pandas 删除重复的多列并忽略方向[重复]

blmhpbnm 于 2023-08-01 发布在其他

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drop duplicates on multiple columns irrespective of the order (a/b == b/a) [duplicate]（1个答案）
Efficient way in Pandas for removing columns with duplicate values in different columns（1个答案）
19小时前关闭。
我有以下dataframe：

data = {
    'person1_name': ['John_Ethan_Wayne', 'John_Ethan_Wayne', 'Michael_Wayne', 'Michael_Wayne', 'Patrick_Wayne', 'Patrick_Wayne'],
    'family1_name': ['Wayne', 'Wayne', 'Wayne', 'Wayne', 'Wayne', 'Wayne'],
    'person2_name': ['Michael_Wayne', 'Patrick_Wayne', 'Patrick_Wayne', 'John_Ethan_Wayne', 'John_Ethan_Wayne', 'Michael_Wayne'],
    'family2_name': ['Wayne', 'Wayne', 'Wayne', 'Wayne', 'Wayne', 'Wayne']
}

df = pd.DataFrame(data)

     person1_name family1_name      person2_name family2_name
 John_Ethan_Wayne        Wayne     Michael_Wayne        Wayne
 John_Ethan_Wayne        Wayne     Patrick_Wayne        Wayne
    Michael_Wayne        Wayne     Patrick_Wayne        Wayne
    Michael_Wayne        Wayne  John_Ethan_Wayne        Wayne
    Patrick_Wayne        Wayne  John_Ethan_Wayne        Wayne
    Patrick_Wayne        Wayne     Michael_Wayne        Wayne

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我想删除(person1_name, family1_name)和(person2_name, family2_name)的副本，忽略关系的方向。
最终结果应为：

person1_name family1_name      person2_name family2_name
 John_Ethan_Wayne        Wayne     Michael_Wayne        Wayne
    Michael_Wayne        Wayne     Patrick_Wayne        Wayne
    Patrick_Wayne        Wayne  John_Ethan_Wayne        Wayne

型

pandas

来源：https://stackoverflow.com/questions/76802206/drop-duplicates-multi-column-and-ignoring-direction

3条答案

按热度按时间

nmpmafwu1#

在您给出的示例中，以下内容就足够了：

df[df.person1_name < df.person2_name]

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这是因为在两行的情况下：
A、B
B、A
它删除了B，A，因为B < A的计算结果为False。

赞(0）回复(0）举报 2023-08-01

zyfwsgd62#

import pandas as pd

data = {
    'person1_name': ['John_Ethan_Wayne', 'John_Ethan_Wayne', 'Michael_Wayne', 'Michael_Wayne', 'Patrick_Wayne', 'Patrick_Wayne'],
    'family1_name': ['Wayne', 'Wayne', 'Wayne', 'Wayne', 'Wayne', 'Wayne'],
    'person2_name': ['Michael_Wayne', 'Patrick_Wayne', 'Patrick_Wayne', 'John_Ethan_Wayne', 'John_Ethan_Wayne', 'Michael_Wayne'],
    'family2_name': ['Wayne', 'Wayne', 'Wayne', 'Wayne', 'Wayne', 'Wayne']
}

df = pd.DataFrame(data)

df['combined'] = df.apply(lambda row: frozenset({(row['person1_name'], row['family1_name']), (row['person2_name'], row['family2_name'])}), axis=1)

df = df.drop_duplicates(subset=['combined'])

df = df.sort_values(by=['person1_name', 'person2_name'])

df = df.reset_index(drop=True)

df = df.drop(columns='combined')

print(df)

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赞(0）回复(0）举报 2023-08-01

35g0bw713#

df['combined_names'] = df[['person1_name', 'family1_name', 'person2_name', 'family2_name']].agg(sorted, axis=1)

unique_combinations = df.drop_duplicates(subset='combined_names').drop(columns='combined_names')

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赞(0）回复(0）举报 2023-08-01

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pandas 删除重复的多列并忽略方向[重复]

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