{"id":95328,"date":"2021-07-19T13:08:59","date_gmt":"2021-07-19T17:08:59","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=95328"},"modified":"2022-11-21T09:47:45","modified_gmt":"2022-11-21T14:47:45","slug":"how-to-run-python-from-r-studio-part-i","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/how-to-run-python-from-r-studio-part-i\/","title":{"rendered":"How to Run Python from R Studio &#8211; Part I"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Modern data science is fundamentally&nbsp;<em>multi-lingual.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">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\u2019s not uncommon to also know one\u2019s way around JavaScript.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Personally, I prefer to use R for data analysis. But, until recently, I\u2019d 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\u2019s&nbsp;<a href=\"https:\/\/robotwealth.com\/financial-data-manipulation-in-dplyr-for-quant-traders\/\">tidyverse<\/a>&nbsp;are really maturing, so I\u2019m doing more and more of this without leaving R.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">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.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-reticulate-to-the-rescue\">Reticulate to the rescue<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Thanks to the&nbsp;<code>reticulate<\/code>&nbsp;package (<code>install.packages('reticulate')<\/code>) and its integration with R Studio, we can run our Python code without ever leaving the comfort of home.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Some useful features of&nbsp;<code>reticulate<\/code>&nbsp;include:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Ability to call Python flexibly from within R:<br><ul><li>sourcing Python scripts<\/li><li>importing Python modules<\/li><li>using Python interactively in an R session<\/li><li>embedding Python code in an R Markdown document<\/li><\/ul><\/li><li>Direct object translation (eg\u00a0<code>pandas.DataFrame<\/code>\u2013<code>data.frame<\/code>,\u00a0<code>numpy.array<\/code>\u2013<code>matrix<\/code>\u00a0etc)<\/li><li>Ability to bind to different Python environments<\/li><\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">For me, the main benefit of&nbsp;<code>reticulate<\/code>&nbsp;is streamlining my workflow. In this post, I\u2019ll share an example. It\u2019s trivial and we could replace this Python script with R code in no time at all, but I\u2019m sure you have more complex Python scripts that you don\u2019t feel like re-writing in R\u2026<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-scraping-etf-constituents-with-python-from-r-studio\">Scraping ETF Constituents with Python from R Studio<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">I have a Python script,&nbsp;<code>download_spdr_holdings.py<\/code>&nbsp;for scraping ETF constituents from the SPDR website:<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\"> \n&#8220;&#8221;&#8221;download ETF holdings to csv file&#8221;&#8221;&#8221;<br>\nimport pandas as pd<br>\ndef get_holdings(spdr_ticker):<br>\n    url = f&#8217;https:\/\/www.sectorspdr.com\/sectorspdr\/IDCO.Client.Spdrs.Holdings\/Export\/ExportCsv?symbol={spdr_ticker}&#8217;<br>\n    df = pd.read_csv(url, skiprows=1).to_csv(f'{spdr_ticker}_holdings.csv&#8217;, index=False)<br>\n    return df<br>\nif __name__ == &#8220;__main__&#8221;:<br>\n    tickers = [&#8216;XLB&#8217;, &#8216;XLE&#8217;, &#8216;XLF&#8217;, &#8216;XLI&#8217;, &#8216;XLK&#8217;, &#8216;XLP&#8217;, &#8216;XLU&#8217;, &#8216;XLV&#8217;, &#8216;XLY&#8217;]<br>\n    for t in tickers:<br>\n        get_holdings(t)\n<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This simple script contains a function for saving the current constituents of a SPDR ETF to a CSV file. When called as a module\u00a0<code>python -m download_spdr_holdings<\/code>, the script loops through a bunch of ETF tickers and saves their constituents to individual CSV files.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The intent is that these CSV files then get read into an R session where any actual analysis takes place.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With&nbsp;<code>reticulate<\/code>, I can remove the disk I\/O operations and read my data directly into my R session, using my existing Python script.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Connect reticulate to Python<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">First, I need to tell&nbsp;<code>reticulate<\/code>&nbsp;about the Python environment I want it to use.&nbsp;<code>reticulate<\/code>&nbsp;is smart enough to use the version of Python found on your&nbsp;<code>PATH<\/code>&nbsp;by default, but I have a Conda environment running Python 3.7 named \u201cpy37\u201d that I\u2019d like to use. Hooking&nbsp;<code>reticulate<\/code>&nbsp;into that environment is as easy as doing:<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\"> \nlibrary(reticulate)<br>\nreticulate::use_condaenv(&#8220;py37&#8221;)\n<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><code>reticulate<\/code>&nbsp;is flexible in its ability to hook into your various Python environments. In addition to&nbsp;<code>use_condaenv()<\/code>&nbsp;for Conda environments, there\u2019s&nbsp;<code>use_virtualenv()<\/code>&nbsp;for virtual environments and&nbsp;<code>use_python()<\/code>&nbsp;to specify a Python version that isn\u2019t on your&nbsp;<code>PATH<\/code>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Bring Python code to R<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">To use my Python script as is directly in R Studio, I could source it by doing&nbsp;<code>reticulate::source_python(\"download_spdr_holdings.py\")<\/code>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">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&nbsp;<code>get_holdings<\/code>&nbsp;to my R session, and I can call it as I would any R function.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For instance,&nbsp;<code>get_holdings('XLF')<\/code>&nbsp;will scrape the constituents of the XLF ETF and save them to disk.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Pretty cool, no?<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">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&nbsp;<code>def<\/code>&nbsp;and call&nbsp;<code>source_python()<\/code>&nbsp;again. I could also just copy the modified&nbsp;<code>def<\/code>&nbsp;directly in an R Markdown notebook (I just need to specify my chunk as&nbsp;<code>{python}<\/code>&nbsp;rather than&nbsp;<code>{r}<\/code>:<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\"> \nimport pandas as pd<br>\ndef get_holdings(spdr_ticker):<br>\n    &#8220;&#8221;&#8221;read in ETF holdings&#8221;&#8221;&#8221;<br>\n    url = f&#8221;https:\/\/www.sectorspdr.com\/sectorspdr\/IDCO.Client.Spdrs.Holdings\/Export\/ExportCsv?symbol={spdr_ticker}&#8221;<br>\n    df = pd.read_csv(url, skiprows=1, usecols=[i for i in range(3)]) <br>\n    return df\n<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I now have the&nbsp;<code>get_holdings<\/code>&nbsp;function in my R session, and can call it as if it were an R function&nbsp;<strong>attached to the&nbsp;<code>py<\/code>&nbsp;object<\/strong>&nbsp;that&nbsp;<code>reticulate<\/code>&nbsp;creates to hold the Python session:<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\"> \nlibrary(tidyverse)<br>\nxlf <- py$get_holdings('XLF')<br>\nxlf %>%<br>\n  arrange(desc(`Index Weight`)) %>%<br>\n  head(10)\n<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\"> \n##    Symbol                  Company Name Index Weight<br>\n## 1     BAC          Bank of America Corp        7.39%<br>\n## 2     WFC              Wells Fargo &amp; Co        3.90%<br>\n## 3       C                 Citigroup Inc        3.86%<br>\n## 4    SPGI                S&amp;P Global Inc        3.09%<br>\n## 5     CME               CME Group Inc A        2.72%<br>\n## 6     BLK                 BlackRock Inc        2.47%<br>\n## 7     AXP           American Express Co        2.37%<br>\n## 8      GS       Goldman Sachs Group Inc        2.34%<br>\n## 9     MMC    Marsh &amp; McLennan Companies        2.25%<br>\n## 10    ICE Intercontinental Exchange Inc        2.18%\n<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Notice that to use the&nbsp;<code>def<\/code>&nbsp;from the Python session embedded in my R session, I had to ask for it using&nbsp;<code>py$object_name<\/code>&nbsp;\u2013 this is different than if I sourced a Python file directly, in which case the Python function becomes available&nbsp;<em>directly<\/em>&nbsp;in the R session (ie I don\u2019t need&nbsp;<code>py$<\/code>).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Stay tuned for the next installment in which the author will demonstrate importing Python modules<\/em>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Visit Robot Wealth website for additional insight<\/em>: <a href=\"https:\/\/robotwealth.com\/using-python-from-the-comfort-of-r-studio\/\"><em>https:\/\/robotwealth.com\/using-python-from-the-comfort-of-r-studio\/<\/em><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Past performance is not indicative of future results.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Any stock, options or futures symbols displayed are for illustrative purposes only and are not intended to portray recommendations.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Kris Longmore demonstrates how to use reticulate package to scrape ETF constituents with Python from R Studio.<\/p>\n","protected":false},"author":271,"featured_media":80349,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,349,338,350,341,344,342],"tags":[806,297,1224,595,10023,10024,1045],"contributors-categories":[13676],"class_list":["post-95328","post","type-post","status-publish","format-standard","has-post-thumbnail","category-data-science","category-programing-languages","category-python-development","category-ibkr-quant-news","category-quant-asia-pacific","category-quant-development","category-quant-regions","category-r-development","tag-data-science","tag-etfs","tag-pandas","tag-python","tag-r-studio","tag-reticulate-package","tag-tidyverse","contributors-categories-robot-wealth"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is 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