8000 Suggest replacing .apply(int) with .astype(int) for performance · Issue #586 · pypest/pyemu · GitHub
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Suggest replacing .apply(int) with .astype(int) for performance #586

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SaFE-APIOpt opened this issue Apr 21, 2025 · 1 comment
Open

Suggest replacing .apply(int) with .astype(int) for performance #586

SaFE-APIOpt opened this issue Apr 21, 2025 · 1 comment

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@SaFE-APIOpt
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https://github.com/pypest/pyemu/blob/22c26e96761952fc95845213eb628885e8a7fde8/autotest/utils/to_pestpp.py#L42C1-L45C50
Hi, I’d like to suggest a performance improvement to the following assignments:

flow_df.loc[:,"k"] = flow_df.lay.apply(int) - 1
flow_df.loc[:,"i"] = flow_df.row.apply(int) - 1
flow_df.loc[:,"j"] = flow_df.col.apply(int) - 1
flow_df.loc[:,"wsp"] = flow_df.wsp.apply(int) - 1

These can be more efficiently rewritten using Pandas’ vectorized .astype(int) method:

flow_df["k"] = flow_df["lay"].astype(int) - 1
flow_df["i"] = flow_df["row"].astype(int) - 1
flow_df["j"] = flow_df["col"].astype(int) - 1
flow_df["wsp"] = flow_df["wsp"].astype(int) - 1

.apply(int) invokes a Python-level loop with individual function calls per element, which is much slower and more memory-intensive than .astype(int), especially for large DataFrames. Using .astype() leverages optimized, compiled code for bulk operations, making it both faster and more memory-efficient.
@jtwhite79
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sounds like a winner to me. @dbsi-pinkman wanna have a shot at a PR for this?

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@jtwhite79 @SaFE-APIOpt
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