pandas 同一图上的 Python 并排箱线图
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Python Side-by-side box plots on same figure
提问by edesz
I am trying to generate a box plot in Python 2.7 for each categorical value in column E from the Pandas dataframe below
我正在尝试在 Python 2.7 中为下面 Pandas 数据框中的 E 列中的每个分类值生成一个箱线图
A B C D E
0 0.647366 0.317832 0.875353 0.993592 1
1 0.504790 0.041806 0.113889 0.445370 2
2 0.769335 0.120647 0.749565 0.935732 3
3 0.215003 0.497402 0.795033 0.246890 1
4 0.841577 0.211128 0.248779 0.250432 1
5 0.045797 0.710889 0.257784 0.207661 4
6 0.229536 0.094308 0.464018 0.402725 3
7 0.067887 0.591637 0.949509 0.858394 2
8 0.827660 0.348025 0.507488 0.343006 3
9 0.559795 0.820231 0.461300 0.921024 1
I would be willing to do this with Matplotlib or any other plotting library. So far the above code can plot all the categories combined on one plot. Here is the code to generate the above data and produce the plot:
我愿意用 Matplotlib 或任何其他绘图库来做到这一点。到目前为止，上面的代码可以将所有类别组合在一个图上。这是生成上述数据并生成绘图的代码：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
# Data
df = pd.DataFrame(np.random.rand(10,4),columns=list('ABCD'))
df['E'] = [1,2,3,1,1,4,3,2,3,1]
# Boxplot
bp = ax.boxplot(df.iloc[:,:-1].values, widths=0.2)
plt.show()
In this example, the categories are 1,2,3,4. I would like to plot separate boxplots side-by-side on the same figure, for only categories 1 and 2 and show the category names in the legend.
在此示例中，类别为 1、2、3、4。我想在同一图上并排绘制单独的箱线图，仅用于类别 1 和 2，并在图例中显示类别名称。
Is there a way to do this?
有没有办法做到这一点？
Additional Information:
附加信息：
The output should look similar to the 3rd figure from here- replace "Yes","No" by "1","2".
输出应该类似于此处的第三个图- 将“是”、“否”替换为“1”、“2”。
回答by Paul H
Starting with this:
从这个开始：
import numpy
import pandas
from matplotlib import pyplot
import seaborn
seaborn.set(style="ticks")
# Data
df = pandas.DataFrame(numpy.random.rand(10,4), columns=list('ABCD'))
df['E'] = [1, 2, 3, 1, 1, 4, 3, 2, 3, 1]
You've got a couple of options. If separate axes are ok,
你有几个选择。如果单独的轴没问题，
fig, axes = pyplot.subplots(ncols=4, figsize=(12, 5), sharey=True)
df.query("E in [1, 2]").boxplot(by='E', return_type='axes', ax=axes)
If you want 1 axes, I think seaborn will be easier. You just need to clean up your data.
如果你想要 1 个轴，我认为 seaborn 会更容易。你只需要清理你的数据。
ax = (
df.set_index('E', append=True) # set E as part of the index
.stack() # pull A - D into rows
.to_frame() # convert to a dataframe
.reset_index() # make the index into reg. columns
.rename(columns={'level_2': 'quantity', 0: 'value'}) # rename columns
.drop('level_0', axis='columns') # drop junk columns
.pipe((seaborn.boxplot, 'data'), x='E', y='value', hue='quantity', order=[1, 2])
)
seaborn.despine(trim=True)
The cool thing about seaborn is that tweaking the parameters slightly can achieve a lot in terms of the plot's layout. If we switch our hueand xvariables, we get:
seaborn 很酷的一点是，稍微调整参数可以在情节布局方面实现很多。如果我们切换我们的hue和x变量，我们得到：
ax = (
df.set_index('E', append=True) # set E as part of the index
.stack() # pull A - D into rows
.to_frame() # convert to a dataframe
.reset_index() # make the index into reg. columns
.rename(columns={'level_2': 'quantity', 0: 'value'}) # rename columns
.drop('level_0', axis='columns') # drop junk columns
.pipe((seaborn.boxplot, 'data'), x='quantity', y='value', hue='E', hue_order=[1, 2])
)
seaborn.despine(trim=True)
If you're curious, the resulting dataframe looks something like this:
如果你很好奇，结果数据框看起来像这样：
E quantity value
0 1 A 0.935433
1 1 B 0.862290
2 1 C 0.197243
3 1 D 0.977969
4 2 A 0.675037
5 2 B 0.494440
6 2 C 0.492762
7 2 D 0.531296
8 3 A 0.119273
9 3 B 0.303639
10 3 C 0.911700
11 3 D 0.807861
回答by banderlog013
An addition to @Paul_H answer.
@Paul_H 答案的补充。
Side-by-side boxplots on the single matplotlib.axes.Axes, no seaborn:
单一的matplotlib.axes.Axes并排箱线图，没有 seaborn：
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.rand(10,4), columns=list('ABCD'))
df['E'] = [1, 2, 1, 1, 1, 2, 1, 2, 2, 1]
mask_e = df['E'] == 1
# prepare data
data_to_plot = [df[mask_e]['A'], df[~mask_e]['A'],
df[mask_e]['B'], df[~mask_e]['B'],
df[mask_e]['C'], df[~mask_e]['C'],
df[mask_e]['D'], df[~mask_e]['D']]
# Positions defaults to range(1, N+1) where N is the number of boxes to be drawn.
# we willmove them a little, to visually group them
plt.figure(figsize=(10, 6))
box = plt.boxplot(data_to_plot,
positions=[1, 1.6, 2.5, 3.1, 4, 4.6, 5.5, 6.1],
labels=['A1','A0','B1','B0','C1','C0','D1','D0'])
