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Filling NaN values with the mode in pandas can be done by using the fillna() function to replace the NaN values with the mode of the dataframe. This is done by passing the mode value to the fillna() function. The mode is calculated by using the mode() function on the dataframe, and the resulting mode is passed as an argument to the fillna() function. The fillna() function will then replace the NaN values with the mode value.
You can use the following syntax to replace NaN values in a column of a pandas DataFrame with the mode value of the column:
df['col1'] = df['col1'].fillna(df['col1'].mode()[0])
The following example shows how to use this syntax in practice.
Example: Replace Missing Values with Mode in Pandas
Suppose we have the following pandas DataFrame with some missing values:
import numpy as np import pandas as pd #create DataFrame with some NaN values df = pd.DataFrame({'rating': [np.nan, 85, np.nan, 88, 94, 90, 75, 75, 87, 86], 'points': [25, np.nan, 14, 16, 27, 20, 12, 15, 14, 19], 'assists': [5, 7, 7, np.nan, 5, 7, 6, 9, 9, 7], 'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]}) #view DataFrame df rating points assists rebounds 0 NaN 25.0 5.0 11 1 85.0 NaN 7.0 8 2 NaN 14.0 7.0 10 3 88.0 16.0 NaN 6 4 94.0 27.0 5.0 6 5 90.0 20.0 7.0 9 6 75.0 12.0 6.0 6 7 75.0 15.0 9.0 10 8 87.0 14.0 9.0 10 9 86.0 19.0 7.0 7
We can use the fillna() function to fill the NaN values in the rating column with the mode value of the rating column:
#fill NaNs with column mode in 'rating' column df['rating'] = df['rating'].fillna(df['rating'].mode()[0]) #view updated DataFrame df rating points assists rebounds 0 75.0 25.0 5.0 11 1 85.0 NaN 7.0 8 2 75.0 14.0 7.0 10 3 88.0 16.0 NaN 6 4 94.0 27.0 5.0 6 5 90.0 20.0 7.0 9 6 75.0 12.0 6.0 6 7 75.0 15.0 9.0 10 8 87.0 14.0 9.0 10 9 86.0 19.0 7.0 7
The mode value in the rating column was 75 so each of the NaN values in the rating column were filled with this value.
Note: You can find the complete online documentation for the fillna() function .