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I have a dataframe like below:

A B C 4.43 NaN 1.11 3.70 0.48 0.79 2.78 -0.29 1.26 1.78 2.90 1.13 40.70 -0.03 0.55 51.75 0.29 1.45 3.65 1.74 0.37 2.93 1.56 1.64 3.43 NaN NaN 2.93 NaN NaN 10.37 NaN NaN

now If Column A > 7, I want to drop Column B and C like below:

A B C 4.43 NaN 1.11 3.70 0.48 0.79 2.78 -0.29 1.26 1.78 2.90 1.13 40.70 NaN NaN 51.75 NaN NaN 3.65 1.74 0.37 2.93 1.56 1.64 3.43 NaN NaN 2.93 NaN NaN 10.37 NaN NaN

How can I achieve that?

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2 Answers 2

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5

Use DataFrame.mask with default value NaN for replace by mask:

df[['B','C']] = df[['B','C']].mask(df.A > 7)

Or DataFrame.loc with specify np.nan:

df.loc[df.A > 7, ['B','C']] = np.nan 
print (df) A B C 0 4.43 NaN 1.11 1 3.70 0.48 0.79 2 2.78 -0.29 1.26 3 1.78 2.90 1.13 4 40.70 NaN NaN 5 51.75 NaN NaN 6 3.65 1.74 0.37 7 2.93 1.56 1.64 8 3.43 NaN NaN 9 2.93 NaN NaN 10 10.37 NaN NaN
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Another possible answer:

df[['B', 'C']] = df[['B', 'C']].loc[df['A'] <= 7] print(df) A B C 0 4.43 NaN 1.11 1 3.70 0.48 0.79 2 2.78 -0.29 1.26 3 1.78 2.90 1.13 4 40.70 NaN NaN 5 51.75 NaN NaN 6 3.65 1.74 0.37 7 2.93 1.56 1.64 8 3.43 NaN NaN 9 2.93 NaN NaN 10 10.37 NaN NaN
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