Top 10 Data Science Libraries in Pythonby@devansh-agarwal
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Top 10 Data Science Libraries in Python

by DEVANSH AGARWALAugust 27th, 2020
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Python is considered to be the easiest language to learn for beginners. It is also popular because of the dynamic set of applications it has. Python has a monopoly in the Data Analytics market along with widespread usage in fields like Artificial Intelligence, Machine Learning, web development, and desktop app development. We have curated a list of the 10 most popular Python libraries that are used in Data Science. The Top 10 Data Science Libraries are; NumPy, Pandas, Matplotlib and Tensorflow.

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Data Science Libraries that will shine this year.

Python is considered to be the easiest language to learn for beginners. Not only that, but Python is also popular because of the dynamic set of applications it has. Python has a monopoly in the Data Analytics market along with widespread usage in fields like Artificial Intelligence, Machine Learning, web development, and desktop app development. 

Considering the all-around popularity and approbation that Python entails, it is no surprise that it has a rich library-collection attributed to Data Science. Libraries are what speak for Python! You just name it and there's a library for almost everything under the sky.

Taking into consideration, the current market trends, Data Science is one of the most sought after career options. If playing with Data and drawing useful conclusions out of it fascinates you, then this is your thing! Python, being one of the most popular programming languages has a rich library-set for Data Science. Python is majorly used for Data Mining, Data Processing & Modelling, Data Visualization, and Data extraction.

Thus, we have curated a list of the 10 most popular Python libraries that are used in Data Science. Dedicated to all the data enthusiasts and Data Scientist, we hope this listicle add value to you! Thus moving ahead, the Top 10 Data Science Libraries are;


NumPy is a Python library majorly used for data analysis, scientific computations, and data science. NumPy majorly supports multi-dimensional array and matrices. It is one of the most fundamental data science libraries in Python. NumPy is also used internally by Tensorflow and many other Python libraries to perform operations on Tensors. NumPy is more like a general-purpose Python package that 


Pandas is another Python library which is best suited for wrangling and merging data. Pandas is mainly used for easy and quick data manipulation, data aggregation, and data visualization. Pandas is used to create data frames(Python Objects) from a CSV file. 


Matplolib is another useful Python library for Data Visualization. Descriptive analysis and visualizing data is very important for any organization. Matplotlib provides various methods to Visualize data effectively. Matplotlib allows you to quickly make line graphs, pie charts, histograms, and other professional-grade figures. Using Matplotlib, one can customize every aspect of a figure. Matplotlib has interactive features like zooming and planning and saving the Graph in graphics format.


Scikit-Learn is one of the most dynamic and widespread machine learning libraries for classical ML algorithms. It is built on top of two basic Python libraries, which are, NumPy and SciPy. Scikit-Learn provides sustenance to most of the supervised and unsupervised learning algorithms. This library can also be used for data-mining, data gathering, and data analysis, which makes it a great tool for beginners learning ML.

Scikit-learn is a free-of-cost machine learning library attributed to Python. It features various algorithms including classification, regression, and clustering algorithms along with support vector machines, gradient boosting, random forests, k-means, etc.


According to Wikipedia, TensorFlow is a free and open-source programming construct, often referred to as a library for data-flow and differentiable programming which is employed across a wide array of tasks. It is a library that is used for machine learning applications such as neural networks, fuzzy logic, and genetic algorithms.

Tensorflow is, by far, one of the most popular Machine Learning libraries in the world today, it wasn’t the first one to be used, but when it was launched in the market, due to the ease of usage and simple syntax, it witnessed a great upsurge and rapidly to surpassed all the libraries that existed in the market.


Keras is a substantial Machine Learning library for Python. It is a high-level neural networks API which has the potential of running on top of TensorFlow, CNTK, or Theano. It can run smoothly on CPU and GPU indifferently. Keras makes it effortless for ML beginners to build, design, and construct a Neural Network. Easy and quick prototyping is a strong characteristic of Keras.

Keras is a deep learning library that wraps around the functionalities of other libraries like Tensorflow, Theano, or CNTK. Written in Python. Keras has upper-hand on its competitors like Scikit-learn and PyTorch because it runs on top of Tensorflow.


Scrapy is a Python framework that is extensively used for web scrapping. Scrapy is extensively used for extracting, storing, and processing a large amount of web data. Scrapy enables us to handle a large amount of data with ease. 

Some of the major applications of Scrapy include web-scraping, extraction of data, and other information, and this data is eventually used for decision-making purposes. Scrapy is an indispensable component of Data Science as it helps us to gather data, store it compactly, and analyze it to draw meaningful conclusions.


Seaborn is predominantly a data visualization library that is built on top of Matplotlib. This library equips you with the ability to curate informative and statistical visuals along with illustrative graphs. Seaborn makes data visualization, an indispensable part of data exploring and analyzing. The library is best-suited for examining relationships among multiple variables.

Seaborn internally performs all the important semantic mapping and statistical aggregation for producing informative plots. This Python library for data visualization also has tools for picking up colors to customize data-sets in graphs.


SciPy is a Python library that consists of a multitude of modules for integration, linear algebra, mathematical computing, optimization, and statistics. This open-source Python library allows developers and data engineers to get their hands dirty with Fourier transforms, ODE solvers, signal and image processing, and the likes.


The Plotly Python library ( is an interactive, open-source plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases.

Built on top of the Plotly JavaScript library (plotly.js), enables Python users to create beautiful interactive web-based visualizations that can be displayed in Jupyter notebooks, saved to standalone HTML files, or served as part of pure Python-built web applications using Dash.


Thus, to sum up, we can say that the Top 10 Data Science Libraries are essential if you want to build a career in the domain of Data Analytics and the likes. Today, Data is taking over the world, data is more precious than any resource in the IT industry. With data, if cleaned and worked upon properly, you can turn things upside-down. You get insights from data, that can help you pave the way for the successful execution of your company and it's offerings.

Therefore, being acquainted with this cutting-edge technology will help you make a promising career in the industry with a fat payout, of course!

Also, I would love to hear any feedback and review from you. Please tell me what you liked in the comment section below. Happy Learning!✨

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