is there a way we can use when we apply a condition on a df and get two return values: one fulfilling the condition and the second with remaining part?
df_with_condition, df_without_condition = df.[some conditional actions]
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You could split the splitting command into two parts:
df_with_condition = df.[some conditional actions]
df_without_condition = df.[~(some conditional actions)]
Cyrus
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i am familiar with this way of splitting. i am just curious if there's way to do it by one operation – mend4x Oct 26 '22 at 13:26
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Check this: https://stackoverflow.com/questions/33742588/pandas-split-dataframe-by-column-value. You could use the answer that uses mask. – Cyrus Oct 26 '22 at 13:37
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If the question is about performing the boolean splitting task in a single line, you can use a generator expression with groupby:
df_false, df_true = (g for k,g in df.groupby(boolean_condition))
If you want the true condition first, invert the boolean with ~:
df_true, df_false = (g for k,g in df.groupby(~boolean_condition))
Example:
df = pd.DataFrame({'A': [0,1,2,0,1], 'B': list('ABCDE')})
# split by A<1 / A≥1
df1, df2 = (g for k,g in df.groupby(df['A'].ge(1)))
print(df1)
print('---')
print(df2)
Output:
A B
0 0 A
3 0 D
---
A B
1 1 B
2 2 C
4 1 E
mozway
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