How to add items of multiple panda dataseries in a list - python

Question

I am having a list 'final_value' which contains panda series with data below. I need to add the vales of multiple panda series into one series. Can anyone please help me.

Need to add values according to their index value.

I tried final_value[i].add(final_value[i+1]) in a loop. But did not work

Data:

[0      0.000000
1     14.522873
2     21.677418
3     10.791055
4      0.000000
5      7.176507
Name: (FDIUSGD Index, PX_LAST), Length: 6, dtype: float64, 
0    -16.276548
1    -16.276548
2    -15.761264
3    -16.276548
4    -16.276548
5    -17.822402
Name: (USHBMIDX Index, PX_LAST), Length: 6, dtype: float64, 
0      0.000000
1      5.972322
2     15.255200
3     -3.498313
4      2.698414
5      3.083199
Name: (USPHNSA Index, PX_LAST), Length: 6, dtype: float64,

Expected output:

0     -16.276548
1     4.218647
2     21.171354
3    -8.983806
4    -13.578134
5    -7.562696

Thanks!

All series has same length. While copy paste data, i would have missed some rows. — Arvinth Kumar, Sep 16 '17 at 03:59
I got the problem.I will fix it. Ideally length of each series will be same — Arvinth Kumar, Sep 16 '17 at 04:02
Using functools reduce is better than 5x faster than pd.concat and sum — Scott Boston, Sep 16 '17 at 04:26

piRSquared · Answer 1 · 2017-09-16T05:28:35.730

4

final_value = [
    pd.Series(1, range(5)),
    pd.Series(2, range(5)),
    pd.Series(3, range(5))
]

Just use sum

sum(final_value)

0    6
1    6
2    6
3    6
4    6
dtype: int64

Or you could use np.sum

pd.Series(np.sum([s.values for s in final_value], 0), final_value[0].index)

0    6
1    6
2    6
3    6
4    6
dtype: int64

edited Sep 16 '17 at 05:28

answered Sep 16 '17 at 04:51

piRSquared

285,575
57
475
624

1

Timing: 1000 loops, best of 3: 1.25 ms per loop functools reduce still wins. – Scott Boston Sep 16 '17 at 05:22
This wins at golf (-: But now that you brought it up. – piRSquared Sep 16 '17 at 05:25
Hole-in-one Yes, it does. – Scott Boston Sep 16 '17 at 05:30
Timing np.sum method. 10000 loops, best of 3: 164 µs per loop crushes functools reduce. You win! – Scott Boston Sep 16 '17 at 05:32
I bow in your presence, Great Circle One. – Scott Boston Sep 16 '17 at 05:32
Great... now I'm blushing – piRSquared Sep 16 '17 at 05:33

Scott Boston · Answer 2 · 2017-09-16T04:36:24.677

3

Let's try this:

from functools import reduce
reduce(pd.Series.add, final_value)

Example:

print(final_values)

Input df:

[0     0.000000
1    14.522873
2    21.677418
3    10.791055
4     0.000000
5     7.176507
Name: 1, dtype: float64, 0   -16.276548
1   -16.276548
2   -15.761264
3   -16.276548
4   -16.276548
5   -17.822402
Name: 1, dtype: float64, 0     0.000000
1     5.972322
2    15.255200
3    -3.498313
4     2.698414
5     3.083199
Name: 1, dtype: float64]


from functools import reduce
reduce(pd.Series.add,final_value)

Output:

0   -16.276548
1     4.218647
2    21.171354
3    -8.983806
4   -13.578134
5    -7.562696
Name: 1, dtype: float64

Timings....

John's Method

%timeit pd.concat(final_value, axis=1).sum(axis=1)

100 loops, best of 3: 3.06 ms per loop

functools reduce method

%timeit reduce(pd.Series.add, final_value)

1000 loops, best of 3: 551 µs per loop

edited Sep 16 '17 at 04:36

answered Sep 16 '17 at 04:05

Scott Boston

147,308
15
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187

1

@piroot https://stackoverflow.com/questions/8689184/nameerror-name-reduce-is-not-defined-in-python – Scott Boston Sep 16 '17 at 04:09
Thanks for Ur kind help! – Arvinth Kumar Sep 16 '17 at 04:15
1

great solution! – Vaishali Sep 16 '17 at 04:34
@Vaishali Thank you. – Scott Boston Sep 16 '17 at 04:35

Zero · Accepted Answer · 2017-09-16T04:14:08.717

Use pd.concat on axis=1 and sum row-wise

In [597]: pd.concat(final_value, axis=1).sum(axis=1)
Out[597]:
0   -16.276548
1     4.218647
2    21.171354
3    -8.983806
4   -13.578134
5    -7.562703
dtype: float64

Details

In [598]: final_value
Out[598]:
[0     0.000000
 1    14.522873
 2    21.677418
 3    10.791055
 4     0.000000
 5     7.176500
 Name: 1, dtype: float64, 0   -16.276548
 1   -16.276548
 2   -15.761264
 3   -16.276548
 4   -16.276548
 5   -17.822402
 Name: 1, dtype: float64, 0     0.000000
 1     5.972322
 2    15.255200
 3    -3.498313
 4     2.698414
 5     3.083199
 Name: 1, dtype: float64]

How to add items of multiple panda dataseries in a list - python

3 Answers3

Timings....

Linked