1

From the data frame below, how to delete all the rows where columns [B, C, D] all are having null values?

Input data frame:

    A    B    C    D
0  20  NaN  2.0  NaN
1   3  1.0  NaN  2.0
2  44  4.0  NaN  NaN
3  67  NaN  NaN  NaN
4  52  3.0  2.0  NaN
5   8  NaN  NaN  NaN

Desired output:

    A    B    C    D
0  20  NaN  2.0  NaN
1   3  1.0  NaN  2.0
2  44  4.0  NaN  NaN
4  52  3.0  2.0  NaN
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3 Answers 3

Reset to default
3

Let us try dropna

df=df.dropna(thresh=1)
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1 Comment

Thank you . It does the job for above question.
1

Personally, I would use pandas.DataFrame.dropna() method with its parameters how and subset. The code is following.

>>> df = df.dropna(how='all', subset=['B', 'C', 'D'])
>>> print(df)
    A    B    C    D
0  20  NaN  2.0  NaN
1   3  1.0  NaN  2.0
2  44  4.0  NaN  NaN
4  52  3.0  2.0  NaN
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2 Comments

Thank you.That did the trick.I find this method most comfortable.
@jjnair, I am glad I could have helped. However, If answer is correct and helpful, also consider accepting it.
1

You can count the (non NaN) value across a subset of columns:

df[df[['B', 'C', 'D']].count(axis=1) > 0]

It gives as expected:

    A    B    C    D
0  20  NaN  2.0  NaN
1   3  1.0  NaN  2.0
2  44  4.0  NaN  NaN
4  52  3.0  2.0  NaN

and only focuses on the required columns

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