I have an array of nested dictionary:
data = {"A":"a","B":"b","ID":[{"ii":"ABC","jj":"BCD"},{"ii":"AAC","jj":"FFD"}],"Finish":"yes"}
I used,
res = pd.DataFrame.from_dict(data , orient='index')
But the ID is still returned as list of dictionary.
A B ID Finish
a b [{"ii":"ABC","jj":"BCD"},{"aa":"AAC","bb":"FFD"}] yes
But I want everything to be converted to df. Not sure how to do it. Kindly help.
Expected Output:
A B ID.ii ID.jj Finish
a b ABC BCD yes
a b AAC FFD yes
You can achieve this using pandas json_normalize
df = pd.json_normalize(data, meta=['A', 'B'], record_path=['ID'], record_prefix="ID.")
Output
ID.ii ID.jj A B
0 ABC BCD a b
1 AAC FFD a b
record_path - will be used to flatten the specific key record_prefix - is added as a column prefix meta - is the columns that needs to be preserved without flattening
Refer the documentation for examples
To achieve this without using json_normalize
, you can pre-process the input like this-
data = {"A":"a","B":"b","ID":[{"ii":"ABC","jj":"BCD"},{"ii":"AAC","jj":"FFD"}],"Finish":"yes"}
op = {}
for i in data:
if isinstance(data[i], list):
for j in data[i]:
for k in j:
tmp = str(i)+"."+str(k)
if tmp not in op:
op[tmp] = [j[k]]
else:
op[tmp].append(j[k])
else:
op[i] = data[i]
>>> data
{'A': 'a', 'B': 'b', 'ID': [{'ii': 'ABC', 'jj': 'BCD'}, {'ii': 'AAC', 'jj': 'FFD'}], 'Finish': 'yes'}
>>> op
{'A': 'a', 'B': 'b', 'ID.ii': ['ABC', 'AAC'], 'ID.jj': ['BCD', 'FFD'], 'Finish': 'yes'}
After this you can directly use
>>> pd.DataFrame(op)
A B ID.ii ID.jj Finish
0 a b ABC BCD yes
1 a b AAC FFD yes
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