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kshirsagarsiddharth/DV0101EN-2-3-1-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py-v2.0.ipynb

Created December 18, 2019 14:54
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DV0101EN-2-3-1-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py-v2.0.ipynb
{
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"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Pie Charts, Box Plots, Scatter Plots, and Bubble Plots</font></h1>"
]
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"source": [
"## Introduction\n",
"\n",
"In this lab session, we continue exploring the Matplotlib library. More specificatlly, we will learn how to create pie charts, box plots, scatter plots, and bubble charts."
]
},
{
"cell_type": "markdown",
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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 *p*andas](#0)<br>\n",
"2. [Downloading and Prepping Data](#2)<br>\n",
"3. [Visualizing Data using Matplotlib](#4) <br>\n",
"4. [Pie Charts](#6) <br>\n",
"5. [Box Plots](#8) <br>\n",
"6. [Scatter Plots](#10) <br>\n",
"7. [Bubble Plots](#12) <br> \n",
"</div>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
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"source": [
"# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
"\n",
"Toolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).\n",
"\n",
"Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website.\n",
"\n",
"The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data."
]
},
{
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},
"source": [
"# Downloading and Prepping Data <a id=\"2\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
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},
"source": [
"Import primary modules."
]
},
{
"cell_type": "code",
"execution_count": 1,
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"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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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",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Solving environment: done\n",
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 4.5.11\n",
" latest version: 4.8.0\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base -c defaults conda\n",
"\n",
"\n",
"\n",
"# All requested packages already installed.\n",
"\n"
]
}
],
"source": [
"!conda install -c anaconda xlrd --yes"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Download the dataset and read it into a *pandas* dataframe."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data downloaded and read into a dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2\n",
" )\n",
"\n",
"print('Data downloaded and read into a dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
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},
"source": [
"Let's take a look at the first five items in our dataset."
]
},
{
"cell_type": "code",
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's find out how many entries there are in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(195, 43)\n"
]
}
],
"source": [
"# print the dimensions of the dataframe\n",
"print(df_can.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
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},
"source": [
"Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* for a detailed description of this preprocessing."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 38)\n"
]
}
],
"source": [
"# clean up the dataset to remove unnecessary columns (eg. REG) \n",
"df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
"\n",
"# let's rename the columns so that they make sense\n",
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
"\n",
"# for sake of consistency, let's also make all column labels of type string\n",
"df_can.columns = list(map(str, df_can.columns))\n",
"\n",
"# set the country name as index - useful for quickly looking up countries using .loc method\n",
"df_can.set_index('Country', inplace=True)\n",
"\n",
"# add total column\n",
"df_can['Total'] = df_can.sum(axis=1)\n",
"\n",
"# years that we will be using in this lesson - useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"print('data dimensions:', df_can.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Visualizing Data using Matplotlib<a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Import `Matplotlib`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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"new_sheet": false,
"run_control": {
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matplotlib version: 3.1.1\n"
]
},
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
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" <th>...</th>\n",
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" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
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" <tr>\n",
" <th>Afghanistan</th>\n",
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" <td>39</td>\n",
" <td>47</td>\n",
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" <td>496</td>\n",
" <td>...</td>\n",
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" <td>1746</td>\n",
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" <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",
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" <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",
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" <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]"
]
},
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"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"\n",
"mpl.style.use('ggplot') # optional: for ggplot-like style\n",
"\n",
"# check for latest version of Matplotlib\n",
"print('Matplotlib version: ', mpl.__version__) # >= 2.0.0\n",
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"source": [
"# Pie Charts <a id=\"6\"></a>\n",
"\n",
"A `pie chart` is a circualr graphic that displays numeric proportions by dividing a circle (or pie) into proportional slices. You are most likely already familiar with pie charts as it is widely used in business and media. We can create pie charts in Matplotlib by passing in the `kind=pie` keyword.\n",
"\n",
"Let's use a pie chart to explore the proportion (percentage) of new immigrants grouped by continents for the entire time period from 1980 to 2013. "
]
},
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"source": [
"Step 1: Gather data. \n",
"\n",
"We will use *pandas* `groupby` method to summarize the immigration data by `Continent`. The general process of `groupby` involves the following steps:\n",
"\n",
"1. **Split:** Splitting the data into groups based on some criteria.\n",
"2. **Apply:** Applying a function to each group independently:\n",
" .sum()\n",
" .count()\n",
" .mean() \n",
" .std() \n",
" .aggregate()\n",
" .apply()\n",
" .etc..\n",
"3. **Combine:** Combining the results into a data structure."
]
},
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"source": [
"<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">"
]
},
{
"cell_type": "code",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>1986</th>\n",
" <th>1987</th>\n",
" <th>1988</th>\n",
" <th>1989</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Continent</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Africa</th>\n",
" <td>3951</td>\n",
" <td>4363</td>\n",
" <td>3819</td>\n",
" <td>2671</td>\n",
" <td>2639</td>\n",
" <td>2650</td>\n",
" <td>3782</td>\n",
" <td>7494</td>\n",
" <td>7552</td>\n",
" <td>9894</td>\n",
" <td>...</td>\n",
" <td>27523</td>\n",
" <td>29188</td>\n",
" <td>28284</td>\n",
" <td>29890</td>\n",
" <td>34534</td>\n",
" <td>40892</td>\n",
" <td>35441</td>\n",
" <td>38083</td>\n",
" <td>38543</td>\n",
" <td>618948</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia</th>\n",
" <td>31025</td>\n",
" <td>34314</td>\n",
" <td>30214</td>\n",
" <td>24696</td>\n",
" <td>27274</td>\n",
" <td>23850</td>\n",
" <td>28739</td>\n",
" <td>43203</td>\n",
" <td>47454</td>\n",
" <td>60256</td>\n",
" <td>...</td>\n",
" <td>159253</td>\n",
" <td>149054</td>\n",
" <td>133459</td>\n",
" <td>139894</td>\n",
" <td>141434</td>\n",
" <td>163845</td>\n",
" <td>146894</td>\n",
" <td>152218</td>\n",
" <td>155075</td>\n",
" <td>3317794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Europe</th>\n",
" <td>39760</td>\n",
" <td>44802</td>\n",
" <td>42720</td>\n",
" <td>24638</td>\n",
" <td>22287</td>\n",
" <td>20844</td>\n",
" <td>24370</td>\n",
" <td>46698</td>\n",
" <td>54726</td>\n",
" <td>60893</td>\n",
" <td>...</td>\n",
" <td>35955</td>\n",
" <td>33053</td>\n",
" <td>33495</td>\n",
" <td>34692</td>\n",
" <td>35078</td>\n",
" <td>33425</td>\n",
" <td>26778</td>\n",
" <td>29177</td>\n",
" <td>28691</td>\n",
" <td>1410947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Latin America and the Caribbean</th>\n",
" <td>13081</td>\n",
" <td>15215</td>\n",
" <td>16769</td>\n",
" <td>15427</td>\n",
" <td>13678</td>\n",
" <td>15171</td>\n",
" <td>21179</td>\n",
" <td>28471</td>\n",
" <td>21924</td>\n",
" <td>25060</td>\n",
" <td>...</td>\n",
" <td>24747</td>\n",
" <td>24676</td>\n",
" <td>26011</td>\n",
" <td>26547</td>\n",
" <td>26867</td>\n",
" <td>28818</td>\n",
" <td>27856</td>\n",
" <td>27173</td>\n",
" <td>24950</td>\n",
" <td>765148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Northern America</th>\n",
" <td>9378</td>\n",
" <td>10030</td>\n",
" <td>9074</td>\n",
" <td>7100</td>\n",
" <td>6661</td>\n",
" <td>6543</td>\n",
" <td>7074</td>\n",
" <td>7705</td>\n",
" <td>6469</td>\n",
" <td>6790</td>\n",
" <td>...</td>\n",
" <td>8394</td>\n",
" <td>9613</td>\n",
" <td>9463</td>\n",
" <td>10190</td>\n",
" <td>8995</td>\n",
" <td>8142</td>\n",
" <td>7677</td>\n",
" <td>7892</td>\n",
" <td>8503</td>\n",
" <td>241142</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 1980 1981 1982 1983 1984 1985 \\\n",
"Continent \n",
"Africa 3951 4363 3819 2671 2639 2650 \n",
"Asia 31025 34314 30214 24696 27274 23850 \n",
"Europe 39760 44802 42720 24638 22287 20844 \n",
"Latin America and the Caribbean 13081 15215 16769 15427 13678 15171 \n",
"Northern America 9378 10030 9074 7100 6661 6543 \n",
"\n",
" 1986 1987 1988 1989 ... 2005 \\\n",
"Continent ... \n",
"Africa 3782 7494 7552 9894 ... 27523 \n",
"Asia 28739 43203 47454 60256 ... 159253 \n",
"Europe 24370 46698 54726 60893 ... 35955 \n",
"Latin America and the Caribbean 21179 28471 21924 25060 ... 24747 \n",
"Northern America 7074 7705 6469 6790 ... 8394 \n",
"\n",
" 2006 2007 2008 2009 2010 \\\n",
"Continent \n",
"Africa 29188 28284 29890 34534 40892 \n",
"Asia 149054 133459 139894 141434 163845 \n",
"Europe 33053 33495 34692 35078 33425 \n",
"Latin America and the Caribbean 24676 26011 26547 26867 28818 \n",
"Northern America 9613 9463 10190 8995 8142 \n",
"\n",
" 2011 2012 2013 Total \n",
"Continent \n",
"Africa 35441 38083 38543 618948 \n",
"Asia 146894 152218 155075 3317794 \n",
"Europe 26778 29177 28691 1410947 \n",
"Latin America and the Caribbean 27856 27173 24950 765148 \n",
"Northern America 7677 7892 8503 241142 \n",
"\n",
"[5 rows x 35 columns]"
]
},
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}
],
"source": [
"# group countries by continents and apply sum() function \n",
"df_continents = df_can.groupby('Continent', axis=0).sum()\n",
"\n",
"# note: the output of the groupby method is a `groupby' object. \n",
"# we can not use it further until we apply a function (eg .sum())\n",
"print(type(df_can.groupby('Continent', axis=0)))\n",
"\n",
"df_continents.head()"
]
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"Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
"- `autopct` - is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
"- `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
"- `shadow` - Draws a shadow beneath the pie (to give a 3D feel)."
]
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{
"data": {
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\n",
"text/plain": [
"<Figure size 360x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# autopct create %, start angle represent starting point\n",
"df_continents['Total'].plot(kind='pie',\n",
" figsize=(5, 6),\n",
" autopct='%1.1f%%', # add in percentages\n",
" startangle=90, # start angle 90° (Africa)\n",
" shadow=True, # add shadow \n",
" )\n",
"\n",
"plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
"plt.axis('equal') # Sets the pie chart to look like a circle.\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
"\n",
"* Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
"* Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
"* Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
"* **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
"explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
"\n",
"df_continents['Total'].plot(kind='pie',\n",
" figsize=(15, 6),\n",
" autopct='%1.1f%%', \n",
" startangle=90, \n",
" shadow=True, \n",
" labels=None, # turn off labels on pie chart\n",
" pctdistance=1.12, # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
" colors=colors_list, # add custom colors\n",
" explode=explode_list # 'explode' lowest 3 continents\n",
" )\n",
"\n",
"# scale the title up by 12% to match pctdistance\n",
"plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
"\n",
"plt.axis('equal') \n",
"\n",
"# add legend\n",
"plt.legend(labels=df_continents.index, loc='upper left') \n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
"\n",
"**Note**: You might need to play with the explore values in order to fix any overlapping slice values."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"colors_list = ['gold','yellowgreen','lightcoral','lightskyblue','lightgreen','pink']\n",
"explode_list = [0.1,0,0,0,0.1,0.1]\n",
"df_continents['2013'].plot(kind='pie',\n",
" figsize = (15,6),\n",
" autopct = '%1.1f%%',\n",
" startangle = 90,\n",
" shadow = True,\n",
" labels = None,\n",
" pctdistance = 1.12,\n",
" colors = colors_list,\n",
" explode = explode_list)\n",
"plt.axis('equal')\n",
"plt.legend(labels = df_continents.index,loc='upper left')\n",
"plt.show()\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
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"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"explode_list = [0.1, 0, 0, 0, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
"-->\n",
"\n",
"<!--\n",
"df_continents['2013'].plot(kind='pie',\n",
" figsize=(15, 6),\n",
" autopct='%1.1f%%', \n",
" startangle=90, \n",
" shadow=True, \n",
" labels=None, # turn off labels on pie chart\n",
" pctdistance=1.12, # the ratio between the pie center and start of text label\n",
" explode=explode_list # 'explode' lowest 3 continents\n",
" )\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # scale the title up by 12% to match pctdistance\n",
"plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
"plt.axis('equal') \n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # add legend\n",
"plt.legend(labels=df_continents.index, loc='upper left') \n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # show plot\n",
"plt.show()\n",
"-->"
]
},
{
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"source": [
"# Box Plots <a id=\"8\"></a>\n",
"\n",
"A `box plot` is a way of statistically representing the *distribution* of the data through five main dimensions: \n",
"\n",
"- **Minimun:** Smallest number in the dataset.\n",
"- **First quartile:** Middle number between the `minimum` and the `median`.\n",
"- **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
"- **Third quartile:** Middle number between `median` and `maximum`.\n",
"- **Maximum:** Highest number in the dataset."
]
},
{
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"source": [
"<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/boxplot_complete.png\" width=440, align=\"center\">"
]
},
{
"cell_type": "markdown",
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"source": [
"To make a `box plot`, we can use `kind=box` in `plot` method invoked on a *pandas* series or dataframe.\n",
"\n",
"Let's plot the box plot for the Japanese immigrants between 1980 - 2013."
]
},
{
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"source": [
"Step 1: Get the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Country</th>\n",
" <th>Japan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>246</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country Japan\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# to get a dataframe, place extra square brackets around 'Japan'.\n",
"df_japan = df_can.loc[['Japan'], years].transpose()\n",
"df_japan.head()"
]
},
{
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"source": [
"Step 2: Plot by passing in `kind='box'`."
]
},
{
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"execution_count": 12,
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"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_japan.plot(kind='box', figsize=(8, 6))\n",
"\n",
"plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
"plt.ylabel('Number of Immigrants')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can immediately make a few key observations from the plot above:\n",
"1. The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and median number of immigrants is around 900 (median).\n",
"2. 25% of the years for period 1980 - 2013 had an annual immigrant count of ~500 or fewer (First quartile).\n",
"2. 75% of the years for period 1980 - 2013 had an annual immigrant count of ~1100 or fewer (Third quartile).\n",
"\n",
"We can view the actual numbers by calling the `describe()` method on the dataframe."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Country</th>\n",
" <th>Japan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>34.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>814.911765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>337.219771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>198.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>529.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>902.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1079.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1284.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country Japan\n",
"count 34.000000\n",
"mean 814.911765\n",
"std 337.219771\n",
"min 198.000000\n",
"25% 529.000000\n",
"50% 902.000000\n",
"75% 1079.000000\n",
"max 1284.000000"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_japan.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's analyize these two countries further using box plots.\n",
"\n",
"**Question:** Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the dataset for China and India and call the dataframe **df_CI**."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Country</th>\n",
" <th>China</th>\n",
" <th>India</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>5123</td>\n",
" <td>8880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>6682</td>\n",
" <td>8670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>3308</td>\n",
" <td>8147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>1863</td>\n",
" <td>7338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>1527</td>\n",
" <td>5704</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country China India\n",
"1980 5123 8880\n",
"1981 6682 8670\n",
"1982 3308 8147\n",
"1983 1863 7338\n",
"1984 1527 5704"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI = df_can.loc[['China','India'],years].transpose()\n",
"df_CI.head()\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
"df_CI.head()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's view the percentages associated with both countries using the `describe()` method."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Country</th>\n",
" <th>China</th>\n",
" <th>India</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>34.000000</td>\n",
" <td>34.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>19410.647059</td>\n",
" <td>20350.117647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>13568.230790</td>\n",
" <td>10007.342579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1527.000000</td>\n",
" <td>4211.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5512.750000</td>\n",
" <td>10637.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>19945.000000</td>\n",
" <td>20235.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>31568.500000</td>\n",
" <td>28699.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>42584.000000</td>\n",
" <td>36210.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country China India\n",
"count 34.000000 34.000000\n",
"mean 19410.647059 20350.117647\n",
"std 13568.230790 10007.342579\n",
"min 1527.000000 4211.000000\n",
"25% 5512.750000 10637.750000\n",
"50% 19945.000000 20235.000000\n",
"75% 31568.500000 28699.500000\n",
"max 42584.000000 36210.000000"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_CI.describe()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.describe()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Plot data."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_CI.plot(kind='box',figsize=(8,6))\n",
"plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
"plt.ylabel('Number of Immigrants')\n",
"\n",
"plt.show()\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_CI.plot(kind='box', figsize=(10, 7))\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
"plt.xlabel('Number of Immigrants')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can observe that, while both countries have around the same median immigrant population (~20,000), China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to *False*. You can also specify a different color in case you are not a big fan of the default red color."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# horizontal box plots\n",
"df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\n",
"\n",
"plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
"plt.xlabel('Number of Immigrants')\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Subplots**\n",
"\n",
"Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
"\n",
"To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**. \n",
"\n",
"Typical syntax is : <br>\n",
"```python\n",
" fig = plt.figure() # create figure\n",
" ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
"```\n",
"Where\n",
"- `nrows` and `ncols` are used to notionally split the figure into (`nrows` \\* `ncols`) sub-axes, \n",
"- `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` * `ncols` as shown below.\n",
"\n",
"<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig5Subplots_V2.png\" width=500 align=\"center\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure() # create figure\n",
"\n",
"ax0 = fig.add_subplot(1, 2, 1) # add subplot 1 (1 row, 2 columns, first plot)\n",
"ax1 = fig.add_subplot(1, 2, 2) # add subplot 2 (1 row, 2 columns, second plot). See tip below**\n",
"\n",
"# Subplot 1: Box plot\n",
"df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 6), ax=ax0) # add to subplot 1\n",
"ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')\n",
"ax0.set_xlabel('Number of Immigrants')\n",
"ax0.set_ylabel('Countries')\n",
"\n",
"# Subplot 2: Line plot\n",
"df_CI.plot(kind='line', figsize=(20, 6), ax=ax1) # add to subplot 2\n",
"ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')\n",
"ax1.set_ylabel('Number of Immigrants')\n",
"ax1.set_xlabel('Years')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
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"source": [
"** * Tip regarding subplot convention **\n",
"\n",
"In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that the a 3 digit number can be given instead, where the hundreds represent `nrows`, the tens represent `ncols` and the units represent `plot_number`. For instance,\n",
"```python\n",
" subplot(211) == subplot(2, 1, 1) \n",
"```\n",
"produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned)."
]
},
{
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"source": [
"Let's try something a little more advanced. \n",
"\n",
"Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\n",
"\n",
"**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the *decades* `1980s`, `1990s`, and `2000s`."
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
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"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>India</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>8880</td>\n",
" <td>8670</td>\n",
" <td>8147</td>\n",
" <td>7338</td>\n",
" <td>5704</td>\n",
" <td>4211</td>\n",
" <td>7150</td>\n",
" <td>...</td>\n",
" <td>36210</td>\n",
" <td>33848</td>\n",
" <td>28742</td>\n",
" <td>28261</td>\n",
" <td>29456</td>\n",
" <td>34235</td>\n",
" <td>27509</td>\n",
" <td>30933</td>\n",
" <td>33087</td>\n",
" <td>691904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>5123</td>\n",
" <td>6682</td>\n",
" <td>3308</td>\n",
" <td>1863</td>\n",
" <td>1527</td>\n",
" <td>1816</td>\n",
" <td>1960</td>\n",
" <td>...</td>\n",
" <td>42584</td>\n",
" <td>33518</td>\n",
" <td>27642</td>\n",
" <td>30037</td>\n",
" <td>29622</td>\n",
" <td>30391</td>\n",
" <td>28502</td>\n",
" <td>33024</td>\n",
" <td>34129</td>\n",
" <td>659962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
" <td>Europe</td>\n",
" <td>Northern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>22045</td>\n",
" <td>24796</td>\n",
" <td>20620</td>\n",
" <td>10015</td>\n",
" <td>10170</td>\n",
" <td>9564</td>\n",
" <td>9470</td>\n",
" <td>...</td>\n",
" <td>7258</td>\n",
" <td>7140</td>\n",
" <td>8216</td>\n",
" <td>8979</td>\n",
" <td>8876</td>\n",
" <td>8724</td>\n",
" <td>6204</td>\n",
" <td>6195</td>\n",
" <td>5827</td>\n",
" <td>551500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Philippines</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>6051</td>\n",
" <td>5921</td>\n",
" <td>5249</td>\n",
" <td>4562</td>\n",
" <td>3801</td>\n",
" <td>3150</td>\n",
" <td>4166</td>\n",
" <td>...</td>\n",
" <td>18139</td>\n",
" <td>18400</td>\n",
" <td>19837</td>\n",
" <td>24887</td>\n",
" <td>28573</td>\n",
" <td>38617</td>\n",
" <td>36765</td>\n",
" <td>34315</td>\n",
" <td>29544</td>\n",
" <td>511391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>978</td>\n",
" <td>972</td>\n",
" <td>1201</td>\n",
" <td>900</td>\n",
" <td>668</td>\n",
" <td>514</td>\n",
" <td>691</td>\n",
" <td>...</td>\n",
" <td>14314</td>\n",
" <td>13127</td>\n",
" <td>10124</td>\n",
" <td>8994</td>\n",
" <td>7217</td>\n",
" <td>6811</td>\n",
" <td>7468</td>\n",
" <td>11227</td>\n",
" <td>12603</td>\n",
" <td>241600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United States of America</th>\n",
" <td>Northern America</td>\n",
" <td>Northern America</td>\n",
" <td>Developed regions</td>\n",
" <td>9378</td>\n",
" <td>10030</td>\n",
" <td>9074</td>\n",
" <td>7100</td>\n",
" <td>6661</td>\n",
" <td>6543</td>\n",
" <td>7074</td>\n",
" <td>...</td>\n",
" <td>8394</td>\n",
" <td>9613</td>\n",
" <td>9463</td>\n",
" <td>10190</td>\n",
" <td>8995</td>\n",
" <td>8142</td>\n",
" <td>7676</td>\n",
" <td>7891</td>\n",
" <td>8501</td>\n",
" <td>241122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1172</td>\n",
" <td>1429</td>\n",
" <td>1822</td>\n",
" <td>1592</td>\n",
" <td>1977</td>\n",
" <td>1648</td>\n",
" <td>1794</td>\n",
" <td>...</td>\n",
" <td>5837</td>\n",
" <td>7480</td>\n",
" <td>6974</td>\n",
" <td>6475</td>\n",
" <td>6580</td>\n",
" <td>7477</td>\n",
" <td>7479</td>\n",
" <td>7534</td>\n",
" <td>11291</td>\n",
" <td>175923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>185</td>\n",
" <td>371</td>\n",
" <td>290</td>\n",
" <td>197</td>\n",
" <td>1086</td>\n",
" <td>845</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>4930</td>\n",
" <td>4714</td>\n",
" <td>4123</td>\n",
" <td>4756</td>\n",
" <td>4547</td>\n",
" <td>4422</td>\n",
" <td>3309</td>\n",
" <td>3338</td>\n",
" <td>2394</td>\n",
" <td>148358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Republic of Korea</th>\n",
" <td>Asia</td>\n",
" <td>Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1011</td>\n",
" <td>1456</td>\n",
" <td>1572</td>\n",
" <td>1081</td>\n",
" <td>847</td>\n",
" <td>962</td>\n",
" <td>1208</td>\n",
" <td>...</td>\n",
" <td>5832</td>\n",
" <td>6215</td>\n",
" <td>5920</td>\n",
" <td>7294</td>\n",
" <td>5874</td>\n",
" <td>5537</td>\n",
" <td>4588</td>\n",
" <td>5316</td>\n",
" <td>4509</td>\n",
" <td>142581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Poland</th>\n",
" <td>Europe</td>\n",
" <td>Eastern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>863</td>\n",
" <td>2930</td>\n",
" <td>5881</td>\n",
" <td>4546</td>\n",
" <td>3588</td>\n",
" <td>2819</td>\n",
" <td>4808</td>\n",
" <td>...</td>\n",
" <td>1405</td>\n",
" <td>1263</td>\n",
" <td>1235</td>\n",
" <td>1267</td>\n",
" <td>1013</td>\n",
" <td>795</td>\n",
" <td>720</td>\n",
" <td>779</td>\n",
" <td>852</td>\n",
" <td>139241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>Asia</td>\n",
" <td>Western Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1409</td>\n",
" <td>1119</td>\n",
" <td>1159</td>\n",
" <td>789</td>\n",
" <td>1253</td>\n",
" <td>1683</td>\n",
" <td>2576</td>\n",
" <td>...</td>\n",
" <td>3709</td>\n",
" <td>3802</td>\n",
" <td>3467</td>\n",
" <td>3566</td>\n",
" <td>3077</td>\n",
" <td>3432</td>\n",
" <td>3072</td>\n",
" <td>1614</td>\n",
" <td>2172</td>\n",
" <td>115359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>France</th>\n",
" <td>Europe</td>\n",
" <td>Western Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>1729</td>\n",
" <td>2027</td>\n",
" <td>2219</td>\n",
" <td>1490</td>\n",
" <td>1169</td>\n",
" <td>1177</td>\n",
" <td>1298</td>\n",
" <td>...</td>\n",
" <td>4429</td>\n",
" <td>4002</td>\n",
" <td>4290</td>\n",
" <td>4532</td>\n",
" <td>5051</td>\n",
" <td>4646</td>\n",
" <td>4080</td>\n",
" <td>6280</td>\n",
" <td>5623</td>\n",
" <td>109091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jamaica</th>\n",
" <td>Latin America and the Caribbean</td>\n",
" <td>Caribbean</td>\n",
" <td>Developing regions</td>\n",
" <td>3198</td>\n",
" <td>2634</td>\n",
" <td>2661</td>\n",
" <td>2455</td>\n",
" <td>2508</td>\n",
" <td>2938</td>\n",
" <td>4649</td>\n",
" <td>...</td>\n",
" <td>1945</td>\n",
" <td>1722</td>\n",
" <td>2141</td>\n",
" <td>2334</td>\n",
" <td>2456</td>\n",
" <td>2321</td>\n",
" <td>2059</td>\n",
" <td>2182</td>\n",
" <td>2479</td>\n",
" <td>106431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Viet Nam</th>\n",
" <td>Asia</td>\n",
" <td>South-Eastern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>1191</td>\n",
" <td>1829</td>\n",
" <td>2162</td>\n",
" <td>3404</td>\n",
" <td>7583</td>\n",
" <td>5907</td>\n",
" <td>2741</td>\n",
" <td>...</td>\n",
" <td>1852</td>\n",
" <td>3153</td>\n",
" <td>2574</td>\n",
" <td>1784</td>\n",
" <td>2171</td>\n",
" <td>1942</td>\n",
" <td>1723</td>\n",
" <td>1731</td>\n",
" <td>2112</td>\n",
" <td>97146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Romania</th>\n",
" <td>Europe</td>\n",
" <td>Eastern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>375</td>\n",
" <td>438</td>\n",
" <td>583</td>\n",
" <td>543</td>\n",
" <td>524</td>\n",
" <td>604</td>\n",
" <td>656</td>\n",
" <td>...</td>\n",
" <td>5048</td>\n",
" <td>4468</td>\n",
" <td>3834</td>\n",
" <td>2837</td>\n",
" <td>2076</td>\n",
" <td>1922</td>\n",
" <td>1776</td>\n",
" <td>1588</td>\n",
" <td>1512</td>\n",
" <td>93585</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Continent \\\n",
"Country \n",
"India Asia \n",
"China Asia \n",
"United Kingdom of Great Britain and Northern Ir... Europe \n",
"Philippines Asia \n",
"Pakistan Asia \n",
"United States of America Northern America \n",
"Iran (Islamic Republic of) Asia \n",
"Sri Lanka Asia \n",
"Republic of Korea Asia \n",
"Poland Europe \n",
"Lebanon Asia \n",
"France Europe \n",
"Jamaica Latin America and the Caribbean \n",
"Viet Nam Asia \n",
"Romania Europe \n",
"\n",
" Region \\\n",
"Country \n",
"India Southern Asia \n",
"China Eastern Asia \n",
"United Kingdom of Great Britain and Northern Ir... Northern Europe \n",
"Philippines South-Eastern Asia \n",
"Pakistan Southern Asia \n",
"United States of America Northern America \n",
"Iran (Islamic Republic of) Southern Asia \n",
"Sri Lanka Southern Asia \n",
"Republic of Korea Eastern Asia \n",
"Poland Eastern Europe \n",
"Lebanon Western Asia \n",
"France Western Europe \n",
"Jamaica Caribbean \n",
"Viet Nam South-Eastern Asia \n",
"Romania Eastern Europe \n",
"\n",
" DevName 1980 \\\n",
"Country \n",
"India Developing regions 8880 \n",
"China Developing regions 5123 \n",
"United Kingdom of Great Britain and Northern Ir... Developed regions 22045 \n",
"Philippines Developing regions 6051 \n",
"Pakistan Developing regions 978 \n",
"United States of America Developed regions 9378 \n",
"Iran (Islamic Republic of) Developing regions 1172 \n",
"Sri Lanka Developing regions 185 \n",
"Republic of Korea Developing regions 1011 \n",
"Poland Developed regions 863 \n",
"Lebanon Developing regions 1409 \n",
"France Developed regions 1729 \n",
"Jamaica Developing regions 3198 \n",
"Viet Nam Developing regions 1191 \n",
"Romania Developed regions 375 \n",
"\n",
" 1981 1982 1983 \\\n",
"Country \n",
"India 8670 8147 7338 \n",
"China 6682 3308 1863 \n",
"United Kingdom of Great Britain and Northern Ir... 24796 20620 10015 \n",
"Philippines 5921 5249 4562 \n",
"Pakistan 972 1201 900 \n",
"United States of America 10030 9074 7100 \n",
"Iran (Islamic Republic of) 1429 1822 1592 \n",
"Sri Lanka 371 290 197 \n",
"Republic of Korea 1456 1572 1081 \n",
"Poland 2930 5881 4546 \n",
"Lebanon 1119 1159 789 \n",
"France 2027 2219 1490 \n",
"Jamaica 2634 2661 2455 \n",
"Viet Nam 1829 2162 3404 \n",
"Romania 438 583 543 \n",
"\n",
" 1984 1985 1986 ... \\\n",
"Country ... \n",
"India 5704 4211 7150 ... \n",
"China 1527 1816 1960 ... \n",
"United Kingdom of Great Britain and Northern Ir... 10170 9564 9470 ... \n",
"Philippines 3801 3150 4166 ... \n",
"Pakistan 668 514 691 ... \n",
"United States of America 6661 6543 7074 ... \n",
"Iran (Islamic Republic of) 1977 1648 1794 ... \n",
"Sri Lanka 1086 845 1838 ... \n",
"Republic of Korea 847 962 1208 ... \n",
"Poland 3588 2819 4808 ... \n",
"Lebanon 1253 1683 2576 ... \n",
"France 1169 1177 1298 ... \n",
"Jamaica 2508 2938 4649 ... \n",
"Viet Nam 7583 5907 2741 ... \n",
"Romania 524 604 656 ... \n",
"\n",
" 2005 2006 2007 \\\n",
"Country \n",
"India 36210 33848 28742 \n",
"China 42584 33518 27642 \n",
"United Kingdom of Great Britain and Northern Ir... 7258 7140 8216 \n",
"Philippines 18139 18400 19837 \n",
"Pakistan 14314 13127 10124 \n",
"United States of America 8394 9613 9463 \n",
"Iran (Islamic Republic of) 5837 7480 6974 \n",
"Sri Lanka 4930 4714 4123 \n",
"Republic of Korea 5832 6215 5920 \n",
"Poland 1405 1263 1235 \n",
"Lebanon 3709 3802 3467 \n",
"France 4429 4002 4290 \n",
"Jamaica 1945 1722 2141 \n",
"Viet Nam 1852 3153 2574 \n",
"Romania 5048 4468 3834 \n",
"\n",
" 2008 2009 2010 \\\n",
"Country \n",
"India 28261 29456 34235 \n",
"China 30037 29622 30391 \n",
"United Kingdom of Great Britain and Northern Ir... 8979 8876 8724 \n",
"Philippines 24887 28573 38617 \n",
"Pakistan 8994 7217 6811 \n",
"United States of America 10190 8995 8142 \n",
"Iran (Islamic Republic of) 6475 6580 7477 \n",
"Sri Lanka 4756 4547 4422 \n",
"Republic of Korea 7294 5874 5537 \n",
"Poland 1267 1013 795 \n",
"Lebanon 3566 3077 3432 \n",
"France 4532 5051 4646 \n",
"Jamaica 2334 2456 2321 \n",
"Viet Nam 1784 2171 1942 \n",
"Romania 2837 2076 1922 \n",
"\n",
" 2011 2012 2013 \\\n",
"Country \n",
"India 27509 30933 33087 \n",
"China 28502 33024 34129 \n",
"United Kingdom of Great Britain and Northern Ir... 6204 6195 5827 \n",
"Philippines 36765 34315 29544 \n",
"Pakistan 7468 11227 12603 \n",
"United States of America 7676 7891 8501 \n",
"Iran (Islamic Republic of) 7479 7534 11291 \n",
"Sri Lanka 3309 3338 2394 \n",
"Republic of Korea 4588 5316 4509 \n",
"Poland 720 779 852 \n",
"Lebanon 3072 1614 2172 \n",
"France 4080 6280 5623 \n",
"Jamaica 2059 2182 2479 \n",
"Viet Nam 1723 1731 2112 \n",
"Romania 1776 1588 1512 \n",
"\n",
" Total \n",
"Country \n",
"India 691904 \n",
"China 659962 \n",
"United Kingdom of Great Britain and Northern Ir... 551500 \n",
"Philippines 511391 \n",
"Pakistan 241600 \n",
"United States of America 241122 \n",
"Iran (Islamic Republic of) 175923 \n",
"Sri Lanka 148358 \n",
"Republic of Korea 142581 \n",
"Poland 139241 \n",
"Lebanon 115359 \n",
"France 109091 \n",
"Jamaica 106431 \n",
"Viet Nam 97146 \n",
"Romania 93585 \n",
"\n",
"[15 rows x 38 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_top15 = df_can.sort_values(['Total'],ascending=False).head(15)\n",
"df_top15\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
"df_top15\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"run_control": {
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},
"source": [
"Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\n",
" 1. Create a list of all years in decades 80's, 90's, and 00's.\n",
" 2. Slice the original dataframe df_can to create a series for each decade and sum across all years for each country.\n",
" 3. Merge the three series into a new data frame. Call your dataframe **new_df**."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
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"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>1980s</th>\n",
" <th>1990s</th>\n",
" <th>2000s</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>82154</td>\n",
" <td>180395</td>\n",
" <td>303591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>32003</td>\n",
" <td>161528</td>\n",
" <td>340385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
" <td>179171</td>\n",
" <td>261966</td>\n",
" <td>83413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Philippines</th>\n",
" <td>60764</td>\n",
" <td>138482</td>\n",
" <td>172904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>10591</td>\n",
" <td>65302</td>\n",
" <td>127598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United States of America</th>\n",
" <td>76824</td>\n",
" <td>56915</td>\n",
" <td>75173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran (Islamic Republic of)</th>\n",
" <td>21477</td>\n",
" <td>54871</td>\n",
" <td>65794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>14796</td>\n",
" <td>70421</td>\n",
" <td>49678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Republic of Korea</th>\n",
" <td>16259</td>\n",
" <td>38189</td>\n",
" <td>68183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Poland</th>\n",
" <td>57602</td>\n",
" <td>64864</td>\n",
" <td>13629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>24918</td>\n",
" <td>49245</td>\n",
" <td>30906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>France</th>\n",
" <td>17137</td>\n",
" <td>30028</td>\n",
" <td>41297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jamaica</th>\n",
" <td>34328</td>\n",
" <td>40329</td>\n",
" <td>22733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Viet Nam</th>\n",
" <td>30638</td>\n",
" <td>37726</td>\n",
" <td>21274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Romania</th>\n",
" <td>7613</td>\n",
" <td>33659</td>\n",
" <td>45515</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1980s 1990s 2000s\n",
"Country \n",
"India 82154 180395 303591\n",
"China 32003 161528 340385\n",
"United Kingdom of Great Britain and Northern Ir... 179171 261966 83413\n",
"Philippines 60764 138482 172904\n",
"Pakistan 10591 65302 127598\n",
"United States of America 76824 56915 75173\n",
"Iran (Islamic Republic of) 21477 54871 65794\n",
"Sri Lanka 14796 70421 49678\n",
"Republic of Korea 16259 38189 68183\n",
"Poland 57602 64864 13629\n",
"Lebanon 24918 49245 30906\n",
"France 17137 30028 41297\n",
"Jamaica 34328 40329 22733\n",
"Viet Nam 30638 37726 21274\n",
"Romania 7613 33659 45515"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"years_80s = list(map(str,range(1980,1990)))\n",
"years_90s = list(map(str,range(1990,2000)))\n",
"years_00s = list(map(str,range(2000,2010)))\n",
"df_80s = df_top15.loc[:,years_80s].sum(axis=1)#this sum is done along the rows\n",
"df_90s = df_top15.loc[:,years_90s].sum(axis=1)\n",
"df_00s = df_top15.loc[:,years_00s].sum(axis=1)\n",
"\n",
"new_df = pd.DataFrame({'1980s':df_80s,'1990s':df_90s,'2000s':df_00s})\n",
"new_df\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # create a list of all years in decades 80's, 90's, and 00's\n",
"years_80s = list(map(str, range(1980, 1990))) \n",
"years_90s = list(map(str, range(1990, 2000))) \n",
"years_00s = list(map(str, range(2000, 2010))) \n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # slice the original dataframe df_can to create a series for each decade\n",
"df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
"df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
"df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # merge the three series into a new data frame\n",
"new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # display dataframe\n",
"new_df.head()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's learn more about the statistics associated with the dataframe using the `describe()` method."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
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"new_sheet": false,
"run_control": {
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"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980s</th>\n",
" <th>1990s</th>\n",
" <th>2000s</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>15.000000</td>\n",
" <td>15.000000</td>\n",
" <td>15.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>44418.333333</td>\n",
" <td>85594.666667</td>\n",
" <td>97471.533333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>44190.676455</td>\n",
" <td>68237.560246</td>\n",
" <td>100583.204205</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>7613.000000</td>\n",
" <td>30028.000000</td>\n",
" <td>13629.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>16698.000000</td>\n",
" <td>39259.000000</td>\n",
" <td>36101.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>30638.000000</td>\n",
" <td>56915.000000</td>\n",
" <td>65794.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>59183.000000</td>\n",
" <td>104451.500000</td>\n",
" <td>105505.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>179171.000000</td>\n",
" <td>261966.000000</td>\n",
" <td>340385.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1980s 1990s 2000s\n",
"count 15.000000 15.000000 15.000000\n",
"mean 44418.333333 85594.666667 97471.533333\n",
"std 44190.676455 68237.560246 100583.204205\n",
"min 7613.000000 30028.000000 13629.000000\n",
"25% 16698.000000 39259.000000 36101.500000\n",
"50% 30638.000000 56915.000000 65794.000000\n",
"75% 59183.000000 104451.500000 105505.500000\n",
"max 179171.000000 261966.000000 340385.000000"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"new_df.describe()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"new_df.describe()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 3: Plot the box plots."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
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"collapsed": false,
"deletable": true,
"editable": true,
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},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Immigration from top 15 countries for decades 80s, 90s and 2000s')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"new_df.plot(kind=\"box\")\n",
"plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
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},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"new_df.plot(kind='box', figsize=(10, 6))\n",
"-->\n",
"\n",
"<!--\n",
"plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
"-->\n",
"\n",
"<!--\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
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"source": [
"Note how the box plot differs from the summary table created. The box plot scans the data and identifies the outliers. In order to be an outlier, the data value must be:<br>\n",
"* larger than Q3 by at least 1.5 times the interquartile range (IQR), or,\n",
"* smaller than Q1 by at least 1.5 times the IQR.\n",
"\n",
"Let's look at decade 2000s as an example: <br>\n",
"* Q1 (25%) = 36,101.5 <br>\n",
"* Q3 (75%) = 105,505.5 <br>\n",
"* IQR = Q3 - Q1 = 69,404 <br>\n",
"\n",
"Using the definition of outlier, any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\n",
"\n",
"Outlier > 105,505.5 + (1.5 * 69,404) <br>\n",
"Outlier > 209,611.5"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
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"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1980s</th>\n",
" <th>1990s</th>\n",
" <th>2000s</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>82154</td>\n",
" <td>180395</td>\n",
" <td>303591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>32003</td>\n",
" <td>161528</td>\n",
" <td>340385</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1980s 1990s 2000s\n",
"Country \n",
"India 82154 180395 303591\n",
"China 32003 161528 340385"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# let's check how many entries fall above the outlier threshold \n",
"new_df[new_df['2000s']> 209611.5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"China and India are both considered as outliers since their population for the decade exceeds 209,611.5. \n",
"\n",
"The box plot is an advanced visualizaiton tool, and there are many options and customizations that exceed the scope of this lab. Please refer to [Matplotlib documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot) on box plots for more information."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Scatter Plots <a id=\"10\"></a>\n",
"\n",
"A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the datapoints are connected together by a line in a line plot, they are not connected in a scatter plot. The data in a scatter plot is considered to express a trend. With further analysis using tools like regression, we can mathematically calculate this relationship and use it to predict trends outside the dataset.\n",
"\n",
"Let's start by exploring the following:\n",
"\n",
"Using a `scatter plot`, let's visualize the trend of total immigrantion to Canada (all countries combined) for the years 1980 - 2013."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the dataset. Since we are expecting to use the relationship betewen `years` and `total population`, we will convert `years` to `int` type."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
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},
"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>year</th>\n",
" <th>total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1980</td>\n",
" <td>99137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1981</td>\n",
" <td>110563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1982</td>\n",
" <td>104271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1983</td>\n",
" <td>75550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1984</td>\n",
" <td>73417</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year total\n",
"0 1980 99137\n",
"1 1981 110563\n",
"2 1982 104271\n",
"3 1983 75550\n",
"4 1984 73417"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we can use the sum() method to get the total population per year\n",
"df_tot = pd.DataFrame(df_can[years].sum(axis=0))\n",
"\n",
"# change the years to type int (useful for regression later on)\n",
"df_tot.index = map(int, df_tot.index)\n",
"\n",
"# reset the index to put in back in as a column in the df_tot dataframe\n",
"df_tot.reset_index(inplace = True)\n",
"\n",
"# rename columns\n",
"df_tot.columns = ['year', 'total']\n",
"\n",
"# view the final dataframe\n",
"df_tot.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
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"read_only": false
}
},
"source": [
"Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
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"deletable": true,
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},
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}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
"\n",
"plt.title('Total Immigration to Canada from 1980 - 2013')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Number of Immigrants')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how the scatter plot does not connect the datapoints together. We can clearly observe an upward trend in the data: as the years go by, the total number of immigrants increases. We can mathematically analyze this upward trend using a regression line (line of best fit). "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"So let's try to plot a linear line of best fit, and use it to predict the number of immigrants in 2015.\n",
"\n",
"Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
"- `x`: x-coordinates of the data. \n",
"- `y`: y-coordinates of the data. \n",
"- `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5.56709228e+03, -1.09261952e+07])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = df_tot['year'] # year on x-axis\n",
"y = df_tot['total'] # total on y-axis\n",
"fit = np.polyfit(x, y, deg=1)\n",
"\n",
"fit"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a*x + b`, our output has 2 elements `[5.56709228e+03, -1.09261952e+07]` with the the slope in position 0 and intercept in position 1. \n",
"\n",
"Step 2: Plot the regression line on the `scatter plot`."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'No. Immigrants = 5567 * Year + -10926195'"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
"\n",
"plt.title('Total Immigration to Canada from 1980 - 2013')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Number of Immigrants')\n",
"\n",
"# plot line of best fit\n",
"plt.plot(x, fit[0] * x + fit[1], color='red') # recall that x is the Years\n",
"plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(2000, 150000))\n",
"\n",
"plt.show()\n",
"\n",
"# print out the line of best fit\n",
"'No. Immigrants = {0:.0f} * Year + {1:.0f}'.format(fit[0], fit[1]) "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"source": [
"Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\n",
"```python\n",
"No. Immigrants = 5567 * Year - 10926195\n",
"No. Immigrants = 5567 * 2015 - 10926195\n",
"No. Immigrants = 291,310\n",
"```\n",
"When compared to the actuals from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp), we see that Canada accepted 271,845 immigrants in 2015. Our estimated value of 291,310 is within 7% of the actual number, which is pretty good considering our original data came from United Nations (and might differ slightly from CIC data).\n",
"\n",
"As a side note, we can observe that immigration took a dip around 1993 - 1997. Further analysis into the topic revealed that in 1993 Canada introcuded Bill C-86 which introduced revisions to the refugee determination system, mostly restrictive. Further amendments to the Immigration Regulations cancelled the sponsorship required for \"assisted relatives\" and reduced the points awarded to them, making it more difficult for family members (other than nuclear family) to immigrate to Canada. These restrictive measures had a direct impact on the immigration numbers for the next several years."
]
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"source": [
"**Question**: Create a scatter plot of the total immigration from Denmark, Norway, and Sweden to Canada from 1980 to 2013?"
]
},
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"Step 1: Get the data:\n",
" 1. Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it **df_countries**.\n",
" 2. Sum the immigration numbers across all three countries for each year and turn the result into a dataframe. Name this new dataframe **df_total**.\n",
" 3. Reset the index in place.\n",
" 4. Rename the columns to **year** and **total**.\n",
" 5. Display the resulting dataframe."
]
},
{
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"execution_count": 32,
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{
"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>Year</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>627</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>10</td>\n",
" <td>331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>11</td>\n",
" <td>381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>12</td>\n",
" <td>411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>13</td>\n",
" <td>481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>14</td>\n",
" <td>345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>15</td>\n",
" <td>352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>16</td>\n",
" <td>301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>17</td>\n",
" <td>338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>18</td>\n",
" <td>217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>19</td>\n",
" <td>287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20</td>\n",
" <td>287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>21</td>\n",
" <td>343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>22</td>\n",
" <td>293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>23</td>\n",
" <td>327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>24</td>\n",
" <td>291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>25</td>\n",
" <td>324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>26</td>\n",
" <td>293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>27</td>\n",
" <td>363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>28</td>\n",
" <td>339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>29</td>\n",
" <td>323</td>\n",
" </tr>\n",
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" <th>30</th>\n",
" <td>30</td>\n",
" <td>297</td>\n",
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" <th>31</th>\n",
" <td>31</td>\n",
" <td>276</td>\n",
" </tr>\n",
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" <td>287</td>\n",
" </tr>\n",
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" <th>33</th>\n",
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" <td>280</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Year Total\n",
"0 0 669\n",
"1 1 678\n",
"2 2 627\n",
"3 3 333\n",
"4 4 252\n",
"5 5 285\n",
"6 6 336\n",
"7 7 387\n",
"8 8 373\n",
"9 9 387\n",
"10 10 331\n",
"11 11 381\n",
"12 12 411\n",
"13 13 481\n",
"14 14 345\n",
"15 15 352\n",
"16 16 301\n",
"17 17 338\n",
"18 18 217\n",
"19 19 287\n",
"20 20 287\n",
"21 21 343\n",
"22 22 293\n",
"23 23 327\n",
"24 24 291\n",
"25 25 324\n",
"26 26 293\n",
"27 27 363\n",
"28 28 339\n",
"29 29 323\n",
"30 30 297\n",
"31 31 276\n",
"32 32 287\n",
"33 33 280"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### type your answer here\n",
"df_countries = df_can.loc[['Denmark','Norway','Sweden'],years]\n",
"df_total = df_countries.sum(axis=0)\n",
"df_total = pd.DataFrame(df_total)\n",
"df_total.index = map(int,df_tot.index)\n",
"df_total.reset_index(inplace=True)\n",
"df_total.columns = [\"Year\",\"Total\"]\n",
"df_total\n",
"\n",
"\n",
"\n"
]
},
{
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"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # create df_countries dataframe\n",
"df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # create df_total by summing across three countries for each year\n",
"df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # reset index in place\n",
"df_total.reset_index(inplace=True)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # rename columns\n",
"df_total.columns = ['year', 'total']\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # change column year from string to int to create scatter plot\n",
"df_total['year'] = df_total['year'].astype(int)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # show resulting dataframe\n",
"df_total.head()\n",
"-->"
]
},
{
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"source": [
"Step 2: Generate the scatter plot by plotting the total versus year in **df_total**."
]
},
{
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"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Number of Immigrants')"
]
},
"execution_count": 34,
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{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"### type your answer here\n",
"df_total.index = map(int,df_total.index)\n",
"df_total.plot(x=\"Year\",y=\"Total\",kind=\"scatter\",figsize = (10,6),color=\"darkblue\")\n",
"plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Number of Immigrants')\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # generate scatter plot\n",
"df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # add title and label to axes\n",
"plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Number of Immigrants')\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # show plot\n",
"plt.show()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Bubble Plots <a id=\"12\"></a>\n",
"\n",
"A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The datapoints are replaced with bubbles, and the size of the bubble is determined by the third variable 'z', also known as the weight. In `maplotlib`, we can pass in an array or scalar to the keyword `s` to `plot()`, that contains the weight of each point.\n",
"\n",
"**Let's start by analyzing the effect of Argentina's great depression**.\n",
"\n",
"Argentina suffered a great depression from 1998 - 2002, which caused widespread unemployment, riots, the fall of the government, and a default on the country's foreign debt. In terms of income, over 50% of Argentines were poor, and seven out of ten Argentine children were poor at the depth of the crisis in 2002. \n",
"\n",
"Let's analyze the effect of this crisis, and compare Argentina's immigration to that of it's neighbour Brazil. Let's do that using a `bubble plot` of immigration from Brazil and Argentina for the years 1980 - 2013. We will set the weights for the bubble as the *normalized* value of the population for each year."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and bring it in the dataframe."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\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>Country</th>\n",
" <th>Year</th>\n",
" <th>Afghanistan</th>\n",
" <th>Albania</th>\n",
" <th>Algeria</th>\n",
" <th>American Samoa</th>\n",
" <th>Andorra</th>\n",
" <th>Angola</th>\n",
" <th>Antigua and Barbuda</th>\n",
" <th>Argentina</th>\n",
" <th>Armenia</th>\n",
" <th>...</th>\n",
" <th>United States of America</th>\n",
" <th>Uruguay</th>\n",
" <th>Uzbekistan</th>\n",
" <th>Vanuatu</th>\n",
" <th>Venezuela (Bolivarian Republic of)</th>\n",
" <th>Viet Nam</th>\n",
" <th>Western Sahara</th>\n",
" <th>Yemen</th>\n",
" <th>Zambia</th>\n",
" <th>Zimbabwe</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1980</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>...</td>\n",
" <td>9378</td>\n",
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" <td>0</td>\n",
" <td>103</td>\n",
" <td>1191</td>\n",
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" <td>11</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1981</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>67</td>\n",
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" <td>0</td>\n",
" <td>3</td>\n",
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" <td>426</td>\n",
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" <td>...</td>\n",
" <td>10030</td>\n",
" <td>132</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>117</td>\n",
" <td>1829</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" <td>114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1982</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>71</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>626</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>9074</td>\n",
" <td>146</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>174</td>\n",
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" <tr>\n",
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" <td>1983</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>69</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>241</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>7100</td>\n",
" <td>105</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>124</td>\n",
" <td>3404</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1984</td>\n",
" <td>71</td>\n",
" <td>0</td>\n",
" <td>63</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>42</td>\n",
" <td>237</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>6661</td>\n",
" <td>90</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>142</td>\n",
" <td>7583</td>\n",
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" <td>32</td>\n",
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],
"text/plain": [
"Country Year Afghanistan Albania Algeria American Samoa Andorra Angola \\\n",
"0 1980 16 1 80 0 0 1 \n",
"1 1981 39 0 67 1 0 3 \n",
"2 1982 39 0 71 0 0 6 \n",
"3 1983 47 0 69 0 0 6 \n",
"4 1984 71 0 63 0 0 4 \n",
"\n",
"Country Antigua and Barbuda Argentina Armenia ... \\\n",
"0 0 368 0 ... \n",
"1 0 426 0 ... \n",
"2 0 626 0 ... \n",
"3 0 241 0 ... \n",
"4 42 237 0 ... \n",
"\n",
"Country United States of America Uruguay Uzbekistan Vanuatu \\\n",
"0 9378 128 0 0 \n",
"1 10030 132 0 0 \n",
"2 9074 146 0 0 \n",
"3 7100 105 0 0 \n",
"4 6661 90 0 0 \n",
"\n",
"Country Venezuela (Bolivarian Republic of) Viet Nam Western Sahara Yemen \\\n",
"0 103 1191 0 1 \n",
"1 117 1829 0 2 \n",
"2 174 2162 0 1 \n",
"3 124 3404 0 6 \n",
"4 142 7583 0 0 \n",
"\n",
"Country Zambia Zimbabwe \n",
"0 11 72 \n",
"1 17 114 \n",
"2 11 102 \n",
"3 7 44 \n",
"4 16 32 \n",
"\n",
"[5 rows x 196 columns]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can_t = df_can[years].transpose() # transposed dataframe\n",
"\n",
"# cast the Years (the index) to type int\n",
"df_can_t.index = map(int, df_can_t.index)\n",
"\n",
"# let's label the index. This will automatically be the column name when we reset the index\n",
"df_can_t.index.name = 'Year'\n",
"\n",
"# reset index to bring the Year in as a column\n",
"df_can_t.reset_index(inplace=True)\n",
"\n",
"# view the changes\n",
"df_can_t.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Create the normalized weights. \n",
"\n",
"There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling) to bring all values into the range [0,1]. The general formula is:\n",
"\n",
"<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig3FeatureScaling.png\" align=\"center\">\n",
"\n",
"where *`X`* is an original value, *`X'`* is the normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the datapoints are scaled to a value between 0-1 accordingly.\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [],
"source": [
"# normalize Brazil data\n",
"norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\n",
"\n",
"# normalize Argentina data\n",
"norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 3: Plot the data. \n",
"- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \n",
"- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
" - multiply weights by 2000 to scale it up on the graph, and,\n",
" - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000)."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fbc95abdfd0>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Brazil\n",
"ax0 = df_can_t.plot(kind='scatter',\n",
" x='Year',\n",
" y='Brazil',\n",
" figsize=(14, 8),\n",
" alpha=0.5, # transparency\n",
" color='green',\n",
" s=norm_brazil * 2000 + 10, # pass in weights \n",
" xlim=(1975, 2015)\n",
" )\n",
"\n",
"# Argentina\n",
"ax1 = df_can_t.plot(kind='scatter',\n",
" x='Year',\n",
" y='Argentina',\n",
" alpha=0.5,\n",
" color=\"blue\",\n",
" s=norm_argentina * 2000 + 10,\n",
" ax = ax0\n",
" )\n",
"\n",
"ax0.set_ylabel('Number of Immigrants')\n",
"ax0.set_title('Immigration from Brazil and Argentina from 1980 - 2013')\n",
"ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The size of the bubble corresponds to the magnitude of immigrating population for that year, compared to the 1980 - 2013 data. The larger the bubble, the more immigrants in that year.\n",
"\n",
"From the plot above, we can see a corresponding increase in immigration from Argentina during the 1998 - 2002 great depression. We can also observe a similar spike around 1985 to 1993. In fact, Argentina had suffered a great depression from 1974 - 1990, just before the onset of 1998 - 2002 great depression. \n",
"\n",
"On a similar note, Brazil suffered the *Samba Effect* where the Brazilian real (currency) dropped nearly 35% in 1999. There was a fear of a South American financial crisis as many South American countries were heavily dependent on industrial exports from Brazil. The Brazilian government subsequently adopted an austerity program, and the economy slowly recovered over the years, culminating in a surge in 2010. The immigration data reflect these events."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question**: Previously in this lab, we created box plots to compare immigration from China and India to Canada. Create bubble plots of immigration from China and India to visualize any differences with time from 1980 to 2013. You can use **df_can_t** that we defined and used in the previous example."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 1: Normalize the data pertaining to China and India."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"jupyter": {
"outputs_hidden": true
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"df_can_t = df_can[years].transpose()\n",
"df_can_.index = map(int,df_can_t.index)\n",
"df_can_t.index.name='Year'\n",
"df_can_t.reset_index(inplace=True)\n",
"norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
"norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # normalize China data\n",
"norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
"-->\n",
"\n",
"<!--\n",
"# normalize India data\n",
"norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Step 2: Generate the bubble plots."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'norm_china' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-38-d8648f89b043>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# transparency\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'green'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm_china\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2000\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# pass in weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mxlim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1975\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2015\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m )\n",
"\u001b[0;31mNameError\u001b[0m: name 'norm_china' is not defined"
]
}
],
"source": [
"### type your answer here\n",
"\n",
"ax0 = df_can_t.plot(kind='scatter',\n",
" x='Year',\n",
" y='China',\n",
" figsize=(14, 8),\n",
" alpha=0.5, # transparency\n",
" color='green',\n",
" s=norm_china * 2000 + 10, # pass in weights \n",
" xlim=(1975, 2015)\n",
" )\n",
"\n",
"ax1 = df_can_t.plot(kind='scatter',\n",
" x='Year',\n",
" y='India',\n",
" alpha=0.5,\n",
" color=\"blue\",\n",
" s=norm_india * 2000 + 10,\n",
" ax = ax0\n",
" )\n",
"\n",
"\n",
"ax0.set_ylabel('Number of Immigrants')\n",
"ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
"ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # China\n",
"ax0 = df_can_t.plot(kind='scatter',\n",
" x='Year',\n",
" y='China',\n",
" figsize=(14, 8),\n",
" alpha=0.5, # transparency\n",
" color='green',\n",
" s=norm_china * 2000 + 10, # pass in weights \n",
" xlim=(1975, 2015)\n",
" )\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # India\n",
"ax1 = df_can_t.plot(kind='scatter',\n",
" x='Year',\n",
" y='India',\n",
" alpha=0.5,\n",
" color=\"blue\",\n",
" s=norm_india * 2000 + 10,\n",
" ax = ax0\n",
" )\n",
"-->\n",
"\n",
"<!--\n",
"ax0.set_ylabel('Number of Immigrants')\n",
"ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
"ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
"-->"
]
},
{
"cell_type": "markdown",
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"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
"\n",
"This notebook was recently revamped by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
]
},
{
"cell_type": "markdown",
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},
"source": [
"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week2_LAB2)."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
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}
},
"source": [
"<hr>\n",
"\n",
"Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
],
"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": "3.6.7"
},
"widgets": {
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"version": "1.1.2"
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"nbformat": 4,
"nbformat_minor": 4
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