No more sleepless nights due to a nested dict, json, list or whatsoever
Project description
No more sleepless nights due to a nested dict, json, list or whatsoever
A convenient way to handle nested iterables! Works only with Python 3.9 and up!
Updates
2022/09/30: Fixed some problems with ProtectedDict and ProtectedList,ProtectedTuple
2022/09/30: Can be used as a generator now: from flatten_any_dict_iterable_or_whatsoever import fla_tu
2022/09/30: New functions: get_from_original_iter,set_in_original_iter, create_random_dict
2022/09/30: Added doc strings
How to use the new functions get_from_original_iter,set_in_original_iter,
data={'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
....]
list(fla_tu(data))
[(5, ('level1', 't1', 's1', 'col1')),
(4, ('level1', 't1', 's1', 'col2')),
(4, ('level1', 't1', 's1', 'col3')),
(9, ('level1', 't1', 's1', 'col4')),
....]
#After having flattened the iterable using fla_tu(), you will have a list of tuples:
#You can use now:
get_from_original_iter(iterable=data, keys=('level1', 't1', 's1', 'col1'))
Out[6]: 5
#to access the values.
set_in_original_iter(iterable=data, keys=('level1', 't1', 's1', 'col1'), value=1000000000000000000)
#to change values of the ORIGINAL ITERABLE!.
Out[8]:
{'level1': {'t1': {'s1': {'col1': 1000000000000000000,
'col2': 4,
'col3': 4,
'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
THIS FUNCTION RETURNS >>>NONE<<<
BECAUSE IT CHANGES THE ORIGINAL ITERABLE!
BE CAREFUL WHAT YOU ARE DOING!!
#DON'T USE data=set_in_original_iter(iterable=data, keys=('level1', 't1', 's1', 'col1'), value=1000000000000000000)
#If you still need the original data, use:
from copy import deepcopy
data2 = deepcopy(data)
list(fla_tu(data))
set_in_original_iter(iterable=data, keys=('level1', 't1', 's1', 'col1'), value=1000000000000000000)
data will be changed
data2 remains unchanged
from flatten_any_dict_iterable_or_whatsoever import ProtectedList,ProtectedDict,ProtectedTuple
from flatten_any_dict_iterable_or_whatsoever import fla_tu
#without protection
data={'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},
'level2': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 9, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 13},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}
pprint(list(fla_tu(data)))
print('------------------------------------------')
#with protection
data={'level1': {'t1': {'s1': ProtectedDict({'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}),
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't3': ProtectedDict({'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}})},
'level2': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 9, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},
's3': {'col1': 11, 'col2': ProtectedList([8,3,5,23,'342342']), 'col3': 2, 'col4': 13},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't3': {'s1': {'col1': 1, 'col2': (2,3,4,5), 'col3': ProtectedTuple((2,3,4,5,'32123')), 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}
pprint(list(list(fla_tu(data)))) #Without protection
[(5, ('level1', 't1', 's1', 'col1')),
(4, ('level1', 't1', 's1', 'col2')),
(4, ('level1', 't1', 's1', 'col3')),
(9, ('level1', 't1', 's1', 'col4')),
(1, ('level1', 't1', 's2', 'col1')),
(5, ('level1', 't1', 's2', 'col2')),
(4, ('level1', 't1', 's2', 'col3')),
(8, ('level1', 't1', 's2', 'col4')),
(11, ('level1', 't1', 's3', 'col1')),
(8, ('level1', 't1', 's3', 'col2')),
(2, ('level1', 't1', 's3', 'col3')),
(9, ('level1', 't1', 's3', 'col4')),
(5, ('level1', 't1', 's4', 'col1')),
(4, ('level1', 't1', 's4', 'col2')),
(4, ('level1', 't1', 's4', 'col3')),
(9, ('level1', 't1', 's4', 'col4')),
(5, ('level1', 't2', 's1', 'col1')),
(4, ('level1', 't2', 's1', 'col2')),
(4, ('level1', 't2', 's1', 'col3')),
(9, ('level1', 't2', 's1', 'col4')),
(1, ('level1', 't2', 's2', 'col1')),
(5, ('level1', 't2', 's2', 'col2')),
(4, ('level1', 't2', 's2', 'col3')),
(8, ('level1', 't2', 's2', 'col4')),
(11, ('level1', 't2', 's3', 'col1')),
(8, ('level1', 't2', 's3', 'col2')),
(2, ('level1', 't2', 's3', 'col3')),
(9, ('level1', 't2', 's3', 'col4')),
(5, ('level1', 't2', 's4', 'col1')),
(4, ('level1', 't2', 's4', 'col2')),
(4, ('level1', 't2', 's4', 'col3')),
(9, ('level1', 't2', 's4', 'col4')),
(1, ('level1', 't3', 's1', 'col1')),
(2, ('level1', 't3', 's1', 'col2')),
(3, ('level1', 't3', 's1', 'col3')),
(4, ('level1', 't3', 's1', 'col4')),
(5, ('level1', 't3', 's2', 'col1')),
(6, ('level1', 't3', 's2', 'col2')),
(7, ('level1', 't3', 's2', 'col3')),
(8, ('level1', 't3', 's2', 'col4')),
(9, ('level1', 't3', 's3', 'col1')),
(10, ('level1', 't3', 's3', 'col2')),
(11, ('level1', 't3', 's3', 'col3')),
(12, ('level1', 't3', 's3', 'col4')),
(13, ('level1', 't3', 's4', 'col1')),
(14, ('level1', 't3', 's4', 'col2')),
(15, ('level1', 't3', 's4', 'col3')),
(16, ('level1', 't3', 's4', 'col4')),
(5, ('level2', 't1', 's1', 'col1')),
(4, ('level2', 't1', 's1', 'col2')),
(9, ('level2', 't1', 's1', 'col3')),
(9, ('level2', 't1', 's1', 'col4')),
(1, ('level2', 't1', 's2', 'col1')),
(5, ('level2', 't1', 's2', 'col2')),
(4, ('level2', 't1', 's2', 'col3')),
(5, ('level2', 't1', 's2', 'col4')),
(11, ('level2', 't1', 's3', 'col1')),
(8, ('level2', 't1', 's3', 'col2')),
(2, ('level2', 't1', 's3', 'col3')),
(13, ('level2', 't1', 's3', 'col4')),
(5, ('level2', 't1', 's4', 'col1')),
(4, ('level2', 't1', 's4', 'col2')),
(4, ('level2', 't1', 's4', 'col3')),
(20, ('level2', 't1', 's4', 'col4')),
(5, ('level2', 't2', 's1', 'col1')),
(4, ('level2', 't2', 's1', 'col2')),
(4, ('level2', 't2', 's1', 'col3')),
(9, ('level2', 't2', 's1', 'col4')),
(1, ('level2', 't2', 's2', 'col1')),
(5, ('level2', 't2', 's2', 'col2')),
(4, ('level2', 't2', 's2', 'col3')),
(8, ('level2', 't2', 's2', 'col4')),
(11, ('level2', 't2', 's3', 'col1')),
(8, ('level2', 't2', 's3', 'col2')),
(2, ('level2', 't2', 's3', 'col3')),
(9, ('level2', 't2', 's3', 'col4')),
(5, ('level2', 't2', 's4', 'col1')),
(4, ('level2', 't2', 's4', 'col2')),
(4, ('level2', 't2', 's4', 'col3')),
(9, ('level2', 't2', 's4', 'col4')),
(1, ('level2', 't3', 's1', 'col1')),
(2, ('level2', 't3', 's1', 'col2')),
(3, ('level2', 't3', 's1', 'col3')),
(4, ('level2', 't3', 's1', 'col4')),
(5, ('level2', 't3', 's2', 'col1')),
(6, ('level2', 't3', 's2', 'col2')),
(7, ('level2', 't3', 's2', 'col3')),
(8, ('level2', 't3', 's2', 'col4')),
(9, ('level2', 't3', 's3', 'col1')),
(10, ('level2', 't3', 's3', 'col2')),
(11, ('level2', 't3', 's3', 'col3')),
(12, ('level2', 't3', 's3', 'col4')),
(13, ('level2', 't3', 's4', 'col1')),
(14, ('level2', 't3', 's4', 'col2')),
(15, ('level2', 't3', 's4', 'col3')),
(16, ('level2', 't3', 's4', 'col4'))]
------------------------------------------ #With protection
[({'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}, ('level1', 't1', 's1')),
(1, ('level1', 't1', 's2', 'col1')),
(5, ('level1', 't1', 's2', 'col2')),
(4, ('level1', 't1', 's2', 'col3')),
(8, ('level1', 't1', 's2', 'col4')),
(11, ('level1', 't1', 's3', 'col1')),
(8, ('level1', 't1', 's3', 'col2')),
(2, ('level1', 't1', 's3', 'col3')),
(9, ('level1', 't1', 's3', 'col4')),
(5, ('level1', 't1', 's4', 'col1')),
(4, ('level1', 't1', 's4', 'col2')),
(4, ('level1', 't1', 's4', 'col3')),
(9, ('level1', 't1', 's4', 'col4')),
(5, ('level1', 't2', 's1', 'col1')),
(4, ('level1', 't2', 's1', 'col2')),
(4, ('level1', 't2', 's1', 'col3')),
(9, ('level1', 't2', 's1', 'col4')),
(1, ('level1', 't2', 's2', 'col1')),
(5, ('level1', 't2', 's2', 'col2')),
(4, ('level1', 't2', 's2', 'col3')),
(8, ('level1', 't2', 's2', 'col4')),
(11, ('level1', 't2', 's3', 'col1')),
(8, ('level1', 't2', 's3', 'col2')),
(2, ('level1', 't2', 's3', 'col3')),
(9, ('level1', 't2', 's3', 'col4')),
(5, ('level1', 't2', 's4', 'col1')),
(4, ('level1', 't2', 's4', 'col2')),
(4, ('level1', 't2', 's4', 'col3')),
(9, ('level1', 't2', 's4', 'col4')),
({'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}},
('level1', 't3')),
(5, ('level2', 't1', 's1', 'col1')),
(4, ('level2', 't1', 's1', 'col2')),
(9, ('level2', 't1', 's1', 'col3')),
(9, ('level2', 't1', 's1', 'col4')),
(1, ('level2', 't1', 's2', 'col1')),
(5, ('level2', 't1', 's2', 'col2')),
(4, ('level2', 't1', 's2', 'col3')),
(5, ('level2', 't1', 's2', 'col4')),
(11, ('level2', 't1', 's3', 'col1')),
([8, 3, 5, 23, '342342'], ('level2', 't1', 's3', 'col2')),
(2, ('level2', 't1', 's3', 'col3')),
(13, ('level2', 't1', 's3', 'col4')),
(5, ('level2', 't1', 's4', 'col1')),
(4, ('level2', 't1', 's4', 'col2')),
(4, ('level2', 't1', 's4', 'col3')),
(20, ('level2', 't1', 's4', 'col4')),
(5, ('level2', 't2', 's1', 'col1')),
(4, ('level2', 't2', 's1', 'col2')),
(4, ('level2', 't2', 's1', 'col3')),
(9, ('level2', 't2', 's1', 'col4')),
(1, ('level2', 't2', 's2', 'col1')),
(5, ('level2', 't2', 's2', 'col2')),
(4, ('level2', 't2', 's2', 'col3')),
(8, ('level2', 't2', 's2', 'col4')),
(11, ('level2', 't2', 's3', 'col1')),
(8, ('level2', 't2', 's3', 'col2')),
(2, ('level2', 't2', 's3', 'col3')),
(9, ('level2', 't2', 's3', 'col4')),
(5, ('level2', 't2', 's4', 'col1')),
(4, ('level2', 't2', 's4', 'col2')),
(4, ('level2', 't2', 's4', 'col3')),
(9, ('level2', 't2', 's4', 'col4')),
(1, ('level2', 't3', 's1', 'col1')),
(2, ('level2', 't3', 's1', 'col2', 0)),
(3, ('level2', 't3', 's1', 'col2', 1)),
(4, ('level2', 't3', 's1', 'col2', 2)),
(5, ('level2', 't3', 's1', 'col2', 3)),
((2, 3, 4, 5, '32123'), ('level2', 't3', 's1', 'col3')),
(4, ('level2', 't3', 's1', 'col4')),
(5, ('level2', 't3', 's2', 'col1')),
(6, ('level2', 't3', 's2', 'col2')),
(7, ('level2', 't3', 's2', 'col3')),
(8, ('level2', 't3', 's2', 'col4')),
(9, ('level2', 't3', 's3', 'col1')),
(10, ('level2', 't3', 's3', 'col2')),
(11, ('level2', 't3', 's3', 'col3')),
(12, ('level2', 't3', 's3', 'col4')),
(13, ('level2', 't3', 's4', 'col1')),
(14, ('level2', 't3', 's4', 'col2')),
(15, ('level2', 't3', 's4', 'col3')),
(16, ('level2', 't3', 's4', 'col4'))]
Install
pip install flatten-any-dict-iterable-or-whatsoever
Usage
from flatten_any_dict_iterable_or_whatsoever import flatten_nested_something_to_list_of_tuples
flatten_nested_something_to_list_of_tuples(nested_iter_that_drives_me_crazy) #That's it! :)
Examples
https://stackoverflow.com/questions/72990265/convert-nested-list-in-dictionary-to-dataframe/72990346
before:
{'a': 'test',
'b': 1657,
'c': 'asset',
'd': [['2089', '0.0'], ['2088', '0.0']],
'e': [['2088', '0.0'], ['2088', '0.0'], ['2088', '0.00']],
'f': [['2088', '0.0', 'x', 'foo'],
['2088', '0.0', 'bar', 'i'],
['2088', '0.00', 'z', '0.2']],
'x': ['test1', 'test2']}
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: test keys: ('a',)
value: 1657 keys: ('b',)
value: asset keys: ('c',)
value: 2089 keys: ('d', 0)
value: 0.0 keys: ('d', 0)
value: 2088 keys: ('d', 1)
value: 0.0 keys: ('d', 1)
value: 2088 keys: ('e', 0)
value: 0.0 keys: ('e', 0)
value: 2088 keys: ('e', 1)
value: 0.0 keys: ('e', 1)
value: 2088 keys: ('e', 2)
value: 0.00 keys: ('e', 2)
value: 2088 keys: ('f', 0)
value: 0.0 keys: ('f', 0)
value: x keys: ('f', 0)
value: foo keys: ('f', 0)
value: 2088 keys: ('f', 1)
value: 0.0 keys: ('f', 1)
value: bar keys: ('f', 1)
value: i keys: ('f', 1)
value: 2088 keys: ('f', 2)
value: 0.00 keys: ('f', 2)
value: z keys: ('f', 2)
value: 0.2 keys: ('f', 2)
value: test1 keys: ('x',)
value: test2 keys: ('x',)
https://stackoverflow.com/questions/73430585/how-to-convert-a-list-of-nested-dictionaries-includes-tuples-as-a-dataframe
before:
[{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 2, 'Name': 'A', 'num': 68}),
'final_value': 118},
{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 4, 'Name': 'A', 'num': 67}),
'final_value': 117},
{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 6, 'Name': 'A', 'num': 67}),
'final_value': 117}]
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: A keys: (0, 'cb', 0, 'Name')
value: 1 keys: (0, 'cb', 0, 'ID')
value: 50 keys: (0, 'cb', 0, 'num')
value: A keys: (0, 'cb', 1, 'Name')
value: 2 keys: (0, 'cb', 1, 'ID')
value: 68 keys: (0, 'cb', 1, 'num')
value: 118 keys: (0, 'final_value')
value: A keys: (1, 'cb', 0, 'Name')
value: 1 keys: (1, 'cb', 0, 'ID')
value: 50 keys: (1, 'cb', 0, 'num')
value: A keys: (1, 'cb', 1, 'Name')
value: 4 keys: (1, 'cb', 1, 'ID')
value: 67 keys: (1, 'cb', 1, 'num')
value: 117 keys: (1, 'final_value')
value: A keys: (2, 'cb', 0, 'Name')
value: 1 keys: (2, 'cb', 0, 'ID')
value: 50 keys: (2, 'cb', 0, 'num')
value: A keys: (2, 'cb', 1, 'Name')
value: 6 keys: (2, 'cb', 1, 'ID')
value: 67 keys: (2, 'cb', 1, 'num')
value: 117 keys: (2, 'final_value')
https://stackoverflow.com/questions/69943509/problems-when-flatten-a-dict
before:
[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],
'application_details': {'email': None, 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '1'},
{'application_contacts': [{'adress': 'Z', 'email': None}],
'application_details': {'email': 'testy@test_a.com', 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '2'},
{'application_contacts': [{'adress': 'Y', 'email': None}],
'application_details': {'email': 'testy@test_a.com', 'phone': None},
'employer': {'Name': 'Nom', 'email': None},
'id': '3'}]
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: 1 keys: (0, 'id')
value: None keys: (0, 'application_details', 'phone')
value: None keys: (0, 'application_details', 'email')
value: Nom keys: (0, 'employer', 'Name')
value: None keys: (0, 'employer', 'email')
value: test@test.com keys: (0, 'application_contacts', 0, 'email')
value: X keys: (0, 'application_contacts', 0, 'adress')
value: 2 keys: (1, 'id')
value: None keys: (1, 'application_details', 'phone')
value: testy@test_a.com keys: (1, 'application_details', 'email')
value: Nom keys: (1, 'employer', 'Name')
value: None keys: (1, 'employer', 'email')
value: None keys: (1, 'application_contacts', 0, 'email')
value: Z keys: (1, 'application_contacts', 0, 'adress')
value: 3 keys: (2, 'id')
value: None keys: (2, 'application_details', 'phone')
value: testy@test_a.com keys: (2, 'application_details', 'email')
value: Nom keys: (2, 'employer', 'Name')
value: None keys: (2, 'employer', 'email')
value: None keys: (2, 'application_contacts', 0, 'email')
value: Y keys: (2, 'application_contacts', 0, 'adress')
https://stackoverflow.com/questions/62765371/convert-nested-dataframe-to-a-simple-dataframeframe
before:
{'A': [1, 2, 3],
'B': [4, 5, 6],
'departure': [{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'},
{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'},
{'actual': None,
'actual_runway': None,
'airport': 'Findel',
'delay': None,
'estimated': '2020-07-07T06:30:00+00:00',
'estimated_runway': None,
'gate': None,
'iata': 'LUX',
'icao': 'ELLX',
'scheduled': '2020-07-07T06:30:00+00:00',
'terminal': None,
'timezone': 'Europe/Luxembourg'}]}
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: 1 keys: ('A',)
value: 2 keys: ('A',)
value: 3 keys: ('A',)
value: 4 keys: ('B',)
value: 5 keys: ('B',)
value: 6 keys: ('B',)
value: Findel keys: ('departure', 0, 'airport')
value: Europe/Luxembourg keys: ('departure', 0, 'timezone')
value: LUX keys: ('departure', 0, 'iata')
value: ELLX keys: ('departure', 0, 'icao')
value: None keys: ('departure', 0, 'terminal')
value: None keys: ('departure', 0, 'gate')
value: None keys: ('departure', 0, 'delay')
value: 2020-07-07T06:30:00+00:00 keys: ('departure', 0, 'scheduled')
value: 2020-07-07T06:30:00+00:00 keys: ('departure', 0, 'estimated')
value: None keys: ('departure', 0, 'actual')
value: None keys: ('departure', 0, 'estimated_runway')
value: None keys: ('departure', 0, 'actual_runway')
value: Findel keys: ('departure', 1, 'airport')
value: Europe/Luxembourg keys: ('departure', 1, 'timezone')
value: LUX keys: ('departure', 1, 'iata')
value: ELLX keys: ('departure', 1, 'icao')
value: None keys: ('departure', 1, 'terminal')
value: None keys: ('departure', 1, 'gate')
value: None keys: ('departure', 1, 'delay')
value: 2020-07-07T06:30:00+00:00 keys: ('departure', 1, 'scheduled')
value: 2020-07-07T06:30:00+00:00 keys: ('departure', 1, 'estimated')
value: None keys: ('departure', 1, 'actual')
value: None keys: ('departure', 1, 'estimated_runway')
value: None keys: ('departure', 1, 'actual_runway')
value: Findel keys: ('departure', 2, 'airport')
value: Europe/Luxembourg keys: ('departure', 2, 'timezone')
value: LUX keys: ('departure', 2, 'iata')
value: ELLX keys: ('departure', 2, 'icao')
value: None keys: ('departure', 2, 'terminal')
value: None keys: ('departure', 2, 'gate')
value: None keys: ('departure', 2, 'delay')
value: 2020-07-07T06:30:00+00:00 keys: ('departure', 2, 'scheduled')
value: 2020-07-07T06:30:00+00:00 keys: ('departure', 2, 'estimated')
value: None keys: ('departure', 2, 'actual')
value: None keys: ('departure', 2, 'estimated_runway')
value: None keys: ('departure', 2, 'actual_runway')
https://stackoverflow.com/questions/64359762/constructing-a-pandas-dataframe-with-columns-and-sub-columns-from-nested-diction
before:
{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},
'level2': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 9, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 13},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},
't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},
's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},
's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},
's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},
't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},
's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},
's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},
's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: 5 keys: ('level1', 't1', 's1', 'col1')
value: 4 keys: ('level1', 't1', 's1', 'col2')
value: 4 keys: ('level1', 't1', 's1', 'col3')
value: 9 keys: ('level1', 't1', 's1', 'col4')
value: 1 keys: ('level1', 't1', 's2', 'col1')
value: 5 keys: ('level1', 't1', 's2', 'col2')
value: 4 keys: ('level1', 't1', 's2', 'col3')
value: 8 keys: ('level1', 't1', 's2', 'col4')
value: 11 keys: ('level1', 't1', 's3', 'col1')
value: 8 keys: ('level1', 't1', 's3', 'col2')
value: 2 keys: ('level1', 't1', 's3', 'col3')
value: 9 keys: ('level1', 't1', 's3', 'col4')
value: 5 keys: ('level1', 't1', 's4', 'col1')
value: 4 keys: ('level1', 't1', 's4', 'col2')
value: 4 keys: ('level1', 't1', 's4', 'col3')
value: 9 keys: ('level1', 't1', 's4', 'col4')
value: 5 keys: ('level1', 't2', 's1', 'col1')
value: 4 keys: ('level1', 't2', 's1', 'col2')
value: 4 keys: ('level1', 't2', 's1', 'col3')
value: 9 keys: ('level1', 't2', 's1', 'col4')
value: 1 keys: ('level1', 't2', 's2', 'col1')
value: 5 keys: ('level1', 't2', 's2', 'col2')
value: 4 keys: ('level1', 't2', 's2', 'col3')
value: 8 keys: ('level1', 't2', 's2', 'col4')
value: 11 keys: ('level1', 't2', 's3', 'col1')
value: 8 keys: ('level1', 't2', 's3', 'col2')
value: 2 keys: ('level1', 't2', 's3', 'col3')
value: 9 keys: ('level1', 't2', 's3', 'col4')
value: 5 keys: ('level1', 't2', 's4', 'col1')
value: 4 keys: ('level1', 't2', 's4', 'col2')
value: 4 keys: ('level1', 't2', 's4', 'col3')
value: 9 keys: ('level1', 't2', 's4', 'col4')
value: 1 keys: ('level1', 't3', 's1', 'col1')
value: 2 keys: ('level1', 't3', 's1', 'col2')
value: 3 keys: ('level1', 't3', 's1', 'col3')
value: 4 keys: ('level1', 't3', 's1', 'col4')
value: 5 keys: ('level1', 't3', 's2', 'col1')
value: 6 keys: ('level1', 't3', 's2', 'col2')
value: 7 keys: ('level1', 't3', 's2', 'col3')
value: 8 keys: ('level1', 't3', 's2', 'col4')
value: 9 keys: ('level1', 't3', 's3', 'col1')
value: 10 keys: ('level1', 't3', 's3', 'col2')
value: 11 keys: ('level1', 't3', 's3', 'col3')
value: 12 keys: ('level1', 't3', 's3', 'col4')
value: 13 keys: ('level1', 't3', 's4', 'col1')
value: 14 keys: ('level1', 't3', 's4', 'col2')
value: 15 keys: ('level1', 't3', 's4', 'col3')
value: 16 keys: ('level1', 't3', 's4', 'col4')
value: 5 keys: ('level2', 't1', 's1', 'col1')
value: 4 keys: ('level2', 't1', 's1', 'col2')
value: 9 keys: ('level2', 't1', 's1', 'col3')
value: 9 keys: ('level2', 't1', 's1', 'col4')
value: 1 keys: ('level2', 't1', 's2', 'col1')
value: 5 keys: ('level2', 't1', 's2', 'col2')
value: 4 keys: ('level2', 't1', 's2', 'col3')
value: 5 keys: ('level2', 't1', 's2', 'col4')
value: 11 keys: ('level2', 't1', 's3', 'col1')
value: 8 keys: ('level2', 't1', 's3', 'col2')
value: 2 keys: ('level2', 't1', 's3', 'col3')
value: 13 keys: ('level2', 't1', 's3', 'col4')
value: 5 keys: ('level2', 't1', 's4', 'col1')
value: 4 keys: ('level2', 't1', 's4', 'col2')
value: 4 keys: ('level2', 't1', 's4', 'col3')
value: 20 keys: ('level2', 't1', 's4', 'col4')
value: 5 keys: ('level2', 't2', 's1', 'col1')
value: 4 keys: ('level2', 't2', 's1', 'col2')
value: 4 keys: ('level2', 't2', 's1', 'col3')
value: 9 keys: ('level2', 't2', 's1', 'col4')
value: 1 keys: ('level2', 't2', 's2', 'col1')
value: 5 keys: ('level2', 't2', 's2', 'col2')
value: 4 keys: ('level2', 't2', 's2', 'col3')
value: 8 keys: ('level2', 't2', 's2', 'col4')
value: 11 keys: ('level2', 't2', 's3', 'col1')
value: 8 keys: ('level2', 't2', 's3', 'col2')
value: 2 keys: ('level2', 't2', 's3', 'col3')
value: 9 keys: ('level2', 't2', 's3', 'col4')
value: 5 keys: ('level2', 't2', 's4', 'col1')
value: 4 keys: ('level2', 't2', 's4', 'col2')
value: 4 keys: ('level2', 't2', 's4', 'col3')
value: 9 keys: ('level2', 't2', 's4', 'col4')
value: 1 keys: ('level2', 't3', 's1', 'col1')
value: 2 keys: ('level2', 't3', 's1', 'col2')
value: 3 keys: ('level2', 't3', 's1', 'col3')
value: 4 keys: ('level2', 't3', 's1', 'col4')
value: 5 keys: ('level2', 't3', 's2', 'col1')
value: 6 keys: ('level2', 't3', 's2', 'col2')
value: 7 keys: ('level2', 't3', 's2', 'col3')
value: 8 keys: ('level2', 't3', 's2', 'col4')
value: 9 keys: ('level2', 't3', 's3', 'col1')
value: 10 keys: ('level2', 't3', 's3', 'col2')
value: 11 keys: ('level2', 't3', 's3', 'col3')
value: 12 keys: ('level2', 't3', 's3', 'col4')
value: 13 keys: ('level2', 't3', 's4', 'col1')
value: 14 keys: ('level2', 't3', 's4', 'col2')
value: 15 keys: ('level2', 't3', 's4', 'col3')
value: 16 keys: ('level2', 't3', 's4', 'col4')
https://stackoverflow.com/questions/61984148/how-to-handle-nested-lists-and-dictionaries-in-pandas-dataframe
before:
{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},
{'review_critic': 'ABC', 'review_score': 90},
{'review_critic': '123', 'review_score': 90}],
'genres': ['Sports', 'Golf'],
'score': 85,
'title': 'Golf Simulator',
'url': 'http://example.com/golf-simulator'}
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: Golf Simulator keys: ('title',)
value: Sports keys: ('genres',)
value: Golf keys: ('genres',)
value: 85 keys: ('score',)
value: XYZ keys: ('critic_reviews', 0, 'review_critic')
value: 90 keys: ('critic_reviews', 0, 'review_score')
value: ABC keys: ('critic_reviews', 1, 'review_critic')
value: 90 keys: ('critic_reviews', 1, 'review_score')
value: 123 keys: ('critic_reviews', 2, 'review_critic')
value: 90 keys: ('critic_reviews', 2, 'review_score')
value: http://example.com/golf-simulator keys: ('url',)
https://stackoverflow.com/questions/72146094/problems-matching-values-from-nested-dictionary
before:
{'_links': {'next': None, 'prev': None},
'limit': 250,
'offset': 0,
'runs': [{'assignedto_id': None,
'blocked_count': 0,
'completed_on': None,
'config': None,
'config_ids': [],
'created_by': 1,
'created_on': 1651790693,
'custom_status1_count': 0,
'custom_status2_count': 0,
'custom_status3_count': 0,
'custom_status4_count': 0,
'custom_status5_count': 0,
'custom_status6_count': 0,
'custom_status7_count': 0,
'description': None,
'failed_count': 1,
'id': 13,
'include_all': False,
'is_completed': False,
'milestone_id': None,
'name': '2022-05-05-testrun',
'passed_count': 2,
'plan_id': None,
'project_id': 1,
'refs': None,
'retest_count': 0,
'suite_id': 1,
'untested_count': 0,
'updated_on': 1651790693,
'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13'},
{'assignedto_id': None,
'blocked_count': 0,
'completed_on': 1650989972,
'config': None,
'config_ids': [],
'created_by': 5,
'created_on': 1650966329,
'custom_status1_count': 0,
'custom_status2_count': 0,
'custom_status3_count': 0,
'custom_status4_count': 0,
'custom_status5_count': 0,
'custom_status6_count': 0,
'custom_status7_count': 0,
'description': None,
'failed_count': 0,
'id': 9,
'include_all': False,
'is_completed': True,
'milestone_id': None,
'name': 'This is a new test run',
'passed_count': 0,
'plan_id': None,
'project_id': 1,
'refs': None,
'retest_count': 0,
'suite_id': 1,
'untested_count': 3,
'updated_on': 1650966329,
'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9'}],
'size': 2}
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: None keys: ('_links', 'next')
value: None keys: ('_links', 'prev')
value: 250 keys: ('limit',)
value: 0 keys: ('offset',)
value: None keys: ('runs', 0, 'assignedto_id')
value: 0 keys: ('runs', 0, 'blocked_count')
value: None keys: ('runs', 0, 'completed_on')
value: None keys: ('runs', 0, 'config')
value: 1 keys: ('runs', 0, 'created_by')
value: 1651790693 keys: ('runs', 0, 'created_on')
value: 0 keys: ('runs', 0, 'custom_status1_count')
value: 0 keys: ('runs', 0, 'custom_status2_count')
value: 0 keys: ('runs', 0, 'custom_status3_count')
value: 0 keys: ('runs', 0, 'custom_status4_count')
value: 0 keys: ('runs', 0, 'custom_status5_count')
value: 0 keys: ('runs', 0, 'custom_status6_count')
value: 0 keys: ('runs', 0, 'custom_status7_count')
value: None keys: ('runs', 0, 'description')
value: 1 keys: ('runs', 0, 'failed_count')
value: 13 keys: ('runs', 0, 'id')
value: False keys: ('runs', 0, 'include_all')
value: False keys: ('runs', 0, 'is_completed')
value: None keys: ('runs', 0, 'milestone_id')
value: 2022-05-05-testrun keys: ('runs', 0, 'name')
value: 2 keys: ('runs', 0, 'passed_count')
value: None keys: ('runs', 0, 'plan_id')
value: 1 keys: ('runs', 0, 'project_id')
value: None keys: ('runs', 0, 'refs')
value: 0 keys: ('runs', 0, 'retest_count')
value: 1 keys: ('runs', 0, 'suite_id')
value: 0 keys: ('runs', 0, 'untested_count')
value: 1651790693 keys: ('runs', 0, 'updated_on')
value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13 keys: ('runs', 0, 'url')
value: None keys: ('runs', 1, 'assignedto_id')
value: 0 keys: ('runs', 1, 'blocked_count')
value: 1650989972 keys: ('runs', 1, 'completed_on')
value: None keys: ('runs', 1, 'config')
value: 5 keys: ('runs', 1, 'created_by')
value: 1650966329 keys: ('runs', 1, 'created_on')
value: 0 keys: ('runs', 1, 'custom_status1_count')
value: 0 keys: ('runs', 1, 'custom_status2_count')
value: 0 keys: ('runs', 1, 'custom_status3_count')
value: 0 keys: ('runs', 1, 'custom_status4_count')
value: 0 keys: ('runs', 1, 'custom_status5_count')
value: 0 keys: ('runs', 1, 'custom_status6_count')
value: 0 keys: ('runs', 1, 'custom_status7_count')
value: None keys: ('runs', 1, 'description')
value: 0 keys: ('runs', 1, 'failed_count')
value: 9 keys: ('runs', 1, 'id')
value: False keys: ('runs', 1, 'include_all')
value: True keys: ('runs', 1, 'is_completed')
value: None keys: ('runs', 1, 'milestone_id')
value: This is a new test run keys: ('runs', 1, 'name')
value: 0 keys: ('runs', 1, 'passed_count')
value: None keys: ('runs', 1, 'plan_id')
value: 1 keys: ('runs', 1, 'project_id')
value: None keys: ('runs', 1, 'refs')
value: 0 keys: ('runs', 1, 'retest_count')
value: 1 keys: ('runs', 1, 'suite_id')
value: 3 keys: ('runs', 1, 'untested_count')
value: 1650966329 keys: ('runs', 1, 'updated_on')
value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9 keys: ('runs', 1, 'url')
value: 2 keys: ('size',)
https://stackoverflow.com/questions/73708706/how-to-get-values-from-list-of-nested-dictionaries/73839430#73839430
before:
{'results': [{'end_time': '2021-01-21',
'key': 'q1',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['1']},
{'end_time': '2021-01-21',
'key': 'q2',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['False']},
{'end_time': '2021-01-21',
'key': 'q3',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['3']},
{'end_time': '2021-01-21',
'key': 'q4',
'result_type': 'multipleChoice',
'start_time': '2021-01-21',
'value': ['3']}]}
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: q1 keys: ('results', 0, 'key')
value: 1 keys: ('results', 0, 'value')
value: 2021-01-21 keys: ('results', 0, 'end_time')
value: 2021-01-21 keys: ('results', 0, 'start_time')
value: multipleChoice keys: ('results', 0, 'result_type')
value: q2 keys: ('results', 1, 'key')
value: False keys: ('results', 1, 'value')
value: 2021-01-21 keys: ('results', 1, 'end_time')
value: 2021-01-21 keys: ('results', 1, 'start_time')
value: multipleChoice keys: ('results', 1, 'result_type')
value: q3 keys: ('results', 2, 'key')
value: 3 keys: ('results', 2, 'value')
value: 2021-01-21 keys: ('results', 2, 'end_time')
value: 2021-01-21 keys: ('results', 2, 'start_time')
value: multipleChoice keys: ('results', 2, 'result_type')
value: q4 keys: ('results', 3, 'key')
value: 3 keys: ('results', 3, 'value')
value: 2021-01-21 keys: ('results', 3, 'end_time')
value: 2021-01-21 keys: ('results', 3, 'start_time')
value: multipleChoice keys: ('results', 3, 'result_type')
https://stackoverflow.com/questions/70811820/pandas-multiindex-from-nested-dictionary
before:
{'A': [1, 2],
'B': [2, 3],
'Coords': [{'X': [1, 2, 3], 'Y': [1, 2, 3], 'Z': [1, 2, 3]},
{'X': [2, 3], 'Y': [2, 3], 'Z': [2, 3]}]}
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: 1 keys: ('A',)
value: 2 keys: ('A',)
value: 2 keys: ('B',)
value: 3 keys: ('B',)
value: 1 keys: ('Coords', 0, 'X')
value: 2 keys: ('Coords', 0, 'X')
value: 3 keys: ('Coords', 0, 'X')
value: 1 keys: ('Coords', 0, 'Y')
value: 2 keys: ('Coords', 0, 'Y')
value: 3 keys: ('Coords', 0, 'Y')
value: 1 keys: ('Coords', 0, 'Z')
value: 2 keys: ('Coords', 0, 'Z')
value: 3 keys: ('Coords', 0, 'Z')
value: 2 keys: ('Coords', 1, 'X')
value: 3 keys: ('Coords', 1, 'X')
value: 2 keys: ('Coords', 1, 'Y')
value: 3 keys: ('Coords', 1, 'Y')
value: 2 keys: ('Coords', 1, 'Z')
value: 3 keys: ('Coords', 1, 'Z')
https://stackoverflow.com/questions/72017771/key-error-when-accessing-a-nested-dictionary
before:
[{'blocks': [{'block_id': 'BJNTn',
'text': {'text': 'You have a new message.',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'WPn/l',
'text': {'text': '*Heard By*\nFriend',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': '5yp',
'text': {'text': '*Which Direction? *\nNorth',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'fKEpF',
'text': {'text': '*Which Destination*\nNew York',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'qjAH',
'text': {'text': '*New Customer:*\\Yes',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'yt4',
'elements': [{'action_id': '+bc',
'text': {'bar': 'View results',
'emoji': True,
'type': 'plain_text'},
'type': 'button',
'url': 'www.example.com/results'}],
'type': 'actions'},
{'block_id': 'IBr',
'text': {'text': ' ', 'type': 'mrkdwn', 'verbatim': False},
'type': 'section'}],
'bot_id': 'BPD4K3SJW',
'subtype': 'bot_message',
'text': "This content can't be displayed.",
'timestamp': '1650905606.755969',
'type': 'message',
'username': 'admin'},
{'blocks': [{'block_id': 'Smd',
'text': {'text': 'You have a new message.',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': '6YaLt',
'text': {'text': '*Heard By*\nOnline Search',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'w3o',
'text': {'text': '*Which Direction: *\nNorth',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'PTQ',
'text': {'text': '*Which Destination? *\nMiami',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'JCfSP',
'text': {'text': '*New Customer? *\nNo',
'type': 'mrkdwn',
'verbatim': False},
'type': 'section'},
{'block_id': 'yt4',
'elements': [{'action_id': '+bc',
'text': {'bar': 'View results',
'emoji': True,
'type': 'plain_text'},
'type': 'button',
'url': 'www.example.com/results'}],
'type': 'actions'},
{'block_id': 'RJOA',
'text': {'text': ' ', 'type': 'mrkdwn', 'verbatim': False},
'type': 'section'}],
'bot_id': 'BPD4K3SJW',
'subtype': 'bot_message',
'text': "This content can't be displayed.",
'timestamp': '1650899428.077709',
'type': 'message',
'username': 'admin'}]
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: message keys: (0, 'type')
value: bot_message keys: (0, 'subtype')
value: This content can't be displayed. keys: (0, 'text')
value: 1650905606.755969 keys: (0, 'timestamp')
value: admin keys: (0, 'username')
value: BPD4K3SJW keys: (0, 'bot_id')
value: section keys: (0, 'blocks', 0, 'type')
value: BJNTn keys: (0, 'blocks', 0, 'block_id')
value: mrkdwn keys: (0, 'blocks', 0, 'text', 'type')
value: You have a new message. keys: (0, 'blocks', 0, 'text', 'text')
value: False keys: (0, 'blocks', 0, 'text', 'verbatim')
value: section keys: (0, 'blocks', 1, 'type')
value: WPn/l keys: (0, 'blocks', 1, 'block_id')
value: mrkdwn keys: (0, 'blocks', 1, 'text', 'type')
value: *Heard By*
Friend keys: (0, 'blocks', 1, 'text', 'text')
value: False keys: (0, 'blocks', 1, 'text', 'verbatim')
value: section keys: (0, 'blocks', 2, 'type')
value: 5yp keys: (0, 'blocks', 2, 'block_id')
value: mrkdwn keys: (0, 'blocks', 2, 'text', 'type')
value: *Which Direction? *
North keys: (0, 'blocks', 2, 'text', 'text')
value: False keys: (0, 'blocks', 2, 'text', 'verbatim')
value: section keys: (0, 'blocks', 3, 'type')
value: fKEpF keys: (0, 'blocks', 3, 'block_id')
value: mrkdwn keys: (0, 'blocks', 3, 'text', 'type')
value: *Which Destination*
New York keys: (0, 'blocks', 3, 'text', 'text')
value: False keys: (0, 'blocks', 3, 'text', 'verbatim')
value: section keys: (0, 'blocks', 4, 'type')
value: qjAH keys: (0, 'blocks', 4, 'block_id')
value: mrkdwn keys: (0, 'blocks', 4, 'text', 'type')
value: *New Customer:*\Yes keys: (0, 'blocks', 4, 'text', 'text')
value: False keys: (0, 'blocks', 4, 'text', 'verbatim')
value: actions keys: (0, 'blocks', 5, 'type')
value: yt4 keys: (0, 'blocks', 5, 'block_id')
value: button keys: (0, 'blocks', 5, 'elements', 0, 'type')
value: +bc keys: (0, 'blocks', 5, 'elements', 0, 'action_id')
value: plain_text keys: (0, 'blocks', 5, 'elements', 0, 'text', 'type')
value: View results keys: (0, 'blocks', 5, 'elements', 0, 'text', 'bar')
value: True keys: (0, 'blocks', 5, 'elements', 0, 'text', 'emoji')
value: www.example.com/results keys: (0, 'blocks', 5, 'elements', 0, 'url')
value: section keys: (0, 'blocks', 6, 'type')
value: IBr keys: (0, 'blocks', 6, 'block_id')
value: mrkdwn keys: (0, 'blocks', 6, 'text', 'type')
value: keys: (0, 'blocks', 6, 'text', 'text')
value: False keys: (0, 'blocks', 6, 'text', 'verbatim')
value: message keys: (1, 'type')
value: bot_message keys: (1, 'subtype')
value: This content can't be displayed. keys: (1, 'text')
value: 1650899428.077709 keys: (1, 'timestamp')
value: admin keys: (1, 'username')
value: BPD4K3SJW keys: (1, 'bot_id')
value: section keys: (1, 'blocks', 0, 'type')
value: Smd keys: (1, 'blocks', 0, 'block_id')
value: mrkdwn keys: (1, 'blocks', 0, 'text', 'type')
value: You have a new message. keys: (1, 'blocks', 0, 'text', 'text')
value: False keys: (1, 'blocks', 0, 'text', 'verbatim')
value: section keys: (1, 'blocks', 1, 'type')
value: 6YaLt keys: (1, 'blocks', 1, 'block_id')
value: mrkdwn keys: (1, 'blocks', 1, 'text', 'type')
value: *Heard By*
Online Search keys: (1, 'blocks', 1, 'text', 'text')
value: False keys: (1, 'blocks', 1, 'text', 'verbatim')
value: section keys: (1, 'blocks', 2, 'type')
value: w3o keys: (1, 'blocks', 2, 'block_id')
value: mrkdwn keys: (1, 'blocks', 2, 'text', 'type')
value: *Which Direction: *
North keys: (1, 'blocks', 2, 'text', 'text')
value: False keys: (1, 'blocks', 2, 'text', 'verbatim')
value: section keys: (1, 'blocks', 3, 'type')
value: PTQ keys: (1, 'blocks', 3, 'block_id')
value: mrkdwn keys: (1, 'blocks', 3, 'text', 'type')
value: *Which Destination? *
Miami keys: (1, 'blocks', 3, 'text', 'text')
value: False keys: (1, 'blocks', 3, 'text', 'verbatim')
value: section keys: (1, 'blocks', 4, 'type')
value: JCfSP keys: (1, 'blocks', 4, 'block_id')
value: mrkdwn keys: (1, 'blocks', 4, 'text', 'type')
value: *New Customer? *
No keys: (1, 'blocks', 4, 'text', 'text')
value: False keys: (1, 'blocks', 4, 'text', 'verbatim')
value: actions keys: (1, 'blocks', 5, 'type')
value: yt4 keys: (1, 'blocks', 5, 'block_id')
value: button keys: (1, 'blocks', 5, 'elements', 0, 'type')
value: +bc keys: (1, 'blocks', 5, 'elements', 0, 'action_id')
value: plain_text keys: (1, 'blocks', 5, 'elements', 0, 'text', 'type')
value: View results keys: (1, 'blocks', 5, 'elements', 0, 'text', 'bar')
value: True keys: (1, 'blocks', 5, 'elements', 0, 'text', 'emoji')
value: www.example.com/results keys: (1, 'blocks', 5, 'elements', 0, 'url')
value: section keys: (1, 'blocks', 6, 'type')
value: RJOA keys: (1, 'blocks', 6, 'block_id')
value: mrkdwn keys: (1, 'blocks', 6, 'text', 'type')
value: keys: (1, 'blocks', 6, 'text', 'text')
value: False keys: (1, 'blocks', 6, 'text', 'verbatim')
https://stackoverflow.com/questions/73643077/how-to-transform-a-list-of-nested-dictionaries-into-a-data-frame-pd-json-normal
before:
[{'apple': {'price': 4, 'units': 3}},
{'banana': {'price': 2, 'units': 20}},
{'orange': {'price': 5, 'units': 15}}]
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: 3 keys: (0, 'apple', 'units')
value: 4 keys: (0, 'apple', 'price')
value: 20 keys: (1, 'banana', 'units')
value: 2 keys: (1, 'banana', 'price')
value: 15 keys: (2, 'orange', 'units')
value: 5 keys: (2, 'orange', 'price')
https://stackoverflow.com/questions/58110440/opening-nested-dict-in-a-single-column-to-multiple-columns-in-pandas
before:
{'simple25b': {'hands': {'0': {'currency': 'rm',
'handId': 'xyz',
'time': '2019-09-23 11:00:01'},
'1': {'currency': 'rm',
'handId': 'abc',
'time': '2019-09-23 11:01:18'}}},
'simple5af': {'hands': {'0': {'currency': 'rm',
'handId': 'akg',
'time': '2019-09-23 10:53:22'},
'1': {'currency': 'rm',
'handId': 'mzc',
'time': '2019-09-23 10:54:15'},
'2': {'currency': 'rm',
'handId': 'swk',
'time': '2019-09-23 10:56:03'},
'3': {'currency': 'rm',
'handId': 'pQc',
'time': '2019-09-23 10:57:15'},
'4': {'currency': 'rm',
'handId': 'ywh',
'time': '2019-09-23 10:58:53'}}}}
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: xyz keys: ('simple25b', 'hands', '0', 'handId')
value: 2019-09-23 11:00:01 keys: ('simple25b', 'hands', '0', 'time')
value: rm keys: ('simple25b', 'hands', '0', 'currency')
value: abc keys: ('simple25b', 'hands', '1', 'handId')
value: 2019-09-23 11:01:18 keys: ('simple25b', 'hands', '1', 'time')
value: rm keys: ('simple25b', 'hands', '1', 'currency')
value: akg keys: ('simple5af', 'hands', '0', 'handId')
value: 2019-09-23 10:53:22 keys: ('simple5af', 'hands', '0', 'time')
value: rm keys: ('simple5af', 'hands', '0', 'currency')
value: mzc keys: ('simple5af', 'hands', '1', 'handId')
value: 2019-09-23 10:54:15 keys: ('simple5af', 'hands', '1', 'time')
value: rm keys: ('simple5af', 'hands', '1', 'currency')
value: swk keys: ('simple5af', 'hands', '2', 'handId')
value: 2019-09-23 10:56:03 keys: ('simple5af', 'hands', '2', 'time')
value: rm keys: ('simple5af', 'hands', '2', 'currency')
value: pQc keys: ('simple5af', 'hands', '3', 'handId')
value: 2019-09-23 10:57:15 keys: ('simple5af', 'hands', '3', 'time')
value: rm keys: ('simple5af', 'hands', '3', 'currency')
value: ywh keys: ('simple5af', 'hands', '4', 'handId')
value: 2019-09-23 10:58:53 keys: ('simple5af', 'hands', '4', 'time')
value: rm keys: ('simple5af', 'hands', '4', 'currency')
https://stackoverflow.com/questions/62059970/how-can-i-convert-nested-dictionary-to-pd-dataframe-faster
before:
{'file': 'name',
'main': [{'answer': [{'comment': 'It is defined as',
'user': 'John',
'value': [{'my_value': 5, 'value_2': 10},
{'my_value': 24, 'value_2': 30}]},
{'comment': 'as John said above it simply means',
'user': 'Sam',
'value': [{'my_value': 9, 'value_2': 10},
{'my_value': 54, 'value_2': 19}]}],
'closed': 'no',
'question': 'what is ?',
'question_no': 'Q.1'}]}
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: name keys: ('file',)
value: Q.1 keys: ('main', 0, 'question_no')
value: what is ? keys: ('main', 0, 'question')
value: John keys: ('main', 0, 'answer', 0, 'user')
value: It is defined as keys: ('main', 0, 'answer', 0, 'comment')
value: 5 keys: ('main', 0, 'answer', 0, 'value', 0, 'my_value')
value: 10 keys: ('main', 0, 'answer', 0, 'value', 0, 'value_2')
value: 24 keys: ('main', 0, 'answer', 0, 'value', 1, 'my_value')
value: 30 keys: ('main', 0, 'answer', 0, 'value', 1, 'value_2')
value: Sam keys: ('main', 0, 'answer', 1, 'user')
value: as John said above it simply means keys: ('main', 0, 'answer', 1, 'comment')
value: 9 keys: ('main', 0, 'answer', 1, 'value', 0, 'my_value')
value: 10 keys: ('main', 0, 'answer', 1, 'value', 0, 'value_2')
value: 54 keys: ('main', 0, 'answer', 1, 'value', 1, 'my_value')
value: 19 keys: ('main', 0, 'answer', 1, 'value', 1, 'value_2')
value: no keys: ('main', 0, 'closed')
https://stackoverflow.com/questions/39634369/4-dimensional-nested-dictionary-to-pandas-data-frame
before:
{'orders': [{'created_at': '2016-09-20T22:04:49+02:00',
'email': 'test@aol.com',
'id': 4314127108,
'line_items': [{'destination_location': {'address1': 'Teststreet '
'12',
'address2': '',
'city': 'Berlin',
'country_code': 'DE',
'id': 2383331012,
'name': 'Test Test',
'zip': '10117'},
'gift_card': False,
'name': 'Blueberry Cup'},
{'destination_location': {'address1': 'Teststreet '
'12',
'address2': '',
'city': 'Berlin',
'country_code': 'DE',
'id': 2383331012,
'name': 'Test Test',
'zip': '10117'},
'gift_card': False,
'name': 'Strawberry Cup'}]}]}
Let's flatten it: flatten_nested_something_to_list_of_tuples(data)
value: 2016-09-20T22:04:49+02:00 keys: ('orders', 0, 'created_at')
value: test@aol.com keys: ('orders', 0, 'email')
value: 4314127108 keys: ('orders', 0, 'id')
value: Teststreet 12 keys: ('orders', 0, 'line_items', 0, 'destination_location', 'address1')
value: keys: ('orders', 0, 'line_items', 0, 'destination_location', 'address2')
value: Berlin keys: ('orders', 0, 'line_items', 0, 'destination_location', 'city')
value: DE keys: ('orders', 0, 'line_items', 0, 'destination_location', 'country_code')
value: 2383331012 keys: ('orders', 0, 'line_items', 0, 'destination_location', 'id')
value: Test Test keys: ('orders', 0, 'line_items', 0, 'destination_location', 'name')
value: 10117 keys: ('orders', 0, 'line_items', 0, 'destination_location', 'zip')
value: False keys: ('orders', 0, 'line_items', 0, 'gift_card')
value: Blueberry Cup keys: ('orders', 0, 'line_items', 0, 'name')
value: Teststreet 12 keys: ('orders', 0, 'line_items', 1, 'destination_location', 'address1')
value: keys: ('orders', 0, 'line_items', 1, 'destination_location', 'address2')
value: Berlin keys: ('orders', 0, 'line_items', 1, 'destination_location', 'city')
value: DE keys: ('orders', 0, 'line_items', 1, 'destination_location', 'country_code')
value: 2383331012 keys: ('orders', 0, 'line_items', 1, 'destination_location', 'id')
value: Test Test keys: ('orders', 0, 'line_items', 1, 'destination_location', 'name')
value: 10117 keys: ('orders', 0, 'line_items', 1, 'destination_location', 'zip')
value: False keys: ('orders', 0, 'line_items', 1, 'gift_card')
value: Strawberry Cup keys: ('orders', 0, 'line_items', 1, 'name')
#The code that I used
from pprint import pprint
def print__(data_, stacklink,original_):
print(stacklink)
print(f'before:')
pprint(original_)
print('\nLet\'s flatten it: flatten_nested_something_to_list_of_tuples(data)\n')
for value, keys in data_:
print(f'value: {str(value).ljust(40)} keys: {keys}')
print('\n\n\n')
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