如果一个字典中的值存在于另一个字典中，则将两个字典合并为一个答案

【问题标题】：Combining two dictionaries into one if the value from the one dictionary exist in another如果一个字典中的值存在于另一个字典中，则将两个字典合并为一个
【发布时间】：2021-09-03 13:59:17
【问题描述】：

我正在尝试构建一个名为 author_venues 的字典，其中作者姓名是键，值是他们发布的场所列表。

给了我两本字典：一个示例 author_pubs 字典，其中作者姓名是键，出版物 ID 列表是值

defaultdict(list,
            {'José A. Blakeley': ['2',
              '25',
              '2018',
              '2185',
              '94602',
              '145114',
              '182779',
              '182780',
              '299422',
              '299426',
              '299428',
              '299558',
              '302125',
              '511816',
              '521294',
              '597967',
              '598123',
              '598125',
              '598130',
              '598132',
              '598134',
              '598136',
              '598620',
              '600180',
              '600221',
              '642049',
              '643606',
              '808458',
              '832249',
              '938531',
              '939047',
              '1064640',
              '1064641',
              '1065929',
              '1118153',
              '1269074',
              '2984279',
              '3154713',
              '3169639',
              '3286099',
              '3494140'],
             'Yuri Breitbart': ['3',
              '4',
              '76914',
              '113875',
              '140847',
              '147900',
              '147901',
              '150951',
              '176221',
              '176896',
              '182963',
              '200336',
              '262940',
              '285098',
              '285564',
              '299526',
              '301313',
              '303418',
              '304160',
              '400040',
              '400041',
              '400174',
              '400175',
              '402178',
              '482506',
              '482785',
              '544757',
              '545233',
              '545429',
              '559737',
              '559761',
              '559765',
              '559783',
              '559785',
              '597889',
              '598201',
              '598202',
              '598203',
              '599325',
              '599899',
              '620806',
              '636455',
              '641884',
              '642157',
              '654200',
              '654201',
              '740600',
              '740602',
              '833336',
              '844280',
              '856032',
              '856222',
              '888870',
              '934979',
              '938228',
              '941484',
              '945339',
              '949548',
              '971592',
              '971593',
              '972813',
              '972958',
              '1064100',
              '1064690',
              '1064691',
              '1064693',
              '1064694',
              '1078369',
              '1078370',
              '1089675',
              '1095084',
              '1121956',
              '1122006',
              '1122610',
              '1127610',
              '1138059',
              '1138061',
              '1141938',
              '1227365',
              '1278703',
              '1319498',
              '2818906',
              '2876867',
              '2978458',
              '3015058',
              '3223418'],

场地名称为键，发布 ID 列表为值的示例场地_pubs 字典

defaultdict(list,
            {'Modern Database Systems': ['2',
              '3',
              '4',
              '5',
              '6',
              '7',
              '8',
              '9',
              '10',
              '11',
              '12',
              '13',
              '14',
              '15',
              '16',
              '17',
              '18',
              '19',
              '20',
              '21',
              '22',
              '23',
              '24',
              '25',
              '26',
              '27',
              '28',
              '29',
              '30',
              '31',
              '32',
              '33',
              '34',
              '1203459',
              '3000615',
              '3000616',
              '3000617',
              '3000618',
              '3000619',
              '3000620',
              '3000621',
              '3000622',
              '3000623',
              '3000624',
              '3000625',
              '3000626'],
             'Object-Oriented Concepts, Databases, and Applications': ['36',
              '37',
              '38',
              '39',
              '40',
              '41',
              '42',
              '43',
              '44',
              '45',
              '46',
              '47',
              '48',
              '49',
              '50',
              '51',
              '52',
              '53',
              '54',
              '55',
              '56',
              '57',
              '58',
              '59'],
             'The INGRES Papers': ['60',
              '61',
              '62',
              '63',
              '64',
              '65',
              '66',
              '67',
              '68',
              '69'],
             'Temporal Databases': ['168',
              '169',
              '170',
              '171',
              '172',
              '173',
              '174',
              '175',
              '176',
              '177',
              '178',
              '179',
              '180',
              '181',
              '182',
              '183',
              '184',
              '185',
              '186',
              '187',
              '188',
              '189',
              '190',
              '627582',
              '627584',
              '627588',
              '627589',
              '627591',
              '627592',
              '627593',
              '627594',
              '627596',
              '627600',
              '627601',
              '627602',
              '627603',
              '627604',
              '627605',
              '627608',
              '627613',
              '627615',
              '627616',
              '627617'],

生成的字典应类似于 {'author':['venue1','venue2','venue3']} author_venue = defaultdict(list)

这是我写的代码：

for k,v in author_pubs.items():
    for item in v:
        for x,y in venue_pubs.items():
            if item in y:
                venue = x
                author_venue[k].append(venue)

但是这个循环需要很长时间，因为我有超过 300 万条记录

请帮忙！

【问题讨论】：

标签： python list dictionary filter

【解决方案1】：

您可以“反转”字典venue_pubs 以加快搜索速度：

from collections import defaultdict


author_pubs = {
    "author1": [1, 2, 3],
    "author2": [3, 4, 5],
}

venue_pubs = {
    "xxx1": [1, 4, 20],
    "xxx2": [4, 30, 40],
}

# "invert" dictionary `venue_pubs`:
tmp = defaultdict(list)
for k, v in venue_pubs.items():
    for val in v:
        tmp[val].append(k)


author_venue = defaultdict(list)

for k, v in author_pubs.items():
    for item in v:
        venues = tmp.get(item)
        if not venues is None:
            author_venue[k].extend(venues)

print(author_venue)

打印：

defaultdict(<class 'list'>, {'author1': ['xxx1'], 'author2': ['xxx1', 'xxx2']})

编辑：删除重复项：

# ...

for k in author_venue:
    author_venue[k] = list(set(author_venue[k]))

print(author_venue)

【讨论】：

非常感谢！还有一个问题，我如何只为每个作者保留唯一的场地？
@YoungZ 看set() 只需将author_venue 字典中list 的值转换为set 即可