I have a Python script, download_spdr_holdings.py for scraping ETF constituents from the SPDR website: """download ETF holdings to csv file"""ĭf = pd.read_csv(url, skiprows=1).to_csv(f': import pandas as pdĭf = pd.read_csv(url, skiprows=1, usecols=) It’s trivial and we could replace this Python script with R code in no time at all, but I’m sure you have more complex Python scripts that you don’t feel like re-writing in R… Scraping ETF Constituents with Python from R Studio Ability to bind to different Python environmentsįor me, the main benefit of reticulate is streamlining my workflow.Direct object translation (eg pandas.DataFrame– ame, numpy.array– matrix etc).embedding Python code in an R Markdown document.using Python interactively in an R session.Ability to call Python flexibly from within R:.Some useful features of reticulate include: Thanks to the reticulate package ( install.packages('reticulate')) and its integration with R Studio, we can run our Python code without ever leaving the comfort of home. Other data scientists who work in bigger teams would likely have even more of a need to switch contexts regularly. Tools for doing this sort of thing in R’s tidyverse are really maturing, so I’m doing more and more of this without leaving R.īut I also have a pile of Python scripts that I used to lean on, and it would be nice to be able to continue to leverage that past work. But, until recently, I’d tend to reach for Python for anything more general, like scraping web data or interacting with an API. Personally, I prefer to use R for data analysis. Modern data science is fundamentally multi-lingual.Īt a minimum, most data scientists are comfortable working in R, Python and SQL many add Java and/or Scala to their toolkit, and it’s not uncommon to also know one’s way around JavaScript.
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