python-scikit-learnUse imputer for one column only
import numpy as np
import pandas as pd
from sklearn import impute
im = impute.SimpleImputer(missing_values=np.nan, strategy='mean')
df = pd.DataFrame([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]], columns=['A', 'B', 'C'])
df['B'] = im.fit_transform(df[['B']])ctrl + cfrom sklearn importimport module from scikit-learn | import numpyimport Numpy module |
import pandas as pdload Pandas module | .SimpleImputer(univariate imputer for completing missing values with simple strategies. |
df[['B']]impute values for | |
Usage example
import numpy as np
import pandas as pd
from sklearn import impute
im = impute.SimpleImputer(missing_values=np.nan, strategy='mean')
df = pd.DataFrame([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]], columns=['A', 'B', 'C'])
df['B'] = im.fit_transform(df[['B']])
print(df)output
A B C
0 7 2.0 3
1 4 3.5 6
2 10 5.0 9
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