So I can't seem to figure out how to effectively implement this. I'm looking to nest a flattened dictionary into dictionary of dictionaries of lists, based on specific keys as inputs. Trying so desperately to learn
Given that my data looks like this:
data= [
{
"player": "Kevin Durant",
"team": "Thunder",
"location": "Oklahoma City",
"points": 15
},
{
"player": "Jeremy Lin",
"team": "Lakers",
"location": "Los Angeles",
"points": 22
},
{
"player": "Kobe Bryant",
"team": "Lakers",
"location": "Los Angeles",
"points": 51
},
{
"player": "Blake Griffin",
"team": "Clippers",
"location": "Los Angeles",
"points": 26
}
]
I'd want to return something like this if I give it parameters like reorder(data,['location','team','player']) for an example
result={
"Los Angeles": {
"Clippers": {
"Blake Griffin": [
{
"points": 26
}
]
},
"Lakers": {
"Kobe Bryant": [
{
"points": 51
}
],
"Jeremy Lin": [
{
"points": 22
}
]
}
},
"Oklahoma City": {
"Thunder": {
"Kevin Durant": [
{
"points": 15
}
]
}
},
}
You can use the setdefault function to automatically build the nesting levels as you go through the data:
data= [
{
"player": "Kevin Durant",
"team": "Thunder",
"location": "Oklahoma City",
"points": 15
},
{
"player": "Jeremy Lin",
"team": "Lakers",
"location": "Los Angeles",
"points": 22
},
{
"player": "Kobe Bryant",
"team": "Lakers",
"location": "Los Angeles",
"points": 51
},
{
"player": "Blake Griffin",
"team": "Clippers",
"location": "Los Angeles",
"points": 26
}
]
nested = dict()
for d in data:
nested.setdefault(d["location"],dict()) \
.setdefault(d["team"], dict()) \
.setdefault(d["player"], list()) \
.append({"points":d["points"]})
output:
print(nested)
{ 'Oklahoma City':
{
'Thunder':
{ 'Kevin Durant': [{'points': 15}] }
},
'Los Angeles':
{
'Lakers':
{
'Jeremy Lin': [{'points': 22}],
'Kobe Bryant': [{'points': 51}]
},
'Clippers':
{ 'Blake Griffin': [{'points': 26}] }
}
}
[EDIT] Generalizing the approach
If you have to do this kind of thing often and on different types of dictionaries or hierarchies, you could generalize it in a function:
def dictNesting(data,*levels):
result = dict()
for d in data:
r = result
for level in levels[:-1]:
r = r.setdefault(d[level],dict())
r = r.setdefault(d[levels[-1]],list())
r.append({k:v for k,v in d.items() if k not in levels})
return result
You would then give the function a list of dictionaries followed by the names of the keys you want to nest:
byLocation = dictNesting(data,"location","team")
{ 'Oklahoma City':
{ 'Thunder': [
{'player': 'Kevin Durant', 'points': 15}]
},
'Los Angeles':
{'Lakers': [
{'player': 'Jeremy Lin', 'points': 22},
{'player': 'Kobe Bryant', 'points': 51}],
'Clippers': [
{'player': 'Blake Griffin', 'points': 26}]
}
}
If you want to group the same data in a different way, you just need to change the order of the field names:
byPlayer = dictNesting(data,"player","location","team")
{ 'Kevin Durant':
{ 'Oklahoma City':
{ 'Thunder': [{'points': 15}] }
},
'Jeremy Lin':
{ 'Los Angeles':
{'Lakers': [{'points': 22}]}
},
'Kobe Bryant':
{ 'Los Angeles':
{'Lakers': [{'points': 51}]}
},
'Blake Griffin':
{ 'Los Angeles':
{'Clippers': [{'points': 26}]}
}
}
From there you can have some fun with the function and improve it to aggregate the data at the lowest nesting level:
def dictNesting(data,*levels,aggregate=False):
result = dict()
for d in data:
r = result
for level in levels[:-1]:
r = r.setdefault(d[level],dict())
r = r.setdefault(d[levels[-1]],[list,dict][aggregate]())
content = ( (k,v) for k,v in d.items() if k not in levels)
if aggregate:
for k,v in content: r.setdefault(k,list()).append(v)
else:
r.append(dict(content))
return result
output:
byCity = dictNesting(data,"location","team",aggregate=True)
{ 'Oklahoma City':
{'Thunder':
{'player': ['Kevin Durant'], 'points': [15]}},
'Los Angeles':
{'Lakers':
{'player': ['Jeremy Lin', 'Kobe Bryant'], 'points': [22, 51]},
'Clippers':
{'player': ['Blake Griffin'], 'points': [26]}
}
}
lakersPlayers = byCity["Los Angeles"]["Lakers"]["player"]
# ['Jeremy Lin', 'Kobe Bryant']
lakersPoints = sum(byCity["Los Angeles"]["Lakers"]["points"])
# 73
Related
i have my sample data as
b = [{"id": 1, "name": {"d_name": "miranda", "ingredient": "orange"}, "score": 1.123},
{"id": 20, "name": {"d_name": "limca", "ingredient": "lime"}, "score": 4.231},
{"id": 3, "name": {"d_name": "coke", "ingredient": "water"}, "score": 4.231},
{"id": 2, "name": {"d_name": "fanta", "ingredient": "water"}, "score": 4.231},
{"id": 3, "name": {"d_name": "dew", "ingredient": "water & sugar"}, "score": 2.231}]
i need to sort such that score ASC, name DESC, id ASC (by relational db notation).
So far, i have implemented
def sort_func(e):
return (e['score'], e['name']['d_name'], e['id'])
a = b.sort(key=sort_func, reverse=False)
This works for score ASC, name ASC, id ASC.
but for score ASC, name DESC, id ASC if i try to sort by name DESC it throws error. because of unary - error in -e['name']['d_name'].
How can i approach this problem, from here ? Thanks,
Edit 1:
i need to make this dynamic such that there can be case such as e['name'['d_name'] ASC, e['name']['ingredient'] DESC. How can i handle this type of dynamic behaviour ?
You can sort by -score, name, -id with reverse=True:
from pprint import pprint
b = [
{
"id": 1,
"name": {"d_name": "miranda", "ingredient": "orange"},
"score": 1.123,
},
{
"id": 20,
"name": {"d_name": "limca", "ingredient": "lime"},
"score": 4.231,
},
{
"id": 3,
"name": {"d_name": "coke", "ingredient": "water"},
"score": 4.231,
},
{
"id": 2,
"name": {"d_name": "fanta", "ingredient": "water"},
"score": 4.231,
},
{
"id": 3,
"name": {"d_name": "dew", "ingredient": "water & sugar"},
"score": 2.231,
},
]
pprint(
sorted(
b,
key=lambda k: (-k["score"], k["name"]["d_name"], -k["id"]),
reverse=True,
)
)
Prints:
[{'id': 1,
'name': {'d_name': 'miranda', 'ingredient': 'orange'},
'score': 1.123},
{'id': 3,
'name': {'d_name': 'dew', 'ingredient': 'water & sugar'},
'score': 2.231},
{'id': 20, 'name': {'d_name': 'limca', 'ingredient': 'lime'}, 'score': 4.231},
{'id': 2, 'name': {'d_name': 'fanta', 'ingredient': 'water'}, 'score': 4.231},
{'id': 3, 'name': {'d_name': 'coke', 'ingredient': 'water'}, 'score': 4.231}]
I have 2 lists, looking like:
temp_data:
{
"id": 1,
"name": "test (replaced)",
"code": "test",
"last_update": "2020-01-01",
"online": false,
"data": {
"temperature": [
{
"date": "2019-12-17",
"value": 23.652905748126333
},
...
]}
hum_data:
{
"id": 1,
"name": "test (replaced)",
"code": "test",
"last_update": "2020-01-01",
"online": false,
"data": {
"humidity": [
{
"date": "2019-12-17",
"value": 23.652905748126333
},
...
]}
I need to merge the 2 lists to 1 without duplicating data. What is the easiest/efficient way? After merging, I want something like this:
{
"id": 1,
"name": "test",
"code": "test",
"last_update": "2020-01-01",
"online": false,
"data": {
"temperature": [
{
"date": "2019-12-17",
"value": 23.652905748126333
},
...
],
"humidity": [
{
"date": "2019-12-17",
"value": 23.652905748126333
},
...
Thanks for helping.
If your lists hum_data and temp_data are not sorted then first sort them and then concatenate the dictionaries pair-wise.
# To make comparisons for sorting
compare_function = lambda value : value['id']
# sort arrays before to make later concatenation easier
temp_data.sort(key=compare_function)
hum_data.sort(key=compare_function)
combined_data = temp_data.copy()
# concatenate the dictionries using the update function
for hum_row, combined_row in zip(hum_data, combined_data):
combined_row['data'].update(hum_row['data'])
# combined hum_data and temp_data
combined_data
If the lists are already sorted then you just need to concatenate dictionary by dictionary.
combined_data = temp_data.copy()
# concatenate the dictionries using the update function
for hum_row, combined_row in zip(hum_data, combined_data):
combined_row['data'].update(hum_row['data'])
# combined hum_data and temp_data
combined_data
With that code I got the following result:
[
{
'id': 1,
'name': 'test (replaced)',
'code': 'test',
'last_update': '2020-01-01',
'online': False,
'data': {
'temperature': [{'date': '2019-12-17', 'value': 1}],
'humidity': [{'date': '2019-12-17', 'value': 1}]}
},
{
'id': 2,
'name': 'test (replaced)',
'code': 'test',
'last_update': '2020-01-01',
'online': False,
'data': {
'temperature': [{'date': '2019-12-17', 'value': 2}],
'humidity': [{'date': '2019-12-17', 'value': 2}]}
}
]
I want to remove some duplicates in my merged dictionary.
My data:
mongo_data = [{
'url': 'https://goodreads.com/',
'variables': [{'key': 'Harry Potter', 'value': '10.0'},
{'key': 'Discovery of Witches', 'value': '8.5'},],
'vendor': 'Fantasy'
},{
'url': 'https://goodreads.com/',
'variables': [{'key': 'Hunger Games', 'value': '10.0'},
{'key': 'Maze Runner', 'value': '5.5'},],
'vendor': 'Dystopia'
},{
'url': 'https://kindle.com/',
'variables': [{'key': 'Divergent', 'value': '9.0'},
{'key': 'Lord of the Rings', 'value': '9.0'},],
'vendor': 'Fantasy'
},{
'url': 'https://kindle.com/',
'variables': [{'key': 'The Handmaids Tale', 'value': '10.0'},
{'key': 'Divergent', 'value': '9.0'},],
'vendor': 'Fantasy'
}]
My code:
for key, group in groupby(mongo_data, key=lambda chunk: chunk['url']):
search = {"url": key, "results": []}
for vendor, group2 in groupby(group, key=lambda chunk2: chunk2['vendor']):
result = {
"genre": vendor,
"data": [{'key': key['key'], 'value': key['value']}
for result2 in group2
for key in result2["variables"]],
}
search["results"].append(result)
searches.append(search)
My result:
[
{
"url": "https://goodreads.com/",
"results": [
{
"genre": "Fantasy",
"data": [
{
"key": "Harry Potter",
"value": "10.0"
},
{
"key": "Discovery of Witches",
"value": "8.5"
}
]
},
{
"genre": "Dystopia",
"data": [
{
"key": "Hunger Games",
"value": "10.0"
},
{
"key": "Maze Runner",
"value": "5.5"
}
]
}
]
},
{
"url": "https://kindle.com/",
"results": [
{
"genre": "Fantasy",
"data": [
{
"key": "Divergent",
"value": "9.0"
},
{
"key": "Lord of the Rings",
"value": "9.0"
},
{
"key": "The Handmaids Tale",
"value": "10.0"
},
{
"key": "Divergent",
"value": "9.0"
}
]
}
}
]
}
]
I do not want any duplicates in my structure. I am not sure on how to take them out. My expected result can be seen below.
Expected result:
[
{
"url": "https://goodreads.com/",
"results": [
{
"genre": "Fantasy",
"data": [
{
"key": "Harry Potter",
"value": "10.0"
},
{
"key": "Discovery of Witches",
"value": "8.5"
}
]
},
{
"genre": "Dystopia",
"data": [
{
"key": "Hunger Games",
"value": "10.0"
},
{
"key": "Maze Runner",
"value": "5.5"
}
]
}
]
},
{
"url": "https://kindle.com/",
"results": [
{
"genre": "Fantasy",
"data": [
{
"key": "Divergent",
"value": "9.0"
},
{
"key": "Lord of the Rings",
"value": "9.0"
},
{
"key": "The Handmaids Tale",
"value": "10.0"
}
]
}
}
]
}
]
Divergent is getting repeated in the last list of dictionaries. When I merged my dictionaries even the duplicates inside https://kindle.com/-->Fantasy got merged into one. Is there a way for me to remove the duplicate dictionary?
I want the https://kindle.com/ part to look like:
{
"url": "https://kindle.com/",
"results": [
{
"genre": "Fantasy",
"data": [
{
"key": "Divergent",
"value": "9.0"
},
{
"key": "Lord of the Rings",
"value": "9.0"
},
{
"key": "The Handmaids Tale",
"value": "10.0"
}
]
}
}
]
}
You can try convert those dict to a set of tuple first and then convert back to a list of dict later:
mongo_data = [{
'url': 'https://goodreads.com/',
'variables': [{'key': 'Harry Potter', 'value': '10.0'},
{'key': 'Discovery of Witches', 'value': '8.5'},],
'vendor': 'Fantasy'
},{
'url': 'https://goodreads.com/',
'variables': [{'key': 'Hunger Games', 'value': '10.0'},
{'key': 'Maze Runner', 'value': '5.5'},],
'vendor': 'Dystopia'
},{
'url': 'https://kindle.com/',
'variables': [{'key': 'Divergent', 'value': '9.0'},
{'key': 'Lord of the Rings', 'value': '9.0'},],
'vendor': 'Fantasy'
},{
'url': 'https://kindle.com/',
'variables': [{'key': 'The Handmaids Tale', 'value': '10.0'},
{'key': 'Divergent', 'value': '9.0'},],
'vendor': 'Fantasy'
}]
from itertools import groupby
searches = []
for key, group in groupby(mongo_data, key=lambda chunk: chunk['url']):
search = {"url": key, "results": []}
for vendor, group2 in groupby(group, key=lambda chunk2: chunk2['vendor']):
result = {
"genre": vendor,
"data": set((key['key'], key['value'])
for result2 in group2
for key in result2["variables"]),
}
result['data'] = [{"key": tup[0], "value": tup[1]} for tup in result['data']]
search["results"].append(result)
searches.append(search)
searches
Output:
[{'results': [{'data': [{'key': 'Harry Potter', 'value': '10.0'},
{'key': 'Discovery of Witches', 'value': '8.5'}],
'genre': 'Fantasy'},
{'data': [{'key': 'Maze Runner', 'value': '5.5'},
{'key': 'Hunger Games', 'value': '10.0'}],
'genre': 'Dystopia'}],
'url': 'https://goodreads.com/'},
{'results': [{'data': [{'key': 'The Handmaids Tale', 'value': '10.0'},
{'key': 'Lord of the Rings', 'value': '9.0'},
{'key': 'Divergent', 'value': '9.0'}],
'genre': 'Fantasy'}],
'url': 'https://kindle.com/'}]
Basically I´m reading a pandas dataframe and converting it to Json. I´m a beginner in coding, but I know that is preferable to use apply function instead iterrows (and I already tried to use apply function, but some difficulties in understand the syntax and find out my solution arose)!!
===============================
Data that I´m reading from excel
id label id_customer label_customer part_number number_customer product label_product key country value_product
6 Sao Paulo CUST-99992 Brazil 982 10 sho1564 shoes SH-99 Chile 1.5
6 Sao Paulo CUST-99992 Brazil 982 10 sn47282 sneakers SN-71 Germany 43.8
6 Sao Paulo CUST-43535 Argentina 435 15 sk84393 skirt SK-11 Netherlands 87.1
92 Hong Hong CUST-88888 China 785 58 ca40349 cap CA-82 Russia 3.95
===============================
CODE:
import pandas as pd
import json
df = pd.read_excel(path)
result = []
for labels, df1 in df.groupby(['id', 'label'],sort=False):
id_, label = labels
record = {'id': int(id_), 'label': label, 'Customer': []}
for inner_labels, df2 in df1.groupby(['id_customer', 'label_customer'],sort=False):
id_,label = inner_labels
record['Customer'].append({
'id': id_,
'label': label,
'Number': [{'part': str(p), 'number_customer': str(s)} for p, s in zip(df2['part_number'], df2['number_customer'])]
})
result.append(record)
===============================
Json I'm getting:
[
{
"id": 6,
"label": "Sao Paulo",
"Customer": [
{
"id": "CUST-99992",
"label": "Brazil",
"Number": [
{
"part": "982",
"number_customer": "10"
},
{
"part": "982",
"number_customer": "10"
}
]
},
{
"id": "CUST-43535",
"label": "Argentina",
"Number": [
{
"part": "435",
"number_customer": "15"
}
]
}
]
},
{
"id": 92,
"label": "Hong Kong",
"Customer": [
{
"id": "CUST-88888",
"label": "China",
"Number": [
{
"part": "785",
"number_customer": "58"
}
]
}
]
}
]
===============================
Json expected:
[
{
"id": 6,
"label": "Sao Paulo",
"Customer": [
{
"id": "CUST-99992",
"label": "Brazil",
"Number": [
{
"part": "982",
"number_customer": "10",
"Procucts": [
{
"product": "sho1564",
"label_product": "shoes",
"Order": [
{
"key": "SH-99",
"country": "Chile",
"value_product": "1.5"
}
]
},
{
"product": "sn47282",
"label_product": "sneakers",
"Order": [
{
"key": "SN-71",
"country": "Germany",
"value_product": "43.8"
}
]
}
]
}
]
},
{
"id": "CUST-43535",
"label": "Argentina",
"Number": [
{
"part": "435",
"number_customer": "15",
"Procucts": [
{
"product": "sk84393",
"label_product": "skirt",
"Order": [
{
"key": "SK-11",
"country": "Netherlands",
"value_product": "87.1"
}
]
}
]
}
]
}
]
},
{
"id": 92,
"label": "Hong Kong",
"Customer": [
{
"id": "CUST-88888",
"label": "China",
"Number": [
{
"part": "785",
"number_customer": "58",
"Procucts": [
{
"product": "ca40349",
"label_product": "cap",
"Order": [
{
"key": "CA-82",
"country": "Russia",
"value_product": "3.95"
}
]
}
]
}
]
}
]
}
]
===============================
Look that id and label is group of information even as id_customer and label customer is another group, part_number and number_customer is another, product and label_product another, key, country and value_product another.
My expected Json depends of my information inside my dataframe.
Can somebody help me in any way pls?
import pandas as pd
import json
df = pd.read_excel(path)
result = []
for labels, df1 in df.groupby(['id', 'label'], sort=False):
id_, label = labels
record = {'id': int(id_), 'label': label, 'Customer': []}
for inner_labels, df2 in df1.groupby(['id_customer', 'label_customer'], sort=False):
id_, label = inner_labels
customer = {'id': id_, 'label': label, 'Number': []}
for inner_labels, df3 in df2.groupby(['part_number', 'number_customer'], sort=False):
p, s = inner_labels
number = {'part': str(p), 'number_customer': str(s), 'Products': []}
for inner_labels, df4 in df3.groupby(['product', 'label_product'], sort=False):
p, lp = inner_labels
product = {'product': p, 'label_product': lp, 'Order': []}
for k, c, v in zip(df4['key'], df4['country'], df4['value_product']):
product['Order'].append({'key': k, 'country': c, 'value_product': v})
number['Products'].append(product)
customer['Number'].append(number)
record['Customer'].append(customer)
result.append(record)
Hope this is of use!
from io import StringIO
import pandas as pd
import json
csv = """id,label,id_customer,label_customer,part_number,number_customer,product,label_product,key,country,value_product
6,Sao Paulo,CUST-99992,Brazil,982,10,sho1564,shoes,SH-99,Chile,1.5
6,Sao Paulo,CUST-99992,Brazil,982,10,sn47282,sneakers,SN-71,Germany,43.8
6,Sao Paulo,CUST-43535,Argentina,435,15,sk84393,skirt,SK-11,Netherlands,87.1
92,Hong Hong,CUST-88888,China,785,58,ca40349,cap,CA-82,Russia,3.95"""
csv = StringIO(csv)
df = pd.read_csv(csv)
def split(df, groupby, json_func):
for x, group in df.groupby(groupby):
yield json_func(group, *x)
a = list(split(df, ['id', 'label'], lambda grp, id_, label: {"id": id_, "label": label, "Customer": list(
split(grp, ['id_customer', 'label_customer'], lambda grp_1, id_cust, label_cust: {"id": id_cust, "label": label_cust, "Number": list(
split(grp_1, ['part_number', 'number_customer'], lambda grp_2, part, num_cust: {"part": part, "number_customer": num_cust, "Products": list(
split(grp_2, ['product', 'label_product'], lambda grp_3, product, label_product: {"product": product, "label_product": label_product, "Order": list(
split(grp_3, ['key', 'country', 'value_product'], lambda _, key, country, value_product: {"key": key, "country": country, "value_product": value_product}))}
))})
)}))}))
display(a)
I would like to create a dictionary containing a nested structure of dictionaries, like bellow :
{
"Jaque": {
"ES": {
"Madrid": [
{
"experience": 9
}
]
},
"FR": {
"Lyon": [
{
"experience": 11.4
}
],
"Paris": [
{
"experience": 20
}
]
}
},
"James": {
"UK": {
"London": [
{
"experience": 10.9
}
]
}
},
"Henry": {
"UK": {
"London": [
{
"experience": 15
}
]
}
},
"Joe": {
"US": {
"Boston": [
{
"experience": 100
}
]
}
}
}
}
My input is a list of dictionaries of this format:
c = [{
"country": "US",
"city": "Boston",
"name": "Joe",
"experience": 100
},
{
"country": "FR",
"city": "Paris",
"name": "Jaque",
"experience": 20
},
{
"country": "FR",
"city": "Lyon",
"name": "Jaque",
"experience": 11.4
},
{
"country": "ES",
"city": "Madrid",
"name": "Jaque",
"experience": 9
},
{
"country": "UK",
"city": "London",
"name": "Henry",
"experience": 15
},
{
"country": "UK",
"city": "London",
"name": "James",
"experience": 10.9
}
]
My first approach was to create the nested dict, step by step:
dd = dict.fromkeys([i.get("name") for i in c],defaultdict(dict))
#will create
# dd = {'Joe': defaultdict(<class 'dict'>, {}), 'Jaque': defaultdict(<class 'dict'>, {}), 'James': defaultdict(<class 'dict'>, {}), 'Henry': defaultdict(<class 'dict'>, {})}
for i in dd:
for j in c:
#verify if name from d is in dict j
if i in j.values():
dd[i]=dict(zip([a.get("country") for a in c if i in a.values() ],[b.get("city") for b in c if i in b.values() ]))
# dd will become
#{'Joe': {'US': 'Boston'}, 'Jaque': {'FR': 'Lyon', 'ES': 'Madrid'}, 'Henry': {'UK': 'London'}, 'James': {'UK': 'London'}}
Now I can't figure a way to create/update the nested structure of dict dd. Is there a more dynamic way to create dict? Thx
You could use itertools.groupby to organize the list similarly to your expected output and then loop to convert to a dict.
from itertools import groupby
from operator import itemgetter
data = [{"country": "US", "city": "Boston", "name": "Joe", "experience": 100 }, {"country": "FR", "city": "Paris", "name": "Jaque", "experience": 20 }, {"country": "FR", "city": "Lyon", "name": "Jaque", "experience": 11.4 }, {"country": "ES", "city": "Madrid", "name": "Jaque", "experience": 9 }, {"country": "UK", "city": "London", "name": "Henry", "experience": 15 }, {"country": "UK", "city": "London", "name": "James", "experience": 10.9 } ]
result = {}
for key, values in groupby(sorted(data, key=itemgetter('name')), key=itemgetter('name')):
result[key] = {
v['country']: {v['city']: [{'experience': v['experience']}]} for v in values
}
print(result)
# {'Henry': {'UK': {'London': [{'experience': 15}]}}, 'James': {'UK': {'London': [{'experience': 10.9}]}}, 'Jaque': {'FR': {'Lyon': [{'experience': 11.4}]}, 'ES': {'Madrid': [{'experience': 9}]}}, 'Joe': {'US': {'Boston': [{'experience': 100}]}}}
You can use recursion with itertools.groupby:
from itertools import groupby
def group(d, keys = None):
key, *keys = keys
new_d = {a:list(b) for a, b in groupby(sorted(d, key=lambda x:x[key]), key=lambda x:x[key])}
t = {a:[{c:d for c, d in k.items() if c != key} for k in b] for a, b in new_d.items()}
return {a:group(b, keys) if not all(len(i) == 1 for i in b) else b for a, b in t.items()}
result = group(data, keys = ['name', 'country', 'city', 'experience'])
import json
print(json.dumps(result, indent=4)))
Output:
{
"Henry": {
"UK": {
"London": [
{
"experience": 15
}
]
}
},
"James": {
"UK": {
"London": [
{
"experience": 10.9
}
]
}
},
"Jaque": {
"ES": {
"Madrid": [
{
"experience": 9
}
]
},
"FR": {
"Lyon": [
{
"experience": 11.4
}
],
"Paris": [
{
"experience": 20
}
]
}
},
"Joe": {
"US": {
"Boston": [
{
"experience": 100
}
]
}
}
}