[Python] mastering python data analysis textbook cumulative distribution code not working

I'm reading Mastering Python Data Analysis and I've come across some code from the textbook that doesn't seem to compile properly, I'm not the most familiar with Python so I haven't been able to figure out what's went wrong. The textbook shows code that is intended to be used to plot a cumulative distribution function, however it seems like when I call this function it throws an error. If anyone has some insights as to what may be going wrong, I'd appreciate the help in learning this.

my imports for this bit of code:

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


This is the function as shown in the textbook to plot the cumulative distribution (note, I've added the print statements to try and figure out where it's breaking):

# 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)


This is the method shown by the textbook that causes the error:

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);


This is the error message I'm given:

---------------------------------------------------------------------------
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,)

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