How many Python Packages are there?

The total number of Python packages in existence exceeds 200,000 (and that figure only includes those stored on PyPI, the official Python Package Index). Python packages offer a user-friendly and effective solution to tackle challenging issues in a variety of domains, including scientific computing, data visualisation, data modelling, and many more.

It is better to complete complicated things piecemeal, one subtask at a time. To accomplish tasks, programmers build and use modules, which are collections of related code kept in independent files.

Some of the most significant Python packages are listed below:

1. NumPy

Without any specialised Python packages, we can do fundamental mathematical operations. However, the NumPy module will make our lives as programmers' lot easier if we need to perform any kind of difficult math.

Tools are available in NumPy to assist in creating multi-dimensional arrays and carrying out calculations on the data they contain. We can complete common statistical procedures, solve algebraic equations, and much more.

Although NumPy is a useful Python package for many general-purpose programming activities, it's especially crucial if we want to undertake machine learning because it forms a portion of the framework for libraries like TensorFlow.

we can use NumPy for:

• contemporary array operations (e.g., add, multiply, slice, reshape, index).
• encompassing mathematical operations.
• number generation at random.
• routines for linear algebra.
• e.g., Fourier transformations

With NumPy, we can use comprehensible Python syntax with the computing capability of executable code. It makes sense why NumPy is used by such a large ecosystem of Python libraries and packages. These comprise several well-known packages, including pandas, Seaborn, SciPy, OpenCV, and many others.

2. Pendulum

One well-liked Python Date Time library for simple Date Time manipulation is the pendulum. It offers a more streamlined and user-friendly API. It makes the issue of intricate date manipulations including time zones, which are not properly handled in native datetime instances, more manageable.

If user have now done any Python programming at all, we're undoubtedly already aware of how to utilise the datetime module to control dates and times in an application.

Datetime is excellent for this kind of fundamental work, while the Pendulum Python module facilitates more intricate date- and time-related scripting. It manages time zones automatically and is more user-friendly.

The best part is that Pendulum may easily take the place of datetime. This implies that we can use it with date- and time-based programmes we've already created. With a few minor exceptions, Without any code modifications, Pendulum will continue to operate normally while providing additional features not present in conventional datetime. It is descended from the common datetime library but offers superior features. Consequently, we may add Pendulums Datetime instances to projects that already make use of the developed datetime class.

3. Pandas

Pandas is the go-to Python library if we work with tabular, time series, or matrix data. It is regarded as a quick, effective, and user-friendly tool for data analysis and modification. A data frame is a unique kind of two-dimensional data structure that works with data frame elements. Like Excel spreadsheets or database tables, data frames contain rows and columns.

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

• Transferring and reading data among SQL databases, Excel, and CSV files.
• altering and rotating datasets.
• datasets that have been sliced, indexed, and subset.
• combining and changing data.
• the joining and merging of datasets.

Pandas is the name of the most popular open-source Python library , data science, data processing, and machine learning tasks. It is built on top of the separate package NumPy, which enables multi-dimensional arrays. One of the most popular data wrangling tools is Pandas, which is often offered in all Python distributions. It works well with a variety of different data science modules.

Pandas is based on the of two essential Python libraries: NumPy for arithmetic computations and Matplotlib for data presentation. Many of the methods in matplotlib and NumPy are easier to use thanks to Pandas, which acts as a wrapper for all these libraries. For instance, the.plot() function in pandas combines several matplotlib functions into one method, allowing developers to plot a chart in a little amount of code.

3. Matplotlib

The most popular data exploration and visualisation library is Matplotlib. It can be used to create simple graphs such as linear plots, histograms, scatter graph, bar graphs, and pie charts. With the help of this library, you can also produce animated and interactive visualisations. Matplotlib is the foundation of every other visualisation library.

When it comes to formatting and decorating plots, the library offers a lot of versatility. Labels, grids, legends, and other display options are all up to you. However, we'll need to write a significant amount of code to produce intricate and visually beautiful plots.

4. MoviePy

MoviePy is to films what Pillow is to photographs. For common import, edit, and export procedures involving video files, it provides several functions. Additionally, it enables us to add titles to videos and rotate them 90 ° (if for some reason we decide we want to do that).

Like Pillow, MoviePy is not intended to function as a tool for sophisticated data manipulation. If we're creating a video editing application, we'll likely also need to rely on OpenCV (which supports both videos and photos) to add the sophisticated features that MoviePy is lacking. However, MoviePy does a good job of handling most common video-related Python programming tasks.

5. Requests

Writing the code that sends Http can be difficult, in large part because HTTP does not exactly format data in a way that is straightforward for people to interpret.

This issue is addressed by the Requests Python module (motto: "HTTP for Humans") by automating several the time-consuming steps that you would typically have to carry out to submit HTTP requests from any application. It eliminates the requirement for POST form encoding or query string addition. Additionally, it automatically maintains connections with HTTP servers, doing so without the need to write a tonne of code.

Requests is a must-have package for any application that transfers data via HTTP, to put it simply.

The purpose of this module is to improve the responsiveness and usability of Python HTTP requests. We can avoid manually adding query strings to URLs by using the simple JSON technique supplied by Requests.

We may modify, examine, approve, and configure HTTP requests using Requests.

• Headers, multi-part files, and parameters should be added.
• automated decompression of data
• When uploading, do so simultaneously

6. PyQt

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

7. Pillow

If we work with picture data. It is a clone of the Python Image Library (PIL), which has evolved into a user-friendly and effective tool for manipulating images in Python.

Pillow allows us to:

• Open and store pictures from various file types (JPEG, PNG, GIF, PDF, etc.).
• Create picture thumbnails.
• Utilize a variety of picture filters (e.g. SMOOTH, BLUR, SHARPEN).
• This application for picture alteration is excellent for novices and offers decent image processing capacity.

8. PyTest

This package offers a wide range of modules for trying out new code, including simple functional tests for libraries and apps as well as large unit tests.

One of the most popular Python packages amongst programmers is pytest thanks to its straightforward syntax and broad feature set. This framework for test automation offers:

• Support enabling test discovery built-in.
• Modular test setup fixtures (e.g. setting up the database connection, URL, input data).
• (315+ external plugins) Rich plugin architecture.
• internal unit tests.

Being able to test new code is crucial if we have a Python development project of the any complexity. You may achieve this with the aid of a few of modules provided by the Pytest package. Pytest can assist users in writing it, whether it's a straightforward unit test or a more intricate functional test.

9. Pywin32

Pywin32 is a necessary package, particularly for Windows Python development. we can interact with Windows registry, just use Windows clipboard, and many other things thanks to its access to many native Windows API calls.

Building a cross-platform Python application won't benefit much from Pywin32, but Windows developers may discover that they prefer it over native Windows tooling.