如何在Matplotlib中制作一个马赛克图[英] How to make a mosaic plot in Matplotlib

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问题描述

我知道您可以使用来自 statsmodels 的 马赛克图 但是当你的类别有一些空值时有点令人沮丧(喜欢这里).我想知道它是否存在像 matplotlib 或 seaborn 这样的图形库的解决方案,这会更方便.

我认为这对 seaborn 来说是一个不错的功能,因为列联表经常使用 pandas 构建.但是,似乎 它不会很快实施.

最后,如何制作一个 3 维的马赛克图,以及可能的空类别?

这是一个通用的马赛克图(来自 wikipedia)在此处输入图片描述

推荐答案

由于python中没有任何东西,这是我编写的代码.最后一个维度的大小现在应该是 1(即常规表)或 2.随意更新代码来解决这个问题,但是超过 3 个可能无法阅读.它有点长,但它完成了工作.下面的例子.

选项很少,大部分都是不言自明的,否则:

  • dic_color_row:一个字典,其中键是最外层的索引(在下面的示例中为 Index_1),值是颜色,避免使用黑色/灰色
  • pad:图的每个条之间的空间
  • alpha_label:第 3 维使用 alpha 技巧进行区分,在图例中会呈现为深灰色/浅灰色,并且可以更改每个标签的名称(类似于 col_labels 或 row_labels)
  • color_label:为 y-tick 标签添加背景颜色.[对/错]

    def mosaic_plot(df, dic_color_row, row_labels=None, col_labels=None, alpha_label=None, top_label="Size",
                    x_label=None, y_label=None, pad=0.01, color_ylabel=False, ax=None, order="Size"):
        """ 
    
        From a contingency table NxM, plot a mosaic plot with the values inside. There should be a double-index for rows
        e.g.
                                             3   4   1   0   2  5
            Index_1          Index_2                       
            AA               C               0   0   0   2   3  0
                             P               6   0   0  13   0  0
            BB               C               0   2   0   0   0  0
                             P              45   1  10  10   1  0
            CC               C               0   6  35  15  29  0
                             P               1   1   0   2   0  0
            DD               C               0  56   0   3   0  0
                             P              30   4   2   0   1  9
    
        order: how columns are order, by default, from the biggest to the smallest in term of category. Possible values are 
            - "Size" [default]
            - "Normal" : as the columns are order in the input df
            - list of column names to reorder the column
        top_label: Size of each columns. The label can be changed to adapt to your value. 
                   If `False`, nothing is displayed and the secondary legend is set on top instead of on right.  
        """
    
        is_multi = len(df.index.names) == 2
        if ax == None:
            fig, ax = plt.subplots(1,1, figsize=(len(df.columns), len(df.index.get_level_values(0).unique())))
    
        size_col = df.sum().sort_values(ascending=False)
        prop_com = size_col.div(size_col.sum())
    
        if order == "Size":
            df = df[size_col.index.values]
        elif order == "Normal":
            prop_com = prop_com[df.columns]
            size_col = size_col[df.columns]
        else:
            df = df[order]
            prop_com = prop_com[order]
            size_col = size_col[order]
    
        if is_multi:
            inner_index = df.index.get_level_values(1).unique()
            prop_ii0 = (df.swaplevel().loc[inner_index[0]]/(df.swaplevel().loc[inner_index[0]]+df.swaplevel().loc[inner_index[1]])).fillna(0)
            alpha_ii = 0.5
            true_y_labels = df.index.levels[0]
        else:
            alpha_ii = 1
            true_y_labels = df.index
    
        Yt = (df.groupby(level=0).sum().iloc[:,0].div(df.groupby(level=0).sum().iloc[:,0].sum())+pad).cumsum() - pad
        Ytt = df.groupby(level=0).sum().iloc[:,0].div(df.groupby(level=0).sum().iloc[:,0].sum())
    
        x = 0    
        for j in df.groupby(level=0).sum().iteritems():
            bot = 0
            S = float(j[1].sum())
            for lab, k in j[1].iteritems():
                bars = []
                ax.bar(x, k/S, width=prop_com[j[0]], bottom=bot, color=dic_color_row[lab], alpha=alpha_ii, lw=0, align="edge")
                if is_multi:
                    ax.bar(x, k/S, width=prop_com[j[0]]*prop_ii0.loc[lab, j[0]], bottom=bot, color=dic_color_row[lab], lw=0, alpha=1, align="edge")
                bot += k/S + pad
            x += prop_com[j[0]] + pad
    
        ## Aesthetic of the plot and ticks
        # Y-axis
        if row_labels == None:
            row_labels = Yt.index
        ax.set_yticks(Yt - Ytt/2)
        ax.set_yticklabels(row_labels)
    
        ax.set_ylim(0, 1 + (len(j[1]) - 1) * pad)
        if y_label == None:
            y_label = df.index.names[0]
        ax.set_ylabel(y_label)
    
        # X-axis
        if col_labels == None:
            col_labels = prop_com.index
        xticks = (prop_com + pad).cumsum() - pad - prop_com/2.
        ax.set_xticks(xticks)
        ax.set_xticklabels(col_labels)
        ax.set_xlim(0, prop_com.sum() + pad * (len(prop_com)-1))
    
        if x_label == None:
            x_label = df.columns.name
        ax.set_xlabel(x_label)
    
        # Top label
        if top_label:
            ax2 = ax.twiny()
            ax2.set_xlim(*ax.get_xlim())
            ax2.set_xticks(xticks) 
            ax2.set_xticklabels(size_col.values.astype(int))
            ax2.set_xlabel(top_label)
            ax2.tick_params(top=False, right=False, pad=0, length=0)
    
        # Ticks and axis settings
    
        ax.tick_params(top=False, right=False, pad=5)
        sns.despine(left=0, bottom=False, right=0, top=0, offset=3)
    
        # Legend
        if is_multi: 
            if alpha_label == None:
                alpha_label = inner_index
            bars = [ax.bar(np.nan, np.nan, color="0.2", alpha=[1, 0.5][b]) for b in range(2)]
            if top_label:
                plt.legend(bars, alpha_label, loc='center left', bbox_to_anchor=(1, 0.5), ncol=1, )
            else:
                plt.legend(bars, alpha_label, loc="lower center", bbox_to_anchor=(0.5, 1), ncol=2)
        plt.tight_layout(rect=[0, 0, .9, 0.95])
        if color_ylabel:
            for tick, label in zip(ax.get_yticklabels(), true_y_labels):
                tick.set_bbox(dict( pad=5, facecolor=dic_color_row[label]))
                tick.set_color("w")
                tick.set_fontweight("bold")
    
        return ax
    

使用交叉制表后得到的数据框:

df
Index_1          Index_2         v   w   x   y   z
AA               Q               0   0   0   2   3 
AA               P               6   0   0  13   0
BB               Q               0   2   0   0   0
BB               P              45   1  10  10   1
CC               Q               0   6   0  15   9
CC               P               0   1   0   2   0
DD               Q               0  56   0   3   0
DD               P              30   4   2   0   1

确保您有 2 列作为索引:

df.set_index(["Index_1", "Index_2"], inplace=True)

然后只需调用:

mosaic_plot(df,
           {"AA":"r", "BB":"b", "CC":"y", "DD":"g"}, # dict of color, mandatory
           x_label='My Category', 
           )

它并不完美,但我希望它可以帮助其他人.mosaicplot python

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