Split pandas dataframe on boolian using boolian function

Question

I have a function

def return_true_false(a,b,c):
    '''
    returns true if stuff, else returns false
    '''

I then apply this function to a Dataframe twice to split the dataframe on the result

df_True =  df[df.apply(lambda x: return_true_false(x[a],x[b],x[c]),axis=1)]
df_false =  df[df.apply(lambda x: not return_true_false(x[a],x[b],x[c]),axis=1)]

However, this does the calculation twice on each row. My question is, is there a way to split this dataset on a function and only go through the dataset once?

Chris Adams · Accepted Answer · 2019-03-08 16:43:36Z

1

IIUC, run it once assigning the result (to mask for example), then using boolean indexing:

mask = df.apply(lambda x: return_true_false(x[a],x[b],x[c]),axis=1)

df_True = df[mask]
df_false = df[~mask]

answered Mar 8, 2019 at 16:43

Chris Adams

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1 Comment

Phil Over a year ago

Thank you! This seems so obvious in hindsight

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