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@codeflash-ai codeflash-ai bot commented Jun 27, 2025

📄 6,392% (63.92x) speedup for groupby_mean in src/numpy_pandas/dataframe_operations.py

⏱️ Runtime : 278 milliseconds 4.28 milliseconds (best of 338 runs)

📝 Explanation and details

Here is an optimized rewrite of your program. The main bottleneck in your original code is the use of df.iloc[i][col] inside a Python loop, which is extremely slow (iloc is not efficient for row-wise access in a loop, since it creates new Series each time and is pure Python). We can extract both columns as numpy arrays (fast), then use a single loop over these pre-extracted arrays, vastly reducing overhead. However, the fastest approach is to use pandas' own highly optimized groupby mechanism, which is written in C. Computing group means with groupby().mean() is both correct and orders of magnitude faster.

I'll preserve your function signature and structure, but internally use vectorized pandas operations for speed, then convert the output to a dict as in your original output.

If it is absolutely required not to use groupby():
Here is a version that manually aggregates the data but without the per-row iloc access overhead:

Both versions will run much faster than the original code.
If maximum speed is the goal, always use the first version with groupby().mean().

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 47 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 groupby_mean

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

def test_single_group_single_row():
    # One group, one row
    df = pd.DataFrame({'A': ['x'], 'B': [10]})
    codeflash_output = groupby_mean(df, 'A', 'B') # 61.9μs -> 43.0μs (43.8% faster)

def test_single_group_multiple_rows():
    # One group, multiple rows
    df = pd.DataFrame({'A': ['x', 'x', 'x'], 'B': [1, 2, 3]})
    codeflash_output = groupby_mean(df, 'A', 'B') # 132μs -> 43.5μs (205% faster)

def test_multiple_groups():
    # Multiple groups, each with multiple rows
    df = pd.DataFrame({'A': ['x', 'y', 'x', 'y'], 'B': [1, 2, 3, 4]})
    codeflash_output = groupby_mean(df, 'A', 'B'); result = codeflash_output # 167μs -> 43.9μs (281% faster)

def test_groups_with_single_and_multiple_rows():
    # Some groups have one row, others have multiple
    df = pd.DataFrame({'A': ['a', 'b', 'a', 'c'], 'B': [5, 10, 15, 20]})
    codeflash_output = groupby_mean(df, 'A', 'B'); result = codeflash_output # 165μs -> 43.8μs (278% faster)

def test_float_values():
    # Value column contains floats
    df = pd.DataFrame({'G': ['g1', 'g2', 'g1', 'g2'], 'V': [1.5, 2.5, 3.5, 4.5]})
    codeflash_output = groupby_mean(df, 'G', 'V'); result = codeflash_output # 165μs -> 43.8μs (278% faster)

def test_negative_and_zero_values():
    # Value column contains negative and zero values
    df = pd.DataFrame({'K': ['a', 'a', 'b', 'b'], 'V': [-1, 1, 0, 0]})
    codeflash_output = groupby_mean(df, 'K', 'V'); result = codeflash_output # 164μs -> 43.9μs (275% faster)

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

def test_empty_dataframe():
    # DataFrame is empty
    df = pd.DataFrame({'A': [], 'B': []})
    codeflash_output = groupby_mean(df, 'A', 'B') # 1.38μs -> 43.2μs (96.8% slower)

def test_group_col_missing():
    # group_col does not exist
    df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
    with pytest.raises(KeyError):
        groupby_mean(df, 'C', 'B')

def test_value_col_missing():
    # value_col does not exist
    df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
    with pytest.raises(KeyError):
        groupby_mean(df, 'A', 'C')

def test_group_col_with_nan():
    # group_col contains NaN
    df = pd.DataFrame({'A': ['x', None, 'y', 'x'], 'B': [1, 2, 3, 4]})
    codeflash_output = groupby_mean(df, 'A', 'B'); result = codeflash_output # 168μs -> 45.2μs (273% faster)

def test_value_col_with_nan():
    # value_col contains NaN
    df = pd.DataFrame({'A': ['x', 'y', 'x'], 'B': [1, float('nan'), 3]})
    codeflash_output = groupby_mean(df, 'A', 'B'); result = codeflash_output # 135μs -> 44.0μs (208% faster)

def test_group_col_with_mixed_types():
    # group_col contains mixed types
    df = pd.DataFrame({'A': ['x', 1, 'x', 1], 'B': [1, 2, 3, 4]})
    codeflash_output = groupby_mean(df, 'A', 'B'); result = codeflash_output # 168μs -> 44.2μs (281% faster)

def test_value_col_with_non_numeric():
    # value_col contains non-numeric values (should raise TypeError)
    df = pd.DataFrame({'A': ['x', 'y'], 'B': ['foo', 'bar']})
    with pytest.raises(TypeError):
        groupby_mean(df, 'A', 'B')

def test_all_groups_unique():
    # Each group appears only once
    df = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': [1, 2, 3]})
    codeflash_output = groupby_mean(df, 'A', 'B'); result = codeflash_output # 134μs -> 44.1μs (204% faster)

def test_all_groups_same():
    # All rows belong to the same group
    df = pd.DataFrame({'A': ['g'] * 5, 'B': [2, 4, 6, 8, 10]})
    codeflash_output = groupby_mean(df, 'A', 'B'); result = codeflash_output # 200μs -> 44.0μs (355% faster)

def test_duplicate_group_and_value_names():
    # group_col and value_col have the same name
    df = pd.DataFrame({'A': ['x', 'y', 'x', 'y'], 'B': [1, 2, 3, 4], 'C': [5, 6, 7, 8]})
    df['A'] = df['B']  # Now both group and value columns are 'B'
    codeflash_output = groupby_mean(df, 'B', 'B'); result = codeflash_output # 130μs -> 20.1μs (552% faster)

def test_group_col_is_index():
    # group_col is actually the index
    df = pd.DataFrame({'B': [1, 2, 3]}, index=['x', 'y', 'x'])
    df = df.reset_index()
    codeflash_output = groupby_mean(df, 'index', 'B'); result = codeflash_output # 134μs -> 39.2μs (244% faster)

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

def test_large_number_of_groups():
    # 1000 unique groups, each with 1 value
    n = 1000
    df = pd.DataFrame({'G': list(range(n)), 'V': list(range(n))})
    codeflash_output = groupby_mean(df, 'G', 'V'); result = codeflash_output # 22.6ms -> 346μs (6428% faster)
    for i in range(n):
        pass

def test_large_group_sizes():
    # 10 groups, each with 100 values
    n_groups = 10
    group_size = 100
    data = {'G': [], 'V': []}
    for g in range(n_groups):
        data['G'].extend([g] * group_size)
        data['V'].extend(range(g * group_size, (g + 1) * group_size))
    df = pd.DataFrame(data)
    codeflash_output = groupby_mean(df, 'G', 'V'); result = codeflash_output
    for g in range(n_groups):
        expected = sum(range(g * group_size, (g + 1) * group_size)) / group_size

def test_large_uniform_group():
    # All rows in one group, 1000 rows
    df = pd.DataFrame({'G': ['a'] * 1000, 'V': list(range(1000))})
    codeflash_output = groupby_mean(df, 'G', 'V'); result = codeflash_output # 33.1ms -> 208μs (15810% faster)

def test_large_randomized_groups():
    # 100 groups, 10 rows each, groups shuffled
    import random
    n_groups = 100
    group_size = 10
    groups = []
    values = []
    for g in range(n_groups):
        groups.extend([g] * group_size)
        values.extend([g * 10 + i for i in range(group_size)])
    combined = list(zip(groups, values))
    random.shuffle(combined)
    shuffled_groups, shuffled_values = zip(*combined)
    df = pd.DataFrame({'G': shuffled_groups, 'V': shuffled_values})
    codeflash_output = groupby_mean(df, 'G', 'V'); result = codeflash_output
    for g in range(n_groups):
        expected = sum([g * 10 + i for i in range(group_size)]) / group_size

def test_performance_large_dataframe():
    # Test that function completes on a large DataFrame (1000 rows, 100 groups)
    import time
    n_rows = 1000
    n_groups = 100
    df = pd.DataFrame({
        'G': [i % n_groups for i in range(n_rows)],
        'V': [i for i in range(n_rows)]
    })
    start = time.time()
    codeflash_output = groupby_mean(df, 'G', 'V'); result = codeflash_output # 22.4ms -> 315μs (6993% faster)
    duration = time.time() - start
# 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 groupby_mean

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

def test_single_group_single_row():
    # One group, one row
    df = pd.DataFrame({'group': ['A'], 'value': [10]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 62.3μs -> 43.3μs (43.8% faster)

def test_single_group_multiple_rows():
    # One group, multiple rows
    df = pd.DataFrame({'group': ['A', 'A', 'A'], 'value': [1, 2, 3]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 133μs -> 43.6μs (205% faster)

def test_multiple_groups_equal_size():
    # Multiple groups, each with same number of rows
    df = pd.DataFrame({'group': ['A', 'A', 'B', 'B'], 'value': [1, 3, 2, 4]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 166μs -> 43.9μs (279% faster)

def test_multiple_groups_unequal_size():
    # Multiple groups, different sizes
    df = pd.DataFrame({'group': ['A', 'A', 'B', 'C', 'C', 'C'], 'value': [1, 3, 2, 4, 5, 7]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 233μs -> 44.5μs (424% faster)

def test_groups_with_negative_and_zero_values():
    # Groups with negative and zero values
    df = pd.DataFrame({'group': ['A', 'A', 'B', 'B'], 'value': [0, -2, 2, 0]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 165μs -> 43.8μs (278% faster)

def test_groups_with_float_values():
    # Groups with float values
    df = pd.DataFrame({'group': ['A', 'B', 'A', 'B'], 'value': [1.5, 2.5, 3.5, 4.5]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 166μs -> 44.0μs (279% faster)

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

def test_empty_dataframe():
    # DataFrame is empty
    df = pd.DataFrame({'group': [], 'value': []})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 1.42μs -> 42.9μs (96.7% slower)

def test_group_col_missing():
    # group_col does not exist
    df = pd.DataFrame({'g': ['A'], 'value': [1]})
    with pytest.raises(KeyError):
        groupby_mean(df, 'group', 'value')

def test_value_col_missing():
    # value_col does not exist
    df = pd.DataFrame({'group': ['A'], 'val': [1]})
    with pytest.raises(KeyError):
        groupby_mean(df, 'group', 'value')

def test_group_col_with_nan():
    # group_col contains NaN
    df = pd.DataFrame({'group': ['A', None, 'B', 'A'], 'value': [1, 2, 3, 5]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 167μs -> 45.1μs (271% faster)

def test_value_col_with_nan():
    # value_col contains NaN
    import math
    df = pd.DataFrame({'group': ['A', 'A', 'B'], 'value': [1.0, float('nan'), 3.0]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 131μs -> 43.7μs (202% faster)

def test_group_col_with_mixed_types():
    # group_col contains mixed types
    df = pd.DataFrame({'group': ['A', 1, 'A', 1], 'value': [2, 4, 6, 8]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 166μs -> 44.2μs (277% faster)

def test_value_col_with_mixed_types():
    # value_col contains ints and floats
    df = pd.DataFrame({'group': ['A', 'A', 'B'], 'value': [1, 2.5, 3]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 131μs -> 43.6μs (202% faster)

def test_group_col_all_same():
    # All group_col values are the same
    df = pd.DataFrame({'group': ['X'] * 10, 'value': list(range(10))})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 368μs -> 44.5μs (728% faster)

def test_value_col_all_same():
    # All values in value_col are the same
    df = pd.DataFrame({'group': ['A', 'B', 'A', 'B'], 'value': [7, 7, 7, 7]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 166μs -> 43.7μs (281% faster)

def test_group_col_all_unique():
    # Every row is its own group
    df = pd.DataFrame({'group': list('ABCDE'), 'value': [1, 2, 3, 4, 5]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 203μs -> 44.3μs (359% faster)

def test_value_col_all_nan():
    # All values are NaN
    import math
    df = pd.DataFrame({'group': ['A', 'A', 'B'], 'value': [float('nan')] * 3})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 133μs -> 44.3μs (202% faster)

def test_group_col_with_empty_string():
    # group_col contains empty string
    df = pd.DataFrame({'group': ['', 'A', ''], 'value': [1, 2, 3]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 131μs -> 43.7μs (201% faster)

def test_value_col_with_zero():
    # value_col contains zeros
    df = pd.DataFrame({'group': ['A', 'B', 'A'], 'value': [0, 0, 0]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 132μs -> 43.6μs (204% faster)

def test_non_string_group_col():
    # group_col is not a string (e.g., int)
    df = pd.DataFrame({0: ['A', 'B', 'A'], 1: [1, 2, 3]})
    codeflash_output = groupby_mean(df, 0, 1); result = codeflash_output # 144μs -> 45.1μs (221% faster)

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

def test_large_number_of_rows_single_group():
    # 1000 rows, single group
    df = pd.DataFrame({'group': ['A'] * 1000, 'value': list(range(1000))})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 33.2ms -> 207μs (15912% faster)

def test_large_number_of_rows_multiple_groups():
    # 1000 rows, 10 groups
    n = 1000
    groups = ['G' + str(i % 10) for i in range(n)]
    values = [i for i in range(n)]
    df = pd.DataFrame({'group': groups, 'value': values})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 33.5ms -> 270μs (12287% faster)
    # For each group Gi, mean is mean of all values at indices where i % 10 == group number
    for i in range(10):
        group = 'G' + str(i)
        group_indices = list(range(i, n, 10))
        expected_mean = sum(group_indices) / len(group_indices)

def test_large_number_of_groups_each_one_row():
    # 1000 groups, each with one row
    n = 1000
    df = pd.DataFrame({'group': [f'G{i}' for i in range(n)], 'value': [i for i in range(n)]})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 33.4ms -> 311μs (10616% faster)
    for i in range(n):
        pass

def test_large_number_of_groups_each_multiple_rows():
    # 100 groups, each with 10 rows
    n_groups = 100
    n_per_group = 10
    groups = [f'G{i}' for i in range(n_groups) for _ in range(n_per_group)]
    values = [j for i in range(n_groups) for j in range(n_per_group)]
    df = pd.DataFrame({'group': groups, 'value': values})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 33.4ms -> 282μs (11737% faster)
    for i in range(n_groups):
        pass

def test_large_scale_mixed_types():
    # 500 rows, mixed int/str group keys, float/int values
    n = 500
    groups = [i if i % 2 == 0 else str(i) for i in range(n)]
    values = [float(i) if i % 3 == 0 else i for i in range(n)]
    df = pd.DataFrame({'group': groups, 'value': values})
    codeflash_output = groupby_mean(df, 'group', 'value'); result = codeflash_output # 16.7ms -> 172μs (9598% faster)
    # Check a few representative groups
    for i in [0, 1, 2, 3, 10, 11, 250, 251, 498, 499]:
        group = i if i % 2 == 0 else str(i)
        # Find all indices with this group
        indices = [j for j in range(n) if (j if j % 2 == 0 else str(j)) == group]
        expected_mean = sum(float(j) if j % 3 == 0 else j for j in indices) / len(indices)
# 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-groupby_mean-mcfa14jd and push.

Codeflash

Here is an optimized rewrite of your program. The main bottleneck in your original code is the use of `df.iloc[i][col]` inside a Python loop, which is extremely slow (`iloc` is not efficient for row-wise access in a loop, since it creates new Series each time and is pure Python). We can extract both columns as numpy arrays (fast), then use a single loop over these pre-extracted arrays, vastly reducing overhead. However, **the fastest approach is to use pandas' own highly optimized groupby mechanism**, which is written in C. Computing group means with `groupby().mean()` is both correct and orders of magnitude faster.

I'll preserve your function signature and structure, but internally use vectorized pandas operations for speed, then convert the output to a dict as in your original output.




**If it is absolutely required not to use groupby():**
Here is a version that manually aggregates the data but without the per-row iloc access overhead: 



Both versions will run **much** faster than the original code.  
If maximum speed is the goal, always use the first version with `groupby().mean()`.
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Jun 27, 2025
@codeflash-ai codeflash-ai bot requested a review from KRRT7 June 27, 2025 20:40
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