id变更计数生成了错误的值

huangapple
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英文:

Count of id with change generates wrong values

问题

这是您提供的代码和描述的翻译结果：

  1. 我有一个看起来像这样的数据框：
  3. ```python
  4. import pandas as pd
  6. data = {
  7.     'api_spec_id': [213, 213, 213, 345, 345, 345, 678, 678, 678, 123, 123],
  8.     'type': ['BR', 'BR', 'NBR', 'NBR', 'NBR', 'NBR', 'BR', 'BR', 'BR', 'BR', 'BR']
  9. }
  11. df = pd.DataFrame(data)

我试图计算4种情况，一种情况是在所有api_spec_id的行中，type=BR，另一种情况是在至少有一行的api_spec_id中，type是BR。

这是我正在使用的代码，但似乎是错误的，因为它为最后两种情况生成相同的输出：

  1. import pandas as pd
  3. 至少有一个破坏性变更 = df[df['type'] == 'BR']['api_spec_id'].nunique()
  5. 包括所有提交的破坏性变更 = df.groupby('api_spec_id').apply(lambda x: 'NBR' not in x['type'].unique()) \
  6.                                 .sum()
  8. 至少有一个非破坏性变更 = df[df['type'] == 'NBR']['api_spec_id'].nunique()
  10. 包括所有提交的非破坏性变更 = df.groupby('api_spec_id').apply(lambda x: 'BR' not in x['type'].unique()) \
  11.                                     .sum()

对于我发送的示例数据框，预期输出将是：

  1. 至少有一个破坏性变更 = 3
  2. 包括所有提交的破坏性变更 = 3
  3. 至少有一个非破坏性变更 = 2
  4. 包括所有提交的非破坏性变更 = 1

我有点困在这个问题上，任何建议或想法将不胜感激。

  2. 希望这能帮助您理解代码并获得所需的结果。如果您有任何进一步的问题或需要进一步的协助，请随时提问。
  4. <details>
  5. <summary>英文:</summary>
  7. I have a df which looks like this:
  9. ```python
  10. import pandas as pd
  12. data = {
  13.     &#39;api_spec_id&#39;: [213, 213, 213, 345, 345, 345, 678, 678, 678, 123, 123],
  14.     &#39;type&#39;: [&#39;BR&#39;, &#39;BR&#39;, &#39;NBR&#39;, &#39;NBR&#39;, &#39;NBR&#39;, &#39;NBR&#39;, &#39;BR&#39;, &#39;BR&#39;, &#39;BR&#39;, &#39;BR&#39;, &#39;BR&#39;]
  15. }
  17. df = pd.DataFrame(data)

I am trying to count 4 cases, one where every for all rows in api_spec_id,the type= BR and second where for atleast one row in api_spec_id, the type is BR.

This is the code I am working with but it seems wrong as it is generating the same output for the last two:

  1. import pandas as pd
  3. at_least_one_breaking_change = df[df[&#39;type&#39;] == &#39;BR&#39;][&#39;api_spec_id&#39;].nunique()
  5. all_commits_including_breaking = df.groupby(&#39;api_spec_id&#39;).apply(lambda x: &#39;NBR&#39; not in x[&#39;type&#39;].unique()) \
  6.                                 .sum()
  8. at_least_one_non_breaking_change = df[df[&#39;type&#39;] == &#39;NBR&#39;][&#39;api_spec_id&#39;].nunique()
  10. all_commits_including_non_breaking = df.groupby(&#39;api_spec_id&#39;).apply(lambda x: &#39;BR&#39; not in x[&#39;type&#39;].unique()) \
  11.                                     .sum()

The expected output for the sample df I sent will be:

  1. at_least_one_breaking_change = 3
  2. all_commits_including_breaking = 3
  3. at_least_one_non_breaking_change = 2
  4. all_commits_including_non_breaking = 1

I am a bit stuck on this and any suggestions or ideas will be greatly appreciated.

答案1

得分: 1

  1. 我认为你可以使用 `pd.crosstab`：
  3. ```python
  4. m = pd.crosstab(df['api_spec_id'], df['type']).astype(bool)
  6. 至少有一个破坏性变化 = sum(m['BR'])
  7. 包括破坏性变化的所有提交 = sum(m['BR'] & ~m['NBR'])
  9. 至少有一个非破坏性变化 = sum(m['NBR'])
  10. 包括非破坏性变化的所有提交 = sum(m['NBR'] & ~m['BR'])

输出：

  1. >>> 至少有一个破坏性变化
  2. 3
  4. >>> 包括破坏性变化的所有提交
  5. 2
  7. >>> 至少有一个非破坏性变化
  8. 2
  10. >>> 包括非破坏性变化的所有提交
  11. 1
  13. >>> m
  14. type            BR    NBR
  15. api_spec_id              
  16. 123           True  False
  17. 213           True   True
  18. 345          False   True
  19. 678           True  False
  2. <details>
  3. <summary>英文:</summary>
  5. I think you can use `pd.crosstab`:

m = pd.crosstab(df['api_spec_id'], df['type']).astype(bool)

at_least_one_breaking_change = sum(m['BR'])
all_commits_including_breaking = sum(m['BR'] & ~m['NBR'])

at_least_one_non_breaking_change = sum(m['NBR'])
all_commits_including_non_breaking = sum(m['NBR'] & ~m['BR'])

  2. Output:

>>> at_least_one_breaking_change
3

>>> all_commits_including_breaking
2

>>> at_least_one_non_breaking_change
2

>>> all_commits_including_non_breaking
1

>>> m
type BR NBR
api_spec_id
123 True False
213 True True
345 False True
678 True False

  2. </details>
  6. # 答案2
  7. **得分**: 0
  9. 我已经看过并运行了你的代码，它的输出是：
  11. [![你的代码的输出][1]][1]
  13. 这个代码中的条件有点错误。
  15. 看一下更新，
  17. ```python
  18. import pandas as pd
  20. at_least_one_breaking_change = df[df['type'] == 'BR']['api_spec_id'].nunique()
  22. all_commits_including_breaking = df.groupby('api_spec_id').apply(lambda x: 'NBR' in x['type'].unique()) \
  23.                                 .sum()
  25. at_least_one_non_breaking_change = df[df['type'] == 'NBR']['api_spec_id'].nunique()
  27. all_commits_including_non_breaking = df.groupby('api_spec_id').apply(lambda x: 'Breaking' in x['type'].unique()) \
  28.                                     .sum()

此外，没有"Breaking"类型。

英文:

I have seen and run your code it's output is :

id变更计数生成了错误的值

Conditions in this code are bit wrong.

Look at the updates,

  1. import pandas as pd
  4. at_least_one_breaking_change = df[df[&#39;type&#39;] == &#39;BR&#39;][&#39;api_spec_id&#39;].nunique()
  6. all_commits_including_breaking = df.groupby(&#39;api_spec_id&#39;).apply(lambda x: &#39;NBR&#39; in x[&#39;type&#39;].unique()) \
  7.                                 .sum()
  9. at_least_one_non_breaking_change = df[df[&#39;type&#39;] == &#39;NBR&#39;][&#39;api_spec_id&#39;].nunique()
  11. all_commits_including_non_breaking = df.groupby(&#39;api_spec_id&#39;).apply(lambda x: &#39;Breaking&#39; in x[&#39;type&#39;].unique()) \
  12.                                     .sum()

Also, there is no type of &quot;Breaking&quot;.

huangapple
  • 本文由 发表于 2023年6月12日 01:16:03
  • 转载请务必保留本文链接：https://go.coder-hub.com/76451633.html
  • pandas
  • python
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