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        "description": "# Split numpy arrays into partitions by one or multiple columns and apply window function to each partition.\n\n\nThis module tries to replicate `select window_function() over (partition by ... order by ...) ...` functionality, commonly found in SQL databases.\n\nThe following window functions are available out of the box: `row_number()`, `top()`, `avg()`. \n\n\n## Usage examples:\n\n```\n    >>> from partition import apply_over_partition\n    >>> from partition.window import row_number, top, avg\n\n    >>> data = np.array([[1,1,3], [2,2,3], [1,1,4]], dtype=np.float32)\n    >>> partition_by_col_indexes = (0, 1)\n    >>> value_col_indexes = (2,)\n    >>> value_ordering = (-1,)  # descending order\n    >>> f = avg\n    >>> f_kwargs = dict(vcol=2, top_n=2)\n    >>> 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)\n    array([3.5, 3. , 3.5])\n\n    >>> f = avg\n    >>> f_kwargs = dict(vcol=2, top_n=1)\n    >>> 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)\n    array([4., 3., 4.])\n\n    >>> f = row_number\n    >>> f_kwargs = dict()\n    >>> 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)\n    array([1, 0, 0])\n\n    >>> f = top\n    >>> f_kwargs = dict(n=1)\n    >>> 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)\n    array([False,  True,  True])\n\n```",
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