Find the best Python tutorials to learn and polish your coding skills in Python. These tutorials are suited for all programmers.

R vs Python — which is better for data analysis and data science? Compare the two languages side by side for an objective answer!

Top 10 Python Libraries to learn in 2025 are TensorFlow,Scikit-Learn,Numpy,Keras,PyTorch,LightGBM,Eli5,SciPy,Theano,Pandas. Read this blog to learn in detail.

This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.

🔥🔥🔥This R, Python and SAS Comparison video you will learn the difference between Python vs R vs SAS and whether you should learn r, python and sas for dat...

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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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Python continues to take leading positions in solving data science tasks and challenges. Last year we made a blog post overviewing the Python’s libraries that proved to be the most helpful at that moment. This year, we expanded our list with new libraries and gave a fresh look to the ones we already talked about, focusing… Read More »Top 20 Python libraries for data science in 2018

Demonstrating different types of colormaps Free Python Code with explanation: https://www.clcoding.com/2024/06/demonstrating-different-types-of.html

Learn Python online: Python tutorials for developers of all skill levels, Python books and courses, Python news, code examples, articles, and more.

In the R programming language, there is a way to construct a data frame from scratch and add observations. Here's how to do it.

Computer Science With Python and Pygame

This article is a beginner-to-intermediate-level walkthrough on Python and matplotlib that mixes theory with example.

This is part 2 of the mega numpy tutorial. In this part, I go into the details of the advanced features of numpy arrays that are essential for data analysis and manipulations.

How to learn Python for data science (plus NumPy, Pandas, Scikit-Learn) efficiently, including a complete self-study curriculum with detailed action steps.

Understand the constructs of the Python programming language and use them to build data science projects Key Features Learn the basics of developing applications with Python and deploy your first … - Selection from Learn Python by Building Data Science Applications [Book]

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