CODEWITHSUNDEEP
pythonpandas
Mohamed Ali JAMAOUI
Mohamed Ali JAMAOUI

Reputation: 14699

Get the min of [0, x] element wise for a column

I need to compute a column where the value is the result of a vectorized operation over other columns: 

df["new_col"] = df["col1"] - min(0,df["col2"])

It turned out, however, that I cannot use min as in the above syntax. So, what is the right way to get the min between zero and a given value of pandas column?

Upvotes: 7

Views: 4771

Answers (3)

DeepSpace
DeepSpace

Reputation: 81664

I think that the other answers aren't what you meant. They take the minimum value in df['col2'] and compare it to 0 (and thus always return the same value), while you wanted the minimum between each value in col2 and 0:

df = pd.DataFrame(data={'a': [2, 3], 'b': [-1, 1]})

df['new_col'] = map(lambda a, b: a - min(0, b), df['a'], df['b'])

print df

>>    a  b  new_col
   0  2 -1        3
   1  3  1        3

Upvotes: 0

firelynx
firelynx

Reputation: 32224

You could use some masking and a temporary column. Totally ignoring the 'min' function.

magicnumber = 0
tempcol = df['col2']
mask = tempcol < magicnumber
tempcol.loc[df[~mask].index] = magicnumber
df['col1'] - tempcol

Or you can use a lambda function:

magicnumber = 0
df['col1'] - df['col2'].apply(lambda x: np.min(magicnumber, x))

OR you can apply over two columns:

df['magicnumber'] = 0
df['col1'] - df[['col2', 'magicnumber']].apply(np.min, axis=1)

Upvotes: 1

tmdavison
tmdavison

Reputation: 69183

you can use numpy.minimum to find the element-wise minimum of an array

import numpy as np
df["new_col"] = df["col1"] - np.minimum(0,df["col2"])

Upvotes: 9

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