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SQL PARTITION BY and window functions for NumPy

Project description

Split numpy arrays into partitions by one or multiple columns and apply window function to each partition.

This module tries to replicate select window_function() over (partition by ... order by ...) ... functionality, commonly found in SQL databases.

The following window functions are available out of the box: row_number(), top(), avg().

Usage examples:

    >>> from partition import apply_over_partition
    >>> from partition.window import row_number, top, avg

    >>> data = np.array([[1,1,3], [2,2,3], [1,1,4]], dtype=np.float32)
    >>> partition_by_col_indexes = (0, 1)
    >>> value_col_indexes = (2,)
    >>> value_ordering = (-1,)  # descending order
    >>> f = avg
    >>> f_kwargs = dict(vcol=2, top_n=2)
    >>> apply_over_partition(data=data, partition_by_col_indexes=partition_by_col_indexes, value_col_indexes=value_col_indexes, value_ordering=value_ordering, f=f, f_kwargs=f_kwargs)
    array([3.5, 3. , 3.5])

    >>> f = avg
    >>> f_kwargs = dict(vcol=2, top_n=1)
    >>> apply_over_partition(data=data, partition_by_col_indexes=partition_by_col_indexes, value_col_indexes=value_col_indexes, value_ordering=value_ordering, f=f, f_kwargs=f_kwargs)
    array([4., 3., 4.])

    >>> f = row_number
    >>> f_kwargs = dict()
    >>> apply_over_partition(data=data, partition_by_col_indexes=partition_by_col_indexes, value_col_indexes=value_col_indexes, value_ordering=value_ordering, f=f, f_kwargs=f_kwargs)
    array([1, 0, 0])

    >>> f = top
    >>> f_kwargs = dict(n=1)
    >>> apply_over_partition(data=data, partition_by_col_indexes=partition_by_col_indexes, value_col_indexes=value_col_indexes, value_ordering=value_ordering, f=f, f_kwargs=f_kwargs)
    array([False,  True,  True])

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