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Python Pandas - Pie Plot

A pie plot is a circular chart that shows data as slices, with each slice representing a portion of the dataset. It is useful for visualizing the relative sizes of different data values. In a pie chart, the size of each slice, including its angle, area, and arc length, corresponds to the value or percentage it represents. The total of all percentages equals 100%, and the sum of all slice angles is 360.

For instance, consider a dataset that records the pass percentages of boys and girls in a classroom. A pie plot visually represents this data, where each slice indicates the proportion of boys and girls passing.

In this tutorial, we will learn how to create and customize pie plots in Python using the Pandas library.

In Pandas, pie plot can be created using the plot.pie() method for both the Series and DataFrames objects. This method internally use Matplotlib and return either a matplotlib.axes.Axes object or NumPy array np.ndarray of Axes when subplots parameter is set to True.

• DataFrame.plot.pie(): Creates pie plot for one or more columns in a DataFrame.

• Series.plot.pie(): Creates a pie plot for a specific column or Series.

Following is the syntax of the plot.pie() method for both the Series and DataFrames objects −

Series.plot.pie(**kwargs)

Where,

• y: Specify the column label or position to plot. Required when using DataFrame.plot.pie() without subplots=True.

• **kwargs: Additional keyword arguments to customize the plot appearance.

This example demonstrates using the Series.plot.pie() method on a pandas Series object to generate the pie plot.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create a Pandas Series
series = pd.Series(3 * np.random.rand(4), index=["a", "b", "c", "d"])

# Generate a pie plot
series.plot.pie(figsize=(7, 4))

# Set title and Display the plot
plt.title('Basic Pie Plot')
plt.show()

Following is the output of the above code −

This example uses the DataFrame.plot.pie() method for the creating the pie plot.

import pandas as pd
import matplotlib.pyplot as plt

# Create a DataFrame with pass percentages
data = {"Category": ["Boys", "Girls"], "Pass Percentage": [75, 85]}

df = pd.DataFrame(data)

# Plotting the pie chart
df.plot.pie(y="Pass Percentage", labels=df["Category"], figsize=(7, 4))

# Set title and Display the plot
plt.title('Pie Plot for DataFrame')
plt.show()

After executing the above code, we get the following output −

Pandas allows customization of pie plots through various parameters such as, labels, colors, autopct, fontsize, and more.

This example demonstrates customizing the pie plot using the addition keyword arguments.

import pandas as pd
import matplotlib.pyplot as plt

# Create a DataFrame with pass percentages
data = {
    "Category": ["Boys", "Girls"],
    "Pass Percentage": [75, 85]
}

df = pd.DataFrame(data)

# Plotting the pie chart
df.plot.pie(y="Pass Percentage", labels=df["Category"], 
autopct="%.1f%%", colors=["skyblue", "pink"], figsize=(7, 4))

# Set title and 
plt.title('Customizing a Pie Plot')
plt.show()

Following is the output of the above code −

When plotting a pie plot from a DataFrame, you must specify a column using the y parameter or use subplots=True to plot multiple pie charts for all numerical columns.

This example demonstrates creating pie plot for all DataFrame columns in a multiple subplots.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Create a DataFrame 
df = pd.DataFrame(3 * np.random.rand(4, 2), 
index=["a", "b", "c", "d"], 
columns=["Column1", "Column2"])

# Plotting the pie chart
df.plot.pie(subplots=True, autopct="%.1f%%", figsize=(7, 4), title='Pie Plots for All Columns')

# Display the Plot
plt.show()

Following is the output of the above code −

Pandas plot.pie() method easily handles the missing values (NaN), if your data contains any. The method automatically fills the missing data with 0 when creating a pie plot.

The following example shows how the plot.pie() method handles the missing values.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create a Pandas Series
series = pd.Series([1, 2, np.nan, 4], index=["A", "B", "C", "D"])

# Generate a pie plot
series.plot.pie(figsize=(7, 4))

# Set title and Display the plot
plt.title('Pie Plot Handling Missing Values')
plt.show()

On executing the above code we will get the following output −

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