From ‘R vs Python’ to ‘R and Python’

In this article, you'll learn to leverage the best of both ‘Python and R’ in a single project.

If you are into Data Science, the two programming languages that immediately come to mind are R and Python. However, instead of considering them as two options, more often than not, we end up comparing the two. R and Python, are excellent tools in their own right but are very often conceived as rivals. If you type R vs Python , in your Google search bar, you instantly get a plethora of resources on topics which talk about the supremacy of one over the other.

One of the reasons for such an outlook is because people have divided the Data Science field into camps based on the choice of the programming language they use. There is an R camp and a Python camp and history is a testimony to the fact that camps cannot live in harmony. Members of both the camps fervently believe that their choice of language is superior to the other. So, in a way, divergence doesn’t lie with the tools but with the people using those tools.

Why not use Both?

There are people in the Data Science community who are using both Python and R, but their percentage is small. On the other hand, there are a lot of people who are committed to only one programming language but wished they had access to some of the capabilities of their adversary. For instance, R users sometimes yearn for the object-oriented capacities that are native to Python and similarly, some Python users long for the wide range of the statistical distributions that are available within R.

The figure above shows the results of the survey conducted by Red Monk in the third quarter of 2018. These results are based on the popularity of the languages on Stack Overflow as well as on Github and clearly show that both R and Python are rated quite high. Therefore, there is no inherent reason as to why we cannot work with both of them on the same project. Our ultimate goal should be to do better analytics and derive better insights and choice of a programming language should not be a hindrance in achieving that.

Overview of R and Python

Let’s have a look at the various aspects of these languages and what’s good and not so good about them.

Python

Since its release in 1991, Python has been extremely popular and is widely used in data processing. Some of the reasons for its wide popularity are:

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

However, Python doesn’t have specialized packages for statistical computing, unlike R.

R

R’s first release came in 1995 and since then it has gone on to become one of the most used tools for data science in the industry.

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

Performance wise R is not the fastest language and can be a memory glutton sometimes when dealing with large datasets.

Leveraging the best of Both Worlds

Could we utilize the statistical prowess of R along with the programming capabilities of Python? Well, when we can easily embed SQL code within either R or Python script, why not blend R and Python together?

There are basically two approaches by which we can use both Python and R side by side in a single project.

R within Python

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

PypeR provides a simple way to access R from Python through pipes. PypeR is also included in Python’s Package Index which provides a more convenient way for installation. PypeR is especially useful when there is no need for frequent interactive data transfers between Python and R. By running R through pipe, the Python program gains flexibility in sub-process controls, memory control, and portability across popular operating system platforms, including Windows, GNU Linux and Mac OS

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

pyRserve uses Rserve as an RPC connection gateway. Through such a connection, variables can be set in R from Python, and also R-functions can be called remotely. R objects are exposed as instances of Python-implemented classes, with R functions as bound methods to those objects in a number of cases.

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

rpy2 runs embedded R in a Python process. It creates a framework that can translate Python objects into R objects, pass them into R functions, and convert R output back into Python objects. rpy2 is used more often since it is one which is being actively developed.

One advantage of using R within Python is that we would able to use R’s awesome packages like ggplot2, tidyr, dplyr et al easily in Python. As an example let’s see how we can easily use ggplot2 for mapping in Python.

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

[https://rpy2.github.io/doc/latest/html/graphics.html#geometry](https://rpy2.github.io/doc/latest/html/graphics.html#geometry](https://rpy2.github.io/doc/latest/html/graphics.html#geometry) "https://rpy2.github.io/doc/latest/html/graphics.html#geometry](https://rpy2.github.io/doc/latest/html/graphics.html#geometry)")

Resources

You may want to have a look at the following resources for more in-depth review of rpy2:

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

Python with R

We can run R scripts in Python by using one of the alternatives below:

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

This package implements an interface to Python via Jython. It is intended for other packages to be able to embed python code along with R.

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

rPython is again a Package Allowing R to Call Python. It makes it possible to run Python code, make function calls, assign and retrieve variables, etc. from R.

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

SnakeCharmR is a modern overhauled version of rPython. It is a fork from ‘rPython’ which uses ‘jsonlite’ and has a lot of improvements over rPython.

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

PythonInR makes accessing Python from within R very easy by providing functions to interact with Python from within R.

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

The reticulate package provides a comprehensive set of tools for interoperability between Python and R. Out of all the above alternatives, this one is the most widely used, more so because it is being aggressively developed by Rstudio. Reticulate embeds a Python session within the R session, enabling seamless, high-performance interoperability. The package enables you to reticulate Python code into R, creating a new breed of a project that weaves together the two languages.

The reticulate package provides the following facilities:

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

Resources

Some great resources on using the reticulate package are:

Object-oriented language
General Purpose
Has a lot of extensions and incredible community support
Simple and easy to understand and learn
packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning activities.

Conclusion

Both R and Python are quite robust languages and either one of them is actually sufficient to carry on the Data Analysis task. However, there are definitely some high and low points for both of them and if we could utilize the strengths of both, we could end up doing a much better job. Either way, having knowledge of both will make us more flexible thereby increasing our chances of being able to work in different environments.

References:

Interfacing R and Python — Andrew Collier

http://blog.yhat.com/tutorials/rpy2-combing-the-power-of-r-and-python.html

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Machine Learning, Data Science and Deep Learning with Python

Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks. Introducing Tensorflow, Using Tensorflow, Introducing Keras, Using Keras, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Learning Deep Learning, Machine Learning with Neural Networks, Deep Learning Tutorial with Python

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks

Explore the full course on Udemy (special discount included in the link): http://learnstartup.net/p/BkS5nEmZg

In less than 3 hours, you can understand the theory behind modern artificial intelligence, and apply it with several hands-on examples. This is machine learning on steroids! Find out why everyone’s so excited about it and how it really works – and what modern AI can and cannot really do.

In this course, we will cover:
• Deep Learning Pre-requistes (gradient descent, autodiff, softmax)
• The History of Artificial Neural Networks
• Deep Learning in the Tensorflow Playground
• Deep Learning Details
• Introducing Tensorflow
• Using Tensorflow
• Introducing Keras
• Using Keras to Predict Political Parties
• Convolutional Neural Networks (CNNs)
• Using CNNs for Handwriting Recognition
• Recurrent Neural Networks (RNNs)
• Using a RNN for Sentiment Analysis
• The Ethics of Deep Learning
• Learning More about Deep Learning

At the end, you will have a final challenge to create your own deep learning / machine learning system to predict whether real mammogram results are benign or malignant, using your own artificial neural network you have learned to code from scratch with Python.

Separate the reality of modern AI from the hype – by learning about deep learning, well, deeply. You will need some familiarity with Python and linear algebra to follow along, but if you have that experience, you will find that neural networks are not as complicated as they sound. And how they actually work is quite elegant!

This is hands-on tutorial with real code you can download, study, and run yourself.

How to get started with Python for Deep Learning and Data Science

A step-by-step guide to setting up Python for Deep Learning and Data Science for a complete beginner

You can code your own Data Science or Deep Learning project in just a couple of lines of code these days. This is not an exaggeration; many programmers out there have done the hard work of writing tons of code for us to use, so that all we need to do is plug-and-play rather than write code from scratch.

You may have seen some of this code on Data Science / Deep Learning blog posts. Perhaps you might have thought: “Well, if it’s really that easy, then why don’t I try it out myself?”

If you’re a beginner to Python and you want to embark on this journey, then this post will guide you through your first steps. A common complaint I hear from complete beginners is that it’s pretty difficult to set up Python. How do we get everything started in the first place so that we can plug-and-play Data Science or Deep Learning code?

This post will guide you through in a step-by-step manner how to set up Python for your Data Science and Deep Learning projects. We will:

Set up Anaconda and Jupyter Notebook
Create Anaconda environments and install packages (code that others have written to make our lives tremendously easy) like tensorflow, keras, pandas, scikit-learn and matplotlib.

Once you’ve set up the above, you can build your first neural network to predict house prices in this tutorial here:

Build your first Neural Network to predict house prices with Keras

Setting up Anaconda and Jupyter Notebook

The main programming language we are going to use is called Python, which is the most common programming language used by Deep Learning practitioners.

The first step is to download Anaconda, which you can think of as a platform for you to use Python “out of the box”.

Visit this page: https://www.anaconda.com/distribution/ and scroll down to see this:

This tutorial is written specifically for Windows users, but the instructions for users of other Operating Systems are not all that different. Be sure to click on “Windows” as your Operating System (or whatever OS that you are on) to make sure that you are downloading the correct version.

This tutorial will be using Python 3, so click the green Download button under “Python 3.7 version”. A pop up should appear for you to click “Save” into whatever directory you wish.

Once it has finished downloading, just go through the setup step by step as follows:

Click Next

Click “I Agree”

Click Next

Choose a destination folder and click Next

Click Install with the default options, and wait for a few moments as Anaconda installs

Click Skip as we will not be using Microsoft VSCode in our tutorials

Click Finish, and the installation is done!

Once the installation is done, go to your Start Menu and you should see some newly installed software:

You should see this on your start menu

Click on Anaconda Navigator, which is a one-stop hub to navigate the apps we need. You should see a front page like this:

Anaconda Navigator Home Screen

Click on ‘Launch’ under Jupyter Notebook, which is the second panel on my screen above. Jupyter Notebook allows us to run Python code interactively on the web browser, and it’s where we will be writing most of our code.

A browser window should open up with your directory listing. I’m going to create a folder on my Desktop called “Intuitive Deep Learning Tutorial”. If you navigate to the folder, your browser should look something like this:

Navigating to a folder called Intuitive Deep Learning Tutorial on my Desktop

On the top right, click on New and select “Python 3”:

Click on New and select Python 3

A new browser window should pop up like this.

Browser window pop-up

Congratulations — you’ve created your first Jupyter notebook! Now it’s time to write some code. Jupyter notebooks allow us to write snippets of code and then run those snippets without running the full program. This helps us perhaps look at any intermediate output from our program.

To begin, let’s write code that will display some words when we run it. This function is called print. Copy and paste the code below into the grey box on your Jupyter notebook:

print("Hello World!")

Your notebook should look like this:

Entering in code into our Jupyter Notebook

Now, press Alt-Enter on your keyboard to run that snippet of code:

Press Alt-Enter to run that snippet of code

You can see that Jupyter notebook has displayed the words “Hello World!” on the display panel below the code snippet! The number 1 has also filled in the square brackets, meaning that this is the first code snippet that we’ve run thus far. This will help us to track the order in which we have run our code snippets.

Instead of Alt-Enter, note that you can also click Run when the code snippet is highlighted:

Click Run on the panel

If you wish to create new grey blocks to write more snippets of code, you can do so under Insert.

Jupyter Notebook also allows you to write normal text instead of code. Click on the drop-down menu that currently says “Code” and select “Markdown”:

Now, our grey box that is tagged as markdown will not have square brackets beside it. If you write some text in this grey box now and press Alt-Enter, the text will render it as plain text like this:

If we write text in our grey box tagged as markdown, pressing Alt-Enter will render it as plain text.

There are some other features that you can explore. But now we’ve got Jupyter notebook set up for us to start writing some code!

Setting up Anaconda environment and installing packages

Now we’ve got our coding platform set up. But are we going to write Deep Learning code from scratch? That seems like an extremely difficult thing to do!

The good news is that many others have written code and made it available to us! With the contribution of others’ code, we can play around with Deep Learning models at a very high level without having to worry about implementing all of it from scratch. This makes it extremely easy for us to get started with coding Deep Learning models.

For this tutorial, we will be downloading five packages that Deep Learning practitioners commonly use:

Set up Anaconda and Jupyter Notebook
Create Anaconda environments and install packages (code that others have written to make our lives tremendously easy) like tensorflow, keras, pandas, scikit-learn and matplotlib.

The first thing we will do is to create a Python environment. An environment is like an isolated working copy of Python, so that whatever you do in your environment (such as installing new packages) will not affect other environments. It’s good practice to create an environment for your projects.

Click on Environments on the left panel and you should see a screen like this:

Anaconda environments

Click on the button “Create” at the bottom of the list. A pop-up like this should appear:

A pop-up like this should appear.

Name your environment and select Python 3.7 and then click Create. This might take a few moments.

Once that is done, your screen should look something like this:

Notice that we have created an environment ‘intuitive-deep-learning’. We can see what packages we have installed in this environment and their respective versions.

Now let’s install some packages we need into our environment!

The first two packages we will install are called Tensorflow and Keras, which help us plug-and-play code for Deep Learning.

On Anaconda Navigator, click on the drop down menu where it currently says “Installed” and select “Not Installed”:

A whole list of packages that you have not installed will appear like this:

Search for “tensorflow”, and click the checkbox for both “keras” and “tensorflow”. Then, click “Apply” on the bottom right of your screen:

A pop up should appear like this:

Click Apply and wait for a few moments. Once that’s done, we will have Keras and Tensorflow installed in our environment!

Using the same method, let’s install the packages ‘pandas’, ‘scikit-learn’ and ‘matplotlib’. These are common packages that data scientists use to process the data as well as to visualize nice graphs in Jupyter notebook.

This is what you should see on your Anaconda Navigator for each of the packages.

Pandas:

Installing pandas into your environment

Scikit-learn:

Installing scikit-learn into your environment

Matplotlib:

Installing matplotlib into your environment

Once it’s done, go back to “Home” on the left panel of Anaconda Navigator. You should see a screen like this, where it says “Applications on intuitive-deep-learning” at the top:

Now, we have to install Jupyter notebook in this environment. So click the green button “Install” under the Jupyter notebook logo. It will take a few moments (again). Once it’s done installing, the Jupyter notebook panel should look like this:

Click on Launch, and the Jupyter notebook app should open.

Create a notebook and type in these five snippets of code and click Alt-Enter. This code tells the notebook that we will be using the five packages that you installed with Anaconda Navigator earlier in the tutorial.

import tensorflow as tf

import keras

import pandas

import sklearn

import matplotlib

If there are no errors, then congratulations — you’ve got everything installed correctly:

A sign that everything works!

If you have had any trouble with any of the steps above, please feel free to comment below and I’ll help you out!

*Originally published by Joseph Lee Wei En at *medium.freecodecamp.org

===================================================================

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Best Python Libraries For Data Science & Machine Learning

Best Python Libraries For Data Science & Machine Learning | Data Science Python Libraries

This video will focus on the top Python libraries that you should know to master Data Science and Machine Learning. Here’s a list of topics that are covered in this session:

Introduction To Data Science And Machine Learning
Why Use Python For Data Science And Machine Learning?
Python Libraries for Data Science And Machine Learning
Python libraries for Statistics
Python libraries for Visualization
Python libraries for Machine Learning
Python libraries for Deep Learning
Python libraries for Natural Language Processing

Thanks for reading ❤

If you liked this post, share it with all of your programming buddies!

From ‘R vs Python’ to ‘R and Python’

Why not use Both?

Overview of R and Python

Python

R

Leveraging the best of Both Worlds

R within Python

Resources

Python with R

Resources

Conclusion

Learn More

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks

How to get started with Python for Deep Learning and Data Science

Setting up Anaconda and Jupyter Notebook

Setting up Anaconda environment and installing packages

Learn More

Best Python Libraries For Data Science & Machine Learning

Further reading about Python