Python is the simplest and easiest programming language, ever found, in the programming world. Everything about it is so simple, its syntax, commands, libraries. You can literally understand the code by reading it. Quite convenient, isn’t it??
It’s a module-rich programming language that has a wide stack of applications in the IT world. You see, this is the reason it’s been ruling over the developer’s hearts. If you’ve switched to Python after spending years on other languages, you’re going to love it.
But, apart from its simplicity, it has other benefits as well. Python is considered as one of the best Data Analytics tools for Big Data due to a large set of data processing libraries it has in-store for you. That is why this branch of python is also known as ‘Python Data Analytics’.
In this blog, I’ve curated 5 Best Python Libraries for Data Science and going to give a brief about them-
Numerical Python, abbreviated as NumPy, is the most popular Machine Learning package for scientific computation and data analytics in Python. It’s a general-purpose, N-dimensional array processing package.
You would be aware that Python does not provide an array data structure but with the NumPy library, one can create and perform all kinds of manipulations on an array.
It is to be noted that a Python list may contain any object type whether it is a character, number, tuple, whereas, the multi-dimensional NumPy arrays are homogeneous, i.e. they can contain only one object type at a time. Though, the advantage with these same object types is that it makes the comprehension of the required storage size for the array easier.
Features & Applications-
Pandas, stands for Python Data Analysis, is an open-source Python library package that contains fast, flexible, high-performance, and easy-to-use data analysis tools to perform various computing operations on data in Python programming language.
The variety of functionalities it possesses, i.e. data reading, manipulation, aggregation, and visualization, makes it the perfect tool to be used for data analysis in Machine Learning and Big Data Analytics.
‘Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time-series data both easy and intuitive.’
It is well suited for heterogeneous object types, ordered as well as the disordered, tabular, arbitrary matrix, and other different forms of statistical and observational data sets.
Features and Functions-
TensorFlow is an open-source, end-to-end platform used to create and deploy Machine Learning powered applications. It’s comprehensive libraries, the wide ecosystem of tools and active community resources let the developers push the limits beyond ML and help them to build Deep Learning models.
Its architecture consists of three main parts-
TensorFlow manages all aspects of the ML system with its different APIs. It was first developed to work on an Artificial Intelligence (AI) model. The term ‘TensorFlow’ was derived from its core framework, i.e. Tensor. A tensor is an N-dimensional matrix that represents all types of data; the shape of this data defines the dimensionality of the matrix.
The specialty of the TensorFlow library is that it uses Graph Computation- It allows the developer to visualize the construction of the neural network with Tensorboard.
Its key features are-
It has a large community on GitHub compared to other frameworks.
SciPy, also known as Scientific Python, is a collection of mathematical functions and algorithms and uses NumPy extension for more numerical operations. It contains several domain-specific modules and toolboxes that are used in signal and image processing, statistics, linear algebra, interpolation, optimization, integration, special functions, Fourier Transform, etc.
SciPy provides high-level classes and commands to the user for visualizing and manipulating data sets. Apart from that, it also provides an additional benefit of making powerful and robust programming language to develop modern programs and applications.
It’s a great module for data processing and fits well in the list of system-prototyping environment rivaling systems, such as MATLAB, Octave, R-Lab, IDL, and SciLab.
Features & Applications:
While the other libraries, mentioned above, are used to manipulate, operate and visualize the data sets, Matplotlib is specifically built for Data Visualization only.
“Matplotlib is a comprehensive MATLAB-style data visualization library for creating static, animated, and interactive visualizations in Python.”
In simple words, Matplotlib is a data plotting and graphing library in Python that can generate a wide variety of different charts and graphs of various formats, such as scatter graphs, line charts, bar charts, heat maps, pie charts, and 3D plots. It processes the data by visualizing or plotting it in the form of a graph, diagram or chart.
Apart from these five libraries, as discussed above, there are several other wonderful Python library modules available for data science that are special in their own functionalities and must be known to you and the world. If you passionately want to become a Data Science Specialist then the more data processing libraries you learn, the better.
Hope this article is useful for you. As you know Python is a popular programming language and you can get the best 10 tricks and facts about python programming easily by click on it.
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By Payal Mittal