Top 5 Python Libraries For Data Science In 2021 - Data Science Consulting Services in Australia

Top 5 Python Libraries For Data Science In 2021

Python Libraries for Data Science - OptiSol Brisbane, Australia

Python Overview

Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of its features support functional programming and aspect-oriented programming (including metaprogramming and metaobjects (magic methods).

Many other paradigms are supported via extensions, including design by contract and logic programming. Python is a programming language that lets you work quickly and integrate systems more effectively.

Why Python?

Python is used by data scientists and developers, which makes it easy to collaborate across your organization through its simple syntax.

It provides great functionality to deal with mathematics, statistics, and scientific function.

Programming languages like Python are used at every step in the data science process such as access data from the database, cleaning, and sort data, analyze and visualize data.

What are Python Libraries?

Python’s standard library is very extensive, contains built-in modules (written in C) that provide access to system functionality such as file I/O that would otherwise be inaccessible to Python programmers, as well as modules written in Python that provide standardized solutions for many problems that occur in everyday programming.

Some of these modules are explicitly designed to encourage and enhance the portability of Python programs by abstracting away platform-specifics into platform-neutral APIs.

In addition to the standard library, there is a growing collection of several thousand components (from individual programs and modules to packages and entire application development frameworks), available from the Python Package Index.

5 Top Python Libraries for Data Science in 2021


  • NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
  • NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter.
  • Using NumPy in Python gives functionality comparable to MATLAB since they are both interpreted, and they both allow the user to write fast programs if most operations work on arrays or matrices instead of scalars.
  • NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use.
  • With this power comes simplicity: a solution in NumPy is often clear and elegant.


  • SciPy is a free and open-source Python library used for scientific computing and technical computing.
  • It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and other tasks common in science and engineering.
  • The SciPy ecosystem includes general and specialized tools for data management and computation, productive experimentation, and high-performance computing.
  • The basic data structure used by SciPy is a multidimensional array provided by the NumPy module.
  • NumPy provides some functions for linear algebra, Fourier transforms, and random number generation, but not with the generality of the equivalent functions in SciPy.


  • Pandas is a software library written for the Python programming language for data manipulation and analysis.
  • Pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.
  • Pandas are mainly used for data analysis. Pandas allow importing data from various file formats such as comma-separated-values, JSON, SQL, Microsoft Excel.
  • Pandas allow various data manipulation operations such as merging, reshaping, selecting, as well as data cleaning, and data wrangling features.
  • It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.
  • Additionally, it has the broader goal of becoming the most powerful and flexible open-source data analysis/manipulation tool available in any language.


  • Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.
  • Keras contains numerous implementations of commonly used neural network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier to simplify the coding necessary for writing deep neural network code.
  • Keras allows users to productize deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine.
  • It also allows the use of distributed training of deep learning models on clusters of Graphics processing units (GPU) and tensor processing units (TPU).
  • Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load.
  • It offers consistent & simple APIs, it minimizes the number of user actions required for common use cases and provides clear & actionable error messages.


  • PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab (FAIR).
  • It accelerates the path from research prototyping to production deployment. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling.
  • A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP, and more.
  • It is production ready – Transition seamlessly between eager and graph modes with TorchScript and accelerate the path to production with TorchServe.

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