Function parallel_coordinates

hvplot/plotting/parallel_coordinates.py:9–86

Parallel coordinates plotting. To show a set of points in an n-dimensional space, a backdrop is drawn consisting of n parallel lines. A point in n-dimensional space is represented as a polyline with vertices on the parallel axes; the position of the vertex on the i-th axis corr

(
    data,
    class_column,
    cols=None,
    alpha=0.5,
    width=600,
    height=300,
    var_name='variable',
    value_name='value',
    cmap=None,
    colormap=None,
    **kwds,
)

@with_hv_extension
def parallel_coordinates(
    data,
    class_column,
    cols=None,
    alpha=0.5,
    width=600,
    height=300,
    var_name='variable',
    value_name='value',
    cmap=None,
    colormap=None,
    **kwds,
):
    """
    Parallel coordinates plotting.

    To show a set of points in an n-dimensional space, a backdrop is drawn
    consisting of n parallel lines. A point in n-dimensional space is
    represented as a polyline with vertices on the parallel axes; the
    position of the vertex on the i-th axis corresponds to the i-th coordinate
    of the point.

    Parameters
    ----------
    frame : DataFrame
        The DataFrame to be plotted.
    class_column : str
        Column name containing class names
    cols : list, optional
        A list of column names to use
    alpha : float, optional
        The transparency of the lines. Default is 0.5.
    cmap/colormap : str or colormap object, optional
        Colormap to use for groups. Default to Colorcet's ``glasbey_category10``.

    Returns
    -------
    obj : HoloViews object
        The HoloViews representation of the plot.

    See Also
    --------
    pandas.plotting.parallel_coordinates : matplotlib version of this routine
    """
    # Transform the dataframe to be used in Vega-Lite
    if cols is not None:
        data = data[list(cols) + [class_column]]
    cols = data.columns
    df = data.reset_index()
    index = (set(df.columns) - set(cols)).pop()
    assert index in df.columns
    df = df.melt([index, class_column], var_name=var_name, value_name=value_name)

    labelled = [] if var_name == 'variable' else ['x']
    if value_name != 'value':
        labelled.append('y')
    options = {
        'Curve': dict(kwds, labelled=labelled, alpha=alpha, width=width, height=height),