python - Replace non-zero values in a pandas dataframe with 1 -


i have pandas dataframe 'result'. 1 of attribute in data frame 'transaction' contain value 0 if it's non cash transaction , real number if transaction cash transaction.this attribute like:

result['transaction'] = [0,0,0,23.2,432,12,0,0,56.4]

i want change value of attribute such non-zero values replaced 1. resultant attribute should this:

result['transaction'] = [0,0,0,1,1,1,0,0,1]

how can this?

original dataframe:

in [701]: df out[701]:      col1 0    0.0 1    0.0 2    0.0 3   23.2 4  432.0 5   12.0 6    0.0 7    0.0 8   56.4

you can use df.where filter , assign:

in [696]: df.col1 = df.where(df.col1 == 0, 1)  in [697]: df out[697]:     col1 0   0.0 1   0.0 2   0.0 3   1.0 4   1.0 5   1.0 6   0.0 7   0.0 8   1.0

you can use boolean indexing simpler predicate:

in [676]: df[df.col1 != 0] = 1  in [677]: df out[677]:     col1 0   0.0 1   0.0 2   0.0 3   1.0 4   1.0 5   1.0 6   0.0 7   0.0 8   1.0

you may use df.map:

in [685]: df[df.col1.map(lambda x: x != 0)] = 1  in [686]: df out[686]:     col1 0   0.0 1   0.0 2   0.0 3   1.0 4   1.0 5   1.0 6   0.0 7   0.0 8   1.0

note that, every method, can tack on .astype(int) if want rid of floating point part of output.


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