如何在特定条件下使用循环从数据框中求和某些值?

Question

我有一个字典和一个有两列的DataFrame:name和salary。我想对姓名与字典中各个值相匹配的薪水求和。这是我目前得到的。我想把经理、职员和分析师的工资分别加起来。

import pandas as pd
a = ['manager','sales','clerk','manager','analayst','sales','manager','analayst' ,'sales','clerk','clerk','analayst']
b = [45000,78000,12000,45000,96000,78000,56000,95000,84000,75000,95000,
26000]
df = pd.DataFrame({'name':a,'salary':b})
sum = 0
k = 0
c = []
for i in a:
if i not in c:
c.append(i)
for j in range(len(df)):
while k < len(c):
p = c[k]
print(p)

d = df[df['name'] == p]['salary'].sum()
k += 1[1]

Answer 1

您可以使用group by函数轻松分离经理，职员和分析师等总和。这有助于您轻松解决您的求和分隔形式

a=['manager','sales','clerk','manager','analayst','sales','manager','analayst' ,'sales','clerk','clerk','analayst']
b=[45000,78000,12000,45000,96000,78000,56000,95000,84000,75000,95000, 26000]
df=pd.DataFrame({'name':a,'salary':b})
df.groupby("name")["salary"].sum()

Answer 2

我猜你想要这样的输出-

>>> a=['manager','sales','clerk','manager','analayst','sales','manager','analayst' ,'sales','clerk','clerk','analayst']
>>> b=[45000,78000,12000,45000,96000,78000,56000,95000,84000,75000,95000,26000]
>>> import pandas as pd
>>> 
>>> df=pd.DataFrame({'name':a,'salary':b})
>>> 
>>> 
>>> df.groupby('name').sum().reset_index()
name  salary
0  analayst  217000
1     clerk  182000
2   manager  146000
3     sales  240000
>>> 

如果是，你可以很容易地做到这一点，通过groupby，它使你能够分组/集群你的数据点，并在每个子集上，你可以执行你想要的聚合

Answer 3

使用说明:

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.sum.html

下面的是结果的代码片段。

>>> import pandas as pd
>>> a = ['manager', 'sales', 'clerk', 'manager', 'analyst', 'sales', 'manager', 'analyst', 'sales', 'clerk',  'clerk', 'analyst']
>>> b = [45000, 78000, 12000, 45000, 96000, 78000, 56000, 95000, 84000, 75000, 95000, 26000]
>>> df = pd.DataFrame({'name': a, 'salary': b})
>>> df
name  salary
0   manager   45000
1     sales   78000
2     clerk   12000
3   manager   45000
4   analyst   96000
5     sales   78000
6   manager   56000
7   analyst   95000
8     sales   84000
9     clerk   75000
10    clerk   95000
11  analyst   26000
>>> df.groupby(['name']).sum()
salary
name
analyst  217000
clerk    182000
manager  146000
sales    240000