Feb 25, 2024 · Feb 23, 2024 · Feb 23, 2024 · Feb 23, 2024 · Feb 23, 2024 · Feb 23, 2024
diff --git a/Doc/library/statistics.rst b/Doc/library/statistics.rst
 .. function:: kde(data, h, kernel='normal')

   `Kernel Density Estimation (KDE)
   <https://www.itm-conferences.org/articles/itmconf/pdf/2018/08/itmconf_sam2018_00037.pdf>`_
   creates an estimated continuous probability density function from *data*
   containing a fixed number of discrete samples.
   <https://www.itm-conferences.org/articles/itmconf/pdf/2018/08/itmconf_sam2018_00037.pdf>`_:
   Create a continuous probability density function from discrete samples.

   The basic idea is to smooth the data using `a kernel function such as a
 normal distribution, triangular distribution, or uniform distribution
 <https://en.wikipedia.org/wiki/Kernel_(statistics)#Kernel_functions_in_common_use>`_.
   The basic idea is to smooth the data using `a kernel function
 <https://en.wikipedia.org/wiki/Kernel_(statistics)`_.
 to help draw inferences about a population from a sample.

   The degree of smoothing is controlled by the scaling parameter *h*
   which is called the bandwidth.  Smaller values emphasize local
diff --git a/Lib/statistics.py b/Lib/statistics.py
    which is called the bandwidth.  Smaller values emphasize local
    features while larger values give smoother results.

    The kernel determines the relative weights of the sample data
    points.  Generally, the choice of kernel shape does not matter
    as much as the more influential bandwidth smoothing parameter.

    Kernels that give some weight to every sample point:

       normal or gauss
       triweight
       cosine

    A StatisticsError will be raised if the data sequence is empty.

    Example
    -------
Original file line number	Diff line number	Diff line change
Expand Up		@@ -264,13 +264,12 @@ However, for reading convenience, most of the examples show sorted sequences.
		.. function:: kde(data, h, kernel='normal')

		`Kernel Density Estimation (KDE)
		<https://www.itm-conferences.org/articles/itmconf/pdf/2018/08/itmconf_sam2018_00037.pdf>`_
		creates an estimated continuous probability density function from data
		containing a fixed number of discrete samples.
		<https://www.itm-conferences.org/articles/itmconf/pdf/2018/08/itmconf_sam2018_00037.pdf>`_:
		Create a continuous probability density function from discrete samples.

		The basic idea is to smooth the data using `a kernel function such as a
		normal distribution, triangular distribution, or uniform distribution
		<https://en.wikipedia.org/wiki/Kernel_(statistics)#Kernel_functions_in_common_use>`_.
		The basic idea is to smooth the data using `a kernel function
		<https://en.wikipedia.org/wiki/Kernel_(statistics)`_.
		to help draw inferences about a population from a sample.

		The degree of smoothing is controlled by the scaling parameter h
		which is called the bandwidth. Smaller values emphasize local
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -814,6 +814,10 @@ def kde(data, h, kernel='normal'):
		which is called the bandwidth. Smaller values emphasize local
		features while larger values give smoother results.

		The kernel determines the relative weights of the sample data
		points. Generally, the choice of kernel shape does not matter
		as much as the more influential bandwidth smoothing parameter.

		Kernels that give some weight to every sample point:

		normal or gauss
Expand All		@@ -830,6 +834,8 @@ def kde(data, h, kernel='normal'):
		triweight
		cosine

		A StatisticsError will be raised if the data sequence is empty.

		Example
		-------

Expand Down