How to split in Pandas cells with multiple data





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I'm trying to extract data from this output (multiplied by 23 col but i will do on a post basis):



                         0                                                                                             22

2014  {'tag': 'operatingrevenue', 'value': 182795000}                      



[1 rows x 23 columns]


How can I extract in order to have a table, indexed by years, with the columns populated with the 'tag' and the values as the relative value of that tag?



     |   operatingrevenue   



2014 |      182795000


I specify that i'm working with pandas and python 3.7



Thanks






python python-3.x pandas







share|improve this question




















  • 1





    what's your expected output?

    – AkshayNevrekar
    Nov 16 '18 at 11:24






  • 1





    Could you update your question with an example dataframe, so that we can help.

    – Philip
    Nov 16 '18 at 11:24











  • Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.

    – Giangi
    Nov 16 '18 at 11:40











  • That original sample data isnt very clear. can you update?

    – MEdwin
    Nov 16 '18 at 11:42











  • It should be clearer now. I have 23 columns but i will work on those with a loop.

    – Giangi
    Nov 16 '18 at 11:51


















1















I'm trying to extract data from this output (multiplied by 23 col but i will do on a post basis):



                         0                                                                                             22

2014  {'tag': 'operatingrevenue', 'value': 182795000}                      



[1 rows x 23 columns]


How can I extract in order to have a table, indexed by years, with the columns populated with the 'tag' and the values as the relative value of that tag?



     |   operatingrevenue   



2014 |      182795000


I specify that i'm working with pandas and python 3.7



Thanks






python python-3.x pandas







share|improve this question




















  • 1





    what's your expected output?

    – AkshayNevrekar
    Nov 16 '18 at 11:24






  • 1





    Could you update your question with an example dataframe, so that we can help.

    – Philip
    Nov 16 '18 at 11:24











  • Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.

    – Giangi
    Nov 16 '18 at 11:40











  • That original sample data isnt very clear. can you update?

    – MEdwin
    Nov 16 '18 at 11:42











  • It should be clearer now. I have 23 columns but i will work on those with a loop.

    – Giangi
    Nov 16 '18 at 11:51














1












1








1








I'm trying to extract data from this output (multiplied by 23 col but i will do on a post basis):



                         0                                                                                             22

2014  {'tag': 'operatingrevenue', 'value': 182795000}                      



[1 rows x 23 columns]


How can I extract in order to have a table, indexed by years, with the columns populated with the 'tag' and the values as the relative value of that tag?



     |   operatingrevenue   



2014 |      182795000


I specify that i'm working with pandas and python 3.7



Thanks






python python-3.x pandas







share|improve this question
















I'm trying to extract data from this output (multiplied by 23 col but i will do on a post basis):



                         0                                                                                             22

2014  {'tag': 'operatingrevenue', 'value': 182795000}                      



[1 rows x 23 columns]


How can I extract in order to have a table, indexed by years, with the columns populated with the 'tag' and the values as the relative value of that tag?



     |   operatingrevenue   



2014 |      182795000


I specify that i'm working with pandas and python 3.7



Thanks






python python-3.x pandas




python python-3.x pandas






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 16 '18 at 11:50







Giangi

















asked Nov 16 '18 at 11:20









GiangiGiangi

84




84








  • 1





    what's your expected output?

    – AkshayNevrekar
    Nov 16 '18 at 11:24






  • 1





    Could you update your question with an example dataframe, so that we can help.

    – Philip
    Nov 16 '18 at 11:24











  • Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.

    – Giangi
    Nov 16 '18 at 11:40











  • That original sample data isnt very clear. can you update?

    – MEdwin
    Nov 16 '18 at 11:42











  • It should be clearer now. I have 23 columns but i will work on those with a loop.

    – Giangi
    Nov 16 '18 at 11:51














  • 1





    what's your expected output?

    – AkshayNevrekar
    Nov 16 '18 at 11:24






  • 1





    Could you update your question with an example dataframe, so that we can help.

    – Philip
    Nov 16 '18 at 11:24











  • Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.

    – Giangi
    Nov 16 '18 at 11:40











  • That original sample data isnt very clear. can you update?

    – MEdwin
    Nov 16 '18 at 11:42











  • It should be clearer now. I have 23 columns but i will work on those with a loop.

    – Giangi
    Nov 16 '18 at 11:51








1




1





what's your expected output?

– AkshayNevrekar
Nov 16 '18 at 11:24





what's your expected output?

– AkshayNevrekar
Nov 16 '18 at 11:24




1




1





Could you update your question with an example dataframe, so that we can help.

– Philip
Nov 16 '18 at 11:24





Could you update your question with an example dataframe, so that we can help.

– Philip
Nov 16 '18 at 11:24













Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.

– Giangi
Nov 16 '18 at 11:40





Done, thanks a lot to everyone. I'm struggling since 2 days, i tried to do it on my own but i don't know what to look for.

– Giangi
Nov 16 '18 at 11:40













That original sample data isnt very clear. can you update?

– MEdwin
Nov 16 '18 at 11:42





That original sample data isnt very clear. can you update?

– MEdwin
Nov 16 '18 at 11:42













It should be clearer now. I have 23 columns but i will work on those with a loop.

– Giangi
Nov 16 '18 at 11:51





It should be clearer now. I have 23 columns but i will work on those with a loop.

– Giangi
Nov 16 '18 at 11:51












1 Answer
1






active

oldest

votes


















0














If this data is representative of your case, it should work:



import pandas as pd



df = pd.DataFrame({0:  [{'tag': 'operatingrevenue', 'value': 182795000},

                        {'tag': 'operatingrevenue', 'value': 182796000}],

                   1:  [{'tag': 'cashdividendspershare', 'value': 1.82},

                        {'tag': 'cashdividendspershare', 'value': 1.92}]},

                  index=[2014, 2015])



df.columns = [i['tag'] for i in df.iloc[0].values]



df = df.applymap(lambda x: x['value'])



df



      operatingrevenue  cashdividendspershare

2014         182795000                   1.82

2015         182796000                   1.92





share|improve this answer
























  • amazing, thanks a lot. It works super well

    – Giangi
    Nov 16 '18 at 12:04












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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









0














If this data is representative of your case, it should work:



import pandas as pd



df = pd.DataFrame({0:  [{'tag': 'operatingrevenue', 'value': 182795000},

                        {'tag': 'operatingrevenue', 'value': 182796000}],

                   1:  [{'tag': 'cashdividendspershare', 'value': 1.82},

                        {'tag': 'cashdividendspershare', 'value': 1.92}]},

                  index=[2014, 2015])



df.columns = [i['tag'] for i in df.iloc[0].values]



df = df.applymap(lambda x: x['value'])



df



      operatingrevenue  cashdividendspershare

2014         182795000                   1.82

2015         182796000                   1.92





share|improve this answer
























  • amazing, thanks a lot. It works super well

    – Giangi
    Nov 16 '18 at 12:04
















0














If this data is representative of your case, it should work:



import pandas as pd



df = pd.DataFrame({0:  [{'tag': 'operatingrevenue', 'value': 182795000},

                        {'tag': 'operatingrevenue', 'value': 182796000}],

                   1:  [{'tag': 'cashdividendspershare', 'value': 1.82},

                        {'tag': 'cashdividendspershare', 'value': 1.92}]},

                  index=[2014, 2015])



df.columns = [i['tag'] for i in df.iloc[0].values]



df = df.applymap(lambda x: x['value'])



df



      operatingrevenue  cashdividendspershare

2014         182795000                   1.82

2015         182796000                   1.92





share|improve this answer
























  • amazing, thanks a lot. It works super well

    – Giangi
    Nov 16 '18 at 12:04














0












0








0







If this data is representative of your case, it should work:



import pandas as pd



df = pd.DataFrame({0:  [{'tag': 'operatingrevenue', 'value': 182795000},

                        {'tag': 'operatingrevenue', 'value': 182796000}],

                   1:  [{'tag': 'cashdividendspershare', 'value': 1.82},

                        {'tag': 'cashdividendspershare', 'value': 1.92}]},

                  index=[2014, 2015])



df.columns = [i['tag'] for i in df.iloc[0].values]



df = df.applymap(lambda x: x['value'])



df



      operatingrevenue  cashdividendspershare

2014         182795000                   1.82

2015         182796000                   1.92





share|improve this answer













If this data is representative of your case, it should work:



import pandas as pd



df = pd.DataFrame({0:  [{'tag': 'operatingrevenue', 'value': 182795000},

                        {'tag': 'operatingrevenue', 'value': 182796000}],

                   1:  [{'tag': 'cashdividendspershare', 'value': 1.82},

                        {'tag': 'cashdividendspershare', 'value': 1.92}]},

                  index=[2014, 2015])



df.columns = [i['tag'] for i in df.iloc[0].values]



df = df.applymap(lambda x: x['value'])



df



      operatingrevenue  cashdividendspershare

2014         182795000                   1.82

2015         182796000                   1.92






share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 16 '18 at 11:58









zipazipa

16.3k31738




16.3k31738













  • amazing, thanks a lot. It works super well

    – Giangi
    Nov 16 '18 at 12:04



















  • amazing, thanks a lot. It works super well

    – Giangi
    Nov 16 '18 at 12:04

















amazing, thanks a lot. It works super well

– Giangi
Nov 16 '18 at 12:04





amazing, thanks a lot. It works super well

– Giangi
Nov 16 '18 at 12:04




















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