Convert name value pairs into new pandas data columns












-2















How do you convert a column in a Python Pandas DataFrame that has one column with name value pairs into additional columns within the same dataframe.



The column (attrs) with the named value pairs has values like :






[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]





So for the first record, the new columns I am trying to create would be attr_id7, attr_id8, attr_id9, attr_id10, attr_id11 and have values 4.00,2.50,1750,false,false



Considering converting column content into proper Python dictionary and then using something like the answer Splitting dictionary/list inside a Pandas Column into Separate Columns






python json pandas







share|improve this question

























  • If these are row-specific, it might be better to unpack the attr_id into a column and val into a column, though I'm not sure if val would have a namespace collision within pandas or not, so you may want to proceed with caution on that particular column name

    – C.Nivs
    Nov 19 '18 at 4:07
















-2















How do you convert a column in a Python Pandas DataFrame that has one column with name value pairs into additional columns within the same dataframe.



The column (attrs) with the named value pairs has values like :






[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]





So for the first record, the new columns I am trying to create would be attr_id7, attr_id8, attr_id9, attr_id10, attr_id11 and have values 4.00,2.50,1750,false,false



Considering converting column content into proper Python dictionary and then using something like the answer Splitting dictionary/list inside a Pandas Column into Separate Columns






python json pandas







share|improve this question

























  • If these are row-specific, it might be better to unpack the attr_id into a column and val into a column, though I'm not sure if val would have a namespace collision within pandas or not, so you may want to proceed with caution on that particular column name

    – C.Nivs
    Nov 19 '18 at 4:07














-2












-2








-2








How do you convert a column in a Python Pandas DataFrame that has one column with name value pairs into additional columns within the same dataframe.



The column (attrs) with the named value pairs has values like :






[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]





So for the first record, the new columns I am trying to create would be attr_id7, attr_id8, attr_id9, attr_id10, attr_id11 and have values 4.00,2.50,1750,false,false



Considering converting column content into proper Python dictionary and then using something like the answer Splitting dictionary/list inside a Pandas Column into Separate Columns






python json pandas







share|improve this question
















How do you convert a column in a Python Pandas DataFrame that has one column with name value pairs into additional columns within the same dataframe.



The column (attrs) with the named value pairs has values like :






[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]





So for the first record, the new columns I am trying to create would be attr_id7, attr_id8, attr_id9, attr_id10, attr_id11 and have values 4.00,2.50,1750,false,false



Considering converting column content into proper Python dictionary and then using something like the answer Splitting dictionary/list inside a Pandas Column into Separate Columns






[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]





[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]

[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}]





python json pandas




python json pandas






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 19 '18 at 5:10







user468648

















asked Nov 19 '18 at 3:57









user468648user468648

152312




152312













  • If these are row-specific, it might be better to unpack the attr_id into a column and val into a column, though I'm not sure if val would have a namespace collision within pandas or not, so you may want to proceed with caution on that particular column name

    – C.Nivs
    Nov 19 '18 at 4:07



















  • If these are row-specific, it might be better to unpack the attr_id into a column and val into a column, though I'm not sure if val would have a namespace collision within pandas or not, so you may want to proceed with caution on that particular column name

    – C.Nivs
    Nov 19 '18 at 4:07

















If these are row-specific, it might be better to unpack the attr_id into a column and val into a column, though I'm not sure if val would have a namespace collision within pandas or not, so you may want to proceed with caution on that particular column name

– C.Nivs
Nov 19 '18 at 4:07





If these are row-specific, it might be better to unpack the attr_id into a column and val into a column, though I'm not sure if val would have a namespace collision within pandas or not, so you may want to proceed with caution on that particular column name

– C.Nivs
Nov 19 '18 at 4:07












1 Answer
1






active

oldest

votes


















1














Maybe something like the below:



import pandas as pd

import numpy as np



l=[[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],

[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],

[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],]



d = 

for i in l:

    q={}

    for x in i:

        q['attr_id{}'.format(x['attr_id'])]=x['val']

    d.append(q)



df = pd.DataFrame(d)

print(df)


.



  attr_id10 attr_id11 attr_id7 attr_id8 attr_id9

0     false     false     4.00     2.50     1750

1     false     false     2.00     1.00      NaN

2     false     false      NaN      NaN      NaN





share|improve this answer























    Your Answer






    StackExchange.ifUsing("editor", function () {
    StackExchange.using("externalEditor", function () {
    StackExchange.using("snippets", function () {
    StackExchange.snippets.init();
    });
    });
    }, "code-snippets");

    StackExchange.ready(function() {
    var channelOptions = {
    tags: "".split(" "),
    id: "1"
    };
    initTagRenderer("".split(" "), "".split(" "), channelOptions);

    StackExchange.using("externalEditor", function() {
    // Have to fire editor after snippets, if snippets enabled
    if (StackExchange.settings.snippets.snippetsEnabled) {
    StackExchange.using("snippets", function() {
    createEditor();
    });
    }
    else {
    createEditor();
    }
    });

    function createEditor() {
    StackExchange.prepareEditor({
    heartbeatType: 'answer',
    autoActivateHeartbeat: false,
    convertImagesToLinks: true,
    noModals: true,
    showLowRepImageUploadWarning: true,
    reputationToPostImages: 10,
    bindNavPrevention: true,
    postfix: "",
    imageUploader: {
    brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
    contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
    allowUrls: true
    },
    onDemand: true,
    discardSelector: ".discard-answer"
    ,immediatelyShowMarkdownHelp:true
    });


    }
    });














    draft saved

    draft discarded


















    StackExchange.ready(
    function () {
    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53368124%2fconvert-name-value-pairs-into-new-pandas-data-columns%23new-answer', 'question_page');
    }
    );

    Post as a guest















    Required, but never shown

























    1 Answer
    1






    active

    oldest

    votes








    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    1














    Maybe something like the below:



    import pandas as pd
    
import numpy as np
    

    
l=[[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
    
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
    
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],]
    

    
d = 
    
for i in l:
    
    q={}
    
    for x in i:
    
        q['attr_id{}'.format(x['attr_id'])]=x['val']
    
    d.append(q)
    

    
df = pd.DataFrame(d)
    
print(df)


    .



      attr_id10 attr_id11 attr_id7 attr_id8 attr_id9
    
0     false     false     4.00     2.50     1750
    
1     false     false     2.00     1.00      NaN
    
2     false     false      NaN      NaN      NaN





    share|improve this answer




























      1














      Maybe something like the below:



      import pandas as pd
      
import numpy as np
      

      
l=[[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
      
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
      
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],]
      

      
d = 
      
for i in l:
      
    q={}
      
    for x in i:
      
        q['attr_id{}'.format(x['attr_id'])]=x['val']
      
    d.append(q)
      

      
df = pd.DataFrame(d)
      
print(df)


      .



        attr_id10 attr_id11 attr_id7 attr_id8 attr_id9
      
0     false     false     4.00     2.50     1750
      
1     false     false     2.00     1.00      NaN
      
2     false     false      NaN      NaN      NaN





      share|improve this answer


























        1












        1








        1







        Maybe something like the below:



        import pandas as pd
        
import numpy as np
        

        
l=[[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
        
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
        
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],]
        

        
d = 
        
for i in l:
        
    q={}
        
    for x in i:
        
        q['attr_id{}'.format(x['attr_id'])]=x['val']
        
    d.append(q)
        

        
df = pd.DataFrame(d)
        
print(df)


        .



          attr_id10 attr_id11 attr_id7 attr_id8 attr_id9
        
0     false     false     4.00     2.50     1750
        
1     false     false     2.00     1.00      NaN
        
2     false     false      NaN      NaN      NaN





        share|improve this answer













        Maybe something like the below:



        import pandas as pd
        
import numpy as np
        

        
l=[[{'attr_id': 7, 'val': '4.00'}, {'attr_id': 8, 'val': '2.50'}, {'attr_id': 9, 'val': '1750'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
        
[{'attr_id': 7, 'val': '2.00'}, {'attr_id': 8, 'val': '1.00'}, {'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],
        
[{'attr_id': 11, 'val': 'false'}, {'attr_id': 10, 'val': 'false'}],]
        

        
d = 
        
for i in l:
        
    q={}
        
    for x in i:
        
        q['attr_id{}'.format(x['attr_id'])]=x['val']
        
    d.append(q)
        

        
df = pd.DataFrame(d)
        
print(df)


        .



          attr_id10 attr_id11 attr_id7 attr_id8 attr_id9
        
0     false     false     4.00     2.50     1750
        
1     false     false     2.00     1.00      NaN
        
2     false     false      NaN      NaN      NaN






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 19 '18 at 4:20









        tengteng

        817721




        817721






























            draft saved

            draft discarded




















































            Thanks for contributing an answer to Stack Overflow!


            • Please be sure to answer the question. Provide details and share your research!

            But avoid …



            • Asking for help, clarification, or responding to other answers.

            • Making statements based on opinion; back them up with references or personal experience.


            To learn more, see our tips on writing great answers.




            draft saved


            draft discarded














            StackExchange.ready(
            function () {
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53368124%2fconvert-name-value-pairs-into-new-pandas-data-columns%23new-answer', 'question_page');
            }
            );

            Post as a guest















            Required, but never shown





















































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown

































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown







            -

            Popular posts from this blog

            Guess what letter conforming each word

            Image
            6 $begingroup$ This is an entry in the Fortnightly Topic Challenge #41: Short and Sweet. 1 letter only could be mean anything . With an additional letter in front , could be used as medicine component and something you definitely learn or at least heard once. Add another letter in front , you would possibly hear that many times . Add an additional letter in front , you would found an independent country . With an additional letter, in the end , you would found something related to apple . Add another letter in the end , you would found something bad . Hint: Each letter is unique . word knowledge letters chemistry share | improve this question ...
            Read more

            Run scheduled task as local user group (not BUILTIN)

            Image
            0 I am trying to run a scheduled task with User Group (not a single user). This is working fine when I in the field "when running a task, use the following user account:" insert BUILTIN User Group (for example: Administrators or Users). But when I try it with the created Local User Group (example: TestGroup) it is not working. The Users in the TestGroup are the same as in the Administrators group. What could be the problem? Are there some restrictions to the Created User Group compared to BUILTINUsers? scheduled-tasks taskscheduler windowstaskschedule share | improve this question asked Nov 15 '18 at 15:36 Far...
            Read more

            Port of Spain

            Image
            For ports in the country of Spain, see List of ports in Spain. City in City of Port of Spain, Trinidad and Tobago Port of Spain City City of Port of Spain Top to bottom (left to right): Skyline of Port of Spain from Gulf of Paria; National Academy for the Performing Arts, Independence Square, Eric Williams Plaza; MovieTowne, Port of Port of Spain Motto(s):  "We Gather Strength As We Go Along" Port of Spain Coordinates: 10°40′N 61°31′W  /  10.667°N 61.517°W  / 10.667; -61.517 Coordinates: 10°40′N 61°31′W  /  10.667°N 61.517°W  / 10.667; -61.517 Country   Trinidad and Tobago Jurisdiction City of Port of Spain Settled 1560 Incorporated (town) 1853 Incorporated (city) 1914 Government  • Mayor Joel Martinez  • Governing body City Corporation Area [1]  • City 12 km 2 (5 sq mi) Elevation [2] 66 m (217 ft) Population (2011) [1]  • City 37,074  • Rank 3rd  • Density 3,090/km 2 (8,000/sq mi)...
            Read more