如何在Python中绘制多个级别的groupby直方图?

问题描述

我有一个这样的数据框，

I have a dataframe like this,

col1    col2     col3
A       east        5
A       west        7
A       east        1
A       east        6
B       east        2
B       west        9
B       east        8
...
Z       west        4

我知道如何使用

df[df.col1.isin(ix)].groupby('col1').hist()

如何使用上面的数据框获得2级分组和绘制直方图?对于每个col1组直方图，我希望它们在单独的图中.但是对于每个col2组.我希望他们像这样.

How can I get a 2 levels groupby and draw histograms by using the dataframe above?For each col1 group histogram I want them in a separate plot. But for each col2 group. I hope them in one like this.

例如，最终结果将是来自A-Z的26个直方图.每个内部都有2个图(east&west).

For example, the final results will be 26 histograms from A-Z. Each inside has 2 plots(east&west).

推荐答案

使用 seaborn :

• pandas.DataFrame.sort_values 在col1上的打印顺序也将从A到Z

Using seaborn:

• pandas.DataFrame.sort_values on col1 will also order the plot sequence from A to Z

import string
import numpy as np
import pandas as pd
import random

alpha_list = [random.choice(list(string.ascii_uppercase)) for _ in range(10_000)]
coor_list = [random.choice(['east', 'west']) for _ in range(10_000)]
rand_val = [np.random.randint(10) for _ in range(10_000)]
df = pd.DataFrame({'col1': alpha_list, 'col2': coor_list, 'col3': rand_val})
df.sort_values(by='col1', inplace=True)

col1  col2  col3
   A  west     1
   A  east     3
   A  west     9
   A  west     5
   A  west     7
   A  east     1
   A  east     5
   A  west     2
   A  east     2
   A  west     2

情节:

• 使用col_wrap
选择每行显示的数字
• 构建结构化的多图网格
• seaborn.FacetGrid
• seaborn.distplot

Plot:

• Select the number displayed per row with col_wrap
• Building structured multi-plot grids
• seaborn.FacetGrid
• seaborn.distplot

g = sns.FacetGrid(df, col='col1', hue='col2', col_wrap=7)
g.map(sns.distplot, 'col3', hist_kws=dict(edgecolor='black'), bins=range(0, 11, 1), kde=False)
plt.xlabel('Value Range')
plt.ylabel('Frequency')
plt.legend()
plt.xticks(range(1, 11, 1))
plt.show()

• 如果要在每个图形上打勾，请在plt.show()之前的行上放置以下代码:
• if you want tick labels on each graph, put the following code on the line before plt.show():

for ax in g.axes.flatten():
    ax.tick_params(labelbottom=True)

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