Question

我正在尝试添加一个新的计算字段。我正在尝试Adding calculated column(s) to a dataframe in pandas中的第二好的答案，因为它看起来很整洁，因此在我看来似乎是最好的。请随时提供更好的选择。

无论哪种方式，我的初始代码都在下面：

import pandas as pd    

#https://github.com/sivabalanb/Data-Analysis-with-Pandas-and-Python/blob/master/nba.csv
dt_nba = pd.read_csv("data//nba.csv")  

#note this is just basic function.  I want to pass partitioned data like team's average salary
def GetSalaryIncrement(val):
    return val * 1.1

dt_nba["SalaryPlus10Percent"] = map(GetSalaryIncrement,dt_nba["Salary"])

dt_nba[["Name","Team","Salary","SalaryPlus10Percent"]][:5]

但是，结果却不是我所期望的：

+----+---------------+----------------+--------------+--------------------------------+
| ID |     Name      |      Team      |    Salary    |      SalaryPlus10Percent       |
+----+---------------+----------------+--------------+--------------------------------+
|  0 | Avery Bradley | Boston Celtics | 7730337.0000 | <map object at 0x7fb819e9b7b8> |
|  1 | Jae Crowder   | Boston Celtics | 6796117.0000 | <map object at 0x7fb819e9b7b8> |
|  2 | John Holland  | Boston Celtics | nan          | <map object at 0x7fb819e9b7b8> |
|  3 | R.J. Hunter   | Boston Celtics | 1148640.0000 | <map object at 0x7fb819e9b7b8> |
|  4 | Jonas Jerebko | Boston Celtics | 5000000.0000 | <map object at 0x7fb819e9b7b8> |
+----+---------------+----------------+--------------+--------------------------------+

我特别希望传递“窗口/汇总数据”，以便在该窗口中适当地忽略Nan值。

在T-SQL中的示例我可以这样做：

-- INCREASE EACH PLAYERS SALARY BY 10% OF AVERAGE SALARY OF THE TEAM
SELECT NewSalary= Salary + (.1 * AVG(Salary) OVER (PARTITION BY Team))
FROM nba_data

如果可能的话，我想在熊猫那做。谢谢。

Answer 1

我认为您正在寻找

dt_nba["Salary"]=dt_nba["Salary"].map(GetSalaryIncrement)

您也可以使用

GetSalaryIncrement(dt_nba["Salary"])

dt_nba["Salary"].apply(GetSalaryIncrement)

要计算INCREASE EACH PLAYERS SALARY BY 10% OF AVERAGE SALARY OF THE TEAM

dt_nba['Newsa']=dt_nba.groupby('Team')['Salary'].transform('mean')*0.1+dt_nba["Salary"]

熊猫数据框使用分区数据添加新的计算字段

1 个答案: