groupby-lib 0.6.1

High-performance extension package for pandas with fast groupby operations

groupby-lib

A high-performance extension package for pandas that provides fast groupby operations using NumPy and Numba acceleration intended for large data sets. Performance improvements increase as data is scaled up (see benchmarking below). It also provides a more flexible API and convenience methods in the group-by space like adding margins and calculating ratios.

Key Features**

• Over 10x faster when grouping with categorical keys (5x faster than Polars)
• ~3-4x faster group-by reductions when grouping by numerical keys
• Support inline filtering with Boolean masks to avoid expensive copies. This also facilitates re-use of GroupBy objects (whose construction is the most expensive part)
• 40x faster rolling stats/EMAs (6x faster than Polars)
• Compatible/Interoperable with Numpy/Pandas/Polars, including pyarrow-backed Pandas objects (even chunked arrays).
• Less verbosity, especially as compared with Polars
• Highly flexible inputs - any 1D object, collection of 1D objects (list, dict, etc) or 2D object from Pandas/Polars/Numpy accepted both for group keys and values to be aggregated
• Two modes of use - GroupBy class and (Series/DataFrame).groupby_fast monkey patch
• Optional margins for linear reductions - particularly useful with multi-key groupings
• Conveniences methods like GroupBy.ratio/subset_ratio/density
• 4x faster crosstabs (8x faster when margins are requested)

**Performance comparisons assuming multiple cores and large data

Overview

groupby-lib enhances Pandas groupby functionality with optimized implementations that leverage NumPy arrays and Numba's just-in-time compilation for significant performance improvements. The package is designed to work seamlessly with pandas DataFrames and Series while providing additional flexibility for various array types.

Set Up

Faster GroupBy Operations

Optimized group-by operations, particularly with categorical data (uses the existing factorization) and with multi-threading on large datasets across both row and columns axes. NB: the benchmarking below is after all numba just-in-time compilations have been completed.

The mask argument - inline filtering

Inline filtering such that Series/DataFrames do not have to be filtered before group-by operations. This saves time and memory directly, and also promotes re-use of GroupBy objects which boosts performance dramatically (the majority of run-time in most group-by ops is in the factorization step)

Multi-key and Margins

Rolling aggregations and EMAs

Optionally return results aligned to inputs (fastest) or sorted by groups first á la pandas (slower)

Quantiles

Crosstabs / Pivots

(DataFrame.pivot_table_fast coming soon)

Flexible Input Types:

Support for NumPy arrays, pandas Series/DataFrames backed by NumPy or Arrow, and Polars Series/DataFrames. Here are some examples which are not exhaustive

from groupby_lib.groupby import GroupBy

arr = np.random.randint(0, 10, 10000)
keys = arr % 3 + 2

# Create GroupBy object
gb = GroupBy(keys)

# Perform aggregations
gb.agg(arr, agg_func=["sum", "mean", "count", "min", "max"])
     sum      mean  count  min  max
2  18054  4.489928   4021    0    9
3  12085  4.035058   2995    1    7
4  14749  4.942694   2984    2    8

# on dict of arrays / Series
gb.std(dict(x=arr, x_squared=arr ** 2, pd_series=pd.Series(arr)))
           x	x_squared	pd_series
2	3.351510	31.601683	3.351510
3	2.452965	20.108977	2.452965
4	2.429882	24.677406	2.429882

# build a GroupBy from a dict/list of keys or a DataFrame
key_list = [keys, pd.Series(["A", "B"]).repeat(5000)]
GroupBy(key_list).size()
3  A    1537
   B    1497
2  A    2027
   B    1990
4  A    1436
   B    1513

• Pandas Compatible: Results are returned as pandas Series/DataFrames
• Multi-threading: Automatic parallelization for large datasets across both row and column axes

Installation

pip install groupby-lib

The GroupBy Class

The core of groupby-lib is the GroupBy class located in groupby_lib.groupby.core. This class provides efficient groupby operations with a pandas-like API.

Basic Usage

Supported Aggregation Methods

The GroupBy class supports various aggregation/selection functions:

• sum()/mean() - Sum/mean of values in each group
• min()/max() - Min/Max value in each group
• var()/std() - Variance/Std. Dev. of values in each group
• count() - Count of non-null values in each group
• size() - Total count of values in each group (including nulls)
• median()/quantile() - Median value/quantiles in each group
• head()/tail() - The top/bottom N rows/elements in each group
• first()/last() - The first/last non-null elements in each group
• nth() - The nth row/element in each group
• shift() - Shift elements group-wise
• diff() - Diff elements group-wise
• rolling_[min()/max()/sum()/mean()] - rolling aggregations
• cum[min()/max()/count()/sum()] - cumulative aggregations
• apply() - Apply any numpy compatbile function returning a scalar
• ema() - Exponetially weighted moving averages, optionally based on times

Working with Pandas Data

# Using pandas Series
df = pd.DataFrame({
    'category': ['A', 'B', 'A', 'C', 'B', 'A'],
    'value1': [1, 2, 3, 4, 5, 6],
    'value2': [10, 20, 30, 40, 50, 60]
})

# Group by category
gb = GroupBy(df['category'])

# Aggregate single column
mean_value1 = gb.mean(df['value1'])
print(mean_value1)
# Output:
# A    3.333333
# B    3.500000
# C    4.000000
# Name: value1, dtype: float64

# Aggregate multiple columns
result = gb.sum(df[['value1', 'value2']])
print(result)
# Output:
#    value1  value2
# A      10     100
# B       7      70
# C       4      40

Multi-level Grouping

# Group by multiple keys
keys1 = ['A', 'A', 'B', 'B', 'A', 'B']
keys2 = ['X', 'Y', 'X', 'Y', 'X', 'Y']
values = [1, 2, 3, 4, 5, 6]

gb = GroupBy([keys1, keys2])
result = gb.sum(values)
print(result)
# Output:
# A  X    6
#    Y    2
# B  X    3
#    Y   10
# dtype: int64

Using Masks for Filtering

# Apply mask to filter data during aggregation
keys = ['A', 'B', 'A', 'C', 'B', 'A']
values = [1, 2, 3, 4, 5, 6]
mask = np.array([True, False, True, True, False, True])

gb = GroupBy(keys)
result = gb.sum(values, mask=mask)
print(result)
# Output: Only includes values where mask is True
# A    10  # (1 + 3 + 6)
# C     4  # (4)
# dtype: int64

Transform Operations

Transform operations return results with the same shape as the input:

keys = ['A', 'B', 'A', 'B', 'A']
values = [1, 2, 3, 4, 5]

gb = GroupBy(keys)
transformed = gb.sum(values, transform=True)
print(transformed)
# Output: Each position shows the group sum
# 0    9  # Sum of group A
# 1    6  # Sum of group B  
# 2    9  # Sum of group A
# 3    6  # Sum of group B
# 4    9  # Sum of group A
# dtype: int64

Generic Aggregation with agg()

# Single aggregation function
gb = GroupBy(['A', 'B', 'A', 'B'])
result = gb.agg([1, 2, 3, 4], 'mean')

# Multiple aggregation functions
result = gb.agg([1, 2, 3, 4], ['sum', 'mean', 'min', 'max'])
print(result)
# Output:
#    sum  mean  min  max
# A    4   2.0    1    3
# B    6   3.0    2    4

Ratio Calculations

# Calculate ratios between two value sets
numerator = [1, 2, 3, 4]
denominator = [10, 20, 30, 40]
keys = ['A', 'B', 'A', 'B']

gb = GroupBy(keys)
ratio = gb.ratio(numerator, denominator)
print(ratio)
# Output:
# A    0.133333  # (1+3)/(10+30)
# B    0.100000  # (2+4)/(20+40)
# dtype: float64

Head Operation

Get the first n rows from each group:

keys = ['A', 'A', 'B', 'B', 'A', 'B']
values = [1, 2, 3, 4, 5, 6]

gb = GroupBy(keys)
first_two = gb.head(values, n=2)
print(first_two)
# Output: First 2 values from each group
# A  0    1
#    1    2
# B  0    3
#    1    4
# dtype: int64

Pivot Tables

Create pivot tables using the crosstab function:

from groupby_lib.groupby import crosstab

# Sample data
index_keys = ['Jan', 'Feb', 'Jan', 'Feb']
column_keys = ['A', 'A', 'B', 'B']  
values = [10, 20, 30, 40]

result = crosstab(
    index=index_keys,
    columns=column_keys,
    values=values,
    agg_func='sum'
)
print(result)
# Output:
#      A   B
# Feb  20  40
# Jan  10  30

Performance Benefits

groupby-lib provides significant performance improvements for large datasets:

• Numba Acceleration: JIT compilation for fast numerical operations
• Multi-threading: Automatic parallelization for datasets > 1M rows
• Memory Efficiency: Optimized memory usage patterns
• Reduced Overhead: Minimal pandas overhead for core operations

Dependencies

• Python 3.10+
• NumPy
• pandas
• numba
• pyarrow
• polars

Development

See CLAUDE.md for development guidelines including:

• Code style conventions
• Testing requirements
• Build and lint commands
• Contribution guidelines