Styjun

I am trying to work with two columns in a dataframe created from a JSON file. One column contains strings of country names, the other column contains a list of dictionaries. Each dictionary represents a given economic or socioeconomic project. Each list varies in size. Each dictionary contains two keys, 'code' and 'name', and there exists only one value for each key. The value for code is a string number i.e. '8' and the value for name is a string title i.e. 'human development'.
So, each country in the country column can appear more than once and with a list of dictionaries in the projects column. Most likely, there are redundant dictionaries. I would like to parse the information into two dataframes.
First, I would like to bag all the code values in a list so that when a country name appears in the country column, there is a list of code values right beside it in the projects column. When I am done gathering all the code values, I will have to count the number of distinct code values that correspond to a given country.
One dataframe should not have its dimensions altered. It should have the name of a country in the country column and the projects column should have a list of string numbers from the code key (already contained in the list of dictionaries for that element), instead of a list of dictionaries. So, one list of code values per country, even if the country appears more than once.[HELP]
The other dataframe will have its dimensionality altered. It will have all the DISTINCT code values collected in a list inside the projects column for a given country in the country column, instead of several appearances of a given country with potentially redundant elements from its list of string numbers. So, one country and one list of distinct code values. [HELP]

import pandas as pd
import numpy as np
import json
from pandas.io.json import json_normalize

df = pd.read_json('projects.json')
df_adj = df[['country','projects']]

for list_entry in df_adj['projects']:
 print(list_entry)

OUTPUT:
['code': '8', 'name': 'Human development', 'code': '11', 'name': '']
['code': '1', 'name': 'Economic management', 'code': '6', 'name': 'Social protection and risk management']
['code': '5', 'name': 'Trade and integration', 'code': '2', 'name': 'Public sector governance', 'code': '11', 'name': 'Environment and natural resources management', 'code': '6', 'name': 'Social protection and risk management']
['code': '7', 'name': 'Social dev/gender/inclusion', 'code': '7', 'name': 'Social dev/gender/inclusion']
['code': '5', 'name': 'Trade and integration', 'code': '4', 'name': 'Financial and private sector development']
['code': '6', 'name': 'Social protection and risk management', 'code': '6', 'name': '']
['code': '2', 'name': 'Public sector governance', 'code': '4', 'name': 'Financial and private sector development']
['code': '11', 'name': 'Environment and natural resources management', 'code': '8', 'name': '']
['code': '10', 'name': 'Rural development', 'code': '7', 'name': '']
['code': '2', 'name': 'Public sector governance', 'code': '2', 'name': 'Public sector governance', 'code': '2', 'name': 'Public sector governance']
['code': '10', 'name': 'Rural development', 'code': '2', 'name': '']
['code': '10', 'name': 'Rural development', 'code': '6', 'name': 'Social protection and risk management', 'code': '6', 'name': 'Social protection and risk management', 'code': '11', 'name': 'Environment and natural resources management']