Python libraries are required for data visualisation because they provide powerful tools to transform complex data into visual representations that are easier to understand and gain insights from. Python libraries offer ready-made plotting functions that abstract away the complexity of creating visualisations from scratch, enabling you to generate publication-quality plots with just a few lines of code. They integrate seamlessly with common data formats used in analysis, provide extensive customisation options, and leverage optimised rendering techniques to visualise even large datasets efficiently. Overall, Python’s data visualisation ecosystem accelerates data-driven decision making by simplifying the process of exploring, presenting, and communicating data insights in a visually appealing manner.
Here are the best Python libraries for data visualisation along with their key features, pros, cons, and why they are suitable for certain use cases.
Best Python Libraries for Data Visualisation in 2025
1. Matplotlib
Matplotlib is a great choice if you need highly customised static visualisations for publications or reports. Its versatility and integration with other data analysis tools make it suitable for a wide range of use cases.
Key Features:
- Provides static, publication-quality 2D and 3D data visualisations
- Supports a wide range of plot types like line plots, scatter plots, bar charts, histograms, etc.
- Highly customizable with control over figure size, colors, text, etc.
- Integrates well with other data analysis libraries like NumPy, Pandas, etc.
Pros
- Extensive documentation and large user community
- Familiar MATLAB-like syntax for those coming from that background
- Highly versatile and customizable
- Can save plots in various formats like PNG, PDF, SVG
Cons
- Low-level library requiring more code for basic plots
- Limited options for interactive visualisations.
- Steep learning curve for complex visualisations.
2. Plotly
Plotly is an excellent choice if you need to create modern, interactive visualisations for exploratory data analysis, dashboards or web applications. Its ease of use and integration with data formats make it very productive.
Key Features:
- Creates interactive, web-based visualisations.
- Supports over 40 chart types including 3D charts, maps, statistical charts.
- Can be used in Jupyter notebooks, web apps, dashboards.
- Integrates well with Pandas, NumPy, and other data formats.
Pros
- Highly interactive visualisations with hover tooltips, zoom, pan etc.
- Well-designed with modern aesthetics out-of-the-box
- Extensive documentation and examples
Cons
- Steep learning curve for complex visualisations
- Can be slow for very large datasets
- Limited customisation options compared to low-level libraries
3. Seaborn
Seaborn is ideal if you are working with statistical data and need to quickly visualise distributions, correlations, regressions etc. Its integration with Pandas makes it a great fit for data analysis and exploration tasks.
Key Features:
- Built on top of Matplotlib, provides higher-level statistical visualisations
- Supports many statistical plot types like heatmaps, violin plots, regression plots
- Seamless integration with Pandas dataframes
- Attractive default styles with easy customisation
Pros
- Simple and intuitive interface for common statistical visualisations
- Attractive default styles that can be easily tweaked
- Tight integration with Pandas for data analysis workflows
Cons
- Limited flexibility for highly customised plots
- Mainly focused on statistical visualisations, not general purpose
- Requires understanding of statistical concepts
4. Bokeh
Bokeh is well-suited if you need to visualise large datasets interactively, create dashboards with linked plots, or visualise real-time streaming data. Its performance and flexibility make it powerful for advanced use cases.
Key Features:
- Creates interactive, web-based visualisations like Plotly
- Supports streaming/updating data sources for real-time viz
- Provides high-level and low-level interfaces
- Can export visualisations as JSON, HTML, notebooks etc.
Pros
- Highly interactive with linking/brushing across multiple plots
- Fast and efficient for large/streaming datasets
- Flexible with high and low-level interfaces
Cons
- Relatively new with smaller community compared to Matplotlib
- Limited options for common statistical visualisations
- More complex setup compared to other libraries
5. Altair
Altair is a declarative statistical visualisation library based on Vega and Vega-Lite. It has a simple, consistent and integrated API for building complex visualisations.
Key Features:
- Supports a wide range of modern visualisation types
- Can build visualisations from Pandas dataframes
- Outputs visualisations as JSON data that can be rendered in browsers, notebooks etc.
- Designed for analysis, not final product visualisation
Pros
- Simple grammar makes it easy to get started
- Leverages the capabilities of the Vega/Vega-Lite visualisation grammars
- Integrates well with other data science libraries like Pandas
Cons
- Limited customisation options compared to low-level libraries
- Primarily focused on statistical visualisations
6. Pygal
Key Features:
- Supports over 20 different chart types
- Charts can be rendered as SVG, PNG, base64 etc.
- Simple API to generate charts from Python data structures
Pros
- Easy to get started and use
- Good for basic charts to embed in web apps/reports
- Decent customisation options
Cons
- Limited to static SVG output, no interactivity
- Not suitable for complex statistical visualisations
- Smaller community compared to other libraries
7. Gleam
Gleam allows creating interactive visualisations and dashboards without HTML/JS.
Key Features:
- Build web apps/dashboards from Python scripts
- Supports integration with other viz libraries
- Apps can be embedded anywhere
Pros
- No need to write HTML/JS for web deployment
- Facilitates sharing interactive visualisations
Cons
- Relatively new library with smaller community
- Limited customisation compared to JavaScript libraries
8. Ggplot2
ggplot is a decent choice for basic statistical plotting needs, especially for those familiar with ggplot2 in R, it lacks the versatility, customisation capabilities and community support of other major Python visualisation libraries.
Key Features:
- Grammar of Graphics Approach.
- Powerful faceting capabilities.
- Handle different data types and structures seamlessly.
- Offers extensive options for customising plots.
Pros
- Integration with Tidyverse
- Attractive default styles
- Consistent syntax using the + operator
Cons
- Steep learning curve
- 2D visualisations
- Limited options for creating interactive
9. Geoplotlib
Geoplotlib is an open-source Python library that simplifies the process of creating a wide variety of static and interactive geographical visualisations and maps directly from data, while leveraging Python’s data science capabilities. Its hardware acceleration allows working with large spatial datasets efficiently.
Key Features:
- Provides implementations of common spatial visualisation.
- Supports hardware-accelerated interactive visualisations.
- Integrates with Python data analysis libraries like NumPy, SciPy, Pandas etc.
- Capable of visualising large datasets efficiently.
Pros
- Simplifies exploratory spatial data analysis
- Avoids complexities of converting to HTML/JavaScript.
- Consistent API and layered approach.
Cons
- Relatively new library with smaller community.
- Limited to 2D map visualisations.
- Requires understanding of mapping/geographical concepts
10. Folium
Folium is a Python library that wraps the Leaflet.js JavaScript library, enabling the creation of interactive maps. It integrates seamlessly with Python’s data manipulation libraries like Pandas and Geopandas, allowing users to visualise geospatial data on high-quality, interactive maps. Folium supports various map tiles, markers, and layers, making it ideal for geographic data visualisation and analysis
Key Features:
- Folium supports a wide range of map types, including basic maps, choropleth maps, heatmaps, and more.
- It can integrate with various Leaflet plugins, extending its functionality with features like marker clustering and drawing tools.
- It can easily visualise GeoJSON data, making it suitable for working with various geospatial data formats.
- It renders maps as HTML files, inline in Jupyter Notebooks, or as static images.
Pros
- Easy to learn and use, with a simple and intuitive API.
- Leverages the power and interactivity of the Leaflet.js library.
- Highly customizable and extensible through plugins.
Cons
- Limited support for advanced cartographic projections and coordinate systems.
- Performance may degrade with very large datasets, as it relies on client-side rendering.
- Limited support for 3D visualisations and advanced mapping techniques.
How to Choose the Best Python Libraries For Data Visualisation
- Type of Visualisation
- Basic Plots: For simple line plots, bar charts, histograms, etc., Matplotlib is a solid choice due to its flexibility and extensive customisation options.
- Statistical Visualisations: Seaborn is built on top of Matplotlib and is ideal for creating aesthetically pleasing statistical plots with less code.
- Interactive Visualisations: Plotly and Bokeh are excellent for creating interactive and web-based visualisations. Plotly is known for its ease of use and wide range of chart types, while Bokeh offers high customisation and performance for large datasets.
- Ease of Use
- Beginner-Friendly: Libraries like Seaborn and Plotly are more user-friendly and require less code to produce complex visualisations compared to Matplotlib.
- Advanced Customisation: Matplotlib offers extensive customisation options, making it suitable for users who need fine-grained control over their plots.
- Integration with Data Analysis Libraries
- Pandas Integration: Libraries like Seaborn and ggplot integrate seamlessly with Pandas, making it easy to visualise data directly from dataframes.
- Geospatial Data: For visualising geographical data, Folium and Geoplotlib are specialised libraries that provide robust support for creating maps and handling geospatial data.
- Performance
