We also want to be able to understand what each slice of the pie represents. To do this, we can either:
- use a legend to label each color, or
- put labels on the chart itself.
budget_data = [500, 1000, 750, 300, 100] budget_categories = ['marketing', 'payroll', 'engineering', 'design', 'misc'] plt.pie(budget_data) plt.legend(budget_categories)
This puts the category names into a legend on the chart:
#option 2 plt.pie(budget_data, labels=budget_categories)
This puts the category names into labels next to each corresponding slice:
One other useful labeling tool for pie charts is adding the percentage of the total that each slice occupies. Matplotlib can add this automatically with the keyword
autopct. We pass in string formatting instructions to format the labels how we want. Some common formats are:
'%0.2f'— 2 decimal places, like
'%0.2f%%'— 2 decimal places, but with a percent sign at the end, like
4.08%. You need two consecutive percent signs because the first one acts as an escape character, so that the second one gets displayed on the chart.
'%d%%'— rounded to the nearest
intand with a percent sign at the end, like
So, a full call to
plt.pie might look like:
plt.pie(budget_data, labels=budget_categories, autopct='%0.1f%%')
and the resulting chart would look like:
Add a legend to the chart you made in the previous exercise by passing in a list of labels to
plt.legend. For the labels, use the list
Add a percentage to each slice using Matplotlib’s
autopct parameter. Go to one decimal point of precision.