Timeline for Pandas merge a "%Y%M%D" date column with a "%H:%M:%S" time column
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| May 18, 2021 at 6:40 | comment | added | Andrea Ciufo | Thank you @riskypenguin I missed the specific answer :) | |
| May 18, 2021 at 6:39 | vote | accept | Andrea Ciufo | ||
| May 12, 2021 at 10:21 | comment | added | riskypenguin |
Thank you for the input, I was expecting df.apply to be faster than that. I reduced it to "can".
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| May 12, 2021 at 10:18 | history | edited | riskypenguin | CC BY-SA 4.0 |
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| May 12, 2021 at 3:28 | comment | added | tdy |
"can and should" seems too conclusive though. apply(axis=1) is usually the slowest option. in this case, it's 2x slower than pd.to_datetime() at 200 rows, 3x slower at 2K rows, 3.5x slower at 2M rows.
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| May 8, 2021 at 17:59 | history | edited | riskypenguin | CC BY-SA 4.0 |
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| May 8, 2021 at 17:54 | history | edited | riskypenguin | CC BY-SA 4.0 |
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| May 8, 2021 at 17:48 | history | answered | riskypenguin | CC BY-SA 4.0 |