distfit运行fit_transform报object无dtype属性错误如何解决

我正在尝试复现《如何使用Python确定最优拟合数据分布》一文中描述的结果，使用的代码如下：

import numpy as np
from distfit import distfit

# 生成10000个均值为0、标准差为3的正态分布样本
X = np.random.normal(0, 3, 10000)

# 初始化distfit
dist = distfit()

# 计算数据的最优拟合概率分布
dist.fit_transform(X)

在Jupyter环境运行上述代码时，抛出如下错误：

[distfit] >fit..
[distfit] >transform..
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-8-02f73e7f157d> in <module>
      9 
     10 # Determine best-fitting probability distribution for data
---> 11 dist.fit_transform(X)

~\Anaconda3\lib\site-packages\distfit\distfit.py in fit_transform(self, X, verbose)
    275         self.fit(verbose=verbose)
    276         # Transform X based on functions
---> 277         self.transform(X, verbose=verbose)
    278         # Store
    279         results = _store(self.alpha,

~\Anaconda3\lib\site-packages\distfit\distfit.py in transform(self, X, verbose)
    214         if self.method=='parametric':
    215             # Compute best distribution fit on the empirical X
---> 216             out_summary, model = _compute_score_distribution(X, X_bins, y_obs, self.distributions, self.stats, verbose=verbose)
    217             # Determine confidence intervals on the best fitting distribution
    218             model = _compute_cii(self, model, verbose=verbose)

~\Anaconda3\lib\site-packages\distfit\distfit.py in _compute_score_distribution(data, X, y_obs, DISTRIBUTIONS, stats, verbose)
    906     model['params'] = (0.0, 1.0)
    907     best_score = np.inf
---> 908     df = pd.DataFrame(index=range(0, len(DISTRIBUTIONS)), columns=['distr', 'score', 'LLE', 'loc', 'scale', 'arg'])
    909     max_name_len = np.max(list(map(lambda x: len(x.name), DISTRIBUTIONS)))
    910 

~\Anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
    346                                  dtype=dtype, copy=copy)
    347         elif isinstance(data, dict):
---> 348             mgr = self._init_dict(data, index, columns, dtype=dtype)
    349         elif isinstance(data, ma.MaskedArray):
    350         import numpy.ma.mrecords as mrecords

~\Anaconda3\lib\site-packages\pandas\core\frame.py in _init_dict(self, data, index, columns, dtype)
    449                     nan_dtype = dtype
    450                 v = construct_1d_arraylike_from_scalar(np.nan, len(index),
---> 451                                                        nan_dtype)
    452                 arrays.loc[missing] = [v] * missing.sum()
    453 

~\Anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in construct_1d_arraylike_from_scalar(value, length, dtype)
   1194     else:
   1195         if not isinstance(dtype, (np.dtype, type(np.dtype))):
-> 1196             dtype = dtype.dtype
   1197 
   1198         # coerce if we have nan for an integer dtype

AttributeError: type object 'object' has no attribute 'dtype'

这个错误是旧版本distfit库与2.0及以上版本pandas不兼容导致的：旧版distfit在内部创建空DataFrame存储拟合结果时，没有正确处理pandas新版本的dtype校验逻辑，触发了属性不存在的报错。

按优先级从高到低选择以下任意一种方法即可：

• 升级distfit到最新版本：这是最稳妥的解决方法，最新版distfit已经修复了该兼容性问题。在终端执行命令pip install --upgrade distfit，执行完成后重启Jupyter内核，重新运行代码即可正常运行。
• 降级pandas到兼容版本：如果因为项目依赖限制无法升级distfit，可以将pandas降级到1.5.3的稳定兼容版本，执行命令pip install pandas==1.5.3，完成后重启Jupyter内核即可。
• 临时补丁方案：如果既不能升级distfit也不能降级pandas，可以在导入distfit前加入如下补丁代码，手动修正dtype处理逻辑：

import numpy as np
import pandas as pd
# 打兼容补丁
from pandas.core import dtypes
original_func = dtypes.cast.construct_1d_arraylike_from_scalar
def patched_func(value, length, dtype):
    if dtype is object:
        dtype = np.dtype('O')
    return original_func(value, length, dtype)
dtypes.cast.construct_1d_arraylike_from_scalar = patched_func

# 之后再导入和使用distfit
from distfit import distfit

内容的提问来源于stack exchange，提问作者Mark