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    <p>Combine the series into a frame which automatically aligns by the index</p> <pre><code>In [51]: index Out[51]: [datetime.datetime(2013, 8, 26, 18, 33, 48, 990974), datetime.datetime(2013, 8, 26, 18, 33, 49, 990974), datetime.datetime(2013, 8, 26, 18, 33, 50, 990974), datetime.datetime(2013, 8, 26, 18, 33, 51, 990974), datetime.datetime(2013, 8, 26, 18, 33, 52, 990974)] In [52]: a = pd.Series([1,4,5,7,8], index = index) In [53]: b = pd.Series([2,3,6,7,8], index = index) In [54]: a Out[54]: 2013-08-26 18:33:48.990974 1 2013-08-26 18:33:49.990974 4 2013-08-26 18:33:50.990974 5 2013-08-26 18:33:51.990974 7 2013-08-26 18:33:52.990974 8 dtype: int64 In [55]: b Out[55]: 2013-08-26 18:33:48.990974 2 2013-08-26 18:33:49.990974 3 2013-08-26 18:33:50.990974 6 2013-08-26 18:33:51.990974 7 2013-08-26 18:33:52.990974 8 dtype: int64 In [56]: df = DataFrame({ 'a' : a, 'b' : b }) In [57]: df Out[57]: a b 2013-08-26 18:33:48.990974 1 2 2013-08-26 18:33:49.990974 4 3 2013-08-26 18:33:50.990974 5 6 2013-08-26 18:33:51.990974 7 7 2013-08-26 18:33:52.990974 8 8 </code></pre> <p>Min/Max</p> <pre><code>In [9]: df.max(1) Out[9]: 2013-08-26 18:33:48.990974 2 2013-08-26 18:33:49.990974 4 2013-08-26 18:33:50.990974 6 2013-08-26 18:33:51.990974 7 2013-08-26 18:33:52.990974 8 Freq: S, dtype: int64 In [10]: df.min(1) Out[10]: 2013-08-26 18:33:48.990974 1 2013-08-26 18:33:49.990974 3 2013-08-26 18:33:50.990974 5 2013-08-26 18:33:51.990974 7 2013-08-26 18:33:52.990974 8 Freq: S, dtype: int64 </code></pre> <p>Index of min/max</p> <pre><code>In [11]: df.idxmax(1) Out[11]: 2013-08-26 18:33:48.990974 b 2013-08-26 18:33:49.990974 a 2013-08-26 18:33:50.990974 b 2013-08-26 18:33:51.990974 a 2013-08-26 18:33:52.990974 a Freq: S, dtype: object In [12]: df.idxmin(1) Out[12]: 2013-08-26 18:33:48.990974 a 2013-08-26 18:33:49.990974 b 2013-08-26 18:33:50.990974 a 2013-08-26 18:33:51.990974 a 2013-08-26 18:33:52.990974 a Freq: S, dtype: object </code></pre>
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    1. POGet min and max elements for 2 corresponding series in pandas
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    1. POGet min and max elements for 2 corresponding series in pandas
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    1. COLooking at the original poster's desired output, I think you want to add ``axis=1``.
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      1. USDan Allan
    2. CO@DanAllan thanks.....
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    3. COThank you for detailed response.. What would be performance implications compared to arithmetic solution? Based on DataFrame internals, is it more expensive to create new data frame vs a few additions/subtractions?
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