Alfie Mellor

Alfie Mellor

1560835890

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

Learn More

☞ An A-Z of useful Python tricks

☞ A Complete Machine Learning Project Walk-Through in Python

☞ A Feature Selection Tool for Machine Learning in Python

☞ Machine Learning: how to go from Zero to Hero

☞ Learning Python: From Zero to Hero

☞ Introduction to PyTorch and Machine Learning

☞ NumPy Tutorial for Beginners

☞ Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development

☞ Machine Learning A-Z™: Hands-On Python & R In Data Science

☞ Python for Data Science and Machine Learning Bootcamp

☞ Data Science, Deep Learning, & Machine Learning with Python

☞ Deep Learning A-Z™: Hands-On Artificial Neural Networks

#python #r #data-science #machine-learning #deep-learning

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Buddha Community

From ‘R vs Python’ to ‘R and Python’
Ray Patel

Ray Patel

1619518440

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

…

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

August Larson

August Larson

1624422360

R vs Python: What Should Beginners Learn?

Let go of any doubts or confusion, make the right choice and then focus and thrive as a data scientist.

I currently lead a research group with data scientists who use both R and Python. I have been in this field for over 14 years. I have witnessed the growth of both languages over the years and there is now a thriving community behind both.

I did not have a straightforward journey and learned many things the hard way. However, you can avoid making the mistakes I made and lead a more focussed, more rewarding journey and reach your goals quicker than others.

Before I dive in, let’s get something out of the way. R and Python are just tools to do the same thing. Data Science. Neither of the tools is inherently better than the other. Both the tools have been evolving over years (and will likely continue to do so).

Therefore, the short answer on whether you should learn Python or R is: it depends.

The longer answer, if you can spare a few minutes, will help you focus on what really matters and avoid the most common mistakes most enthusiastic beginners aspiring to become expert data scientists make.

#r-programming #python #perspective #r vs python: what should beginners learn? #r vs python #r

Ray Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Art Lind

Art Lind

1602968400

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development