5Z9fvnz5yuXLlx/Ys+xHDbczpzraLm8M3WnDKMQwiDb0Pxb66cip/PMI1NGFGLrQhlGIoQttGIUYutCGYcVwOLB7Zh4E7AP8Y0S8sl7W9JTqXOtou7wxDKa8MQym/AYW9OnIiPjZJIsKYNth1NGFGLrQhlGIoQttGIUYutCGEYlhyfjpksxcGRH7AJ+JiD+j+RfGXOtou7wxdKcNoxDDINqwnoV+JGxb4IXAX0/wuHZIdXQhhi60YRRi6EIbRiGGLrRhFGK4OiJ2HZ+ovzyeDowBj2jYhrnW0XZ5YxhMeWMYTPkNLOgjYcAXgM0y8yf9CyLi3CHV0YUYutCGUYihC20YhRi60IZRiOGFwLreGZm5DnhhRPx7g/KDqKPt8sYwmPLGMJjyG/C2RZIkSS1Y6KcjJUmSFiSTMEmSpBaYhEmSJLVgoXfMl6T1RMSpwG2Z+aKeeU8APgc8PDNXtRacJPXwSJikrnkF8NSI2A8gIu4OnAAcMcgELCKWDKouSYuTV0dK6pyIWA68C3g48GZg18x8SkTcBTgSOAy4J/BV4O/re8LdBUhgb+DuwE/qZRfUdX4SuBG4P/A44GmZee5QGyapUzwSJqlzMvPTwA+BTwErgJfUi14DPA14PLADcDPwvp6iXwB2Brajumn2J/qqfi7VLYM2B/53nsKXtEh4JExSJ0XEtsCvgDdl5nH1vIuBF2fm1+vpHYFLgI0z846+8mPAb6kGTL25PhK2trevmSTNhR3zJXVSZq6OiDXAL3pm3wf4z4joTbhKYJuI+C3wTuBZVLchGV9njOqIGcDl8xu1pMXEJEzSYnIF8NzM/G7/gog4FHgq8CTgUmBrqiNhvTfm9dSBpIGxT5ikxeQjwDsi4j4AEbFNRBxQL9scuI3q5tibAG9vJ0RJi4VJmKTF5FjgS8A5EXETcB6wZ73sY8BV9eMX9TJJmjd2zJckSWqBR8IkSZJaYBImSZLUApMwSZKkFpiESZIktcAkTJIkqQUmYZIkSS0wCZMkSWqBSZgkSVILTMIkSZJa8P8Bl1bVULefupsAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# step 2: plot data\n",
"df_iceland.plot(kind='bar', figsize=(10, 6))\n",
"\n",
"plt.xlabel('Year') # add to x-label to the plot\n",
"plt.ylabel('Number of immigrants') # add y-label to the plot\n",
"plt.title('Icelandic immigrants to Canada from 1980 to 2013') # add title to the plot\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The bar plot above shows the total number of immigrants broken down by each year. We can clearly see the impact of the financial crisis; the number of immigrants to Canada started increasing rapidly after 2008. \n",
"\n",
"Let's annotate this on the plot using the `annotate` method of the **scripting layer** or the **pyplot interface**. We will pass in the following parameters:\n",
"- `s`: str, the text of annotation.\n",
"- `xy`: Tuple specifying the (x,y) point to annotate (in this case, end point of arrow).\n",
"- `xytext`: Tuple specifying the (x,y) point to place the text (in this case, start point of arrow).\n",
"- `xycoords`: The coordinate system that xy is given in - 'data' uses the coordinate system of the object being annotated (default).\n",
"- `arrowprops`: Takes a dictionary of properties to draw the arrow:\n",
" - `arrowstyle`: Specifies the arrow style, `'->'` is standard arrow.\n",
" - `connectionstyle`: Specifies the connection type. `arc3` is a straight line.\n",
" - `color`: Specifes color of arror.\n",
" - `lw`: Specifies the line width.\n",
"\n",
"I encourage you to read the Matplotlib documentation for more details on annotations: \n",
"http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_iceland.plot(kind='bar', figsize=(10, 6), rot=90) # rotate the bars by 90 degrees\n",
"\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.title('Icelandic Immigrants to Canada from 1980 to 2013')\n",
"\n",
"# Annotate arrow\n",
"plt.annotate('', # s: str. Will leave it blank for no text\n",
" xy=(32, 70), # place head of the arrow at point (year 2012 , pop 70)\n",
" xytext=(28, 20), # place base of the arrow at point (year 2008 , pop 20)\n",
" xycoords='data', # will use the coordinate system of the object being annotated \n",
" arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='blue', lw=2)\n",
" )\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's also annotate a text to go over the arrow. We will pass in the following additional parameters:\n",
"- `rotation`: rotation angle of text in degrees (counter clockwise)\n",
"- `va`: vertical alignment of text [‘center’ | ‘top’ | ‘bottom’ | ‘baseline’]\n",
"- `ha`: horizontal alignment of text [‘center’ | ‘right’ | ‘left’]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_iceland.plot(kind='bar', figsize=(10, 6), rot=90) \n",
"\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Number of Immigrants')\n",
"plt.title('Icelandic Immigrants to Canada from 1980 to 2013')\n",
"\n",
"# Annotate arrow\n",
"plt.annotate('', # s: str. will leave it blank for no text\n",
" xy=(32, 70), # place head of the arrow at point (year 2012 , pop 70)\n",
" xytext=(28, 20), # place base of the arrow at point (year 2008 , pop 20)\n",
" xycoords='data', # will use the coordinate system of the object being annotated \n",
" arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='blue', lw=2)\n",
" )\n",
"\n",
"# Annotate Text\n",
"plt.annotate('2008 - 2011 Financial Crisis', # text to display\n",
" xy=(28, 30), # start the text at at point (year 2008 , pop 30)\n",
" rotation=72.5, # based on trial and error to match the arrow\n",
" va='bottom', # want the text to be vertically 'bottom' aligned\n",
" ha='left', # want the text to be horizontally 'left' algned.\n",
" )\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Horizontal Bar Plot**\n",
"\n",
"Sometimes it is more practical to represent the data horizontally, especially if you need more room for labelling the bars. In horizontal bar graphs, the y-axis is used for labelling, and the length of bars on the x-axis corresponds to the magnitude of the variable being measured. As you will see, there is more room on the y-axis to label categetorical variables.\n",
"\n",
"\n",
"**Question:** Using the scripting layter and the `df_can` dataset, create a *horizontal* bar plot showing the *total* number of immigrants to Canada from the top 15 countries, for the period 1980 - 2013. Label each country with the total immigrant count."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data pertaining to the top 15 countries."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\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",
"\\\\ # sort dataframe on 'Total' column (descending)\n",
"df_can.sort_values(by='Total', ascending=True, inplace=True)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # get top 15 countries\n",
"df_top15 = df_can['Total'].tail(15)\n",
"df_top15\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot data:\n",
" 1. Use `kind='barh'` to generate a bar chart with horizontal bars.\n",
" 2. Make sure to choose a good size for the plot and to label your axes and to give the plot a title.\n",
" 3. Loop through the countries and annotate the immigrant population using the anotate function of the scripting interface."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"### type your answer here\n",
"\n",
"\n",
"\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",
"\\\\ # generate plot\n",
"df_top15.plot(kind='barh', figsize=(12, 12), color='steelblue')\n",
"plt.xlabel('Number of Immigrants')\n",
"plt.title('Top 15 Conuntries Contributing to the Immigration to Canada between 1980 - 2013')\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # annotate value labels to each country\n",
"for index, value in enumerate(df_top15): \n",
" label = format(int(value), ',') # format int with commas\n",
" \n",
" # place text at the end of bar (subtracting 47000 from x, and 0.1 from y to make it fit within the bar)\n",
" plt.annotate(label, xy=(value - 47000, index - 0.10), color='white')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was originally created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
"\n",
"This notebook was recently revamped by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
]
},
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"source": [
"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week2_LAB1)."
]
},
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"source": [
"<hr>\n",
"\n",
"Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
],
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