20 Must-Know Python Libraries for Data Science

Vidya Gopinath for keySkillset Vidya Gopinath for keySkillset
Vidya Gopinath for keySkillset
20 Must-Know Python Libraries for Data Science

Imagine this: you're starting a new project, and it requires some complex logic or functionality. You dive into coding, only to realize that someone else has already solved the exact problem you're facing. They've encapsulated their solution into a neat package or library, ready to be used by anyone. That's the beauty of Python!

Now, if you wish to know 12 real world applications of Python, you can look up this blog.

What are Python Libraries 

Python, hailed as the best language to learn, offers a plethora of libraries that make your coding journey a breeze. These libraries are pre-written collections of code, designed to tackle specific tasks or provide useful functionalities. Whether you're delving into data science, machine learning, web development, or even simple mathematics, Python has got you covered.

The Python library ecosystem is a vibrant universe teeming with possibilities. It's like having an arsenal of tools at your disposal, waiting to be utilized to their fullest potential. With these libraries, you can significantly enhance your productivity, accelerate development, and create robust applications with ease.

The List of Libraries 

Now, you might be wondering, "Which Python libraries should I explore?" Well, in the realm of data science, here's a curated list of the top 20  Python libraries that are stealing the spotlight in 2023:

  1. NumPy: A fundamental library for numerical computing in Python.
  2. Pandas: Data manipulation and analysis made easy.
  3. Matplotlib: Creating captivating visualizations and plots.
  4. Seaborn: Statistical data visualization at its finest.
  5. Scikit-learn: Machine learning for everyone.
  6. TensorFlow: Building and training neural networks.
  7. PyTorch: Deep learning made accessible.
  8. Keras: High-level neural networks simplified.
  9. SciPy: Scientific computing and optimization.
  10. NLTK: Natural Language Processing (NLP) made simple.
  11. OpenCV: Computer vision at your fingertips.
  12. Plotly: Interactive and dynamic visualizations.
  13. Gensim: Topic modeling and document similarity.
  14. Scrapy: Empowering Web Crawling and Data Extraction
  15. SpaCy: Unleash the Power of NLP Awesomeness!
  16. BeautifulSoup: Unleash the Magic of Data Scraping!
  17. LightGBM: Boost Your Data Science Projects with Lightning Speed!
  18. Theano: Unleash the Power of Mathematical Expression Manipulation!
  19. Ggplot: Unleash the Power of the Grammar of Graphics!
  20. Altair: Power of Declarative Statistical Visualization


Picture this: you're diving into the realm of data analysis, and you need a tool that can handle vast amounts of numerical data with lightning-fast speed. That's where NumPy comes into play, the fundamental package for numerical computation in Python.

With an impressive 18,000 comments on GitHub and a thriving community of 700 contributors, NumPy has become a powerhouse in the field. Its key feature is the introduction of N-dimensional arrays, revolutionizing how data is handled. 

Packed with precompiled functions optimized for speed, NumPy excels at array-oriented computing, enabling efficient manipulation of large datasets. It finds extensive applications in data analysis, forming the backbone of libraries like SciPy and scikit-learn. With NumPy, you can unleash the full potential of your data, bid farewell to slow computations, and embrace the speed and efficiency of this remarkable library.

So, immerse yourself in the world of NumPy, where N-dimensional arrays reign supreme, and join the countless data enthusiasts who have harnessed its power. Let NumPy be your trusted companion in the captivating realm of numerical computation, empowering you to elevate your data analysis game to new heights. Experience the true power of Python in data science and unlock a world of possibilities.


Discover the power of Pandas, the must-have Python library for data science. Alongside NumPy and matplotlib, Pandas dominates the field with its widespread popularity and usage. With an impressive 17,000 comments on GitHub and a thriving community of 1,200 contributors, Pandas excels in data analysis and cleaning. 

Its fast and flexible data structures, like data frames, simplify working with structured data. Pandas offers elegant syntax, rich functionalities, and high-level abstraction, making it a go-to tool for tasks such as data wrangling, ETL jobs, and time-series analysis.

 From academia to finance and beyond, Pandas empowers users to effortlessly unlock insights and drive impactful decisions through data. Join the Pandas revolution and take your data science endeavors to new heights.


When it comes to creating visually appealing and informative plots and graphs, Matplotlib takes the spotlight. With its impressive library of visualizations, Matplotlib is a go-to plotting tool for data scientists. With over 26,000 comments on GitHub and a vibrant community of 700 contributors, it has become an essential library for data visualization.

Matplotlib offers a wide range of features, making it a suitable replacement for MATLAB while being free and open-source. It supports multiple backends and output types, ensuring compatibility across different operating systems and output formats. Additionally, Matplotlib provides an object-oriented API, enabling users to seamlessly embed plots into their applications.

The library finds numerous applications in the data science domain, including correlation analysis of variables, visualizing confidence intervals of models, outlier detection using scatter plots, and gaining insights into data distributions. With Matplotlib, data scientists can transform complex data into compelling visual representations, facilitating a deeper understanding of their datasets.


When it comes to creating visually appealing and informative statistical graphs, Seaborn takes the lead by building upon the low-level interface of Matplotlib. With its high-level interface, Seaborn offers a range of features that make data visualization a breeze.

Seaborn provides a rich collection of plots, including relational plots, categorical plots, distribution plots, regression plots, matrix plots, and multi-plot grids. It also offers themes to style Matplotlib visualizations, allowing users to create visually consistent and aesthetically pleasing graphs. Additionally, Seaborn seamlessly integrates with NumPy and Pandas, making it convenient for working with data in these formats. Its speed in visualizing univariate and bivariate data sets it apart as a powerful tool.

One of the major advantages of Seaborn is its efficiency. It takes care of much of the visualization process, enabling users to easily pass their entire dataset for visualization. The interactive and informative representation of data provided by Seaborn allows for quick insights and exploration. However, Seaborn does have some limitations. The visualizations it produces are not highly interactive, and customization options are limited to the styles provided by Seaborn itself.

Seaborn finds its applications in visualizing data in an aesthetically pleasing manner, making it a popular choice in various integrated development environments (IDEs) and data analysis workflow


Alright, folks, now let us dive into the fantastic world of Scikit-learn! This Python library is like a magical toolbox for data scientists, packed with all the machine learning algorithms you could ever dream of. Picture it as the cool kid that effortlessly blends in with NumPy and SciPy, making your data analysis journey a breeze.

With Scikit-learn by your side, you can conquer the data science kingdom with clustering, classification, regression, model selection, and even dimensionality reduction. It's like having a personal army of algorithms ready to tackle any prediction challenge you throw their way. And hey, it doesn't stop there! Scikit-learn also spices things up with classification metrics to keep you on your toes.

The best part? This library comes with a free pass! Yep, it's open-source, so you can unleash your data-crunching powers without any pesky licensing restrictions holding you back. But, fair warning, while Scikit-learn is a solid foundation, it's not the go-to guru for those deep, complex machine learning quests. And let's be real, when it comes to heavy-duty datasets, it might struggle a bit to keep up.

Now, imagine a world where you can spot spam, recognize images, predict drug responses, forecast stock prices, slice and dice customer segments, and get wild with experimental groupings—all thanks to Scikit-learn. It's like a data science superhero, swooping in to save the day with its accessibility, algorithmic charm, and open-source vibes. Just remember, when you venture into the realm of specialization or monstrous datasets, Scikit-learn might not be your knight in shining armor.

So, grab your Python hat and get ready to rock the data science realm with Scikit-learn and have you exclaim, "Who needs pandas anyway?!" Okay, maybe not exactly, but you get the point.


TensorFlow, the superstar of Python libraries for data science, is a powerhouse when it comes to training and inference with deep neural networks. With its open-source nature, TensorFlow offers a wealth of features for machine learning and data science tasks. From data preparation to model building, deployment, and MLOps implementation, TensorFlow has got you covered across various platforms.

One major advantage of TensorFlow is its high-level Keras API, which makes model building a breeze. It provides robust support for ML production, offering scalability, easy debugging, extensive architectural support, and seamless library management. However, TensorFlow may have some limitations on Windows and is known to be relatively slower compared to other frameworks. Additionally, its focus is more on model execution rather than training.

TensorFlow finds applications in diverse domains. Companies like Airbnb, Airbus, and GE leverage TensorFlow for tasks such as image classification, object detection, and extracting insights from satellite images. It's the go-to library for speech and image recognition, text-based applications, and time-series analysis.

So, get ready to embrace TensorFlow's versatility and tap into its deep learning prowess. With its vibrant community and frequent updates, you'll have the latest features at your fingertips. It's time to let TensorFlow unleash its magic and take your data science adventures to the next level!


Hold on tight, data science enthusiasts, because PyTorch is here to rock your world! As one of the top Python libraries, PyTorch is a scientific computing package that harnesses the power of graphics processing units (GPUs) to deliver mind-blowing performance. It's like the cool kid on the block, designed to provide maximum flexibility and speed for deep learning research.

PyTorch is famous for its two standout features. First, it excels at tensor computations with robust GPU acceleration support. It's like giving your computations a turbo boost, taking them to a whole new level of speed and efficiency. Second, PyTorch empowers developers to build deep neural networks using a tape-based autograd system. It's like having a personal assistant that automatically calculates gradients, making training models a breeze.

In the ever-evolving world of data science, bridging the gap between research and practice can be a challenge. But fear not! PyTorch is the superhero package that simplifies this journey. It provides a seamless transition from theory and research to practical training and development in the realm of machine learning. It's like having a supportive guide by your side, ensuring you smoothly navigate the intricacies of cutting-edge research.

So, whether you're exploring groundbreaking ideas or diving into complex models, PyTorch has got your back. It combines the best of both worlds—flexibility and speed—to give you an edge in the world of data science. Get ready to unleash the power of PyTorch and take your deep learning adventures to new heights!


Get ready for the Keras experience! This powerful interface to TensorFlow is all about simplicity and efficiency. With Keras, you can dive into deep learning with ease, running experiments quickly and effortlessly. It scales like a champ, making it perfect for large-scale production settings. And the best part? You can deploy Keras anywhere—from websites to mobile devices and even as a web API. It's like having a versatile, user-friendly ally by your side.

Keras tightly integrates with TensorFlow 2, covering the entire machine learning pipeline. It's the ideal starting point for newcomers to deep learning, offering pre-trained models and support for multiple GPUs and TPUs. However, it's worth noting that pushing Keras beyond its intended use can sometimes lead to low-level API issues. Certain features like data pre-processing and basic machine learning algorithms could use some improvement. And while Keras prioritizes user-friendliness, there may be a trade-off in speed for certain applications.

When it comes to applications, Keras excels in image recognition. With its arsenal of pre-trained models like Xception, ResNet, MobileNet, and ImageNet, you'll unlock the secrets hidden within images in no time.

So, buckle up and let Keras guide you through the exciting world of deep learning. It's your ticket to efficient experimentation and impressive results. Get ready to conquer the neural network realm with Keras by your side!


Prepare to dive into the fascinating world of SciPy! This open-source package is a powerhouse for scientific and technical computing. With modules covering integration, optimization, interpolation, linear algebra, statistics, image processing, and more, SciPy is a go-to tool for math and science enthusiasts. Fun fact: SciPy relies on the mighty NumPy for its magic!

One of SciPy's standout features is its utility functions for signal processing, statistics, and optimization. It's like having a Swiss Army knife for your scientific computing needs. Plus, SciPy plays well with other Python libraries, offering better cross-functionality and opening doors to exciting possibilities. It even supports parallel programming for specific web and database routines, unleashing the power of multiprocessing.

While SciPy has numerous advantages, it's not without its quirks. One challenge is dealing with NaN (Not a Number) values, which can be tricky to interpret and work with, especially when not all packages support them. Additionally, for those without a strong background in mathematics, SciPy can be a bit complex. It's a tool designed to aid scientific and mathematical exploration, so a solid understanding of the underlying concepts is helpful.

When it comes to applications, SciPy is a math lover's dream. From solving differential equations to performing linear algebra operations, SciPy shines in research and scientific computation. It's your trusty companion for number crunching and data manipulation, making it an invaluable asset for prototyping systems and data processing.

So, gear up for an adventure with SciPy! Embrace its high-level commands, interactive Python sessions, and the support of a thriving Python community. Just remember, SciPy may not be your go-to for plotting functions, as its primary focus is on numerical objects and algorithms. Get ready to unlock the secrets of scientific computing with the help of SciPy!


Introducing NLTK, the go-to library for statistical language processing! With a collection of libraries and applications, NLTK empowers computers to understand and respond to human language. It's a powerful tool used in education and research, making it a popular choice in the Python ecosystem.

NLTK offers a range of features that enhance language processing. Tokenization, for instance, breaks down text into smaller chunks called tokens, enabling deeper analysis. Sentiment analysis becomes a breeze with NLTK's built-in classifier, allowing you to gauge the positive and negative sentiments towards a specific topic. Additionally, NLTK aids in removing stop words and names, perfect for recommendation systems.

One of NLTK's notable advantages is its broad language support. It covers the largest number of languages among similar packages, making it a versatile choice for multilingual applications. However, using NLTK may come with a steep learning curve, as it can be challenging to master. Moreover, its performance may not meet the demands of real-world production usage, as it tends to be slower compared to other alternatives.

NLTK finds its applications in various domains. It excels in sentiment analysis, enabling you to analyze online reviews with ease. Additionally, you can leverage the nltk.chat module to build chatbots, providing engaging and interactive conversations.

So, if you're ready to dive into the exciting world of language processing, NLTK is your trusty companion. Just be prepared for a learning curve and consider its performance limitations in high-demand production environments. Let NLTK unleash the power of natural language understanding in your projects!


Introducing OpenCV, the powerhouse library for computer vision, machine learning, and image processing! With access to over 2,500 methods, OpenCV enables various applications like object identification and facial recognition. It supports multiple programming languages, making it highly versatile and widely used across industries.

OpenCV's features are impressive. It can process images and videos to identify objects, faces, or even handwriting. Being open-source, it's quick and easy to implement, with customizable code to meet specific business needs. Its extensive algorithm collection, with over 2,500 modern and classic algorithms, allows for diverse tasks. The active community surrounding OpenCV ensures readily available assistance for all users, and it efficiently utilizes hardware acceleration and multicore systems.

However, OpenCV has limitations in facial recognition, such as sensitivity to pose variations and occlusion interference. Nonetheless, it finds applications in various domains. From removing watermarks and cleaning backgrounds in images to facial and object detection and recognition, OpenCV is a versatile tool.

OpenCV's BSD license offers it as a free and powerful solution for machine learning and computer vision. Its open-source nature allows for customization, and it provides a shared architecture for seamless integration into commercial products.

So, if you're diving into computer vision and image processing, OpenCV is your go-to companion. Its speed, vast algorithm collection, and wide community support make it a valuable asset. Just keep in mind its limitations in facial recognition and enjoy the endless possibilities of computer vision with OpenCV!


Let's explore the fascinating world of Plotly! With a massive user base of over 50 million users globally, Plotly is an open-source Python 3D data visualization framework that brings data to life. It is built on the Plotly JavaScript library (plotly.js) and serves as a web-based data visualization tool. Plotly supports a wide range of chart types, including scatter plots, histograms, line charts, bar charts, box plots, and 3D graphs, among others. What sets Plotly apart is its inclusion of contour plots, adding a unique feature to its data visualization capabilities.

One of the key features of Plotly is its versatility. It can be used to create web-based data visualizations that can be embedded in Jupyter notebooks, Dash web apps, or exported as standalone HTML files. With Plotly, you can create interactive and dynamic visualizations that enhance data exploration and understanding.

Plotly offers a range of benefits. Its hover tool allows you to easily spot outliers or abnormalities in your data points, enabling deeper analysis. Moreover, Plotly provides extensive customization options, allowing you to tailor your graphs to be visually appealing and easily interpretable by others.

While Plotly offers numerous advantages, there are some considerations to keep in mind. Staying up-to-date with different Plotly tools, such as Chart Studio and Express, can be challenging, as well as navigating potentially outdated documentation. Additionally, the initial setup of Plotly without an online account may require some effort, and there can be a significant amount of code involved in creating visualizations. Furthermore, linking multiple graphs to the same source dataset can be complex, despite Plotly's support for dictionaries, lists, and DataFrames.


Let's delve into the world of Gensim, the popular open-source NLP language library dedicated to topic modeling. Gensim, short for Generate Similar, leverages modern statistical ML techniques to tackle complex tasks like corpora processing and topic identification.

Gensim boasts an array of impressive features. It excels in handling large and web-scale corpora through its online training algorithms, offering scalability without the need for the entire input corpus to reside in RAM simultaneously. Moreover, Gensim is platform agnostic, supporting Windows, Linux, and Mac OS, and it leverages efficient multicore implementations for high-speed processing and retrieval.

There are several advantages to using Gensim. It efficiently processes large text files without the need to load them into memory, making it ideal for memory-constrained environments. Additionally, Gensim's use of unsupervised models eliminates the need for manual annotations or document hand tagging.

However, it's important to note that Gensim is limited to unsupervised text modeling and does not provide a full-fledged NLP pipeline implementation. Despite this, it has been extensively utilized and referenced in numerous academic, commercial, and research applications. Gensim also offers streamlined implementations for fastText and Word2vec, enabling tasks such as word embedding text classification and reconstructing linguistic context.

Discover the power of Gensim for topic modeling and join the multitude of researchers and practitioners who have leveraged its capabilities. Share this fascinating library with a friend who shares your enthusiasm for NLP and tag them in the comments! Let's unlock the potential of Gensim together.


Scrapy is an awesome Python library for web crawling. It's fast, open-source, and super popular. With Scrapy, you can easily grab data from web pages using XPath selectors. It's like having a superhero sidekick for scraping structured data!

Why do people love Scrapy? Well, it's perfect for building spider bots, those cool programs that crawl websites and fetch all kinds of useful information. Plus, it's not limited to just web pages. Scrapy can handle APIs too, so you can gather data from all sorts of sources. It's all about being efficient and not repeating yourself!

Data scientists and web scrapers dig Scrapy because it's a rockstar at mining data and automating tests. It's built with Python and works like a charm on different operating systems. So whether you're on Linux, Windows, BSD, or Mac, Scrapy has got your back.

Scrapy comes packed with awesome features. It knows how to collect and extract data from HTML/XML sources without breaking a sweat. You can export your data in various formats like JSON, CSV, or XML. And if you're feeling adventurous, you can even extend Scrapy's powers with custom functionality. It's flexible like that!

But hey, Scrapy does have a few quirks. It can't handle websites that rely too much on JavaScript. So keep that in mind if you're dealing with those fancy interactive sites. Oh, and the installation process might be a bit different depending on your operating system. But once you're up and running, the web crawling magic begins!

So, if you're into web scraping, Scrapy is your secret weapon. It's fast, versatile, and makes data extraction a breeze. Get ready to explore the web like never before with Scrapy by your side. It's time to unleash the crawling power!


Get ready to dive into the world of advanced NLP with SpaCy! It's a Python and Cython library that plays well with deep learning frameworks like TensorFlow and PyTorch. SpaCy is all about performance and efficiency, making it a top choice for NLP enthusiasts.

With SpaCy, you can tap into the power of convolutional neural network models. These models can do cool stuff like tagging, parsing, and named entity recognition. And guess what? SpaCy has got your back with tokenization for over 50 languages. Talk about versatility!

Just like its buddy NLTK, SpaCy rocks at tokenization. It can split text into meaningful chunks for analysis. But that's not all! SpaCy can also perform part-of-speech tagging, helping you identify whether a word is a noun, verb, adjective, and more. And let's not forget about Named Entity Recognition (NER) – it's perfect for spotting and classifying those important named entities.

Here's the scoop on SpaCy's pros: it's faster than NLTK, super easy to learn and use, and it harnesses the power of neural networks to train its models. Talk about efficiency and cutting-edge technology!

Now, every superhero has a weakness, and SpaCy is no exception. While it's a fantastic library, it's not as customizable as NLTK. If your task doesn't fit one of SpaCy's prebuilt models, you might need to get a bit creative. But hey, sometimes a little constraint can spark some ingenious solutions!

So, where can you apply the magic of SpaCy? Well, it's perfect for analyzing online reviews and doing sentiment analysis. You can even use it to automate the summarization of resumes, thanks to its Named Entity Recognition powers. And if you're working on search autocomplete or autocorrect, SpaCy has got your back.

Get ready to unleash the NLP awesomeness with SpaCy. It's fast, fun, and packed with powerful features. Let's dive into the world of natural language processing and conquer the textual realm together!


Get ready to scrape web data like a pro with BeautifulSoup! It's a Python library that's all about extracting HTML and XML source data. BeautifulSoup helps you develop web crawlers to retrieve structured data and make sense of the web.

With the version, BS4 (BeautifulSoup 4), you can transform messy web data into neat XML structures for easy analysis. It automatically handles encodings, making data interpretation a breeze. Searching through parsed documents is a breeze with BeautifulSoup. It's beginner-friendly, well-documented, and supported by a strong community.

However, speed can be a downside, but multithreading can help boost performance. Just keep in mind that using proxies can be a bit tricky in complex projects. Ready to uncover hidden treasures in web data? Let BeautifulSoup be your guide!


Harness the power of LightGBM, the go-to Python library for implementing gradient-boosting algorithms. With its high-performance implementation, LightGBM takes your data science projects to new heights.

Featuring seamless integration with popular Python libraries like Pandas, Scikit-Learn, and XGBoost, LightGBM makes it easy to incorporate its capabilities into your existing workflow. No hassle, just seamless collaboration.

But what sets LightGBM apart is its lightning-fast speed and memory efficiency. It's built to handle large datasets and high-dimensional feature spaces with ease. Say goodbye to sluggish performance and hello to rapid insights.

Customization is key, and LightGBM understands that. With a wide range of hyperparameters at your disposal, you can fine-tune your models to achieve optimal performance for your specific datasets and use cases. The power is in your hands.

Unlock the potential of LightGBM in various applications, from anomaly detection to time series analysis and natural language processing. It's a versatile tool that empowers you to tackle complex challenges and uncover valuable insights.

Boost your data science projects with LightGBM. Get ready for speed, efficiency, and limitless possibilities. It's time to shine a light on your data.


Step into the world of Theano, the Python package that revolutionizes mathematical expression manipulation. With a focus on efficiency, Theano lets you effortlessly manipulate and evaluate matrix-valued expressions.

Integrating seamlessly with NumPy, Theano takes advantage of NumPy's ndarray objects, amplifying its capabilities. But Theano doesn't stop there—it goes beyond by calculating derivatives for multiple functions, empowering you to explore complex mathematical realms.

Speed is the name of the game, and Theano excels. By dynamically generating C code, it turbocharges expression evaluation, delivering lightning-fast results. It also harnesses the power of GPUs for data-intensive operations, achieving up to 140 times faster performance.

While newer versions have emerged, Theano remains a pioneer in the deep learning arena. Though development ceased in 2017, its influence is immortal. From computer vision tasks like handwriting recognition to driving advancements in deep learning, Theano continues to leave its mark.

Embrace Theano and unlock a world of mathematical possibilities. Let it be your ally in unraveling the intricacies of mathematical expressions, taking your data analysis to new heights.


Step into the world of Ggplot, where the grammar of graphics comes alive. Originally built for R, gPlot can now be harnessed in Python using the plotnine package.

Unleash your creativity with Ggplot's impressive features. Simply follow the format of data, x, y, and let your imagination run wild. From complex plots derived from data frames to customizable visualizations, Ggplot empowers you to bring your data to life with ease.

With its programmatic interface,  Ggplot puts you in control. Define variables, choose display options, and fine-tune visual properties effortlessly. Enjoy the richness of statistical transformations, scales, facets, coordinated systems, and themes, all at your fingertips.

Experience the pros of Ggplot's grammar of graphics. Achieve more with less code as the underlying principles ensure efficiency and flexibility. Benefit from its refined and comprehensive graphics system, offering a mature solution for all your visualization needs.

While Ggplot's visuals outshine other libraries, it's worth noting some considerations. Compared to more fundamental graphics solutions, Ggplot may be slower. Modifying default colors can be a bit challenging, but the stunning visuals make it worthwhile. Additionally, certain plot types may require adjustments to the data structure.

Regardless of your data's complexity, Ggplot is your go-to package for quick and captivating visuals. Let your creativity shine as you delve into the world of Ggplot and unlock the true potential of the grammar of graphics.


Discover Altair, a powerful declarative statistical visualization package inspired by the visualization grammar of Vega. With Altair, you can effortlessly create aesthetic and impactful visualizations with minimal code.

Enjoy the benefits of Altair's simplicity and versatility. By simply changing the "mark" attribute, you can unlock a variety of plot types, while keeping the base code intact. Altair focuses on the relationship between data columns, making the code concise and easy to understand. Plus, implementing interactivity and faceting is a breeze.

While Altair offers impressive features, it's essential to consider some limitations. Customization options are limited, and plotting complex machine-learning models can be challenging. Additionally, 3D visualization is not supported in Altair for Python.

Altair finds its applications in automatically generating various graph types for smaller data frames. It is a powerful tool for statistical data visualization, allowing you to create captivating visualizations such as bar charts, pie charts, histograms, scatterplots, and more. Altair's dependencies, including Python, NumPy, and Pandas, are seamlessly installed during the setup process. Whether you're using Jupyter Notebooks or JupyterLab, Altair empowers you to create interactive and stunning data visualizations effortlessly.

Experience the key features of Altair, such as data aggregation, fast experimentation, dynamic filtering, and connecting multiple plots. Benefit from its simplicity and visually appealing representations that enhance data understanding.

While Altair may not support 3D visualizations and lacks some basic plots like boxplots, its ease of use and flexibility make it a valuable tool for statistical visualization needs.

Unleash the power of Altair and take your data visualizations to new heights!


These are by no means an all comprehensive list of the Python libraries. Still, these libraries cover a wide spectrum of data science tasks, from handling data manipulation and visualization to implementing advanced machine learning algorithms. With their collective power, you can unlock the full potential of your data and gain valuable insights.

So, why reinvent the wheel when you have an extensive collection of battle-tested tools at your disposal? Embrace the Python library ecosystem, explore these top 30 libraries, and witness the true power of code reusability. Your journey in mastering Python and data science will reach new heights with these invaluable resources by your side. To master Python basics the simulation way, you can check out the Python Beginner course at keySkillset.

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