NVIDIA becoming the world’s most valuable company and Python becoming the world’s most popular computing language are both due to the explosion of data science (DS), machine learning (ML), and artificial intelligence (AI) workflows in this Internet age. A few years ago, Python and R both seemed like strong contenders for these applications, as both are easy to use interpreted languages with powerful data-handling capabilities, include mature ecosystems of open-source libraries and tools, and have robust communities of passionate users and developers (plus a few detractors!). Historically, R was even considered a stronger language for data exploration and statistical analysis, so why is Python now so much further ahead? In this post, we will unpack why Python rose to nearly complete dominance of data-oriented applications, highlighting what Python offers that is difficult for R to match, along with areas where R is still the right choice.

How Did We Get Here?

R and Python both emerged in the early 1990s, with R created specifically for statistical analysis and data exploration and visualization, while Python was developed for general-purpose applications, systems, and web programming, without any inherent support for numerical datasets. R quickly became popular in academic statistics and research environments, supplanting proprietary systems like S-Plus but remaining narrowly focused on data-centric workflows. Meanwhile, Python evolved gradually into a broad, general-purpose tool, adding specific support for array computing, statistical analysis, and visualization only much later than in R, as add-on packages like NumPy, SciPy, and pandas rather than core features of the language. This history explains much about how Python and R ended up, as we will see below.

Key Reasons Python Dominates in Data Science and AI Workflows

1. Breadth of Ecosystem

Perhaps paradoxically, Python succeeds at DS, ML, and AI workflows because it also supports all the tasks that are not DS, ML, or AI. R has always provided well-tested, convenient statistical functions for working with data, but those functions make up only a small part of any overall practical workflow. Real workflows involve code for accessing data from many different sources (from files, databases, and web servers, to cameras and microphones), then filtering and cleaning up the data, training models using the data, and performing these steps on cloud platforms like AWS, Azure, and GCP using infrastructure tools like Docker and Kubernetes. These “practical” considerations typically aren’t covered by the R language itself, but they must be in place and connected up before the statistical function can be invoked, and Python excels at all those interfacing and “glue” operations.

Thus Python wins because a single, unified Python codebase can handle:

  • Data acquisition and signal processing with OpenCV, torchvision, and pyo
  • Natural language processing with spaCy and NLTK
  • Data preprocessing with pandas or Polars
  • Feature engineering with scikit-learn
  • Visualization with matplotlib, plotly, or bokeh
  • Model training with PyTorch or TensorFlow
  • API deployment via FastAPI
  • Big-data scaling via Dask or Ray
  • Orchestration with Airflow

R, while excellent for key steps in the analysis, doesn’t have the same end-to-end coverage. Many production workflows built around R require “gluing” it to other languages, which greatly increases complexity and makes it hard to find people who can manage the whole process. Organizations can simply use Python for all of these tasks, minimizing friction and letting people move freely between projects without having to re-learn languages.

2. AI and Machine Learning Leadership

In part because of Python’s ecosystem coverage for “the things around DS/ML/AI,” Python has become the de facto language of modern AI research and development. Virtually every major deep learning and machine learning framework—TensorFlow, PyTorch, JAX, Keras, scikit-learn, XGBoost, LightGBM—is either written in or exposes Python APIs first.

R does have solid ML packages like caret and mlr3, and also interfaces to TensorFlow or Keras. But these are typically wrappers around Python libraries or in addition to the Python interfaces. This situation creates a dependency chain: cutting-edge ML and AI features arrive first in Python, then later (if at all) in R.

For AI practitioners, this time lag is decisive. When new architectures like transformers, diffusion models, or reinforcement learning frameworks emerge, Python developers can experiment immediately. R users often wait months or years for wrappers or integration; they are out of the loop.

3. Community and Talent Pool

A language is only as strong as the community behind it. On this front, Python has eclipsed R by sheer scale.

  • GitHub activity: Python is consistently the most active language for working with data in open-source repositories, far ahead of R (and behind only JavaScript, which is not widely applicable to DS/AI/ML workflows).
  • Stack Overflow questions: Python has an order of magnitude more active discussions than R, reflecting both adoption and support.
  • Education: Python is taught in computer science programs worldwide, from introductory programming to advanced AI courses, and is also taught at an introductory level in many other degree programs. R is common mainly in certain niche domains like statistics and epidemiology.

For enterprise organizations, Python’s dominance translates into a much deeper talent pool. Hiring data scientists or engineers who know Python is significantly easier than finding R specialists. R specialists with broad background in the complete range of interfacing and orchestration tasks required for enterprise DS/AI/ML workflows are even rarer.

4. Integration with Production Systems

AI/ML models only create value when deployed. Python makes deployment seamless because it integrates naturally with backend services, APIs, and cloud platforms.

Some examples:

  • FastAPI allows teams to deploy ML models as REST APIs with minimal boilerplate.
  • ONNX provides a standardized way to export Python-trained models to run in C++, Java, or mobile environments.
  • MLOps tools like MLflow, Kubeflow, and Vertex AI all have strong Python support.

By contrast, deploying R models often requires either containerizing R runtimes or exporting models into another format. While solutions like plumber (for REST APIs) exist, they are out of the mainstream compared to their Python equivalents.

5. Learning Curve and Accessibility

Python’s design philosophy—”readability counts”—makes it an approachable language for beginners. A Python script often resembles pseudocode, which lowers the barrier to entry for those without a computer science background. For example, training a simple model with scikit-learn looks like this code, which most technical users can scan and understand at a basic level:

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train, y_train)
preds = model.predict(X_test)

6. Performance and Scalability

Both Python and R are considered “slow” because they are interpreted rather than compiled, and so they depend crucially on other technologies when dealing with large datasets or for fast responses. Python has a much richer ecosystem for working with large and complex datasets quickly:

  • NumPy and pandas leverage heavily optimized C and Fortran code under the hood.
  • Numba, Cython, and PyPy provide just-in-time compilation for speedups.
  • Dask and Ray scale workflows across clusters of many different processors.
  • PySpark allows running big-data workloads on Hadoop clusters.
  • GPU acceleration using RAPIDS and CUDA is well supported by Python ML frameworks.

R has tools like data.table (highly optimized) and integrations with Spark, but Python offers vastly more flexibility for scaling from a laptop to a cloud cluster.

7. Visualization and Communication

Historically, visualization and web applications were two of R’s biggest advantages, thanks to ggplot2 for elegant, declarative plotting, and shiny for building interactive dashboards. For years, people were wondering when there would finally be a “Shiny for Python.”

Thanks to the momentum around Python, it has now caught up significantly and even surpassed R in many areas. Python visualization libraries like Seaborn, Plotly, Altair, Bokeh, and matplotlib provide nearly equivalent capabilities to ggplot2 (though not always with the same programming elegance), and Python-only tools like Datashader provide big-data rendering capabilities far beyond what is available from R or other languages.

Meanwhile, Shiny itself is now available for Python, along with Streamlit, Panel, Dash, and many other tools for building interactive dashboards directly in Python. Panel in particular makes it possible to build full-featured, complex web applications beyond what is practical in Shiny. And any of these tools integrate smoothly with the rest of the Python ecosystem, simplifying the overall project compared to combining R with other languages.

8. Industry Momentum

Momentum matters. Over the past decade, surveys from Kaggle, JetBrains, and Stack Overflow consistently show Python surpassing R in popularity among data scientists.

  • The Kaggle State of Data Science Survey (last published in 2022) found Python used by over 90% of respondents, compared to under 30% for R, with Python increasing and R usage steadily decreasing since 2018.
  • Job postings for “data scientist” roles overwhelmingly list Python as a required skill, with R often listed as “nice to have.”

This industry consensus reinforces itself: Companies adopt Python because talent is available, and talent learns Python because companies demand it.

When R Still Shines

To be clear, R is not obsolete. There are scenarios where R remains an excellent choice:

  • Pure statistical analysis: R’s built-in statistical tests, distributions, and models are unmatched.
  • Academic research: In fields like epidemiology and ecology, R remains the standard.
  • Visualization-first projects: ggplot2’s grammar of graphics is clear, clean, and expressive.
  • IDE support: RStudio provides a powerful, clear, and consistent developer experience. There are many competing editors for Python providing a less-polished overall experience.

So if workflow is primarily about certain fields of academic research, abstract statistical modeling, and reporting, rather than end-to-end AI systems, or if you prioritize a consistent user experience over having access to a wide range of capabilities, R may still be a better fit.

A Case Study: AI Workflow in Practice

Imagine a company building a recommendation system for an e-commerce platform. Here’s how the workflow might unfold in Python:

  1. Data ingestion: Read terabytes of clickstream logs with Dask or PySpark.
  2. Feature engineering: Transform user history into embeddings with pandas and scikit-learn.
  3. Model training: Train a neural collaborative filtering model in PyTorch on GPUs.
  4. Experiment tracking: Log metrics with MLflow.
  5. Deployment: Expose the trained model as a REST API using FastAPI.
  6. Monitoring: Use Prometheus and Grafana to track latency and drift.

In R, steps 1–3 are possible but more awkward (often requiring Python wrappers). Steps 4–6 are rarely done natively in R. The result is fragmentation: analysts in R, engineers in Python, and painful handoffs between them.

By using Python end-to-end, the company avoids these silos.

The Future Outlook

As AI evolves, the dominance of Python is likely to persist. Several trends reinforce this:

  • Generative AI: Frameworks like Hugging Face Transformers and diffusion models release Python APIs first.
  • Hardware acceleration: NVIDIA, AMD, and Intel all optimize libraries for Python ML stacks.
  • Cross-language support: Projects like PyO3 (Python ↔ Rust) and ONNX mean Python will remain the hub, even if computation happens elsewhere.
  • Education: Universities increasingly teach Python-first data science.

R will continue to thrive in niche domains, but its influence in industry-scale AI is likely to shrink further.

Conclusion

Both R and Python are powerful, open-source tools that have shaped modern data science. But when it comes to end-to-end AI workflows, Python is typically the better choice. Its advantages include:

  • A vast and versatile ecosystem
  • Leadership in AI and machine learning frameworks
  • Integration with production systems
  • A massive community and talent pool
  • Strong support for scalability and deployment

R remains invaluable for statistical analysis, visualization, and domain-specific research. Yet for organizations seeking to build, deploy, and scale AI systems, Python is the language that bridges data science and software engineering.

In short: if your work centers on exploring data, publishing statistical research, or making static reports, R may be your best friend. But if your goal is to operationalize AI, deliver value in production, and future-proof your workflows, Python is not just the safer bet—it’s the dominant force shaping the field.

If you’re interested in turning your R workflows into modern high-performance, flexible, AI-ready Python tools, our Professional Services team can help. Reach out to our highly experienced team of engineers to discuss your current R workflows and how we can transition them to Python workflows with a rich future ahead of them.