Timeline for Pandas: DataFrame within DataFrame
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| Jul 31, 2013 at 6:09 | vote | accept | jorge.santos | ||
| Jul 31, 2013 at 6:09 | vote | accept | jorge.santos | ||
| Jul 31, 2013 at 6:09 | |||||
| Jul 31, 2013 at 0:42 | comment | added | Jeff |
you don't need to be that fancy df['scalar1'] = 234 will work
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| Jul 30, 2013 at 21:06 | comment | added | jorge.santos | Thank you Jeff. The idea of doing this is because I need to combine these data frames with another bunch of scalar values. For example row1: DF_a, np.nan, 104, 105 | row2: np.nan, DF_b, 234, 213. This assuming i have the columns Cat1, Cat2, Scalar1, Scalar2. i guess this still possible using the multi-index approach, would i just need to broadcast the scalar value across all items of cat1/cat2? thanks again | |
| Jul 30, 2013 at 19:59 | comment | added | Phillip Cloud |
There's also the option to create a hierarchically indexed DataFrame using Panel.to_frame(filter_observations=False).
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| Jul 30, 2013 at 19:32 | history | edited | Jeff | CC BY-SA 3.0 |
added 77 characters in body
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| Jul 30, 2013 at 19:17 | history | answered | Jeff | CC BY-SA 3.0 |