CODEWITHSUNDEEP
pythonjsonpandasdataframe
Akash Bachu
Akash Bachu

Reputation: 47

Nested json to pandas dataframe

I have a nested JSON file, I flattened it and got a list back which looks like this;

[{patient_0_order: 1234,
   patient_0_id: a1,
   patient_0_time: 01/01/2016,
   patient_0_desc: xyz,
   patient_1_order: 2313,
   patient_1_id: b1,
   patient_1_time: 02/01/2016,
   patient_1_desc: def,
   patient_2_order: 9876,
   patient_2_id: c1,
   patient_2_time: 03/01/2016,
   patient_2_desc: ghi,
   patient_3_order: 0075,
   patient_3_id: d1,
   patient_3_time: 04/01/2016,
   patient_3_desc: klm,
   patient_4_order: 6268,
   patient_4_id: e1,
   patient_4_time: 05/01/2016,
   patient_4_desc: pqr}`]

Now I want to convert the list into a data frame such that each row takes one patient like below.

       patient_order    patient_id       patient_time    patient_desc 
  0      1234                a1          01/01/2016        xyz
  1      2313                b1          02/01/2016        def
  2      9876                c1          03/01/2016        ghi
  3      0075                d1          04/01/2016        klm
  4      6268                e1          05/01/2016        pqr

I tried using pandas.DataFrame(list) and it gave me a dataframe with 1 row * 20 columns table which is not I want.

Any help and suggestions would be greatly appreciated.

Upvotes: 0

Views: 355

Answers (2)

Rocky Li
Rocky Li

Reputation: 5958

'Here's how you can convert the json object (dictionary):

old_dict = json.loads('YOUR JSON STRING')[0]
col_names = ['order', 'id', 'time', 'desc']
# Reorganize the dictionary.
new_dict = {col: {k: v for k, v in old_dict.iteritems() if col in k} for col in col_names}
df = pd.DataFrame(new_dict)

should return what you want.

Upvotes: 1

Wiggy A.
Wiggy A.

Reputation: 496

Here we go, this works. Probably not the prettiest it could be, but it works and I'll probably come back to clean this later.

original = [{"patient_0_order": 1234, "patient_0_id": 123, "patient_1_id": 12, "patient_1_order": 1255}]
original = original[0]

elems = []

current_patient = 0
current_d = {}
total_elems = len(original.keys())

for index, i in enumerate(sorted(original.keys(), key=lambda x: int(x.split("_")[1]))):
   key_details = i.split("_")
   # This will be used in the dataframe as a column name
   key_name = key_details[2]
   # The number specific to this patient
   patient_num = int(key_details[1])
   # Checking if we're still on the same patient
   if patient_num == current_patient:
      current_d[key_name] = original[i]
   # Checks if this is the last element
   if index == total_elems-1:
      elems.append(current_d)
   # Checks if we've moved on to the next patient and moves on accordingly
   if patient_num != current_patient:
      elems.append(current_d)
      # Starting off the new dictionary for this patient with the current key
      current_d = {key_name: original[i]}
      current_patient = patient_num

df = pd.DataFrame(elems)

And feel free to modify the key_name method to adjust how you want the columns to be named! Adding a 'patient_' to it will get what you have in the question.

Upvotes: 1

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