How to replace NaN values by Zeroes in a column of a Pandas DataFrame?

To replace NaN values by zeroes or other values in a column of a Pandas DataFrame, we can use df.fillna() method.

Steps

• Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.

• Print the input DataFrame, df.

• Use df.fillna(0) to replace NaN in DataFrame with value 0.

• Similarly use df.fillna(5) and df.fillna(7) to replace NaN in DataFrame with 5 and 7, respectively.

• Print the replaced NaN, DataFrame.

Example

 Live Demo

import pandas as pd import numpy as np df = pd.DataFrame(    {       "x": [5, np.nan, 1, np.nan],       "y": [np.nan, 1, np.nan, 10],       "z": [np.nan, 1, np.nan, np.nan]    } ) print "Input series is:
", df print "After replacing NaN with 0:
", df.fillna(0) print "After replacing NaN with 5:
", df.fillna(5) print "After replacing NaN with 7:
", df.fillna(7)

Output

Input series is:     x  y    z 0 5.0  NaN  NaN 1 NaN  1.0  1.0 2 1.0  NaN  NaN 3 NaN  10.0 NaN After replacing NaN with 0:     x   y    z 0 5.0   0.0  0.0 1 0.0   1.0  1.0 2 1.0   0.0  0.0 3 0.0  10.0  0.0 After replacing NaN with 5:    x    y    z 0 5.0  5.0   5.0 1 5.0  1.0   1.0 2 1.0  5.0   5.0 3 5.0  10.0  5.0 After replacing NaN with 7:     x    y     z 0  5.0  7.0   7.0 1  7.0  1.0   1.0 2  1.0  7.0   7.0 3  7.0  10.0  7.0