more about Pandas入门 DataFrame的常用函数
Waffle Charts Tutorial
Prepping Data
Let’s download, import and clean our primary Canadian Immigration dataset using pandas read_excel() method for any visualization.
df_can = pd.read_excel('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx',
sheet_name='Canada by Citizenship',
skiprows=range(20),
skipfooter=2)
print('Data downloaded and read into a dataframe!')
# 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)
Waffle Charts
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.
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
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.
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
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))
Denmark: 0.32255663965602777
Norway: 0.1924094592359848
Sweden: 0.48503390110798744
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)
Total number of tiles is 400
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))
Denmark: 129
Norway: 77
Sweden: 194
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!')
Waffle chart populated!
Let’s take a peek at how the matrix looks like.
waffle_chart
array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,
2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2.,
2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3.]])
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?
Function packed
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)
Total number of tiles is 400
Denmark: 129
Norway: 77
Sweden: 194
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.