flatten-everything 0.3

Flattens everything - lists,tuples,dicts,np,pd

Install it:

pip install flatten-everything

Import it:

from flatten_everything import flatten_everything, ProtectedDict,ProtectedList,ProtectedTuple,ProtectedSet

Use it:

#Let's create some random data
import numpy as np
from collections import UserDict, OrderedDict
import pandas as pd
a = np.array([2, 3, 4], dtype=np.uint32)
b = np.array([5, 6, 7], dtype=np.uint32)
data = { # https://www.geeksforgeeks.org/converting-nested-json-structures-to-pandas-dataframes/
 "company": "XYZ pvt ltd",
 "location": "London",
 "info": {
 "president": "Rakesh Kapoor",
 "contacts": {"email": "[e-mail]", "tel": "9876543210"},
 },
}
data2 = [ # https://www.geeksforgeeks.org/converting-nested-json-structures-to-pandas-dataframes/
 {
 "id": "001",
 "company": "XYZ pvt ltd",
 "location": "London",
 "info": {
 "president": "Rakesh Kapoor",
 "contacts": {"email": "[e-mail]", "tel": "9876543210"},
 },
 },
 {
 "id": "002",
 "company": "PQR Associates",
 "location": "Abu Dhabi",
 "info": {
 "president": "Neelam Subramaniyam",
 "contacts": {"email": "[e-mail]", "tel": "8876443210"},
 },
 },
]
dataf = pd.json_normalize(data)
dicti2 = {
 "name": {"name": "John", "age": "27", "sex": "Male"},
 "Peter2": {"name": "Marie", "age": "22", "sex": "Female"},
 "sdfsdf": {"name": "Luna", "age": "24", "sex": "Female"},
 "Peter": {"name": "Peter", "age": "29", "sex": "Male"},
}
dicti = {
 "name": {"name": "John", "age": "27", "sex": "Male"},
 "Peter2": {"name": "Marie", "age": "22", "sex": "Female"},
 "sdfsdf": {"name": "Luna", "age": "24", "sex": "Female"},
 "Peter": {"name": Peter", "age": "29", "sex": "Male"},
}
odi = OrderedDict(dicti2)
ada = UserDict(dicti2)
testlistnotprotected1 = {
 "mytest": [
 ["dd", "xxaa"],
 [[[[(data, 111, {33, 44, 22})]]], [data2]],
 dataf,
 {"blabla": data2},
 "stads",
 (444, 4),
 21.2,
 ["sda", "sadfrs"],
 ("bababa", 44, [111, 111, 111]),
 dicti,
 a,
 b,
 ["colkey"],
 odi,
 ada,
 np.arange(27).reshape((3, 3, 3)),
 ]
}
flattened1 = list((flatten_everything(testlistnotprotected1)))
#output:
['dd',
 'xxaa',
 'XYZ pvt ltd',
 'London',
 'Rakesh Kapoor',
 '[e-mail]',
 '9876543210',
 111,
 33,
 44,
 22,
 '001',
 'XYZ pvt ltd',
 'London',
 'Rakesh Kapoor',
 '[e-mail]',
 '9876543210',
 '002',
 'PQR Associates',
 'Abu Dhabi',
 'Neelam Subramaniyam',
 '[e-mail]',
 '8876443210',
 'XYZ pvt ltd',
 'London',
 'Rakesh Kapoor',
 '[e-mail]',
 '9876543210',
 '001',
 'XYZ pvt ltd',
 'London',
 'Rakesh Kapoor',
 '[e-mail]',
 '9876543210',
 '002',
 'PQR Associates',
 'Abu Dhabi',
 'Neelam Subramaniyam',
 '[e-mail]',
 '8876443210',
 'stads',
 444,
 4,
 21.2,
 'sda',
 'sadfrs',
 'bababa',
 44,
 111,
 111,
 111,
 'John',
 '27',
 'Male',
 'Marie',
 '22',
 'Female',
 'Luna',
 '24',
 'Female',
 'Peter',
 '29',
 'Male',
 2,
 3,
 4,
 5,
 6,
 7,
 'colkey',
 'John',
 '27',
 'Male',
 'Marie',
 '22',
 'Female',
 'Luna',
 '24',
 'Female',
 'Peter',
 '29',
 'Male',
 'name',
 'Peter2',
 'sdfsdf',
 'Peter',
 0,
 1,
 2,
 3,
 4,
 5,
 6,
 7,
 8,
 9,
 10,
 11,
 12,
 13,
 14,
 15,
 16,
 17,
 18,
 19,
 20,
 21,
 22,
 23,
 24,
 25,
 26]
#You can also protect lists, dicts, tuples and sets from getting flattened
testlistprotected2 = {
    "mytest": [
        ["dd", "xxaa"],
        [[[[(data, 111, {33, 44, 22})]]], [data2]],
        dataf,
        ProtectedDict({"blabla": data2}),
        "stads",
        (444, 4),
        21.2,
        ProtectedList(["sda", "sadfrs"]),
        ProtectedTuple(("bababa", 44, [111, 111, 111])),
        dicti,
        a,
        b,
        ["colkey"],
        odi,
        ada,
        np.arange(27).reshape((3, 3, 3)),
    ]
}
flattened2 = list((flatten_everything(testlistprotected2)))
#output:
['dd',
 'xxaa',
 'XYZ pvt ltd',
 'London',
 'Rakesh Kapoor',
 '[e-mail]',
 '9876543210',
 111,
 33,
 44,
 22,
 '001',
 'XYZ pvt ltd',
 'London',
 'Rakesh Kapoor',
 '[e-mail]',
 '9876543210',
 '002',
 'PQR Associates',
 'Abu Dhabi',
 'Neelam Subramaniyam',
 '[e-mail]',
 '8876443210',
 'XYZ pvt ltd',
 'London',
 'Rakesh Kapoor',
 '[e-mail]',
 '9876543210',
 {'blabla': [{'id': '001',
    'company': 'XYZ pvt ltd',
    'location': 'London',
    'info': {'president': 'Rakesh Kapoor',
     'contacts': {'email': '[e-mail]', 'tel': '9876543210'}}},
   {'id': '002',
    'company': 'PQR Associates',
    'location': 'Abu Dhabi',
    'info': {'president': 'Neelam Subramaniyam',
     'contacts': {'email': '[e-mail]', 'tel': '8876443210'}}}]},
 'stads',
 444,
 4,
 21.2,
 ['sda', 'sadfrs'],
 ('bababa', 44, [111, 111, 111]),
 'John',
 '27',
 'Male',
 'Marie',
 '22',
 'Female',
 'Luna',
 '24',
 'Female',
 'Peter',
 '29',
 'Male',
 2,
 3,
 4,
 5,
 6,
 7,
 'colkey',
 'John',
 '27',
 'Male',
 'Marie',
 '22',
 'Female',
 'Luna',
 '24',
 'Female',
 'Peter',
 '29',
 'Male',
 'name',
 'Peter2',
 'sdfsdf',
 'Peter',
 0,
 1,
 2,
 3,
 4,
 5,
 6,
 7,
 8,
 9,
 10,
 11,
 12,
 13,
 14,
 15,
 16,
 17,
 18,
 19,
 20,
 21,
 22,
 23,
 24,
 25,
 26]