CODEWITHSUNDEEP
pythonpandaspandas-groupby
Novin Shahroudi
Novin Shahroudi

Reputation: 680

Pandas combine columns on groupby

I intend to combine columns of a Pandas DataFrame after groupby. I looked for options that I can use but none of them does what I'm looking for. The closest option was .agg() which performs on values of a column, however, I want to calculate a statistic of all features for every given groupbyed row.

I am looking for something like this:

dataset.groupby(['company', 'team']).combine(new_cols=['features_mean'], to_combine=['feature 1':'feature 2'], funcs=[np.mean], axis=1)

enter image description here

Upvotes: 1

Views: 138

Answers (2)

Novin Shahroudi
Novin Shahroudi

Reputation: 680

I realized that I don't even need to use groupby. I can simply use apply:

dataset['new measure'] = dataset.apply(lambda r: r['Feature 1':'Feature 12'].mean(), axis=1)

This post helped!

However, it runs slow due to using of for loop in the implementation.

Upvotes: 0

jezrael
jezrael

Reputation: 863741

Use loc with mean:

dataset['new measure'] = dataset.loc[:, 'Feature 1':'Feature 12'].mean(axis=1)

Sample:

dataset = pd.DataFrame({'A':list('abcdef'),
                   'Feature 1':[4,5,4,5,5,4],
                   'Feature 2':[7,8,9,4,2,3],
                   'Feature 3':[1,3,5,7,1,0],
                   'Feature 4':[5,3,6,9,2,4],
                   'F':list('aaabbb')})

#print (dataset)

dataset['new measure'] = dataset.loc[:, 'Feature 1':'Feature 4'].mean(axis=1)
print (dataset)
   A  F  Feature 1  Feature 2  Feature 3  Feature 4  new measure
0  a  a          4          7          1          5         4.25
1  b  a          5          8          3          3         4.75
2  c  a          4          9          5          6         6.00
3  d  b          5          4          7          9         6.25
4  e  b          5          2          1          2         2.50
5  f  b          4          3          0          4         2.75

Upvotes: 1

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