根據熊貓中的行數據創建新行和新列

Question

我有一個數據框，其中每個行都有行，每個用戶加入我的網站並進行購買。

+---+-----+--------------------+---------+--------+-----+
|   | uid |        msg         |  _time  | gender | age |
+---+-----+--------------------+---------+--------+-----+
| 0 |   1 | confirmed_settings | 1/29/15 | M      |  37 |
| 1 |   1 | sale               | 4/13/15 | M      |  37 |
| 2 |   3 | confirmed_settings | 4/19/15 | M      |  35 |
| 3 |   4 | confirmed_settings | 2/21/15 | M      |  21 |
| 4 |   5 | confirmed_settings | 3/28/15 | M      |  18 |
| 5 |   4 | sale               | 3/15/15 | M      |  21 |
+---+-----+--------------------+---------+--------+-----+

我想改變數據框使每一行是一個UID獨特，有一個叫列sale和confirmed_settings具有動作的時間戳。 請注意，並非每個用戶都有sale ，但是每個用戶都有confirmed_settings 。 如下所示：

+---+-----+--------------------+---------+---------+--------+-----+
|   | uid | confirmed_settings |  sale   |  _time  | gender | age |
+---+-----+--------------------+---------+---------+--------+-----+
| 0 |   1 | 1/29/15            | 4/13/15 | 1/29/15 | M      |  37 |
| 1 |   3 | 4/19/15            | null    | 4/19/15 | M      |  35 |
| 2 |   4 | 2/21/15            | 3/15/15 | 2/21/15 | M      |  21 |
| 3 |   5 | 3/28/15            | null    | 3/28/15 | M      |  18 |
+---+-----+--------------------+---------+---------+--------+-----+

完成此操作的最佳熊貓習慣用語/功能是什么？

Answer 1

不知道這是否是最佳解決方案，但應該可以：

In [1]: df
Out[1]:
   uid                 msg    _time gender  age
0    1  confirmed_settings  1/29/15      M   37
1    1                sale  4/13/15      M   37
2    3  confirmed_settings  4/19/15      M   35
3    4  confirmed_settings  2/21/15      M   21
4    5  confirmed_settings  3/28/15      M   18
5    4                sale  3/15/15      M   21

In [2]: df1 = df.pivot(index='uid', columns='msg', values='_time').reset_index()
In [3]: df1 = df1.merge(df[['uid', 'gender', 'age']].drop_duplicates(), on='uid')

In [4]: df1
Out[4]: 
   uid confirmed_settings     sale gender  age
0    1            1/29/15  4/13/15      M   37
2    3            4/19/15      NaN      M   35
3    4            2/21/15  3/15/15      M   21
5    5            3/28/15      NaN      M   18