README.md

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
filter_parent_key: Parent Keys only to be considered should be string or list,if none of key specified is in data,will return empty dataframe

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