I have a pandas dataframe with 10 columns and I want to fill missing values for all columns except one (lets say that column is called test). Currently, if I do this:
df.fillna(df.median(), inplace=True)
It replaces NA values in all columns with median value, how do I exclude specific column(s) without specifying ALL the other columns
you can use pd.DataFrame.drop to help out
df.drop('unwanted_column', 1).fillna(df.median())
Or pd.Index.difference
df.loc[:, df.columns.difference(['unwanted_column'])].fillna(df.median())
Or just
df.loc[:, df.columns != 'unwanted_column']
Input to difference function should be passed as an array (Edited).
Just select whatever columns you want using pandas' column indexing:
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame({'A': [np.nan, 5, 2, np.nan, 3], 'B': [np.nan, 4, 3, 5, np.nan], 'C': [np.nan, 4, 3, 2, 1]})
>>> df
A B C
0 NaN NaN NaN
1 5.0 4.0 4.0
2 2.0 3.0 3.0
3 NaN 5.0 2.0
4 3.0 NaN 1.0
>>> cols = ['A', 'B']
>>> df[cols] = df[cols].fillna(df[cols].median())
>>> df
A B C
0 3.0 4.0 NaN
1 5.0 4.0 4.0
2 2.0 3.0 3.0
3 3.0 5.0 2.0
4 3.0 4.0 1.0
