基于 Python 列表从 yaml 文件中检索数据

Question

我在 ipython 工作； 我有一个 Yaml 文件和一个与我的 Yaml 文件对应的 [thomas] id 列表（thomas：文件的第三行）。 下面只是文件的一小部分。 完整的文件可以在这里找到（ 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

我想解析该文件，并为我的列表中与 [thomas:] 中的 Id 对应的每个 id 我想检索以下内容：[fec] :（可能不止一个，我需要所有这些） [姓名：] [第一个：] [中间：] [最后一个：]; [生物：] [生日：]; [terms:]（很可能有多个词条，我需要所有词条）[type:] [start:] [state:] [party:]。 最后，也可能存在 fec 数据不可用的情况。

1）我应该如何存储数据？ 我对 Python（我的第一种编程语言）还是比较陌生，不确定如何存储数据。 直觉上，我会说字典； 然而，最重要的是易于访问和数据检索。 以前，我将类似的嵌套数据存储为 csv。 这种方法看起来有点笨重。 如果我可以制作一个字典（我正在检索的数据）的列表（来自我拥有的 thomas id），这似乎是理想的。

2) 我不确定如何设置 for/while 语句，以便我只检索与我的 thomas id 列表相对应的数据。

我开始编写我期望将信息写入 CSV 的代码：

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()

我收到以下错误：

---------------------------------------------------------------------------
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

我认为您可以尝试解析 YAML 并将其加载到数据框，对其进行规范化：

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())

输出：

  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': ...

更新：

以下版本将过滤您的输入数据，因此只会处理包含“thomas”和“fec”的记录：

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')

输出：

   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，如您所见，这将复制您的行（请参阅： id.thomas和id.fec ），以便它可以显示为数据框

更新2

您可能还想将“id.fec”中的列表转换为列，但我会在其他数据框中进行：

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

print(df_fec.head())

输出：

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