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在 AND 和 OR 不等於的條件組合上合並 pandas dataframe

[英]Merge pandas dataframe on a combination of conditions with AND and OR not Equal

給定:下面的兩個數據框

df1:
| Company  | Package | Badge Number | Work Date  |
|----------|---------|--------------|------------|
| Compnay1 | X       | 1            | 2020-01-01 |
| Company2 | X       | 2            | 2020-01-01 |

df2:
| Company  | Package | Badge Number | Work Date  |
|----------|---------|--------------|------------|
| Compnay1 | X       | 1            | 2020-01-01 |
| Compnay1 | Y       | 1            | 2020-01-01 |
| Company2 | X       | 1            | 2020-01-01 |
| Company2 | Y       | 1            | 2020-01-01 |
| Company2 | X       | 2            | 2020-01-01 |

需要什么:我需要編寫類似於此 SQL 語句的 python 代碼。

SELECT * 
FROM df1
INNER JOIN df2
ON df1.[Badge Number] = df2.[Badge Number]
AND df1.[Work Date] = df2.[Work Date]
AND (df1.[Company] != df2.[Company] OR df1.[Package] != df2.[Package])

結果:

| df1.Company | df1.Package | df1.Badge Number | df1.Work Date | df2.Company | df2.Package | df2.Badge Number | df2.Work Date |
|-------------|-------------|------------------|---------------|-------------|-------------|------------------|---------------|
| Compnay1    | X           | 1                | 2020-01-01    | Compnay1    | Y           | 1                | 2020-01-01    |
| Compnay1    | X           | 1                | 2020-01-01    | Company2    | X           | 1                | 2020-01-01    |
| Compnay1    | X           | 1                | 2020-01-01    | Company2    | Y           | 1                | 2020-01-01    |

這可以純粹在 pandas 中完成,而無需在 python 代碼中編寫 SQL 查詢嗎?

一個想法是使用DataFrame.merge

df = df1.merge(df2, on=['Badge Number','Work Date'])

然后過濾:

df [(df['Company_x'] != df['Company_y']) | (df['Package_x'] != df['Package_y'])]

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