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From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Wed, 30 Jul 2025 03:32:12 +0000
Subject: [PATCH] =?UTF-8?q?=E2=9A=A1=EF=B8=8F=20Speed=20up=20function=20`f?=
 =?UTF-8?q?illna`=20by=205,211%?=
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The optimized version achieves a **5211% speedup** by replacing an inefficient row-by-row loop with pandas' vectorized operations.

**Key Performance Issues in Original Code:**
- **Row-by-row iteration**: The `for i in range(len(df))` loop processes each row individually, which is extremely slow for pandas DataFrames
- **Inefficient NA checking**: `pd.isna(df.iloc[i][column])` performs individual cell access and NA checking in each iteration
- **Slow assignment**: `result.iloc[i, df.columns.get_loc(column)] = value` uses positional indexing for each assignment

**Optimizations Applied:**
1. **Vectorized NA detection**: `mask = result[column].isna()` creates a boolean mask for all NA values in one operation
2. **Vectorized assignment**: `result.loc[mask, column] = value` assigns the fill value to all NA positions simultaneously

**Why This Creates Massive Speedup:**
- **Eliminates Python loop overhead**: Instead of 10,000+ Python iterations (as seen in profiler), the optimized version performs bulk operations at the C level within pandas
- **Memory locality**: Vectorized operations process contiguous memory blocks efficiently
- **Single-pass operations**: The original code made multiple passes through data (NA check + assignment), while the optimized version does everything in two vectorized operations

**Performance Characteristics by Test Case:**
- **Large datasets with many NAs**: Massive gains (25,000%+ faster) as the original's O(n) loop becomes O(1) vectorized operations
- **Small datasets with no NAs**: Slight slowdown (~46% slower) due to vectorization overhead being larger than simple loop for tiny datasets
- **Mixed scenarios**: Generally 10-65% faster for datasets with some NAs

The optimization is particularly effective for larger datasets where the vectorization benefits far outweigh the setup costs, making it ideal for real-world data processing scenarios.
---
 src/numpy_pandas/dataframe_operations.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/numpy_pandas/dataframe_operations.py b/src/numpy_pandas/dataframe_operations.py
index cb4cda2..174996a 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 = {}
@@ -106,9 +109,9 @@ def apply_function(df: pd.DataFrame, column: str, func: Callable) -> List[Any]:
 
 def fillna(df: pd.DataFrame, column: str, value: Any) -> pd.DataFrame:
     result = df.copy()
-    for i in range(len(df)):
-        if pd.isna(df.iloc[i][column]):
-            result.iloc[i, df.columns.get_loc(column)] = value
+    # Replace NA values in the specified column using efficient vectorized operation
+    mask = result[column].isna()
+    result.loc[mask, column] = value
     return result