2

I have DataFrame which contains dictionaries in the columns.

Can be created as below

 lis = [
     {'id': '1', 
     'author': {'self': 'A', 
     'displayName': 'A'}, 
     'created': '2018-12-18', 
     'items': {'field': 'status', 
         'fromString': 'Backlog'}}, 
     {'id': '2', 
     'author': {'self': 'B', 
     'displayName': 'B'}, 
     'created': '2018-12-18', 
     'items': {'field': 'status', 
         'fromString': 'Funnel'}}] 

pd.DataFrame(lis)  

                              author     created id                                           items
0  {'self': 'A', 'displayName': 'A'}  2018-12-18  1  {'field': 'status', 'fromString': 'Backlog'}
1  {'self': 'B', 'displayName': 'B'}  2018-12-18  2   {'field': 'status', 'fromString': 'Funnel'}

I want to convert this info multi level DataFrame.

I have been trying with 

pd.MultiIndex.from_product(lis) 
pd.MultiIndex.from_frame(pd.DataFrame(lis))

But not able to get the result i am looking for.Basically i want like below:

        author               created        id       items

self       displayName                             field   fromString
 A             A            2018-12-18       1      status   Backlog
 B             B            2018-12-18       2      status   Funnel

Any suggestions on how i can achieve this ?

Thanks

Praveen
  • 2,137
  • 1
  • 18
  • 21

2 Answers2

3

You can use json.json_normalize - but columns names are flattened with . separator:

from pandas.io.json import json_normalize

lis = [
     {'id': '1', 
     'author': {'self': 'A', 
     'displayName': 'A'}, 
     'created': '2018-12-18', 
     'items': {'field': 'status', 
         'fromString': 'Backlog'}}, 
     {'id': '2', 
     'author': {'self': 'B', 
     'displayName': 'B'}, 
     'created': '2018-12-18', 
     'items': {'field': 'status', 
         'fromString': 'Funnel'}}] 

df = json_normalize(lis)
print (df)
  id     created author.self author.displayName items.field items.fromString
0  1  2018-12-18           A                  A      status          Backlog
1  2  2018-12-18           B                  B      status           Funnel

For MulitIndex in columns and in index - first create Mulitiindex by all columns without . by DataFrame.set_index and then use str.split:

df = df.set_index(['id','created'])
df.columns = df.columns.str.split('.', expand=True)
print (df)
              author               items           
                self displayName   field fromString
id created                                         
1  2018-12-18      A           A  status    Backlog
2  2018-12-18      B           B  status     Funnel

If need MulitIndex in columns - it is possible, but get missing values in columns names:

df.columns = df.columns.str.split('.', expand=True)
print (df)
   id     created author               items           
  NaN         NaN   self displayName   field fromString
0   1  2018-12-18      A           A  status    Backlog
1   2  2018-12-18      B           B  status     Funnel

Missing values should be replaced by empty string:

df = df.rename(columns= lambda x: '' if x != x else x)
print (df)
  id     created author               items           
                   self displayName   field fromString
0  1  2018-12-18      A           A  status    Backlog
1  2  2018-12-18      B           B  status     Funnel
jezrael
  • 822,522
  • 95
  • 1,334
  • 1,252
1

Try the below, Hope this would help.

df = pd.io.json.json_normalize(lis)
print(sorted(df.columns))

tupleList = [tuple(values.split(".")) if "." in values else (values,None) for values in sorted(df.columns)]

df.columns=pd.MultiIndex.from_tuples(tuplelist)
print(df)

Ouput will be as given below 

author              created     id   items
displayName self    NaN         NaN  field  fromString
    A       A        2018-12-18  1   status  Backlog
    B       B        2018-12-18  2   status  Funnel
Shishir Naresh
  • 743
  • 4
  • 10