Handling deeply nested JSON with Python

I first came across JSON back in 2015. As I predicted back then it's taken off in a huge way due to the growth in node based back ends and growth in JavaScript.

I do miss the structure of SQL databases.

To process JSON with Python we can use a list or dict comprehension, however, custom code is typically needed for different JSON data.

Another option is to flatten JSON to a key value dictionary.

I use a flatten function to do this. Thanks to a stack overflow post.

I've been able to use the function below to process very deeply nested JSON.

Look at the JSON below - taken from the Google Maps Distance API.

{
  "destination_addresses": [
    "Washington, DC, USA",
    "Philadelphia, PA, USA",
    "Santa Barbara, CA, USA",
    "Miami, FL, USA",
    "Austin, TX, USA",
    "Napa County, CA, USA"
  ],
  "origin_addresses": ["New York, NY, USA"],
  "rows": [
    {
      "elements": [
        {
          "distance": {
            "text": "227 mi",
            "value": 365468
          },
          "duration": {
            "text": "3 hours 54 mins",
            "value": 14064
          },
          "status": "OK"
        },
        {
          "distance": {
            "text": "94.6 mi",
            "value": 152193
          },
          "duration": {
            "text": "1 hour 44 mins",
            "value": 6227
          },
          "status": "OK"
        },
        {
          "distance": {
            "text": "2,878 mi",
            "value": 4632197
          },
          "duration": {
            "text": "1 day 18 hours",
            "value": 151772
          },
          "status": "OK"
        },
        {
          "distance": {
            "text": "1,286 mi",
            "value": 2069031
          },
          "duration": {
            "text": "18 hours 43 mins",
            "value": 67405
          },
          "status": "OK"
        },
        {
          "distance": {
            "text": "1,742 mi",
            "value": 2802972
          },
          "duration": {
            "text": "1 day 2 hours",
            "value": 93070
          },
          "status": "OK"
        },
        {
          "distance": {
            "text": "2,871 mi",
            "value": 4620514
          },
          "duration": {
            "text": "1 day 18 hours",
            "value": 152913
          },
          "status": "OK"
        }
      ]
    }
  ],
  "status": "OK"
}

Here's the recursive function I use for different json sources.

def flatten(dictionary, parent_key=False, separator='.', log=False):
    """
    Turn a nested dictionary into a flattened dictionary
    :param dictionary: The dictionary to flatten
    :param parent_key: The string to prepend to dictionary's keys
    :param separator: The string used to separate flattened keys
    :param log : Bool used to control logging to the terminal
    :return: A flattened dictionary
    """

    items = []
    for key, value in dictionary.items():
        if log: print('checking:',key)
        new_key = str(parent_key) + separator + key if parent_key else key
        if isinstance(value, collections.abc.MutableMapping):
            if log: print(new_key,': dict found')
            if not value.items():
                if log: print('Adding key-value pair:',new_key,None)
                items.append((new_key,None))
            else:
                items.extend(flatten(value, new_key, separator).items())
        elif isinstance(value, list):
            if log: print(new_key,': list found')
            if len(value):
                for k, v in enumerate(value):
                    items.extend(flatten({str(k): v}, new_key).items())
            else:
                if log: print('Adding key-value pair:',new_key,None)
                items.append((new_key,None))
        else:
            if log: print('Adding key-value pair:',new_key,value)
            items.append((new_key, value))
    return dict(items)

Calling this function transforms the json into a flat dict:

Having done that it's simple to make any further transforms you need using a pandas dataframe.

Here's some sample code as an example to extract distance values. Python version greater than 3.3 is required to run the code below.

with open('data.json') as f:
    data = json.load(f)

# Flatten json to dict
a = flatten(data)

# Load to dataframe
df = pd.DataFrame.from_dict(a, orient='index', dtype=str)

# Filter accordingly
df = df[df.index.str.contains("distance.text")]
pprint(df)

Here's the complete code:

import json
import pandas as pd
from pprint import pprint
import collections.abc


def flatten(dictionary, parent_key=False, separator='.', log=False):
    """
    Turn a nested dictionary into a flattened dictionary
    :param dictionary: The dictionary to flatten
    :param parent_key: The string to prepend to dictionary's keys
    :param separator: The string used to separate flattened keys
    :param log : Bool used to control logging to the terminal
    :return: A flattened dictionary
    """

    items = []
    for key, value in dictionary.items():
        if log: print('checking:',key)
        new_key = str(parent_key) + separator + key if parent_key else key
        if isinstance(value, collections.abc.MutableMapping):
            if log: print(new_key,': dict found')
            if not value.items():
                if log: print('Adding key-value pair:',new_key,None)
                items.append((new_key,None))
            else:
                items.extend(flatten(value, new_key, separator).items())
        elif isinstance(value, list):
            if log: print(new_key,': list found')
            if len(value):
                for k, v in enumerate(value):
                    items.extend(flatten({str(k): v}, new_key).items())
            else:
                if log: print('Adding key-value pair:',new_key,None)
                items.append((new_key,None))
        else:
            if log: print('Adding key-value pair:',new_key,value)
            items.append((new_key, value))
    return dict(items)



with open('data.json') as f:
    data = json.load(f)

# Flatten json to dict
a = flatten(data)


# Load to dataframe
df = pd.DataFrame.from_dict(a, orient='index', dtype=str)

# Filter accordingly
df = df[df.index.str.contains("distance.text")]
pprint(df)