Create a column for Month from a column of date (however the date column does not contain the month information)

Question

I have a data like this, and want to create a column called 'Month'

+---------+------------------+------+------+
| Name    | Task             | Team | Date |
+---------+------------------+------+------+
| John    | Market study     | A    | 1    |
+---------+------------------+------+------+
| Michael | Customer service | B    | 1    |
+---------+------------------+------+------+
| Joanna  | Accounting       | C    | 1    |
+---------+------------------+------+------+
| John    | Accounting       | B    | 2    |
+---------+------------------+------+------+
| Michael | Customer service | A    | 2    |
+---------+------------------+------+------+
| Joanna  | Market study     | C    | 2    |
+---------+------------------+------+------+
| John    | Customer service | C    | 1    |
+---------+------------------+------+------+
| Michael | Market study     | A    | 1    |
+---------+------------------+------+------+
| Joanna  | Customer service | B    | 1    |
+---------+------------------+------+------+
| John    | Market study     | A    | 2    |
+---------+------------------+------+------+
| Michael | Customer service | B    | 2    |
+---------+------------------+------+------+
| Joanna  | Accounting       | C    | 2    |
+---------+------------------+------+------+

So basicly, I have the date information, but the date does not contain the month it belongs to. However, I know that if it occurs the first time, then it will belong the Month 1, if it occurs for the second time, then it will belong to Month 2. So for example, date 1 occurs 3 time, then interrupted by date 2. So the first 3 time belongs to month 1, and the next 3 times occur, it belong to Month 2. So I would like my result like this:

+---------+------------------+------+------+---------+
| Name    | Task             | Team | Date | Month   |
+---------+------------------+------+------+---------+
| John    | Market study     | A    | 1    | Month 1 |
+---------+------------------+------+------+---------+
| Michael | Customer service | B    | 1    | Month 1 |
+---------+------------------+------+------+---------+
| Joanna  | Accounting       | C    | 1    | Month 1 |
+---------+------------------+------+------+---------+
| John    | Accounting       | B    | 2    | Month 1 |
+---------+------------------+------+------+---------+
| Michael | Customer service | A    | 2    | Month 1 |
+---------+------------------+------+------+---------+
| Joanna  | Market study     | C    | 2    | Month 1 |
+---------+------------------+------+------+---------+
| John    | Customer service | C    | 1    | Month 2 |
+---------+------------------+------+------+---------+
| Michael | Market study     | A    | 1    | Month 2 |
+---------+------------------+------+------+---------+
| Joanna  | Customer service | B    | 1    | Month 2 |
+---------+------------------+------+------+---------+
| John    | Market study     | A    | 2    | Month 2 |
+---------+------------------+------+------+---------+
| Michael | Customer service | B    | 2    | Month 2 |
+---------+------------------+------+------+---------+
| Joanna  | Accounting       | C    | 2    | Month 2 |
+---------+------------------+------+------+---------+

I do not have any idea, other than just using some loops. Thank you all.

Answer 1

If I understand the question correctly, you may do following: create mask s to separate each consective values into separate group. From s, create mask s1 to each value of each group. Groupby s1 and Date and doing cumcount and map to create desired output:

s = df.Date.ne(df.Date.shift()).cumsum()
s1 = df.Date.groupby(s).cumcount()

df['Month'] = df.groupby([s1, 'Date']).Name.cumcount().add(1).map(lambda x: 'Month '+str(x))

Out[897]:
       Name              Task Team  Date    Month
0      John      Market-study    A     1  Month 1
1   Michael  Customer-service    B     1  Month 1
2    Joanna        Accounting    C     1  Month 1
3      John        Accounting    B     2  Month 1
4   Michael  Customer-service    A     2  Month 1
5    Joanna      Market-study    C     2  Month 1
6      John  Customer-service    C     1  Month 2
7   Michael      Market-study    A     1  Month 2
8    Joanna  Customer-service    B     1  Month 2
9      John      Market-study    A     2  Month 2
10  Michael  Customer-service    B     2  Month 2
11   Joanna        Accounting    C     2  Month 2