Timeline for answer to Does pandas iterrows have performance issues? by Moto Koto
Current License: CC BY-SA 4.0
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| Jan 10, 2023 at 4:18 | comment | added | tylerl |
This solution is great as it’s very easy to replace an existing .iterrows() loop with this and it’s many times faster without using up all memory & crashing. Is anyone aware of drawbacks or limitations to this method? So far all I’ve found is that dfa.values will automatically choose a dtype that’s compatible with all the columns’ dtypes and convert all the data to that single dtype, which AFAIK isn’t optimal for your DF but usually won’t break anything. NOTE: pandas.pydata.org/docs/reference/api/… recommends using .to_numpy() instead of .values.
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| Sep 19, 2022 at 18:21 | history | edited | Peter Mortensen | CC BY-SA 4.0 |
Active reading [<https://en.wikipedia.org/wiki/NumPy> <https://www.youtube.com/watch?v=1Dax90QyXgI&t=17m54s>].
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| May 15, 2021 at 15:11 | review | Late answers | |||
| May 15, 2021 at 15:13 | |||||
| May 15, 2021 at 14:56 | review | First posts | |||
| May 15, 2021 at 17:34 | |||||
| May 15, 2021 at 14:55 | history | answered | Moto Koto | CC BY-SA 4.0 |