We go over different packages and approaches for leveraging multicore processing with R and Python. Learn more about parallel processing with our video and step-by-step guide.

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Here is a list of the top 15 Python libraries used in Data Science and Machine Learning.

Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java … - Selection from Data Science with Java [Book]

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This cheat sheet, along with explanations, was first published on DataCamp. Click on the picture to zoom in. To view other cheat sheets (Python, R, Machine Learning, Probability, Visualizations, Deep Learning, Data Science, and so on) click here.  To view a better version of the cheat sheet and read the explanations, click here.  Top DSC… Read More »Data Science in Python: Pandas Cheat Sheet

Gear up to speed and have concepts and commands handy in Data Science, Data Mining, and Machine learning algorithms with these cheat sheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark, Matlab, and Java.

Just like how Thanos claimed to be inevitable in The Avengers, the direct or indirect use of python has become inevitable for R users in recent years. Fret not R users, you don’t have to abandon your favourite IDE, Rstudio, when using python. With the reticulate package you can use python in Rstudio and even have a mixture of R and python code running in the same session. If you blog with blogdown, you don’t have to migrate to another platform to write about your python projects. With the help of reticulate, you can continue publishing content on your blogdown site. An analogy of reticulate will be like a translator between R and python. library(tidyverse) library(reticulate) Setup reticulate has a default approach to instruct R where to find python, which environment and version to use. There are three approaches to manually configure this. virtualenv where you specify the directory of python virtual environment use_python where you specify the path where your ‘python’ resides. use_condaenv where you specify the name of the specific Conda environment to use. You can access the name(s) of the available environments via conda_list()[[1]] conda_list()[[1]] %>% use_condaenv() Let’s check which python version, environment and configuration has been bind to this R session. py_config() #not run to maintain privacy Running python Amend {r} in your code chunk to {python} to run python code. For this post, I will add #{python/r} in my code chunks to make it explicit that I ran the code as a python or a r code chunk. #{python} Plist= [11,22,33,44,55,66] print(Plist) ## [11, 22, 33, 44, 55, 66] #{python} def Psq_fun (x): value= x*x return(value) print(Psq_fun(9)) ## 81 Alternatively, you can execute python scripts in your r chunks using the function py_run_string. You will need to wrap your python scripts within the quotation marks "". #{r} py_run_string("def Pten (x): value= x*10 return(value)") Let’s run the above in a python code chunk. #{python} Pten(2) ## 20 Do note that python objects/functions are not explicitly displayed in your global environment (you are after all working in an R global environment by default). #{r} ls() ## character(0) Nevertheless, be assured that you can still access them in future python and r chunks. #{python} print(Psq_fun(9)) ## 81 #{python} print(Plist) ## [11, 22, 33, 44, 55, 66] Accessing python You can access previously ran python functions/objects in your r chunks using the prefix py combined with the R’s dollar sign syntax $. #{r} py$Psq_fun(4) ## [1] 16 #{r} py$Plist ## [1] 11 22 33 44 55 66 Alternatively, you can directly evaluate previous python objects/functions in r chunks using the py_eval function. #{r} py_eval("Plist") ## [1] 11 22 33 44 55 66 #{r} py_eval("Psq_fun(2)") ## [1] 4 Accessing R Likewise, you can access R objects/functions in your python chunks using the prefix r combined with a punctuation mark . #{r} (Rvec% class() ## [1] "python.builtin.list" "python.builtin.object" Previously, I mentioned that python objects do not exist in your global R environment when you run the python script directly inside {python} code chunks or with python_run_string. However, when you create python objects in {r} code chunks, the python object is saved in the R environment. #{r} # convert R object into python Python_in_Renv% r_to_py() # check if the converted object lives in the `R` environment. https://stackoverflow.com/questions/1169248/test-if-a-vector-contains-a-given-element "Python_in_Renv" %in% ls() ## [1] TRUE For python objects living in the R global environment, you can convert it back to a R object with py_to_r. Python_in_Renv %>% py_to_r() %>% class() ## [1] "numeric" Layout At times you may decide to display R and python code side by side to compare which language is superior. Html document static The first outlay is static where the page is divided into half and content appears on either half of the page. You will sandwich text and code chunks meant to be on the left and right with the respective html/css code. #Start of column partition #Left column Text: `R` code will be on the left Chunk of R code ``{r}`` #Right column Text: `python` code will be on the right Chunk of Python code ``{python}`` # End of column partition Dynamic An alternative will be a where different languages are compartmentalized into their respective tabs and the tabs are displayed next to each another. Users decide which language they wish to view and therefore which tab to highlight. You will use the {.tabset} function with headers to create the tabs and end the tab section with ##. ## Level 2 heading {.tabset} ### Level 3 heading (first tab i.e. left tab) Text: R code will be on the left tab Chunk of R code ``{r}`` ### Level 3 heading (next tab i.e. right tab) Text: python code will be on the right tab Chunk of python code ``{python}`` ## Blog Unfortunately, the above do not work when building a site with blogdown despite trying several Hugo themes. I’ve adapted code from here which successfully created two columns on this site. Text: R code will be on the left Code: ``{r}`` Text: python code will be on the left Code: ``{python}`` R code on the left #{r} Rroot_fun ## function(x){ ## value= x^.5 ## print(value) ## } python code on the right #{python} print(Pten) ## Resources The adoption of reticulate in data science projects is endless. For example, Manuel Tilgner used R for data wrangling and pre-processing and python via reticulate to do some prediction. Martin Henze used python again via reticulate to do some prediction and used R’s almighty ggplot to visualize the results. Sean Lopp used reticulate to run some python code to create a Shiny app. If you prefer a video presentation of a little bit of all the above, you can watch Dan Chen’s presentation during the late 2019 Rstats DC Conference.

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We identify the 6 tools in the modern open-source Data Science ecosystem, examine the Python vs R question, and determine which tools are used the most with Deep Learning and Big Data.

This list of lists contains books, notebooks, presentations, cheat sheets, and tutorials covering all aspects of data science, machine learning, deep learning, statistics, math, and more, with most documents featuring Python or R code and numerous illustrations or case studies. All this material is available for free, and consists of content mostly created in 2019… Read More »40+ Modern Tutorials Covering All Aspects of Machine Learning

Online Certification Course on Applied Data Science with Python Batch will start on June 12, 2021 || Weekend Batch Conference Code: EICTIITR-SUM-24 Experts from Academia/Industry Prof. R. Balasubramanian, CSE Department, IIT Roorkee Prof. Sumit Kalra, CSE Department, IIT Jodhpur Mr. Pranav Pravin Gandhi, Uniconverge Technologies Pvt. Ltd. EICT Principal Investigator and Coordinator Prof. Sanjeev Manhas […]

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