Packages in Python

                                                               Important Packages in Python

Please don't take a felony if your favorite Python library or framework didn't make this list of top ten Python packages. The Python ecosystem has generated so many valuable packages that listing all of them on a top 100 list would be impossible, let alone a top 10 list. Still, the packages described below are useful for general-purpose Python programming.

>NumPy: Basic mathematical operations can be performed without any special Python packages. However, if you need to do any kind of complex math, the NumPy package will simplify your life a lot. NumPy provides tools for creating multidimensional arrays and performing calculations on them. The program allows you to solve algebraic formulas, perform common statistical operations, and much more. Despite being a valuable Python package for general-purpose programming, NumPy is particularly useful if you plan to do machine learning because it provides part of the foundation for libraries like TensorFlow.

>Pendulum: - If you have even a little Python programming experience, you probably know that you can use the DateTime module to manage dates and times within an application. Datetime is great for basic tasks along these lines. Nevertheless, the Pendulum Python package simplifies coding with dates and times. The package is more intuitive, and it manages time zones automatically. Additionally, Pendulum is designed to be a drop-in replacement for DateTime. So, you can use it with code you've already written based on DateTime. Unless there are a few exceptions, Pendulum will work just as well as plain old DateTime, without the need to modify the code.

>Pandas: - Pandas Python package provides fast, flexible, and expressive data structures that can be utilized to work with "relational" or labeled data. It is planned to be the building blocks to do practical, real-world data analysis in Python.

>MoviePy:-MoviePy is like Pillow for videos. It supports a range of functions for importing, modifying, and exporting video files. Additionally, it lets you insert titles into videos and rotate videos 90 degrees (if you desire).

>Pytest:- Python development projects of any complexity require you to perform tests on new code. Pytest provides a variety of modules to help you accomplish this. Pytest can help you write a simple unit test or a more complex functional test.

>Pywin32: - Pywin32 is a must-have package for Windows Python programming in particular. It provides access to many native Windows API functions, such as interacting with the Windows registry, using the Windows clipboard, and more. If you're writing a cross-platform Python app, Pywin32 lacks much support, but Windows developers might prefer it over native Windows tools.

>Python Imaging Library:-The Python imaging library, also known as PIL or Pillow, is a Python must-have if your application interacts with images. Code that opens, modifies, and saves images in a variety of formats is easy to write. If you're doing more advanced work (like image recognition, in which case OpenCV would be a good package to consider), Pillow won't cut it on its own. For basic image importing, manipulation, and exporting, Pillow is your best option.

>PyQt:-  PyQT, another Python package for building GUIs, is also a strong contender. It provides bindings to Qt, which is also cross-platform—designed for more thoughtful GUI programming than Tkinter. Therefore, PyQT may be overkill if you're building an app with a pretty simple interface - such as a window with buttons and text fields - but it's a great tool for complex, multi-dimensional GUIs.

> Scikit-learn:- In Python, Scikit-learn is arguably the most important library for machine learning. As a consequence of cleaning and manipulating data with Pandas or NumPy, scikit-learn is used to build machine learning models. It has tons of tools for predictive modeling and analysis.

>TensorFlow:- TensorFlow is one of the most popular Python libraries for building neural networks. It uses multi-dimensional arrays, also known as tensors, to perform several operations on an input.

>Plotly:- Plotly is a great tool for building visualizations as it is very powerful, easy to use, and has a big advantage of allowing you to interact with the visualizations.

>Seaborn:- Seaborn is an effective library for creating different visualizations using Matplotlib. The creation of amplified data visuals is one of the most important features of Seaborn. Correlations that are not immediately apparent can be illustrated visually, allowing Data Scientists to understand the models better.

>SciPy:- SciPy is primarily used for its scientific functions and mathematical functions derived from NumPy. This library provides stats functions, optimization functions, and signal processing functions. It includes functions for computing integrals numerically to solve differential equations and provide optimization.

>Statsmodels: - Statsmodels is a great tool for performing hardcore statistics. With this multifunctional library, you get features from Matplotlib, data handling from Pandas, formula handling from Pasty and NumPy, and it is built on NumPy and SciPy.>Statsmodels: - Statsmodels is a great tool for performing hardcore statistics. In particular, it's useful for creating statistical models, like OLS and performing statistical tests.

>Keras: - Keras is mainly used to create deep learning models, such as neural networks. It's built on top of TensorFlow and Theano and makes it simple to build neural networks. Keras generates a computational graph using back-end infrastructure, making it relatively slow compared to other libraries.

>Gradio: - With Gradio, you can build and deploy web apps for your machine learning models in just three lines of code. It serves the same purpose as Streamlight or Flask, but it's much faster and easier to set up a model.

How do I create a Python package?

> Our first step is to create a directory and give it a name, preferably related to its operation.

> Then, we added the classes and the required functions to it.

> Lastly, we create an __init__.py file inside the directory so that Python knows the directory is a package.

How do Python packages work?

Modules are files containing Python code in run-time for specific user-specific code. In addition, a package modifies the user-interpreted code so that it becomes easily usable in the run time. In Python, a "module" is mainly a namespace containing locally extracted variables.

A package consists of Python files and a file named __init__.py. This means that every directory inside the Python path contains a file named __init.py, which Python will treat as a package.

Conclusion: - Packages are a crucial building block in programming. Without packages, we'd spend lots of time writing already written code. Imagine having to write new code every time you wanted to parse a file in a certain format. You wouldn't get anything done! That's why we always use packages.