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.
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.
For this lesson, we will focus on the Canadian Immigration data
Import Primary Modules. The first thing we'll do is import two key data analysis modules: pandas and Numpy.
import numpy as np # useful for many scientific computing in Python
import pandas as pd # primary data structure library
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.
You would need to run the following line of code to install the xlrd module:
!conda install -c anaconda xlrd --yes
Download 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 lab for the rational and detailed description of the changes.
Clean up the dataset to remove columns that are not informative to us for visualization (eg. Type, AREA, REG).
df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)
# let's view the first five elements and see how the dataframe was changed
df_can.head()
Notice how the columns Type, Coverage, AREA, REG, and DEV got removed from the dataframe.
Rename some of the columns so that they make sense.
df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)
# let's view the first five elements and see how the dataframe was changed
df_can.head()
Notice how the column names now make much more sense, even to an outsider.
For consistency, ensure that all column labels of type string.
# let's examine the types of the column labels
all(isinstance(column, str) for column in df_can.columns)
Notice how the above line of code returned False when we tested if all the column labels are of type string. So let's change them all to string type.
df_can.columns = list(map(str, df_can.columns))
# let's check the column labels types now
all(isinstance(column, str) for column in 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)
# let's view the first five elements and see how the dataframe was changed
df_can.head()
Notice how the country names now serve as indices.
Add total column.
df_can['Total'] = df_can.sum(axis=1)
# let's view the first five elements and see how the dataframe was changed
df_can.head()
Now the dataframe has an extra column that presents the total number of immigrants from each country in the dataset from 1980 - 2013. So if we print the dimension of the data, we get:
print ('data dimensions:', df_can.shape)
So now our dataframe has 38 columns instead of 37 columns that we had before.
# finally, let's create a list of years from 1980 - 2013
# this will come in handy when we start plotting the data
years = list(map(str, range(1980, 2014)))
years
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.style.use('ggplot') # optional: for ggplot-like style
# check for latest version of Matplotlib
print('Matplotlib version: ', mpl.__version__) # >= 2.0.0
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.style.use('ggplot') # optional: for ggplot-like style
# check for latest version of Matplotlib
print('Matplotlib version: ', mpl.__version__) # >= 2.0.0
A waffle chart is an interesting visualization that is normally created to display progress toward goals.
It is commonly an effective option when you are trying to add interesting visualization features to a visual that consists mainly of cells, such as an Excel dashboard.
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.
values: Values corresponding to categories or classes.
height: Defined height of waffle chart.
width: Defined width of waffle chart.
colormap: Colormap class
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.
Word clouds (also known as text clouds or tag clouds) work in a simple way: the more a specific word appears in a source of textual data (such as a speech, blog post, or database), the bigger and bolder it appears in the word cloud.
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!')
# 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!
Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
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')