df.apply(hurst_function) 报错：必须是实数，而不是元组，在 Python 中。

huangapple
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英文:

df.apply(hurst_function) gave TypeError: must be real number, not tuple in, Python

问题

以下是您要翻译的部分：

"I have a column in form of a data-frame that contains the ratio of some numbers.
On that df col, I want to apply hurst function using df.apply() method.

I don't know if the error is with the df.apply or with the hurst_function.
Consider the code which calculates hurst exponent on a col using the df.apply method:

  1. import hurst 
  3. def hurst_function(df_col_slice):
  4.     display(df_col_slice)
  5.     return hurst.compute_Hc(df_col_slice)
  7. def func(df_col):
  8.     
  9.     results = round(df_col.rolling(101).apply(hurst_function)[100:],1)
  10.     return results
  12. func(df_col)

I get the error:

  1. Input In [73], in func(df_col)
  2. ---> 32     results = round(df_col.rolling(101).apply(hurst_function)[100:],1)
  4. File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\window\rolling.py:1843, in Rolling.apply(self, func, raw, engine, engine_kwargs, args, kwargs)
  5.    1822 @doc(
  6.    1823     template_header,
  7.    1824     create_section_header("Parameters"),
  8.    (...)
  10. ... (中间部分省略)
  12. TypeError: must be real number, not tuple

What can I do to solve this?

Edit: display(df_col_slice) is giving the following output:

  1. 0      0.282043
  2. 1      0.103355
  3. 2      0.537766
  4. 3      0.491976
  5. 4      0.535050
  6.          ...   
  7. 96     0.022696
  8. 97     0.438995
  9. 98    -0.131486
  10. 99     0.248250
  11. 100    1.246463
  12. Length: 101, dtype: float64

"

英文:

I have a column in form of a data-frame that contains the ratio of some numbers.
On that df col, I want to apply hurst function using df.apply() method.

I don't know if the error is with the df.apply or with the hurst_function.
Consider the code which calculates hurst exponent on a col using the df.apply method:

  1. import hurst 
  3. def hurst_function(df_col_slice):
  4.     display(df_col_slice)
  5.     return hurst.compute_Hc(df_col_slice)
  7. def func(df_col):
  8.     
  9.     results = round(df_col.rolling(101).apply(hurst_function)[100:],1)
  10.     return results
  12. func(df_col)

I get the error:

  1. Input In [73], in func(df_col)
  2. ---> 32     results = round(df_col.rolling(101).apply(hurst_function)[100:],1)
  4. File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\window\rolling.py:1843, in Rolling.apply(self, func, raw, engine, engine_kwargs, args, kwargs)
  5.    1822 @doc(
  6.    1823     template_header,
  7.    1824     create_section_header("Parameters"),
  8.    (...)
  9.    1841     kwargs: dict[str, Any] | None = None,
  10.    1842 ):
  11. -> 1843     return super().apply(
  12.    1844         func,
  13.    1845         raw=raw,
  14.    1846         engine=engine,
  15.    1847         engine_kwargs=engine_kwargs,
  16.    1848         args=args,
  17.    1849         kwargs=kwargs,
  18.    1850     )
  20. File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\window\rolling.py:1315, in RollingAndExpandingMixin.apply(self, func, raw, engine, engine_kwargs, args, kwargs)
  21.    1312 else:
  22.    1313     raise ValueError("engine must be either 'numba' or 'cython'")
  23. -> 1315 return self._apply(
  24.    1316     apply_func,
  25.    1317     numba_cache_key=numba_cache_key,
  26.    1318     numba_args=numba_args,
  27.    1319 )
  29. File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\window\rolling.py:590, in BaseWindow._apply(self, func, name, numba_cache_key, numba_args, **kwargs)
  30.     587     return result
  31.     589 if self.method == "single":
  32. --> 590     return self._apply_blockwise(homogeneous_func, name)
  33.     591 else:
  34.     592     return self._apply_tablewise(homogeneous_func, name)
  36. File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\window\rolling.py:442, in BaseWindow._apply_blockwise(self, homogeneous_func, name)
  37.     437 """
  38.     438 Apply the given function to the DataFrame broken down into homogeneous
  39.     439 sub-frames.
  40.     440 """
  41.     441 if self._selected_obj.ndim == 1:
  42. --> 442     return self._apply_series(homogeneous_func, name)
  43.     444 obj = self._create_data(self._selected_obj)
  44.     445 if name == "count":
  45.     446     # GH 12541: Special case for count where we support date-like types
  47. File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\window\rolling.py:431, in BaseWindow._apply_series(self, homogeneous_func, name)
  48.     428 except (TypeError, NotImplementedError) as err:
  49.     429     raise DataError("No numeric types to aggregate") from err
  50. --> 431 result = homogeneous_func(values)
  51.     432 return obj._constructor(result, index=obj.index, name=obj.name)
  53. File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\window\rolling.py:582, in BaseWindow._apply.<locals>.homogeneous_func(values)
  54.     579     return func(x, start, end, min_periods, *numba_args)
  55.     581 with np.errstate(all="ignore"):
  56. --> 582     result = calc(values)
  57.     584 if numba_cache_key is not None:
  58.     585     NUMBA_FUNC_CACHE[numba_cache_key] = func
  60. File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\window\rolling.py:579, in BaseWindow._apply.<locals>.homogeneous_func.<locals>.calc(x)
  61.     571 start, end = window_indexer.get_window_bounds(
  62.     572     num_values=len(x),
  63.     573     min_periods=min_periods,
  64.     574     center=self.center,
  65.     575     closed=self.closed,
  66.     576 )
  67.     577 self._check_window_bounds(start, end, len(x))
  68. --> 579 return func(x, start, end, min_periods, *numba_args)
  70. File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\window\rolling.py:1342, in RollingAndExpandingMixin._generate_cython_apply_func.<locals>.apply_func(values, begin, end, min_periods, raw)
  71.    1339 if not raw:
  72.    1340     # GH 45912
  73.    1341     values = Series(values, index=self._on)
  74. -> 1342 return window_func(values, begin, end, min_periods)
  76. File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\_libs\window\aggregations.pyx:1315, in pandas._libs.window.aggregations.roll_apply()
  78. TypeError: must be real number, not tuple

What can I do to solve this?

Edit: display(df_col_slice) is giving the following output:

  1. 0      0.282043
  2. 1      0.103355
  3. 2      0.537766
  4. 3      0.491976
  5. 4      0.535050
  6.          ...   
  7. 96     0.022696
  8. 97     0.438995
  9. 98    -0.131486
  10. 99     0.248250
  11. 100    1.246463
  12. Length: 101, dtype: float64

答案1

得分: 3

hurst.compute_Hc 函数返回一个包含 3 个值的元组：

H，c，vals = compute_Hc(df_col_slice)

其中，H 是赫斯特指数，而 c 是某个常数。

但是，pandas._libs.window.aggregations.roll_apply() 期望其参数（函数）返回一个单一的标量，它是滚动窗口的减小结果。

这就是为什么你的 hurst_function 函数需要从 vals 返回某个特定值。

英文:

hurst.compute_Hc function returns a tuple of 3 values:

  1. H, c, vals = compute_Hc(df_col_slice)

where H is the Hurst exponent , and c - is some constant.

But, pandas._libs.window.aggregations.roll_apply() expects its argument (function) to return a single (scalar) which is the reduced result of a rolling window.

That's why your hurst_function function need to return a certain value from vals.

huangapple
  • 本文由 发表于 2023年2月6日 17:44:04
  • 转载请务必保留本文链接：https://go.coder-hub.com/75359622.html
  • apply
  • dataframe
  • math
  • python
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