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How to use Pandas to get the count of every combination inclusive


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10















I am trying to figure out what combination of clothing customers are buying together. I can figure out the exact combination, but the problem I can't figure out is the count that includes the combination + others.



For example, I have:



Cust_num  Item    Rev
Cust1 Shirt1 $40
Cust1 Shirt2 $40
Cust1 Shorts1 $40
Cust2 Shirt1 $40
Cust2 Shorts1 $40


This should result in:



Combo                  Count
Shirt1,Shirt2,Shorts1 1
Shirt1,Shorts1 2


The best I can do is unique combinations:



Combo                 Count
Shirt1,Shirt2,Shorts1 1
Shirt1,Shorts1 1


I tried:



df = df.pivot(index='Cust_num',columns='Item').sum()
df[df.notnull()] = "x"
df = df.loc[:,"Shirt1":].replace("x", pd.Series(df.columns, df.columns))
col = df.stack().groupby(level=0).apply(','.join)
df2 = pd.DataFrame(col)
df2.groupby([0]).size().reset_index(name='counts')


But that is just the unique counts.










share|improve this question







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  • 3





    I feel like this is one sort of problem pandas would not be suitable for.

    – coldspeed
    23 hours ago


















10















I am trying to figure out what combination of clothing customers are buying together. I can figure out the exact combination, but the problem I can't figure out is the count that includes the combination + others.



For example, I have:



Cust_num  Item    Rev
Cust1 Shirt1 $40
Cust1 Shirt2 $40
Cust1 Shorts1 $40
Cust2 Shirt1 $40
Cust2 Shorts1 $40


This should result in:



Combo                  Count
Shirt1,Shirt2,Shorts1 1
Shirt1,Shorts1 2


The best I can do is unique combinations:



Combo                 Count
Shirt1,Shirt2,Shorts1 1
Shirt1,Shorts1 1


I tried:



df = df.pivot(index='Cust_num',columns='Item').sum()
df[df.notnull()] = "x"
df = df.loc[:,"Shirt1":].replace("x", pd.Series(df.columns, df.columns))
col = df.stack().groupby(level=0).apply(','.join)
df2 = pd.DataFrame(col)
df2.groupby([0]).size().reset_index(name='counts')


But that is just the unique counts.










share|improve this question







New contributor




lys_dad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
















  • 3





    I feel like this is one sort of problem pandas would not be suitable for.

    – coldspeed
    23 hours ago














10












10








10








I am trying to figure out what combination of clothing customers are buying together. I can figure out the exact combination, but the problem I can't figure out is the count that includes the combination + others.



For example, I have:



Cust_num  Item    Rev
Cust1 Shirt1 $40
Cust1 Shirt2 $40
Cust1 Shorts1 $40
Cust2 Shirt1 $40
Cust2 Shorts1 $40


This should result in:



Combo                  Count
Shirt1,Shirt2,Shorts1 1
Shirt1,Shorts1 2


The best I can do is unique combinations:



Combo                 Count
Shirt1,Shirt2,Shorts1 1
Shirt1,Shorts1 1


I tried:



df = df.pivot(index='Cust_num',columns='Item').sum()
df[df.notnull()] = "x"
df = df.loc[:,"Shirt1":].replace("x", pd.Series(df.columns, df.columns))
col = df.stack().groupby(level=0).apply(','.join)
df2 = pd.DataFrame(col)
df2.groupby([0]).size().reset_index(name='counts')


But that is just the unique counts.










share|improve this question







New contributor




lys_dad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.












I am trying to figure out what combination of clothing customers are buying together. I can figure out the exact combination, but the problem I can't figure out is the count that includes the combination + others.



For example, I have:



Cust_num  Item    Rev
Cust1 Shirt1 $40
Cust1 Shirt2 $40
Cust1 Shorts1 $40
Cust2 Shirt1 $40
Cust2 Shorts1 $40


This should result in:



Combo                  Count
Shirt1,Shirt2,Shorts1 1
Shirt1,Shorts1 2


The best I can do is unique combinations:



Combo                 Count
Shirt1,Shirt2,Shorts1 1
Shirt1,Shorts1 1


I tried:



df = df.pivot(index='Cust_num',columns='Item').sum()
df[df.notnull()] = "x"
df = df.loc[:,"Shirt1":].replace("x", pd.Series(df.columns, df.columns))
col = df.stack().groupby(level=0).apply(','.join)
df2 = pd.DataFrame(col)
df2.groupby([0]).size().reset_index(name='counts')


But that is just the unique counts.







python pandas






share|improve this question







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lys_dad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











share|improve this question







New contributor




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Check out our Code of Conduct.









share|improve this question




share|improve this question






New contributor




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asked yesterday









lys_dadlys_dad

565




565




New contributor




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New contributor





lys_dad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






lys_dad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.








  • 3





    I feel like this is one sort of problem pandas would not be suitable for.

    – coldspeed
    23 hours ago














  • 3





    I feel like this is one sort of problem pandas would not be suitable for.

    – coldspeed
    23 hours ago








3




3





I feel like this is one sort of problem pandas would not be suitable for.

– coldspeed
23 hours ago





I feel like this is one sort of problem pandas would not be suitable for.

– coldspeed
23 hours ago












4 Answers
4






active

oldest

votes


















8














Using pandas.DataFrame.groupby:



grouped_item = df.groupby('Cust_num')['Item']
subsets = grouped_item.apply(lambda x: set(x)).tolist()
Count = [sum(s2.issubset(s1) for s1 in subsets) for s2 in subsets]
combo = grouped_item.apply(lambda x:','.join(x))
combo = combo.reset_index()
combo['Count']=Count


Output:



  Cust_num                   Item  Count
0 Cust1 Shirt1,Shirt2,Shorts1 1
1 Cust2 Shirt1,Shorts1 2





share|improve this answer
























  • How is finding subsets finding inclusive combination of df['Item']? Subsets are not combinations. Below is output of what you doing: {' Shirt2', ' Shorts1', ' Shirt1'}: True followed by {' Shorts1', ' Shirt1'}: True and then {' Shorts1', ' Shirt1'}: True. Then you sum then to get [1,2]. I agree my approach i did is wrong so is yours. I would think @ResidentSleeper has correct answer.

    – Lee Mtoti
    20 hours ago













  • @Chris, I think you need to find combination of Item first which would give you your subsets. You would sum unique. FYI, I did not mock your comment. I was asking a question just like you asked me a question. I did not throw a hissy or downvote you. Thanks for sharing

    – Lee Mtoti
    20 hours ago











  • @LeeMtoti Apologies for a strong language. I've deleted it. BTW, grouped_item contains a combination of each CustN's choice of clothing. lambda x: set(x) was implemented for a issubset comparison. As you pointed out, issubset returns True if and only if a set is contained in other set, which I still believe is what OP wants.

    – Chris
    20 hours ago











  • I believe the term combination is subtle misleading. My understanding is a set of each customer's choices of Item. Hope this makes my answer and intention clearer.

    – Chris
    20 hours ago











  • Apologies if the language wasn't correct. I wasn't sure how to describe it. I tried this with the first 1,000 customers, and it worked! I'm running it now with the full list.

    – lys_dad
    12 hours ago



















2














Late answer, but you can use:



df = df.groupby(['Cust_num'], as_index=False).agg(','.join).drop(columns=['Rev']).set_index(['Item']).rename_axis("combo").rename(columns={"Cust_num": "Count"})
df['Count'] = df['Count'].str.replace(r'Cust','')




combo                   Count                 
Shirt1,Shirt2,Shorts1 1
Shirt1,Shorts1 2





share|improve this answer

































    2














    I think you need to create a combination of items first.



    How to get all possible combinations of a list’s elements?



    I used the function from Dan H's answer.



    from itertools import chain, combinations
    def all_subsets(ss):
    return chain(*map(lambda x: combinations(ss, x), range(0, len(ss)+1)))

    uq_items = df.Item.unique()

    list(all_subsets(uq_items))

    [(),
    ('Shirt1',),
    ('Shirt2',),
    ('Shorts1',),
    ('Shirt1', 'Shirt2'),
    ('Shirt1', 'Shorts1'),
    ('Shirt2', 'Shorts1'),
    ('Shirt1', 'Shirt2', 'Shorts1')]


    And use groupby each customer to get their items combination.



    ls = []

    for _, d in df.groupby('Cust_num', group_keys=False):
    # Get all possible subset of items
    pi = np.array(list(all_subsets(d.Item)))

    # Fliter only > 1
    ls.append(pi[[len(l) > 1 for l in pi]])


    Then convert to Series and use value_counts().



    pd.Series(np.concatenate(ls)).value_counts()

    (Shirt1, Shorts1) 2
    (Shirt2, Shorts1) 1
    (Shirt1, Shirt2, Shorts1) 1
    (Shirt1, Shirt2) 1





    share|improve this answer


























    • This probably works, but I keep getting a "Memory Error". I'll have to look for a solution for the memory issue as I can't use my desktop for this project (sensitive data).

      – lys_dad
      13 hours ago











    • I did the first 1000 customers and it worked! Any suggestion for low memory laptops?

      – lys_dad
      12 hours ago











    • @lys_dad The accepted answer has already solved your memory problem, right?

      – ResidentSleeper
      9 hours ago











    • It does, yes. But thank you for your elegant solution!

      – lys_dad
      7 hours ago



















    0














    My version which I believe is easier to understand



    new_df = df.groupby("Cust_num").agg({lambda x: ''.join(x.unique())})

    new_df ['count'] = range(1, len(new_df ) + 1)


    Output:



                                Item      Rev count
    <lambda> <lambda>
    Cust_num
    Cust1 Shirt1 Shirt2 Shorts1 $40 1
    Cust2 Shirt1 Shorts1 $40 2


    Since you do not need the Rev column, you can drop it:



    new_df = new_df = new_df.drop(columns=["Rev"]).reset_index()

    new_df


    Output:



      Cust_num                    Item count
    <lambda>
    0 Cust1 Shirt1 Shirt2 Shorts1 1
    1 Cust2 Shirt1 Shorts1 2


    This edit is to respond to @Chris by looking at his approach written using list comprehension. He created an list of sets:



    [{' Shirt1', ' Shirt2', ' Shorts1'}, {' Shirt1', ' Shorts1'}]


    Then next step finds the subsets:



    for s1 in subsets:
    for s2 in subsets:
    if s2.issubset(s1):
    print("{}: {}".format(s2,s2.issubset(s1)))


    Output:



    {' Shirt2', ' Shorts1', ' Shirt1'}: True
    {' Shorts1', ' Shirt1'}: True
    {' Shorts1', ' Shirt1'}: True


    You asked me to explain myself and I did. However after thinking about it, I realized your approach was wrong too. As such I was not mocking you, but thanking you for making me think about my solution. Also thanks to @ResidentSleeper for his solution.






    share|improve this answer


























    • @Chris thank for mocking my comment and down voting my answer. I did not downvote your answer out of the spirit of learning from each other.

      – Lee Mtoti
      20 hours ago











    • @Chris thanks for removing the downvote. Whole point is to learn from each other while helping each other.

      – Lee Mtoti
      19 hours ago














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    4 Answers
    4






    active

    oldest

    votes








    4 Answers
    4






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    8














    Using pandas.DataFrame.groupby:



    grouped_item = df.groupby('Cust_num')['Item']
    subsets = grouped_item.apply(lambda x: set(x)).tolist()
    Count = [sum(s2.issubset(s1) for s1 in subsets) for s2 in subsets]
    combo = grouped_item.apply(lambda x:','.join(x))
    combo = combo.reset_index()
    combo['Count']=Count


    Output:



      Cust_num                   Item  Count
    0 Cust1 Shirt1,Shirt2,Shorts1 1
    1 Cust2 Shirt1,Shorts1 2





    share|improve this answer
























    • How is finding subsets finding inclusive combination of df['Item']? Subsets are not combinations. Below is output of what you doing: {' Shirt2', ' Shorts1', ' Shirt1'}: True followed by {' Shorts1', ' Shirt1'}: True and then {' Shorts1', ' Shirt1'}: True. Then you sum then to get [1,2]. I agree my approach i did is wrong so is yours. I would think @ResidentSleeper has correct answer.

      – Lee Mtoti
      20 hours ago













    • @Chris, I think you need to find combination of Item first which would give you your subsets. You would sum unique. FYI, I did not mock your comment. I was asking a question just like you asked me a question. I did not throw a hissy or downvote you. Thanks for sharing

      – Lee Mtoti
      20 hours ago











    • @LeeMtoti Apologies for a strong language. I've deleted it. BTW, grouped_item contains a combination of each CustN's choice of clothing. lambda x: set(x) was implemented for a issubset comparison. As you pointed out, issubset returns True if and only if a set is contained in other set, which I still believe is what OP wants.

      – Chris
      20 hours ago











    • I believe the term combination is subtle misleading. My understanding is a set of each customer's choices of Item. Hope this makes my answer and intention clearer.

      – Chris
      20 hours ago











    • Apologies if the language wasn't correct. I wasn't sure how to describe it. I tried this with the first 1,000 customers, and it worked! I'm running it now with the full list.

      – lys_dad
      12 hours ago
















    8














    Using pandas.DataFrame.groupby:



    grouped_item = df.groupby('Cust_num')['Item']
    subsets = grouped_item.apply(lambda x: set(x)).tolist()
    Count = [sum(s2.issubset(s1) for s1 in subsets) for s2 in subsets]
    combo = grouped_item.apply(lambda x:','.join(x))
    combo = combo.reset_index()
    combo['Count']=Count


    Output:



      Cust_num                   Item  Count
    0 Cust1 Shirt1,Shirt2,Shorts1 1
    1 Cust2 Shirt1,Shorts1 2





    share|improve this answer
























    • How is finding subsets finding inclusive combination of df['Item']? Subsets are not combinations. Below is output of what you doing: {' Shirt2', ' Shorts1', ' Shirt1'}: True followed by {' Shorts1', ' Shirt1'}: True and then {' Shorts1', ' Shirt1'}: True. Then you sum then to get [1,2]. I agree my approach i did is wrong so is yours. I would think @ResidentSleeper has correct answer.

      – Lee Mtoti
      20 hours ago













    • @Chris, I think you need to find combination of Item first which would give you your subsets. You would sum unique. FYI, I did not mock your comment. I was asking a question just like you asked me a question. I did not throw a hissy or downvote you. Thanks for sharing

      – Lee Mtoti
      20 hours ago











    • @LeeMtoti Apologies for a strong language. I've deleted it. BTW, grouped_item contains a combination of each CustN's choice of clothing. lambda x: set(x) was implemented for a issubset comparison. As you pointed out, issubset returns True if and only if a set is contained in other set, which I still believe is what OP wants.

      – Chris
      20 hours ago











    • I believe the term combination is subtle misleading. My understanding is a set of each customer's choices of Item. Hope this makes my answer and intention clearer.

      – Chris
      20 hours ago











    • Apologies if the language wasn't correct. I wasn't sure how to describe it. I tried this with the first 1,000 customers, and it worked! I'm running it now with the full list.

      – lys_dad
      12 hours ago














    8












    8








    8







    Using pandas.DataFrame.groupby:



    grouped_item = df.groupby('Cust_num')['Item']
    subsets = grouped_item.apply(lambda x: set(x)).tolist()
    Count = [sum(s2.issubset(s1) for s1 in subsets) for s2 in subsets]
    combo = grouped_item.apply(lambda x:','.join(x))
    combo = combo.reset_index()
    combo['Count']=Count


    Output:



      Cust_num                   Item  Count
    0 Cust1 Shirt1,Shirt2,Shorts1 1
    1 Cust2 Shirt1,Shorts1 2





    share|improve this answer













    Using pandas.DataFrame.groupby:



    grouped_item = df.groupby('Cust_num')['Item']
    subsets = grouped_item.apply(lambda x: set(x)).tolist()
    Count = [sum(s2.issubset(s1) for s1 in subsets) for s2 in subsets]
    combo = grouped_item.apply(lambda x:','.join(x))
    combo = combo.reset_index()
    combo['Count']=Count


    Output:



      Cust_num                   Item  Count
    0 Cust1 Shirt1,Shirt2,Shorts1 1
    1 Cust2 Shirt1,Shorts1 2






    share|improve this answer












    share|improve this answer



    share|improve this answer










    answered 23 hours ago









    ChrisChris

    3,791522




    3,791522













    • How is finding subsets finding inclusive combination of df['Item']? Subsets are not combinations. Below is output of what you doing: {' Shirt2', ' Shorts1', ' Shirt1'}: True followed by {' Shorts1', ' Shirt1'}: True and then {' Shorts1', ' Shirt1'}: True. Then you sum then to get [1,2]. I agree my approach i did is wrong so is yours. I would think @ResidentSleeper has correct answer.

      – Lee Mtoti
      20 hours ago













    • @Chris, I think you need to find combination of Item first which would give you your subsets. You would sum unique. FYI, I did not mock your comment. I was asking a question just like you asked me a question. I did not throw a hissy or downvote you. Thanks for sharing

      – Lee Mtoti
      20 hours ago











    • @LeeMtoti Apologies for a strong language. I've deleted it. BTW, grouped_item contains a combination of each CustN's choice of clothing. lambda x: set(x) was implemented for a issubset comparison. As you pointed out, issubset returns True if and only if a set is contained in other set, which I still believe is what OP wants.

      – Chris
      20 hours ago











    • I believe the term combination is subtle misleading. My understanding is a set of each customer's choices of Item. Hope this makes my answer and intention clearer.

      – Chris
      20 hours ago











    • Apologies if the language wasn't correct. I wasn't sure how to describe it. I tried this with the first 1,000 customers, and it worked! I'm running it now with the full list.

      – lys_dad
      12 hours ago



















    • How is finding subsets finding inclusive combination of df['Item']? Subsets are not combinations. Below is output of what you doing: {' Shirt2', ' Shorts1', ' Shirt1'}: True followed by {' Shorts1', ' Shirt1'}: True and then {' Shorts1', ' Shirt1'}: True. Then you sum then to get [1,2]. I agree my approach i did is wrong so is yours. I would think @ResidentSleeper has correct answer.

      – Lee Mtoti
      20 hours ago













    • @Chris, I think you need to find combination of Item first which would give you your subsets. You would sum unique. FYI, I did not mock your comment. I was asking a question just like you asked me a question. I did not throw a hissy or downvote you. Thanks for sharing

      – Lee Mtoti
      20 hours ago











    • @LeeMtoti Apologies for a strong language. I've deleted it. BTW, grouped_item contains a combination of each CustN's choice of clothing. lambda x: set(x) was implemented for a issubset comparison. As you pointed out, issubset returns True if and only if a set is contained in other set, which I still believe is what OP wants.

      – Chris
      20 hours ago











    • I believe the term combination is subtle misleading. My understanding is a set of each customer's choices of Item. Hope this makes my answer and intention clearer.

      – Chris
      20 hours ago











    • Apologies if the language wasn't correct. I wasn't sure how to describe it. I tried this with the first 1,000 customers, and it worked! I'm running it now with the full list.

      – lys_dad
      12 hours ago

















    How is finding subsets finding inclusive combination of df['Item']? Subsets are not combinations. Below is output of what you doing: {' Shirt2', ' Shorts1', ' Shirt1'}: True followed by {' Shorts1', ' Shirt1'}: True and then {' Shorts1', ' Shirt1'}: True. Then you sum then to get [1,2]. I agree my approach i did is wrong so is yours. I would think @ResidentSleeper has correct answer.

    – Lee Mtoti
    20 hours ago







    How is finding subsets finding inclusive combination of df['Item']? Subsets are not combinations. Below is output of what you doing: {' Shirt2', ' Shorts1', ' Shirt1'}: True followed by {' Shorts1', ' Shirt1'}: True and then {' Shorts1', ' Shirt1'}: True. Then you sum then to get [1,2]. I agree my approach i did is wrong so is yours. I would think @ResidentSleeper has correct answer.

    – Lee Mtoti
    20 hours ago















    @Chris, I think you need to find combination of Item first which would give you your subsets. You would sum unique. FYI, I did not mock your comment. I was asking a question just like you asked me a question. I did not throw a hissy or downvote you. Thanks for sharing

    – Lee Mtoti
    20 hours ago





    @Chris, I think you need to find combination of Item first which would give you your subsets. You would sum unique. FYI, I did not mock your comment. I was asking a question just like you asked me a question. I did not throw a hissy or downvote you. Thanks for sharing

    – Lee Mtoti
    20 hours ago













    @LeeMtoti Apologies for a strong language. I've deleted it. BTW, grouped_item contains a combination of each CustN's choice of clothing. lambda x: set(x) was implemented for a issubset comparison. As you pointed out, issubset returns True if and only if a set is contained in other set, which I still believe is what OP wants.

    – Chris
    20 hours ago





    @LeeMtoti Apologies for a strong language. I've deleted it. BTW, grouped_item contains a combination of each CustN's choice of clothing. lambda x: set(x) was implemented for a issubset comparison. As you pointed out, issubset returns True if and only if a set is contained in other set, which I still believe is what OP wants.

    – Chris
    20 hours ago













    I believe the term combination is subtle misleading. My understanding is a set of each customer's choices of Item. Hope this makes my answer and intention clearer.

    – Chris
    20 hours ago





    I believe the term combination is subtle misleading. My understanding is a set of each customer's choices of Item. Hope this makes my answer and intention clearer.

    – Chris
    20 hours ago













    Apologies if the language wasn't correct. I wasn't sure how to describe it. I tried this with the first 1,000 customers, and it worked! I'm running it now with the full list.

    – lys_dad
    12 hours ago





    Apologies if the language wasn't correct. I wasn't sure how to describe it. I tried this with the first 1,000 customers, and it worked! I'm running it now with the full list.

    – lys_dad
    12 hours ago













    2














    Late answer, but you can use:



    df = df.groupby(['Cust_num'], as_index=False).agg(','.join).drop(columns=['Rev']).set_index(['Item']).rename_axis("combo").rename(columns={"Cust_num": "Count"})
    df['Count'] = df['Count'].str.replace(r'Cust','')




    combo                   Count                 
    Shirt1,Shirt2,Shorts1 1
    Shirt1,Shorts1 2





    share|improve this answer






























      2














      Late answer, but you can use:



      df = df.groupby(['Cust_num'], as_index=False).agg(','.join).drop(columns=['Rev']).set_index(['Item']).rename_axis("combo").rename(columns={"Cust_num": "Count"})
      df['Count'] = df['Count'].str.replace(r'Cust','')




      combo                   Count                 
      Shirt1,Shirt2,Shorts1 1
      Shirt1,Shorts1 2





      share|improve this answer




























        2












        2








        2







        Late answer, but you can use:



        df = df.groupby(['Cust_num'], as_index=False).agg(','.join).drop(columns=['Rev']).set_index(['Item']).rename_axis("combo").rename(columns={"Cust_num": "Count"})
        df['Count'] = df['Count'].str.replace(r'Cust','')




        combo                   Count                 
        Shirt1,Shirt2,Shorts1 1
        Shirt1,Shorts1 2





        share|improve this answer















        Late answer, but you can use:



        df = df.groupby(['Cust_num'], as_index=False).agg(','.join).drop(columns=['Rev']).set_index(['Item']).rename_axis("combo").rename(columns={"Cust_num": "Count"})
        df['Count'] = df['Count'].str.replace(r'Cust','')




        combo                   Count                 
        Shirt1,Shirt2,Shorts1 1
        Shirt1,Shorts1 2






        share|improve this answer














        share|improve this answer



        share|improve this answer








        edited 22 hours ago

























        answered 23 hours ago









        Pedro LobitoPedro Lobito

        50.6k16138172




        50.6k16138172























            2














            I think you need to create a combination of items first.



            How to get all possible combinations of a list’s elements?



            I used the function from Dan H's answer.



            from itertools import chain, combinations
            def all_subsets(ss):
            return chain(*map(lambda x: combinations(ss, x), range(0, len(ss)+1)))

            uq_items = df.Item.unique()

            list(all_subsets(uq_items))

            [(),
            ('Shirt1',),
            ('Shirt2',),
            ('Shorts1',),
            ('Shirt1', 'Shirt2'),
            ('Shirt1', 'Shorts1'),
            ('Shirt2', 'Shorts1'),
            ('Shirt1', 'Shirt2', 'Shorts1')]


            And use groupby each customer to get their items combination.



            ls = []

            for _, d in df.groupby('Cust_num', group_keys=False):
            # Get all possible subset of items
            pi = np.array(list(all_subsets(d.Item)))

            # Fliter only > 1
            ls.append(pi[[len(l) > 1 for l in pi]])


            Then convert to Series and use value_counts().



            pd.Series(np.concatenate(ls)).value_counts()

            (Shirt1, Shorts1) 2
            (Shirt2, Shorts1) 1
            (Shirt1, Shirt2, Shorts1) 1
            (Shirt1, Shirt2) 1





            share|improve this answer


























            • This probably works, but I keep getting a "Memory Error". I'll have to look for a solution for the memory issue as I can't use my desktop for this project (sensitive data).

              – lys_dad
              13 hours ago











            • I did the first 1000 customers and it worked! Any suggestion for low memory laptops?

              – lys_dad
              12 hours ago











            • @lys_dad The accepted answer has already solved your memory problem, right?

              – ResidentSleeper
              9 hours ago











            • It does, yes. But thank you for your elegant solution!

              – lys_dad
              7 hours ago
















            2














            I think you need to create a combination of items first.



            How to get all possible combinations of a list’s elements?



            I used the function from Dan H's answer.



            from itertools import chain, combinations
            def all_subsets(ss):
            return chain(*map(lambda x: combinations(ss, x), range(0, len(ss)+1)))

            uq_items = df.Item.unique()

            list(all_subsets(uq_items))

            [(),
            ('Shirt1',),
            ('Shirt2',),
            ('Shorts1',),
            ('Shirt1', 'Shirt2'),
            ('Shirt1', 'Shorts1'),
            ('Shirt2', 'Shorts1'),
            ('Shirt1', 'Shirt2', 'Shorts1')]


            And use groupby each customer to get their items combination.



            ls = []

            for _, d in df.groupby('Cust_num', group_keys=False):
            # Get all possible subset of items
            pi = np.array(list(all_subsets(d.Item)))

            # Fliter only > 1
            ls.append(pi[[len(l) > 1 for l in pi]])


            Then convert to Series and use value_counts().



            pd.Series(np.concatenate(ls)).value_counts()

            (Shirt1, Shorts1) 2
            (Shirt2, Shorts1) 1
            (Shirt1, Shirt2, Shorts1) 1
            (Shirt1, Shirt2) 1





            share|improve this answer


























            • This probably works, but I keep getting a "Memory Error". I'll have to look for a solution for the memory issue as I can't use my desktop for this project (sensitive data).

              – lys_dad
              13 hours ago











            • I did the first 1000 customers and it worked! Any suggestion for low memory laptops?

              – lys_dad
              12 hours ago











            • @lys_dad The accepted answer has already solved your memory problem, right?

              – ResidentSleeper
              9 hours ago











            • It does, yes. But thank you for your elegant solution!

              – lys_dad
              7 hours ago














            2












            2








            2







            I think you need to create a combination of items first.



            How to get all possible combinations of a list’s elements?



            I used the function from Dan H's answer.



            from itertools import chain, combinations
            def all_subsets(ss):
            return chain(*map(lambda x: combinations(ss, x), range(0, len(ss)+1)))

            uq_items = df.Item.unique()

            list(all_subsets(uq_items))

            [(),
            ('Shirt1',),
            ('Shirt2',),
            ('Shorts1',),
            ('Shirt1', 'Shirt2'),
            ('Shirt1', 'Shorts1'),
            ('Shirt2', 'Shorts1'),
            ('Shirt1', 'Shirt2', 'Shorts1')]


            And use groupby each customer to get their items combination.



            ls = []

            for _, d in df.groupby('Cust_num', group_keys=False):
            # Get all possible subset of items
            pi = np.array(list(all_subsets(d.Item)))

            # Fliter only > 1
            ls.append(pi[[len(l) > 1 for l in pi]])


            Then convert to Series and use value_counts().



            pd.Series(np.concatenate(ls)).value_counts()

            (Shirt1, Shorts1) 2
            (Shirt2, Shorts1) 1
            (Shirt1, Shirt2, Shorts1) 1
            (Shirt1, Shirt2) 1





            share|improve this answer















            I think you need to create a combination of items first.



            How to get all possible combinations of a list’s elements?



            I used the function from Dan H's answer.



            from itertools import chain, combinations
            def all_subsets(ss):
            return chain(*map(lambda x: combinations(ss, x), range(0, len(ss)+1)))

            uq_items = df.Item.unique()

            list(all_subsets(uq_items))

            [(),
            ('Shirt1',),
            ('Shirt2',),
            ('Shorts1',),
            ('Shirt1', 'Shirt2'),
            ('Shirt1', 'Shorts1'),
            ('Shirt2', 'Shorts1'),
            ('Shirt1', 'Shirt2', 'Shorts1')]


            And use groupby each customer to get their items combination.



            ls = []

            for _, d in df.groupby('Cust_num', group_keys=False):
            # Get all possible subset of items
            pi = np.array(list(all_subsets(d.Item)))

            # Fliter only > 1
            ls.append(pi[[len(l) > 1 for l in pi]])


            Then convert to Series and use value_counts().



            pd.Series(np.concatenate(ls)).value_counts()

            (Shirt1, Shorts1) 2
            (Shirt2, Shorts1) 1
            (Shirt1, Shirt2, Shorts1) 1
            (Shirt1, Shirt2) 1






            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited 20 hours ago

























            answered 23 hours ago









            ResidentSleeperResidentSleeper

            37210




            37210













            • This probably works, but I keep getting a "Memory Error". I'll have to look for a solution for the memory issue as I can't use my desktop for this project (sensitive data).

              – lys_dad
              13 hours ago











            • I did the first 1000 customers and it worked! Any suggestion for low memory laptops?

              – lys_dad
              12 hours ago











            • @lys_dad The accepted answer has already solved your memory problem, right?

              – ResidentSleeper
              9 hours ago











            • It does, yes. But thank you for your elegant solution!

              – lys_dad
              7 hours ago



















            • This probably works, but I keep getting a "Memory Error". I'll have to look for a solution for the memory issue as I can't use my desktop for this project (sensitive data).

              – lys_dad
              13 hours ago











            • I did the first 1000 customers and it worked! Any suggestion for low memory laptops?

              – lys_dad
              12 hours ago











            • @lys_dad The accepted answer has already solved your memory problem, right?

              – ResidentSleeper
              9 hours ago











            • It does, yes. But thank you for your elegant solution!

              – lys_dad
              7 hours ago

















            This probably works, but I keep getting a "Memory Error". I'll have to look for a solution for the memory issue as I can't use my desktop for this project (sensitive data).

            – lys_dad
            13 hours ago





            This probably works, but I keep getting a "Memory Error". I'll have to look for a solution for the memory issue as I can't use my desktop for this project (sensitive data).

            – lys_dad
            13 hours ago













            I did the first 1000 customers and it worked! Any suggestion for low memory laptops?

            – lys_dad
            12 hours ago





            I did the first 1000 customers and it worked! Any suggestion for low memory laptops?

            – lys_dad
            12 hours ago













            @lys_dad The accepted answer has already solved your memory problem, right?

            – ResidentSleeper
            9 hours ago





            @lys_dad The accepted answer has already solved your memory problem, right?

            – ResidentSleeper
            9 hours ago













            It does, yes. But thank you for your elegant solution!

            – lys_dad
            7 hours ago





            It does, yes. But thank you for your elegant solution!

            – lys_dad
            7 hours ago











            0














            My version which I believe is easier to understand



            new_df = df.groupby("Cust_num").agg({lambda x: ''.join(x.unique())})

            new_df ['count'] = range(1, len(new_df ) + 1)


            Output:



                                        Item      Rev count
            <lambda> <lambda>
            Cust_num
            Cust1 Shirt1 Shirt2 Shorts1 $40 1
            Cust2 Shirt1 Shorts1 $40 2


            Since you do not need the Rev column, you can drop it:



            new_df = new_df = new_df.drop(columns=["Rev"]).reset_index()

            new_df


            Output:



              Cust_num                    Item count
            <lambda>
            0 Cust1 Shirt1 Shirt2 Shorts1 1
            1 Cust2 Shirt1 Shorts1 2


            This edit is to respond to @Chris by looking at his approach written using list comprehension. He created an list of sets:



            [{' Shirt1', ' Shirt2', ' Shorts1'}, {' Shirt1', ' Shorts1'}]


            Then next step finds the subsets:



            for s1 in subsets:
            for s2 in subsets:
            if s2.issubset(s1):
            print("{}: {}".format(s2,s2.issubset(s1)))


            Output:



            {' Shirt2', ' Shorts1', ' Shirt1'}: True
            {' Shorts1', ' Shirt1'}: True
            {' Shorts1', ' Shirt1'}: True


            You asked me to explain myself and I did. However after thinking about it, I realized your approach was wrong too. As such I was not mocking you, but thanking you for making me think about my solution. Also thanks to @ResidentSleeper for his solution.






            share|improve this answer


























            • @Chris thank for mocking my comment and down voting my answer. I did not downvote your answer out of the spirit of learning from each other.

              – Lee Mtoti
              20 hours ago











            • @Chris thanks for removing the downvote. Whole point is to learn from each other while helping each other.

              – Lee Mtoti
              19 hours ago


















            0














            My version which I believe is easier to understand



            new_df = df.groupby("Cust_num").agg({lambda x: ''.join(x.unique())})

            new_df ['count'] = range(1, len(new_df ) + 1)


            Output:



                                        Item      Rev count
            <lambda> <lambda>
            Cust_num
            Cust1 Shirt1 Shirt2 Shorts1 $40 1
            Cust2 Shirt1 Shorts1 $40 2


            Since you do not need the Rev column, you can drop it:



            new_df = new_df = new_df.drop(columns=["Rev"]).reset_index()

            new_df


            Output:



              Cust_num                    Item count
            <lambda>
            0 Cust1 Shirt1 Shirt2 Shorts1 1
            1 Cust2 Shirt1 Shorts1 2


            This edit is to respond to @Chris by looking at his approach written using list comprehension. He created an list of sets:



            [{' Shirt1', ' Shirt2', ' Shorts1'}, {' Shirt1', ' Shorts1'}]


            Then next step finds the subsets:



            for s1 in subsets:
            for s2 in subsets:
            if s2.issubset(s1):
            print("{}: {}".format(s2,s2.issubset(s1)))


            Output:



            {' Shirt2', ' Shorts1', ' Shirt1'}: True
            {' Shorts1', ' Shirt1'}: True
            {' Shorts1', ' Shirt1'}: True


            You asked me to explain myself and I did. However after thinking about it, I realized your approach was wrong too. As such I was not mocking you, but thanking you for making me think about my solution. Also thanks to @ResidentSleeper for his solution.






            share|improve this answer


























            • @Chris thank for mocking my comment and down voting my answer. I did not downvote your answer out of the spirit of learning from each other.

              – Lee Mtoti
              20 hours ago











            • @Chris thanks for removing the downvote. Whole point is to learn from each other while helping each other.

              – Lee Mtoti
              19 hours ago
















            0












            0








            0







            My version which I believe is easier to understand



            new_df = df.groupby("Cust_num").agg({lambda x: ''.join(x.unique())})

            new_df ['count'] = range(1, len(new_df ) + 1)


            Output:



                                        Item      Rev count
            <lambda> <lambda>
            Cust_num
            Cust1 Shirt1 Shirt2 Shorts1 $40 1
            Cust2 Shirt1 Shorts1 $40 2


            Since you do not need the Rev column, you can drop it:



            new_df = new_df = new_df.drop(columns=["Rev"]).reset_index()

            new_df


            Output:



              Cust_num                    Item count
            <lambda>
            0 Cust1 Shirt1 Shirt2 Shorts1 1
            1 Cust2 Shirt1 Shorts1 2


            This edit is to respond to @Chris by looking at his approach written using list comprehension. He created an list of sets:



            [{' Shirt1', ' Shirt2', ' Shorts1'}, {' Shirt1', ' Shorts1'}]


            Then next step finds the subsets:



            for s1 in subsets:
            for s2 in subsets:
            if s2.issubset(s1):
            print("{}: {}".format(s2,s2.issubset(s1)))


            Output:



            {' Shirt2', ' Shorts1', ' Shirt1'}: True
            {' Shorts1', ' Shirt1'}: True
            {' Shorts1', ' Shirt1'}: True


            You asked me to explain myself and I did. However after thinking about it, I realized your approach was wrong too. As such I was not mocking you, but thanking you for making me think about my solution. Also thanks to @ResidentSleeper for his solution.






            share|improve this answer















            My version which I believe is easier to understand



            new_df = df.groupby("Cust_num").agg({lambda x: ''.join(x.unique())})

            new_df ['count'] = range(1, len(new_df ) + 1)


            Output:



                                        Item      Rev count
            <lambda> <lambda>
            Cust_num
            Cust1 Shirt1 Shirt2 Shorts1 $40 1
            Cust2 Shirt1 Shorts1 $40 2


            Since you do not need the Rev column, you can drop it:



            new_df = new_df = new_df.drop(columns=["Rev"]).reset_index()

            new_df


            Output:



              Cust_num                    Item count
            <lambda>
            0 Cust1 Shirt1 Shirt2 Shorts1 1
            1 Cust2 Shirt1 Shorts1 2


            This edit is to respond to @Chris by looking at his approach written using list comprehension. He created an list of sets:



            [{' Shirt1', ' Shirt2', ' Shorts1'}, {' Shirt1', ' Shorts1'}]


            Then next step finds the subsets:



            for s1 in subsets:
            for s2 in subsets:
            if s2.issubset(s1):
            print("{}: {}".format(s2,s2.issubset(s1)))


            Output:



            {' Shirt2', ' Shorts1', ' Shirt1'}: True
            {' Shorts1', ' Shirt1'}: True
            {' Shorts1', ' Shirt1'}: True


            You asked me to explain myself and I did. However after thinking about it, I realized your approach was wrong too. As such I was not mocking you, but thanking you for making me think about my solution. Also thanks to @ResidentSleeper for his solution.







            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited 19 hours ago

























            answered 22 hours ago









            Lee MtotiLee Mtoti

            13410




            13410













            • @Chris thank for mocking my comment and down voting my answer. I did not downvote your answer out of the spirit of learning from each other.

              – Lee Mtoti
              20 hours ago











            • @Chris thanks for removing the downvote. Whole point is to learn from each other while helping each other.

              – Lee Mtoti
              19 hours ago





















            • @Chris thank for mocking my comment and down voting my answer. I did not downvote your answer out of the spirit of learning from each other.

              – Lee Mtoti
              20 hours ago











            • @Chris thanks for removing the downvote. Whole point is to learn from each other while helping each other.

              – Lee Mtoti
              19 hours ago



















            @Chris thank for mocking my comment and down voting my answer. I did not downvote your answer out of the spirit of learning from each other.

            – Lee Mtoti
            20 hours ago





            @Chris thank for mocking my comment and down voting my answer. I did not downvote your answer out of the spirit of learning from each other.

            – Lee Mtoti
            20 hours ago













            @Chris thanks for removing the downvote. Whole point is to learn from each other while helping each other.

            – Lee Mtoti
            19 hours ago







            @Chris thanks for removing the downvote. Whole point is to learn from each other while helping each other.

            – Lee Mtoti
            19 hours ago












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