Seaborn和pandas绘制柱状图(countplot,barplot)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

# 读取employee数据集
employee = pd.read_csv('data/employee.csv', parse_dates=['HIRE_DATE', 'JOB_DATE']) #parse_dates将哪一列解析为时间索引
employee.head()

	UNIQUE_ID	POSITION_TITLE	DEPARTMENT	BASE_SALARY	RACE	EMPLOYMENT_TYPE	GENDER	EMPLOYMENT_STATUS	HIRE_DATE	JOB_DATE
0	0	ASSISTANT DIRECTOR (EX LVL)	Municipal Courts Department	121862.0	Hispanic/Latino	Full Time	Female	Active	2006-06-12	2012-10-13
1	1	LIBRARY ASSISTANT	Library	26125.0	Hispanic/Latino	Full Time	Female	Active	2000-07-19	2010-09-18
2	2	POLICE OFFICER	Houston Police Department-HPD	45279.0	White	Full Time	Male	Active	2015-02-03	2015-02-03
3	3	ENGINEER/OPERATOR	Houston Fire Department (HFD)	63166.0	White	Full Time	Male	Active	1982-02-08	1991-05-25
4	4	ELECTRICIAN	General Services Department	56347.0	White	Full Time	Male	Active	1989-06-19	1994-10-22

用seaborn画出每个部门的柱状图 

# 用seaborn画出每个部门的柱状图
import pandas as pd
import seaborn as sns
sns.countplot(y='DEPARTMENT', data=employee)

要是用pandas来做，需要先聚合数据 

# 要是用pandas来做，需要先聚合数据
employee['DEPARTMENT'].value_counts().plot(kind='barh')

用seaborn找到每个种族的平均工资

# 用seaborn找到每个种族的平均工资
ax = sns.barplot(x='RACE', y='BASE_SALARY', data=employee) # barplot条形图
ax.figure.set_size_inches(16, 4) #set_size_inches,matplotlib 包中提供的函数，用于设置图形的尺寸

用pandas来做，需要先按照race分组

# 用pandas来做，需要先按照race分组
avg_sal = employee.groupby('RACE', sort=False)['BASE_SALARY'].mean()
ax = avg_sal.plot(kind='bar', rot=0, figsize=(16,4), width=.8)
ax.set_xlim(-.5, 5.5) #set_xlim(初始值,末值)
ax.set_ylabel('Mean Salary')

seaborn还支持在分组内使用第三个参数

# seaborn还支持在分组内使用第三个参数
ax = sns.barplot(x='RACE', y='BASE_SALARY', hue='GENDER', # hue  目标值是什么target
                          data=employee, palette='Greys')
ax.figure.set_size_inches(16,4) #set_size_inches,matplotlib 包中提供的函数，用于设置图形的尺寸

pandas则要对race和gender同时分组，并对gender做unstack 

# pandas则要对race和gender同时分组，并对gender做unstack
employee.groupby(['RACE', 'GENDER'], sort=False)['BASE_SALARY'] \
                 .mean().unstack('GENDER') \
                 .plot(kind='bar', figsize=(16,4), rot=0,
                       width=.8, cmap='Greys')