如何在Python中对Plotly/Dash Mekko图表中的列进行排序和着色？

Question

我试着做两件事：

对每列应用一种颜色，而不是对每个单独的项目应用一种颜色

对底部值最大的列进行排序（这不太重要）

这是我当前的代码（改编自堆栈溢出）。此时，变量颜色可以是任何颜色（如果列多于颜色，则应重复）

我尝试应用一个数字id（参见注释行），但是因为我必须为mekko透视数据，所以数字有点丢失

任何帮助都将不胜感激。非常感谢

下面是一个示例数据文件：https://www.dropbox.com/s/lgf7oyrir33lpd4/sample_data.csv?dl=0

def chartMekko(excel,stack,colors=colors):
    data = excel
# pivot topic_label to columns
    pivot_data=data.pivot_table(index=['topic_label_group'],columns=[stack], values='cluster_frequency', aggfunc=np.sum).fillna(0)
    names = pivot_data.index.tolist()
id = []
# cols = []
# for i in range(len(names)):
#     id.append(names[i][1])
#     cols.append(names[i][0])
    pio.renderers.default ='iframe_connected'
def stacked_bar_width_plot(df, value_cols, width_col, colors=colors, **subplot):
"""A stacked column plot with variable bar width.
        :param df
        :param list value_cols: columns of `df`, already normalized (sum=1 for every line).
        :param str width_col: column of `df`, unbounded, used (i) as label (ii) to compute width.
        :param dict subplot: optional figure/row/col
        """
        categories = df.index.to_list()
        width = df[width_col]
        x = np.cumsum([0] + list(width[:-1]))
        colors = colors * len(value_cols)
        figure = subplot.pop('figure', go.Figure())
for colname, color in zip(value_cols, colors):
            figure.add_trace(go.Bar(name=colname, x=x, y=df[colname], width=width, offset=0, marker_color=color,customdata=categories,hovertemplate="<br>".join([
"<b>" + colname + "</b>",
"# of verbatims: %{width}",
"Pct in "+colname+": %{y}",
"<extra></extra>"])
            ), **subplot)
return figure.update_xaxes(
tickvals=x + np.array(width) / 2,
range=[0, np.sum(width)],
ticktext=categories, **subplot
        ) \
            .update_yaxes(range=[0, 1], row=subplot.get('row'), col=subplot.get('col')) \
            .update_layout(barmode='stack', bargap=0,showlegend=False,margin=dict(l=5, r=25, t=20, b=10),)

def mekko_plot(df, unit_name=None, colors=colors, **subplot):
"""A mekko plot is a normalized stacked column plot with variable bar width.
        :param DataFrame df: already indexed by category, with only numeric columns:
                    there will be one stacked-bar per line (X), labeled after the index, with one bar per column.
        :param str unit_name: used to populate hover.
        :param list colors: color of each column. None for default palette (blue, red, ...).
        The rest (title..) is easily added afterwards.
        """
# Normalize then defer to stacked_bar_width_plot plot.
        value_cols = df.columns
        w = pd.DataFrame({unit_name: df.sum(axis='columns')})
        w[value_cols] = df.div(w[unit_name], axis='index')
return stacked_bar_width_plot(w, value_cols, width_col=unit_name, colors=colors, **subplot)
    mekko = mekko_plot(pivot_data, unit_name='cluster_frequency')
    charts = dcc.Graph(figure=mekko, id='chartTest', style={})
return mekko