Learn
Different Plot Types
Pie Chart Labeling

We also want to be able to understand what each slice of the pie represents. To do this, we can either:

1. use a legend to label each color, or
2. put labels on the chart itself.

#### Method 1

``````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:

#### Method 2

``````#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 `4.08`
• `'%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 `int` and with a percent sign at the end, like `4%`.

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:

### Instructions

1.

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 `payment_method_names`.

2.

Add a percentage to each slice using Matplotlib’s `autopct` parameter. Go to one decimal point of precision.