Write a program to load the JSON data file 'NYC0-US-Senators-2019.json' *** and perform a data analysis over the file. It should load the file as a list of dictionary and then allow the user to perform the following functions:
Consider writing 1. and 2. as functions which take the list of senators as input and output a dictionary of the party and gender counts respectively.
HINT: Build dictionary of the values and and count them. That way you don't have to know the the parties or genders in advance. Basically this works like the word count example from the severance reading and is a common approach used in computing to aggegrate values.
Sample run:
Analysis of US Senators:
Party Counts {'republican': 53, 'democrat': 45, 'independent': 2}
Gender Counts {'male': 75, 'female': 25}*** This file was extracted from: https://www.govtrack.us/api/v2/role?current=true&role_type=senator
In [15]:
# Write code
def count_gender(senators):
gender_dict = {}
for senator in senators:
gender = senator['person']['gender'].lower()
gender_dict[gender] = gender_dict.get(gender,0) +1
return gender_dict
def count_party(senators):
party_dict = {}
for senator in senators:
party = senator['party'].lower()
party_dict[party] = party_dict.get(party,0) +1
return party_dict
file = 'NYC0-US-Senators-2019.json'
with open(file, 'r') as f:
senators = json.load(f)
gender = count_gender(senators)
party = count_party(senators)
print("Analysis of US Senators: ")
print(f"Party Counts {party}")
print(f"Gender Counts {gender}")
In [5]:
# code to update the senators in case you want fresh data!
import requests
import json
senators = requests.get('https://www.govtrack.us/api/v2/role?current=true&role_type=senator').json()['objects']
with open('NYC0-US-Senators-2019.json','w') as f:
f.write(json.dumps(senators))