Use Different Languages in One Jupyter Notebook: Python, R and Julia
date
Sep 25, 2024
slug
all-languages-for-data-analysis-in-one-notebook-python-r-and-julia
status
Published
summary
The article discusses integrating Python, R, and Julia in a single Jupyter Notebook to enhance data analysis efficiency. It outlines prerequisites for setup, including installing necessary kernels and libraries, and provides examples of how to write code in each language within the notebook. The integration allows for combining machine learning, fast data preparation, and visualization seamlessly.
tags
Data Analysis
Python
ML
type
Post
I recently faced an efficiency challenge in my data analysis pipeline: Python and R were too slow, even with parallelization. To address this, I began learning Julia.
Surprisingly, I discovered a method to integrate all major data analysis languages in a single notebook. Now we can seamlessly combine machine learning (in Python), fast data preparation (using Julia), and result visualization with
ggplot2 (supported in R).Prerequisites
- Python, R, Julia installed
- Jupyter Notebook installed
- Install Julia kernel:
- Enter Julia REPL
- For example, we want to add a Julia kernel in Jupyter using multi-threads:
installkernel("Julia (4 threads)", env=Dict("JULIA_NUM_THREADS"=>"4"))
- Install
PyCallandRCallin Julia
Usage
- Then write jupyter notebooks as
Result
Alternative
- 2Sigma, BeakerX, https://github.com/twosigma/beakerx