In this article, you’ll see the difference between of R and Python for data scientists and data analysts.
R vs Python: The challenge under ten categories
With the massive growth in the importance of Big Data, machine learning, and data science in the software industry or software service companies, two languages have emerged as the most favourable ones for the developers. R and Python have become the two most popular and favourite languages for the data scientists and data analysts. Both of these are similar, yet, different in their ways which makes it difficult for the developers to pick one out of the two.
R is considered to be the best programming language for any statistician as it possesses an extensive catalogue of statistical and graphical methods. On the other hand, Python does pretty much the same work as R, but data scientists or data analysts prefer it because of its simplicity and high performance. Now both the programming languages are free and open source and were developed in the early 90s.
R is a powerful scripting language, and highly flexible with a vibrant community and resource back whereas Python is a widely used object-oriented language which is easy to learn and debug.
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If they look at the ease of learning, R has a steep learning curve, and people with less or no experience in programming finds it difficult in the beginning. However, once you get a grip of the language, it is not that hard to understand. Python, on the other hand, emphasises productivity and code readability, which makes it one of the simplest programming languages. It is a preferable language for the beginners as well as the experienced developers due to its ease of learning and understandability.
start_time <- Sys.time() df <- read.csv("~/desktop/medium/library-collection-inventory.csv") end_time <- Sys.time() end_time - start_time #Time difference of 3.317888 mins
import time import pandas as pd start = time.time() y1 = pd.read_csv('~/desktop/medium/library-collection-inventory.csv') end = time.time() print("Time difference of " + str(end - start) + " seconds") #Time difference of 92.6236419678 seconds
If we compare the speed, R took almost twice as long to load the 4.5 gigabyte .csv file than Python pandas. On the other hand, Python is high-level programming language, and it has been the choice for building critical yet fast applications.
In the case of data handling capabilities, R is convenient for analysis due to the vast number of packages, readily practical tests, and the advantage of using formulas. However, it can also be used for fundamental data analysis without the installation of any package. Moreover, only the big data sets required packages like plyr, data.table. Now in the initial stages, the Python packages for data analysis were an issue. However, this has improved with the recent versions numpy and pandas are used for data analysis in Python, and both these languages are suitable for parallel computation.
Now, if we consider graphics and visualisation, a picture is what a thousand words. Visualised data is understood efficiently and more effectively than raw values. R consists of numerous packages that provide advanced graphical capabilities like the ggplot2 is used for customised graphs. Now visualisations are essential while choosing data analysis software and Python has some amazing visualisation libraries such as seaborn and bokeh. It has many libraries when compared to R, but they are more complex and also gives a tidy output.
With a rise in popularity in deep learning two new packages have been added to the R community, KerasR and RStudio’s Keras. Now both the packages provide an R interface to the Python deep learning package. It’s a high-level neural networks API which is written in Python and capable of running on top of either Tensorflow or Microsoft cognitive toolkit. Now getting started with Keras is one of the easiest ways to get familiar with deep learning in Python, and that also explains why the KerasR and Keras packages provide an interface for this fantastic package for the R users.
Now, if we compare the flexibility of both the languages, it is easy to use complicated formulas in R and also the statistical tests, and models are readily available and easily used. On the other hand, Python is a flexible language when it comes to working on something new or building from scratch. It is also used for scripting a website or other applications.
“[Comparison of top data science
libraries for Python, R and Scala
[Infographic]](https://medium.com/activewizards-machine-learning-company/comparison-of-top-data-science-libraries-for-python-r-and-scala-infographic-574069949267)”” “https://medium.com/activewizards-machine-learning-company/comparison-of-top-data-science-libraries-for-python-r-and-scala-infographic-574069949267)””) — Igor
Now if we look at the code repository and libraries, Comprehensive R Archive Network (CRAN) is a vast repository of the R packages to which users can easily contribute. The packages consist of R functions, data, and compiled code which can be installed using just one line. It also has a long list of popular packages such as the plyr, dplyr, data.table and many more. On the other hand, Python consists of pip package index which is a repository of Python software and libraries. Although users can contribute to pip, it is a complicated process. The dependencies and installation of Python libraries can be tiring tasks at times. Some popular libraries of Python are pandas, numpy and matplotlib.
Now if we look at the popularity of both the languages, they started from the same level a decade ago. However, Python witnessed a massive growth in popularity and was ranked first in 2016 as compared to R that ranked sixth in the list. Also, the Python users are more loyal to their language when compared to the users of the other. As the percentage of people switching from R to Python is twice as large as Python to R.
Python (Yellow) — R (Blue)
Now, when we consider the job scenario, the software companies have been more inclined towards technologies such as machine learning, artificial intelligence and Big Data which explains the growth in the demand for Python developers. Although both languages can be used for statistics and analysis. Python has a slight edge over the other due to its simplicity and ranks higher on job trends.
In the case of community and customer support, usually, commercial software’s offered paid customer service. However, R and Python do not have customer service support which means you are on your own if you face any trouble. However, both the languages have online communities for help, And Python has greater community support when compared to R.
So now, we are done with all the parameters of comparison. We can say that it was a tough fight between the two. However, Python emerges to be the winner due to its immense popularity and simplicity when compared to R.
So what do you think? Let me know about your opinion in the comment section below, till then thank you and happy learning.
#python #r #data-science
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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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.
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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:
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Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines “just go with the version your favorite tutorial was written in, and check out the differences later on.”
But what if you are starting a new project and have the choice to pick? I would say there is currently no “right” or “wrong” as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use.
However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project.The its good to join best python training program which help to improve your skills.
What is Python 2?
Python 2 made code development process easier than earlier versions. It implemented technical details of Python Enhancement Proposal (PEP). Python 2.7 (last version in 2.x ) is no longer under development and in 2020 will be discontinued.
What is Python 3?
On December 2008, Python released version 3.0. This version was mainly released to fix problems that exist in Python 2. The nature of these changes is such that Python 3 was incompatible with Python 2.
It is backward incompatible Some features of Python 3 have been backported to Python 2.x versions to make the migration process easy in Python 3.
Python 3 syntax is simpler and easily understandable whereas Python 2 syntax is comparatively difficult to understand.
Python 3 default storing of strings is Unicode whereas Python 2 stores need to define Unicode string value with “u.”
Python 3 value of variables never changes whereas in Python 2 value of the global variable will be changed while using it inside for-loop.
Python 3 exceptions should be enclosed in parenthesis while Python 2 exceptions should be enclosed in notations.
Python 3 rules of ordering comparisons are simplified whereas Python 2 rules of ordering comparison are complex.
Python 3 offers Range() function to perform iterations whereas, In Python 2, the xrange() is used for iterations.
Which Python Version to Use?
When it comes to Python version 2 vs. 3 today, Python 3 is the outright winner. That’s because Python 2 won’t be available after 2020. Mass Python 3 adoption is the clear direction of the future.
After considering declining support for Python 2 programming language and added benefits from upgrades to Python 3, it is always advisable for a new developer to select Python version 3. However, if a job demands Python 2 capabilities, that would be an only compelling reason to use this version.
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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.
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')
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