import pandas as pd
import numpy as np
from tabulate import tabulate
pandas 给dataframe 赋值操作1
df = pd.read_csv('data/employee_sample.csv',index_col=0)
print(tabulate(df,headers=df.columns,tablefmt='simple'))
GENDER RACE DEPARTMENT YEARS EXPERIENCE SALARY
-------- -------- ------ ------------------ ------------------ --------
Tom Male White Engineering 23 107962
Niko Male Black Engineering 1 30347
Penelope Female White Engineering 12 60258
Aria Female Black Engineering 8 43618
Sofia Female Black Parks & Recreation 23 26125
Dean Male Black Parks & Recreation 3 33592
Zach Male White Parks & Recreation 4 37565
- 给dataframe 加1列 如下
- 给df增加score列并使用单个值给该列赋值
df['score'] =100
print(tabulate(df,headers=df.columns,tablefmt='simple'))
GENDER RACE DEPARTMENT YEARS EXPERIENCE SALARY score
-------- -------- ------ ------------------ ------------------ -------- -------
Tom Male White Engineering 23 107962 100
Niko Male Black Engineering 1 30347 100
Penelope Female White Engineering 12 60258 100
Aria Female Black Engineering 8 43618 100
Sofia Female Black Parks & Recreation 23 26125 100
Dean Male Black Parks & Recreation 3 33592 100
Zach Male White Parks & Recreation 4 37565 100
- 使用np.array给df增加rate列并赋值
rates = np.round(np.random.rand(7),2)
rates
array([0.69, 0.16, 0.9 , 0.21, 0.25, 0.5 , 0.09])
df['rate']=rates
print(tabulate(df,headers=df.columns,tablefmt='simple'))
GENDER RACE DEPARTMENT YEARS EXPERIENCE SALARY score rate
-------- -------- ------ ------------------ ------------------ -------- ------- ------
Tom Male White Engineering 23 107962 100 0.69
Niko Male Black Engineering 1 30347 100 0.16
Penelope Female White Engineering 12 60258 100 0.9
Aria Female Black Engineering 8 43618 100 0.21
Sofia Female Black Parks & Recreation 23 26125 100 0.25
Dean Male Black Parks & Recreation 3 33592 100 0.5
Zach Male White Parks & Recreation 4 37565 100 0.09
- 同上
score = np.random.randint(0,100,len(df))
score
array([60, 20, 84, 34, 71, 0, 0])
df['score']=score
print(tabulate(df,headers=df.columns,tablefmt='simple'))
GENDER RACE DEPARTMENT YEARS EXPERIENCE SALARY score rate
-------- -------- ------ ------------------ ------------------ -------- ------- ------
Tom Male White Engineering 23 107962 60 0.69
Niko Male Black Engineering 1 30347 20 0.16
Penelope Female White Engineering 12 60258 84 0.9
Aria Female Black Engineering 8 43618 34 0.21
Sofia Female Black Parks & Recreation 23 26125 71 0.25
Dean Male Black Parks & Recreation 3 33592 0 0.5
Zach Male White Parks & Recreation 4 37565 0 0.09
- 使用list列表给df增加floor列，并且赋值
floor=[10,2,3,4,9,2,4]
df['floor']=floor
print(tabulate(df,headers=df.columns,tablefmt='simple'))
GENDER RACE DEPARTMENT YEARS EXPERIENCE SALARY score rate floor
-------- -------- ------ ------------------ ------------------ -------- ------- ------ -------
Tom Male White Engineering 23 107962 60 0.69 10
Niko Male Black Engineering 1 30347 20 0.16 2
Penelope Female White Engineering 12 60258 84 0.9 3
Aria Female Black Engineering 8 43618 34 0.21 4
Sofia Female Black Parks & Recreation 23 26125 71 0.25 9
Dean Male Black Parks & Recreation 3 33592 0 0.5 2
Zach Male White Parks & Recreation 4 37565 0 0.09 4
- 使用Series给df增加lastname列，并且赋值
last_name = pd.Series(['Smith', 'Jones', 'Williams', 'Green', 'Brown', 'Simpson', 'Peters'])
last_name
0 Smith
1 Jones
2 Williams
3 Green
4 Brown
5 Simpson
6 Peters
dtype: object
df['lastname']=last_name
print(tabulate(df,headers=df.columns,tablefmt='simple'))
GENDER RACE DEPARTMENT YEARS EXPERIENCE SALARY score rate floor lastname
-------- -------- ------ ------------------ ------------------ -------- ------- ------ ------- ----------
Tom Male White Engineering 23 107962 60 0.69 10 nan
Niko Male Black Engineering 1 30347 20 0.16 2 nan
Penelope Female White Engineering 12 60258 84 0.9 3 nan
Aria Female Black Engineering 8 43618 34 0.21 4 nan
Sofia Female Black Parks & Recreation 23 26125 71 0.25 9 nan
Dean Male Black Parks & Recreation 3 33592 0 0.5 2 nan
Zach Male White Parks & Recreation 4 37565 0 0.09 4 nan
为啥上个例子lastname的值都是Nan？原因是index不匹配
last_name.index
RangeIndex(start=0, stop=7, step=1)
df.index
Index(['Tom', 'Niko', 'Penelope', 'Aria', 'Sofia', 'Dean', 'Zach'], dtype='object')
index一个是数值，一个是字符串，现在搞成一样的
last_name=pd.Series(last_name.values,index=df.index)
last_name
Tom Smith
Niko Jones
Penelope Williams
Aria Green
Sofia Brown
Dean Simpson
Zach Peters
dtype: object
df['lastname']=last_name
print(tabulate(df,headers=df.columns,tablefmt='simple'))
GENDER RACE DEPARTMENT YEARS EXPERIENCE SALARY score rate floor lastname
-------- -------- ------ ------------------ ------------------ -------- ------- ------ ------- ----------
Tom Male White Engineering 23 107962 60 0.69 10 Smith
Niko Male Black Engineering 1 30347 20 0.16 2 Jones
Penelope Female White Engineering 12 60258 84 0.9 3 Williams
Aria Female Black Engineering 8 43618 34 0.21 4 Green
Sofia Female Black Parks & Recreation 23 26125 71 0.25 9 Brown
Dean Male Black Parks & Recreation 3 33592 0 0.5 2 Simpson
Zach Male White Parks & Recreation 4 37565 0 0.09 4 Peters
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