Top Python Libraries for Data Scientists

Python is often the programming language of choice for developers and data scientists who want to work in machine learning.

Python is a general-purpose programming language that can be used across multiple domains and applications, including web development, scientific computing, data science, and machine learning.

Python's history goes back all the way to 1989 when Guido van Rossum created it as part of an effort by Amoeba to develop their own portable operating system for use on personal computers. The project was abandoned but Guido decided to keep working on the language he'd been writing over those years. He released Python 1.0 in 1991 and has continued developing new versions since then: 2.0 was released in 1994; 3.0 came out in 2008; 3D was released in 2010; PY 4 was unveiled in 2013; 5 launched on December 3rd 2015.

Nowadays, Python is one of the most widely used programming languages, thanks to its great scalability, versatility and simplicity. It can be used for rapid prototyping or for production-ready software development. Since Python is easy to learn and its syntax is simple to understand, it has become an ideal choice for both beginners as well as experienced programmers.

Python’s popularity has increased rapidly in recent years due to its simplicity and ease of use while handling big data sets or performing complex mathematics. With its high-level built-in set of libraries such as NumPy (Numerical Python), Pandas (Python Data Analysis Library), Matplotlib (mathematical plotting library) etc., it makes sense why so many data scientists prefer using this language over other options such as R or SAS when analyzing large datasets.

You can download and use Python for free. It's open source, meaning that its code is available to the public. You can also contribute to its development through GitHub, where you can find thousands of developers working on it.

Python has an active community of developers who are constantly adding new features and creating libraries for data science. If you are new to Python, the best way to learn it is by attending meetups and conferences. There are many resources available on the internet that can help you master the language including online courses, books, tutorials and videos.

The language it's very easy to learn (especially if you're already familiar with programming), and it comes with a lot of useful libraries built in so you don't have to spend time reinventing the wheel while tackling data science projects on your own.

Python has a wide range of libraries that can make the lives of data scientists a lot easier. These libraries are designed for different purposes such as data analysis/visualization/machine learning etc.

The top Python libraries for data science are:

Python’s library support is one of its biggest strengths. There are a number of libraries available to help you get the job done, including pandas, scikit-learn and TensorFlow.

A general rule of thumb is that if there’s a task you need to do in Python and it involves manipulating or processing data, there will be a library for it.

To make things easier for myself (and other people), I have compiled this list.

TensorFlow

TensorFlow is a free and open-source platform for machine learning built by Google. It is an end-to-end open-source platform for machine learning, with all the tools and libraries you need to build your first neural network.

This library includes various tools, libraries and resources that allow developers to create and deploy machine learning applications. This makes it easier for data scientists to use deep learning without having to worry about implementing their own custom backends or dealing with low-level infrastructure details such as memory management or parallelism issues.

NumPy

NumPy is a Python module for scientific computing. It provides a multidimensional array object and tools for working with these arrays. The arrays are packed in a binary format, which means that they are compressed and can be efficiently stored in files, memory buffers, disks etc. NumPy also includes many functions for performing operations on arrays, e.g., linear algebra, random number generation etc.

Pandas

Pandas is an open source Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. It is free to use under a BSD license.

Matplotlib

Matplotlib is the basic package for data visualization in Python. It's the most widely used library for data visualization in Python. Many other libraries are built on top of Matplotlib, so that you can use them without having to write any code yourself.

Keras

Keras is an open-source neural network library written in Python. It can run on top of TensorFlow, MXNet, or Theano. Keras was developed with a focus on enabling fast experimentation. It is easy to use and runs on both CPU and GPU. We will be using Keras to develop our neural networks because it's widely used by data scientists across all industries.

Scikit-learn

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means etc

PyTorch

PyTorch is a Python-based scientific computing package that provides a wide range of algorithms for deep learning. It also provides two high-level features: A rich set of neural networks, including auto-differentiation and dynamic computational graphs

Seaborn

Seaborn is a Python visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics, without the need to learn any advanced mathematics or programming techniques.

Seaborn was created by Wes McKinney (creator of Pandas) in 2014 to solve the problem of creating good looking plots. Seaborn allows you to use interactive widgets, such as sliders and dropdowns, to change settings while viewing the effects they have on your graph.