.fillna()

The .fillna() function returns a new DataFrame object with NA values replaced with a specified value. The original DataFrame object, used to call the method, remains unchanged.

Syntax

df = dataframevalue.fillna(value)

dataframevalue is the DataFrame with the source data and value is the value used to fill holes. value can be a scalar such as 0, or it can be a DataFrame specifying replacement values for each column. Column labels not in value won’t be filled.

.fillna() has the following parameters:

Example

In the following example, the .fillna() method is used to fill in NA values in a DataFrame first with a scalar, then with a dict:

import pandas as pd
import numpy as np

d = {'col 1' : [1,2,3,np.nan], 'col 2' : ['A','B',np.nan,'D'], 'col 3' : [5,np.nan,7,8], 'col 4' : [np.nan,'F','G','H']}
df = pd.DataFrame(data = d)

print(f'Original df:\n{df}\n')

first_fillna = df.fillna(0)
print(f'After first fillna():\n{first_fillna}\n')

second_fillna = df.fillna({'col 1':0,'col 2':'X','col 3':0,'col 4':'X'})
print(f'After second fillna():\n{second_fillna}\n')

The output from these instances of the .fillna() method is shown below:

Original df:
   col 1 col 2  col 3 col 4
0    1.0     A    5.0   NaN
1    2.0     B    NaN     F
2    3.0   NaN    7.0     G
3    NaN     D    8.0     H

After first fillna():
   col 1 col 2  col 3 col 4
0    1.0     A    5.0     0
1    2.0     B    0.0     F
2    3.0     0    7.0     G
3    0.0     D    8.0     H

After second fillna():
   col 1 col 2  col 3 col 4
0    1.0     A    5.0     X
1    2.0     B    0.0     F
2    3.0     X    7.0     G
3    0.0     D    8.0     H