Question

我知道你可以使用mosaic plot from statsmodels但是当你的类别有一些空值（like here）时会有点令人沮丧。我想知道它是否存在一个像matplotlib或seaborn这样的图形库的解决方案，这将更方便。

我认为这对于seaborn来说是一个很好的功能，因为列联表常常是用熊猫建造的。但是很快就会出现it won't be implemented。

最后，如何制作具有3个维度和可能的空类别的马赛克图？

这是一个通用的马赛克图（来自wikipedia）

Answer 1

由于python中没有任何内容，这里是我编写的代码。最后一个维度的大小应为1（即常规表）或2。可以随意更新代码来解决这个问题，但是超过3可能无法读取。它有点长，但它完成了这项工作。示例如下。

选项很少，大部分是自解释的，否则：

dic_color_row：一个字典，其中键是最外面的索引（下面示例中为Index_1），值为颜色，避免黑/灰色
pad：情节每个小节之间的空格
alpha_label：第三维使用alpha技巧进行区分，在它们之间，它将在图例中呈现为深灰色/浅灰色，您可以更改每个标签的名称（类似于{{1} }或col_labels）
row_labels：为y-tick标签添加背景颜色。 [是/否]
```
color_label
```

使用交叉制表后的数据帧：

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

确保您有2列作为索引：

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

然后只需致电：

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

它并不完美，但我希望它能帮助别人。

如何在Matplotlib中制作马赛克图

1 个答案: