Construct Pandas Dataframe From Nested Dictionaries Having List As Item

I have several dictionary data and I want to convert to Pandas DataFrame. However, due to unnecessary key '0' (for me), I've obtained undesirable format of DataFrame when I convert

Solution 1:

You should simply drop a level from your nested dict to make life easier. The code below drops the unnecessary part of your dicts and concatenates the dataframes from each of the dicts together.

all_dicts=[dict1,dict2,dict3]
df=pd.concat([pd.DataFrame({k:v[0] for k,v in d.items()}) for d in all_dicts])
df.index=pd.MultiIndex.from_product([['dict1','dict2','dict3'],['A','B']])

>>> df 
             1      2      3      4      5
dict1 A -0.022  0.269  0.118  0.057 -0.916
      B -0.017  0.271  0.119  0.061 -0.924
dict2 A  0.384  0.485  0.465  0.456 -0.479
      B  0.398  0.489  0.469  0.468 -0.482
dict3 A -0.323 -0.535 -0.336 -0.140  0.175
      B -0.321 -0.534 -0.336 -0.142  0.177

Solution 2:

You can simply modify your input data and convert it to DataFrame:

import itertools

lst = [dict1, dict2, dict3]
dict = {}

for k in dict1:
    temp = [l[k].itervalues().next() for l in lst]
    dict[k] = list(itertools.chain(*temp))

dict['row']  = ['A','B']*len(lst)
dict['dict'] = ['dict'+str(i+1) for i inrange(len(lst)) for n inrange(2)]

In [23]: pd.DataFrame(dict)
Out[23]:
       12345dict row
0 -0.0220.2690.1180.057 -0.916  dict1   A
1 -0.0170.2710.1190.061 -0.924  dict1   B
20.3840.4850.4650.456 -0.479  dict2   A
30.3980.4890.4690.468 -0.482  dict2   B
4 -0.323 -0.535 -0.336 -0.1400.175  dict3   A
5 -0.321 -0.534 -0.336 -0.1420.177  dict3   B