CODEWITHSUNDEEP
pythonpandas
Zanam
Zanam

Reputation: 4807

Pandas intersect multiple series based on index

I have a list of pandas.core.series.Series. The size of list is 10. Each series in the list has an index and a value.

I want to intersect the 10 pandas.core.series.Series in the list to obtain one final DataFrame of all values with common index as the new index. It should have 10 columns for values corresponding to elements in the original list.

Is there a pythonic way to do this instead of using series intersection multiple times?

Upvotes: 3

Views: 2395

Answers (2)

piRSquared
piRSquared

Reputation: 294586

Consider the list of series los

los = [
    pd.Series([1, 2, 3], [0, 2, 4], name='A'),
    pd.Series([1, 2, 3], [1, 2, 4], name='B'),
    pd.Series([1, 2, 3], [4, 3, 2], name='C')
]

Using pd.concat with parameter join='inner'
'inner' is required to get the intersection of indices

pd.concat(los, axis=1, join='inner')

   A  B  C
4  3  3  1
2  2  2  3

Had we not used join='inner'

pd.concat(los, axis=1)

     A    B    C
0  1.0  NaN  NaN
1  NaN  1.0  NaN
2  2.0  2.0  3.0
3  NaN  NaN  2.0
4  3.0  3.0  1.0

Which is not the intersection

Upvotes: 3

Gene Burinsky
Gene Burinsky

Reputation: 10243

The command pd.concat is intended specifically for this. Note that, in my example, the indexes are all the same but pd.concat will intersect indexes automatically. 

## generate data
series_list = [pd.Series(np.random.randn(10)) for x in range(10)]

## output example
In [13]: pd.concat(series_list, axis=1)
Out[13]:
          0         1         2         3         4         5         6  \
0  0.859256 -2.283672  0.539067 -0.745864 -2.658162  0.353576  0.482614
1  0.999823  0.155528 -0.579598  0.356863 -0.135860 -0.406351  0.888127
2 -0.727666 -1.571515  0.639486  0.394803  0.478038  0.244531 -1.422910
3  0.582662  1.469884 -3.337026 -0.407253 -2.351327 -0.676233  0.018020
4  1.173179  0.211506 -0.360274 -0.299976 -0.479131  1.735279  0.549555
5 -1.589117 -2.037131 -1.843098  0.066606  0.166258 -1.444472  1.534016
6 -0.275819 -0.978669  2.299632  0.807746 -1.358762  1.190374 -0.668204
7  0.933350 -0.536032  1.285759  0.677269 -0.385078 -0.357661  2.085237
8  0.167977  0.090950  1.220945  1.085571 -0.486978  0.848816 -0.559023
9 -1.006001 -0.168631  1.501675 -0.351409  1.719402  0.337982 -0.776788

          7         8         9
0  0.015773  2.356775 -1.288013
1  1.292615  1.272147  0.347335
2  1.410002 -0.364822 -0.372589
3 -2.306940 -0.816853  2.565389
4 -1.815764 -1.547376  1.104517
5 -1.561681 -0.373882  0.582264
6 -1.272563 -0.317298 -0.446855
7  1.179011 -1.402293  0.424124
8 -1.839095  1.278204 -1.166991
9  0.950620  0.681596 -1.908053

Upvotes: 1

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