Best Python Visualization Libraries: Which one to Choose?

Python is one of the most preferred programming languages for data science and data visualization needs. Dada visualization refers to the discipline of visually representing data through formats like graphs, images, plots and other such forms. Afterall visual representation of complex and huge datasets is much easier to understand, analyse, identify patterns and get other important business centric insights rather than going through thousands of excel files.

Python Visualization Libraries to use in 2024

Most common mistake clients make when searching for the best Python visualization library for their project is searching for “most popular python data visualization library” or “most used python data visualization library”.

Such keywords will give you a list of the top trending tools and libraries, however its relevance to your project may/may not be the exact match you’re looking for. Which is why we have specified use cases and categorized the tools in this Python libraries list for data visualization so you know which one is best suited for your project.

Notice💡

We have curated this Python Visualization Library List based on our project experiences and after consulting with various Python developers in our circle. If you feel we missed out any noteworthy Python visualization library, kindly mail your suggestions to [email protected], with a basic explanation as to why you think it’s a worthy addition.

1.Matplotlib – Visualization with Python (GitHub: Fork – 7k | Star – 17.9K)

Matplotlib is one of the most popular comprehensive Python data visualization libraries. It is also one of the oldest contenders which has managed being people’s favourite plotting library amongst Python developers.

One of the biggest benefits of matplotlib being a decade-old Python plot library is that many modern libraries provide full-support and seamless integrating with it, with most ongoing projects.

You can install Matplotlib from the official PyPi repository by clicking here.

Matplotlib is the best Python Visualization Library when you want to: 

• Customize the visualization of these plots and charts.
• Visualize data derived for Pandas DataFrames or NumPy arrays.
• Export vector graphic output formats such as PDF, SVG, PNG and others.
• Build basic plots and charts like bar, histograms, scatter plots and more.
• Build interactive plots by integrating Matplotlib with Jupyter or IPython notebooks.
• Get plotting functionality for other Python plotting libraries like scikit-learn and statsmodel.

2.Plotly – Open-Source Graphing Library for Python (GitHub: Fork –2.4k | Star – 14K)

Plotly is another popular open-source Python graph library. It was originally developed for JavaScript but it also supports charting and graphing functionality for Python, R, MATLAB, Arduino, Julia and Perl and other such programming languages.

Most often, Plotly is closely compared to matplotlib in the Python community, if we need to make a quick comparison between the two, Matplotlib is more beginner friendly and convenient tool whereas Plotly Python is an elaborate data visualization tool with better charting and plotting capabilities.

You can install Plotly Python from the official PyPi repository by clicking here.

Plotly is the best Python graph visualization library when you want to: 

• Get interactive data visualization with controls like zoom, pan, hover tooltips, and more.
• Integrate with web apps and dashboards by using Flash or Django Python frameworks.
• Visualize large datasets that might choke other Python static charting libraries. It supports WebGL/svg rendering and lazy loading that helps with big data.
• Visualize scientific data through 3D charts, statistical graphs, geographical maps and more.
• Enable seamless collaboration and sharing of the visuals amongst global teams and stakeholders.

3.Seaborn – Statistical Data Visualization (GitHub: Fork – 1.8K | Star – 11K)

Seaborn is a matplotlib based data visualization library for Python. It is mainly liked by the Python community for its capability to create high-level mesmerising Python charts with just a few lines of code.

This Python data visualization library provides pleasing and modern styles and color pallets which makes it instantly appealing and provides you with rich graphing capabilities. However, since it is based on matplotlib, you will need to master that library to truly utilize Seaborn’s customization options.

You can install Seaborn from the official PyPi repository by clicking here.

Seaborn is one of the best Python visualization packages when you want to: 

• Create appealing statistical graphs with Seaborn’s heatmaps, distributions and clusters.
• Create quick Python visualizations for Pandas DataFrame due to Seaborn’s seamless integration with the framework.
• Map dataframe columns to visual properties with ease.
• Visualize statistical Python models from Statsmodel or SciPy.
• Visualize large and complex datasets by combining Seaborn with matplotlib and Pandas.

4.Altair – Declarative Visualization in Python (GitHub: Fork – 747 | Star – 8.4K)

Atlair claims to be a declarative statistical visualization library for Python. With over 68.3K users on its GitHub repository and over 140 contributors, it is clear that many people support its claim. It is based on Vega-Lite grammar which allows developers to spend lesser time worrying about coding and allows more insights to explore the data.

Altair follows an API-driven data visualization method in Python. They have different kind of charting options displayed on their official site with their proper code examples for you to try them on. Altair defines the links in data columns in the x and y axis when it creates any visuals.

You can install Altair from the official PyPi repository by clicking here.

Altair is the best declarative statistical visualization library when you want to: 

• Create faster and interactive Python data visualizations.
• Make web-based dashboard and plots.
• Make exploratory plots.
• Work with huge datasets by integrating Apache Arrow and Pandas with Altair.
• Customize your plot design aspects like axes, colors, tooltips & more.
• Create publication quality static plots in PNG, SVG and other formats.

5.Bokeh – Interactive Visualizations for Modern Web Browsers (GitHub: Fork – 4.1K | Star – 17.9K)

Bokeh is an interactive tool for data visualization in Python that provides modern charting capabilities for web browsers. It is most preferable when you need to develop dashboards or interactive plots for complicated data assets. It is based on The Grammar of Graphics like GGPlot, however it is based on Python whereas GGPlot supports multiple programming languages and is mainly used with R.

Bokeh comes with three different levels for creating visualizations. Level one focuses on creating quick data plots, level two controls the building blocks of the plot and level three is for experienced developers that can create and customize charts as per their needs with no pre-defined defaults. You might be able to handle level 1 and level 2 type of charting requirements yourself, but we recommend you to hire Python developers with proven experience and expertise to integrate level 3 into your project.

You can install Bokeh from the official PyPi repository by clicking here.

Bokeh is the best interactive Python data visualization tool when you need to: 

• Fulfil common plotting requirements
• Create highly interactive and scalable visualizations like graphs
• Create highly custom-made visualizations
• Want to create quick charts with complete control over their configuration
• When you need output formats like HTML and notebooks.

Also Read: – Python Optimization: Performance, Tips & Tricks

6.PyGal – Beautiful Python Charting (GitHub: Fork – 417 | Star – 2.6K)

PyGal is a beautiful Python charting library that is primarily used for building SVG graphs and chart visualizations for Python data. PyGal allows developers to create proper graphs with minimal lines of code.

You can embed charts made with PyGal into web pages just like with Bokeh or Plotly. However, a stark difference that makes PyGal standout is that it can output the charts in SVG format which means you will be able to analyse your Python chart clearly without any quality reduction when you upscale or downscale them. One limitation of this SVG base approach can be that it can get overly complicated with complex data. So, using PyGal for small to medium Python visualization needs is ideal.

You can install PyGal from the official PyPi repository by clicking here.

PyGal is an ideal Python chart library when you need to: 

• Build dynamic and interactive graphs with Flask or Django.
• Make SVG presentations with higher interactivity.
• Visualize data from small web apps with quick and efficient graphs.
• Create visually appealing charts in lesser lines of code.

7.Geoplotlib – Python Toolbox for visualizing Geographical Data & Making Maps (GitHub: Fork – 175 | Star – 1K)

Geoplotlib is a collection of Python visualization tools for making maps based on geographical data. It is a visualization tool with a specific target – to develop hardware-accelerated and highly interactive data visualization in Python. This geographical graphing library provides access for a variety of maps such as heatmaps, equivalent area maps and point density maps.

Geoplotlib’s architecture is based on top of various popular Python projects such as SciPy, NumPy and OpenGL’s framework and architecture. You would also need to set up NumPy and Pyglet (object-oriented programming interface) as prerequisites to using Geoplotlib.

You can install Geoplotlib from the official PyPi repository by clicking here.

Geoplotlib is one of the  ideal Python visualization packages when you need to: 

• Draw various type of maps such as heatmaps, dot density maps or equivalent area maps.
• You need to create highly interactive maps with features like panning and zooming.
• Choose a comprehensive Python mapping library that handles data importing, map projecting and map tile transfers with ease.
• Want to process large datasets with Geoplotlib’s hardware accelerated top-notch rendering.

8.Folium – Python Data, Leaflet.JS Maps(GitHub: Fork – 2.2K | Star – 6.4K)

Folium is one of the easiest and beginner friendly library for carrying out data visualization with Python and leaflet.js library. You can manipulate your data using Python and visualize it using Leaflet map using folium.

With Folium you can create highly interactive maps with different overlays, tilesets, geospatial data visualization and more. You can easily integrate it with Pandas and GeoPandas for plotting spatial data on interactive maps.

You can install Folium from the official PyPi repository by clicking here.

Folium is ideal Python visualization mapping tool when you need to: 

• Map using Pandas or GeoPandas.
• Create interactive map for data exploration.
• Create travel route mapping for GPS or flight paths.
• Create web-based maps and dashboard with ease.
• Choose from a custom range of map styling options.
• Enable real-time mapping with IoT integration for weather, traffic and other use cases.
• Visualise geospatial data like choropleth maps, marker clusters and heatmap overlays.

9.Missingno -Missing Data Visualization Module(GitHub: Fork – 465 | Star – 3.6K)

As the name suggests, Missingno is a reliable missing data visualization module that can help you quickly overlook the completeness of your Python dataset. It does that with an easy-to-understand visual summary in place of scrolling through endless columns of text and numbers to find the problem areas.

Moreover, Missingno allows developers to filter and sort their chunks of data based on spot correlations or completion with a dendrogram or a heatmap.

You can install Missingno from the official PyPi repository by clicking here.

Missingno is ideal Python visualization library when you need to: 

• Visualize missing data using dendrograms, heatmaps, bar and matrix.
• Compare and contrast missing value across different datasets and features.
• Make communication easier with stakeholders.
• Impute missing data.

Also Read: – 10 Essential Python Libraries for Data Science to Use

How to choose the right Data Visualization Tool for Python?

Visualizing big data in Python is made easier thanks to the vast list of Python libraries for data visualization. However, since there are so many options to choose from, it can be difficult to find the right fit for your project. Here are the key considerations to look for when selecting the right Python data visualization library for your project:

1. Ease of Use and Learning Curve

• How easy is it to create basic visualization with the chosen library?
• Does the library come with proper documentation and training modules?
• Is the chosen Python data visualization library easy to use for non-technical users?
• Do you need to hire Python developers with considerable experience to use these libraries?

2. Customization Options

• How flexible is the customization option for changing the appearance of the provided visualizations?
• Does customization take first priority or important priority for your Python data visualization needs?
• Does the library provide support for implementing consistent styling?

3. Interactivity and Responsiveness

• Are you looking for a basic Python library for graphs or you need interactive elements like zooming, panning or on-hover effects?
• Do you need to create interactive dashboards or reports using this library?

4. List of Available Chart Types

• Do you have specific chart type requirement for your project? If so, does the chosen library have support for them?
• What is the complexity level of data visualization capabilities you need?

5. Compatibility with existing Data Analysis Tools

• Will this Python graphing library support and work with your existing data analysis tools and data type?
• Is it compatible with your Python environment and version?

6. Performance Capabilities and Load Handling

• Can the Python code visualizer library deal with your anticipated data volume?
• Are there potential limitations or performance bottlenecks you should be aware of?
• Will it be able to handle data aggregation, down sampling and other advanced data visualization needs?

7. Output Formats

• How do you need the output file format and style to be? Reports, web applications, presentations?
• Are these formats and types supported by your chosen Python visualizer?

8. Licensing and Cost

• Does the licensing of the tool match your budget and project needs?
• Do you need to pay additional premium for utilizing your desired features?
• Are there cheaper or free alternatives available?

9. Platform Independence and Security

• Does this Python visualize library work on all platforms utilized by your teams?
• Are there Python Security concerns or additional setup needed to ensure data safety & privacy?

Wrapping Up

These are the most reliable, scalable and noteworthy Python visualization libraries, tools and packages that you can use for your Python data visualization needs. However, the list of libraries available is exhaustively large and we couldn’t cover every library in detail. Hence, here is a quick guide to selecting the best Python visualization library as per their primary use cases:

We will keep updating this space for more Python data visualization assets and removing ones that get deprecated or discontinue support. Keep checking this space for your Python visualization needs.