How Many Python Packages Are There

There are over 200,000 Python packages in existence (this number only includes packages stored on PyPI, the official Python package index). Python packages provide user-friendly and efficient solutions to challenging problems in a wide range of fields, including scientific computing, data visualization, data modeling, and more.

Complex things are best accomplished piecemeal, one subtask at a time. To accomplish tasks, programmers create and use modules, which are collections of related code stored in separate files.

Below is a list of some of the most important Python packages.

1. NumPy

We can perform basic mathematical operations without any dedicated Python packages. However, if we need to perform any difficult mathematical operations, the NumPy module will make our lives as programmers much easier.

The tools in NumPy help us create multidimensional arrays and perform calculations on the data within them. We can perform common statistical procedures, solve algebraic equations, and more.

While NumPy is a Python package useful for many general programming activities, it’s particularly important if we want to do machine learning, as it forms part of the framework for libraries like TensorFlow.

We can use NumPy to:

• Modern array operations (e.g., addition, multiplication, slicing, reshaping, indexing).
• Mathematical operations covered.
• Randomly generate numbers.
• Procedures for linear algebra.
• For example, Fourier transforms

With NumPy, we can use understandable Python syntax with the computational power of executable code. This explains why NumPy is used by such a large ecosystem of Python libraries and packages. This includes several well-known packages, including pandas, seaborn, and python. href=”https://geek-docs.com/scipy/scipy-top-tutorials/1000100_scipy_index.html” rel=”noopener” target=”_blank” title=”SciPy Tutorials”>SciPy, OpenCV, and many other packages.

2. Pendulum

A popular Python datetime library for simple datetime manipulation is pendulum. It provides a more streamlined and user-friendly API. It makes it easier to manage complex date manipulation issues, including time zones, which are not handled correctly by native datetime instances.

If you’ve done any Python programming at this point, you’ve undoubtedly already learned how to use the datetime module to control dates and times in your applications.

While datetime is great for basic tasks like this, the pendulum Python module is beneficial for more complex date and time scripts. It automatically manages time zones and is more user-friendly.

Most importantly, Pendulum can easily replace datetime. This means we can use it with existing date- and time-based programs. With some minor exceptions, no code changes are required; Pendulum will continue to function normally, while providing additional functionality not found in traditional datetime. It is an evolution of the standard datetime library, but offers superior functionality. Therefore, we can add Pendulum’s Datetime instances to projects that already use the developed datetime class.

3. Pandas

If you’re working with tabular, time series, or matrix data, Pandas is the go-to Python library. It’s considered a fast, efficient, and user-friendly tool for data analysis and manipulation. A data frame is a unique two-dimensional data structure that works with data frame elements. Like an Excel spreadsheet or database table, a data frame contains rows and columns.

Pandas can be leveraged for a variety of purposes, such as:

• Transferring and reading data between SQL databases, Excel, and CSV files.
• Transform and pivot datasets.
• Slice, index, and subset datasets.
• Combine and transform data.
• Concatenate and merge datasets.

Pandas is the name of the most popular open-source Python library for data science, data processing, and machine learning tasks. It’s built on top of the standalone NumPy package, which implements multidimensional arrays. One of the most popular data processing tools is Pandas, which is commonly included in all Python distributions. It works well with a variety of different data science modules.

Pandas is built on two fundamental Python libraries: NumPy for mathematical calculations and Matplotlib for data display. Because Pandas acts as a wrapper around these libraries, many methods in Matplotlib and NumPy are easier to use. For example, the .plot() function in Pandas combines several Matplotlib functions into a single method, allowing developers to draw charts with less code.

3. Matplotlib

The most popular data exploration and visualization library is Matplotlib. It can be used to create simple graphs such as line graphs, bar graphs, scatter plots, bar charts, and pie charts. With the help of this library, you can also create animated and interactive visualizations. Matplotlib is the foundation of all other visualization libraries.

This library offers a lot of versatility when it comes to formatting and decorating plots. Labels, grids, legends, and other display options are all up to you. However, creating complex, visually appealing plots requires a significant amount of code.

4. MoviePy

MoviePy is to movies what Pillow is to photos. It provides several functions for common import, editing, and export procedures involving video files. Furthermore, it allows us to add titles to videos and rotate them 90° (if for some reason we decide to do so).

Like Pillow, MoviePy is not intended to be a complex data processing tool. If we were creating a video editing application, we might also rely on OpenCV (which supports both video and photos) to add sophisticated functionality that MoviePy lacks. However, MoviePy does a good job of handling most common video-related Python programming tasks.

5. Requests

Writing code to send HTTP requests can be difficult, largely because HTTP doesn’t exactly format data in a way that humans can directly interpret.

The Requests Python module (motto: “HTTP for Humans”) solves this problem by automating several time-consuming steps that you typically have to perform when submitting HTTP requests from any application. It eliminates the need for POST form encoding or query strings. Furthermore, it automatically maintains connections to HTTP servers without requiring extensive code.

Simply put, Requests is a must-have package for any application that transfers data over HTTP.

This module aims to improve the responsiveness and usability of HTTP requests in Python. We can avoid manually adding query strings to URLs by using the simple JSON techniques provided by Requests.

We can use Requests to modify, inspect, approve, and configure HTTP requests.

• Headers, multipart files, and parameters should be added.
• Automatically decompress data
• During upload, simultaneously

6. PyQt

Despite what was said earlier, PyQt, another Python library for creating GUIs, is a serious challenger. It provides cross-platform bindings to (you guessed it) the Qt toolkit. Compared to Tkinter, it’s designed for more demanding GUI programming. This means that if you’re developing an application with a straightforward user experience, PyQt may be overkill.

7. Pillow

If you’re working with image data, Pillow is a clone of the Python Imaging Library (PIL) that has evolved into a user-friendly and efficient tool for manipulating images in Python.

Pillow enables us to

• Open and save images of various file types (JPEG, PNG, GIF, PDF, etc.).
• Create image thumbnails.
• Apply various image filters (such as Smooth, Blur, and Sharpen).
• This image modification application is excellent for beginners and offers decent image processing capabilities.

8. PyTest

This package provides a wide range of modules for trying out new code, including simple functional tests for libraries and applications as well as extensive unit tests.

One of the most popular Python packages among programmers is pytest, due to its straightforward syntax and extensive functionality. This test automation framework provides…

• Built-in test discovery support.
• Modular test setup fixtures (e.g., setting up database connections, URLs, input data).
• (315+ external plugins) Rich plugin architecture.
• Internal unit tests.

Being able to test new code is crucial for any complex Python development project. You can achieve this with the help of several modules provided by the pytest package. Pytest helps users write both simple unit tests and more complex functional tests.

9. Pywin32

Pywin32 is an essential package, especially for Python development on Windows. It allows us to interact with the Windows registry, use the Windows clipboard, and much more, thanks to its access to many native Windows APIs.

Building a cross-platform Python application won’t benefit much from Pywin32, but Windows developers may find they prefer it to native Windows tools.