pandas 箱图，在每个子图中按不同的ylim分组

问题描述

我有一个数据框，我想将其绘制为:

I have a dataframe and I would like to plot it as:

>>> X = pd.DataFrame(np.random.normal(0, 1, (100, 3)))
>>> X['NCP'] = np.random.randint(0, 5, 100)
>>> X[X['NCP'] == 0] += 100
>>> X.groupby('NCP').boxplot()

结果是我想要的，但是所有子图都具有相同的ylim.这使得无法正确地可视化结果.如何为每个子图设置不同的ylim?

The result is what I want but all the subplots have the same ylim. This makes impossible to visualize the result properly. How can I set different ylim for each subplot?

推荐答案

您要的是分别为每个轴设置y轴.我认为应该是ax.set_ylim([a, b]).但是每次我为每个轴运行它时，所有轴都会更新.

What you asked for was to set the y axis separately for each axes. I believe that should be ax.set_ylim([a, b]). But every time I ran it for each axes it updated for all.

因为我无法弄清楚如何直接回答您的问题，所以我提供了解决方法.

Because I couldn't figure out how to answer your question directly, I'm providing a work around.

X = pd.DataFrame(np.random.normal(0, 1, (100, 3)))
X['NCP'] = np.random.randint(0, 5, 100)
X[X['NCP'] == 0] += 100

groups = X.groupby('NCP')

print groups.groups.keys()

# This gets a number of subplots equal to the number of groups in a single 
# column.  you can adjust this yourself if you need.
fig, axes = plt.subplots(len(groups.groups), 1, figsize=[10, 12])

# Loop through each group and plot boxplot to appropriate axis
for i, k in enumerate(groups.groups.keys()):
    group = groups.get_group(k)
    group.boxplot(ax=axes[i], return_type='axes')

subplots文档

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