The widely used relational database management system is known as MysqlDB. The MysqlDB doesn’t understand and accept the value of ‘Nan’, thus there is a need to convert the ‘Nan’ value coming from Pandas or Numpy to ‘None’. In this article, we will see how we can replace Pandas or Numpy ‘Nan’ with a ‘None’.

Replacing NaN with None in Pandas

Example 1: The data frame, for which ‘Nan’ is to be replaced with ‘None’, is as follows:

The provided code uses the Pandas library to replace ‘NaN’ values in a DataFrame df with ‘None’. It does so by using the replace() method with a dictionary mapping where keys (in this case, ‘np.nan’) are replaced by their corresponding values (in this case, ‘None’). The resulting DataFrame ‘replaced_df’ contains ‘None’ in place of ‘NaN’ values.

Output:

Example 2: The data frame, for which ‘Nan’ is to be replaced with ‘None’, is as follows:

The provided code uses the Pandas library to replace ‘NaN’ values in a DataFrame df with ‘None’. It does so by using the replace() method with a dictionary mapping where keys (in this case, ‘np.nan’) are replaced by their corresponding values (in this case, ‘None’). The resulting DataFrame ‘replaced_df’ contains ‘None’ in place of ‘NaN’ values.

Output

Replacing NaN with None in NumPy

Example 1: The data frame, for which ‘Nan’ is to be replaced with ‘None’ , is as follows:

[ 1. nan  3.  6.  7.]

It creates a NumPy array named temp, replaces the np.nan values with None using np.where, and then prints the modified temp array.

Output:

[1.0 None 3.0 6.0 7.0]

Example 2: The data frame, for which ‘Nan’ is to be replaced with ‘None’ , is as follows:

[ 4.  5. nan nan  7.]

It creates a NumPy array named temp, replaces the np.nan values with None using np.where, and then prints the modified temp array.

Output

[4.0 5.0 None None 7.0]