ℕ𝕖𝕤𝕥𝕖𝕕𝔽𝕖𝕥𝕔𝕙

Outline

1. Overview
2. Installation
3. Usage
4. Examples
   1. Fetch Value
   2. Set Value
   3. Flatten Nested Lists
5. How to Contribute

Overview

• NestedFetch provides syntactic sugar 🍬 to deal with a nested python dictionary or a nested list 🐍
• You can get, set, update and flatten values from a deeply nested dictionary or a list with a more concise, easier and a KeyError, IndexError free way 😌

data = {
        "league": "Champions League",
        "matches": [
            {
                "match_id": "match_1",
                "goals": [
                {
                    "time": 13,
                    "scorrer": "Lionel Messi",
                    "assist": "Luis Suarez"
                },
                {
                    "time": 78,
                    "scorrer": "Luis Suarez",
                    "assist": "Ivan Rakitic"
                }]
            },
            {
                "match_id": "match_2",
                "goals": [
                {
                    "time": 36,
                    "scorrer": "C. Ronaldo",
                    "assist": "Luka Modric"
                }]
            }]
        }

Installation

NestedFetch works with Python3.
You can directly install it via pip

$ pip3 install nestedfetch

Usage

Import the methods from the package.

from nestedfetch import nested_get, nested_set

No need to instantiate any object, just use the methods specifying valid parameters.

Examples

Fetch Data

nested_get(data, keys, default=None, flatten=False)

@Arguments
data : dict / list
keys => List of sequential keys leading to the desired value to fetch
default => Specifies the default value to be returned if any specified key is not present. If not specified, it will be None
flatten => Specifies whether to flatten the returned value

@Return
Returns the fetched value if it exists, or returns specified default value

• Fetch simple nested data :

data = {
            'name': 'Jesse Pinkman',
            'details': {
                'address':{
                    'city': 'Albuquerque'
                }
            }
        }
res = nested_get(data,['details','address','city'])
# res = Albuquerque

• Fetch simple nested data with default value:

data = {
            'name': 'Jesse Pinkman',
            'details': {
                'address':{
                    'city': 'Albuquerque'
                }
            }
        }
res = nested_get(data,['details','address','state'], default=-1)
# res = -1

• Fetch nested data:

data = {
            'name': 'Jesse Pinkman',
            'details': {
                'address':[{
                    'city': 'Albuquerque'
                },{
                    'city': 'El Paso'
                }]
            }
        }
res = nested_get(data,['details','address','city'])
# res = ['Albuquerque','El Paso']

• Fetch nested data with default value:

data = {
            'name': 'Jesse Pinkman',
            'details': {
                'address':[{
                    'city': 'Albuquerque'
                },{
                    'city': 'El Paso'
                },{
                    'state': 'New Mexico'
                }]
            }
        }
res = nested_get(data,['details','address','city'], default= None)
# res = ['Albuquerque','El Paso', None]

• Fetch nested data by specifing index:

data = {
            'name': 'Walter White',
            'details': {
                'address':[{
                    'city': 'Albuquerque'
                },{
                    'city': 'El Paso'
                }]
            }
        }
res = nested_get(data,['details','address','city', 0])
# res = Albuquerque

• Fetch nested data without flatten:

data = {
        "league": "Champions League",
        "matches": [
            {
                "match_id": "match_1",
                "goals": [
                {
                    "time": 13,
                    "scorrer": "Lionel Messi",
                    "assist": "Luis Suarez"
                },
                {
                    "time": 78,
                    "scorrer": "Luis Suarez",
                    "assist": "Ivan Rakitic"
                }]
            },
            {
                "match_id": "match_2",
                "goals": [
                {
                    "time": 36,
                    "scorrer": "C. Ronaldo",
                    "assist": "Luka Modric"
                }]
            }]
        }
res = nested_get(data,['matches','goals','scorrer'])
# res = [['Lionel Messi', 'Luis Suarez'], ['C. Ronaldo']]

• Fetch nested data with flatten:

data = {
        "league": "Champions League",
        "matches": [
            {
                "match_id": "match_1",
                "goals": [
                {
                    "time": 13,
                    "scorrer": "Lionel Messi",
                    "assist": "Luis Suarez"
                },
                {
                    "time": 78,
                    "scorrer": "Luis Suarez",
                    "assist": "Ivan Rakitic"
                }]
            },
            {
                "match_id": "match_2",
                "goals": [
                {
                    "time": 36,
                    "scorrer": "C. Ronaldo",
                    "assist": "Luka Modric"
                }]
            }]
        }
res = nested_get(data,['matches','goals','scorrer'], flatten=True)
# res = ['Lionel Messi', 'Luis Suarez', 'C. Ronaldo']

Set / Update Data

nested_set(data, keys, value, create_missing=False):

@Arguments
data => dict / list
keys => List of sequential keys leading to the desired value to set / update
value => Specifies the value to set / update
create_missing => Specifies whether to create new key while building up if the specified key does not exists

@Return
Returns the number of values updated

• Update value of simple nested data :

data = {
            'name': 'Jesse Pinkman',
            'details': {
                'address':{
                    'city': 'Albuquerque'
                }
            }
        }
res = nested_set(data,['details','address','city'], "Denver")
# res = 1

# data = {
#             'name': 'Jesse Pinkman',
#             'details': {
#                 'address':{
#                     'city': 'Denver'
#                 }
#             }
#         }

• Update nested data:

data = {
            'name': 'Jesse Pinkman',
            'details': {
                'address':[{
                    'city': 'Albuquerque'
                },{
                    'city': 'El Paso'
                }]
            }
        }
res = nested_set(data,['details','address','city'], "Denver")
# res = 2

# data = {
#     'name': 'Jesse Pinkman',
#     'details': {
#         'address':[{
#             'city': 'Denver'
#         },{
#             'city': 'Denver'
#         }]
#     }
# }

• Update nested data with index:

data = {
            'name': 'Jesse Pinkman',
            'details': {
                'address':[{
                    'city': 'Albuquerque'
                },{
                    'city': 'El Paso'
                }]
            }
        }
res = nested_set(data,['details','address',0,'city'], "Denver")
# res = 1

# data = {
#     'name': 'Jesse Pinkman',
#     'details': {
#         'address':[{
#             'city': 'Denver'
#         },{
#             'city': 'El Paso'
#         }]
#     }
# }

• Set nested data with create_missing :

data = {
            'name': 'Jesse Pinkman',
            'details': {
                'address':{
                    'city': 'Albuquerque'
                }
            }
        }
res = nested_set(data,['details','address','state'], "New Mexico", create_missing=True)
# res = 1

# data = {
#             'name': 'Jesse Pinkman',
#             'details': {
#                 'address':{
#                     'city': 'Denver',
#                     'state': 'New Mexico'
#                 }
#             }
#         }

Flatten Nested Lists

flatten_data(data):

@Arguments
data => list of list

@Return
Returns the flattened list

• Flatten List of Lists

data = [[
    ['This','is'],
    ['flattened', 'data']
]]

res = flatten_data(data)
# res = ['This','is','flattened','data']

How to contribute

Contributions are welcome 😇.
Feel free to submit a patch, report a bug 🐛 or ask for a feature 🐣.
Please open an issue first to encourage and keep track of potential discussions 📝.