From 7d48e45324afc780f80e55a90ceb82cc3e26b7db Mon Sep 17 00:00:00 2001
From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Wed, 30 Jul 2025 03:30:36 +0000
Subject: [PATCH] =?UTF-8?q?=E2=9A=A1=EF=B8=8F=20Speed=20up=20function=20`a?=
 =?UTF-8?q?pply=5Ffunction`=20by=201,957%?=
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The optimization replaces a manual row-by-row iteration with pandas' vectorized `map()` operation, resulting in a dramatic ~20x speedup.

**Key changes:**
- **Eliminated `df.iloc[i][column]` access pattern**: The original code uses `df.iloc[i][column]` inside a loop, which is extremely inefficient. Each `iloc` call triggers pandas' positional indexing machinery, creating significant overhead for every row access.
- **Leveraged vectorized operations**: The optimized version uses `df[column].map(func)`, which operates directly on the pandas Series using optimized C code paths rather than Python iteration.
- **Removed explicit loop and list building**: Instead of manually appending to a result list, the operation is performed in a single vectorized call and converted to a list at the end.

**Why this is faster:**
The original implementation has O(n) calls to `iloc`, each with substantial overhead for index resolution and type checking. The line profiler shows that `df.iloc[i][column]` consumes 98% of the execution time (396ms out of 405ms total). In contrast, `Series.map()` leverages pandas' internal optimizations and vectorized operations, processing the entire column at once with minimal per-element overhead.

**Performance characteristics by test case:**
- **Small DataFrames (3-4 rows)**: Modest improvements or slight regressions due to vectorization overhead being comparable to the small dataset size
- **Large DataFrames (1000+ rows)**: Massive speedups (2600-4200% faster) where vectorization truly shines, as the fixed overhead is amortized across many elements
- **Edge cases**: Consistent behavior with slightly better performance for exception handling scenarios due to faster failure detection

The optimization is most effective for medium to large datasets where the vectorization benefits outweigh the setup costs.
---
 src/numpy_pandas/dataframe_operations.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/numpy_pandas/dataframe_operations.py b/src/numpy_pandas/dataframe_operations.py
index cb4cda2..bb98c0e 100644
--- a/src/numpy_pandas/dataframe_operations.py
+++ b/src/numpy_pandas/dataframe_operations.py
@@ -66,14 +66,17 @@ def pivot_table(
 
         def agg_func(values):
             return sum(values) / len(values)
+
     elif aggfunc == "sum":
 
         def agg_func(values):
             return sum(values)
+
     elif aggfunc == "count":
 
         def agg_func(values):
             return len(values)
+
     else:
         raise ValueError(f"Unsupported aggregation function: {aggfunc}")
     grouped_data = {}
@@ -97,11 +100,8 @@ def agg_func(values):
 
 
 def apply_function(df: pd.DataFrame, column: str, func: Callable) -> List[Any]:
-    result = []
-    for i in range(len(df)):
-        value = df.iloc[i][column]
-        result.append(func(value))
-    return result
+    # Use vectorized map for better performance
+    return list(df[column].map(func))
 
 
 def fillna(df: pd.DataFrame, column: str, value: Any) -> pd.DataFrame: