|
for index,row in df.iterrows(): |
Current code:
for index, row in df.iterrows():
...
Recommended replacement:
for row in df.itertuples(index=True):
...
iterrows() constructs each row as a Pandas Series, resulting in significant overhead due to object creation, index binding, and repeated type inference. This becomes increasingly inefficient with larger DataFrames.
In contrast, itertuples() yields lightweight namedtuples at the Cython level, allowing for faster row-wise access with minimal memory usage. Benchmark results show that itertuples() is up to 50x faster on large datasets while producing the same output structure for read-only access.
PDF-Parser/app.py
Line 72 in 3c33e2f
Current code:
Recommended replacement:
iterrows() constructs each row as a Pandas Series, resulting in significant overhead due to object creation, index binding, and repeated type inference. This becomes increasingly inefficient with larger DataFrames.
In contrast, itertuples() yields lightweight namedtuples at the Cython level, allowing for faster row-wise access with minimal memory usage. Benchmark results show that itertuples() is up to 50x faster on large datasets while producing the same output structure for read-only access.