如何在箱线图中放置多个中值？

Question

我只发现代码可以将中值放入箱线图中，我试过了。但由于我的箱线图是多重的，所以它无法获取 x-tick 获取定位器。如何找到箱线图的次要刻度定位器，我已经尝试过但仍然无法获得多个箱线图位置的位置。有什么建议可以改善这个情节吗？

df = pd.DataFrame([['Apple', 10, 'A'],['Apple', 8, 'B'],['Apple', 10, 'C'],
              ['Apple', 5, 'A'],['Apple', 7, 'B'],['Apple', 9, 'C'],
              ['Apple', 3, 'A'],['Apple', 5, 'B'],['Apple', 4, 'C'],
              ['Orange', 3, 'A'],['Orange', 4, 'B'],['Orange', 6, 'C'],
              ['Orange', 2, 'A'],['Orange', 8, 'B'],['Orange', 4, 'C'],
              ['Orange', 8, 'A'],['Orange', 10, 'B'],['Orange', 1, 'C']])

df.columns = ['item', 'score', 'grade']


fig = plt.figure(figsize=(6, 3), dpi=150)

ax = sns.boxplot(x='item', y='score', data=df, hue='grade', palette=sns.color_palette('husl'))
ax.legend(loc='lower right', bbox_to_anchor=(1.11, 0), ncol=1, fontsize = 'x-small').set_title('')

medians = df.groupby(['item','grade'])['score'].median().values
median_labels = [str(np.round(s, 2)) for s in medians]

pos = range(len(medians))
for tick,label in zip(pos, ax.get_xticklabels()):
    ax.text(pos[tick], medians[tick], median_labels[tick], 
            horizontalalignment='center', size='xx-small', color='w', weight='semibold', bbox=dict(facecolor='#445A64'))

Answer 1

众所周知，Seaborn 很难使用。下面的代码有效，但如果其中一个类别为空且未绘制箱线图，则可能会中断，例如，使用风险自负：

df = pd.DataFrame([['Apple', 10, 'A'],['Apple', 8, 'B'],['Apple', 10, 'C'],
              ['Apple', 5, 'A'],['Apple', 7, 'B'],['Apple', 9, 'C'],
              ['Apple', 3, 'A'],['Apple', 5, 'B'],['Apple', 4, 'C'],
              ['Orange', 3, 'A'],['Orange', 4, 'B'],['Orange', 6, 'C'],
              ['Orange', 2, 'A'],['Orange', 8, 'B'],['Orange', 4, 'C'],
              ['Orange', 8, 'A'],['Orange', 10, 'B'],['Orange', 1, 'C']])

df.columns = ['item', 'score', 'grade']


width = 0.8
hue_col = 'grade'

fig, plt.figure(figsize=(6, 3), dpi=150)
ax = sns.boxplot(x='item', y='score', data=df, hue=hue_col, palette=sns.color_palette('husl'), width=width)
ax.legend(loc='lower right', bbox_to_anchor=(1.11, 0), ncol=1, fontsize = 'x-small').set_title('')

# get the offsets used by boxplot when hue-nesting is used
# https://github.com/mwaskom/seaborn/blob/c73055b2a9d9830c6fbbace07127c370389d04dd/seaborn/categorical.py#L367
n_levels = len(df[hue_col].unique())
each_width = width / n_levels
offsets = np.linspace(0, width - each_width, n_levels)
offsets -= offsets.mean()

medians = df.groupby(['item','grade'])['score'].median()

for x0,(_,med0) in enumerate(medians.groupby(level=0)):
    for off,(_,med1) in zip(offsets,med0.groupby(level=1)):
        ax.text(x0+off, med1.item(), '{:.0f}'.format(med1.item()), 
            horizontalalignment='center', va='center', size='xx-small', color='w', weight='semibold', bbox=dict(facecolor='#445A64'))

一般来说，为避免任何意外，如果您想修改 seaborn 绘图，我建议您指定 order 和 hue_order，以便按照预先确定的顺序绘制绘图。这是另一个能够处理缺失类别的版本：

df = pd.DataFrame([['Apple', 8, 'B'],['Apple', 10, 'C'],
              ['Apple', 7, 'B'],['Apple', 9, 'C'],
              ['Apple', 5, 'B'],['Apple', 4, 'C'],
              ['Orange', 3, 'A'],['Orange', 6, 'C'],
              ['Orange', 2, 'A'],['Orange', 4, 'C'],
              ['Orange', 8, 'A'],['Orange', 1, 'C']])

df.columns = ['item', 'score', 'grade']


order = ['Apple', 'Orange']
hue_col = 'grade'
hue_order = ['A','B','C']
width = 0.8

fig, plt.figure(figsize=(6, 3), dpi=150)
ax = sns.boxplot(x='item', y='score', data=df, hue=hue_col, palette=sns.color_palette('husl'), width=width,
                order=order, hue_order=hue_order)
ax.legend(loc='lower right', bbox_to_anchor=(1.11, 0), ncol=1, fontsize = 'x-small').set_title('')

# get the offsets used by boxplot when hue-nesting is used
# https://github.com/mwaskom/seaborn/blob/c73055b2a9d9830c6fbbace07127c370389d04dd/seaborn/categorical.py#L367
n_levels = len(df[hue_col].unique())
each_width = width / n_levels
offsets = np.linspace(0, width - each_width, n_levels)
offsets -= offsets.mean()

medians = df.groupby(['item','grade'])['score'].median()
medians = medians.reindex(pd.MultiIndex.from_product([order,hue_order]))

for x0,(_,med0) in enumerate(medians.groupby(level=0)):
    for off,(_,med1) in zip(offsets,med0.groupby(level=1)):
        if not np.isnan(med1.item()):
            ax.text(x0+off, med1.item(), '{:.0f}'.format(med1.item()), 
                horizontalalignment='center', va='center', size='xx-small', color='w', weight='semibold', bbox=dict(facecolor='#445A64'))