Question

我有一个看起来像这样的数据框：

data = {
"Name": ["Brad Pitt", "Brad Pitt", "Brad Pitt", "Rowan Atkinson", "Leonardo DiCaprio", "Leonardo DiCaprio", 
          "Uma Thurman", "Uma Thurman", "John Travolta", "John Travolta", "Brad Pitt", "Brad Pitt", 
         "Leonardo DiCaprio", "Leonardo DiCaprio"], 
"Movie": ["Moneyball", "Moneyball", "Mr. & Mrs. Smith", "The Lion King", "Inception", "Inception",
          "Pulp fiction", "Pulp fiction", "Pulp fiction", "Pulp fiction", "Twelve Monkeys", "Twelve Monkeys",
          "Titanic", "Titanic"],
"Rating": [7, 7, 7.8, 9, 8.2, 8.2, 7.9, 7.9, 7.9, 7.9, 8, 8, 9.1, 9.1], 
"Votes": [70, 70, 120, 150, 110, 110, 85, 85, 85, 85, 60, 60, 170, 170],
"Genre": ["Biography", "Drama", "Comedy", "Animation", "Action", "Thriller", 
          "Crime", "Drama", "Crime", "Drama", "Mystery", "Triller", 
          "Drama", "Romance"]
    }
import pandas as pd
films = pd.DataFrame(data)
films

我想应用一些操作使其看起来像这样：

在1）电影中，我为每个演员放了movie.count（），2）评分成为唯一电影的平均评分，并且3）演员对唯一电影的投票汇总。

请帮助弄清楚如何进行此转换。谢谢。

Answer 1

首先，您可以按名称和电影分组以删除重复项，然后按名称分组以聚合其余部分：

In [25]: films.groupby(["Name", "Movie"]).first().reset_index().groupby("Name")
    ...: .agg({"Movie": "count", "Rating": "mean", "Votes": "sum"})
Out[25]:
                   Movie  Rating  Votes
Name
Brad Pitt              3    7.60    250
John Travolta          1    7.90     85
Leonardo DiCaprio      2    8.65    280
Rowan Atkinson         1    9.00    150
Uma Thurman            1    7.90     85

Answer 2

我将首先处理重复项，然后进行分组，而不是使用嵌套的groupby。

%timeit films.drop_duplicates(['Movie', 'Name']).groupby(['Name']).agg({'Movie' : 'count', 'Rating' : 'mean', 'Votes' : 'sum'})
2.55 ms ± 122 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)


%timeit films.groupby(["Name", "Movie"]).first().reset_index().groupby("Name").agg({"Movie": "count", "Rating": "mean", "Votes": "sum"})
6.92 ms ± 143 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)


                   Movie    Rating  Votes
Name            
Brad Pitt           3       7.60    250
John Travolta       1       7.90    85
Leonardo DiCaprio   2       8.65    280
Rowan Atkinson      1       9.00    150
Uma Thurman         1       7.90    85

熊猫数据框转换。一次应用少量算术运算

2 个答案: