Retrieving data from a yaml file based on a Python list

Question

I'm working in ipython; I have a Yaml file and a list of [thomas] ids corresponding to my Yaml file (thomas: -third row down on the file). Below is just a small snippet of the file. The complete file can be found here (https://github.com/108michael/congress-legislators/blob/master/legislators-historical.yaml)

   - id:
    bioguide: C000858
    thomas: '00246'
    lis: S215
    govtrack: 300029
    opensecrets: N00002091
    votesmart: 53288
    icpsr: 14809
    fec:
    - S0ID00057
    wikipedia: Larry Craig
    house_history: 11530
  name:
    first: Larry
    middle: E.
    last: Craig
  bio:
    birthday: '1945-07-20'
    gender: M
    religion: Methodist
  terms:
  - type: rep
    start: '1981-01-05'
    end: '1983-01-03'
    state: ID
    district: 1
    party: Republican
  - type: rep
    start: '1983-01-03'
    end: '1985-01-03'
    state: ID
    district: 1
    party: Republican

I want to parse the file and for every id in my list that corresponds to an Id in [thomas:] I want to retrieve the following: [fec]: (there could be more than one of these, I need all of them) [name:] [first:] [middle:] [last:]; [bio:] [birthday:]; [terms:] (it is likely that there is more than one term, I need for all terms) [type:] [start:] [state:] [party:]. Finally, there may also be instances where the fec data is not available.

1) How should I store the data? I am still relatively new to Python (my first programing language) and am not sure how to store the data. Intuitively, I would say dictionary; however what is paramount is ease of access and data retrieval. Previously, I have stored similarly nested data as csv. This method seems a little bit bulky. It seems that it would be ideal if I could just make a list (from the thomas ids that I have) of dictionaries (the data I am retrieving).

2) I'm not sure how to set up the for/while statements so that I only retrieve data corresponding to my list of thomas ids.

I started with writing what I expect would be the code for writing the info to CSV:

import pandas as pd
import yaml
import glob
import CSV
df = pd.concat((pd.read_csv(f, names=['date','bill_id','sponsor_id']) for f in glob.glob('/home/jayaramdas/anaconda3/df/s11?_s_b')))

outputfile = open('sponsor_details', 'W', newline='')
outputwriter = csv.writer(outputfile)

df = df.drop_duplicates('sponsor_id')
sponsor_list = df['sponsor_id'].tolist()

with open('legislators-historical.yaml', 'r') as f:
    data = yaml.load(f)

    for sponsor in sponsor_list:
        where sponsor == data[0]['thomas']:
            x = data[0]['thomas']
            a = data[0]['name']['first']
            b = data[0]['name']['middle']
            c = data[0]['name']['last']
            d = data[0]['bio']['gender']
            e = data[0]['bio']['religion']

            for fec in data[0]['id']:
                c = fec.get('fec')    

                for terms in data[0]['id']:
                    t = terms.get('type')  
                    s = terms.get('start')  
                    state = terms.get('state')
                    p = terms.get('party')

    outputwriter.writerow([x, a, b, c, d, e, c, t, s, state, p])
    outputfile.flush()

I get the following error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-48-057d25de7e11> in <module>()
     15 
     16     for sponsor in sponsor_list:
---> 17         if sponsor == data[0]['thomas']:
     18             x = data[0]['thomas']
     19             a = data[0]['name']['first']

KeyError: 'thomas'

Answer 1

I think you may try to parse YAML and load it to data frame, normalizing it:

import pandas as pd
from yaml import safe_load

with open('legislators-historical.yaml', 'r') as f:
    df = pd.json_normalize(safe_load(f))

print(df.head())

Output:

  bio.birthday bio.gender bio.religion id.bioguide       id.fec  id.govtrack  \
0   1943-12-02          M   Protestant     A000109  [S6CO00168]       300003
1   1745-04-02          M          NaN     B000226          NaN       401222
2   1742-03-21          M          NaN     B000546          NaN       401521
3   1743-06-16          M          NaN     B001086          NaN       402032
4   1730-07-22          M          NaN     C000187          NaN       402334

   id.house_history  id.icpsr id.lis id.opensecrets id.thomas  id.votesmart  \
0              8410     29108   S250      N00009082     00011         26783
1               NaN       507    NaN            NaN       NaN           NaN
2              9479       786    NaN            NaN       NaN           NaN
3             10177      1260    NaN            NaN       NaN           NaN
4             10687      1538    NaN            NaN       NaN           NaN

     id.wikipedia  name.first name.last name.middle  \
0    Wayne Allard       Wayne    Allard          A.
1             NaN     Richard   Bassett         NaN
2             NaN  Theodorick     Bland         NaN
3   Aedanus Burke     Aedanus     Burke         NaN
4  Daniel Carroll      Daniel   Carroll         NaN

                                               terms
0  [{'party': 'Republican', 'type': 'rep', 'state...
1  [{'party': 'Anti-Administration', 'type': 'sen...
2  [{'end': '1791-03-03', 'district': 9, 'type': ...
3  [{'end': '1791-03-03', 'district': 2, 'type': ...
4  [{'end': '1791-03-03', 'district': 6, 'type': ...

UPDATE:

the following version will filter your input data so only records containing "thomas" and "fec" will be processed:

import pandas as pd
from yaml import safe_load

def read_yaml(fn):
    with open(fn, 'r') as fi:
        return safe_load(fi)

def filter_data(data):
    result_data = []
    for x in data:
        if 'id' not in x:   continue
        if 'fec' not in x['id']:    continue
        if 'thomas' not in x['id']: continue
        result_data.append(x)
    return result_data


fn = 'aaa.yaml'


df = pd.json_normalize(filter_data(read_yaml(fn)), 'terms', [['id', 'fec'], ['id', 'thomas']])
print(df.head())

df.to_csv('out.csv')

Output:

   class  district         end       party       start state type  \
0    NaN         4  1993-01-03  Republican  1991-01-03    CO  rep
1    NaN         4  1995-01-03  Republican  1993-01-05    CO  rep
2    NaN         4  1997-01-03  Republican  1995-01-04    CO  rep
3      2       NaN  2003-01-03  Republican  1997-01-07    CO  sen
4      2       NaN  2009-01-03  Republican  2003-01-07    CO  sen

                        url id.thomas     id.fec
0                       NaN     00011  S6CO00168
1                       NaN     00011  S6CO00168
2                       NaN     00011  S6CO00168
3                       NaN     00011  S6CO00168
4  http://allard.senate.gov     00011  S6CO00168

PS as you see this will duplicate your rows (see: id.thomas and id.fec) so that it can be shown as a data frame

UPDATE2

You may also want to convert lists in 'id.fec' into columns, but i would do it in additional data frame:

df_fec = df['id.fec'].apply(pd.Series)

print(df_fec.head())

Output:

           0          1
0  S8AR00112  H2AR01022
1  S8AR00112  H2AR01022
2  S8AR00112  H2AR01022
3  S8AR00112  H2AR01022
4  S6CO00168        NaN