Data Visualization (Waffle Charts, Word Clouds, and Regression Plots)
Third tutorial of Data Visualization in Python using Google Colab.
- Waffle Charts, Word Clouds, and Regression Plots
- Exploring Datasets with pandas and Matplotlib
- Downloading and Prepping Data
- Visualizing Data using Matplotlib
- Waffle Charts
- Word Clouds
- Regression Plots
Waffle Charts, Word Clouds, and Regression Plots
In this lab, we will learn how to create word clouds and waffle charts. Furthermore, we will start learning about additional visualization libraries that are based on Matplotlib, namely the library seaborn, and we will learn how to create regression plots using the seaborn library.
Exploring Datasets with pandas and Matplotlib
Toolkits: The course heavily relies on pandas and Numpy for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is Matplotlib.
Dataset: Immigration to Canada from 1980 to 2013 - International migration flows to and from selected countries - The 2015 revision from United Nation's website
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.
Import Primary Modules:
import numpy as np # useful for many scientific computing in Python
import pandas as pd # primary data structure library
from PIL import Image # converting images into arrays
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:
!conda install -c anaconda xlrd --yesDownload the dataset and read it into a pandas dataframe:
df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',
sheet_name='Canada by Citizenship',
skiprows=range(20),
skipfooter=2)
print('Data downloaded and read into a dataframe!')
Let's take a look at the first five items in our dataset
df_can.head()
Let's find out how many entries there are in our dataset
# print the dimensions of the dataframe
print(df_can.shape)
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.
# clean up the dataset to remove unnecessary columns (eg. REG)
df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis = 1, inplace = True)
# let's rename the columns so that they make sense
df_can.rename (columns = {'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace = True)
# for sake of consistency, let's also make all column labels of type string
df_can.columns = list(map(str, df_can.columns))
# set the country name as index - useful for quickly looking up countries using .loc method
df_can.set_index('Country', inplace = True)
# add total column
df_can['Total'] = df_can.sum (axis = 1)
# years that we will be using in this lesson - useful for plotting later on
years = list(map(str, range(1980, 2014)))
print ('data dimensions:', df_can.shape)
Import matplotlib:
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches # needed for waffle Charts
mpl.style.use('ggplot') # optional: for ggplot-like style
# check for latest version of Matplotlib
print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0
Let's revisit the previous case study about Denmark, Norway, and Sweden.
# let's create a new dataframe for these three countries
df_dsn = df_can.loc[['Denmark', 'Norway', 'Sweden'], :]
# let's take a look at our dataframe
df_dsn
Unfortunately, unlike R, waffle charts are not built into any of the Python visualization libraries. Therefore, we will learn how to create them from scratch.
Step 1. The first step into creating a waffle chart is determing the proportion of each category with respect to the total.
# compute the proportion of each category with respect to the total
total_values = sum(df_dsn['Total'])
category_proportions = [(float(value) / total_values) for value in df_dsn['Total']]
# print out proportions
for i, proportion in enumerate(category_proportions):
print (df_dsn.index.values[i] + ': ' + str(proportion))
Step 2. The second step is defining the overall size of the waffle chart.
width = 40 # width of chart
height = 10 # height of chart
total_num_tiles = width * height # total number of tiles
print ('Total number of tiles is ', total_num_tiles)
Step 3. The third step is using the proportion of each category to determe it respective number of tiles
# compute the number of tiles for each catagory
tiles_per_category = [round(proportion * total_num_tiles) for proportion in category_proportions]
# print out number of tiles per category
for i, tiles in enumerate(tiles_per_category):
print (df_dsn.index.values[i] + ': ' + str(tiles))
Based on the calculated proportions, Denmark will occupy 129 tiles of the waffle chart, Norway will occupy 77 tiles, and Sweden will occupy 194 tiles.
Step 4. The fourth step is creating a matrix that resembles the waffle chart and populating it.
# initialize the waffle chart as an empty matrix
waffle_chart = np.zeros((height, width))
# define indices to loop through waffle chart
category_index = 0
tile_index = 0
# populate the waffle chart
for col in range(width):
for row in range(height):
tile_index += 1
# if the number of tiles populated for the current category is equal to its corresponding allocated tiles...
if tile_index > sum(tiles_per_category[0:category_index]):
# ...proceed to the next category
category_index += 1
# set the class value to an integer, which increases with class
waffle_chart[row, col] = category_index
print ('Waffle chart populated!')
Let's take a peek at how the matrix looks like.
waffle_chart
As expected, the matrix consists of three categories and the total number of each category's instances matches the total number of tiles allocated to each category.
Step 5. Map the waffle chart matrix into a visual.
# instantiate a new figure object
fig = plt.figure()
# use matshow to display the waffle chart
colormap = plt.cm.coolwarm
plt.matshow(waffle_chart, cmap=colormap)
plt.colorbar()
Step 6. Prettify the chart.
# instantiate a new figure object
fig = plt.figure()
# use matshow to display the waffle chart
colormap = plt.cm.coolwarm
plt.matshow(waffle_chart, cmap=colormap)
plt.colorbar()
# get the axis
ax = plt.gca()
# set minor ticks
ax.set_xticks(np.arange(-.5, (width), 1), minor=True)
ax.set_yticks(np.arange(-.5, (height), 1), minor=True)
# add gridlines based on minor ticks
ax.grid(which='minor', color='w', linestyle='-', linewidth=2)
plt.xticks([])
plt.yticks([])
Step 7. Create a legend and add it to chart.
# instantiate a new figure object
fig = plt.figure()
# use matshow to display the waffle chart
colormap = plt.cm.coolwarm
plt.matshow(waffle_chart, cmap=colormap)
plt.colorbar()
# get the axis
ax = plt.gca()
# set minor ticks
ax.set_xticks(np.arange(-.5, (width), 1), minor=True)
ax.set_yticks(np.arange(-.5, (height), 1), minor=True)
# add gridlines based on minor ticks
ax.grid(which='minor', color='w', linestyle='-', linewidth=2)
plt.xticks([])
plt.yticks([])
# compute cumulative sum of individual categories to match color schemes between chart and legend
values_cumsum = np.cumsum(df_dsn['Total'])
total_values = values_cumsum[len(values_cumsum) - 1]
# create legend
legend_handles = []
for i, category in enumerate(df_dsn.index.values):
label_str = category + ' (' + str(df_dsn['Total'][i]) + ')'
color_val = colormap(float(values_cumsum[i])/total_values)
legend_handles.append(mpatches.Patch(color=color_val, label=label_str))
# add legend to chart
plt.legend(handles=legend_handles,
loc='lower center',
ncol=len(df_dsn.index.values),
bbox_to_anchor=(0., -0.2, 0.95, .1)
)
And there you go! What a good looking delicious waffle chart, don't you think?
Now it would very inefficient to repeat these seven steps every time we wish to create a waffle chart. So let's combine all seven steps into one function called create_waffle_chart. This function would take the following parameters as input:
- categories:Unique categories or classes in dataframe.> 2. values:Values corresponding to categories or classes.> 3. height:Defined height of waffle chart.> 4. width:Defined width of waffle chart.> 5. colormap:Colormap class> 6. value_sign:In order to make our function more generalizable, we will add this parameter to address signs that could be associated with a value such as %, $, and so on. value_sign has a default value of empty string.
def create_waffle_chart(categories, values, height, width, colormap, value_sign=''):
# compute the proportion of each category with respect to the total
total_values = sum(values)
category_proportions = [(float(value) / total_values) for value in values]
# compute the total number of tiles
total_num_tiles = width * height # total number of tiles
print ('Total number of tiles is', total_num_tiles)
# compute the number of tiles for each catagory
tiles_per_category = [round(proportion * total_num_tiles) for proportion in category_proportions]
# print out number of tiles per category
for i, tiles in enumerate(tiles_per_category):
print (df_dsn.index.values[i] + ': ' + str(tiles))
# initialize the waffle chart as an empty matrix
waffle_chart = np.zeros((height, width))
# define indices to loop through waffle chart
category_index = 0
tile_index = 0
# populate the waffle chart
for col in range(width):
for row in range(height):
tile_index += 1
# if the number of tiles populated for the current category
# is equal to its corresponding allocated tiles...
if tile_index > sum(tiles_per_category[0:category_index]):
# ...proceed to the next category
category_index += 1
# set the class value to an integer, which increases with class
waffle_chart[row, col] = category_index
# instantiate a new figure object
fig = plt.figure()
# use matshow to display the waffle chart
colormap = plt.cm.coolwarm
plt.matshow(waffle_chart, cmap=colormap)
plt.colorbar()
# get the axis
ax = plt.gca()
# set minor ticks
ax.set_xticks(np.arange(-.5, (width), 1), minor=True)
ax.set_yticks(np.arange(-.5, (height), 1), minor=True)
# add dridlines based on minor ticks
ax.grid(which='minor', color='w', linestyle='-', linewidth=2)
plt.xticks([])
plt.yticks([])
# compute cumulative sum of individual categories to match color schemes between chart and legend
values_cumsum = np.cumsum(values)
total_values = values_cumsum[len(values_cumsum) - 1]
# create legend
legend_handles = []
for i, category in enumerate(categories):
if value_sign == '%':
label_str = category + ' (' + str(values[i]) + value_sign + ')'
else:
label_str = category + ' (' + value_sign + str(values[i]) + ')'
color_val = colormap(float(values_cumsum[i])/total_values)
legend_handles.append(mpatches.Patch(color=color_val, label=label_str))
# add legend to chart
plt.legend(
handles=legend_handles,
loc='lower center',
ncol=len(categories),
bbox_to_anchor=(0., -0.2, 0.95, .1)
)
Now to create a waffle chart, all we have to do is call the function create_waffle_chart. Let's define the input parameters:
width = 40 # width of chart
height = 10 # height of chart
categories = df_dsn.index.values # categories
values = df_dsn['Total'] # correponding values of categories
colormap = plt.cm.coolwarm # color map class
And now let's call our function to create a waffle chart.
create_waffle_chart(categories, values, height, width, colormap)
There seems to be a new Python package for generating waffle charts called PyWaffle, but it looks like the repository is still being built. But feel free to check it out and play with it.
Luckily, a Python package already exists in Python for generating word clouds. The package, called word_cloud was developed by Andreas Mueller. You can learn more about the package by following this link.
Let's use this package to learn how to generate a word cloud for a given text document.
First, let's install the package.
# install wordcloud
!conda install -c conda-forge wordcloud==1.4.1 --yes
# import package and its set of stopwords
from wordcloud import WordCloud, STOPWORDS
print ('Wordcloud is installed and imported!')
Word clouds are commonly used to perform high-level analysis and visualization of text data. Accordinly, let's digress from the immigration dataset and work with an example that involves analyzing text data. Let's try to analyze a short novel written by Lewis Carroll titled Alice's Adventures in Wonderland. Let's go ahead and download a .txt file of the novel.
# download file and save as alice_novel.txt
!wget --quiet https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/alice_novel.txt
# open the file and read it into a variable alice_novel
alice_novel = open('alice_novel.txt', 'r').read()
print ('File downloaded and saved!')
Next, let's use the stopwords that we imported from word_cloud. We use the function set to remove any redundant stopwords.
stopwords = set(STOPWORDS)
Create a word cloud object and generate a word cloud. For simplicity, let's generate a word cloud using only the first 2000 words in the novel.
# instantiate a word cloud object
alice_wc = WordCloud(
background_color='white',
max_words=2000,
stopwords=stopwords
)
# generate the word cloud
alice_wc.generate(alice_novel)
Awesome! Now that the word cloud is created, let's visualize it.
# display the word cloud
plt.imshow(alice_wc, interpolation='bilinear')
plt.axis('off')
plt.show()
Interesting! So in the first 2000 words in the novel, the most common words are Alice, said, little, Queen, and so on. Let's resize the cloud so that we can see the less frequent words a little better.
fig = plt.figure()
fig.set_figwidth(14) # set width
fig.set_figheight(18) # set height
# display the cloud
plt.imshow(alice_wc, interpolation='bilinear')
plt.axis('off')
plt.show()
Much better! However, said isn't really an informative word. So let's add it to our stopwords and re-generate the cloud.
stopwords.add('said') # add the words said to stopwords
# re-generate the word cloud
alice_wc.generate(alice_novel)
# display the cloud
fig = plt.figure()
fig.set_figwidth(14) # set width
fig.set_figheight(18) # set height
plt.imshow(alice_wc, interpolation='bilinear')
plt.axis('off')
plt.show()
Excellent! This looks really interesting! Another cool thing you can implement with the word_cloud package is superimposing the words onto a mask of any shape. Let's use a mask of Alice and her rabbit. We already created the mask for you, so let's go ahead and download it and call it alice_mask.png.
# download image
!wget --quiet https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/alice_mask.png
# save mask to alice_mask
alice_mask = np.array(Image.open('alice_mask.png'))
print('Image downloaded and saved!')
Let's take a look at how the mask looks like.
fig = plt.figure()
fig.set_figwidth(14) # set width
fig.set_figheight(18) # set height
plt.imshow(alice_mask, cmap=plt.cm.gray, interpolation='bilinear')
plt.axis('off')
plt.show()
Shaping the word cloud according to the mask is straightforward using word_cloud package. For simplicity, we will continue using the first 2000 words in the novel.
# instantiate a word cloud object
alice_wc = WordCloud(background_color='white', max_words=2000, mask=alice_mask, stopwords=stopwords)
# generate the word cloud
alice_wc.generate(alice_novel)
# display the word cloud
fig = plt.figure()
fig.set_figwidth(14) # set width
fig.set_figheight(18) # set height
plt.imshow(alice_wc, interpolation='bilinear')
plt.axis('off')
plt.show()
Really impressive!
Unfortunately, our immmigration data does not have any text data, but where there is a will there is a way. Let's generate sample text data from our immigration dataset, say text data of 90 words.
Let's recall how our data looks like.
df_can.head()
And what was the total immigration from 1980 to 2013?
total_immigration = df_can['Total'].sum()
total_immigration
Using countries with single-word names, let's duplicate each country's name based on how much they contribute to the total immigration.
max_words = 90
word_string = ''
for country in df_can.index.values:
# check if country's name is a single-word name
if len(country.split(' ')) == 1:
repeat_num_times = int(df_can.loc[country, 'Total']/float(total_immigration)*max_words)
word_string = word_string + ((country + ' ') * repeat_num_times)
# display the generated text
word_string
We are not dealing with any stopwords here, so there is no need to pass them when creating the word cloud.
# create the word cloud
wordcloud = WordCloud(background_color='white').generate(word_string)
print('Word cloud created!')
# display the cloud
fig = plt.figure()
fig.set_figwidth(14)
fig.set_figheight(18)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()
According to the above word cloud, it looks like the majority of the people who immigrated came from one of 15 countries that are displayed by the word cloud. One cool visual that you could build, is perhaps using the map of Canada and a mask and superimposing the word cloud on top of the map of Canada. That would be an interesting visual to build!
In lab Pie Charts, Box Plots, Scatter Plots, and Bubble Plots, we learned how to create a scatter plot and then fit a regression line. It took ~20 lines of code to create the scatter plot along with the regression fit. In this final section, we will explore seaborn and see how efficient it is to create regression lines and fits using this library!
Let's first install seaborn
# install seaborn
!conda install -c anaconda seaborn --yes
# import library
import seaborn as sns
print('Seaborn installed and imported!')
Create a new dataframe that stores that total number of landed immigrants to Canada per year from 1980 to 2013.
# we can use the sum() method to get the total population per year
df_tot = pd.DataFrame(df_can[years].sum(axis=0))
# change the years to type float (useful for regression later on)
df_tot.index = map(float, df_tot.index)
# reset the index to put in back in as a column in the df_tot dataframe
df_tot.reset_index(inplace=True)
# rename columns
df_tot.columns = ['year', 'total']
# view the final dataframe
df_tot.head()
With seaborn, generating a regression plot is as simple as calling the regplot function.
import seaborn as sns
ax = sns.regplot(x='year', y='total', data=df_tot)
This is not magic; it is seaborn! You can also customize the color of the scatter plot and regression line. Let's change the color to green.
import seaborn as sns
ax = sns.regplot(x='year', y='total', data=df_tot, color='green')
You can always customize the marker shape, so instead of circular markers, let's use '+'.
import seaborn as sns
ax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+')
Let's blow up the plot a little bit so that it is more appealing to the sight.
plt.figure(figsize=(15, 10))
ax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+')
And let's increase the size of markers so they match the new size of the figure, and add a title and x- and y-labels.
plt.figure(figsize=(15, 10))
ax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+', scatter_kws={'s': 200})
ax.set(xlabel='Year', ylabel='Total Immigration') # add x- and y-labels
ax.set_title('Total Immigration to Canada from 1980 - 2013') # add title
And finally increase the font size of the tickmark labels, the title, and the x- and y-labels so they don't feel left out!
plt.figure(figsize=(15, 10))
sns.set(font_scale=1.5)
ax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+', scatter_kws={'s': 200})
ax.set(xlabel='Year', ylabel='Total Immigration')
ax.set_title('Total Immigration to Canada from 1980 - 2013')
Amazing! A complete scatter plot with a regression fit with 5 lines of code only. Isn't this really amazing?
If you are not a big fan of the purple background, you can easily change the style to a white plain background.
plt.figure(figsize=(15, 10))
sns.set(font_scale=1.5)
sns.set_style('ticks') # change background to white background
ax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+', scatter_kws={'s': 200})
ax.set(xlabel='Year', ylabel='Total Immigration')
ax.set_title('Total Immigration to Canada from 1980 - 2013')
Or to a white background with gridlines.
plt.figure(figsize=(15, 10))
sns.set(font_scale=1.5)
sns.set_style('whitegrid')
ax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+', scatter_kws={'s': 200})
ax.set(xlabel='Year', ylabel='Total Immigration')
ax.set_title('Total Immigration to Canada from 1980 - 2013')
Question: Use seaborn to create a scatter plot with a regression line to visualize the total immigration from Denmark, Sweden, and Norway to Canada from 1980 to 2013.
# create df_countries dataframe
df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()
# create df_total by summing across three countries for each year
df_total = pd.DataFrame(df_countries.sum(axis=1))
# reset index in place
df_total.reset_index(inplace=True)
# rename columns
df_total.columns = ['year', 'total']
# change column year from string to int to create scatter plot
df_total['year'] = df_total['year'].astype(int)
# define figure size
plt.figure(figsize=(15, 10))
# define background style and font size
sns.set(font_scale=1.5)
sns.set_style('whitegrid')
# generate plot and add title and axes labels
ax = sns.regplot(x='year', y='total', data=df_total, color='green', marker='+', scatter_kws={'s': 200})
ax.set(xlabel='Year', ylabel='Total Immigration')
ax.set_title('Total Immigrationn from Denmark, Sweden, and Norway to Canada from 1980 - 2013')
Thank you for completing this lab!
This notebook was created by Alex Aklson. I hope you found this lab interesting and educational. Feel free to contact me if you have any questions!
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.
Copyright © 2019 Cognitive Class. This notebook and its source code are released under the terms of the MIT License.