Python | Visualizing missing values in python using matplotlib

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Plotting missing values

In Python, you can visualize missing values (NaN or null values) in a Pandas dataframe using the missingno library. This library provides several functions for visualizing missing values, including a matrix plot and a bar chart.

Here's an example of how to use the missingno library to visualize missing values in a Pandas dataframe:

import pandas as pd

import missingno as msno

# create a sample dataframe with missing values

data = {'name': ['John', 'Jane', 'Mike', 'Susan'],

'age': [30, None, None, 40],

'gender': ['M', 'F', None, 'F']}

df = pd.DataFrame(data)

# visualize missing values in the dataframe using a matrix plot

msno.matrix(df)

The white spaces in the matrix represent missing values. In this example, we can see that there are two missing values in the age column and one missing value in the gender column.

You can also visualize the number of missing values in each column using a bar chart:

# visualize missing values in the dataframe using a bar chart

msno.bar(df)

This will output a bar chart that shows the number of missing values in each column of the dataframe:

In this example, we can see that the age and gender columns each have two missing values.