Pandas_工资集处理


import numpy as np
import pandas as pd
from pandas import Series,DataFrame

# 1--读取数据文件
file_obj=open('Baltimore_City_Employee_Salaries_FY2016.csv')
salary_df=pd.read_csv(file_obj)
file_obj.close()
salary_df.head()

	Name	JobTitle	AgencyID	Agency	HireDate	AnnualSalary	GrossPay
0	Aaron,Patricia G	Facilities/Office Services II	A03031	OED-Employment Dev (031)	10/24/1979 12:00:00 AM	$56705.00	$54135.44
1	Aaron,Petra L	ASSISTANT STATE'S ATTORNEY	A29045	States Attorneys Office (045)	09/25/2006 12:00:00 AM	$75500.00	$72445.87
2	Abbey,Emmanuel	CONTRACT SERV SPEC II	A40001	M-R Info Technology (001)	05/01/2013 12:00:00 AM	$60060.00	$59602.58
3	Abbott-Cole,Michelle	Operations Officer III	A90005	TRANS-Traffic (005)	11/28/2014 12:00:00 AM	$70000.00	$59517.21
4	Abdal-Rahim,Naim A	EMT Firefighter Suppression	A64120	Fire Department (120)	03/30/2011 12:00:00 AM	$64365.00	$74770.82

# 2--查看数据情况
salary_df.shape  # (13818, 7)
salary_df.describe()
salary_df.info()  # 可以看到 Grosspay 的个数为 13546，不是13818，有缺失值
salary_df.isnull().sum() #  可以看到 Grosspay缺失值的个数为 272

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13818 entries, 0 to 13817
Data columns (total 7 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Name          13818 non-null  object
 1   JobTitle      13818 non-null  object
 2   AgencyID      13818 non-null  object
 3   Agency        13818 non-null  object
 4   HireDate      13818 non-null  object
 5   AnnualSalary  13818 non-null  object
 6   GrossPay      13546 non-null  object
dtypes: object(7)
memory usage: 755.8+ KB


Name              0
JobTitle          0
AgencyID          0
Agency            0
HireDate          0
AnnualSalary      0
GrossPay        272
dtype: int64

# 3--数据清理
# 3-1）删除缺失值
salary_df=salary_df.dropna(how='any',axis=0)  # 删除存在缺失值的整行数据
salary_df.isnull().sum()

Name            0
JobTitle        0
AgencyID        0
Agency          0
HireDate        0
AnnualSalary    0
GrossPay        0
dtype: int64

# 3-2)去除 AnnualSalary，GrossPay 两列中的 $号，并将这两列数据类型转为 float
salary_df['AnnualSalary']=salary_df['AnnualSalary'].str.strip('$')
salary_df['GrossPay']=salary_df['GrossPay'].str.strip('$')
salary_df.head()

	Name	JobTitle	AgencyID	Agency	HireDate	AnnualSalary	GrossPay
0	Aaron,Patricia G	Facilities/Office Services II	A03031	OED-Employment Dev (031)	10/24/1979 12:00:00 AM	56705.00	54135.44
1	Aaron,Petra L	ASSISTANT STATE'S ATTORNEY	A29045	States Attorneys Office (045)	09/25/2006 12:00:00 AM	75500.00	72445.87
2	Abbey,Emmanuel	CONTRACT SERV SPEC II	A40001	M-R Info Technology (001)	05/01/2013 12:00:00 AM	60060.00	59602.58
3	Abbott-Cole,Michelle	Operations Officer III	A90005	TRANS-Traffic (005)	11/28/2014 12:00:00 AM	70000.00	59517.21
4	Abdal-Rahim,Naim A	EMT Firefighter Suppression	A64120	Fire Department (120)	03/30/2011 12:00:00 AM	64365.00	74770.82

salary_df['GrossPay'].dtype  # dtype('O') 不是 dtype('float')
# salary_df['AnnualSalary']=salary_df['AnnualSalary'].astype(float)
# salary_df['GrossPay']=salary_df['GrossPay'].astype(float)  # 直接用下面这句：
salary_df[['AnnualSalary','GrossPay']]=salary_df[['AnnualSalary','GrossPay']].astype(float)
salary_df['GrossPay'].dtype   # dtype('float64')

dtype('O')

# 3-3） 新建一列，用于存放入职月份：
salary_df['month']=salary_df['HireDate'].str.split('/').str[0]  # 或者：
# salary_df['month']=salary_df['HireDate'].str.split('/').str.get(0)  
salary_df.head()
salary_df[['HireDate','month']].head()  # 只查看这两列

	HireDate	month
0	10/24/1979 12:00:00 AM	10
1	09/25/2006 12:00:00 AM	09
2	05/01/2013 12:00:00 AM	05
3	11/28/2014 12:00:00 AM	11
4	03/30/2011 12:00:00 AM	03

# 4--数据探索
# 4-1）工资分布：
salary_df['AnnualSalary'].hist(bins=20)  # 基本呈正态分布，高薪的人员较少，30000美元左右的人最多

<matplotlib.axes._subplots.AxesSubplot at 0x21999d528c8>

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# 4-2）入职月份统计：
month=salary_df['month'].value_counts()
month.plot(kind='barh')  # 6月份入职人数最多，HR最忙

<matplotlib.axes._subplots.AxesSubplot at 0x219a114ef08>

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# 5-聚合运算
# 5-1）计算年薪平均值和职位个数：
salary_df['AnnualSalary'].mean()  # 53507.98394359959  所有员工的平均年薪
# 5-2) 计算各职位的年薪平均值 看哪个职位的平均工资高,职位数，及该职位的最高工资
salary_df.groupby('JobTitle')['AnnualSalary'].mean()
salary_df.groupby('JobTitle')['AnnualSalary'].agg(['mean','count','max'])

	mean	count	max
JobTitle
911 LEAD OPERATOR	49816.750000	4	50162.0
911 OPERATOR	44829.461538	65	50829.0
911 OPERATOR SUPERVISOR	57203.500000	4	57579.0
ACCOUNT EXECUTIVE	57200.000000	4	57200.0
ACCOUNTANT I	49065.866667	15	57579.0
...	...	...	...
ZONING APPEALS ADVISOR BMZA	53636.000000	1	53636.0
ZONING APPEALS OFFICER	67800.000000	1	67800.0
ZONING ENFORCEMENT OFFICER	65800.000000	1	65800.0
ZONING EXAMINER I	45628.500000	2	48811.0
ZONING EXAMINER II	56150.000000	1	56150.0

1034 rows × 3 columns

# 5-3) 对上述各职位的年薪的平均值进行降序排列
jobtitle_salary_df=salary_df.groupby('JobTitle')['AnnualSalary'].agg(['mean','count','max'])
jobtitle_sort_salary_df=jobtitle_salary_df.sort_values(by='mean',ascending=False)[:30]  # 知识点：df.sort_values(by=列名，ascending=False)
jobtitle_sort_salary_df['mean'].plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x219a52aa788>

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# 5-4) 对上述各职位的人数进行降序排列
jobtitle_salary_df=salary_df.groupby('JobTitle')['AnnualSalary'].agg(['mean','count','max'])
jobtitle_sort_salary_df=jobtitle_salary_df.sort_values(by='count',ascending=False)[:30]  # 知识点：df.sort_values(by=列名，ascending=False)
jobtitle_sort_salary_df['count'].plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x219a6327e48>

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# 我们再看看其他的需求：
# 从头开始：
import numpy as np
import pandas as pd
from pandas import Series,DataFrame

# 1--读取数据文件
file_obj=open('Baltimore_City_Employee_Salaries_FY2016.csv')
salary_df=pd.read_csv(file_obj)
file_obj.close()

# 2--删除含缺失值的行，
salary_df=salary_df.dropna(how='any',axis=0)  # 删除存在缺失值的整行数据
salary_df.isnull().sum()

# 3--去除 AnnualSalary，GrossPay 两列中的 $号，并将这两列数据类型转为 float
salary_df['AnnualSalary']=salary_df['AnnualSalary'].str.strip('$')
salary_df['GrossPay']=salary_df['GrossPay'].str.strip('$')
salary_df[['AnnualSalary','GrossPay']]=salary_df[['AnnualSalary','GrossPay']].astype(float)
salary_df.head()

# 4--查看 AnnualSalary的最大值及所在的行号：
salary_df['AnnualSalary'].max()  # 238772.0
salary_df['AnnualSalary'].argmax()  # 8701  # se.argmax()之前没有学到，这里补充一下