使用Python中的Pandas，为每个组选择最高价值的行 [英] With Pandas in Python, select the highest value row for each group

问题描述

对于Pandas，用于以下数据集

With Pandas, for the following data set

author1,category1,10.00
author1,category2,15.00
author1,category3,12.00
author2,category1,5.00
author2,category2,6.00
author2,category3,4.00
author2,category4,9.00
author3,category1,7.00
author3,category2,4.00
author3,category3,7.00

我想为每个作者获得最高价值

I would like to get the highest value for each author

author1,category2,15.00
author2,category4,9.00
author3,category1,7.00
author3,category3,7.00

(抱歉，我是熊猫小白菜.)

(Apologies, I'm a pandas noob.)

推荐答案

由于您也想检索category列，因此列val上的标准.agg不会提供您想要的内容. (此外，由于author3中有两个值7，@ Padraic Cunningham使用.max()的方法只会返回一个实例，而不是两个实例).您可以定义一个自定义的apply函数来完成任务.

Since you want to retrieve category column as well, a standard .agg on column val won't give you what you want. (also, since there are two values in author3 are 7, the approach by @Padraic Cunningham using.max() will only return one instance instead of both) You can define a customized apply function to accomplish your task.

import pandas as pd

# your data, assume columns names are: author, cat, val
# ===============================
print(df)


    author        cat  val
0  author1  category1   10
1  author1  category2   15
2  author1  category3   12
3  author2  category1    5
4  author2  category2    6
5  author2  category3    4
6  author2  category4    9
7  author3  category1    7
8  author3  category2    4
9  author3  category3    7

# processing
# ====================================
def func(group):
    return group.loc[group['val'] == group['val'].max()]

df.groupby('author', as_index=False).apply(func).reset_index(drop=True)


    author        cat  val
0  author1  category2   15
1  author2  category4    9
2  author3  category1    7
3  author3  category3    7    

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