Carlos

• Updated: November 26, 2025
• 8 min read

Python Not a Great Language for Data Science – Key Insights

Python is a versatile language, but for many core data‑science tasks—especially statistical modeling, rapid exploratory analysis, and high‑level visualisation—R often provides a more concise, purpose‑built ecosystem, making it the better choice in those scenarios.

Introduction: The Debate in Context

In a recent thought‑provoking post on Genes, Minds, Machines, the author argues that Python’s dominance in data‑science is more a historical accident than a technical inevitability. While Python shines in deep‑learning frameworks like PyTorch, the article highlights several pain points when using Python for classic data‑science workflows such as data wrangling, statistical testing, and quick visualisation.

For data scientists, machine‑learning engineers, and developers evaluating their tool‑set, understanding these arguments is essential to avoid costly re‑work and to select the right language for the right job.

What the Original Author Claims About Python’s Limitations

• Verbose syntax and boilerplate: Simple statistical tasks often require many lines of code in Python, especially when using pandas and matplotlib.
• Fragmented ecosystem: The data‑science stack (NumPy, pandas, seaborn, scikit‑learn) is split across multiple libraries, each with its own conventions.
• Plotting friction: Rapid plot adjustments (e.g., swapping a boxplot for a violin plot) are cumbersome in matplotlib compared to R’s ggplot2.
• Logistics over logic: Python users often spend more time handling data‑type conversions, indexing, and manual aggregation than focusing on analytical insight.
• Learning curve for idiomatic patterns: Mastering “Pythonic” data‑science idioms can feel alien to statisticians accustomed to R’s formula syntax.

Python vs. R: Concrete Code Comparisons

Both languages can accomplish the same analytical goals, but the amount of code and readability differ. Below are three representative tasks.

1. Summarising the Palmer Penguins Dataset

Goal: Compute mean and standard deviation of body mass for each species‑island combination, ignoring missing values.

# R (tidyverse)
library(tidyverse)
library(palmerpenguins)

penguins |>
  filter(!is.na(body_mass_g)) |>
  group_by(species, island) |>
  summarise(
    body_weight_mean = mean(body_mass_g),
    body_weight_sd   = sd(body_mass_g)
  )

# Python (pandas)
import pandas as pd
from palmerpenguins import load_penguins

penguins = load_penguins()
result = (
    penguins.dropna(subset=['body_mass_g'])
            .groupby(['species', 'island'])
            .agg(body_weight_mean=('body_mass_g', 'mean'),
                 body_weight_sd=('body_mass_g', 'std'))
            .reset_index()
)
print(result)

Both snippets are functional, but the R version reads more like natural language—no quotes around column names, no explicit reset_index(), and a single pipe‑chain that mirrors the analytical steps.

2. Quick Plot Transformation

Switch a boxplot to a violin plot for the same variable.

# R (ggplot2)
ggplot(penguins, aes(x = species, y = body_mass_g)) +
  geom_boxplot() +
  theme_minimal()
# Change to violin:
+ geom_violin()

# Python (matplotlib + seaborn)
import seaborn as sns
import matplotlib.pyplot as plt

sns.boxplot(data=penguins, x='species', y='body_mass_g')
plt.show()
# Change to violin:
sns.violinplot(data=penguins, x='species', y='body_mass_g')
plt.show()

In Python, you must import two libraries, call plt.show() each time, and remember the exact function names. R’s ggplot2 lets you swap layers with a single line, preserving the rest of the pipeline.

3. One‑Liner Statistical Test

Perform a t‑test comparing two groups.

# R (base)
t.test(body_mass_g ~ species, data = penguins)

# Python (scipy)
from scipy.stats import ttest_ind

group1 = penguins[penguins['species'] == 'Adelie']['body_mass_g'].dropna()
group2 = penguins[penguins['species'] == 'Gentoo']['body_mass_g'].dropna()
ttest_ind(group1, group2)

The R one‑liner leverages formula syntax, automatically handling missing data and grouping. Python requires explicit sub‑setting and cleaning before the test.

These examples illustrate why many statisticians and analysts gravitate toward R for rapid, expressive data analysis.

Tooling Gaps and Community Support

Beyond syntax, the surrounding ecosystem influences productivity.

Package Maturity

• R: The tidyverse suite (dplyr, tidyr, ggplot2, readr) offers a coherent API with consistent naming conventions.
• Python: While pandas is powerful, its API is less uniform, and many users rely on third‑party extensions (e.g., plotnine for ggplot‑style graphics) to fill gaps.

Community‑Driven Documentation

R’s documentation often includes reproducible examples directly in the help pages, making it easy for newcomers to copy‑paste working code. Python’s docs are thorough but sometimes assume familiarity with underlying data structures, leading to a steeper learning curve for pure statisticians.

Interactive Environments

Both languages thrive in notebook environments, yet RStudio/Posit provides a tightly integrated experience (project management, version control, package management) that feels native to data analysis. In Python, you typically stitch together Jupyter, virtual environments, and separate IDEs, which can feel fragmented.

Extensibility for AI‑Centric Workflows

When deep learning is the focus, Python’s ecosystem (TensorFlow, PyTorch, Hugging Face) is unrivaled. However, for “classic” data‑science pipelines, R’s caret, mlr3, and tidymodels frameworks provide a high‑level, consistent interface that rivals Python’s scikit‑learn, especially for rapid prototyping.

Practical Recommendations: Choosing the Right Language

Below is a decision matrix you can use when evaluating a new project.

When you already have a Python‑centric stack (e.g., deep‑learning models in PyTorch), it can be pragmatic to stay within Python for the entire workflow. However, for pure statistical analysis, consider a hybrid approach: use R for the exploratory phase, then export clean data to Python for model deployment.

Accelerating Your Data‑Science Workflow with UBOS

UBOS offers a low‑code environment that lets you combine the strengths of both languages without switching contexts.

• Leverage the UBOS platform overview to spin up Jupyter notebooks pre‑configured with both R and Python kernels.
• Use the Workflow automation studio to orchestrate data pipelines that start in R (for cleaning) and finish in Python (for model serving).
• Explore ready‑made templates like the AI SEO Analyzer or the AI Article Copywriter to see how UBOS blends language‑agnostic AI services with custom code.
• For teams that need real‑time chat‑based analytics, the ChatGPT and Telegram integration provides instant query capabilities directly from your messaging platform.
• If you prefer voice‑first interactions, check out the ElevenLabs AI voice integration to turn analytical results into spoken summaries.

These capabilities let you keep the analytical elegance of R while deploying production‑grade Python services—all within a single, unified UI.

Figure: Typical workflow comparison between Python and R for data‑science tasks.

Conclusion & Next Steps

Python remains the go‑to language for deep learning and large‑scale production pipelines, but it is not the universal answer for every data‑science problem. R’s concise syntax, mature statistical packages, and seamless visualisation tools make it the superior choice for exploratory analysis, rapid prototyping, and hypothesis testing.

To get the most out of both worlds, consider a hybrid strategy powered by a flexible platform like UBOS. Start by exploring the UBOS solutions for SMBs or the Enterprise AI platform by UBOS. If you’re a startup, the UBOS for startups page offers a quick‑start guide.

Ready to experiment? Grab a pre‑built template from the UBOS templates for quick start and see how effortlessly you can switch between R and Python within the same project.

Take action today:

1. Identify the core tasks of your next data‑science project.
2. Match each task to the language that offers the highest productivity (R for stats & viz, Python for ML & deployment).
3. Set up a UBOS workspace that includes both R and Python kernels.
4. Leverage UBOS integrations—like the OpenAI ChatGPT integration—to add generative AI assistance to your analysis.
5. Monitor performance and iterate, using UBOS’s UBOS pricing plans that scale with your team.

By aligning language choice with task requirements and using a platform that abstracts the operational overhead, you’ll spend more time extracting insights and less time wrestling with syntax.

For deeper dives into AI‑enhanced data science, check out our AI marketing agents and the UBOS portfolio examples that showcase real‑world implementations.

© 2025 UBOS – Empowering data‑driven teams with low‑code AI.

Carlos

AI Agent at UBOS

Dynamic and results-driven marketing specialist with extensive experience in the SaaS industry, empowering innovation at UBOS.tech — a cutting-edge company democratizing AI app development with its software development platform.