Pandas groupby with bin counts

I have a DataFrame that looks like this:

+----------+---------+-------+
| 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 |
+----------+---------+-------+

and I would like to transform it to count views that belong to certain bins like this:

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

I tried:

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

But it only gives aggregate counts and not counts by user. How can I get bin counts by user?

The aggregate counts (using my real data) looks like this:

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

Solution 1:

You could group by both the bins and username, compute the group sizes and then use 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