I try to drop null values of column 'Age' in dataframe, which consists of float values, but it doesn't work. I tried
data.dropna(subset=['Age'], how='all')
data['Age'] = data['Age'].dropna()
data=data.dropna(axis=1,how='all')
It works for other columns but not for 'Age'
    Pclass  Fare    Age Sex
0   3   7.2500  22.0    1
1   1   71.2833 38.0    0
2   3   7.9250  26.0    0
3   1   53.1000 35.0    0
4   3   8.0500  35.0    1
5   3   8.4583  NaN 1
6   1   51.8625 54.0    1
7   3   21.0750 2.0 1
                Remove All Duplicate Rows from Pandas DataFrame You can set 'keep=False' in the drop_duplicates() function to remove all the duplicate rows. For E.x, df. drop_duplicates(keep=False) .
The drop_duplicates() method removes duplicate rows. Use the subset parameter if only some specified columns should be considered when looking for duplicates.
data.dropna(subset=['Age']) would work, but you should either set inplace=True or assign it back to data:
data = data.dropna(subset=['Age'])
or
data.dropna(subset=['Age'], inplace=True)
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