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Data visualization is an essential part of data analysis and communication. Visually appealing and informative plots make it easier to understand complex data patterns and convey the findings in an effective way. 

Being a popular language for data analysis and visualization, Python offers a range of options to choose from.

Each of Python visualization libraries has a set of features, advantages, and disadvantages. Of course, the best way to learn about different technologies and visualization libraries in Python is practice. However, to make an informed decision it’s also important to understand the pros and cons of each, as different visualization tools in Python have different syntaxes and semantics.

In this blog, we’ll cover the 7 most popular Python libraries for visualization.

Reasons to Use Python For Data Visualization

Python has become a go-to language in data science. There are multiple reasons why it’s so often chosen for data analysis and beautiful data visualization. Some of them are:

  1. Unlike Excel, for example, which is also frequently used for data visualization, Python can handle large and multiple datasets very quickly.
  2. It gives great data connectivity when working with cloud-based data.
  3. Python offers improved productivity which allows business stakeholders to make data-driven decisions. 
  4. Python's syntax is clear, readable, and beginner-friendly, making it easy for data scientists, analysts, and researchers to learn and use.
  5. It has a vast and mature ecosystem of data visualization libraries
  6. Python is a versatile language that can be used for various tasks in data analysis, machine learning, and web development.

How to Use Python for Data Visualization

Python implementation for data visualization involves leveraging various libraries and tools to create informative and visually appealing representations of data. Here's a step-by-step guide to getting started with data visualization in Python:

1. Install Necessary Libraries

Make sure you have the required visualization packages in Python installed. Common libraries include:

  • Matplotlib
  • Seaborn
  • Plotly
  • Bokeh
  • Altair
  • Holoviews
2. Import Libraries

In your Python script or Jupyter Notebook, import the required data visualization libraries. This allows you to access their functions and features.

3. Load Your Data

Load your dataset into a Pandas DataFrame or another suitable data structure. Ensure your data is in a format compatible with the chosen visualization library.

4. Choose a Library Based on the Use Case

Depending on your visualization needs, choose the appropriate library. For basic plots and customization, Matplotlib and Seaborn are widely used. For interactivity, consider Plotly or Bokeh. Altair and Holoviews offer high-level abstractions for concise visualizations.

5. Create Simple Plots

Start with simple plots like scatter plots, histograms, or bar charts. Use Matplotlib and Seaborn for quick and static visualization of plots.

6. Explore Interactivity

If interactivity is crucial, experiment with Plotly or Bokeh. These libraries allow users to create dynamic plots with zooming, panning, and tooltips.

7. Create Interactive Dashboards with Dash

To create interactive dashboards, explore Dash by Plotly. Dash allows you to build interactive web applications with data visualizations.

8. Experiment with Declarative Syntax

Altair and Holoviews use a declarative syntax, allowing users to express visualizations concisely. Experiment with these libraries for simplicity and ease of use.

9. Save and Share

Save your visualizations as images or interactive HTML files for sharing or embedding in presentations, reports, or websites.

10. Experiment and Customize

Experiment with different libraries and visualization types to find the most suitable for your data. Customize your plots using available options and documentation.

Now, let’s examine each visualization Python library individually.

Top 7 Data Visualization Libraries for Python


Matplotlib facilitates the creation of 2D array plots. It utilizes the NumPy library, written in Python, and is compatible with Python and IPython shells, as well as Jupyter notebooks and web application servers. It allows users to explore trends, behavioral patterns, and correlations through scatter, line, bar, and histogram plots, among others.

Main Features of Matplotlib

  • Matplotlib supports a variety of plot types, including line plots, scatter plots, bar plots, histogram plots, pie charts, 3-D charts, and more. 
  • It has great customization options for plots. Users can control the appearance of plot elements, such as colors, markers, line styles, and labels.
  • Matplotlib integrates with Jupyter Notebooks and NumPy, another popular Python library for numerical computing.
  •  Supports the creation of animated visualizations.
  • Supports multiple backends, allowing users to choose the appropriate backend for their specific use case. Backends include interactive backends for user interfaces and non-interactive backends for saving plots as image files.
  • The Matplotlib gallery provides a collection of example plots and code snippets, serving as a valuable resource for users to learn and adapt code for their specific needs.

Matplotlib Pros and Cons                  

Matplotlib Pros and Cons

Together, these features make Matplotlib the best Python visualization library for many users, solidifying its reputation as the go-to Python visualization library.


Seaborn is also one of the popular visualization libraries in Python.  It’s especially useful for making statistical representations of datasets in Python. Seaborn is built on Matplotlib and benefits from its flexibility, alongside integration with the NumPy and Pandas data structures. It’s quite easy and fast to understand and write.

Main Features of Seaborn

  1. Seaborn provides a high-level interface for creating aesthetically pleasing statistical graphics.
  2. It specializes in statistical data visualization and is particularly effective for visualizing complex relationships in datasets.
  3. Seaborn is designed to produce visually appealing plots with attractive color palettes and default settings.
  4. Provides specialized plots for categorical data, including bar plots, count plots, box plots, violin plots, and more. 
  5. Seamless integration with Pandas DataFrames simplifies the handling of structured data and facilitates quick plot creation directly from DataFrame columns.

Seaborn Pros and Cons

Seaborn Pros and Cons


Distinguished as the best Python visualization library by many users, Plotly is an interactive, open-source, and comprehensive tool for creating declarative visualizations in Python. It offers a rich assortment of visualizations, including scientific charts, 3D graphs, statistical charts, and financial charts. Plotly graphs, ideal for both Jupyter notebooks and standalone HTML files, can be seamlessly viewed online. 

Notably, the powerful library provides options for interactive plot interaction and editing, and its robust API functions flawlessly in both local and web browser modes.

Main Features of Plotly

  1. Plotly offers great interactive plotting capabilities. Users can zoom, pan, and hover over data points to get additional information, enhancing the exploration of complex datasets.
  2. It supports a diverse range of plot types, including line charts, scatter plots, bar charts, histograms, box plots, 3D plots, choropleth maps, and more.
  3. Users can host and publish their visualizations on the Plotly Chart Studio or embed them in web applications and dashboards.
  4. Integrates with Jupyter Notebooks and supports animations.
  5. Plotly Express, a high-level API, simplifies the process of creating common plots with less code.

Plotly Pros and Cons

Plotly Pros and Cons


Bokeh is one of the interactive Python visualization packages. It provides high-performance interactive plots and charts for complex use cases. The charts can be easily modified by using custom JavaScript. Due to its easily compatible nature, it can work with Pandas and Jupyter notebooks.

Main Features of Bokeh

  1. Great interactive visualizations: users can pan, zoom, and hover over data points for dynamic exploration.
  2. Bokeh provides both high-level and low-level APIs, catering to users with different preferences and needs.
  3. Online sharing option and integration with Jupyter Notebooks.
  4. Supports streaming data for real-time analytics and monitoring.
  5. Options for customizing the styling of plots, including colors, markers, layout, and annotations.

Bokeh Pros and Cons

Bokeh Pros and Cons


Plotnine is an implementation of a grammar of graphics based on ggplot2. This package supports the creation of complex plots from data in a data frame. The default settings help create publication-quality plots without requiring a lot of settings and tweaking.

One of the main benefits of Plotnine is its simplicity and ease of use. Its syntax is intuitive and expressive so the users need just a few lines of code to create complex plots.

Main Features of Plotnine

  1. It follows the Grammar of Graphics, a systematic approach to creating visualizations. It provides a structured way to articulate visual intentions, making it easy for users to express complex ideas with simple code.
  2. Faceting allows users to create small multiples, enabling the exploration of multivariate relationships and patterns within subsets of the data.
  3. Provides high-level abstractions for creating complex visualizations with minimal code. Users can create sophisticated plots with concise and intuitive syntax.
  4. Aesthetically pleasing visualizations with well-designed default aesthetics.
  5. Users can easily add custom themes, scales, and geoms to tailor visualizations to their specific needs.

Plotnine Pros and Cons
Plotnine Pros and Cons


Altair is a simple, friendly, and consistent declarative visualization library for Python built on top of Vega-Lite grammar. The declarative approach, the same as in Seaborn, gives users the freedom to concentrate on interpreting the data instead of writing the correct syntax. However, this approach has a downside as well: it gives users less control over customizing the visualization.

Altair is one of the Python visualization packages that gives very pleasant and eye-catching data visualization but requires more effort to understand and write compared to Seaborn, for example.

Main Features of Altair

  1. The declarative syntax makes it easy to create complex visualizations with concise and human-readable code.
  2. Users can add interactivity to plots, such as tooltips, zooming, panning, and selection, to enhance the exploration of data.
  3. Altair can automatically choose the appropriate type of chart based on the data and the user's specifications.
  4. Supports a variety of chart types, including scatter plots, line charts, bar charts, histograms, heat maps, and more.
  5. Altair allows users to bind data directly to visualizations.

Altair Pros and Cons

Altair Pros and Cons


Holoviews is a Python library for creating interactive visualizations with high-level and declarative syntax. It simplifies the process of data visualization by providing a concise and expressive way to create complex plots. The main principle of Holoviews is that data visualization should be as direct and simple as possible.

Main Features of Holoviews

  1. Holoviews provides high-level abstractions for creating complex visualizations with minimal code.
  2. It uses a declarative syntax, making it easy to create expressive visualizations without the need for extensive code.
  3. Supports interactive visualizations, allowing users to explore data dynamically through interactive tools like zooming, panning, and hovering.
  4. Integrates with Pandas DataFrames, simplifying the process of working with structured data and creating visualizations based on DataFrame columns.
  5. This Python graph visualization library is designed to handle large datasets efficiently, making it suitable for the interactive exploration of big data.

Holoviews Pros and Cons

Holoviews Pros and Cons


When choosing Python visualization tools, the most important is to clearly understand your use case and requirements. Different Python visualization libraries have various syntaxes and semantics, so it’s vital to understand the advantages and drawbacks of each.

What’s your favorite Python library for visualization?

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