Left Outer Join两个单列数据框

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英文:

Left Outer Join with two single columned dataframes

问题

我在Pandas Merging 101中没有看到下面提到的情况。

我在理解Pandas文档中如何执行左外连接方面遇到了困难。

  1. import pandas as pd
  3. left_df = pd.DataFrame({ 
  4.                        'user_id': ['Peter', 'John', 'Robert', 'Anna']
  5.                       })
  7. right_df = pd.DataFrame({'user_id': ['Paul', 'Mary', 'John', 'Anna']})
  9. pd.merge(left_df, right_df, on='user_id', how='left')

输出是:

  1.    user_id
  2. 0  Peter
  3. 1  John
  4. 2  Robert
  5. 3  Anna

预期输出是:

  1.    user_id
  2. 0  Peter
  3. 1  Robert

我漏掉了什么？indicator = True参数是否必需（用于创建一个_merge列以进行过滤）以执行左外连接？

英文:

I don't see the below case mentioned in Pandas Merging 101.

<br> <br>
I'm having trouble understanding the Pandas documentation for doing a left outer join.

  1. import pandas as pd
  3. left_df = pd.DataFrame({ 
  4.                        &#39;user_id&#39;: [&#39;Peter&#39;, &#39;John&#39;, &#39;Robert&#39;, &#39;Anna&#39;]
  5.                        
  6.                       })
  8. right_df = pd.DataFrame({&#39;user_id&#39;: [&#39;Paul&#39;, &#39;Mary&#39;, &#39;John&#39;,
  9.                                      &#39;Anna&#39;]
  10.                        })
  13. pd.merge(left_df, right_df, on = &#39;user_id&#39;, how = &#39;left&#39;)

Output is: <br>

  1.  	user_id
  2. 0 	Peter
  3. 1 	John
  4. 2 	Robert
  5. 3 	Anna

Expected output:

  1.  	user_id
  2. 0 	Peter
  3. 1 	Robert

What am I missing? Is the indicator = True parameter a must (to create a _merge column to filter on) for left outer joins?

答案1

得分: 1

你可以使用 merge 并设置 indicator=True，然后只保留值为 left_only 的行，但这不是最佳方法。你可以使用 isin 来获取一个布尔掩码，然后反转它：

  1. left_df[~left_df['user_id'].isin(right_df['user_id'])]
  3.   user_id
  4. 0   Peter
  5. 2  Robert

使用 merge：

  1. (left_df.merge(right_df, on='user_id', how='left', indicator='present')
  2.             .loc[lambda x: x.pop('present') == 'left_only'])
  4.   user_id
  5. 0   Peter
  6. 2  Robert
英文:

You can use merge with indicator=True and keep only rows where value is set to left_only but it's not the best way. You can use isin to get a boolean mask then invert it:

  1. &gt;&gt;&gt; left_df[~left_df[&#39;user_id&#39;].isin(right_df[&#39;user_id&#39;])]
  3.   user_id
  4. 0   Peter
  5. 2  Robert

With merge:

  1. &gt;&gt;&gt; (left_df.merge(right_df, on=&#39;user_id&#39;, how=&#39;left&#39;, indicator=&#39;present&#39;)
  2.             .loc[lambda x: x.pop(&#39;present&#39;) == &#39;left_only&#39;])
  4.   user_id
  5. 0   Peter
  6. 2  Robert

huangapple
  • 本文由 发表于 2023年2月10日 03:47:23
  • 转载请务必保留本文链接：https://go.coder-hub.com/75403715.html
  • dataframe
  • left-join
  • pandas
  • python
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