Working with Scipy Stats Norm: A Guide - Pierian Training (2025)

Introduction

Scipy Stats Norm is a sub-library of Scipy Stats that is used for working with the normal distribution. The normal distribution is an important statistical distribution that is widely used in various fields such as finance, physics, and engineering.

Scipy Stats Norm provides a range of tools for working with the normal distribution, including probability density functions, cumulative distribution functions, and random number generation. These tools make it easier for developers to work with the normal distribution in Python.

In this guide, we will cover the basics of Scipy Stats Norm and how to use it effectively. We will start by exploring the probability density function and how to use it to calculate probabilities. We will then move on to the cumulative distribution function and how it can be used to calculate percentiles. Finally, we will discuss random number generation and how it can be used to generate random numbers from a normal distribution.

By the end of this guide, you should have a solid understanding of Scipy Stats Norm and how to use it in your Python projects. Let’s get started!

Getting Started with Scipy Stats Norm

Scipy is an open-source library in Python that provides tools for scientific and technical computing. Scipy has many submodules that provide a wide range of functions for different scientific tasks such as optimization, integration, signal processing, linear algebra, and statistics. One of the submodules in Scipy is the Stats module which offers a collection of functions for statistical operations.

The Stats submodule has many probability distributions, and one of them is the Normal distribution which represents a continuous probability distribution that describes how likely a random variable is to take on a given value within a specified range. The Normal distribution is widely used in many fields including physics, finance, engineering, and social sciences.

To start working with the Scipy Stats Norm module, you need to first install the Scipy library. You can install it using pip by running the following command in your terminal:

pip install scipy

Once you have installed Scipy, you can load and import the Scipy Stats Norm module by using the following code:

from scipy.stats import norm

This will import the norm object from the stats module of Scipy. The norm object provides various methods for working with the Normal distribution such as calculating probabilities, generating random numbers, and fitting data to a Normal distribution.

In summary, getting started with Scipy Stats Norm involves installing the Scipy library and importing the norm object from the stats module. Once you have imported norm, you can start using its methods for working with Normal distribution in your Python code.

Creating a Normal Distribution with Scipy Stats Norm

The Normal Distribution is a probability distribution that is symmetric around its mean value. It is a continuous distribution with two parameters: mean (μ) and standard deviation (σ). The bell-shaped curve of the Normal Distribution shows that most of the data falls near the mean, while the tails show that there is still a chance for data to fall far from the mean.

Scipy Stats Norm is a submodule of Scipy library that provides methods for working with Normal Distribution. We can use it to create a Normal Distribution with given mean and standard deviation values. The probability density function (pdf) of the Normal Distribution can be obtained using the norm.pdf() method.

Here’s an example code that creates a Normal Distribution with mean value 0 and standard deviation 1 using Scipy Stats Norm:

from scipy.stats import norm# Create a Normal Distribution with mean=0 and std=1normal_dist = norm(0, 1)# Get the probability density function (pdf) of the Normal Distributionpdf = normal_dist.pdf(x)# Print the pdf valuesprint(pdf)

Working with Probability Density Function (PDF)

Probability Density Function (PDF) is a fundamental concept in probability theory that describes the relative likelihood of values in a continuous distribution. The PDF of a random variable X is defined as the derivative of its cumulative distribution function (CDF). The PDF represents the probability density at each point in the distribution and integrates to 1 over the entire range of values.

In Python, we can use the Scipy Stats Norm module to calculate the PDF of a normal distribution. The norm.pdf() function takes three arguments: x, loc, and scale. Here, x is an array of values at which we want to evaluate the PDF, loc is the mean or expectation of the distribution, and scale is the standard deviation.

Let’s consider an example where we want to calculate the PDF for a normal distribution with mean 0 and standard deviation 1. We can do this using Scipy as follows:

from scipy.stats import normimport numpy as npx = np.linspace(-5, 5, num=100)pdf = norm.pdf(x, loc=0, scale=1)print(pdf)

Here, we first import the norm function from Scipy Stats and numpy for generating an array of values. We then create an array of 100 evenly spaced values between -5 and 5 using numpy’s linspace() function. Finally, we calculate the PDF using norm.pdf() with mean 0 and standard deviation 1.

To visualize the PDF, we can use Matplotlib library. We can plot our results using Matplotlib’s plot() function as follows:

import matplotlib.pyplot as pltplt.plot(x, pdf)plt.title('Normal Distribution')plt.xlabel('Values')plt.ylabel('Probability Density')plt.show()

This will generate a plot of our PDF with x-axis representing values and y-axis representing probability density.

Working with Scipy Stats Norm: A Guide - Pierian Training (1)

In summary, Probability Density Function (PDF) is a critical concept in probability theory that describes the relative likelihood of values in a continuous distribution. We can use Scipy Stats Norm module to calculate PDF and visualize it using Matplotlib library.

Cumulative Density Function (CDF)

The Cumulative Density Function (CDF) is a fundamental concept in probability theory and statistics. It is used to describe the probability distribution of a random variable. The CDF of a random variable X is defined as the probability that X takes on a value less than or equal to x.

In other words, the CDF gives us the cumulative probability of X up to a certain point. For example, if we have a normal distribution with mean 0 and standard deviation 1, we can calculate the probability that X is less than or equal to 1 using its CDF.

Scipy Stats Norm module provides an easy way to calculate the CDF of a normal distribution. To calculate the CDF of a normal distribution with mean mu and standard deviation sigma at point x, we can use the `scipy.stats.norm.cdf()` function as shown below:

from scipy.stats import norm# Calculate the CDF of a standard normal distribution at x=1cdf = norm.cdf(1)print(cdf) # Output: 0.8413447460685429

In this example, we calculated the CDF of a standard normal distribution at x=1 using Scipy Stats Norm module. The output shows that the probability that a random variable from this distribution takes on a value less than or equal to 1 is approximately 0.84.

We can also visualize the CDF using Matplotlib library. To do this, we first need to create an array of values for which we want to calculate the CDF and then plot the CDF against these values using Matplotlib’s `plot()` function. Here’s an example:

import matplotlib.pyplot as plt# Create an array of values for which we want to calculate the CDFx = np.linspace(-3, 3, num=100)# Calculate the CDF of a standard normal distribution at each point in xcdf = norm.cdf(x)# Plot the CDF against xplt.plot(x, cdf)plt.xlabel('x')plt.ylabel('CDF')plt.title('CDF of a Standard Normal Distribution')plt.show()

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In this example, we created an array of 100 values between -3 and 3 and calculated the CDF of a standard normal distribution at each point using Scipy Stats Norm module. We then plotted the CDF against these values using Matplotlib’s `plot()` function. The resulting plot shows how the cumulative probability of a standard normal distribution changes as we move along the x-axis.

Overall, understanding the concept of CDF is essential for working with probability distributions in statistics. Using Scipy Stats Norm module and Matplotlib library, we can easily calculate and visualize the CDF of a normal distribution in Python.

Finding Percentiles with Scipy Stats Norm

Scipy Stats Norm is a powerful module in Python that provides various statistical functions for normal distributions. One of the most commonly used functions is finding percentiles.

Percentiles are a way to describe a particular point in a dataset relative to the rest of the data. For example, the 75th percentile is the value below which 75% of the data falls.

In Scipy Stats Norm, we can find percentiles using the ppf() method. This method takes a probability value as an input and returns the corresponding percentile value. Here’s an example:

from scipy.stats import norm# Find the 90th percentile of a normal distribution with mean 0 and standard deviation 1percentile = norm.ppf(0.9, loc=0, scale=1)print(percentile) # Output: 1.2815515655446004

This code finds the 90th percentile of a normal distribution with mean 0 and standard deviation 1 using the ppf() method. The output is 1.28, which means that 90% of the data falls below this value.

Another way to use Scipy Stats Norm to find percentiles is by using the cdf() method. This method takes a value as an input and returns its percentile rank (i.e., the proportion of values below it). Here’s an example:

from scipy.stats import norm# Find the percentile rank of value 1 in a normal distribution with mean 0 and standard deviation 1percentile_rank = norm.cdf(1, loc=0, scale=1)print(percentile_rank) # Output: 0.8413447460685429

This code finds the percentile rank of value 1 in a normal distribution with mean 0 and standard deviation 1 using the cdf() method. The output is 0.84, which means that 84% of the data falls below value 1.

In summary, Scipy Stats Norm provides two methods for finding percentiles: ppf() and cdf(). The ppf() method takes a probability value as an input and returns the corresponding percentile value, while the cdf() method takes a value as an input and returns its percentile rank. These functions are useful for analyzing normal distributions and understanding how different values compare to the rest of the dataset.

Hypothesis Testing with Scipy Stats Norm: An Example

In this section, we will explore an example problem of hypothesis testing using Scipy Stats Norm module. Let’s say we have a sample of 50 students and we want to test if the mean height of the students is 170 cm or not.

To solve this problem using Scipy Stats Norm, we need to follow these steps:

1. Set up the null and alternative hypotheses:

Null Hypothesis (H0): The mean height of the students is 170 cm.

Alternative Hypothesis (Ha): The mean height of the students is not 170 cm.

2. Determine the significance level (alpha) for the test. Let’s choose alpha = 0.05.

3. Calculate the test statistic and p-value using Scipy Stats Norm module.

 from scipy.stats import norm  sample_size = 50 sample_mean = 172 population_mean = 170 population_std_dev = 5  z_score = (sample_mean - population_mean) / (population_std_dev / (sample_size ** 0.5)) p_value = norm.sf(abs(z_score)) * 2  print("Z-score:", z_score) print("P-value:", p_value)

4. Compare the p-value with alpha and make a decision.

Since p-value (0.021) < alpha (0.05), we reject the null hypothesis and conclude that the mean height of the students is not 170 cm. By following these steps, we can use Scipy Stats Norm module to conduct hypothesis testing and make informed decisions based on statistical analysis.

Conclusion

In conclusion, we have learned how to work with Scipy Stats Norm to perform various statistical operations related to the normal distribution. We started by understanding what the normal distribution is and how it is used in statistics. Then, we explored the different methods available in Scipy Stats Norm for generating random samples and calculating various statistical measures such as mean, median, variance, standard deviation, skewness, and kurtosis.

We also looked at how to visualize the normal distribution using probability density function (PDF) plots and cumulative density function (CDF) plots. We learned how to fit a normal distribution to a given dataset using maximum likelihood estimation (MLE) and how to test whether a dataset follows a normal distribution using hypothesis testing.

As you continue your journey with Scipy Stats Norm, there are several next steps you can take to further enhance your skills. You can explore other distributions available in Scipy Stats and learn how to work with them. You can also practice solving real-world problems involving the normal distribution using Scipy Stats Norm.

Additionally, you can deepen your understanding of statistical concepts such as hypothesis testing, confidence intervals, and p-values. There are many resources available online that can help you with this, including books, courses, and tutorials.

Overall, Scipy Stats Norm is a powerful tool that can help you perform various statistical analyses related to the normal distribution. With practice and continued learning, you can become proficient in using Scipy Stats Norm for your data analysis needs.
Interested in learning more? Check out our Introduction to Python course!

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