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The pandas fillna() 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.