json-flattening 1.0.0

Package to flatten json Data

Json-flattening

Flatten Json to Relational Dataframe

Instructions

1. Install

pip install json-flattening

2. Parameters

json_data: Input data to be flattend, should be dict or list of dict
list_split_to_many: If the final node list data should be splitted to one to many relation(i.e to multiple rows)
ignore_parent_key: Parent Keys to be ignored should be string or list

3. Flatten json

from json_flattening import json_flatten

data = [{
    "firstName": "Rack",
    "lastName": "Jackon",
    "gender": "man",
    "age": 24,
    "address": {
        "streetAddress": "126",
        "city": "San Jone",
        "state": "CA",
        "postalCode": "394221"
    },
    "phoneNumbers": [
        { "type": "home", "number": "7383627627" }
    ]
}
{
    "firstName": "rock",
    "lastName": "Jackon",
    "gender": "man",
    "age": 24,
    "address": {
        "streetAddress": "126",
        "city": "San Jone",
        "postalCode": "394221"
    },
    "phoneNumbers": [
        { "type": "home", "number": "7383627627" }
    ]
}]

flatten_data = json_flatten(data)

firstName	lastName	gender	age	address_state	address_streetAddress	address_postalCode	address_city	phoneNumbers_type	phoneNumbers_number
Rack	Jackon	man	24	CA	126	394221	San Jone	home	7383627627
rock	Jackon	man	24	NaN	126	394221	San Jone	home	7383627627

4. With ignore parent Key

flatten_data = json_flatten(data,ignore_parent_key=['phoneNumbers','age'])

firstName	lastName	gender	address_state	address_streetAddress	address_postalCode	address_city
Rack	Jackon	man	CA	126	394221	San Jone
rock	Jackon	man	NaN	126	394221	San Jone

Disclaimer : For heavily complex nested json which has combination of both sub json and other type like integer and string
the relation may not work properly for simple nested works perfect

Change Log

1.0.0 (12/4/2022)

• First Release