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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 _discrete_colorize(categorical, hue, scheme, k, cmap, vmin, vmax):
 """ Creates a discrete colormap, either using an already-categorical data variable or by bucketing a non-categorical ordinal one. If a scheme is provided we compute a distribution for the given data. If one is not provided we assume that the input data is categorical. This code makes extensive use of ``geopandas`` choropleth facilities. Parameters categorical : boolean Whether or not the input variable is already categorical. hue : iterable The data column whose entries are being discretely colorized. Note that although top-level plotter ``hue`` parameters ingest many argument signatures, not just iterables, they are all preprocessed to standardized iterables before this method is called. scheme : str The mapclassify binning scheme to be used for splitting data values (or rather, the the string representation thereof). k : int The number of bins which will be used. This parameter will be ignored if ``categorical`` is True. The default value should be 5---this should be set before this method is called. cmap : ``matplotlib.cm`` instance The `matplotlib` colormap instance which will be used to colorize the geometries. This colormap determines the spectrum; our algorithm determines the cuts. vmin : float A strict floor on the value associated with the "bottom" of the colormap spectrum. Data column entries whose value is below this level will all be colored by the same threshold value. vmax : float A strict cealing on the value associated with the "bottom" of the colormap spectrum. Data column entries whose value is above this level will all be colored by the same threshold value. Returns ------- (cmap, categories, values) : tuple A tuple meant for assignment containing the values for various properties set by this method call. """

    if not categorical:
        binning = _mapclassify_choro(hue, scheme, k=k)
        values = binning.yb
        binedges = [binning.yb.min()] + binning.bins.tolist()
        categories = ['{0:.2f} - {1:.2f}'.format(binedges[i], binedges[i + 1])
                      for i in range(len(binedges) - 1)]
    else:
        categories = np.unique(hue)
        if len(categories) > 10:
            warnings.warn("Generating a colormap using a categorical column with over 10 individual categories. "
                          "This is not recommended!")
        value_map = {v: i for i, v in enumerate(categories)}
        values = [value_map[d] for d in hue]
    cmap = _norm_cmap(values, cmap, mpl.colors.Normalize, mpl.cm, vmin=vmin, vmax=vmax)
    return cmap, categories, values