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📄 5,211% (52.11x) speedup for fillna in src/numpy_pandas/dataframe_operations.py

⏱️ Runtime : 155 milliseconds 2.91 milliseconds (best of 652 runs)

📝 Explanation and details

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.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 42 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
from typing import Any

import pandas as pd
# imports
import pytest  # used for our unit tests
from src.numpy_pandas.dataframe_operations import fillna

# unit tests

# ----------------------
# Basic Test Cases
# ----------------------

def test_fillna_basic_single_nan():
    # Test replacing a single NaN value in a column
    df = pd.DataFrame({'a': [1, None, 3], 'b': [4, 5, 6]})
    codeflash_output = fillna(df, 'a', 2); result = codeflash_output # 85.9μs -> 83.5μs (2.84% faster)
    expected = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})

def test_fillna_basic_multiple_nan():
    # Test replacing multiple NaN values in a column
    df = pd.DataFrame({'a': [None, 2, None], 'b': [1, 2, 3]})
    codeflash_output = fillna(df, 'a', 0); result = codeflash_output # 116μs -> 82.9μs (40.4% faster)
    expected = pd.DataFrame({'a': [0, 2, 0], 'b': [1, 2, 3]})

def test_fillna_basic_no_nan():
    # Test when there are no NaN values in the column
    df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
    codeflash_output = fillna(df, 'a', 0); result = codeflash_output # 33.6μs -> 63.0μs (46.6% slower)

def test_fillna_basic_other_columns_untouched():
    # Test that only the specified column is changed
    df = pd.DataFrame({'a': [None, 2, 3], 'b': [None, None, 3]})
    codeflash_output = fillna(df, 'a', 7); result = codeflash_output # 50.7μs -> 62.1μs (18.4% slower)
    expected = pd.DataFrame({'a': [7, 2, 3], 'b': [None, None, 3]})

def test_fillna_basic_string_value():
    # Test filling with a string value
    df = pd.DataFrame({'a': [None, 'foo', None], 'b': [1, 2, 3]})
    codeflash_output = fillna(df, 'a', 'bar'); result = codeflash_output # 112μs -> 83.1μs (35.3% faster)
    expected = pd.DataFrame({'a': ['bar', 'foo', 'bar'], 'b': [1, 2, 3]})

# ----------------------
# Edge Test Cases
# ----------------------

def test_fillna_edge_all_nan():
    # Test when the entire column is NaN
    df = pd.DataFrame({'a': [None, None, None], 'b': [1, 2, 3]})
    codeflash_output = fillna(df, 'a', 5); result = codeflash_output # 138μs -> 84.0μs (65.4% faster)
    expected = pd.DataFrame({'a': [5, 5, 5], 'b': [1, 2, 3]})

def test_fillna_edge_empty_dataframe():
    # Test with an empty DataFrame
    df = pd.DataFrame({'a': [], 'b': []})
    codeflash_output = fillna(df, 'a', 1); result = codeflash_output # 6.75μs -> 62.8μs (89.2% slower)
    expected = pd.DataFrame({'a': [], 'b': []})

def test_fillna_edge_column_not_exist():
    # Test when the column does not exist
    df = pd.DataFrame({'a': [1, None, 3]})
    with pytest.raises(KeyError):
        fillna(df, 'b', 0) # 21.2μs -> 15.9μs (33.0% faster)

def test_fillna_edge_nan_in_non_target_column():
    # NaNs in a non-target column should not be changed
    df = pd.DataFrame({'a': [1, 2, 3], 'b': [None, 5, None]})
    codeflash_output = fillna(df, 'a', 0); result = codeflash_output # 47.1μs -> 84.8μs (44.4% slower)
    expected = pd.DataFrame({'a': [1, 2, 3], 'b': [None, 5, None]})

def test_fillna_edge_fill_with_nan():
    # Filling NaN with NaN should leave NaNs unchanged
    df = pd.DataFrame({'a': [1, None, 3]})
    codeflash_output = fillna(df, 'a', None); result = codeflash_output # 74.2μs -> 84.0μs (11.7% slower)
    expected = pd.DataFrame({'a': [1, None, 3]})

def test_fillna_edge_column_with_different_types():
    # Test filling NaN in a column with mixed types
    df = pd.DataFrame({'a': [1, None, 'foo', None]})
    codeflash_output = fillna(df, 'a', 99); result = codeflash_output # 62.8μs -> 63.3μs (0.791% slower)
    expected = pd.DataFrame({'a': [1, 99, 'foo', 99]})

def test_fillna_edge_index_preserved():
    # Test that index is preserved after fill
    df = pd.DataFrame({'a': [None, 2]}, index=['x', 'y'])
    codeflash_output = fillna(df, 'a', 7); result = codeflash_output # 44.8μs -> 65.3μs (31.5% slower)
    expected = pd.DataFrame({'a': [7, 2]}, index=['x', 'y'])

def test_fillna_edge_column_with_nan_and_inf():
    # Test that only NaN is filled, not inf
    df = pd.DataFrame({'a': [float('nan'), float('inf'), -float('inf'), 5]})
    codeflash_output = fillna(df, 'a', 0); result = codeflash_output # 56.3μs -> 61.5μs (8.41% slower)
    expected = pd.DataFrame({'a': [0, float('inf'), -float('inf'), 5]})

def test_fillna_edge_fill_with_zero():
    # Fill NaN with zero
    df = pd.DataFrame({'a': [None, 2, None]})
    codeflash_output = fillna(df, 'a', 0); result = codeflash_output # 61.9μs -> 61.3μs (1.02% faster)
    expected = pd.DataFrame({'a': [0, 2, 0]})

def test_fillna_edge_column_with_boolean():
    # Fill NaN in boolean column
    df = pd.DataFrame({'a': [True, None, False]})
    codeflash_output = fillna(df, 'a', True); result = codeflash_output # 46.8μs -> 62.7μs (25.4% slower)
    expected = pd.DataFrame({'a': [True, True, False]})

def test_fillna_edge_column_with_all_types():
    # Fill NaN in column with objects of various types
    df = pd.DataFrame({'a': [None, 1, 'x', 3.14]})
    codeflash_output = fillna(df, 'a', 'filled'); result = codeflash_output # 52.4μs -> 62.5μs (16.2% slower)
    expected = pd.DataFrame({'a': ['filled', 1, 'x', 3.14]})

# ----------------------
# Large Scale Test Cases
# ----------------------

def test_fillna_large_scale_half_nan():
    # DataFrame with 1000 rows, half NaN in target column
    n = 1000
    data = {'a': [None if i % 2 == 0 else i for i in range(n)], 'b': list(range(n))}
    df = pd.DataFrame(data)
    codeflash_output = fillna(df, 'a', 42); result = codeflash_output # 22.5ms -> 86.8μs (25844% faster)
    expected_a = [42 if i % 2 == 0 else i for i in range(n)]
    expected = pd.DataFrame({'a': expected_a, 'b': list(range(n))})

def test_fillna_large_scale_all_nan():
    # DataFrame with 1000 rows, all NaN in target column
    n = 1000
    df = pd.DataFrame({'a': [None]*n, 'b': list(range(n))})
    codeflash_output = fillna(df, 'a', 7); result = codeflash_output # 31.9ms -> 94.5μs (33616% faster)
    expected = pd.DataFrame({'a': [7]*n, 'b': list(range(n))})

def test_fillna_large_scale_no_nan():
    # DataFrame with 1000 rows, no NaN in target column
    n = 1000
    df = pd.DataFrame({'a': list(range(n)), 'b': [None]*n})
    codeflash_output = fillna(df, 'a', 7); result = codeflash_output # 7.36ms -> 86.4μs (8421% faster)

def test_fillna_large_scale_sparse_nan():
    # DataFrame with 1000 rows, sparse NaN (every 100th row)
    n = 1000
    data = {'a': [None if i % 100 == 0 else i for i in range(n)], 'b': list(range(n))}
    df = pd.DataFrame(data)
    codeflash_output = fillna(df, 'a', -1); result = codeflash_output # 7.81ms -> 85.1μs (9079% faster)
    expected_a = [-1 if i % 100 == 0 else i for i in range(n)]
    expected = pd.DataFrame({'a': expected_a, 'b': list(range(n))})

def test_fillna_large_scale_multiple_columns():
    # DataFrame with 1000 rows and multiple columns, fill only target column
    n = 1000
    df = pd.DataFrame({
        'a': [None if i % 2 == 0 else i for i in range(n)],
        'b': [None if i % 3 == 0 else i for i in range(n)],
        'c': list(range(n))
    })
    codeflash_output = fillna(df, 'a', 123); result = codeflash_output # 25.2ms -> 91.7μs (27423% faster)
    expected_a = [123 if i % 2 == 0 else i for i in range(n)]
    expected = pd.DataFrame({'a': expected_a, 'b': [None if i % 3 == 0 else i for i in range(n)], 'c': list(range(n))})
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from typing import Any

import pandas as pd
# imports
import pytest  # used for our unit tests
from src.numpy_pandas.dataframe_operations import fillna

# unit tests

# ------------------- Basic Test Cases -------------------

def test_fillna_basic_numeric():
    # Fill NaN in a numeric column
    df = pd.DataFrame({'a': [1, None, 3, None]})
    codeflash_output = fillna(df, 'a', 0); result = codeflash_output # 70.3μs -> 62.9μs (11.7% faster)
    expected = pd.DataFrame({'a': [1, 0, 3, 0]})

def test_fillna_basic_string():
    # Fill NaN in a string column
    df = pd.DataFrame({'b': ['x', None, 'y', None]})
    codeflash_output = fillna(df, 'b', 'fill'); result = codeflash_output # 64.0μs -> 62.8μs (2.06% faster)
    expected = pd.DataFrame({'b': ['x', 'fill', 'y', 'fill']})

def test_fillna_no_nans():
    # No NaN values, DataFrame should remain unchanged
    df = pd.DataFrame({'a': [1, 2, 3]})
    codeflash_output = fillna(df, 'a', 0); result = codeflash_output # 33.8μs -> 63.0μs (46.3% slower)

def test_fillna_multiple_columns():
    # Only specified column should be filled
    df = pd.DataFrame({'a': [None, 2], 'b': [3, None]})
    codeflash_output = fillna(df, 'a', 9); result = codeflash_output # 44.9μs -> 62.2μs (27.8% slower)
    expected = pd.DataFrame({'a': [9, 2], 'b': [3, None]})

def test_fillna_preserve_other_columns():
    # Other columns should not be altered
    df = pd.DataFrame({'a': [None, 2], 'b': [3, 4]})
    codeflash_output = fillna(df, 'a', 1); result = codeflash_output # 76.7μs -> 83.4μs (8.04% slower)

# ------------------- Edge Test Cases -------------------

def test_fillna_empty_dataframe():
    # Empty DataFrame should return empty DataFrame
    df = pd.DataFrame({'a': []})
    codeflash_output = fillna(df, 'a', 0); result = codeflash_output # 6.79μs -> 62.6μs (89.2% slower)

def test_fillna_all_nans():
    # All values are NaN
    df = pd.DataFrame({'a': [None, None, None]})
    codeflash_output = fillna(df, 'a', 42); result = codeflash_output # 66.2μs -> 63.0μs (5.09% faster)
    expected = pd.DataFrame({'a': [42, 42, 42]})

def test_fillna_column_not_exist():
    # Non-existent column should raise KeyError
    df = pd.DataFrame({'a': [1, None]})
    with pytest.raises(KeyError):
        fillna(df, 'b', 0) # 21.5μs -> 15.9μs (35.4% faster)

def test_fillna_column_with_mixed_types():
    # Mixed types in column, fillna should work for NaN only
    df = pd.DataFrame({'a': [1, 'x', None, 3.5]})
    codeflash_output = fillna(df, 'a', 'filled'); result = codeflash_output # 52.5μs -> 63.4μs (17.2% slower)
    expected = pd.DataFrame({'a': [1, 'x', 'filled', 3.5]})

def test_fillna_with_nan_fill_value():
    # Filling NaN with np.nan should leave NaNs unchanged
    import numpy as np
    df = pd.DataFrame({'a': [None, 2, None]})
    codeflash_output = fillna(df, 'a', np.nan); result = codeflash_output # 65.0μs -> 63.3μs (2.63% faster)

def test_fillna_with_none_fill_value():
    # Filling NaN with None should leave NaNs unchanged
    df = pd.DataFrame({'a': [None, 2, None]})
    codeflash_output = fillna(df, 'a', None); result = codeflash_output # 107μs -> 84.2μs (28.2% faster)

def test_fillna_column_all_non_na():
    # Column with all valid (non-NaN) values
    df = pd.DataFrame({'a': [1, 2, 3]})
    codeflash_output = fillna(df, 'a', 99); result = codeflash_output # 33.1μs -> 62.8μs (47.2% slower)

def test_fillna_with_infinity():
    # Fill NaN with infinity
    import math
    df = pd.DataFrame({'a': [None, 1, None]})
    codeflash_output = fillna(df, 'a', math.inf); result = codeflash_output # 62.2μs -> 61.9μs (0.470% faster)
    expected = pd.DataFrame({'a': [math.inf, 1, math.inf]})

def test_fillna_column_with_all_same_value():
    # All values in column are the same and non-NaN
    df = pd.DataFrame({'a': [5, 5, 5]})
    codeflash_output = fillna(df, 'a', 0); result = codeflash_output # 33.3μs -> 62.3μs (46.6% slower)

def test_fillna_on_indexed_dataframe():
    # DataFrame with custom index
    df = pd.DataFrame({'a': [None, 2]}, index=['x', 'y'])
    codeflash_output = fillna(df, 'a', 7); result = codeflash_output # 44.8μs -> 64.9μs (31.1% slower)
    expected = pd.DataFrame({'a': [7, 2]}, index=['x', 'y'])

def test_fillna_on_multi_column_nan():
    # Only specified column is filled, others remain
    df = pd.DataFrame({'a': [None, 2], 'b': [None, 3]})
    codeflash_output = fillna(df, 'b', 8); result = codeflash_output # 44.9μs -> 62.1μs (27.7% slower)
    expected = pd.DataFrame({'a': [None, 2], 'b': [8, 3]})

# ------------------- Large Scale Test Cases -------------------

def test_fillna_large_dataframe_half_nans():
    # DataFrame with 1000 rows, half NaN in target column
    import numpy as np
    size = 1000
    data = [None if i % 2 == 0 else i for i in range(size)]
    df = pd.DataFrame({'a': data})
    codeflash_output = fillna(df, 'a', -1); result = codeflash_output # 9.70ms -> 65.0μs (14818% faster)
    expected = pd.DataFrame({'a': [-1 if i % 2 == 0 else i for i in range(size)]})

def test_fillna_large_dataframe_all_nans():
    # DataFrame with 1000 NaNs
    df = pd.DataFrame({'a': [None] * 1000})
    codeflash_output = fillna(df, 'a', 12345); result = codeflash_output # 12.5ms -> 78.3μs (15817% faster)
    expected = pd.DataFrame({'a': [12345] * 1000})

def test_fillna_large_dataframe_no_nans():
    # DataFrame with 1000 non-NaN values
    df = pd.DataFrame({'a': list(range(1000))})
    codeflash_output = fillna(df, 'a', 0); result = codeflash_output # 4.91ms -> 63.8μs (7596% faster)

def test_fillna_large_dataframe_multiple_columns():
    # Fill only one column in a large DataFrame with multiple columns
    import numpy as np
    size = 1000
    df = pd.DataFrame({
        'a': [None if i % 2 == 0 else i for i in range(size)],
        'b': [i for i in range(size)],
        'c': [None if i % 3 == 0 else i for i in range(size)]
    })
    codeflash_output = fillna(df, 'a', 999); result = codeflash_output # 25.0ms -> 92.4μs (27008% faster)
    expected = df.copy()
    expected['a'] = [999 if i % 2 == 0 else i for i in range(size)]

def test_fillna_large_dataframe_string_column():
    # Fill NaN in a large string column
    size = 1000
    df = pd.DataFrame({'s': [None if i % 10 == 0 else str(i) for i in range(size)]})
    codeflash_output = fillna(df, 's', 'filled'); result = codeflash_output # 5.74ms -> 82.0μs (6899% faster)
    expected = pd.DataFrame({'s': ['filled' if i % 10 == 0 else str(i) for i in range(size)]})
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-fillna-mdpetka0 and push.

Codeflash

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.
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jul 30, 2025
@codeflash-ai codeflash-ai bot requested a review from aseembits93 July 30, 2025 03:32
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