Recommended Python Libraries

If you are a Pythonista, there is no doubt that you need a sound knowledge of the best Python libraries.

Python is widely used and a popular programming language that has replaced other languages in the tech field. Python has grown so incredibly that even non-IT personalities have noted the language.

It could be used for a backend project, a data science project, games, machine learning, and a whole lot more.

Moreover, I will be walking you through this article with the best Python libraries that you need as a Python developer.

Recommended Python Libraries

There is no doubt that there are a plethora of Python Libraries out there that serve and are best for certain purposes.

I would not hold back on sharing the best Python libraries that you need to know in this guide.

1. Scikit-Learn -

Probably, you have not heard of the Python library, Scikit-learn. Well, this library might not be that popular for you but it is indeed one of the best Python libraries if I must recommend it.

This library works with complex data which is associated with SciPy and NumPy and can use more than a metric.

Features Of Scikit-Learn

Scikit-Learn is a remarkable Python library and course, it has the following unique features.

1. Extraction -

This feature allows you to extract content from images and text using Scikit-Learn.

2. Cross Validation -

It is assuring to check the accuracy of supervised models on unseen data using various methods.

3. Unsupervised Learning Algorithm -

It has a larger spread of algorithms in the offering which starts from factor analysis, and principal component analysis to unsupervised neural networks.

2. Keras -

Compared to other Python libraries, Keras is considered the coolest for machine learning. This library is top-notch when it comes to visualizing graphs, processing data sets, and compiling models.

It also uses Theano and TensorFlow internally for the backend. Although Keras might be slow for machine learning due to its creation of computational graphs by using back-end infrastructure and then making use of it to perform operations.

Features Of Keras

• It supports a variety of network models of a neural network.
• It is flexible, recommended for innovative research, and incredibly expressive because it is modular.
• It makes debugging and exploring easy because it is a complete Python-based framework.

3. TensorFlow -

TensorFlow is an open-source Python library which is popular when it comes to Machine learning.

This library was created by Google which collaborated with Brain Team, and since then it has been used for almost all Google applications for machine learning.

TensorFlow serves purposes and can be used to implement the expression of neural networks as computational graphs easily.

Features Of TensorFlow

• TensorFlow can easily visualize every part of the graph which is not possible with NumPy or Scikit-Learn.
• It is easily trained on GPU and CPU for distributing computing.
• It is highly flexible.
• It has a larger community and allows the training of multiple neural networks and multiple GPUs.

4. NumPy -

Aside from being one of the recommended Python libraries, NumPy ranks at the top position in the list of popular Python libraries.

Although TensorFlow is a Python library, it relies on NumPy internally to carry out multiple operations.

Features Of NumPy

• It promotes healthy interaction between developers in the tech field.
• It makes complex mathematical implementations basic.
• It makes developers flawlessly grasp code.

5. SciPy -

The ideal Python library for fintech, application developers and engineers.

This library contains modules for optimization, linear algebra, integration, and statistics despite being created by NumPy.

It is created from NumPy for mathematical functions, hence its main purpose is to provide all the efficient numerical routines like optimization, numerical integration, and many others using its specific submodules of NumPy.

Conclusion

The above are the recommended Python libraries of mine, and it does seem to be that Python Libraries that are not on my list are condemned, of course not.

Moreover, choosing a Python library is subject to what you want it for.