What are the Different Libraries that Python Offers?

At present, software programmers and data scientists are using various programming languages, and Python happens to be one of them. That being said, it can be a daunting task to master Python. You will come across a plethora of tools and resources available out there, which might make you confused in the long run regarding which one you should use.

Here, we have highlighted some of the most well-known Python libraries that will come of use to every data scientist and software developer.

1. NumPy

The term “NumPy” is an abbreviation for Numerical Python. Apart from helping the developers in scientific computing, this library is also employed extensively for Deep Learning and Machine Learning. NumPy has become quite popular among data scientists because it supports N-dimensional arrays. Being quite complicated, Machine Learning algorithms require multidimensional array operations. Besides this, TensorFlow and several other libraries take the help of NumPy for computing tensors internally. In a nutshell, large multidimensional array objects are supported by NumPy for working with them.

2. TensorFlow

Being created by Google, TensorFlow happens to be another well-known Python library that supports deep learning and machine learning. It is known to help programmers offering Python development services to perform complicated calculations. TensorFlow is likewise ideal for speech recognition, object identification, and other tasks. One can deploy deep learning and machine learning models very easily to GPU, CPU, and other similar platforms. TensorFlow will allow you to use the cloud platform of Google without any problem as well.

3. SciPy

The arrays of this Python library use NumPy since the main functionality of SciPy has been built upon it. Developers make extensive use of SciPy for performing complicated scientific computations at present. It provides several useful functions such as signal processing functions, stats functions, and so on. Apart from this, SciPy also supports multi-dimensional image processing and can solve differential equations.

4. BeautifulSoup

Another excellent Python library happens to beBeautifulSoup which is used for data scraping plus web crawling. It does all these from XML and HTML documents. Being able to identify encodings automatically, this library is known to work with complicated HTML documents. So in case you do not want to make use of an API for gathering information from any website, you will be able to do so with the help of BeautifulSoup without any problem whatsoever.

Must Read: What Are The Uses Of Python When It Comes To Developing Apps?

5. Scrapy

Scrapy happens to be an excellent tool when it comes to scraping information. If we want to recover structured information from the Internet, then this Python library mentioned here will be suitable for us. It does this by building spider bots or crawling programs. Thus, Scrapy can perform web scraping on a large scale, and it can likewise process the data according to your liking. Besides this, you can also use it for storing information in an organized manner.

6. Pandas

This is one of the best Python libraries when it comes to exploring data and analyzing them. It will provide useful tools for loading, manipulating, and preparing structured information of all types. Pandasis based on a couple of data structures, namely, Data Frames and Series. While Series happens to be one-dimensional, Data Frames is 2-dimensional. Put simply; this Python library is imperative for manipulating and visualizing data in the best possible way. You will also not find it difficult to install Pandas which can be achieved by simply running the “pip install pandas” command.

7. Matplotlib

Many organizations are using Matplotlib right now, given that it aids in exploring and visualizing information in several innovative ways. It can be used for different visualizations such as bar charts, line plots, tables, etc. Moreover, this Python library is known to lay down an extensive range of themes, colors, and palettes for customizing the plots. Being one of the handiest libraries, you can likewise export Matplotlib for other apps very easily. It is also feasible to increase the scope of this library using additional features. Nevertheless, one drawback of using Matplotlib is that the developers have to write additional code for creating innovative visualizations. Here, we like to mention that Matplotlib supports most of the plotting libraries out there.

8. Seaborn

This library helps us produce various visualizations, just like the previous one mentioned in this list. It is based on Matplotlib and aids in creating even complicated visualizations, including joint plots and time series. Many developers like to work with Seaborn more than its counterpart, mainly because of the top-notch interface provided by it. It provides us with seamless functionalities that assist in emphasizing the plot and drawing it. In case developers want to learn some Python libraries, then Seaborn has to be one of them.

Conclusion

There are many more Python libraries as well, which we have not covered in this blog. However, it is a fact that all these tools mentioned above will help to create top-notch ML models in Python. Consequently, any data scientist must take full advantage of these libraries while developing high-performing projects.