如何使用groupby对象来获取其他列的总和？ [英] How to use groupby objects to get sums of other columns?

问题描述

我正在玩一个从网络上的一个mlb数据集，以帮助学习。
数据框看起来像

 玩家职位薪水年
 0 Mike Witt Pitcher 1400000 1988 
 1 George Hendrick Outfielder 989333 1988 
 2 Chili Davis Outfielder 950000 1988 
 3 Brian Downing指定的Hitter 900000 1988 
 4 Bob Boone Catcher 883000 1988 
。 
。 
。 
  

作为一个实验，我试图找到通过他们的积累累积最高总薪水的投手事业。

到目前为止，我已经尝试过：

  mask = mlb.Position ==Pitcher
 pitchers = mlb [mask] 
 pitcher_groups = pitchers.groupby（Player）
  

我不知道如何继续使用groupby对象。我知道我需要在每个组中找到工资，并做一些比较...我如何做这个没有for循环？

解决方案

只要做：

  pitcher_groups ['薪资'] sum（）
  

这将对groupby对象的薪水列进行总和。

 在[57]中：
 
 df [df ['Position'] =='Pitcher']。groupby（'Player'）['Salary']。sum（）
出[57]：
玩家
 Mike Witt 1400000 
名称：薪水，dtype：int64 
  

I'm playing with an mlb data set from the web to help learn. The dataframe looks like

    Player             Position          Salary     Year
0   Mike Witt          Pitcher           1400000    1988
1   George Hendrick    Outfielder        989333     1988
2   Chili Davis        Outfielder        950000     1988
3   Brian Downing      Designated Hitter 900000     1988
4   Bob Boone          Catcher           883000     1988
.
. 
.

As an experiment, I'm trying to find the pitcher that has accumulated the highest total salary through their career. mlb is the dataframe.

So far I have tried:

mask = mlb.Position == "Pitcher"
pitchers = mlb[mask]
pitcher_groups = pitchers.groupby("Player")

I'm not sure how to proceed with the groupby object. I know I need to find the salary sum in each group, and do some sort of comparison... How do I do this without for loops?

解决方案

Just do:

pitcher_groups['Salary'].sum()

This sums the salary column on the groupby object.

In [57]:

df[df['Position']=='Pitcher'].groupby('Player')['Salary'].sum()
Out[57]:
Player
Mike Witt    1400000
Name: Salary, dtype: int64

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