# Fitting a Probability Distribution to data using SciPy's Fit Function

by sneha@openweaver.com Updated: Mar 29, 2023

Solution Kit

Fitting a probability distribution to data is identifying a probability distribution that best describes the underlying probability of a given dataset. This is done by finding the distribution parameters (such as the mean, variance, etc.) that best describes the dataset. The parameters are usually estimated using maximum likelihood estimation, which maximizes the likelihood of the data given the estimated parameters. Once the distribution parameters are determined, the distribution can be used to make predictions about future data or make decisions about the data.

SciPy is a library of open source software for scientific computing in Python. It comprises tools and libraries for numerical computation, analysis, and visualization. It includes modules for linear algebra, integration, optimization, signal processing, and statistics. It can be used for various tasks, from basic statistical processing to advanced numerical simulations.

The SciPy fit function is a numerical optimization technique used to fit a function to a given set of data points.

• It is a part of the SciPy library in Python, which is used for numerical computing.
• It uses techniques such as least squares, least absolute deviation, and Nelder-Mead to find an optimal fit for the given data.

Here is an example of fitting a probability distribution to data using SciPy's fit function

Fig 1: Preview of the output that you will get on running this code from your IDE

### Code

In this solution, we use the SciPy fit function.

### Instructions

1. Install Jupyter Notebook on your computer.
2. Open terminal and install the required libraries with following commands.
3. Install SciPy - pip install scipy
4. Install Numpy - pip install numpy
5. Copy the snippet using the 'copy' button and paste it into that file.
6. Run the file using run button.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "Fitting a probability distribution to data using SciPy's fit function" in kandi. You can try any such use case!

### Dependent Libraries

scipyby scipy

Python 11340 Version:v1.11.0rc1

SciPy library main repository

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scipyby scipy

Python 11340 Version:v1.11.0rc1 License: Permissive (BSD-3-Clause)

SciPy library main repository
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numpyby numpy

Python 23755 Version:v1.25.0rc1

The fundamental package for scientific computing with Python.

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numpyby numpy

Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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If you do not have SciPy or numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the numpy page in kandi.

You can search for any dependent library on kandi like numpy/SciPy.

### Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in Python3.9.6
2. The solution is tested on SciPy-Python 1.9.1 version.

Using this solution, we are able to fit a probability distribution to data using SciPy's fit function.

This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to fit a probability distribution to data.

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