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| { | |
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| "source": [ | |
| "<center>\n", | |
| " <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n", | |
| "</center>\n", | |
| "\n", | |
| "# Area Plots, Histograms, and Bar Plots\n", | |
| "\n", | |
| "Estaimted time needed: **30** minutes\n", | |
| "\n", | |
| "## Objectives\n", | |
| "\n", | |
| "After complting this lab you will be able to:\n", | |
| "\n", | |
| "- Create additional labs namely area plots, histogram and bar charts\n" | |
| ] | |
| }, | |
| { | |
| "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>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
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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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) and [**Numpy**](http://www.numpy.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) for data wrangling, analysis, and visualization. The primary plotting library that we are exploring in the course is [Matplotlib](http://matplotlib.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) 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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
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| "new_sheet": false, | |
| "run_control": { | |
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| } | |
| }, | |
| "source": [ | |
| "# Downloading and Prepping Data <a id=\"2\"></a>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
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| } | |
| }, | |
| "source": [ | |
| "Import Primary Modules. The first thing we'll do is import two key data analysis modules: _pandas_ and **Numpy**.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
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| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np # useful for many scientific computing in Python\n", | |
| "import pandas as pd # primary data structure library" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
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| } | |
| }, | |
| "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", | |
| "```\n", | |
| "!conda install -c anaconda xlrd --yes\n", | |
| "```\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Download the dataset and read it into a _pandas_ dataframe.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "button": 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, | |
| "new_sheet": false, | |
| "run_control": { | |
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| } | |
| }, | |
| "source": [ | |
| "Let's take a look at the first five items in our dataset.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "button": 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>Type</th>\n", | |
| " <th>Coverage</th>\n", | |
| " <th>OdName</th>\n", | |
| " <th>AREA</th>\n", | |
| " <th>AreaName</th>\n", | |
| " <th>REG</th>\n", | |
| " <th>RegName</th>\n", | |
| " <th>DEV</th>\n", | |
| " <th>DevName</th>\n", | |
| " <th>1980</th>\n", | |
| " <th>...</th>\n", | |
| " <th>2004</th>\n", | |
| " <th>2005</th>\n", | |
| " <th>2006</th>\n", | |
| " <th>2007</th>\n", | |
| " <th>2008</th>\n", | |
| " <th>2009</th>\n", | |
| " <th>2010</th>\n", | |
| " <th>2011</th>\n", | |
| " <th>2012</th>\n", | |
| " <th>2013</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Afghanistan</td>\n", | |
| " <td>935</td>\n", | |
| " <td>Asia</td>\n", | |
| " <td>5501</td>\n", | |
| " <td>Southern Asia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>16</td>\n", | |
| " <td>...</td>\n", | |
| " <td>2978</td>\n", | |
| " <td>3436</td>\n", | |
| " <td>3009</td>\n", | |
| " <td>2652</td>\n", | |
| " <td>2111</td>\n", | |
| " <td>1746</td>\n", | |
| " <td>1758</td>\n", | |
| " <td>2203</td>\n", | |
| " <td>2635</td>\n", | |
| " <td>2004</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Albania</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>925</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>1</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1450</td>\n", | |
| " <td>1223</td>\n", | |
| " <td>856</td>\n", | |
| " <td>702</td>\n", | |
| " <td>560</td>\n", | |
| " <td>716</td>\n", | |
| " <td>561</td>\n", | |
| " <td>539</td>\n", | |
| " <td>620</td>\n", | |
| " <td>603</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Algeria</td>\n", | |
| " <td>903</td>\n", | |
| " <td>Africa</td>\n", | |
| " <td>912</td>\n", | |
| " <td>Northern Africa</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>80</td>\n", | |
| " <td>...</td>\n", | |
| " <td>3616</td>\n", | |
| " <td>3626</td>\n", | |
| " <td>4807</td>\n", | |
| " <td>3623</td>\n", | |
| " <td>4005</td>\n", | |
| " <td>5393</td>\n", | |
| " <td>4752</td>\n", | |
| " <td>4325</td>\n", | |
| " <td>3774</td>\n", | |
| " <td>4331</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>American Samoa</td>\n", | |
| " <td>909</td>\n", | |
| " <td>Oceania</td>\n", | |
| " <td>957</td>\n", | |
| " <td>Polynesia</td>\n", | |
| " <td>902</td>\n", | |
| " <td>Developing regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Immigrants</td>\n", | |
| " <td>Foreigners</td>\n", | |
| " <td>Andorra</td>\n", | |
| " <td>908</td>\n", | |
| " <td>Europe</td>\n", | |
| " <td>925</td>\n", | |
| " <td>Southern Europe</td>\n", | |
| " <td>901</td>\n", | |
| " <td>Developed regions</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 43 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Type Coverage OdName AREA AreaName REG \\\n", | |
| "0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n", | |
| "1 Immigrants Foreigners Albania 908 Europe 925 \n", | |
| "2 Immigrants Foreigners Algeria 903 Africa 912 \n", | |
| "3 Immigrants Foreigners American Samoa 909 Oceania 957 \n", | |
| "4 Immigrants Foreigners Andorra 908 Europe 925 \n", | |
| "\n", | |
| " RegName DEV DevName 1980 ... 2004 2005 2006 \\\n", | |
| "0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n", | |
| "1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n", | |
| "2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n", | |
| "3 Polynesia 902 Developing regions 0 ... 0 0 1 \n", | |
| "4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n", | |
| "\n", | |
| " 2007 2008 2009 2010 2011 2012 2013 \n", | |
| "0 2652 2111 1746 1758 2203 2635 2004 \n", | |
| "1 702 560 716 561 539 620 603 \n", | |
| "2 3623 4005 5393 4752 4325 3774 4331 \n", | |
| "3 0 0 0 0 0 0 0 \n", | |
| "4 1 0 0 0 0 1 1 \n", | |
| "\n", | |
| "[5 rows x 43 columns]" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_can.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Let's find out how many entries there are in our dataset.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "button": 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, | |
| "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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "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).\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>OdName</th>\n", | |
| " <th>AreaName</th>\n", | |
| " <th>RegName</th>\n", | |
| " <th>DevName</th>\n", | |
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| " <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", | |
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| " <td>47</td>\n", | |
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| " <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", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1450</td>\n", | |
| " <td>1223</td>\n", | |
| " <td>856</td>\n", | |
| " <td>702</td>\n", | |
| " <td>560</td>\n", | |
| " <td>716</td>\n", | |
| " <td>561</td>\n", | |
| " <td>539</td>\n", | |
| " <td>620</td>\n", | |
| " <td>603</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>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": [ | |
| " 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": 6, | |
| "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, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Notice how the columns Type, Coverage, AREA, REG, and DEV got removed from the dataframe.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "#### 2. Rename some of the columns so that they make sense.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "button": 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>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", | |
| " <th>2007</th>\n", | |
| " <th>2008</th>\n", | |
| " <th>2009</th>\n", | |
| " <th>2010</th>\n", | |
| " <th>2011</th>\n", | |
| " <th>2012</th>\n", | |
| " <th>2013</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>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", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1450</td>\n", | |
| " <td>1223</td>\n", | |
| " <td>856</td>\n", | |
| " <td>702</td>\n", | |
| " <td>560</td>\n", | |
| " <td>716</td>\n", | |
| " <td>561</td>\n", | |
| " <td>539</td>\n", | |
| " <td>620</td>\n", | |
| " <td>603</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>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": 8, | |
| "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, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Notice how the column names now make much more sense, even to an outsider.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "#### 3. For consistency, ensure that all column labels of type string.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| }, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "False" | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "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, | |
| "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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "True" | |
| ] | |
| }, | |
| "execution_count": 10, | |
| "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, | |
| "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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "button": 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", | |
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| " .dataframe tbody tr th {\n", | |
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| "</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", | |
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| " <th>1980</th>\n", | |
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| " <th>1985</th>\n", | |
| " <th>1986</th>\n", | |
| " <th>...</th>\n", | |
| " <th>2004</th>\n", | |
| " <th>2005</th>\n", | |
| " <th>2006</th>\n", | |
| " <th>2007</th>\n", | |
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| " <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", | |
| " <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>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>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": 11, | |
| "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, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Notice how the country names now serve as indices.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "#### 5. Add total column.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "button": 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>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>58639</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>15699</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>69439</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>6</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>15</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 58639 \n", | |
| "Albania 716 561 539 620 603 15699 \n", | |
| "Algeria 5393 4752 4325 3774 4331 69439 \n", | |
| "American Samoa 0 0 0 0 0 6 \n", | |
| "Andorra 0 0 0 1 1 15 \n", | |
| "\n", | |
| "[5 rows x 38 columns]" | |
| ] | |
| }, | |
| "execution_count": 12, | |
| "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, | |
| "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:\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "button": 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, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "So now our dataframe has 38 columns instead of 37 columns that we had before.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "button": 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": 14, | |
| "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, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "# Visualizing Data using Matplotlib<a id=\"4\"></a>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Import `Matplotlib` and **Numpy**.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Matplotlib version: 3.3.2\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, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "# Area Plots<a id=\"6\"></a>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "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**.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "button": 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": 16, | |
| "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, | |
| "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`. \n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": { | |
| "button": 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": {}, | |
| "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, | |
| "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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [], | |
| "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, | |
| "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", | |
| "\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", | |
| "```\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [], | |
| "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, | |
| "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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": { | |
| "button": 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>Palau</th>\n", | |
| " <th>Western Sahara</th>\n", | |
| " <th>Marshall Islands</th>\n", | |
| " <th>New Caledonia</th>\n", | |
| " <th>San Marino</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1980</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1981</th>\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>1982</th>\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>1983</th>\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>1984</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "Country Palau Western Sahara Marshall Islands New Caledonia San Marino\n", | |
| "1980 0 0 0 0 1\n", | |
| "1981 0 0 0 0 0\n", | |
| "1982 0 0 0 0 0\n", | |
| "1983 0 0 0 0 0\n", | |
| "1984 0 0 0 0 0" | |
| ] | |
| }, | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "### type your answer here\n", | |
| "df_can.sort_values('Total', ascending = True, inplace=True)\n", | |
| "\n", | |
| "df_least5 = df_can.head(5)\n", | |
| "\n", | |
| "#transpose \n", | |
| "df_least5 = df_least5[years].transpose()\n", | |
| "df_least5.head()\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Text(0.5, 0, 'Years')" | |
| ] | |
| }, | |
| "execution_count": 34, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x720 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "#plot\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=.45, stacked=True, figsize=(20,10))\n", | |
| "\n", | |
| "plt.title(\"Immigerent Trend of bottomm 5\")\n", | |
| "plt.ylabel('No. of Immigrents')\n", | |
| "plt.xlabel('Years')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Double-click **here** for the solution.\n", | |
| "\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", | |
| "-->\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Text(0, 0.5, 'No. of Immigration')" | |
| ] | |
| }, | |
| "execution_count": 37, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x720 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "### type your answer here\n", | |
| "ax = df_least5.plot(kind='area',alpha=0.55, stacked=False, figsize=(20,10))\n", | |
| "\n", | |
| "ax.set_title('Immigration Trend of 5 Countries with Least Contribution to Immigration')\n", | |
| "ax.set_xlabel('Years')\n", | |
| "ax.set_ylabel('No. of Immigration')\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Double-click **here** for the solution.\n", | |
| "\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", | |
| "-->\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "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?\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "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:\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 38, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Country\n", | |
| "Palau 0\n", | |
| "Western Sahara 0\n", | |
| "Marshall Islands 0\n", | |
| "New Caledonia 2\n", | |
| "San Marino 0\n", | |
| "Name: 2013, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 38, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# let's quickly view the 2013 data\n", | |
| "df_can['2013'].head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 39, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "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, | |
| "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", | |
| "\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>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "We can easily graph this distribution by passing `kind=hist` to `plot()`.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 40, | |
| "metadata": { | |
| "button": 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": {}, | |
| "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, | |
| "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:\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 41, | |
| "metadata": { | |
| "button": 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": {}, | |
| "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, | |
| "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](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
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| "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?\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 42, | |
| "metadata": { | |
| "button": 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>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", | |
| " <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>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": 42, | |
| "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": 43, | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<AxesSubplot:ylabel='Frequency'>" | |
| ] | |
| }, | |
| "execution_count": 43, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# generate histogram\n", | |
| "df_can.loc[['Denmark', 'Norway', 'Sweden'], years].plot.hist()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "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" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 44, | |
| "metadata": { | |
| "button": 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": 44, | |
| "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": 45, | |
| "metadata": { | |
| "button": 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": {}, | |
| "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, | |
| "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", | |
| "\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\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 46, | |
| "metadata": { | |
| "button": 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": {}, | |
| "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, | |
| "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", | |
| "\n", | |
| "```python\n", | |
| "import matplotlib\n", | |
| "for name, hex in matplotlib.colors.cnames.items():\n", | |
| " print(name, hex)\n", | |
| "```\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 47, | |
| "metadata": { | |
| "button": 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": {}, | |
| "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, | |
| "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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 53, | |
| "metadata": { | |
| "button": 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": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "### type your answer here\n", | |
| "df_cof = df_can.loc[[\"Greece\", \"Albania\",\"Bulgaria\"], years].transpose()\n", | |
| "\n", | |
| "# let's get the x-tick values\n", | |
| "count, bin_edges = np.histogram(df_cof, 15)\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", | |
| "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()\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Double-click **here** for the solution.\n", | |
| "\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", | |
| "-->\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "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\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "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. \n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 54, | |
| "metadata": { | |
| "button": 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": 54, | |
| "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": 55, | |
| "metadata": { | |
| "button": 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": {}, | |
| "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, | |
| "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", | |
| "\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](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 56, | |
| "metadata": { | |
| "button": 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": {}, | |
| "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, | |
| "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", | |
| "\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’]\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 57, | |
| "metadata": { | |
| "button": 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": {}, | |
| "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, | |
| "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", | |
| "**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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Step 1: Get the data pertaining to the top 15 countries.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": true, | |
| "jupyter": { | |
| "outputs_hidden": 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, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Double-click **here** for the solution.\n", | |
| "\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", | |
| "-->\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Step 2: Plot data:\n", | |
| "\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.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "button": false, | |
| "collapsed": true, | |
| "jupyter": { | |
| "outputs_hidden": 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, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "Double-click **here** for the solution.\n", | |
| "\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", | |
| "-->\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "button": false, | |
| "new_sheet": false, | |
| "run_control": { | |
| "read_only": false | |
| } | |
| }, | |
| "source": [ | |
| "### Thank you for completing this lab!\n", | |
| "\n", | |
| "## Author\n", | |
| "\n", | |
| "<a href=\"https://www.linkedin.com/in/aklson/\" target=\"_blank\">Alex Aklson</a>\n", | |
| "\n", | |
| "### Other Contributors\n", | |
| "\n", | |
| "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n", | |
| "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n", | |
| "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n", | |
| "\n", | |
| "## Change Log\n", | |
| "\n", | |
| "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n", | |
| "| ----------------- | ------- | ---------- | ---------------------------------- |\n", | |
| "| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n", | |
| "| | | | |\n", | |
| "| | | | |\n", | |
| "\n", | |
| "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python", | |
| "language": "python", | |
| "name": "conda-env-python-py" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.11" | |
| }, | |
| "widgets": { | |
| "state": {}, | |
| "version": "1.1.2" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 4 | |
| } |
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