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pandas create a one row dataframe from nested dict

I know the "create pandas dataframe from nested dict" has a lot of entries here but I'm not found the answer that applies to my problem:
I have a dict like this:
{'id': 1,
'creator_user_id': {'id': 12170254,
'name': 'Nicolas',
'email': 'some_mail#some_email_provider.com',
'has_pic': 0,
'pic_hash': None,
'active_flag': True,
'value': 12170254}....,
and after reading with pandas look like this:
df = pd.DataFrame.from_dict(my_dict,orient='index')
print(df)
id 1
creator_user_id {'id': 12170254, 'name': 'Nicolas', 'email': '...
user_id {'id': 12264469, 'name': 'Daniela Giraldo G', ...
person_id {'active_flag': True, 'name': 'Cristina Cardoz...
org_id {'name': 'Cristina Cardozo', 'people_count': 1...
stage_id 2
title Cristina Cardozo
I would like to create a one-row dataframe where, for example, the nested creator_user_id column results in several columns that I after can name: creator_user_id_id, creator_user_id_name, etc.
thank you for your time!

Given you want one row, just use json_normalize()
pd.json_normalize({'id': 1,
'creator_user_id': {'id': 12170254,
'name': 'Nicolas',
'email': 'some_mail#some_email_provider.com',
'has_pic': 0,
'pic_hash': None,
'active_flag': True,
'value': 12170254}})

Converting data frame into a nested dictionary

Below is my subsetted data frame, I am having a hard time trying to convert it into my desired output as I am fairly new to Python. Essentially I want to create a nested dictionary inside a list, with the column names as a value, and then another nested dictionary inside a list. Is this doable?
import pandas as pd
Sector Community Name
0 centre: 10901.0 park: 3238.0
1 northeast: 6958.0 heights: 1955.0
Desired output:
[{'column': 'Sector',
'value': [{'name': 'centre', 'value': 10901.0},
{'name': 'northeast', 'value': 6958.0}]},
{'column': 'Community Name',
'value': [{'name': 'park', 'value': 3238.0},
{'name': 'heights', 'value': 1955.0},
{'name': 'hill', 'value': 1454.0}]}]

From #sushanth's answer, I may add up to this solution. Assume that your dataframe variable is defined as df.
result = []
for header in list(df):
column_values = df[header].to_list()
result.append({
"column" : header,
"value" : [dict(zip(['name', 'value'], str(value).split(":"))) for value in column_values]
})

Using pandas in above case might be a overkill, Here is a solution using python inbuilt functions which you can give a try,
input_ = {"Sector": ["centre: 10901.0", "northeast: 6958.0"],
"Community Name": ["park: 3238.0", "heights: 1955.0"]}
result = []
for k, v in input_.items():
result.append({
"column" : k,
"value" : [dict(zip(['name', 'value'], vv.split(":"))) for vv in v]
})
print(result)
[{'column': 'Sector',
'value': [{'name': 'centre', 'value': ' 10901.0'},
{'name': 'northeast', 'value': ' 6958.0'}]},
{'column': 'Community Name',
'value': [{'name': 'park', 'value': ' 3238.0'},
{'name': 'heights', 'value': ' 1955.0'}]}]

Pandas - Extracting values from a Dataframe column

I have a Dataframe in the below format:
cust_id, cust_details
101, [{'self': 'https://website.com/rest/api/2/customFieldOption/1', 'value': 'Type-A', 'id': '1'},
{'self': 'https://website.com/rest/api/2/customFieldOption/2', 'value': 'Type-B', 'id': '2'},
{'self': 'https://website.com/rest/api/2/customFieldOption/3', 'value': 'Type-C', 'id': '3'},
{'self': 'https://website.com/rest/api/2/customFieldOption/4', 'value': 'Type-D', 'id': '4'}]
102, [{'self': 'https://website.com/rest/api/2/customFieldOption/5', 'value': 'Type-X', 'id': '5'},
{'self': 'https://website.com/rest/api/2/customFieldOption/6', 'value': 'Type-Y', 'id': '6'}]
I am trying to extract for every cust_id all cust_detail values
Expected output:
cust_id, new_value
101,Type-A, Type-B, Type-C, Type-D
102,Type-X, Type-Y

Easy answer:
df['new_value'] = df.cust_details.apply(lambda ds: [d['value'] for d in ds])
More complex, potentially better answer:
Rather than storing lists of dictionaries in the first place, I'd recommend making each dictionary a row in the original dataframe.
df = pd.concat([
df['cust_id'],
pd.DataFrame(
df['cust_details'].explode().values.tolist(),
index=df['cust_details'].explode().index
)
], axis=1)
If you need to group values by id, you can do so via standard groupby methods:
df.groupby('cust_id')['value'].apply(list)
This may seem more complex, but depending on your use case might save you effort in the long-run.

pandas create new columns from dictionaries

a portion of one column 'relatedWorkOrder' in my dataframe looks like this:
{'number': 2552, 'labor': {'name': 'IA001', 'code': '70M0901003'}...}
{'number': 2552, 'labor': {'name': 'IA001', 'code': '70M0901003'}...}
{'number': 2552, 'labor': {'name': 'IA001', 'code': '70M0901003'}...}
My desired output is to have a column 'name','labor_name','labor_code' with their respective values. I can do this using regex extract and replace:
df['name'] = df['relatedWorkOrder'].str.extract(r'{regex}',expand=False).str.replace('something','')
But I have several dictionaries in this column and in this way is tedious, also I'm wondering if it's possible doing this through accessing the keys and values of the dictionary
Any help with that?

You can join the result from pd.json_normalize:
df.join(pd.json_normalize(df['relatedWorkOrder'], sep='_'))

API Call - Multi dimensional nested dictionary to pandas data frame

I need your help with converting a multidimensional dict to a pandas data frame. I get the dict from a JSON file which I retrieve from a API call (Shopify).
response = requests.get("URL", auth=("ID","KEY"))
data = json.loads(response.text)
The "data" dictionary looks as follows:
{'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'}]
}]}
In this case the dictionary has 4 Dimensions and I would like to convert the dict into a pandas data frame. I tried everything ranging from json_normalize() to pandas.DataFrame.from_dict(), yet I did not manage to get anywhere. When I try to convert the dict to a df, I get columns which contain list of lists.
Does anyone know how to approach that?
Thanks
EDITED:
Thank you #piRSquared. Your solution works fine! However, how you solve it if there was another product in the order? Because then it does work. JSON response of an order with 2 products is as follows (goals is to have a second row with the same "created_at". "email" etc. columns):
{'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'}]
}]}
So the df in the end should be on a row by row basis for all sold products. Thank you, I really appreciate your help!

There are a number of ways to do this. This is just a way I decided to do it. You need to explore how you want to see this represented, then figure out how to get there.
df = pd.DataFrame(data['orders'])
df1 = df.line_items.str[0].apply(pd.Series)
df2 = df1.destination_location.apply(pd.Series)
pd.concat([df.drop('line_items', 1), df1.drop('destination_location', 1), df2],
axis=1, keys=['', 'line_items', 'destination_location'])