With Pandas in Python, select the highest value row for each group

Question

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
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Answer 1

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    

Answer 2

import pandas as pd

df = pd.read_csv("in.csv", names=("Author","Cat","Val"))

print(df.groupby(['Author'])['Val'].max())

To get the df:

inds = df.groupby(['Author'])['Val'].transform(max) == df['Val']
df = df[inds]
df.reset_index(drop=True, inplace=True)
print(df)
    Author        Cat  Val
0  author1  category2   15
1  author2  category4    9
2  author3  category1    7
3  author3  category3    7