识别并存储在字典中多次出现的值 (Python)

Question

我有一个字典列表，其中重复了一些“术语”值：

terms_dict = [{'term': 'potato', 'cui': '123AB'}, {'term': 'carrot', 'cui': '222AB'}, {'term': 'potato', 'cui': '456AB'}]

如您所见，术语 'potato' 值出现了不止一次。我想将此 'term' 作为变量存储以备将来参考。然后，从 terms_dict 中删除所有这些重复的术语，只留下列表中的术语 'carrot' 词典。

期望输出：

repeated_terms = ['potato'] ## identified and stored terms that are repeated in terms_dict. 

new_terms_dict = [{'term': 'carrot', 'cui': '222AB'}] ## new dict with the unique term.

想法：

我当然可以创建一个包含独特术语的新词典，但是，我一直坚持实际识别重复的“术语”并将其存储在列表中。

是否有 finding/printing/storing 重复值的 pythonic 方式？

Answer 1

您可以使用 collections.Counter 来完成任务：

from collections import Counter

terms_dict = [
    {"term": "potato", "cui": "123AB"},
    {"term": "carrot", "cui": "222AB"},
    {"term": "potato", "cui": "456AB"},
]

c = Counter(d["term"] for d in terms_dict)

repeated_terms = [k for k, v in c.items() if v > 1]
new_terms_dict = [d for d in terms_dict if c[d["term"]] == 1]

print(repeated_terms)
print(new_terms_dict)

打印：

['potato']
[{'term': 'carrot', 'cui': '222AB'}]

Answer 2

您可以使用 drop_duplicates 和 duplicated 来自 pandas:

>>> import pandas as pd
>>> df = pd.DataFrame(terms_dict)
>>> df.term[df.term.duplicated()].tolist() # repeats
['potato']
>>> df.drop_duplicates('term', keep=False).to_dict('records') # without repeats
[{'term': 'carrot', 'cui': '222AB'}]

识别并存储在字典中多次出现的值 (Python)

Identify & store the values that appear multiple times in a dictionary (Python)

python

dictionary

list

key-value