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pythonpandasdataframelambdaelementwise-operations
Lourenco
Lourenco

Reputation: 3022

Element-wise operation with lambda (pd.DataFrame)

Trying to subtract a constant array from a DatraFrame using lambda.

This is my DataFrame d:

import pandas as pd

d = pd.DataFrame()
d['x'] = pd.Series([1, 2, 3, 4, 5, 6])
d['y'] = pd.Series([11, 22, 33, 44, 55, 66])

A working as expected classical loop approach:

transformed = pd.DataFrame(columns=('x', 'y'))
for index, row in d.iterrows():
  transformed.loc[index] = [row[0] + 5, row[1] + 10]
print(transformed)

Produces:

    x   y
0   6  21
1   7  32
2   8  43
3   9  54
4  10  65
5  11  76

This is the lambda version:

print(d.apply(lambda x: x + [5, 10]))

However, is raising the error: ValueError: operands could not be broadcast together with shapes (6,) (2,)

After reading Pandas documentation, I understood my lambda approach should work. Why doesn't it work?

Upvotes: 2

Views: 618

Answers (2)

jezrael
jezrael

Reputation: 863361

If number of columns is same like length of list simpliest is:

print(d + [5, 10])
    x   y
0   6  21
1   7  32
2   8  43
3   9  54
4  10  65
5  11  76

If there is multiple columns select by list, lengths of lists has to be same:

print(d[['x','y']] + [5, 10])

Upvotes: 3

U13-Forward
U13-Forward

Reputation: 71610

apply is automatically column wise, the axis argument is set to 0 by default.

You need to specify axis=1 for it will calculate row wise:

>>> d.apply(lambda x: x + [5, 10], axis=1)
    x   y
0   6  21
1   7  32
2   8  43
3   9  54
4  10  65
5  11  76
>>>

But tbh in this situation you don't need apply anyway:

>>> d + [5, 10]
    x   y
0   6  21
1   7  32
2   8  43
3   9  54
4  10  65
5  11  76
>>>

Upvotes: 1

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