如何使用多个分类变量归一化海洋计数图 [英] How to normalize a seaborn countplot with multiple categorical variables

问题描述

我为一个数据框的多个类别变量创建了一个难以置信的countplot，但我不想使用百分比而不是计数?

I have created a seaborn countplot for multiple categorical variables of a dataframe but instead of count I want to have percentages?

最佳选择是什么? plo子?我可以使用以下查询来一次获取条形图吗?

What is the best option to use? Barplots? Can I use a query like the below one to get the barplots at once?

for i, col in enumerate(df_categorical.columns):
   plt.figure(i)
   sns.countplot(x=col,hue='Response',data=df_categorical) 

该查询一次为我提供所有变量的countplot

this query gives me the countplot for all variables at once

谢谢！

数据如下:

    State           Response     Coverage   Education   Effective To Date   EmploymentStatus       Gender   Location Code   Marital Status  Policy Type Policy    Renew Offer Type  Sales Channel   Vehicle Class   Vehicle Size    
0   Washington  No  Basic   Bachelor    2/24/11 Employed    F   Suburban    Married Corporate Auto  Corporate L3    Offer1  Agent   Two-Door Car    Medsize  
1   Arizona     No  Extended    Bachelor    1/31/11 Unemployed  F   Suburban    Single  Personal Auto   Personal L3 Offer3  Agent   Four-Door Car   Medsize
2   Nevada      No  Premium Bachelor    2/19/11 Employed    F   Suburban    Married Personal Auto   Personal L3 Offer1  Agent   Two-Door Car    Medsize
3   California  No  Basic   Bachelor    1/20/11 Unemployed  M   Suburban    Married Corporate Auto  Corporate L2    Offer1  Call Center SUV Medsize
4   Washington  No  Basic   Bachelor    2/3/11  Employed    M   Rural   Single  Personal Auto   Personal L1 Offer1  Agent   Four-Door Car   Medsize

推荐答案

考虑一个groupby.transform来计算百分比列，然后将barplot与 x 一起用于原始值列和 y 表示百分比列.

Consider a groupby.transform to calculate percentage column, then run barplot with x for original value column and y for percent column.

数据 (仅将原始发布数据的两个否"转换为是")

from io import StringIO
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

txt = '''
    State           Response     Coverage   Education   "Effective To Date"   EmploymentStatus       Gender   "Location Code"   "Marital Status"  "Policy Type" Policy    "Renew Offer Type"  "Sales Channel"   "Vehicle Class"   "Vehicle Size" 
0   Washington  No  Basic   Bachelor    "2/24/11" Employed    F   Suburban    Married "Corporate Auto"  "Corporate L3"    Offer1  Agent   "Two-Door Car"    Medsize  
1   Arizona     No  Extended    Bachelor  "1/31/11"   Unemployed  F   Suburban    Single  "Personal Auto"   "Personal L3" Offer3  Agent   "Four-Door Car"   Medsize
2   Nevada      Yes  Premium Bachelor    "2/19/11" Employed    F   Suburban    Married "Personal Auto"   "Personal L3" Offer1  Agent   "Two-Door Car"    Medsize
3   California  No  Basic   Bachelor    "1/20/11" Unemployed  M   Suburban    Married "Corporate Auto"  "Corporate L2"    Offer1  "Call Center" SUV Medsize
4   Washington  Yes  Basic   Bachelor    "2/3/11"  Employed    M   Rural   Single  "Personal Auto"   "Personal L1" Offer1  Agent   "Four-Door Car"   Medsize'''

df_categorical = pd.read_table(StringIO(txt), sep="\s+")

图 (两列中多个图的单个图)

fig = plt.figure(figsize=(10,30))

for i, col in enumerate(df_categorical.columns):   
   # PERCENT COLUMN CALCULATION
   df_categorical[col+'_pct'] = df_categorical.groupby(['Response', col])[col]\
                                   .transform(lambda x: len(x)) / len(df_categorical)

   plt.subplot(8, 2, i+1)   
   sns.barplot(x=col, y=col+'_pct', hue='Response', data=df_categorical)\
          .set(xlabel=col, ylabel='Percent')    

plt.tight_layout()
plt.show()
plt.clf()

plt.close('all')

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