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Convert dictionaries with list of values into a dataframe


How to merge two dictionaries in a single expression?How do I sort a list of dictionaries by a value of the dictionary?What is the best way to iterate over a dictionary?Convert two lists into a dictionaryHow do I sort a dictionary by value?Add new keys to a dictionary?Check if a given key already exists in a dictionaryHow do I list all files of a directory?Iterating over dictionaries using 'for' loopsAdding new column to existing DataFrame in Python pandas






.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;








6















Say I have three dictionaries



dictionary_col2
'MOB': [1, 2], 'ASP': [1, 2], 'YIP': [1, 2]


 dictionary_col3

'MOB': ['MOB_L001_R1_001.gz',
 'MOB_L002_R1_001.gz'],
 'ASP': ['ASP_L001_R1_001.gz',
 'ASP_L002_R1_001.gz'],
 'YIP': ['YIP_L001_R1_001.gz',
 'YIP_L002_R1_001.gz']



dictionary_col4

'MOB': ['MOB_L001_R2_001.gz',
 'MOB_L002_R2_001.gz'],
 'ASP': ['ASP_L001_R2_001.gz',
 'ASP_L002_R2_001.gz'],
 'YIP': ['YIP_L001_R2_001.gz',
 'YIP_L002_R2_001.gz']



I wanna convert the above dictionaries into a data frame. I have tried the following,



df = pd.DataFrame([dictionary_col2, dictionary_col3, dictionary_col4])
The df data frame looks like,



 ASP MOB YIP
0 [1, 2] [1, 2] [1, 2]
1 [ASP_L001_R1_001.gz, ASP_L002_R1_001.gz] [MOB_L001_R1_001.gz, MOB_L002_R1_001.gz] [YIP_L001_R1_001.gz, YIP_L002_R1_001.gz]
2 [ASP_L001_R2_001.gz, ASP_L002_R2_001.gz] [MOB_L001_R2_001.gz, MOB_L002_R2_001.gz] [YIP_L001_R2_001.gz, YIP_L002_R2_001.gz]


My aim is to have a data frame with the following columns: 



 col1 col2 col3 col4 
 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz 
 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz 
 ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz 
 ASP 2 ASP_L002_R1_001.gz MOB_L002_R2_001.gz 
 YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
 YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz


Any help/suggestions are appreciated!!






python pandas dictionary







share|improve this question






























    6















    Say I have three dictionaries



    dictionary_col2
    'MOB': [1, 2], 'ASP': [1, 2], 'YIP': [1, 2]


     dictionary_col3

    'MOB': ['MOB_L001_R1_001.gz',
     'MOB_L002_R1_001.gz'],
     'ASP': ['ASP_L001_R1_001.gz',
     'ASP_L002_R1_001.gz'],
     'YIP': ['YIP_L001_R1_001.gz',
     'YIP_L002_R1_001.gz']



    dictionary_col4

    'MOB': ['MOB_L001_R2_001.gz',
     'MOB_L002_R2_001.gz'],
     'ASP': ['ASP_L001_R2_001.gz',
     'ASP_L002_R2_001.gz'],
     'YIP': ['YIP_L001_R2_001.gz',
     'YIP_L002_R2_001.gz']



    I wanna convert the above dictionaries into a data frame. I have tried the following,



    df = pd.DataFrame([dictionary_col2, dictionary_col3, dictionary_col4])
    The df data frame looks like,



     ASP MOB YIP
    0 [1, 2] [1, 2] [1, 2]
    1 [ASP_L001_R1_001.gz, ASP_L002_R1_001.gz] [MOB_L001_R1_001.gz, MOB_L002_R1_001.gz] [YIP_L001_R1_001.gz, YIP_L002_R1_001.gz]
    2 [ASP_L001_R2_001.gz, ASP_L002_R2_001.gz] [MOB_L001_R2_001.gz, MOB_L002_R2_001.gz] [YIP_L001_R2_001.gz, YIP_L002_R2_001.gz]


    My aim is to have a data frame with the following columns: 



     col1 col2 col3 col4 
     MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz 
     MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz 
     ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz 
     ASP 2 ASP_L002_R1_001.gz MOB_L002_R2_001.gz 
     YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
     YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz


    Any help/suggestions are appreciated!!






    python pandas dictionary







    share|improve this question


























      6












      6








      6


      1






      Say I have three dictionaries



      dictionary_col2
      'MOB': [1, 2], 'ASP': [1, 2], 'YIP': [1, 2]


       dictionary_col3

      'MOB': ['MOB_L001_R1_001.gz',
       'MOB_L002_R1_001.gz'],
       'ASP': ['ASP_L001_R1_001.gz',
       'ASP_L002_R1_001.gz'],
       'YIP': ['YIP_L001_R1_001.gz',
       'YIP_L002_R1_001.gz']



      dictionary_col4

      'MOB': ['MOB_L001_R2_001.gz',
       'MOB_L002_R2_001.gz'],
       'ASP': ['ASP_L001_R2_001.gz',
       'ASP_L002_R2_001.gz'],
       'YIP': ['YIP_L001_R2_001.gz',
       'YIP_L002_R2_001.gz']



      I wanna convert the above dictionaries into a data frame. I have tried the following,



      df = pd.DataFrame([dictionary_col2, dictionary_col3, dictionary_col4])
      The df data frame looks like,



       ASP MOB YIP
      0 [1, 2] [1, 2] [1, 2]
      1 [ASP_L001_R1_001.gz, ASP_L002_R1_001.gz] [MOB_L001_R1_001.gz, MOB_L002_R1_001.gz] [YIP_L001_R1_001.gz, YIP_L002_R1_001.gz]
      2 [ASP_L001_R2_001.gz, ASP_L002_R2_001.gz] [MOB_L001_R2_001.gz, MOB_L002_R2_001.gz] [YIP_L001_R2_001.gz, YIP_L002_R2_001.gz]


      My aim is to have a data frame with the following columns: 



       col1 col2 col3 col4 
       MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz 
       MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz 
       ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz 
       ASP 2 ASP_L002_R1_001.gz MOB_L002_R2_001.gz 
       YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
       YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz


      Any help/suggestions are appreciated!!






      python pandas dictionary







      share|improve this question














      Say I have three dictionaries



      dictionary_col2
      'MOB': [1, 2], 'ASP': [1, 2], 'YIP': [1, 2]


       dictionary_col3

      'MOB': ['MOB_L001_R1_001.gz',
       'MOB_L002_R1_001.gz'],
       'ASP': ['ASP_L001_R1_001.gz',
       'ASP_L002_R1_001.gz'],
       'YIP': ['YIP_L001_R1_001.gz',
       'YIP_L002_R1_001.gz']



      dictionary_col4

      'MOB': ['MOB_L001_R2_001.gz',
       'MOB_L002_R2_001.gz'],
       'ASP': ['ASP_L001_R2_001.gz',
       'ASP_L002_R2_001.gz'],
       'YIP': ['YIP_L001_R2_001.gz',
       'YIP_L002_R2_001.gz']



      I wanna convert the above dictionaries into a data frame. I have tried the following,



      df = pd.DataFrame([dictionary_col2, dictionary_col3, dictionary_col4])
      The df data frame looks like,



       ASP MOB YIP
      0 [1, 2] [1, 2] [1, 2]
      1 [ASP_L001_R1_001.gz, ASP_L002_R1_001.gz] [MOB_L001_R1_001.gz, MOB_L002_R1_001.gz] [YIP_L001_R1_001.gz, YIP_L002_R1_001.gz]
      2 [ASP_L001_R2_001.gz, ASP_L002_R2_001.gz] [MOB_L001_R2_001.gz, MOB_L002_R2_001.gz] [YIP_L001_R2_001.gz, YIP_L002_R2_001.gz]


      My aim is to have a data frame with the following columns: 



       col1 col2 col3 col4 
       MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz 
       MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz 
       ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz 
       ASP 2 ASP_L002_R1_001.gz MOB_L002_R2_001.gz 
       YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
       YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz


      Any help/suggestions are appreciated!!






      python pandas dictionary




      python pandas dictionary






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 9 hours ago









      user1017373user1017373

      8401 gold badge13 silver badges27 bronze badges




      8401 gold badge13 silver badges27 bronze badges

























          4 Answers
          4






          active

          oldest

          votes


















          5
















          pd.DataFrame('col2': pd.DataFrame(col2).unstack(),
           'col3': pd.DataFrame(col3).unstack(),
           'col4': pd.DataFrame(col4).unstack()).reset_index(level=0)


          returns



           level_0 col2 col3 col4
          0 ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
          1 ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
          0 YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer

























          • This solution best works for my question as it generates the column name as well. Thank you!

            – user1017373
            8 hours ago


















          4
















          dict_list = [dictionary_col2, dictionary_col3, dictionary_col4]

          df = pd.concat([pd.DataFrame.from_dict(x, orient = 'index').unstack() for x in dict_list], axis = 1)


          output:



          >>> df

           0 1 2
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
           ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
           YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
           ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
           YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer

























          • Thank you for the solution

            – user1017373
            8 hours ago


















          3
















          IIUC, you can do:



          pd.concat([pd.DataFrame(d).stack() for d in (d1,d2,d3)], axis=1)


          Output:



           0 1 2
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
           ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
           YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
           ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
           YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer

























          • Thank you for the solution

            – user1017373
            8 hours ago


















          2
















          What you can do using concat with explode notice in pandas 0.25.0



          pd.concat([pd.Series(x).explode() for x in [d1,d2]],axis=1)





          share|improve this answer



























          • Thanks for your time!

            – user1017373
            8 hours ago













          Your Answer






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          4 Answers
          4






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          4 Answers
          4






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          5
















          pd.DataFrame('col2': pd.DataFrame(col2).unstack(),
           'col3': pd.DataFrame(col3).unstack(),
           'col4': pd.DataFrame(col4).unstack()).reset_index(level=0)


          returns



           level_0 col2 col3 col4
          0 ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
          1 ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
          0 YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer

























          • This solution best works for my question as it generates the column name as well. Thank you!

            – user1017373
            8 hours ago















          5
















          pd.DataFrame('col2': pd.DataFrame(col2).unstack(),
           'col3': pd.DataFrame(col3).unstack(),
           'col4': pd.DataFrame(col4).unstack()).reset_index(level=0)


          returns



           level_0 col2 col3 col4
          0 ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
          1 ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
          0 YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer

























          • This solution best works for my question as it generates the column name as well. Thank you!

            – user1017373
            8 hours ago













          5














          5










          5









          pd.DataFrame('col2': pd.DataFrame(col2).unstack(),
           'col3': pd.DataFrame(col3).unstack(),
           'col4': pd.DataFrame(col4).unstack()).reset_index(level=0)


          returns



           level_0 col2 col3 col4
          0 ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
          1 ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
          0 YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer













          pd.DataFrame('col2': pd.DataFrame(col2).unstack(),
           'col3': pd.DataFrame(col3).unstack(),
           'col4': pd.DataFrame(col4).unstack()).reset_index(level=0)


          returns



           level_0 col2 col3 col4
          0 ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
          1 ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
          0 YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 9 hours ago









          eumiroeumiro

          142k22 gold badges247 silver badges238 bronze badges




          142k22 gold badges247 silver badges238 bronze badges















          • This solution best works for my question as it generates the column name as well. Thank you!

            – user1017373
            8 hours ago

















          • This solution best works for my question as it generates the column name as well. Thank you!

            – user1017373
            8 hours ago
















          This solution best works for my question as it generates the column name as well. Thank you!

          – user1017373
          8 hours ago





          This solution best works for my question as it generates the column name as well. Thank you!

          – user1017373
          8 hours ago













          4
















          dict_list = [dictionary_col2, dictionary_col3, dictionary_col4]

          df = pd.concat([pd.DataFrame.from_dict(x, orient = 'index').unstack() for x in dict_list], axis = 1)


          output:



          >>> df

           0 1 2
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
           ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
           YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
           ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
           YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer

























          • Thank you for the solution

            – user1017373
            8 hours ago















          4
















          dict_list = [dictionary_col2, dictionary_col3, dictionary_col4]

          df = pd.concat([pd.DataFrame.from_dict(x, orient = 'index').unstack() for x in dict_list], axis = 1)


          output:



          >>> df

           0 1 2
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
           ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
           YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
           ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
           YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer

























          • Thank you for the solution

            – user1017373
            8 hours ago













          4














          4










          4









          dict_list = [dictionary_col2, dictionary_col3, dictionary_col4]

          df = pd.concat([pd.DataFrame.from_dict(x, orient = 'index').unstack() for x in dict_list], axis = 1)


          output:



          >>> df

           0 1 2
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
           ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
           YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
           ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
           YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer













          dict_list = [dictionary_col2, dictionary_col3, dictionary_col4]

          df = pd.concat([pd.DataFrame.from_dict(x, orient = 'index').unstack() for x in dict_list], axis = 1)


          output:



          >>> df

           0 1 2
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
           ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
           YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
           ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
           YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 9 hours ago









          Brian JosephBrian Joseph

          7162 silver badges13 bronze badges




          7162 silver badges13 bronze badges















          • Thank you for the solution

            – user1017373
            8 hours ago

















          • Thank you for the solution

            – user1017373
            8 hours ago
















          Thank you for the solution

          – user1017373
          8 hours ago





          Thank you for the solution

          – user1017373
          8 hours ago











          3
















          IIUC, you can do:



          pd.concat([pd.DataFrame(d).stack() for d in (d1,d2,d3)], axis=1)


          Output:



           0 1 2
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
           ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
           YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
           ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
           YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer

























          • Thank you for the solution

            – user1017373
            8 hours ago















          3
















          IIUC, you can do:



          pd.concat([pd.DataFrame(d).stack() for d in (d1,d2,d3)], axis=1)


          Output:



           0 1 2
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
           ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
           YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
           ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
           YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer

























          • Thank you for the solution

            – user1017373
            8 hours ago













          3














          3










          3









          IIUC, you can do:



          pd.concat([pd.DataFrame(d).stack() for d in (d1,d2,d3)], axis=1)


          Output:



           0 1 2
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
           ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
           YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
           ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
           YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz





          share|improve this answer













          IIUC, you can do:



          pd.concat([pd.DataFrame(d).stack() for d in (d1,d2,d3)], axis=1)


          Output:



           0 1 2
          0 MOB 1 MOB_L001_R1_001.gz MOB_L001_R2_001.gz
           ASP 1 ASP_L001_R1_001.gz ASP_L001_R2_001.gz
           YIP 1 YIP_L001_R1_001.gz YIP_L001_R2_001.gz
          1 MOB 2 MOB_L002_R1_001.gz MOB_L002_R2_001.gz
           ASP 2 ASP_L002_R1_001.gz ASP_L002_R2_001.gz
           YIP 2 YIP_L002_R1_001.gz YIP_L002_R2_001.gz






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 9 hours ago









          Quang HoangQuang Hoang

          17.6k2 gold badges14 silver badges27 bronze badges




          17.6k2 gold badges14 silver badges27 bronze badges















          • Thank you for the solution

            – user1017373
            8 hours ago

















          • Thank you for the solution

            – user1017373
            8 hours ago
















          Thank you for the solution

          – user1017373
          8 hours ago





          Thank you for the solution

          – user1017373
          8 hours ago











          2
















          What you can do using concat with explode notice in pandas 0.25.0



          pd.concat([pd.Series(x).explode() for x in [d1,d2]],axis=1)





          share|improve this answer



























          • Thanks for your time!

            – user1017373
            8 hours ago















          2
















          What you can do using concat with explode notice in pandas 0.25.0



          pd.concat([pd.Series(x).explode() for x in [d1,d2]],axis=1)





          share|improve this answer



























          • Thanks for your time!

            – user1017373
            8 hours ago













          2














          2










          2









          What you can do using concat with explode notice in pandas 0.25.0



          pd.concat([pd.Series(x).explode() for x in [d1,d2]],axis=1)





          share|improve this answer















          What you can do using concat with explode notice in pandas 0.25.0



          pd.concat([pd.Series(x).explode() for x in [d1,d2]],axis=1)






          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited 8 hours ago

























          answered 9 hours ago









          WeNYoBenWeNYoBen

          159k8 gold badges55 silver badges86 bronze badges




          159k8 gold badges55 silver badges86 bronze badges















          • Thanks for your time!

            – user1017373
            8 hours ago

















          • Thanks for your time!

            – user1017373
            8 hours ago
















          Thanks for your time!

          – user1017373
          8 hours ago





          Thanks for your time!

          – user1017373
          8 hours ago


















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