⚡️ Speed up function ds_as_cds by 25%

[codeflash-ai]

📄 25% (0.25x) speedup for ds_as_cds in panel/pane/vega.py

⏱️ Runtime : 4.49 milliseconds → 3.59 milliseconds (best of 40 runs)

📝 Explanation and details

The optimization eliminates the inefficient nested loop pattern that was causing significant performance bottlenecks.

Key changes:

• Replaced the nested for-loop structure (for item in dataset: for k in keys: data[k].append(item.get(k))) with a single dictionary comprehension using list comprehensions: {k: np.asarray([item.get(k) for item in dataset]) for k in keys}
• Eliminated the intermediate list-building step and separate numpy conversion phase

Why this is faster:

• Reduced function call overhead: The original code made ~47,000 append() calls (visible in line profiler), while the optimized version builds each list in one go using list comprehension, which is implemented in C and much faster
• Better memory access patterns: Instead of repeatedly accessing and mutating dictionary values, the optimization creates each column's data in a single pass
• Eliminated redundant numpy conversions: The original code built Python lists first, then converted to numpy arrays. The optimized version builds the list and converts to numpy array in one comprehension per key

Performance gains by test case:

• Most effective for larger datasets: 30-40% speedup on large-scale tests (1000+ items)
• Moderate gains (7-15%) on small datasets with multiple keys
• Particularly effective when datasets have missing keys or varied structures, as the item.get(k) pattern remains efficient
• No impact on pandas DataFrame cases since they use a different code path

The line profiler shows the nested loop overhead dropped from 72% of total time to being eliminated entirely, with the new dictionary comprehension becoming the dominant operation at 55% of the reduced total time.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 39 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from __future__ import annotations

import numpy as np
# imports
import pytest  # used for our unit tests
from panel.pane.vega import ds_as_cds

# unit tests

# Basic Test Cases

def test_empty_list_returns_empty_dict():
    # Test that an empty list returns an empty dict
    codeflash_output = ds_as_cds([]) # 1.82μs -> 1.64μs (10.8% faster)

def test_single_dict_in_list():
    # Test a single dict in a list
    codeflash_output = ds_as_cds([{'a': 1, 'b': 2}]); result = codeflash_output # 11.3μs -> 10.0μs (12.3% faster)

def test_multiple_dicts_same_keys():
    # Test multiple dicts with same keys
    data = [{'x': 1, 'y': 2}, {'x': 3, 'y': 4}]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 9.79μs -> 9.13μs (7.24% faster)

def test_multiple_dicts_different_keys():
    # Test multiple dicts with different keys
    data = [{'a': 1}, {'b': 2}, {'a': 3, 'b': 4}]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 10.6μs -> 9.79μs (8.38% faster)

def test_dataframe_input():
    # Test with pandas DataFrame input
    import pandas as pd
    df = pd.DataFrame({'foo': [1, 2, 3], 'bar': [4, 5, 6]})
    codeflash_output = ds_as_cds(df); result = codeflash_output # 82.0μs -> 83.3μs (1.56% slower)

def test_dicts_with_extra_keys():
    # Test dicts with overlapping and extra keys
    data = [{'a': 1, 'b': 2}, {'a': 3, 'c': 4}]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 12.0μs -> 11.0μs (8.93% faster)

# Edge Test Cases

def test_list_with_empty_dicts():
    # Test list containing empty dicts
    data = [{}, {}]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 4.14μs -> 3.33μs (24.3% faster)

def test_list_with_partial_empty_dicts():
    # Test list with some empty dicts and some with keys
    data = [{}, {'x': 1}]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 9.15μs -> 8.31μs (10.2% faster)

def test_dicts_with_none_values():
    # Test dicts with explicit None values
    data = [{'a': None}, {'a': 2}]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 9.04μs -> 7.93μs (14.0% faster)

def test_dicts_with_mixed_types():
    # Test dicts with mixed types for values
    data = [{'x': 1}, {'x': 'foo'}]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 11.1μs -> 10.1μs (9.24% faster)

def test_dicts_with_nested_dicts():
    # Test dicts with nested dicts as values
    nested = {'a': {'b': 1}}
    flat = {'a': 2}
    codeflash_output = ds_as_cds([nested, flat]); result = codeflash_output # 8.94μs -> 7.90μs (13.1% faster)

def test_dataframe_with_missing_values():
    # Test DataFrame with missing values
    import pandas as pd
    df = pd.DataFrame({'a': [1, None, 3], 'b': [None, 2, 4]})
    codeflash_output = ds_as_cds(df); result = codeflash_output # 79.1μs -> 80.7μs (1.94% slower)

def test_non_list_non_dataframe_input():
    # Test input that is not a list or DataFrame (should raise TypeError or behave as per implementation)
    with pytest.raises(TypeError):
        ds_as_cds(123) # 2.28μs -> 2.14μs (6.35% faster)

def test_list_with_non_dict_elements():
    # Test list containing non-dict elements (should raise AttributeError or behave as per implementation)
    data = [{'a': 1}, 2]
    with pytest.raises(AttributeError):
        ds_as_cds(data) # 3.63μs -> 3.60μs (0.973% faster)

def test_dicts_with_unhashable_keys():
    # Test dicts with unhashable keys (should raise TypeError)
    data = [{('a',): 1}, {('b',): 2}]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 12.2μs -> 11.2μs (9.07% faster)

# Large Scale Test Cases

def test_large_list_of_dicts():
    # Test with a large list of dicts
    N = 1000
    data = [{'x': i, 'y': i * 2} for i in range(N)]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 223μs -> 166μs (34.2% faster)

def test_large_dataframe():
    # Test with a large DataFrame
    import pandas as pd
    N = 1000
    df = pd.DataFrame({'a': range(N), 'b': range(N, 2*N)})
    codeflash_output = ds_as_cds(df); result = codeflash_output # 78.7μs -> 80.8μs (2.61% slower)

def test_large_list_with_missing_keys():
    # Test large list with some missing keys
    N = 1000
    data = [{'x': i} if i % 2 == 0 else {'y': i} for i in range(N)]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 233μs -> 178μs (30.6% faster)

def test_large_list_with_extra_keys():
    # Test large list with extra keys appearing only in some dicts
    N = 1000
    data = [{'a': i, 'b': i*2} for i in range(N//2)] + [{'c': i} for i in range(N//2, N)]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 311μs -> 234μs (32.8% faster)

def test_large_list_all_empty_dicts():
    # Test large list of empty dicts
    N = 1000
    data = [{} for _ in range(N)]
    codeflash_output = ds_as_cds(data); result = codeflash_output # 53.7μs -> 38.1μs (40.9% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
from __future__ import annotations

import numpy as np
# imports
import pytest  # used for our unit tests
from panel.pane.vega import ds_as_cds

# unit tests

# --- Basic Test Cases ---

def test_empty_list_returns_empty_dict():
    # Test that an empty list returns an empty dict
    codeflash_output = ds_as_cds([]) # 1.53μs -> 1.34μs (14.7% faster)

def test_single_dict():
    # Test a single dictionary in a list
    codeflash_output = ds_as_cds([{'a': 1, 'b': 2}]); result = codeflash_output # 9.74μs -> 9.17μs (6.27% faster)

def test_multiple_dicts_same_keys():
    # Test multiple dictionaries with identical keys
    dataset = [{'x': 1, 'y': 2}, {'x': 3, 'y': 4}]
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 9.47μs -> 8.67μs (9.23% faster)

def test_multiple_dicts_different_keys():
    # Test multiple dictionaries with different keys
    dataset = [{'a': 1}, {'b': 2}, {'a': 3, 'b': 4}]
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 10.3μs -> 9.70μs (6.53% faster)

def test_dicts_with_none_values():
    # Test dictionaries where some values are None
    dataset = [{'a': None, 'b': 2}, {'a': 3, 'b': None}]
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 9.83μs -> 8.72μs (12.7% faster)

def test_dataframe_input():
    # Test that a pandas DataFrame is converted properly
    import pandas as pd
    df = pd.DataFrame({'foo': [1, 2], 'bar': [3, 4]})
    codeflash_output = ds_as_cds(df); result = codeflash_output # 78.8μs -> 80.9μs (2.69% slower)

# --- Edge Test Cases ---

def test_dicts_with_missing_keys():
    # Some dicts missing keys entirely
    dataset = [{'a': 1}, {}, {'b': 2}]
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 11.0μs -> 9.95μs (10.8% faster)

def test_dicts_with_extra_keys():
    # Dicts with extra/unexpected keys
    dataset = [{'x': 1, 'y': 2}, {'x': 3, 'z': 5}]
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 11.0μs -> 10.2μs (7.97% faster)

def test_dicts_with_non_string_keys():
    # Dicts with non-string keys (should be handled)
    dataset = [{1: 'a', 2: 'b'}, {1: 'c', 2: 'd'}]
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 11.0μs -> 10.4μs (6.03% faster)

def test_dicts_with_mixed_types():
    # Dicts with mixed value types
    dataset = [{'a': 1, 'b': 'x'}, {'a': 2.5, 'b': None}]
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 11.1μs -> 9.77μs (13.3% faster)

def test_dicts_with_nested_dicts():
    # Dicts with values that are themselves dicts
    dataset = [{'a': {'x': 1}}, {'a': {'x': 2}}]
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 7.90μs -> 7.27μs (8.70% faster)

def test_dicts_with_lists_as_values():
    # Dicts with lists as values
    dataset = [{'a': [1,2]}, {'a': [3,4]}]

def test_dataframe_with_missing_values():
    # DataFrame with NaN values
    import pandas as pd
    df = pd.DataFrame({'foo': [1, None], 'bar': [None, 4]})
    codeflash_output = ds_as_cds(df); result = codeflash_output # 78.5μs -> 79.7μs (1.46% slower)

def test_input_is_not_list_or_dataframe():
    # Input is not a list or DataFrame (should raise TypeError)
    with pytest.raises(TypeError):
        ds_as_cds(123) # 2.17μs -> 2.20μs (1.50% slower)

# --- Large Scale Test Cases ---

def test_large_list_of_dicts():
    # Large scale: 1000 dicts with 10 keys
    keys = [f'k{i}' for i in range(10)]
    dataset = [{k: i for k in keys} for i in range(1000)]
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 843μs -> 641μs (31.5% faster)
    for k in keys:
        pass

def test_large_list_with_missing_keys():
    # Large scale: 1000 dicts, some missing keys
    keys = [f'k{i}' for i in range(10)]
    dataset = []
    for i in range(1000):
        d = {}
        for k in keys:
            if i % 2 == 0:
                d[k] = i
        dataset.append(d)
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 920μs -> 707μs (30.0% faster)
    for k in keys:
        pass

def test_large_dataframe():
    # Large scale: DataFrame with 1000 rows and 10 columns
    import pandas as pd
    data = {f'col{i}': list(range(1000)) for i in range(10)}
    df = pd.DataFrame(data)
    codeflash_output = ds_as_cds(df); result = codeflash_output # 159μs -> 163μs (2.93% slower)
    for k in data.keys():
        pass

def test_large_list_with_extra_keys():
    # Large scale: 500 dicts with base keys, 500 with extra keys
    keys = [f'k{i}' for i in range(5)]
    extra_keys = [f'extra{i}' for i in range(5)]
    dataset = [{k: i for k in keys} for i in range(500)]
    dataset += [{**{k: i for k in keys}, **{ek: i for ek in extra_keys}} for i in range(500, 1000)]
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 898μs -> 678μs (32.3% faster)
    all_keys = set(keys) | set(extra_keys)
    for k in keys:
        pass
    for ek in extra_keys:
        pass

def test_large_list_with_varied_types():
    # Large scale: 1000 dicts, alternating types
    dataset = []
    for i in range(1000):
        if i % 2 == 0:
            dataset.append({'a': i, 'b': str(i)})
        else:
            dataset.append({'a': float(i), 'b': None})
    codeflash_output = ds_as_cds(dataset); result = codeflash_output # 252μs -> 196μs (28.8% faster)
# 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-ds_as_cds-mhc07mt7 and push.