Can Python Libraries For Data Science Work With R?

2025-08-09 11:09:28 234

4 Answers

Xavier
Xavier
2025-08-11 10:01:21
I’ve found that Python and R can play nicely together with the right tools. For quick tasks, I use 'reticulate' in R to run Python snippets. For bigger projects, I might write separate scripts in each language and pass data between them. It’s not ideal, but it lets me use Python’s 'tensorflow' and R’s 'shiny' in the same project. The main challenge is keeping dependencies in sync, but it’s manageable with virtual environments.
Declan
Declan
2025-08-12 06:57:35
From my experience, Python and R can coexist in a data science workflow, but it takes some effort. I often use 'feather' or 'parquet' files to share data between the two languages because they’re fast and preserve data types better than CSV. For example, I might clean data in Python with 'pandas', save it as a 'feather' file, and then analyze it in R with 'tidyverse'. It’s not as smooth as staying in one language, but it’s practical.

Sometimes, I’ll even use 'Plumber' in R to create APIs that Python can call, or vice versa with 'Flask' in Python. This is especially useful for deploying models where one language has a clear advantage. The downside? You need to manage two environments, which can be a headache. But if you’re comfortable with both, it’s worth the flexibility.
Evelyn
Evelyn
2025-08-12 11:17:20
I can confidently say that there are ways to make them work together, though it’s not always seamless. Python libraries like 'pandas', 'numpy', and 'scikit-learn' are incredibly powerful, but R has its own strengths, especially in statistical modeling and visualization with packages like 'ggplot2' and 'dplyr'. Tools like 'reticulate' in R allow you to call Python code directly from R, which is a game-changer for integrating workflows.

For example, you can use 'reticulate' to run Python scripts or even import Python modules into R. This means you can leverage Python’s machine learning libraries while still using R for data wrangling or visualization. Another approach is using Jupyter notebooks, where you can mix R and Python cells. It’s not perfect—sometimes there are hiccups with data type conversions or environment setups—but it’s a viable option for those who want the best of both worlds.
Sophia
Sophia
2025-08-12 20:29:55
I’ve spent years switching between Python and R, and while they’re not natively compatible, there are workarounds. One of my favorite tricks is using 'rpy2' in Python to call R functions. This lets me use R’s advanced statistical packages right inside my Python scripts. For instance, I might use 'scipy' for optimization but switch to R’s 'lme4' for mixed-effects models. It’s a bit clunky, but it works.

Another option is exporting data to CSV or JSON and loading it into the other language. Not elegant, but effective. If you’re into reproducibility, tools like 'knitr' or 'R Markdown' can incorporate both Python and R code, though it requires some setup. The key is knowing which tool to use for which task—Python for general-purpose work, R for stats—and bridging the gap when needed.
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