python - 在 pandas Dataframe 中按级别创建计数器，优化

Question

我有这样一个数据集:

user_id  communication_type  
7         Newsletter
7         Newsletter
7         Newsletter
7         Newsletter
7         Conference
7    Upcoming Events
7    Upcoming Events
7    Upcoming Events
7         Conference
7         Conference
7            Webinar
7          Hackathon

期望的输出:

user_id  communication_type    sent_past
7         Newsletter            0
7         Newsletter            1
7         Newsletter            2
7         Newsletter            3
7         Conference            0
7    Upcoming Events            0 
7    Upcoming Events            1
7    Upcoming Events            2  
7         Conference            1
7         Conference            2 
7            Webinar            0
7          Hackathon            0

基本上为特定 user_id 在每个 communication_type 级别获得一个计数器。

对我有用的解决方案:

train['sent_past'] = train.groupby(['user_id','communication_type']).apply(lambda x: x.reset_index()).index.get_level_values(1)

但它在 ~1m 行上非常慢。我该如何优化它？

Answer 1

您可以使用 groupby.cumcount .

df['sent_past'] = df.groupby('communication_type').cumcount()

print(df)

    user_id communication_type  sent_past
0         7         Newsletter          0
1         7         Newsletter          1
2         7         Newsletter          2
3         7         Newsletter          3
4         7         Conference          0
5         7    Upcoming Events          0
6         7    Upcoming Events          1
7         7    Upcoming Events          2
8         7         Conference          1
9         7         Conference          2
10        7            Webinar          0
11        7          Hackathon          0

或者对于 user_id 和 communication_type 的组合使用:

df['sent_past'] = df.groupby(['user_id', 'communication_type']).cumcount()