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Need help translating a nested dictionary into a pandas dataframe

Looking into translating the following nested dictionary which is an API pull from Yelp into a pandas dataframe to run visualization on:
Top 50 Pizzerias in Chicago
{'businesses': [{'alias': 'pequods-pizzeria-chicago',
'categories': [{'alias': 'pizza', 'title': 'Pizza'}],
'coordinates': {'latitude': 41.92187, 'longitude': -87.664486},
'display_phone': '(773) 327-1512',
'distance': 2158.7084581522413,
'id': 'DXwSYgiXqIVNdO9dazel6w',
'image_url': 'https://s3-media1.fl.yelpcdn.com/bphoto/8QJUNblfCI0EDhOjuIWJ4A/o.jpg',
'is_closed': False,
'location': {'address1': '2207 N Clybourn Ave',
'address2': '',
'address3': '',
'city': 'Chicago',
'country': 'US',
'display_address': ['2207 N Clybourn Ave',
'Chicago, IL 60614'],
'state': 'IL',
'zip_code': '60614'},
'name': "Pequod's Pizzeria",
'phone': '+17733271512',
'price': '$$',
'rating': 4.0,
'review_count': 6586,
'transactions': ['restaurant_reservation', 'delivery'],
'url': 'https://www.yelp.com/biz/pequods-pizzeria-chicago?adjust_creative=wt2WY5Ii_urZB8YeHggW2g&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=wt2WY5Ii_urZB8YeHggW2g'},
{'alias': 'lou-malnatis-pizzeria-chicago',
'categories': [{'alias': 'pizza', 'title': 'Pizza'},
{'alias': 'italian', 'title': 'Italian'},
{'alias': 'sandwiches', 'title': 'Sandwiches'}],
'coordinates': {'latitude': 41.890357,
'longitude': -87.633704},
'display_phone': '(312) 828-9800',
'distance': 4000.9990531720227,
'id': '8vFJH_paXsMocmEO_KAa3w',
'image_url': 'https://s3-media3.fl.yelpcdn.com/bphoto/9FiL-9Pbytyg6usOE02lYg/o.jpg',
'is_closed': False,
'location': {'address1': '439 N Wells St',
'address2': '',
'address3': '',
'city': 'Chicago',
'country': 'US',
'display_address': ['439 N Wells St',
'Chicago, IL 60654'],
'state': 'IL',
'zip_code': '60654'},
'name': "Lou Malnati's Pizzeria",
'phone': '+13128289800',
'price': '$$',
'rating': 4.0,
'review_count': 6368,
'transactions': ['pickup', 'delivery'],
'url': 'https://www.yelp.com/biz/lou-malnatis-pizzeria-chicago?adjust_creative=wt2WY5Ii_urZB8YeHggW2g&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=wt2WY5Ii_urZB8YeHggW2g'},
....]
I've tried the below and iterations of it but haven't had any luck.
df = pd.DataFrame.from_dict(topresponse)
Im really new to coding so any advice would be helpful

response["businesses"] is a list of records, so:
df = pd.DataFrame.from_records(response["businesses"])

Python Decision Tree: Creating Relationship using Dictionary from Row data

I have a hierarchical data(more than 10 generation) which tells who a person's parent/children are. i would want to represent this as dict of dict. is there any way to achieve this.
sample input - List of Dict/Dataframe
[{'Name': 'Oli Bob', 'Location': 'United Kingdom', 'Parent': nan}, {'Name': 'Mary May', 'Location': 'Germany', 'Parent': 'Oli Bob'}, {'Name': 'Christine Lobowski', 'Location': 'France', 'Parent': 'Oli Bob'}, {'Name': 'Brendon Philips', 'Location': 'USA', 'Parent': 'Oli Bob'}, {'Name': 'Margret Marmajuke', 'Location': 'Canada', 'Parent': 'Brendon Philips'}, {'Name': 'Frank Harbours', 'Location': 'Russia', 'Parent': 'Brendon Philips'}, {'Name': 'Todd Philips', 'Location': 'United Kingdom', 'Parent': 'Frank Harbours'}, {'Name': 'Jamie Newhart', 'Location': 'India', 'Parent': nan}, {'Name': 'Gemma Jane', 'Location': 'China', 'Parent': nan}, {'Name': 'Emily Sykes', 'Location': 'South Korea', 'Parent': 'Emily Sykes'}, {'Name': 'James Newman', 'Location': 'Japan', 'Parent': nan}]
same data in table form
Desired Output
[
{name:"Oli Bob", location:"United Kingdom", _children:[
{name:"Mary May", location:"Germany"},
{name:"Christine Lobowski", location:"France"},
{name:"Brendon Philips", location:"USA",_children:[
{name:"Margret Marmajuke", location:"Canada"},
{name:"Frank Harbours", location:"Russia",_children:[{name:"Todd Philips", location:"United Kingdom"}]},
]},
]},
{name:"Jamie Newhart", location:"India"},
{name:"Gemma Jane", location:"China", _children:[
{name:"Emily Sykes", location:"South Korea"},
]},
{name:"James Newman", location:"Japan"},
];

Switching keys and values in a dictionary in python with multiple values

Given a dictionary like so (data from geonamescache):
{'3041563': {'geonameid': 3041563,
'name': 'Andorra la Vella',
'latitude': 42.50779,
'longitude': 1.52109,
'countrycode': 'AD',
'population': 20430,
'timezone': 'Europe/Andorra',
'admin1code': '07'},
'290594': {'geonameid': 290594,
'name': 'Umm Al Quwain City',
'latitude': 25.56473,
'longitude': 55.55517,
'countrycode': 'AE',
'population': 62747,
'timezone': 'Asia/Dubai',
'admin1code': '07'},
'291074': {'geonameid': 291074,
'name': 'Ras Al Khaimah City',
'latitude': 25.78953,
'longitude': 55.9432,
'countrycode': 'AE',
'population': 351943,
'timezone': 'Asia/Dubai',
'admin1code': '05'},....
How can I switch the keys with the value 'name', for all items in dict?
Meaning the city name will become the key for each item.
Expected output:
{'Andorra la Vella': {'geonameid': 3041563,
'latitude': 42.50779,
'longitude': 1.52109,
'countrycode': 'AD',
'population': 20430,
'timezone': 'Europe/Andorra',
'admin1code': '07'},
'Umm Al Quwain City': {'geonameid': 290594,
'latitude': 25.56473,
'longitude': 55.55517,
'countrycode': 'AE',
'population': 62747,
'timezone': 'Asia/Dubai',
'admin1code': '07'},
'Ras Al Khaimah City': {'geonameid': 291074,
'latitude': 25.78953,
'longitude': 55.9432,
'countrycode': 'AE',
'population': 351943,
'timezone': 'Asia/Dubai',
'admin1code': '05'},....

Are you looking for an output like this?
{'Andorra la Vella': {'admin1code': '07',
'countrycode': 'AD',
'geonameid': 3041563,
'latitude': 42.50779,
'longitude': 1.52109,
'name': 'Andorra la Vella',
'population': 20430,
'timezone': 'Europe/Andorra'},
'Ras Al Khaimah City': {'admin1code': '05',
'countrycode': 'AE',
'geonameid': 291074,
'latitude': 25.78953,
'longitude': 55.9432,
'name': 'Ras Al Khaimah City',
'population': 351943,
'timezone': 'Asia/Dubai'},
'Umm Al Quwain City': {'admin1code': '07',
'countrycode': 'AE',
'geonameid': 290594,
'latitude': 25.56473,
'longitude': 55.55517,
'name': 'Umm Al Quwain City',
'population': 62747,
'timezone': 'Asia/Dubai'}}
If so, you can create a new dictionary of this format from the existing one. Here is one way you can do it, where dicc is your existing dictionary.
newdic = {}
for key, val in dicc.items():
newdic[val['name']] = val
print(newdic)

Just reassign the values as the keys:
data = ... # Your data here
for geocode, area in data.items():
cityname = area["name"]
area[cityname] = "name"
del area["name"] # if you don’t want the ‘name’ key anymore
data[geocode] = area

sum values of specific keys in a dict

I have a a list of dicts that looks like this:
source_dict = [{'ppl': 10, 'items': 15, 'airport': 'lax', 'city': 'Los Angeles', 'timestamp': 1, 'region': 'North America', 'country': 'United States'},
{'ppl': 20, 'items': 32, 'airport': 'JFK', 'city': 'New York', 'timestamp': 2, 'region': 'North America', 'country': 'United States'},
{'ppl': 50, 'items': 20, 'airport': 'ABC', 'city': 'London', 'timestamp': 1, 'region': 'Europe', 'country': 'United Kingdom'}... ]
#Gets the list of countries in the dict
countries = list(set(stats['country'] for stats in source_dict))
I know I can use an a collection:
counter = collections.Counter()
for d in source_dict:
counter.update(d)
But, want to group by country and get totals for only certain keys not all of them.
So the result should be
{'Country': 'United States', 'p95': 30, 'items':37},
{'Country': 'England', 'ppl': 50, 'items':20},...
Im not sure how to incorporate multiple keys into a counter. To produce that result

This is one approach using collections.defaultdict & collections.Counter.
Ex:
from collections import defaultdict, Counter
source_dict = [{'ppl': 10, 'items': 15, 'airport': 'lax', 'city': 'Los Angeles', 'timestamp': 1, 'region': 'North America', 'country': 'United States'},
{'ppl': 20, 'items': 32, 'airport': 'JFK', 'city': 'New York', 'timestamp': 2, 'region': 'North America', 'country': 'United States'},
{'ppl': 50, 'items': 20, 'airport': 'ABC', 'city': 'London', 'timestamp': 1, 'region': 'Europe', 'country': 'United Kingdom'} ]
result = defaultdict(Counter)
for stats in source_dict:
result[stats['country']].update(Counter({'ppl': stats['ppl'], "items": stats['items']}))
#result = [{'Country': k, **v} for k, v in result.items()] #Required output
print(result)
Output:
defaultdict(<class 'collections.Counter'>,
{'United Kingdom': Counter({'ppl': 50, 'items': 20}),
'United States': Counter({'items': 47, 'ppl': 30})})

In pandas you can do this:
import io
import pandas as pd
dff=io.StringIO("""ppl,items,airport,city,timestamp,region,country
10,15,lax,Los Angeles,1,North America,United States
20,32,JFK,New York,2,North America,United States
50,20,ABC,London,1,Europe,United Kingdom""")
df3=pd.read_csv(dff)
df3
ppl items airport city timestamp region country
0 10 15 lax Los Angeles 1 North America United States
1 20 32 JFK New York 2 North America United States
2 50 20 ABC London 1 Europe United Kingdom
df3.groupby('region').agg({'ppl':'sum', 'items':'sum'})
# ppl items
#region
#Europe 50 20
#North America 30 47

how to find the no. of person from a particular country from the below code?

[
{'Year': 1901,
'Category': 'Chemistry',
'Prize': 'The Nobel Prize in Chemistry 1901',
'Motivation': '"in recognition of the extraordinary services he has rendered by the discovery of the laws of chemical dynamics and osmotic pressure in solutions"',
'Prize Share': '1/1',
'Laureate ID': 160,
'Laureate Type': 'Individual',
'Full Name': "Jacobus Henricus van 't Hoff",
'Birth Date': '1852-08-30',
'Birth City': 'Rotterdam',
'Birth Country': 'Netherlands',
'Sex': 'Male',
'Organization Name': 'Berlin University',
'Organization City': 'Berlin',
'Organization Country': 'Germany',
'Death Date': '1911-03-01',
'Death City': 'Berlin',
'Death Country': 'Germany'},
{'Year': 1901,
'Category': 'Literature',
'Prize': 'The Nobel Prize in Literature 1901',
'Motivation': '"in special recognition of his poetic composition, which gives evidence of lofty idealism, artistic perfection and a rare combination of the qualities of both heart and intellect"',
'Prize Share': '1/1',
'Laureate ID': 569,
'Laureate Type': 'Individual',
'Full Name': 'Sully Prudhomme',
'Birth Date': '1839-03-16',
'Birth City': 'Paris',
'Birth Country': 'France',
'Sex': 'Male',
'Organization Name': '',
'Organization City': '',
'Organization Country': '',
'Death Date': '1907-09-07',
'Death City': 'Châtenay',
'Death Country': 'France'}
]

If you want to find, how many person belong to same birth country only from given list of dict, you can use the following code :
from collections import Counter
li = [each['Birth City'] for each in val if each['Birth City']]
print(dict(Counter(li)))
OUTPUT
{'Rotterdam': 1, 'Paris': 1}