我知道您可以使用 mosaic plot from statsmodels但是当您的类别有一些空值(like here)时，这有点令人沮丧。我想知道它是否存在带有像 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 来说是个不错的功能，因为列联表经常用 pandas 构建。然而似乎it won't be implemented很快。
最后，如何制作 3 维马赛克图，以及可能的空类别？
这是一个通用的马赛克图(来自 wikipedia )
最佳答案
由于 python 中不存在任何内容，因此这是我编写的代码。现在，最后一个维度的大小应为 1(即常规表格)或 2。请随意更新代码来解决这个问题，但是超过 3 个可能无法读取。 它有点长，但它完成了工作。示例如下。
选项很少，大多数都是不言自明的，否则:
dic_color_row:一个字典，其中键是最外层的索引(下面示例中的Index_1)，值是颜色，避免黑色/灰色pad:图中每个条形之间的空间alpha_label:第3维使用alpha trick来区分，它们之间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
确保你有 2 列作为索引:
df.set_index(["Index_1", "Index_2"], inplace=True)
然后调用:
mosaic_plot(df,
{"AA":"r", "BB":"b", "CC":"y", "DD":"g"}, # dict of color, mandatory
x_label='My Category',
)
它并不完美，但我希望它能对其他人有所帮助。
关于python - 如何在 Matplotlib 中制作马赛克图，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/45352585/