6 Best Python Libraries for Data Science

on Data Analytics, Data Driven Strategies • July 14th, 2021 • Write for Hevo

Data is considered to be one of the most valuable assets for any business if leveraged efficiently. As the volume of data with any business increases, its ability to perform quality analysis and make better data-driven decisions also increases. With the increase in businesses’ reliance on data-driven decision-making, they are constantly looking for ways to extract value from their data that will help them boost revenue and adapt to new market trends.

As a result, most businesses across the world have started relying on Data Science and Analytics to derive insights from their data. This article will provide you with a comprehensive understanding of the best Python Libraries for Data Science.

Table of Contents

Introduction to Python

Python Logo
Image Source: https://www.python.org/community/logos/

Python is one of the most popular General-purpose Programming Languages that was released in 1991 and was created by Guido Van Rossum. It can be used for a wide variety of applications such as Server-side Web Development, System Scripting, Data Science and Analytics, Software Development, etc. 

Python is an Interactive, Interpreted, Object-Oriented Programming Language that incorporates Exceptions, Modules, Dynamic Typing, Dynamic Binding, Classes, High-level Dynamic Data Types, etc. It can also be used to make system calls to almost all well-known Operating Systems.

More information about Python can be found here.

Understanding the Key Features of Python

Some of the most well-known features of Python are as follows:

  • Free and Open-Source: Python is available free of cost for everyone and can be easily downloaded and installed from the official website. Open-Source means that the source code is openly available. This gives users with enough knowledge the ability to make changes to the code as per business use cases and product requirements.
  • Easy to Code and Read: Python is considered to be a very beginner-friendly language and hence, most people with basic programming knowledge can easily learn the Python syntax in a few hours. 
  • High-Level: While using Python, developers do not need to have any information on the System Architecture or manage memory usage manually. All this is automatically handled by the Python Interpreter.
  • Portable: A Python code written on one system can easily be transferred to another system and can run without any issues.
  • Interpreted: Python code is processed by the Interpreter at runtime. This means that users do not need to compile the code and then run it similar to other programming languages such as Java, C/C++, etc.
  • Object-Oriented: Python also has support for the Object-Oriented Programming Paradigm which allows users to write readable and reusable code.

Introduction to Data Science

Data Science
Image Source: https://home.kpmg/xx/en/home/insights/2019/07/the-emergence-of-data-science-in-pe.html

Data Science can be defined as the field of study that combines Programming Skills, Knowledge of Mathematics and Statistics, and Domain Expertise to extract meaningful insights from data. Data science practitioners usually leverage Machine Learning algorithms and Artificial Intelligence (AI) systems to perform tasks that would typically require human intelligence. In turn, these algorithms can identify patterns and insights from data that analysts and businesses can leverage to plan their future strategies. Data Scientists typically have an in-depth experience of the following:

  • Business Domain
  • Statistics and Probability
  • Computer Science
  • Written and Verbal Communication
The Data Science Cycle
Image Source: https://ischoolonline.berkeley.edu/data-science/what-is-data-science/

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  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
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6 Best Data Science Libraries Python

The best Data Science Libraries Python are as follows:

1) Best Python Libraries for Data Science: Numpy

Numpy Logo
Image Source: https://commons.wikimedia.org/wiki/File:NumPy_logo_2020.svg

NumPy stands for Numerical Python and is an essential Python library for scientific computing. It is widely used for Machine Learning and Deep Learning applications. All Machine learning algorithms are computationally complex and hence, require multidimensional array operations. NumPy houses support for large multidimensional array objects and also offers numerous tools to work with them.

Some of the most popular Data Science Libraries for Python, such as Pandas, SciKit-Learn, Matplotlib, etc., are built on top of NumPy.

More information about NumPy can be found here.

2) Best Python Libraries for Data Science: Pandas

Pandas Logo
Image Source: https://en.wikipedia.org/wiki/Pandas_(software)

Pandas is considered to be one of the most popular Python libraries for Data Manipulation and Analysis. Pandas makes use of dataframes to hold the required data in memory. It allows users to write simple scripts that can help perform all required ETL using Python operations.

The biggest drawback of using Pandas is that it was designed primarily as a Data Analysis tool and hence, stores all data in memory to perform the required operations. This results in performance issues as the size of the dataset increases and is not considered to be suitable for Big Data applications.

More information on Pandas can be found here.

3) Best Python Libraries for Data Science: Matplotlib

Matplotlib Logo
Image Source: https://matplotlib.org/

Matplotlib is one of the most popular cross-platform Data Visualization and Graphical Plotting libraries for Python. It also has a numerical extension called NumPy. Matplotlib was developed by John Hunter and is currently seen as a robust Open-Source alternative to MATLAB. This Python library can be used by developers to create numerous static, interactive, or animated data visualizations.

A Matplotlib script in Python can easily be structured such that a few lines of code are enough in most cases to generate a visual data plot. The Matplotlib scripting layer houses two APIs:

  • The Pyplot API is a hierarchy of Python code objects and can be referred to using matplotlib.pyplot.
  • An OO (Object-Oriented) API collection of objects that provides direct access to Matplotlib’s backend layers and can be assembled with more flexibility than pyplot.

More information about Matplotlib can be found here

4) Best Python Libraries for Data Science: SciKit-Learn

SciKit-Learn Logo
Image Source: https://commons.wikimedia.org/wiki/File:Scikit_learn_logo_small.svg

SciKit-Learn (Sklearn) was developed by David Cournapeau in 2007 as a Google Summer of Code project and is a widely used library for Machine Learning in Python. This library houses numerous efficient tools for Statistical Modeling and Machine Learning. It is primarily written in Python and built upon SciPy, NumPy, and Matplotlib. SciKit-Learn now offers developers access to a range of Supervised and Unsupervised Machine Learning algorithms via a powerful interface in Python.

The SciKit-Learn stack includes NumPy, SciPy, Matplotlib, IPython, Sympy, and Pandas. All these libraries together allow users to implement Regression, Classification, and Clustering models. Users can also leverage SciKit-Learn to perform Data Pre-processing and Model Selection.

More information about SciKit-Learn can be found here.

5) Best Python Libraries for Data Science: Tensorflow

Tensorflow Logo
Image Source: https://www.tensorflow.org/

TensorFlow is an Open-Source library for complex numerical computation and large-scale Machine Learning and Artificial Intelligence that was developed by the Google Brain team. TensorFlow houses a large number of robust Machine Learning and Deep Learning models and algorithms and allows developers to access them via powerful APIs. It leverages Python to provide developers with a convenient front-end API for building applications with the framework while executing those applications in high-performance C++ internally. 

Tensorflow also gives developers the ability to create a graph of computations where each node in the graph represents a mathematical operation, and each connection represents some data. Hence, developers have to focus solely on the overall logic of the application instead of dealing with low-level details like coming up with proper ways to generate the output of one function and passing it as input to another.

More information about Tensorflow can be found here.

6) Best Python Libraries for Data Science: Keras

Keras Logo
Image Source: https://keras.io/

Keras is a high-level, Deep Learning API that was developed by Francois Chollet and was released in 2015. It is an Open-Source software library that provides an interface for Tensorflow and enables developers to perform fast experiments with Deep Neural Networks. It also houses support for multiple Backend Neural Network Computation.             

Keras is considered relatively easy to learn and work with as it provides developers with a powerful Python frontend along with a high level of abstraction while having the option to implement multiple backends for computation. Although this makes Keras slower than numerous other Deep Learning frameworks, it is still preferred as it is highly beginner-friendly. Keras offers utilities for Compiling Deep Learning Models, Graph Visualisations, and complex dataset analysis. Further, it provides numerous prelabeled datasets that users can easily import and perform the required operations on it directly.

More information about Keras can be found here.

Conclusion

This article provided you with an understanding of the best Python Libraries for Data Science, allowing you to choose the right ones based on your business use case and data requirements.

The first step in implementing any Data Science application is integrating the data from all sources. However, most businesses today have an extremely high volume of data with a dynamic structure, stored across numerous applications. Creating a Data Pipeline from scratch for such data is a complex process since businesses will have to utilize a high amount of resources to develop it and then ensure that it can keep up with the increased data volume and Schema variations. Businesses can instead use automated platforms like Hevo.

Hevo helps you directly transfer data from a source of your choice like Python to a Data Warehouse or desired destination in a fully automated and secure manner without having to write the code or export data repeatedly. It will make your life easier and make data migration hassle-free. It is User-Friendly, Reliable, and Secure.

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