Python seaborn pie chart

How to Create a Pie Chart in Seaborn

We do not have any built-in function to create Pie chart in seaborn, but with the help of Matplotlib, we can create a pie chart and leverage seaborn for color pallets.

A pie chart (or a circle chart) is a circular statistical graphic, which is divided into slices to illustrate numerical proportion. In this article, let us take a look at creating a pie chart using Matplotlib and Seaborn with examples.

Create a simple Pie chart in Seaborn

Let’s say we need to plot a pie chart for sales delivery of cars sold in different locations.

The Pie chart below demonstrates the proportion of numerical sales data for different locations. We represent the sales data in the form of slices to understand it easily.

In the below example, we have used Matplotlib to draw a pie chart, and we are using a pastel color pallet of seaborn.

The seaborn color_palette() method accepts the color pallet in the string format, and it has a wide variety of color pallets such as dark, bright, pastel, Paired etc.

import matplotlib.pyplot as plt import seaborn as sns # car sales data total_sales = [3000, 2245, 1235, 5330, 4200] location = ['Bangalore', 'Delhi', 'Chennai', 'Mumbai', 'Kolkatta'] # Seaborn color palette to plot pie chart colors = sns.color_palette('pastel') # create pie chart using matplotlib plt.pie(total_sales, labels=location, colors=colors) plt.show() 

Create an advanced Pie chart in Seaborn

• We have used autopct property to set the percentage of sales inside each slice, making it more effective.
• We have the highest car sales in Mumbai. We can represent it differently by making it a standout slice using explode property.
• We can rotate the labels in the direction of the slices using the rotatelabel property.
• We have changed the color pallet to bright to make the pie chart look more attractive.

import matplotlib.pyplot as plt import seaborn as sns # car sales data total_sales = [3000, 2245, 1235, 5330, 4200] explode = [0, 0, 0, 0.2, 0] location = ['Bangalore', 'Delhi', 'Chennai', 'Mumbai', 'Kolkatta'] # Seaborn color palette to plot pie chart colors = sns.color_palette('bright') # create pie chart using matplotlib plt.pie(total_sales, labels=location, colors=colors, autopct='%.0f%%', explode=explode, shadow=True, rotatelabels='true') plt.show() 

Output

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Как создать круговую диаграмму в Seaborn

В библиотеке визуализации данных Python Seaborn нет функции по умолчанию для создания круговых диаграмм, но вы можете использовать следующий синтаксис в Matplotlib для создания круговой диаграммы и добавления цветовой палитры Seaborn:

import matplotlib.pyplot as plt import seaborn as sns #define data data = [value1, value2, value3, . ] labels = ['label1', 'label2', 'label3', . ] #define Seaborn color palette to use colors = sns.color_palette('pastel')[ 0:5 ] #create pie chart plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%') plt.show() 

Полный список цветовых палитр см. в документации Seaborn.

В следующих примерах показано, как использовать этот синтаксис на практике.

Пример 1. Круговая диаграмма с цветовой палитрой Pastel Seaborn

В следующем коде показано, как создать круговую диаграмму с использованием « пастельной » цветовой палитры Seaborn:

import matplotlib.pyplot as plt import seaborn as sns #define data data = [15, 25, 25, 30, 5] labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4', 'Group 5'] #define Seaborn color palette to use colors = sns.color_palette('pastel')[ 0:5 ] #create pie chart plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%') plt.show() 

Пример 2. Круговая диаграмма с цветовой палитрой Bright Seaborn

В следующем коде показано, как создать круговую диаграмму с использованием « яркой » цветовой палитры Seaborn:

import matplotlib.pyplot as plt import seaborn as sns #define data data = [15, 25, 25, 30, 5] labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4', 'Group 5'] #define Seaborn color palette to use colors = sns.color_palette('bright')[ 0:5 ] #create pie chart plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%') plt.show() 

Эти два примера иллюстрируют, как создать круговую диаграмму с двумя разными цветовыми палитрами Seaborn.

Тем не менее, есть еще много стилей, которые вы могли бы использовать. Полный список цветовых палитр см. в онлайн-документации .

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How to use Matplotlib and Seaborn to draw pie charts (or their alternatives) in Python?

As part of your data wrangling process you’ll probably need to quickly aggregate and visualize your data, so you can make sense of it, specially when working with huge data sources.

In today’s tutorial we’ll leverage several Python libraries to create some simple pie charts that will help you better document and visualize your analysis.

Pie Charts with Python

In this and other examples, we’ll use a fictitious deliveries dataset. If you would like to follow along through the analysis below, you can download the deliveries dataset from this location.

Let’s get started by importing the Python data analysis libraries into our environment.

#Python3 import pandas as pd import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline sns.set()

Now we’ll acquire the Deliveries dataset.

#import the deliveries data into a Pandas dataframe #it is assumed that the dataset is saved in the same directory of your notebook deliveries = pd.read_csv("deliveries.csv")

We’ll now going to look into the DataFrame header, to get familiar with the data:

Simple Charting in Pandas

For this example, we would like to analyze the tips our delivery drivers received for the different delivery areas (Food, Medicine etc’). We have acquired the data, so we can move on to some data visualization.

#groupby the data by delivery type data = deliveries.groupby("type")["del_tip"].sum() data

type Food 12 Gear 15 Groceries 13 Medicines 18 Name: del_tip, dtype: int64

Now we are able to use the Matplotlib engine embedded in Pandas to quickly show a pie plot:

Here’s the rather simple chart we got:

Note the use of autopct which allows us to format the percentage string, so we can show the precise percentage values.

Example of Matplotlib Pie Charts

That was pretty easy. Let’s now use Matplotlib to make something a bit more fancy here:

#Using matplotlib pie, ax = plt.subplots(figsize=[10,6]) labels = data.keys() plt.pie(x=data, autopct="%.1f%%", explode=[0.05]*4, labels=labels, pctdistance=0.5) plt.title("Delivery Tips by type", fontsize=14); pie.savefig("DeliveryPieChart.png")

The result looks a bit cleaner now:

We added a title and also exploded the pie for clarity. Lastly, we used the plt.savefig() method to export the chart as a graphic file to be used in a presentation or uploaded to a web page.

Can we have Seaborn pie charts?

Pie charts are not directly available in Seaborn, but the sns bar plot chart is a good alternative that Seaborn has readily available for us to use.

As we don’t have the autopct option available in Seaborn, we’ll need to define a custom aggregation using a lambda function to calculate the percentage column.

Using the data from the previous example:

data_per = data.groupby('type').agg(percentage =('del_tip', lambda p: p.sum() / data.sum() * 100)).round(2)

We’ll then use the Seaborn library to render a barplot:

#Seaborn barplot sns.set_style("whitegrid") bar,ax = plt.subplots(figsize=(10,6)) ax = sns.barplot(x=data_per.index, y='percentage', data=data_per, ci=None, palette="muted",orient='v', ) ax.set_title("Pie chart approximation in Seaborn - Total Tip by Delivery Type", fontsize=15) ax.set_xlabel ("Delivery Type") ax.set_ylabel ("Percentage") # calculate the percentages and annotate the sns barplot for rect in ax.patches: ax.text (rect.get_x() + rect.get_width() / 2,rect.get_height(),"%.1f%%"% rect.get_height(), weight='bold' ) bar.savefig("Seaborn_Pie_Chart.png");

Result is not exactly a pie chart, but useful to show the distribution by percentage:

Note: If you are just beginning with Matplotlib/Seaborn then adding labels to barplots patches in Seaborn is not a trivial task, suggest to look into the “for” loop i implemented to accomplish that.

Here’s a variation, the key difference is the fact we are using an horizontal chart (so obviously had to tweak the locations of the data labels accordingly):

#Seaborn Horizontal barplot sns.set_style("whitegrid") bar,ax = plt.subplots(figsize=(10,6)) ax = sns.barplot(x="percentage", y=data_per.index, data=data_per, ci=None, palette="muted",orient='h' ) ax.set_title("Pie chart approximation in Seaborn - Total Tip by Delivery Type", fontsize=15) ax.set_xlabel ("Percentage") ax.set_ylabel ("Delivery Type") for rect in ax.patches: ax.text (rect.get_width(), rect.get_y() + rect.get_height() / 2,"%.1f%%"% rect.get_width(), weight='bold' ) bar.savefig("Seaborn_Bar_Vertical.png");

If you are just beginning with Seaborn, you might want to take a look at our detailed examples on countplots and histograms that you can create in Seaborn.

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