pandas将一组API函数应用于多个数据框。

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英文:

pandas apply a list of api functions to many dataframes

问题

以下是您提供的内容的翻译部分：

我想将一系列 pandas.api 函数应用于多个数据框并获取它们的响应。
我的代码：

  1. panda_apis = [
  2. pd.api.types.infer_dtype,
  3. pd.api.types.is_bool_dtype,
  4. pd.api.types.is_categorical_dtype,
  5. pd.api.types.is_complex_dtype,
  6. pd.api.types.is_datetime64_any_dtype,
  7. pd.api.types.is_datetime64_dtype]
  9. api_df = pd.DataFrame(data={'function':[i.__name__ for i in panda_apis]})
  10. api_df.head()
  1. # 两个示例数据框
  3. df1 = 
  5.                Datetime 	value
  6. 0 	2002-12-31 01:00:00 	5077.0
  7. 1 	2002-12-31 02:00:00 	4939.0
  8. 2 	2002-12-31 03:00:00 	4885.0
  9. 3 	2002-12-31 04:00:00 	4857.0
  11. df2 = 
  12.           Datetime 	    value
  13. 0 	2013-12-31 01:00:00 	1861.0
  14. 1 	2013-12-31 02:00:00 	1835.0
  15. 2 	2013-12-31 03:00:00 	1841.0
  16. 3 	2013-12-31 04:00:00 	1872.0
  17. 4 	2013-12-31 05:00:00 	1934.0
  18. df1 = df1.addprefix('df1_')
  19. api_df[df1.columns] = api_df['function'].apply(lambda x: eval(x(df1)))
  21. 目前的输出：
  23. TypeError: 'str' object is callable
  25. 期望的输出：
  27. ```python
  28. api_df = 
  29.     function                df1_Datetime  df1_value   df2_Datetime  df2_value
  30. 0 	infer_dtype                  string     float  ....
  31. 1 	is_bool_dtype                False      False  ..
  32. 2 	is_categorical_dtype         False      False  ..
  33. 3 	is_complex_dtype             False      False  .. 
  34. 4 	is_datetime64_any_dtype      True       False  ..
  2. 请注意，由于代码的复杂性，可能需要进一步的调试以获得期望的输出。
  4. <details>
  5. <summary>英文:</summary>
  7. I want apply a list of [`pandas.api`][1] functions to many dataframes and get their response. 
  8. My code: 
  10.     panda_apis = [
  11.     pd.api.types.infer_dtype,
  12.     pd.api.types.is_bool_dtype,
  13.     pd.api.types.is_categorical_dtype,
  14.     pd.api.types.is_complex_dtype,
  15.     pd.api.types.is_datetime64_any_dtype,
  16.     pd.api.types.is_datetime64_dtype]
  17.     
  18.     api_df = pd.DataFrame(data={&#39;function&#39;:[i.__name__ for i in panda_apis]})
  19.     api_df.head()
  20.     
  21.         function
  22.     0 	infer_dtype
  23.     1 	is_bool_dtype
  24.     2 	is_categorical_dtype
  25.     3 	is_complex_dtype
  26.     4 	is_datetime64_any_dtype
  27.     
  28.     # Two example dataframes
  29.     
  30.     df1 = 
  31.     
  32.                    Datetime 	value
  33.     0 	2002-12-31 01:00:00 	5077.0
  34.     1 	2002-12-31 02:00:00 	4939.0
  35.     2 	2002-12-31 03:00:00 	4885.0
  36.     3 	2002-12-31 04:00:00 	4857.0
  37.     
  38.     df2 = 
  39.     	          Datetime 	    value
  40.     0 	2013-12-31 01:00:00 	1861.0
  41.     1 	2013-12-31 02:00:00 	1835.0
  42.     2 	2013-12-31 03:00:00 	1841.0
  43.     3 	2013-12-31 04:00:00 	1872.0
  44.     4 	2013-12-31 05:00:00 	1934.0
  45.     df1 = df1.addprefix(&#39;df1_&#39;)
  46.     api_df[df1.columns] = api_df[&#39;function&#39;].apply(lambda x: eval(x(df1)))
  48. Present output: 
  50.     TypeError: &#39;str&#39; object is not callable
  52. Expected output:
  54.     api_df = 
  55.         function                df1_Datetime  df1_value   df2_Datetime  df2_value
  56.     0 	infer_dtype                  string     float  ....
  57.     1 	is_bool_dtype                False      False  ..
  58.     2 	is_categorical_dtype         False      False  ..
  59.     3 	is_complex_dtype             False      False  .. 
  60.     4 	is_datetime64_any_dtype      True       False  .. 
  63.   [1]: https://pandas.pydata.org/docs/reference/api/pandas.api.types.pandas_dtype.html
  65. </details>
  68. # 答案1
  69. **得分**: 1
  71. lst = [df1.add_prefix("df1_"), df2.add_prefix("df2_")]
  73. out = (
  74.     api_df.join(
  75.         pd.concat([pd.DataFrame([
  76.             df.apply(f) for f in panda_apis]) for df in lst], axis=1)
  77.     )
  78. )
  80. *NB : A DataFrame column can't have more than one [dtype][1]*.
  82. Output :
  84. print(out)
  86.                       function df1_Datetime df1_value df2_Datetime df2_value
  87.     0              infer_dtype       string  floating       string  floating
  88.     1            is_bool_dtype        False     False        False     False
  89.     2     is_categorical_dtype        False     False        False     False
  90.     3         is_complex_dtype        False     False        False     False
  91.     4  is_datetime64_any_dtype        False     False        False     False
  92.     5      is_datetime64_dtype        False     False        False     False
  94. With the `Datetime` *s* cast to `datetime64[ns]`, we get :
  96.                       function df1_Datetime df1_value df2_Datetime df2_value
  97.     0              infer_dtype   datetime64  floating   datetime64  floating
  98.     1            is_bool_dtype        False     False        False     False
  99.     2     is_categorical_dtype        False     False        False     False
  100.     3         is_complex_dtype        False     False        False     False
  101.     4  is_datetime64_any_dtype         True     False         True     False
  102.     5      is_datetime64_dtype         True     False         True     False
  104.   [1]: https://pandas.pydata.org/docs/user_guide/basics.html#dtypes
  106. <details>
  107. <summary>英文:</summary>
  109. You can try this :
  111.     lst = [df1.add_prefix(&quot;df1_&quot;), df2.add_prefix(&quot;df2_&quot;)]
  112.     
  113.     out = (
  114.         api_df.join(
  115.             pd.concat([pd.DataFrame([
  116.                 df.apply(f) for f in panda_apis]) for df in lst], axis=1)
  117.         )
  118.     )
  120. *NB : A DataFrame column can&#39;t have more than one [dtype][1]*.
  122. Output :
  124.     print(out)
  125.     
  126.                       function df1_Datetime df1_value df2_Datetime df2_value
  127.     0              infer_dtype       string  floating       string  floating
  128.     1            is_bool_dtype        False     False        False     False
  129.     2     is_categorical_dtype        False     False        False     False
  130.     3         is_complex_dtype        False     False        False     False
  131.     4  is_datetime64_any_dtype        False     False        False     False
  132.     5      is_datetime64_dtype        False     False        False     False
  134. With the `Datetime` *s* cast to `datetime64[ns]`, we get :
  136.                       function df1_Datetime df1_value df2_Datetime df2_value
  137.     0              infer_dtype   datetime64  floating   datetime64  floating
  138.     1            is_bool_dtype        False     False        False     False
  139.     2     is_categorical_dtype        False     False        False     False
  140.     3         is_complex_dtype        False     False        False     False
  141.     4  is_datetime64_any_dtype         True     False         True     False
  142.     5      is_datetime64_dtype         True     False         True     False
  144.   [1]: https://pandas.pydata.org/docs/user_guide/basics.html#dtypes
  147. </details>

huangapple
  • 本文由 发表于 2023年6月13日 06:14:48
  • 转载请务必保留本文链接：https://go.coder-hub.com/76460640.html
  • apply
  • dataframe
  • pandas
  • python
  • types
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