Python pandas.DataFrame: Make whole row NaN according to condition

Use boolean indexing for assign value per condition:

df[df['B'] > 5] = np.nan
print (df)
     A    B
0  1.0  4.0
1  3.0  5.0
2  NaN  NaN
3  NaN  NaN

Or DataFrame.mask which add by default NaNs by condition:

df = df.mask(df['B'] > 5)
print (df)
     A    B
0  1.0  4.0
1  3.0  5.0
2  NaN  NaN
3  NaN  NaN

Thank you Bharath shetty:

df = df.where(~(df['B']>5))

You can also use df.loc[df.B > 5, :] = np.nan

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Example

In [14]: df
Out[14]: 
   A  B
0  1  4
1  3  5
2  4  6
3  8  7

In [15]: df.loc[df.B > 5, :] = np.nan 

In [16]: df
Out[16]: 
     A    B
0  1.0  4.0
1  3.0  5.0
2  NaN  NaN
3  NaN  NaN

in human language df.loc[df.B > 5, :] = np.nan can be translated to:

assign np.nan to any column (:) of the dataframe ( df ) where the condition df.B > 5 is valid.