In [5]:

    

import us
import pandas as pd
import numpy as np


    

In [6]:

    

for state in us.states.STATES:
    print '['+state.name+']({{ site.baseurl }}/states/'+state.abbr+'/)'


    

    




[Alabama]({{ site.baseurl }}/states/AL/)
[Alaska]({{ site.baseurl }}/states/AK/)
[Arizona]({{ site.baseurl }}/states/AZ/)
[Arkansas]({{ site.baseurl }}/states/AR/)
[California]({{ site.baseurl }}/states/CA/)
[Colorado]({{ site.baseurl }}/states/CO/)
[Connecticut]({{ site.baseurl }}/states/CT/)
[Delaware]({{ site.baseurl }}/states/DE/)
[District of Columbia]({{ site.baseurl }}/states/DC/)
[Florida]({{ site.baseurl }}/states/FL/)
[Georgia]({{ site.baseurl }}/states/GA/)
[Hawaii]({{ site.baseurl }}/states/HI/)
[Idaho]({{ site.baseurl }}/states/ID/)
[Illinois]({{ site.baseurl }}/states/IL/)
[Indiana]({{ site.baseurl }}/states/IN/)
[Iowa]({{ site.baseurl }}/states/IA/)
[Kansas]({{ site.baseurl }}/states/KS/)
[Kentucky]({{ site.baseurl }}/states/KY/)
[Louisiana]({{ site.baseurl }}/states/LA/)
[Maine]({{ site.baseurl }}/states/ME/)
[Maryland]({{ site.baseurl }}/states/MD/)
[Massachusetts]({{ site.baseurl }}/states/MA/)
[Michigan]({{ site.baseurl }}/states/MI/)
[Minnesota]({{ site.baseurl }}/states/MN/)
[Mississippi]({{ site.baseurl }}/states/MS/)
[Missouri]({{ site.baseurl }}/states/MO/)
[Montana]({{ site.baseurl }}/states/MT/)
[Nebraska]({{ site.baseurl }}/states/NE/)
[Nevada]({{ site.baseurl }}/states/NV/)
[New Hampshire]({{ site.baseurl }}/states/NH/)
[New Jersey]({{ site.baseurl }}/states/NJ/)
[New Mexico]({{ site.baseurl }}/states/NM/)
[New York]({{ site.baseurl }}/states/NY/)
[North Carolina]({{ site.baseurl }}/states/NC/)
[North Dakota]({{ site.baseurl }}/states/ND/)
[Ohio]({{ site.baseurl }}/states/OH/)
[Oklahoma]({{ site.baseurl }}/states/OK/)
[Oregon]({{ site.baseurl }}/states/OR/)
[Pennsylvania]({{ site.baseurl }}/states/PA/)
[Rhode Island]({{ site.baseurl }}/states/RI/)
[South Carolina]({{ site.baseurl }}/states/SC/)
[South Dakota]({{ site.baseurl }}/states/SD/)
[Tennessee]({{ site.baseurl }}/states/TN/)
[Texas]({{ site.baseurl }}/states/TX/)
[Utah]({{ site.baseurl }}/states/UT/)
[Vermont]({{ site.baseurl }}/states/VT/)
[Virginia]({{ site.baseurl }}/states/VA/)
[Washington]({{ site.baseurl }}/states/WA/)
[West Virginia]({{ site.baseurl }}/states/WV/)
[Wisconsin]({{ site.baseurl }}/states/WI/)
[Wyoming]({{ site.baseurl }}/states/WY/)

In [8]:

    

data = pd.read_csv('../data/legislators.csv')


    

In [9]:

    

data = data[data['in_office'] == 1]


    

In [10]:

    

fullnames = []
for names in zip(data['firstname'], data['lastname']):
    fullnames.append(names[0]+' '+names[1])


    

In [11]:

    

fulldist = []
for this in zip(data['state'],data['district']):
    if len(this[1]) == 2:
        fulldist.append(this[0]+'-'+this[1])
    if len(this[1]) == 1:
        fulldist.append(this[0]+'-0'+this[1])
    if len(this[1]) > 2:
        fulldist.append(this[0]+'-'+this[0])


    

In [12]:

    

df = pd.DataFrame([fullnames,fulldist,list(data['party']),list(data['state']),list(data['bioguide_id'])])


    

In [13]:

    

df = df.T


    

In [16]:

    

df.to_pickle('../cleaned_data/legislator_key_info')


    

In [ ]: