How to Make a Countplot in Seaborn

A countplot is a bar chart that shows the number of observations for each category of a categorical variable. It is a simple and effective way to visualize the distribution of a categorical variable.

sns basic countplot

How to Create a Countplot in Seaborn

To create a countplot in Seaborn, you can use the countplot function. The syntax is as follows:

sns.countplot(x=column_name, data=dataframe)

where column_name is the name of the categorical variable that you want to plot, and dataframe is the name of the dataframe that contains the data.

For example, the following code creates a countplot of the passengers column in the taxis dataset:

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('taxis')

sns.countplot(x='passengers', data=df)
plt.show()

This code will create a bar chart that shows the number of taxis with each number of passengers.

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sns basic countplot

The countplot function has several parameters that you can use to customize the appearance of the countplot.

Countplot rotation

To flip the chart, just change the x argument to y.

sns.countplot(y='passengers', data=df)

sns y countplot

You can also use a orient parameter and set v for a vertical alignment or h for a horizontal one.

Countplot hue

To increase the amount of data in the chart you may use the hue parameter. It is adding an additional sets of data to your counterplot graph. I decided to visualize payments with color of the taxi to check the relationship between them.

sns.countplot(x='payment', data=df, hue='color')

sns countplot hue

Countplot hue_order

The hue_order parameter gives you the possibility of changing the order of a hue. I used simply df[‘color’].value_counts().index[::-1] structure to change the order by index in the reverse order.

sns.countplot(x='payment', data=df, hue='color',
              hue_order = df['color'].value_counts().index[::-1])

sns countplot hue order

Countplot order

To sort the data series use the order parameter. I picked df[‘passengers’].value_counts().index to sort counts.

sns.countplot(x='passengers', data=df, 
              order=df['passengers'].value_counts().index)

sns countplot order

Countplot ascending order

To sort the data series in ascending order, add the reverse indexing using index[::-1].

sns.countplot(x='passengers', data=df, 
              order=df['passengers'].value_counts().index[::-1])

sns countplot order ascending

Countplot color

It is also possible to change the color of your python countplot. I set the color parameter to blue.

sns.countplot(x='passengers', data=df, color='blue')

sns countplot color blue

Countplot palette

If one color is not enough for you or you want to make your chart look more attractive, use the palette parameter. I dediced to use the inferno.

sns.countplot(x='passengers', data=df, palette='inferno')

sns countplot palette inforno

Countplot saturation

For those who care about details, you can also change the color saturation. The default is 0.75. Watch the saturation change as you lower it to 0.2.

sns.countplot(x='passengers', data=df, saturation=0.2)

sns countplot saturation

Countplot dodge

To stack data in a countplot, use a dodge. By default, dodge is set to true. If you change this parameter to false, the count graph will change like this. The data has been accumulated.

sns.countplot(x='payment', data=df, hue='color', dodge=False)

sns countplot dodge

These are all the parameters of the countplot method that I wanted to discuss for you. I hope you will improve your charting skills at Seaborn with this python course.

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For more information on the countplot function, you can refer to the Seaborn documentation.