I have a dataframe with a list of items in the first row and then all the items that were bought with that item in subsequent columns:
df = pd.DataFrame({'1': ['Item 1', 'Item 1', 'Item 1', 'Item 2', 'Item 2', 'Item 2'], '2': ['Item 4', 'Item 5', 'Item 6', 'Item 7', 'Item 8', 'Item 9'], '3': ['Item 10', 'Item 11', 'Item 12', 'Item 13', 'Item 14', 'Item 15']})
I want to merge all the items bought with each item into a single row as below:
new_df = pd.DataFrame({'1': ['Item 1', 'Item 2'], '2': ['Item 4', 'Item 7'], '3': ['Item 10', 'Item 13'], '4': ['Item 5', 'Item 8'], '5': ['Item 11', 'Item 14'], '6': ['Item 6', 'Item 9'], '7': ['Item 12', 'Item 15']})
So, all the items bought with Item 1 form the columns next to it. As you can see in my example I want to keep all items that were bought with each item, even if they are duplicated.
I have been trying to get it to work with a pandas dataframe, however if there was a list generated for each item that would also be fine. I have been trying some kind of groupby and lambda function but I can’t get them to work.
EDIT: Changed numbers to make it more clear how the final df should be organized.
Thanks!
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Answer
TRY:
new_df = df.groupby('1', as_index=False).apply( lambda x: pd.Series(x.values.ravel()[1:]))
OUTPUT:
1 0 1 2 3 4 5 6 7 0 Item 1 Item 4 Item 10 Item 1 Item 5 Item 11 Item 1 Item 6 Item 12 1 Item 2 Item 7 Item 13 Item 2 Item 8 Item 14 Item 2 Item 9 Item 15