#!/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:
import csv
import json
from transparencydata import TransparencyData
from influenceexplorer import InfluenceExplorer
td = TransparencyData('4df4c44769f4411a982d024313deb894')
api = InfluenceExplorer('4df4c44769f4411a982d024313deb894')
# Dictionary for ids->[name, probably party]
crp_ids = {}
with open('CRP_IDs.csv', 'rb') as f:
reader = csv.reader(f)
for row in reader:
if(row[0] != ''):
crp_ids[row[0]] = [row[1],row[2]]
# Dictionary for names-> td_ids
# We can use these td_ids to access the
# influence explorer methods.
#cand_ids = {}
#for cand_id in crp_ids:
# try:
# ent = api.entities.id_lookup(namespace='urn:crp:recipient', id=str(cand_id))
# cand_ids[crp_ids[cand_id][0]] = ent[0][u'id'].encode('ascii')
# print 'found candidate ', crp_ids[cand_id][0]
# except:
# print 'error in finding candidate ' + crp_ids[cand_id][0] + ' in database'
# continue
# 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]
# 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]
from influenceexplorer import InfluenceExplorer
from transparencydata import TransparencyData
import numpy as np
import matplotlib.pyplot as plt
api_key = '81ae602f16f34cbc9fe2643c7691f3d3'
ie = InfluenceExplorer(api_key)
td = TransparencyData(api_key)
city_name = ""
while city_name != "0":
city_name = raw_input("Enter a city: ")
state_name = raw_input("Enter a state abbreviation: ")
bTotal = []
oTotal = []
sTotal = []
hTotal = []
for i in td.earmarks(city=city_name, state=state_name):
print i['fiscal_year'],
print "Budget:", i['budget_amount'],
print "\tOmni:", i['omni_amount'],
print "\tSenate", i['senate_amount'],
print "\tHouse:", i['house_amount'],
print "\n\t",
print i['members'],
print "\n\t",
# 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]
# 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:
# August Guang, February 2013
# corpSearch.py
# Takes in an input term (corporation name) and scrapes the InfluenceExplorer
# to run: python corpSearch.py -i <input>
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]
