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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def hexbin(x, y, size, orientation="pointytop", aspect_scale=1):
    ''' Perform an equal-weight binning of data points into hexagonal tiles.

    For more sophisticated use cases, e.g. weighted binning or scaling
    individual tiles proportional to some other quantity, consider using
    HoloViews.

    Args:
        x (array[float]) :
            A NumPy array of x-coordinates for binning

        y (array[float]) :
            A NumPy array of y-coordinates for binning

        size (float) :
            The size of the hexagonal tiling.

            The size is defined as the distance from the center of a hexagon
            to the top corner for "pointytop" orientation, or from the center
            to a side corner for "flattop" orientation.

        orientation (str, optional) :
            Whether the hex tile orientation should be "pointytop" or
            "flattop". (default: "pointytop")

        aspect_scale (float, optional) :
            Match a plot's aspect ratio scaling.

            When working with a plot with ``aspect_scale != 1``, this
            parameter can be set to match the plot, in order to draw
            regular hexagons (instead of "stretched" ones).

            This is roughly equivalent to binning in "screen space", and
            it may be better to use axis-aligned rectangular bins when
            plot aspect scales are not one.

    Returns:
        DataFrame

        The resulting DataFrame will have columns *q* and *r* that specify
        hexagon tile locations in axial coordinates, and a column *counts* that
        provides the count for each tile.

    .. warning::
        Hex binning only functions on linear scales, i.e. not on log plots.

    '''
    pd = import_required('pandas','hexbin requires pandas to be installed')

    q, r = cartesian_to_axial(x, y, size, orientation, aspect_scale=aspect_scale)

    df = pd.DataFrame(dict(r=r, q=q))
    return df.groupby(['q', 'r']).size().reset_index(name='counts')