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How to Run Python from R Studio – Part I

How to Run Python from R Studio – Part I

Posted July 19, 2021 at 1:08 pm
Kris Longmore
Robot Wealth

Modern data science is fundamentally multi-lingual.

At 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.

Personally, I prefer to use R for data analysis. But, until recently, I’d tend to reach for Python for anything more general, like scraping web data or interacting with an API. 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.

But 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. Other data scientists who work in bigger teams would likely have even more of a need to switch contexts regularly.

Reticulate to the rescue

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.

Some useful features of reticulate include:

  • Ability to call Python flexibly from within R:
    • sourcing Python scripts
    • importing Python modules
    • using Python interactively in an R session
    • embedding Python code in an R Markdown document
  • Direct object translation (eg pandas.DataFramedata.framenumpy.arraymatrix etc)
  • Ability to bind to different Python environments

For me, the main benefit of reticulate is streamlining my workflow. In this post, I’ll share an example. 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

I have a Python script, for scraping ETF constituents from the SPDR website:

“””download ETF holdings to csv file”””
import pandas as pd
def get_holdings(spdr_ticker):
url = f’{spdr_ticker}’
df = pd.read_csv(url, skiprows=1).to_csv(f'{spdr_ticker}_holdings.csv’, index=False)
return df
if __name__ == “__main__”:
tickers = [‘XLB’, ‘XLE’, ‘XLF’, ‘XLI’, ‘XLK’, ‘XLP’, ‘XLU’, ‘XLV’, ‘XLY’]
for t in tickers:

This simple script contains a function for saving the current constituents of a SPDR ETF to a CSV file. When called as a module python -m download_spdr_holdings, the script loops through a bunch of ETF tickers and saves their constituents to individual CSV files.

The intent is that these CSV files then get read into an R session where any actual analysis takes place.

With reticulate, I can remove the disk I/O operations and read my data directly into my R session, using my existing Python script.

Connect reticulate to Python

First, I need to tell reticulate about the Python environment I want it to use. reticulate is smart enough to use the version of Python found on your PATH by default, but I have a Conda environment running Python 3.7 named “py37” that I’d like to use. Hooking reticulate into that environment is as easy as doing:


reticulate is flexible in its ability to hook into your various Python environments. In addition to use_condaenv() for Conda environments, there’s use_virtualenv() for virtual environments and use_python() to specify a Python version that isn’t on your PATH.

Bring Python code to R

To use my Python script as is directly in R Studio, I could source it by doing reticulate::source_python("").

This will cause the Python script to run as if it were called from the command line as a module and will loop through all the tickers and save their constituents to CSV files as before. It will also add the function get_holdings to my R session, and I can call it as I would any R function.

For instance, get_holdings('XLF') will scrape the constituents of the XLF ETF and save them to disk.

Pretty cool, no?

However, the point of this exercise was to skip the disk I/O operations and read the ETF constituents directly into my R session. So I would need to modify my Python def and call source_python() again. I could also just copy the modified def directly in an R Markdown notebook (I just need to specify my chunk as {python} rather than {r}:

import pandas as pd
def get_holdings(spdr_ticker):
“””read in ETF holdings”””
url = f”{spdr_ticker}”
df = pd.read_csv(url, skiprows=1, usecols=[i for i in range(3)])
return df

I now have the get_holdings function in my R session, and can call it as if it were an R function attached to the py object that reticulate creates to hold the Python session:

xlf <- py$get_holdings('XLF')
xlf %>%
arrange(desc(`Index Weight`)) %>%

## Symbol Company Name Index Weight
## 1 BAC Bank of America Corp 7.39%
## 2 WFC Wells Fargo & Co 3.90%
## 3 C Citigroup Inc 3.86%
## 4 SPGI S&P Global Inc 3.09%
## 5 CME CME Group Inc A 2.72%
## 6 BLK BlackRock Inc 2.47%
## 7 AXP American Express Co 2.37%
## 8 GS Goldman Sachs Group Inc 2.34%
## 9 MMC Marsh & McLennan Companies 2.25%
## 10 ICE Intercontinental Exchange Inc 2.18%

Notice that to use the def from the Python session embedded in my R session, I had to ask for it using py$object_name – this is different than if I sourced a Python file directly, in which case the Python function becomes available directly in the R session (ie I don’t need py$).

Stay tuned for the next installment in which the author will demonstrate importing Python modules.

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