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I have a csv file which contains several columns of data, one of which is datetime and the rest are various values. There are multiple rows for which the datetime is the same and only one of the rest of the columns is different.

I need to be able to have only 1 row for each datetime and fill in each column. I have done this using Visual Studio with a data table but would like to be able to do the same in something outside of VS.

What I have tried:

I've looked for Python applications but can't find a suitable data table method.
Posted
Updated 8-May-22 2:53am
Comments
Richard MacCutchan 29-Apr-22 7:56am
   
You can read the data into Excel and manipulate it there.
Maciej Los 29-Apr-22 14:49pm
   
Richard, why?
OP should define what language want to use.
Richard MacCutchan 30-Apr-22 3:29am
   
Why not? If it works then it is a solution.
Member 11109279 30-Apr-22 4:38am
   
The problem is that is works on my computer but there are so many requirements using VS that it hardly ever works when I send it to somebody else and I was looking for a solution other than VS. Preferably Python.
Member 11109279 30-Apr-22 4:43am
   
Were talking about hundreds, if not thousands of rows of data. The person responsible for doing this monthly is doing it that way and it's taking way too much time.
Richard MacCutchan 30-Apr-22 5:01am
   
Maybe you could have put that information in your original question. Remember the only information we have to work on is what you tell us.

You can manipulate Excel data with pandas or A Guide to Excel Spreadsheets in Python With openpyxl – Real Python[^].
   
Your requirement is not quite clear. I'd suggest to use pandas.DataFrame[^] with methods:
- read_csv[^]
- drop_duplicates[^].

For example:
Python
#dataframe creation
df = pd.DataFrame({
    'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
    'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
    'rating': [4, 4, 3.5, 15, 5]
})
#removing duplicates:
df.drop_duplicates(subset=['brand'])


#before:
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

#after:
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
   

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