掌握Python数据分析教材累积分布代码不起作用

Question

我正在阅读《掌握Python数据分析》，我发现教科书中的一些代码似乎无法正确编译，我对Python不是最熟悉，所以我无法弄清楚发生了什么错误的。教科书显示了旨在用于绘制累积分布函数的代码，但是当我调用此函数时，它似乎会抛出错误。如果有人对可能出现的问题有一些见解，我将不胜感激学习这一点的帮助。

我对这段代码的导入：

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

这是教科书中所示的函数，用于绘制累积分布（注意，我添加了打印语句来尝试找出它的突破点）：

# define a function that can plot cumulative distribution function (cdf)
def plot_cdf(data, plot_range=None, scale_to=None, **kwargs):
    print("inputs received: data: {}, plot range: {}, scale to: {}".format(data, plot_range, scale_to))
    num_bins = len(data)
    sorted_data = np.array(sorted(data), dtype=np.float64)
    data_range = sorted_data[-1] - sorted_data[0]
    counts, bin_edges = np.histogram(sorted_data, bins=num_bins)
    print("bin edges: {}".format(bin_edges))
    xvalues = bin_edges[:1]
    print("counts: {}, np.cumsum: {}".format(counts, np.cumsum(counts)))
    yvalues = np.cumsum(counts)
    if plot_range is None:
        xmin = sorted_data[0]
        xmax = sorted_data[-1]
    else:
        xmin, xmax = plot_range
    #pad the arrays
    xvalues = np.concatenate([[xmin, xvalues[0]], xvalues, [xmax]])
    yvalues = np.concatenate([[0.0, 0.0], yvalues, [yvalues.max()]])
    print("x values: {}".format(xvalues))
    print("y values: {}".format(yvalues))
    if scale_to is not None:
        yvalues = yvalues / len(data) * scale_to
    plt.axis([xmin, xmax, 0, yvalues.max()])
    return plt.plot(xvalues, yvalues, **kwargs)

这是课本上展示的导致错误的方法：

xmin = 0
xmax = 3.5
xx = np.linspace(xmin,xmax,200)
plt.plot(xx, rvweib.cdf(xx), color='orange', lw=5)
plot_cdf(weib_variates, plot_range=[xmin, xmax], scale_to=1, lw=2, color='green')
plt.axis([xmin, xmax, 0, 1])
plt.title('Weibul distribution simulation', fontsize=14)
plt.xlabel('Failure Time', fontsize=12);

这是我收到的错误消息：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[19], line 5
      3 xx = np.linspace(xmin,xmax,200)
      4 plt.plot(xx, rvweib.cdf(xx), color='orange', lw=5)
----> 5 plot_cdf(weib_variates, plot_range=[xmin, xmax], scale_to=1, lw=2, color='green')
      6 plt.axis([xmin, xmax, 0, 1])
      7 plt.title('Weibul distribution simulation', fontsize=14)

Cell In[18], line 25, in plot_cdf(data, plot_range, scale_to, **kwargs)
     23     yvalues = yvalues / len(data) * scale_to
     24 plt.axis([xmin, xmax, 0, yvalues.max()])
---> 25 return plt.plot(xvalues, yvalues, **kwargs)

File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\matplotlib\pyplot.py:3794, in plot(scalex, scaley, data, *args, **kwargs)
   3786 @_copy_docstring_and_deprecators(Axes.plot)
   3787 def plot(
   3788     *args: float | ArrayLike | str,
   (...)
   3792     **kwargs,
   3793 ) -> list[Line2D]:
-> 3794     return gca().plot(
   3795         *args,
   3796         scalex=scalex,
   3797         scaley=scaley,
   3798         **({"data": data} if data is not None else {}),
   3799         **kwargs,
   3800     )

File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\matplotlib\axes\_axes.py:1779, in Axes.plot(self, scalex, scaley, data, *args, **kwargs)
   1536 """
   1537 Plot y versus x as lines and/or markers.
   1538 
   (...)
   1776 (``'green'``) or hex strings (``'#008000'``).
   1777 """
   1778 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-> 1779 lines = [*self._get_lines(self, *args, data=data, **kwargs)]
   1780 for line in lines:
   1781     self.add_line(line)

File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\matplotlib\axes\_base.py:296, in _process_plot_var_args.__call__(self, axes, data, *args, **kwargs)
    294     this += args[0],
    295     args = args[1:]
--> 296 yield from self._plot_args(
    297     axes, this, kwargs, ambiguous_fmt_datakey=ambiguous_fmt_datakey)

File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\matplotlib\axes\_base.py:486, in _process_plot_var_args._plot_args(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)
    483     axes.yaxis.update_units(y)
    485 if x.shape[0] != y.shape[0]:
--> 486     raise ValueError(f"x and y must have same first dimension, but "
    487                      f"have shapes {x.shape} and {y.shape}")
    488 if x.ndim > 2 or y.ndim > 2:
    489     raise ValueError(f"x and y can be no greater than 2D, but have "
    490                      f"shapes {x.shape} and {y.shape}")

ValueError: x and y must have same first dimension, but have shapes (4,) and (503,)

Answer 1

您的问题的答案在于回溯。尺寸不匹配。

检查

bin_edges[:1]

，可能您的意思是

bin_edges[1:]

。

我想这是一个证明你理解的练习，日常使用即可

plt.hist(data, bins=num_bins, density=True, cumulative=True, histtype='step')