Python 在pandas中，如何按公共值分组并基于该值拆分为列？

我有一个dataframe，它在“Status”列中具有相同的公共值。我需要将它拆分为两个不同的列及其旁边的URL

我试过了

DataFrame（df.groupby（['Labels'，'Pattern'，'Status]）['Count']）没有按预期工作

我已经附上了df查询和清晰的理解图片

DF

import pandas as pd

df = pd.DataFrame({'Labels': {0: 'Apple',  1: 'Apple',  2: 'Apple',  3: 'Apple',  4: 'Orange',  5: 'Orange',  6: 'Orange',  7: 'Orange',  8: 'Grapes',  9: 'Grapes'}, 'Pattern': {0: 'Red',  1: 'Red',  2: 'Green',  3: 'Green',  4: 'Good',  5: 'Good',  6: 'Bad',  7: 'Bad',  8: 'Violet',  9: 'Violet'}, 'Status': {0: 'Checked',  1: 'Not_Checked',  2: 'Checked',  3: 'Not_Checked',  4: 'Checked',  5: 'Not_Checked',  6: 'Checked',  7: 'Not_Checked',  8: 'Checked',  9: 'Not_Checked'}, 'Count': {0: 79,  1: 221,  2: 3,  3: 306,  4: 13,  5: 297,  6: 28,  7: 281,  8: 20,  9: 290}, 'Some_Link': {0: 'http://www.example.com/',  1: 'http://angle.example.com/',  2: 'https://example.com/',  3: 'http://www.example.com/www.php',  4: 'http://example.com/blow/bag',  5: 'https://www.example.com/?baby=brake',  6: 'https://www.example.org/?afternoon=approval&baseball=arithmetic',  7: 'https://example.net/babies/badge?amount=balance',  8: 'http://www.example.com/boundary/boat.aspx',  9: 'http://www.example.com/sssl.php'}, 'Some_Link2': {0: 'http://www.example.com/beef/approval',  1: 'https://www.example.com/qqa.php',  2: 'https://example.com/aswq.php',  3: 'http://www.example.com/believe/bike.php?amount=blade',  4: 'https://www.example.com/',  5: 'http://www.example.com/?beef=acoustics',  6: 'https://www.example.com/#apparatus',  7: 'https://www.example.com/asd.php',  8: 'http://basketball.example.com/bone/bedroom',  9: 'https://www.example.org/box/back'}})

实际输入

预期产出

与和一起使用，最后展平多索引：

df = df.set_index(['Labels','Pattern','Status']).unstack().sort_index(level=1, axis=1)
df.columns = df.columns.map(lambda x: f'{x[0]}_{x[1]}')
df = df.reset_index()

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与和一起使用，最后展平多索引：

df = df.set_index(['Labels','Pattern','Status']).unstack().sort_index(level=1, axis=1)
df.columns = df.columns.map(lambda x: f'{x[0]}_{x[1]}')
df = df.reset_index()

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签出数据透视表：签出数据透视表：