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Python - How to fill NAN values with mean in Pandas?
For mean, use the mean() function. Calculate the mean for the column with NaN and use the fillna() to fill the NaN values with the mean.
Let us first import the required libraries −
import pandas as pd import numpy as np
Create a DataFrame with 2 columns and some NaN values. We have entered these NaN values using numpy np.NaN −
dataFrame = pd.DataFrame(
{
"Car": ['BMW', 'Lexus', 'Lexus', 'Mustang', 'Bentley', 'Mustang'],"Units": [100, 150, np.NaN, 80, np.NaN, np.NaN]
}
)Finding mean of the column values with NaN i.e, for Units columns here. So, the Units column has 100, 150 and 80; therefore, the mean would be 110 −
meanVal = dataFrame['Units'].mean()
Replace NaNs with the mean of the column where it is located. The mean calculated above is 110, so NaN values will be replaced with 110 −
dataFrame['Units'].fillna(value=meanVal, inplace=True)
Example
Following is the code −
import pandas as pd
import numpy as np
# Create DataFrame
dataFrame = pd.DataFrame(
{
"Car": ['BMW', 'Lexus', 'Lexus', 'Mustang', 'Bentley', 'Mustang'],"Units": [100, 150, np.NaN, 80, np.NaN, np.NaN]
}
)
print"DataFrame ...\n",dataFrame
# finding mean of the column values with NaN i.e, for Units columns here
# so the Units column has 100, 150 and 80; therefore the mean would ne 110
meanVal = dataFrame['Units'].mean()
# Replace NaNs with the mean of the column where it is located
# the mean calculated above is 110, so NaN values will be replaced with 110
dataFrame['Units'].fillna(value=meanVal, inplace=True)
print"\nUpdated Dataframe after filling NaN values with mean...\n",dataFrameOutput
This will produce the following output −
DataFrame ... Car Units 0 BMW 100.0 1 Lexus 150.0 2 Lexus NaN 3 Mustang 80.0 4 Bentley NaN 5 Mustang NaN Updated Dataframe after filling NaN values with mean... Car Units 0 BMW 100.0 1 Lexus 150.0 2 Lexus 110.0 3 Mustang 80.0 4 Bentley 110.0 5 Mustang 110.0
Updated on: 20-Sep-2021
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