Top Python Libraries

Overview

Python Libraries are a set of useful functions that eliminate the need for writing codes from scratch. There are over 137,000 python libraries present today. Python libraries play a vital role in developing machine learning, data science, data visualization, image and data manipulation applications and more.

Python is an ocean of libraries that serve various purposes and as a Python developer, you must have sound knowledge of the best ones. To help you in this, here is an article that brings to you the top Python libraries which are:

TensorFlow

The most popular deep learning framework, TensorFlow is an open-source software library for high-performance numerical computation. It is an iconic math library and is also used for machine learning and deep learning algorithms. TensorFlow was developed by the researchers at the Google Brain team within Google AI organization, and today it is being used by researchers for machine learning algorithms, and by physicists for complex mathematical computations. The following operating systems support TensorFlow: macOS 10.12.6 (Sierra) or later; Ubuntu 16.04 or later; Windows 7 or above; Raspbian 9.0 or later.

PyTorch

Introduced by Facebook in 2017, PyTorch is a Python package which gives the user a blend of 2 high-level features — Tensor computation (like NumPy) with strong GPU acceleration and developing Deep Neural Networks on a tape-based auto diff system. PyTorch provides a great platform to execute Deep Learning models with increased flexibility and speed built to be integrated deeply with Python.

Keras

It is an open-source neural network library written in Python designed to enable fast experimentation with deep neural networks. With deep learning becoming ubiquitous, Keras becomes the ideal choice as it is API designed for humans and not machines according to the creators. With over 200,000 users as of November 2017, Keras has stronger adoption in both the industry and the research community even over TensorFlow or Theano. Before installing Keras, it is advised to install TensorFlow backend engine.

Pandas

Pandas is a machine learning library in Python that provides data structures of high-level and a wide variety of tools for analysis. One of the great feature of this library is the ability to translate complex operations with data using one or two commands. Pandas have so many inbuilt methods for grouping, combining data, and filtering, as well as time-series functionality.

Currently, there are fewer releases of pandas library which includes hundred of new features, bug fixes, enhancements, and changes in API. The improvements in pandas regards its ability to group and sort data, select best suited output for the apply method, and provides support for performing custom types operations. Data Analysis among everything else takes the highlight when it comes to usage of Pandas. But, Pandas when used with other libraries and tools ensure high functionality and good amount of flexibility.

SciPy

This is yet another open-source software used for scientific computing in Python. Apart from that, Scipy is also used for Data Computation, productivity, and high-performance computing and quality assurance. The core Scipy packages are Numpy, SciPy library, Matplotlib, IPython, Sympy, and Pandas.

Matplotlib

All the libraries that we have discussed are capable of a gamut of numeric operations but when it comes to dimensional plotting, Matplotlib steals the show. This open-source library in Python is widely used for publication of quality figures in a variety of hard copy formats and interactive environments across platforms. You can design charts, graphs, pie charts, scatterplots, histograms, error charts, etc. with just a few lines of code.

SymPy

For all the symbolic mathematics, SymPy is the answer. This Python library for symbolic mathematics is an effective aid for computer algebra system (CAS) while keeping the code as simple as possible to be comprehensible and easily extensible. SymPy is written in Python only and can be embedded in other applications and extended with custom functions.

Seaborn

When it comes to visualisation of statistical models like heat maps, Seaborn is among the reliable sources. This Python library is derived from Matplotlib and closely integrated with Pandas data structures.

Numpy

Numpy is considered as one of the most popular machine learning library in Python. TensorFlow and other libraries uses Numpy internally for performing multiple operations on Tensors. Array interface is the best and the most important feature of Numpy. Numpy is very interactive and easy to use. It makes complex mathematical implementations very simple.

It makes coding real easy and grasping the concepts is easy. It’s widely used, hence a lot of open source contribution. This interface can be utilized for expressing images, sound waves, and other binary raw streams as an array of real numbers in N-dimensional. For implementing this library for machine learning having knowledge of Numpy is important for full stack developers.

Scikit-learn

It is a Python library is associated with NumPy and SciPy. It is considered as one of the best libraries for working with complex data. There are a lot of changes being made in this library. One modification is the cross-validation feature, providing the ability to use more than one metric. Lots of training methods like logistics regression and nearest neighbors have received some little improvements. It contains a numerous number of algorithms for implementing standard machine learning and data mining tasks like reducing dimensionality, classification, regression, clustering, and model selection.

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