Plotly vs matplotlib: A quick comparison with visual guides

Data visualization lies at the heart of modern data science, analytics, and business intelligence. Whether you’re creating interactive dashboards for your organization, performing exploratory data analysis on a new dataset, or presenting insights to stakeholders, great visualizations can make all the difference. And Python offers some great solutions for just this!

Two of the most prominent Python libraries for data visualization—Plotly and Matplotlib—offer a wealth of features for creating plots, charts, and dashboards. But it can be a bit overwhelming to figure out which library to pick and learn.

In this article, we’ll break down the similarities and differences between Plotly vs Matplotlib. We’ll discuss their core functionality, strengths, weaknesses, syntax differences, styling, interactivity, and more. By the end, you’ll have a solid understanding of which library is best suited for different use cases—and how each can fit into a modern data science workflow.

If you’re interested in seeing both libraries in action with a side-by-side comparison, you can follow along in this video:

The importance of data visualization

Data visualization is one of the most effective methods to communicate insights, discover patterns, and guide decision-making. Whether you’re a data scientist exploring a new dataset or a business analyst building dashboards for executive reports, the ability to create clear, compelling visuals can make your data accessible and actionable.

• Faster insights: A well-crafted chart or graph can uncover relationships and patterns faster than combing through raw data.
• Better decisions: Visuals help decision-makers see trends and outliers at a glance, enabling them to act quickly.
• Effective communication: Visualizations make it easier to tell a story with data, ensuring your audience understands not just the “what,” but also the “why” behind your findings.

In Python’s data science ecosystem, two major visualization libraries—Matplotlib (the classic, foundational library) and Plotly (the interactive, modern library)—are indispensable tools. Both can be used for exploratory data analysis (EDA) and for building enterprise-grade dashboards and reports, but they both serve specific purposes.

What Is Matplotlib?

Released in 2003, Matplotlib is the bedrock of Python’s data visualization world. Most other Python plotting libraries build on top of Matplotlib in some way (e.g., Seaborn). It excels at creating static, publication-quality plots, from basic line graphs to complex multi-paneled figures. While it's very flexible and easy to get started with, the learning curve can be steep, especially when you want advanced styling or complex subplots.

What Is Plotly?

Plotly is a relatively newer library that focuses on providing interactive visualizations in Python, JavaScript, R, and other languages. It has become increasingly popular for building dynamic and shareable dashboards. With Plotly’s Python API, users can create beautiful visualizations with features such as hover tooltips, zooming, and clickable legend entries - functionality that usually requires more effort when using Matplotlib alone.

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When to use Python data visualization libraries

Before diving into the head-to-head comparison, let’s examine when you might turn to these Python data visualization libraries in your workflow.

Data exploration and analysis

During EDA, you’re experimenting with different plots to see if the data reveals any trends, patterns, or outliers. This stage often involves numerous quick visual checks: histograms of distributions, scatter plots of relationships, box plots for outlier detection, etc. Both Matplotlib and Plotly are excellent choices here.

• Matplotlib: Offers speedy, straightforward ways to generate static plots directly in a Jupyter notebook. If you’ve used pandas, you might already be familiar with the .plot() functions that build on Matplotlib.
• Plotly: Provides interactive plots that let you hover over points to see exact data values, zoom in/out, and toggle data series on and off. This level of interactivity can accelerate your analysis as you explore and refine your hypotheses.

Dashboarding and reporting

When you need to share your findings through a dashboard, web app, or slideshow, your choice of library might shift based on interactivity, aesthetic preferences, and the audiences’ needs.

• Plotly: Offers a built-in approach for interactive, web-ready graphics. Coupled with frameworks like Dash or platforms like Fabi.ai, you can build data-driven applications that stakeholders can explore on their own.
• Matplotlib: Typically used for static or embedded charts, such as those you’d place in a PDF report or presentation. It can also integrate with web frameworks, but it often requires additional libraries or complex setups to achieve interactivity.

Academic and publication use cases

If you’re publishing scientific papers or textbooks, you might want the highest degree of control over your figures to meet strict formatting guidelines.

• Matplotlib: Is often preferred for academic or highly customized plots because you can fine-tune just about any aspect of the figure—fonts, margins, axis scales, etc.
• Plotly: While still highly customizable, Plotly’s real strength lies in interactivity and web-based visuals. It can be used for publications, but academic communities have historically leaned more toward Matplotlib (and libraries built on it, like Seaborn).

Industry and business intelligence

In corporate or industry settings, dashboards and interactive capabilities can be a game-changer, especially for non-technical stakeholders.

• Plotly: Extremely powerful in business settings because interactive charts engage decision-makers, enabling them to filter data in real-time.
• Matplotlib: Still widely used for quick analytics, static reports, and integration within Python machine learning pipelines.

Plotly vs Matplotlib: A comprehensive comparison

Now that we’ve covered the contexts in which you might use these libraries, let’s perform an in-depth comparison of Plotly vs Matplotlib. We’ll look at syntax, styling, interactivity, performance, ecosystem support, and ultimately the pros and cons of each.

Syntax

Matplotlib

Imperative approach: A lot of Matplotlib’s syntax is based on the state-machine environment (particularly when using pyplot), which can feel very similar to MATLAB. For example:

import matplotlib.pyplot as plt

plt.plot(x, y)
plt.title("My Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

You call various plt functions to configure your plot, and these changes apply to the current figure or axes.

Object-oriented approach: Matplotlib also supports an object-oriented style:

fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title("My Plot")
ax.set_xlabel("X-axis")
ax.set_ylabel("Y-axis")
plt.show()

This approach can be cleaner for complex figures with multiple subplots.

Plotly

Declarative, figure-centric: With Plotly, you often work directly with figure objects, making your code more declarative. You create traces (e.g., scatter, bar) and add them to a figure. For instance:

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name='Plot'))
fig.update_layout(title='My Plot',
                  xaxis_title='X-axis',
                  yaxis_title='Y-axis')
fig.show()

Expressiveness with Plotly Express: Plotly Express (plotly.express) simplifies this further, especially for quick explorations. One line can create an interactive chart with minimal hassle:

import plotly.express as px

df = px.data.iris()
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')
fig.show()

Frameworks and syntax for different packages can create passionate debates on both sides of the aisle, but oftentimes it simply comes down to preferences. For example, I’m not a hard-core developer, and when I read the Plotly syntax, even for more complicated charts, it intuitively makes more sense to me. I can read the Matplotlib code just fine, but it just feels less intuitive.

Takeaway on syntax:

• Matplotlib can be more verbose when it comes to advanced styling but remains straightforward for basic plots.
• Plotly might initially seem more complex due to the figure-trace model, but with plotly.express, quick interactive plots are surprisingly easy to create.

A note on syntax and AI: with AI being some pervasive nowadays and fully integrated into data analysis environments like Fabi.ai, syntax is becoming less of a barrier to entry, especially when working with such commonly used and well documented libraries like Matplotlib and Plotly. This doesn’t mean that this should not factor into your decisions when picking one over the other, but it will certainly put more of an emphasis on other strengths as AI continues to improve.

Styling and visual aesthetics

How good a chart looks isn’t simply a matter of taste and aesthetics. Building data visualizations that look great, comply with the brand colors and are easier to read is a critical component of telling a story with data and making an impact on the business.

Matplotlib

• Mature and flexible: Because of its long history, Matplotlib has extensive customization capabilities. You can control every aspect of your plot—axes, ticks, fonts, colors, etc. With enough skill and patience, you can create almost any chart that you can imagine.
• Seaborn integration: Many data scientists layer Seaborn on top of Matplotlib for improved aesthetics and simpler syntax for advanced statistical plots. If you like Matplotlib but you want your charts to just look a bit better out of the box, Seaborn may be a great solution.

Plotly

• Built-in themes: Plotly offers sleek default themes, and you can switch among them easily
• Interactive elements: Hover labels, legends, tooltips, and pop-ups are part of the core Plotly design, which can be further styled to match your needs. If your data contains thousands of data points and it’s important for a user to be able to zoom in to a specific part of the plot, this interactivity will be very important.
• Customization: Although interactive styling can feel more complex to navigate, Plotly’s figure objects can be tweaked to an impressive level of detail.

Takeaway on styling:

• Matplotlib is the gold standard for static, publication-ready visuals but doesn’t usually look aesthetically pleasing right out of the box. With enough elbow grease you can usually make a plot look the way you want, but it won’t be as easy as other libraries.
• Plotly is strong out of the box with interactive, modern aesthetics and can be highly customized with a bit of learning.

Performance and ecosystem

Depending on how much data you’re working with and the granularity of your analysis, this may be a factor. If you’re working with hundreds of thousands or millions or points, performance can become a key factor.

Matplotlib

• Performance: Matplotlib is generally performant for generating static plots, though rendering can slow down if you’re handling extremely large datasets (e.g., millions of points).
• Ecosystem: Being one of the oldest libraries, Matplotlib’s ecosystem is vast—Seaborn, pandas plotting, scikit-learn’s built-in plotting functions, etc., all rely on Matplotlib under the hood.

Plotly

• Performance: Plotly’s performance is also robust, but highly interactive plots may become sluggish with extremely large datasets in the browser. Plotly does provide ways to optimize or reduce data resolution for large-scale visualizations but you may find that working with hundreds of thousands or millions of points on a chart can start to cause issue
• Integration: The Plotly ecosystem includes plotly.js (for direct JavaScript usage), dash (for Python-based web apps), and chart-studio (for sharing). There’s also a strong community contributing advanced visualization types (e.g., 3D surfaces, geographic maps, and more).

Interactivity

Saving the best for the last. The difference in interactivity between these two libraries is really where they stand distinctly apart.

Matplotlib

• Primarily static: Most plots in Matplotlib are static images by default (PNG, PDF, SVG). You can incorporate some interactive elements in Jupyter notebooks (e.g., %matplotlib notebook), but the level of interactivity is generally limited compared to Plotly. If you’re looking to build beautiful, interactive reports and dashboards, Matplotlib will quickly leave you wanting.
• Third-party tools: Tools like mpld3 and bokeh can add interactivity on top of Matplotlib, but these require additional effort and are not as seamless as Plotly’s built-in approach.

Plotly

• Interactive by default: Every plot is designed for user interaction: hover tooltips, zooming, panning, and toggling legend entries come standard.
• Integration with Python dashboarding platform and libraries: Plotly’s companion library, Dash, makes it straightforward to build full-fledged web applications without needing specialized JavaScript knowledge. Certain Python dashboarding platforms such as Fabi.ai, also integrate with Plotly, and make it incredibly easy to create rich, interactive Python data apps and dashboards in minutes with no overhead.

Takeaway on interactivity:

• Matplotlib is best for static and “one-off” plots, especially for PDF reports or academic publications.
• Plotly is unbeatable if your goal is to let users explore data interactively, either in a Jupyter environment or as a web dashboard.

To really see this in action, check out the video that we’ve linked to in the introduction of this article.

Pros and cons of each

We’ve gone through each library in detail above, but let’s take a minute to recap the pros and cons of Matplotlib vs Plotly.

Matplotlib pros

1. Industry standard: Widely recognized, stable, and thoroughly documented.
2. Highly customizable: Fine-grained control over every element, perfect for publication-quality figures.
3. Rich ecosystem: Integrates seamlessly with many Python data tools (NumPy, pandas, Seaborn, etc.).

Matplotlib cons

1. Steeper learning curve for advanced plots: The imperative style can be less intuitive, especially for complex subplots.
2. Limited interactivity: By default, plots are static.
3. Older architecture: Sometimes feels less modern than other libraries (requires more code for certain tasks).

Plotly pros

1. Interactive by default: Great for data exploration, dashboards, and web-based analytics.
2. Modern and aesthetic: Polished visuals out of the box, minimal effort to get an attractive plot.
3. Easy integration with Python dashboard platforms and frameworks: Rapidly build interactive web apps in pure Python using Dash or Fabi.ai.

Plotly cons

1. Learning curve for complex use cases: Detailed figure customization can require digging into Plotly’s documentation. Thankfully, AI can greatly accelerate and simplify this task.
2. Possible performance issues with very large datasets: Interactive rendering in a browser may lag.
3. Less standard in traditional academic circles: Not as commonly used for static plots in scientific papers, though acceptance is growing.

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How platforms like Fabi.ai fit into the picture

Choosing between Plotly and Matplotlib isn’t just about picking a library—it’s also about how you integrate that library into larger analytics workflows. As we’ve alluded to above, Matplotlib works great in static environments on your machine or in Jupyter notebooks, while Plotly works great within modern platforms and frameworks that support interactivity. Dash is affiliated with Plotly and is a great solution to build enterprise-grade Python data apps and dashboards. However, there are more modern and AI-native solutions such as Fabi.ai, they also support Plotly even faster out of the box.

In the right environment, Plotly allows you to create rich, great looking and interactive visuals that you can deploy and share with your stakeholders. This type of workflow is crucial to fostering a collaborative data-driven environment in the enterprise.

Ultimately, whether you choose Plotly, Matplotlib, or a combination of both, a platform like Fabi.ai helps you handle the heavy lifting of deployment and sharing, so you can focus on discovering insights rather than wrestling with infrastructure.

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Conclusion

When it comes to Plotly vs Matplotlib, there’s no one-size-fits-all answer. Each library has its strengths, and the best choice depends on your project’s objectives and the way you intend to present or share your results.

Key takeaways

1. Matplotlib is a time-tested classic—ideal for static, publication-quality plots and those who want granular control over every element. If you need well-established, static visuals or you’re working on academic publications with strict styling guidelines, Matplotlib is a reliable choice.
2. Plotly excels in interactive and modern web-based visualizations. When building dashboards or interactive data explorations—especially for non-technical users—Plotly’s hover, zoom, and toggle features can be incredibly powerful. Coupled with Fabi.ai, it provides an almost seamless path to building full-fledged data-driven applications.
3. Fabi.ai and Similar Platforms can further enhance your data visualization workflow by simplifying the dashboard deployment process, offering drag-and-drop interfaces, built-in data connectors, and automated sharing features. These tools help you focus on insight generation rather than managing technical complexities.

Recommendations

• Use Plotly if:some text
  • You need interactive visualizations with hover, zoom, or clickable features.
  • You want to quickly build shareable dashboards for a non-technical audience.
  • You plan to embed charts into web applications or digital products.
  • You favor aesthetically pleasing plots right out of the box.
• Use Matplotlib if:some text
  • You’re preparing visuals for academic or print publications.
  • You prefer a more traditional, lower-level plotting approach with maximum customization.
  • You’re working in an environment already heavily reliant on Matplotlib or Seaborn.

Final thoughts

In a data-driven world, the ability to quickly and effectively visualize information is a must-have skill. Both Plotly and Matplotlib serve that need but in different ways. The choice often hinges on how interactive you need your visuals to be and the final medium in which your charts will appear. Don’t be afraid to use both libraries within the same project—Matplotlib for static EDA plots, Plotly for final interactive dashboards. Tools like Fabi.ai streamline these processes by offering platforms where you can unify, deploy, and share your visualizations, regardless of the underlying library.

If you’re interested in trying out both libraries side by side and seeing what deploying interactive charts in a collaborative data analysis environment looks like, we invite you to try out Fabi.ai for free.