Using lambda if condition on different columns in Pandas dataframe

Question

I have simple dataframe:

import pandas as pd
frame = pd.DataFrame(np.random.randn(4, 3), columns=list('abc'))

Thus for example:

a   b   c
0   -0.813530   -1.291862   1.330320
1   -1.066475   0.624504    1.690770
2   1.330330    -0.675750   -1.123389
3   0.400109    -1.224936   -1.704173

And then I want to create column “d” that contains value from “c” if c is positive. Else value from “b”.

I am trying:

frame['d']=frame.apply(lambda x: frame['c'] if frame['c']>0 else frame['b'],axis=0)

But getting “ValueError: ('The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().', 'occurred at index a')

I was trying to google how to solve this, but did not succeed. Any tip please?

lambda x: ... as in it takes an argument x which is not used for the logic..... — Tadhg McDonald-Jensen
– Tadhg McDonald-Jensen, Commented May 25, 2016 at 16:40
frame['c']>0 produces a series of values in column c that are greater then 0, which is then tried to use the booleaness of it instead of x['c']>0 which will compare the value at the specific cell to 0 and return a boolean. — Tadhg McDonald-Jensen
– Tadhg McDonald-Jensen, Commented May 25, 2016 at 16:42

MaxU - stand with Ukraine · Accepted Answer · 2016-05-25 16:40:17Z

55

is that what you want?

In [300]: frame[['b','c']].apply(lambda x: x['c'] if x['c']>0 else x['b'], axis=1)
Out[300]:
0   -1.099891
1    0.582815
2    0.901591
3    0.900856
dtype: float64

answered May 25, 2016 at 16:40

MaxU - stand with Ukraine

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1 Comment

jkr Over a year ago

axis=1 is important at the end. Otherwise, it gives keyerror.

piRSquared · Accepted Answer · 2016-05-25 17:14:12Z

8

Solution

use a vectorized approach

frame['d'] = frame.b + (frame.c > 0) * (frame.c - frame.b)

Explanation

This is derived from the sum of

(frame.c > 0) * frame.c  # frame.c if positive

Plus

(frame.c <= 0) * frame.b  # frame.b if c is not positive

However

(frame.c <=0 )

is equivalent to

(1 - frame.c > 0)

and when combined you get

frame['d'] = frame.b + (frame.c > 0) * (frame.c - frame.b)

edited May 25, 2016 at 17:14

answered May 25, 2016 at 17:08

piRSquared

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Comments

John Glen · Accepted Answer · 2022-04-02 15:22:24Z

3

I came by and faced something like this and this how I retrieve new column based on conditions from other columns

df["col3"] = df[["col1", "col2"]].apply(
    lambda x: "return this if first statement is true"
    if (x.col1 == "value1" and x.col2 == "value2")
    else "return this if the statement right below this line is true"
    if (x.col1 == "value1" and x.col2 != "value2")
    else "return this if the below is true"
    if (x.col1 != "value1" and x.col2 == "Value2")
    else "return this because none of the above statements were true",
    axis=1
)

edited Apr 2, 2022 at 15:22

John Glen

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answered Apr 2, 2022 at 11:41

Peter

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