Arrays to row in pandas - pandas

Question

I have following dict which I want to convert into pandas. this dict have nested list which can appear for one node but not other.
dis={"companies": [{"object_id": 123,
"name": "Abd ",
"contact_name": ["xxxx",
"yyyy"],
"contact_id":[1234,
33455]
},
{"object_id": 654,
"name": "DDSPP"},
{"object_id": 987,
"name": "CCD"}
]}
AS
object_id, name, contact_name, contact_id
123,Abd,xxxx,1234
123,Abd,yyyy,
654,DDSPP,,
987,CCD,,
How can i achive this
I was trying to do like
abc = pd.DataFrame(dis).set_index['object_id','contact_name']
but it says
'method' object is not subscriptable

Answer 1

This is inspired from #jezrael answer in this link: Splitting multiple columns into rows in pandas dataframe
Use:
s = {"companies": [{"object_id": 123,
"name": "Abd ",
"contact_name": ["xxxx",
"yyyy"],
"contact_id":[1234,
33455]
},
{"object_id": 654,
"name": "DDSPP"},
{"object_id": 987,
"name": "CCD"}
]}
df = pd.DataFrame(s) #convert into DF
df = df['companies'].apply(pd.Series) #this splits the internal keys and values into columns
split1 = df.apply(lambda x: pd.Series(x['contact_id']), axis=1).stack().reset_index(level=1, drop=True)
split2 = df.apply(lambda x: pd.Series(x['contact_name']), axis=1).stack().reset_index(level=1, drop=True)
df1 = pd.concat([split1,split2], axis=1, keys=['contact_id','contact_name'])
pd.options.display.float_format = '{:.0f}'.format
print (df.drop(['contact_id','contact_name'], axis=1).join(df1).reset_index(drop=True))
Output with regular index:
name object_id contact_id contact_name
0 Abd 123 1234 xxxx
1 Abd 123 33455 yyyy
2 DDSPP 654 nan NaN
3 CCD 987 nan NaN
Is this something you were looking for?

Answer 2

If you have just only one column needs to convert, then you can use something more shortly, like this:
df = pd.DataFrame(d['companies'])
d = df.loc[0].apply(pd.Series)
d[1].fillna(d[0], inplace=True)
df.drop([0],0).append(d.T)
Otherwise, if you need to do this with more then one raw, you can use it, but it have to be modified.