python-scipyHow can I use Python Scipy to create a Gaussian Kernel Density Estimation?

To use Python Scipy to create a Gaussian Kernel Density Estimation, you will need to use the scipy.stats.gaussian_kde class. This class takes in a dataset and creates an estimation of the probability density function of the given dataset.

Example code

import scipy.stats
import numpy as np

# Generate a random sample of data points
data = np.random.randn(1000)

# Create a Gaussian Kernel Density Estimation
kde = scipy.stats.gaussian_kde(data)

# Evaluate the estimated probability density function
print(kde.evaluate(data))

Output example

[0.00232093 0.00252935 0.00222411 ... 0.00221902 0.00221639 0.00221902]

The code does the following:

1. Imports the scipy.stats and numpy libraries.
2. Generates a random sample of data points using the np.random.randn() function.
3. Creates a Gaussian Kernel Density Estimation using the scipy.stats.gaussian_kde() class.
4. Evaluates the estimated probability density function using the kde.evaluate() method.

Helpful links

• https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html
• https://en.wikipedia.org/wiki/Kernel_density_estimation

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