How to Print the Numpy Version and Configuration?

The simplest method for printing the NumPy version and configuration is to run the following code:

Code:

Output:

As you can observe from the code above, we were able to determine the precise NumPy version and configuration we were using. It was a rather quick solution to the problem, but there are alternative ways to obtain the NumPy version. Let us now explore deeper into its depths.

Until now, we've looked at the simplest method for determining the exact NumPy version and settings we're using. Have you ever wondered what NumPy is? Numpy is a robust mathematics toolkit that may be used to create an efficient multi-dimensional storage for generic data. It is possible to specify arbitrary data types. The Numpy library enables quick and easy interaction with a variety of databases. If you haven't already, enter the following command in your terminal to install Numpy.

Let us now verify the version of NumPy Library we are using once you have successfully installed it on your device. There are several methods for determining the version of NumPy. Let's go through each technique one by one, with a suitable methodology and a functional code sample.

Making use of the Version Object

The version object is a Python object that may be used to verify the version of almost any type of module. So we can use this to verify the version of the numpy library. However, before we can verify the version, we must first import the numpy library. Using the import keyword, we may import.

Syntax:

Let us now examine the version of the numpy package in our JUPYTER notebook.

Code:

Output:

We may also verify the version with the version method, which has the corresponding syntax:

Syntax:

It is a similar method that we employed in our simplest approach, which we discussed before in this article.

Using pip, Check for NumPy Version

There are several ways to determine the NumPy version using the pip command. Let us now examine each of them individually. Before proceeding, it should be noted that all of the code examples in this section are executed on the terminal or command prompt.

1. Examine the NumPy version employing show along with pip.

Syntax:

Here, the module name is the name of the package, which in our case is numpy. The show command, when used with pip, will produce the following results.

Code:

Output:

As you can see from the output above, the version title provided us with the precise version of NumPy that is being used in our system.

2. Examine the NumPy Version Employing List and Pip.

Syntax:

The list will return the names of all packages in our system, accompanied by the package version.

Code:

Output:

As you may have seen, that list provided all the packages that are installed on your system; however, while you were able to obtain the precise version of NumPy, you must manually search for numpy to check for its version, which makes the job much more tedious. Further strategies discussed in this section will attempt to solve this challenge and give us an ideal answer.

3. Examine the NumPy Version Employing FINDSTR with Pip List

A command-line function called FINDSTR is used to locate the string, in this example, numpy, from the list of modules that the pip list command returns.

Syntax:

Code:

Output:

4. Examine the NumPy Version Employing the grep Command with Pip Freeze

The grep filter, like the FINDSTR command, examines the list of modules returned by the pip freeze command for a certain pattern of characters and shows all rows that match that pattern.

Syntax:

Code:

Output:

Using the numexpr Module, Examine the NumPy Version.

Numerical expressions executed on numpy arrays are evaluated using the numexpr package. This package's print_versions() function may be used to show the numpy version.

Syntax:

Code :

Output:

As you may have seen up to this point, the NumPy version title in the output tells us what version of NumPy is currently installed on our system.

Using the pkg_resources Module, Examine the NumPy Version

The pkg_resources module included with setuptools offers an API for Python modules to access their source files, as well as enabling flexible applications and architectures to locate plugins automatically. This package aids in the discovery, usage, and provision of Python tools. The get_distribution() function, together with the version() method, will yield the module version.

Syntax:

Consider the following example :

Code :

Output:

Using the importlib.metadata Module, Examine for NumPy Version.

The importlib_metadata module allows access to installed module metadata. The version function in importlib_metadata returns the module version in Python.

Syntax:

Consider the following example, in which we have substituted module_name with numpy, which corresponds to the module name whose version we want to get.

Code :

Output:

We've seen several ways to examine the NumPy version we're using in our devices, but have you ever questioned why it's vital to version a library? Let's look at why it's vital to provide a library with a version number.

What is the Significance of Version Numbers?

Version numbers distinguish one module from another and indicate changes. It would be logistically impossible to track down the changes made in each version without version numbers. Whoever is in charge of conveying changes would get puzzled, and the incorrect package might be deployed.

Version numbers also play a significant role in managing module dependencies. Dependencies are generally always included in packages. Dependents sometimes have dependencies of their own, resulting in a "dependency tree" of necessary packages.

Dependency resolution can quickly become complicated. If you're using Python to create an application and two packages need separate versions of the same package, Python will encounter a version mismatch, and your project may fail to build!

Now that you understand the significance of the Version number, let us look at the significance of the show_config() method, which we used to obtain the NumPy configuration.

The show config() method prints information about different resources in the system on which NumPy was constructed, such as libraries, library directories, include directories, and so on. It is also as crucial as the version number.

This brings us to the conclusion of the article. Kudos! You now have a clear grasp of how to quickly identify NumPy version and configuration, as well as why it is vital to check for version and configuration.