#!/usr/bin/python -tt
# Stephen Muchovej
apikey = 'your_api_key'
import sys
from sunlight import openstates
from transparencydata import TransparencyData
td = TransparencyData(apikey)
import pandas as pd
"""
This program obtains general information on the donors to a particular legislator.
It first obtains all the legislators for the state of CA, and then
cycles through all of those to keep relevant information and put it in a pandas
data frame.
It then writes that pandas dataframe to a mysql database
"""
# obtain the list of legislators in teh current session
all_legs = openstates.legislators(state='ca')
# obtain all donor information for the particular legislator in the past 3 years.
index = -1
for leg in all_legs:
thiscontribution = td.contributions(cycle='2013|2014|2015', recipient_ft=leg['last_name'].lower(), recipient_state='ca')
df = pd.DataFrame(thiscontribution)
# df.columns has the name of the column
index = index + 1
print index
if not df.empty:
# August Guang, February 2013
# corpSearch.py
# Takes in an input term (corporation name) and scrapes the InfluenceExplorer
# to run: python corpSearch.py -i <input> -o <output>
from transparencydata import TransparencyData
import sys, getopt
#import requests
import pprint
import itertools
import json
api = TransparencyData('8f0d91c66d4e428da018c0eb0fa571fc')
# reads CRP_Categories.txt
def readIn(inFile):
with open(inFile, 'r') as fin:
# format is:
# Catcode Catname Catorder Industry Sector Sector_Long
tmp = fin.readlines()
sectData = tmp[1:]
return sectData
# sorts CRP_Categories.txt so that all codes are associated with a sector in a dictionary
# this allows us to look up a code in the dictionary
def sectorDict(sectData):
sectDict = {}
for i in sectData:
sector = i.split()[4]
code = i.split()[0]
