Introduction to isnull().mean() in Python

isnull().mean() is a function in Python that is used to find the percentage of missing values in a DataFrame. This function is available in the Pandas library.

Syntax

The syntax of the isnull().mean() function is as follows:

dataframe.isnull().mean()

Here, dataframe is the name of the Pandas DataFrame that we want to analyze.

Working

The isnull() method returns a DataFrame consisting of boolean values that indicate whether each element of the original DataFrame is missing or not.

After that, the mean() method is applied to calculate the mean of the boolean values across each column. This gives us the percentage of missing values in each column.

For example, if we have a Pandas DataFrame like this:

import pandas as pd

data = {'A': [1, 2, None, 4, None], 
        'B': [None, 6, 7, None, 9], 
        'C': [10, 11, 12, None, 14]}

df = pd.DataFrame(data)

The output of df would be:

     A    B     C
0  1.0  NaN  10.0
1  2.0  6.0  11.0
2  NaN  7.0  12.0
3  4.0  NaN   NaN
4  NaN  9.0  14.0

Now, we can apply the isnull().mean() function to find the percentage of missing values in each column:

percent_missing = df.isnull().mean()

print(percent_missing)

The output would be:

A    0.4
B    0.4
C    0.2
dtype: float64

This output tells us that column A and column B both have 40% missing values, while column C has 20% missing values.

Conclusion

In summary, isnull().mean() is a useful function in Python that can help us analyze missing values in a Pandas DataFrame. By using this function, we can quickly find out the percentage of missing values in each column, which can help us identify potential issues with our data.