Pandas groupby与bin计数

Question

我有一个看起来像这样的DataFrame:

+----------+---------+-------+
| username | post_id | views |
+----------+---------+-------+
| john     |       1 |     3 |
| john     |       2 |    23 |
| john     |       3 |    44 |
| john     |       4 |    82 |
| jane     |       7 |     5 |
| jane     |       8 |    25 |
| jane     |       9 |    46 |
| jane     |      10 |    56 |
+----------+---------+-------+

我想将它转换为计算属于某些二进制文件的视图:

+------+------+-------+-------+--------+
|      | 1-10 | 11-25 | 25-50 | 51-100 |
+------+------+-------+-------+--------+
| john |    1 |     1 |     1 |      1 |
| jane |    1 |     1 |     1 |      1 |
+------+------+-------+-------+--------+

我试过了:

bins = [1, 10, 25, 50, 100]
groups = df.groupby(pd.cut(df.views, bins))
groups.username.count()

但它只提供聚合计数而不是用户计数.我如何获得用户的bin计数？

聚合计数(使用我的实际数据)如下所示:

impressions
(2500, 5000]         2332
(5000, 10000]        1118
(10000, 50000]        570
(50000, 10000000]      14
Name: username, dtype: int64

Answer 1

您可以按箱和用户名进行分组,计算组大小,然后使用unstack():

>>> groups = df.groupby(['username', pd.cut(df.views, bins)])
>>> groups.size().unstack()
views     (1, 10]  (10, 25]  (25, 50]  (50, 100]
username
jane            1         1         1          1
john            1         1         1          1