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As of August 2025, Python is the world’s most popular programming language, with a reach that spans every continent. It leads the PYPL index with 30.5% of the global market and holds over 26% in the TIOBE index, confirming both its dominance and continued momentum. The language’s user base grew by at least 7% in the past year, and nearly 4 in 10 active developers picked up Python within the last two years - a sign of its appeal to newcomers and professionals alike.
Python owes this growth to its versatility and simplicity, but nowhere is it more indispensable than in data analysis and visualization. More than half of all Python developers work with data daily, and in the fields of data science and analytics, Python powers over 80% of professional workflows. From cleaning and transforming large datasets to building machine learning models, it has become the universal tool for turning raw numbers into insights.
In visualization, Python shines. Its ecosystem lets you turn data into charts, graphs, maps, histograms, and full interactive dashboards. The leading chart libraries in 2025 give developers everything they need - from quick prototypes to production-ready tools for advanced analytics.
Today, we take a look at the top choices and ask the difficult question: is there a better way to visualize data?
Having data points in the form of a giant table with rows and columns belongs in the 20th century. Visualizing data with bar charts, graphs, line charts, scatter plots and more helps the end-user understand the numbers they are seeing.
Here are some of the best Python libraries for creating interactive visualizations.
Altair is a free and open source Python library that is commonly used for interactive and statistical visualizations. For businesses looking to do exploratory data analysis and statistical data visualization, this is one of the best free choices out there.
It’s based on Vega and Vega-lite, which ensures that the 3D charts, box plots, bar plots and other visualization types are fully interactive. When using Altair, you can install dependencies such as Numpy and Pandas for data wrangling. It relies on JSON to present the charts and add any specifications or customizations to the chart’s look and feel.
August 2025 Update: Altair remains a favorite for exploratory data analysis and statistical visualization. Its declarative syntax and tight integration with Pandas make it beginner-friendly. Still best for small-to-medium datasets; performance slows with millions of rows.
Plotly, the Python chart library is built on top of Plot.ly, the Javascript library. For dynamic businesses that need fast, interactive dashboards, this chart library is an excellent choice.
It has a rich selection of visualizations to choose from, including line plots, scatter plots, 3D plots and many others. The basic version of Plotly is free to use and if you want access to the more advanced features, you’ll have to opt for a paid plan.
August 2025 Update: Dash is stronger than ever in 2025. With Plotly 6.0 released in March, you now get native support for real-time data streaming and GPU-accelerated rendering. Widely used in finance, healthcare, and IoT for production dashboards.
Based on the Grammar of Graphics, this is one of the most popular Python chart libraries for a wide variety of use cases. For interactive libraries and working with data sets large and small, Bokeh is fast and adaptable. It can be used in HTML pages, JSON objects or apps.
Bokeh is free and open source, which means it won’t cost you much as long as you have a really good Python developer. Because of its interactivity and flexibility, it’s a superb choice for visualizing data in apps.
August 2025 Update: Bokeh continues to be a flexible option for interactive web dashboards, but its growth has slowed. The community still maintains it, but most new projects are leaning toward Dash or Streamlit for speed of setup and modern UI support.
For most Python developers, Matplotlib is the default choice for visualizing data. It’s been around since 2003 and it can be used for interactive visualization across different platforms. You can use it in Python scripts, the Jupyter notebook, and with Python and iPython shells. The interface for the library resembles MATLAB.
It supports a rich variety of visualizations, such as line plots, scatter plots, bar charts, histograms and many others. It’s also open source and you can use it completely free.
August 2025 Update: Still the default workhorse for Python visualization. Its API hasn’t changed much, but improvements in Matplotlib 4.0 added better styling defaults and accessibility. Most developers now combine it with Seaborn for higher-level plots.
If your developers are familiar with visualizing data in R, they’ll love GGplot, as this is the Python port of GGplot2. For plotting and visualizing data with the grammar of graphics approach, this high-level-API library is an excellent choice. It allows developers to combine complex plots from data in one dataframe.
It’s open source and free to use.
August 2025 Update: Rarely updated, mostly of interest to developers switching from R. Most teams now prefer Plotnine for Grammar of Graphics in Python.
Plotnine is the popular visualization library used in the programming language R. In this library, you layer components to create a complete plot. It’s integrated with Pandas and it’s one of the best choices if you need to visualize complex data sets.
GGPlot2 is technically used in R, but libraries such as Plotnine are heavily influenced by it. It’s an excellent choice for R users making a transition to Python.
It’s open source and completely free.
August 2025 Update: Plotnine has gained more stability in 2025 and remains the main Grammar of Graphics library in Python. Still a strong choice for R users transitioning to Python.
Built on top of Matplotlib, Seaborn gives a high-level interface for building statistical visualizations. This is a solid choice if you want to visualize distributions and relationships in data sets.
It’s free to use.
August 2025 Update: Still the go-to for statistical visualization. In 2025, Seaborn 0.13 introduced better categorical plots and simplified theme customization. Most widely used alongside Matplotlib.
For developers looking to create map-type visualizations, Geoplotlib should be the first choice. On top of its narrow use case, it’s also very easy to use and great for beginners. Density maps, heatmaps, dot density maps and other geographic visualizations are a breeze with this chart library type.
Geoplotlib is open source and free.
August 2025 Update: Geoplotlib hasn’t seen much activity recently. For geographic visualization in 2025, most developers prefer Folium, Kepler.gl (via Python bindings), or Plotly’s mapbox integration.
This Python chart library is built with interactivity in mind and it’s great for use in web apps and pages. The difference between Pygal and the rest is that the output files are SVG. This means that there is no loss in quality since these are scalable vector charts.
It’s highly customizable, free and open source.
August 2025 Update: Pygal’s SVG-first output is still unique, but adoption has slowed. The lack of active updates makes it less appealing for production apps in 2025.
If you’re constantly struggling with datasets that are incomplete, this is the right Python library for you. Before visualizing anything, Missingno gives you a high-level interface, showing you the data you’re missing for the dataset to be complete. If you’re new to data analytics and you’re worried about data structure and integrity, you may want to check this one out.
It’s free and open source.
August 2025 Update: Missingno remains the go-to for visualizing missing data. Lightweight, reliable, but largely unchanged. Often paired with Pandas Profiling or YData Profiling for automated EDA in 2025.
This is the Python chart library that helps you turn visualizations into web apps. Gleam lets you easily build a web application interface, where you can choose from a number of input fields your users can control, like filters or selectors. Then, it uses other, existing Python libraries such as Matplotlib and Plotly for creating the data visualizations. If you’re keen on building an app from scratch with Python, this is a good alternative to purchasing off-the-shelf visualization software.
Like most other tools on this list, it’s free to use.
August 2025 Update: Gleam is largely inactive in 2025. If you want Python-to-web apps, Streamlit and Gradio have effectively replaced it as the modern, actively maintained choices.
If getting the job done is all that matters, Leather is as good as it gets. This chart library was intentionally created for those developers who need quick and easy visualizations without the added bells and whistles. You can get end results as SVG charts, but the dashboards you can create are fairly basic, as is the documentation.
It’s also open source and free.
August 2025 Update: Leather is basically abandonware now. Still works for simple SVG charts, but not recommended for new projects.
If you need to visualize geographical data and make it interactive, consider this chart library instead of the more popular variants mentioned above. Folium allows for a certain level of interactivity, letting your end users easily zoom in and out of maps for more intricate details. It uses Leaflet.js as a foundation.
The strong suites of this library are geographical maps, heatmaps, choropleth maps and similar. It’s open source and free to use.
August 2025 Update: Folium is still a top pick for interactive geographic visualizations. Strong integration with Leaflet.js keeps it relevant, but newer frameworks like Deck.gl via Pydeck and Kepler.gl are gaining traction for advanced geospatial analysis.
The Python data visualization ecosystem is evolving fast. Alongside the classic chart libraries, a few newer frameworks are shaping how teams explore and present data today. If you’re building in 2025, these are worth considering:
Streamlit has become the most popular Python framework for turning data scripts into interactive web apps. With just a few lines of code, you can go from a Jupyter notebook to a shareable dashboard. It’s especially loved by data scientists and product teams who need to showcase results quickly without worrying about front-end development.
Gradio is widely used in the AI and machine learning community. It lets you wrap models with a simple visual interface that includes charts, inputs, and even real-time outputs. If you’re experimenting with generative AI or predictive analytics, Gradio makes it easy to present results in a user-friendly way.
Formerly known as Pandas Profiling, YData Profiling is now the go-to tool for automated dataset exploration and data quality checks. It can instantly generate interactive reports that highlight distributions, correlations, and missing values. Many teams use it at the start of any project to spot issues before building visualizations.
Together, these tools reflect the shift toward faster prototyping, AI integration, and data quality awareness in 2025. They don’t replace chart libraries like Matplotlib or Plotly, but they do make it easier to get insights in front of users faster.
The low (or non-existent) cost, the options for customization (color palettes, chart types) and interactivity, the ease of use: Python chart libraries have a lot going for them. However, similarly to Javascript chart libraries, there are some downsides to be aware of as well.
Performance issues: some chart libraries have difficulties handling large datasets. If you have complex data structures and want to visualize huge volumes of data at once, the dashboards you create may take a long time to load. This can be a deal breaker for time-intensive use cases such as embedded analytics where real-time data access is critical.
Customization limitations: while you can customize some of the visualizations (e.g. interactive plots, pie charts) and the way they look, there is another issue. The visualizations should fit in with the rest of your website or app and look great on a phone or in a web browser. That’s where product managers often run into a wall with open source libraries. Our guide for product managers on creating custom charts explains how Luzmo takes a hybrid approach to solve this challenge.
Integration into existing websites or business applications: creating different chart types in Python is one thing but making sure they integrate into a web-based application is another. You’re going to need a really good developer who knows their way around APIs and integrations for web applications.
Complexity and learning curve: there are different types of Python libraries and developers. And while the majority of libraries are open source and anyone can learn them by taking a deep dive in Github, really mastering different types of charts is going to require more than a simple tutorial.
Maintenance and support: if you choose an open source Python library, beware that they will lack the support and maintenance that commercial libraries have.
Licensing and compliance: your business model may have compliance and licensing requirements that will make it difficult to use Python libraries and you’ll have to use visualization tools instead.
Need to visualize data in your software? Instead of relying on open source libraries and hoping your developers can wrangle contour plots and choropleths, get a dedicated tool for the job. If you want a deeper look at how this works in practice, see our guide for product managers on creating custom charts.
At Luzmo, we help businesses visualize data in their app, for themselves and their end users. And you also get access to AI and machine learning features out of the box.
Our embedded analytics platform has a capable API that allows any developer to integrate a dashboard in your product or website within hours, not weeks or months.
Grab a free demo with our team to see how Luzmo can work for you!
Build your first embedded data product now. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
