Pandas - Best way to deal with this data - python-2.7

Question

I hopefully have a very simple question. My company uses a employee shift management software known as humanity which produces hour reports in a really unuseable format.
I need to clean it up so I can apply it in the rest of my analysis but I am at a loss at the best way to do this and I can't figure it out. The data starts looking like this:
Name | Total | Start (Sep 1, 2017) | End (Sep 1, 2017) | Hrs (Sep 1, 2017)
User 1 | 12 | 06:00 | 18:30 | 13
User 2 | 0 | | |
There are obviously many more users and many more dates but it repeats across the columns for additional dates. Here is what I have done to clean it up so far:
data = pd.read_csv("TestReport.csv")
del data["Total"]
cols = [c for c in data.columns if c.lower()[:3] != 'hrs']
data = data[cols]
data.rename(columns=lambda x: re.sub('Start \(', '', x), inplace=True)
data.rename(columns=lambda x: re.sub('End \(', '', x), inplace=True)
data.rename(columns=lambda x: re.sub('\)', '', x), inplace=True)
data.fillna(0, inplace=True)
My end need is to create date fields for start and finish times for each day for each user. With my data now having the column names as a pure month, day, year I think the best way would be just to iterate over each row and add the row value + column name, convert to date time and that will work.
However, I am not positive the best way to go about doing this, or if this would even be the best way.
The most important thing for me is that each user has a combined start and finish date time to be able to use to further analyze their efficiency during their shift on different records.
Let me know if I can provide any more details,
Thank you!
Andy McMaster
*******************Edited to show example*********************
Ideally end goal is to create a series of date ranges for each user. I need to be able to compare these series to my dataframe which holds records for all employees work, then assign each record to the user (team lead) who managed that record.
End would ideally be
Name | Total | Start (Sep 1, 2017) | End (Sep 1, 2017) | Hrs (Sep 1, 2017)
User 1 | 12 | 06:00 Sep 1, 2017 | 18:30 Sep 1, 2017 | 13
User 2 | 0 | | |

Answer 1

All - I discovered at least in my opinion the best way to solve this issue. I stick with the same data cleaning but end up with a little piece like this to create a workable list to add hours and dates together.
month_list = data.columns.tolist()
month_list.remove('Name')
new_list = []
for i in month_list:
if i not in new_list:
new_list.append(i)
for i in new_list:
data[i] = i + " " + data[i].astype(str)
This produces data that looks like:
Name Sep 1, 2017 Sep 1, 2017 Sep 2, 2017 \
0 User 1 Sep 1, 2017 6:00 Sep 1, 2017 18:30 Sep 2, 2017 6:00
1 User 2 Sep 1, 2017 0 Sep 1, 2017 0 Sep 2, 2017 0
2 User 3 Sep 1, 2017 0 Sep 1, 2017 0 Sep 2, 2017 0
3 User 4 Sep 1, 2017 0 Sep 1, 2017 0 Sep 2, 2017 0
4 User 5 Sep 1, 2017 6:00 Sep 1, 2017 12:00 Sep 2, 2017 6:00
Next steps will involve reworkign my code to remove all zero times or creating a filter down the road so as I go through each user, I only am using available times they worked.
Hopefully this will help someone if they ever have a poorly designed timesheet they need to work with.