Get two return values from Pandas apply

Question

I'm trying to return two different values from an apply method but I cant figure out how to get the results I need.

With a function as:

def fun(row):
    s = [sum(row[i:i+2]) for i in range (len(row) -1)]
    ps = s.index(max(s))
    return max(s),ps

and df as:

    6:00    6:15    6:30    
0   3       8       9       
1   60      62      116

I'm trying to return the max value of the row, but i also need to get the index of the first value that produces the max combination.

df["phour"] = t.apply(fun, axis=1)

I can get the output I need, but I don't know how I can get the index in a new column.So far im getting both answer in a tuple

    6:00    6:15    6:30    phour
0   3       8       9       (17, 1)
1   60      62      116     (178, 1)

How can I get the index value in its own column?

Possible duplicate of [Returning multiple values from pandas apply on a DataFrame](http://stackoverflow.com/questions/10751127/returning-multiple-values-from-pandas-apply-on-a-dataframe) — Gordon Bean, Apr 13 '17 at 17:29

score 8 · Answer 1 · answered Sep 29 '18 at 03:05

You can get the index in a separate column like this:

df[['phour','index']] = df.apply(lambda row: pd.Series(list(fun(row))), axis=1)

Or if you modify fun slightly:

def fun(row):
    s = [sum(row[i:i+2]) for i in range (len(row) -1)]
    ps = s.index(max(s))
    return [max(s),ps]

Then the code becomes a little less convoluted:

 df[['phour','index']] = df.apply(lambda row: pd.Series(fun(row)), axis=1)

piRSquared · Accepted Answer · 2017-04-13T17:27:20.660

7

You can apply pd.Series

df.drop('Double', 1).join(df.Double.apply(pd.Series, index=['D1', 'D2']))

   A  B  C  D1  D2
0  1  2  3   1   2
1  2  3  2   3   4
2  3  4  4   5   6
3  4  1  1   7   8

Equivalently

df.drop('Double', 1).join(
    pd.DataFrame(np.array(df.Double.values.tolist()), columns=['D1', 'D2'])
)

setup
using @GordonBean's df

df = pd.DataFrame({'A':[1,2,3,4], 'B':[2,3,4,1], 'C':[3,2,4,1], 'Double': [(1,2), (3,4), (5,6), (7,8)]})

edited Apr 13 '17 at 17:27

answered Apr 13 '17 at 17:25

piRSquared

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I should have looked around a little before just answering the question: http://stackoverflow.com/questions/10751127/returning-multiple-values-from-pandas-apply-on-a-dataframe :) – Gordon Bean Apr 13 '17 at 17:26
This is a lot cleaner. Thanks @piRSquared. – Daniel Apr 13 '17 at 17:27
1

It's very nice! But we can try to make it even little nicer: `df.join(df.pop('Double').apply(pd.Series, index=['D1', 'D2']))` ;-) – MaxU - stand with Ukraine Apr 13 '17 at 18:13

Gordon Bean · Answer 3 · 2017-04-13T17:14:20.450

2

If you are just trying to get the max and argmax, I recommend using the pandas API:

DataFrame.idxmax

So:

df = pd.DataFrame({'A':[1,2,3,4], 'B':[2,3,4,1], 'C':[3,2,4,1]})
df

    A   B   C
0   1   2   3
1   2   3   2
2   3   4   4
3   4   1   1

df['Max'] = df.max(axis=1)
df['ArgMax'] = df.idxmax(axis=1)
df    

    A   B   C   Max ArgMax
0   1   2   3   3   C
1   2   3   2   3   B
2   3   4   4   4   B
3   4   1   1   4   A

Update:
And if you need the actual index value, you can use numpy.ndarray.argmax:

df['ArgMaxNum'] = df[['A','B','C']].values.argmax(axis=1)


    A   B   C   Max ArgMax  ArgMaxNum
0   1   2   3   3   C   2
1   2   3   2   3   B   1
2   3   4   4   4   B   1
3   4   1   1   4   A   0

edited Apr 13 '17 at 17:14

answered Apr 13 '17 at 17:11

Gordon Bean

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I need to get the max of the consecutive values of each row. Using the first row as an example I need to pick between 11 and 17, and know where the index from the max sequence started – Daniel Apr 13 '17 at 17:14
Ah, I see. I'll add another answer. – Gordon Bean Apr 13 '17 at 17:16

score 2 · Answer 4 · answered Apr 13 '17 at 17:19

One way to split out the tuples into separate columns could be with tuple unpacking:

df = pd.DataFrame({'A':[1,2,3,4], 'B':[2,3,4,1], 'C':[3,2,4,1], 'Double': [(1,2), (3,4), (5,6), (7,8)]})
df


    A   B   C   Double
0   1   2   3   (1, 2)
1   2   3   2   (3, 4)
2   3   4   4   (5, 6)
3   4   1   1   (7, 8)

df['D1'] = [d[0] for d in df.Double]
df['D2'] = [d[1] for d in df.Double]
df


    A   B   C   Double  D1  D2
0   1   2   3   (1, 2)  1   2
1   2   3   2   (3, 4)  3   4
2   3   4   4   (5, 6)  5   6
3   4   1   1   (7, 8)  7   8

Thanks this works. It might not be the best and more recommended approach, but it will suffice for what i need. — Daniel, Apr 13 '17 at 17:24

score 1 · Answer 5 · answered Apr 13 '17 at 17:14

1

There's got to be a better way but you can do:

df.merge(pd.DataFrame(((i,j) for 
                       i,j in df.apply(lambda x: fun(x)).values),
                      columns=['phour','index']),
         left_index=True,right_index=True)

answered Apr 13 '17 at 17:14

mechanical_meat

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Thanks for the answer, this works as well, but honestly i cant understand exactly whats going on with the code. I can follow @Gordon Bean a lot better. – Daniel Apr 13 '17 at 17:23

Get two return values from Pandas apply

5 Answers5