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The pandas function is useful for filling in missing values in columns of a pandas DataFrame.

This tutorial provides several examples of how to use this function to fill in missing values for multiple columns of the following pandas DataFrame:

import pandas as pd
import numpy as np

#create DataFrame
df = pd.DataFrame({'team': ['A', np.nan, 'B', 'B', 'B', 'C', 'C', 'C'],
                   'points': [25, np.nan, 15, np.nan, 19, 23, 25, 29],
                   'assists': [5, 7, 7, 9, 12, 9, np.nan, 4],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})

#view DataFrame
print(df)

  team  points  assists  rebounds
0    A    25.0      5.0        11
1  NaN     NaN      7.0         8
2    B    15.0      7.0        10
3    B     NaN      9.0         6
4    B    19.0     12.0         6
5    C    23.0      9.0         5
6    C    25.0      NaN         9
7    C    29.0      4.0        12

Example 1: Fill in Missing Values of All Columns

The following code shows how to fill in missing values with a zero for all columns in the DataFrame:

#replace all missing values with zero
df.fillna(value=0, inplace=True)

#view DataFrame
print(df) 

  team  points  assists  rebounds
0    A    25.0      5.0        11
1    0     0.0      7.0         8
2    B    15.0      7.0        10
3    B     0.0      9.0         6
4    B    19.0     12.0         6
5    C    23.0      9.0         5
6    C    25.0      0.0         9
7    C    29.0      4.0        12

Example 2: Fill in Missing Values of Multiple Columns

The following code shows how to fill in missing values with a zero for just the points and assists columns in the DataFrame:

#replace missing values in points and assists columns with zero
df[['points', 'assists']] = df[['points', 'assists']].fillna(value=0)

#view DataFrame
print(df) 

  team  points  assists  rebounds
0    A    25.0      5.0        11
1  NaN     0.0      7.0         8
2    B    15.0      7.0        10
3    B     0.0      9.0         6
4    B    19.0     12.0         6
5    C    23.0      9.0         5
6    C    25.0      0.0         9
7    C    29.0      4.0        12

Example 3: Fill in Missing Values of Multiple Columns with Different Values

The following code shows how to fill in missing values in three different columns with three different values:

#replace missing values in three columns with three different values
df.fillna({'team':'Unknown', 'points': 0, 'assists': 'zero'}, inplace=True)

#view DataFrame
print(df)

      team  points assists  rebounds
0        A    25.0       5        11
1  Unknown     0.0       7         8
2        B    15.0       7        10
3        B     0.0       9         6
4        B    19.0      12         6
5        C    23.0       9         5
6        C    25.0    zero         9
7        C    29.0       4        12

Notice that each of the missing values in the three columns were replaced with some unique value.