⚔️ The big question is which one should we learn as for someone who is interested in machine learning or large datasets – Python or R? ⚔️ In this article, we will answer this question considering all the aspects of both the languages. ⚖
For a large number of people, data analysis is one of the most important parts of their jobs. The increased availability of data has made computing more powerful and the need for an analytics-driven decision in businesses has brought data science into the limelight. According to a report by IBM, in 2015, there were 2.35 million openings for data analytics jobs in the US. It is expected and estimated that by 2020, the number will rise to 2.72 million. IBM likes to call it “The Quant Crunch”.
In the current era, programming languages like R and Python have been in much demand especially in this quest for data science. Both were developed in the early 1990s. R was mainly for statistical analysis and Python was rather a general-purpose language. Now the big question is which one should we learn as for someone who is interested in machine learning or large datasets – Python or R? In this article, we will answer this question considering all the aspects of both the languages.
Python and R are both open-source, state-of-the-art programming languages. Both languages are oriented toward data science. Learning both of them would be an ideal solution. But since we are to make a comparison let us segregate both the language modules based on their respective qualities.
Python, which is also called the Swiss army knife of coding, is a general-purpose, high-level programming language which focuses on versatility and cleaner programming.
It is easy-to-use and makes replicability and accessibility easier than R. Python is primarily used in the field of Artificial Intelligence and game development.
It is basically a low-level programming language used by statisticians and data miners for developing statistical software, graphical representations, and for data analysis. R Foundation for Statistical Computing has been supporting it. R has one of the richest ecosystems of around 12000 packages in the open-source repository for performing data analysis.
Python is not named after the snake, but rather after the British TV show Monty Python. Influenced by Modula-3 and successor of the ABC programming language, Python was implemented in the year 1989 by Guido van Rossum.
It was initially released in the year 1991 as Python 0.9.0. Python 2.0 and Python 3.0 were released in the year 2000 and 2008 respectively (the latest version of Python is 3.7.3).
Ross Ihaka and Robert Gentleman were the developers of R, which is an implementation of the S programming language created by John Chambers in 1976. Ihaka and Gentleman developed it while working together in New Zealand.
When R was released in 1990, many joined the project to make improvements. It was declared “open-source” in the year 1995. The first version of R was released to the public in the year 2000.
R is a free programming language and is considered to be the best since most statistical languages are not priceless.
It covers a wide range of packages which are used in various fields starting from statistical computing, genomics, machine learning, finance, medicine and so on.
Let us list some key features of R -
Python is an interpreted high-level language and it is extremely versatile. It’s a name you can hear among people who love working with data.
According to the TIOBE Programming Community Index, Python is the 3rd most popular language of 2019 after Java and C.
Let us list five significant reasons why Python is the language for all.
Below are two images which show the difference in the code for displaying “Hello World” in Python and R.
Code for displaying “Hello World” in Python
Code for displaying “Hello World” in R
**Step 1: **Open any browser and go to https://www.python.org/
Step 2: Click on the Downloads option. You will see the latest version of Python(which is Python 3.7.3 and stable too).
**Step 3: **Click on ” Download Python 3.7.x ” option.
**Step 4: **The file named “Python-3.7.x.exe” should start downloading into your standard download folder.
Step 5: After it is downloaded, go to the specified folder and run it. Proceed with the Installation process. After a few minutes or so, you will have your Python IDLE running in your computer.
**Step 1: **Open any browser and go to https://www.python.org/
Step 2: Click on the Downloads option. You will see the latest version of Python(Python 3.7.3).
**Step 3: **Click on “Download Python 3.7.x” option.
**Step 4: **The file named “Python-3.7.x.pkg” should start downloading into your standard download folder.
**Step 5: **After it is downloaded, go to the specified folder and run it. Proceed with the Installation process. After a few minutes or so, you will have your Python IDLE running in your computer.
Step 1: Open any internet browser and go to www.r-project.org.
Step 2: Click on the ”download R” link in the middle of the page under “Getting Started.”
Step 3: Select a CRAN location and click the corresponding link.
Step 4: Click on the “install R for the first time” link at the top of the page.
Step 5: Click on “Download R for Windows” and save the file on your computer. Run the .exe file and follow the installation instructions thereafter.
Step 1: Open any internet browser and go to www.r-project.org.
Step 2: Click the “download R” link in the centre of the page under “Getting Started”.
Step 3: Select a CRAN location (a mirror site) and click the corresponding link.
Step 4: Click on the “Download R for (Mac) OS X” link at the top of the page.
Step 5: Click on the file which contains the latest version of R under “Files”.
Step 6: Save the .pkg file, double-click it to open, and follow the installation instructions thereafter.
Both R and Python have a common free and open-source distribution— Anaconda. Its main functions include applications of machine learning, large-scale data processing, predictive analysis, and data science.
The Anaconda distribution consists around 1400 popular data science packages including Anaconda Navigator, a desktop Graphical User Interface(GUI) which allows users to launch applications and manage the conda package.
Some of the commonly used IDEs of Python are -
Some of the commonly used IDEs of R are -
If you have gathered some knowledge about programming, Python is the language for you. The syntax of Python is much analogous to other languages in comparison to R’s syntax.
R has a non-standardized kind of code which might be a difficulty for people who are new to programming. On the other hand, Python is much readable and focuses on development fruitfulness.
R is a statistical programming language which is mainly used in the academic sector. But the real question is which one is industry-ready?
If we consider this, Python would be a better option. Organizations use Python extensively to develop their production systems.
But since some time now, R has updated their libraries to open-source, industries are also considering it for their work and is being largely used.
This is the most common question which is lurking around everyone for some time. But before settling to the conclusion, let me provide you with two examples.
Consider a situation where we need to cover election data. This is a relatively repetitive and predictable process where we need to collect data and make recurrent analysis and make pies and charts based on that. In this case, Python will provide ease of work.
Now, if we take text analysis, for example, where we need to break paragraphs into phrases and words and analyze patterns, it is better to make use of R.
Conclusively, we can say Python is used for repeated jobs and data manipulation whereas R for heavy statistical projects and situations where we need to dive into one-time datasets.
Machine learning comes in the category of Artificial Intelligence while Statistical learning is a subfield of Statistics. Machine learning focuses on the development of real-world applications and predictive models; while Statistical learning mainly emphasizes on preciseness and uncertainty.
Since R was developed by statisticians, people who have a background in statistics, R would be easier to work with.
Python, on the other hand, is a better choice for those in the data department where they need to perform analysis and also for those in the machine learning sector, especially because of its flexibility.
For software engineering, Python is the one. For an engineering environment, Python is better than R in the larger spectrum. However, you might need to make use of a low-level module like C++ or Java for really efficient coding.
R is always a better option for continuous prototyping and handling datasets. Data visualizations can be performed with R with library packages like ggplot2, HTML widgets, Leaflet. Though Python has made some advances with Matplotlib but still lags behind R in this area.
Python has a special library called the Python requests library which simplifies HTTP requests into a line of code by allowing data from websites. It also contains libraries for organizing data and making an in-depth analysis.
R is not very efficient in collecting information from websites as compared to Python. However, packages like Rvest and magrittr can be used for web scraping, cleaning and breaking down information. You can also insert data from CSV, Excel and from text files into R.
Pandas is the data analysis library of Python. It can work easily with large amounts of data. It allows the user to filter, arrange and display the data in minimal time.
While working with projects, Pandas allows the construction and reconstruction of frameworks. Invalid values like Nan(not a number) can be replaced with a value(such as 0) which will allow ease in numerical analysis. You can scan and clean the illogical data.
Since R was made by statisticians to perform statistical and numerical analysis, data exploration is a privilege to those using R. You can make probability distributions, perform statistical tests and make standard machine learning models.
Optimization techniques, statistical processing, random number generation, signal processing, and machine learning are some basic functionalities of R.
Ask a question and Python is there to help you out. Numerical modelling analysis? There’s Numpy.
Scientific computation and calculation? SciPyi is there.
And for Machine learning algorithms? It is a scikit-learn. By using scikit-learn you can use all the machine learning library packages contained in Python without worrying about the inside complexities.
If you want to perform some particular modeling analysis, you have to go outside of R’s basic library functions.
Poisson’s distribution and mixtures of probability laws are some of the outside library packages used for some specific data modeling analysis.
For data visualization, we can use Python’s distribution—Anaconda.
Matplotlib is used to create graphs and charts using the data stored in Python and for advanced ones and better design, Plot.ly is used.
You might have seen online tutorials on how to learn Python. People use the nbconvert function to create it. With this function, you can convert your snippets of code to HTML documents.
R contains packages for scientific visualization techniques which allows the results to be displayed graphically.
You can create elementary graphs and plots from data matrices and save them in .jpg or PDF formats. This can be done from the basic R libraries.
However, for advance plots or graphs, you can use the ggplot2 function.
Topographic hill shading using Matplotlib
Plot.ly correlation points of the Iris dataset
Both R and Python have become stars in the field of Data Science and Machine Learning.
R had its popularity in the year 2015 – 2016. But in recent years, Python has become more popular.
Python’s popularity has been because of its multi-programming paradigms, easy readability, availability of vast library, and community support. While other programming languages like C, C++ or Java takes around 5 to 7 lines code to print “hello world”, Python saves your time and effort because a single line of code is more than enough to execute it.
Some of the sectors where both R and Python have gained popularity in recent years are –
In the above chart, we can see that gradually other sectors are also adapting R and Python as a preference. Organizations like financial firms, retail organizations, banks and healthcare institutions have started offering job roles in R.
Python is considered to be the fastest growing programming language in the world. According to Stack Overflow developer survey, in 2013, Python overtook R as the most popular language for data science.
According to Forbes, a data scientist is the “sexiest job of the 21st century”. Python is real-life implemented. Basic data science operations are easier in Python as compared to R. In addition to its versatility and easier to code features, developers tend to use it more.
In the year 2016, R was used by 55% data scientists while Python stood at 51%. In the following 2 years, Python increased by 33% and R got reduced by 25%.
So the question is will the slope of R continue going downwards? I guess it will, but not in practice.
R is the statistician’s language. People having mathematics and statistics as their background will never neglect R while creating a data science model. R would be easy and simple to them rather than Python.
So how will we choose?
Since the popularity of R is down-swinging, using R as complementary to Python will be a good combination. This way R would always have a role to play in a data scientist’s toolbox.
Below is a Python’s Jupyter Notebook’s percentage of Monthly Active Users (MAU) on Github survey by Ben Frederickson which shows a sharp increase after 2015.
“Ranking programming languages by Github users” – Ben Frederickson
According to IEEE, which tracks the programming languages by its popularity, Python is currently considered to be the most popular language for Data Scientists worldwide.
Some of the regions in which Python is widely used are mentioned below:
Some of the organizations which use Python language—
Some of the Python job profiles with their basic salary package—
According to Payscale.com, below is a graph depicting average Python salary for India and US.
You can also take up the Python training to learn the basics of the world’s fastest growing and most popular programming language used by data scientists, software engineers, machine learning engineers. This training will be a great introduction to both fundamental programming concepts and the programming language and will also enhance your skill sets.
The graph below highlights the jobs of R programmers from the year 2009 – 2017.
Some of the organizations which use R as a tool for analytics—
R job roles with their basic salary package—
**1) All-in-one language - **Python is an interpreted, interactive, modular, dynamic, portable, object-oriented, high-level programming language which is accessible and easy to learn and has a gentle learning curve.
**2) A handful of Support Libraries - **Python boasts a high number of standard libraries for string operations, operating system interfaces, data manipulation, data collection, machine learning, Internet and so on.
Scikit-learn and Pandas are two tools for data analysis and high-performance structures respectively. If you want to include R-like functions, you have the RPy2 package.
3) Integration - Python has better integration features than R. It can develop Web Services by integrating with Enterprise Application Integration.
Though developers prefer low-level languages like C, C++ or Java, if Python gets integrated with them, the control capabilities of Python gets boosted.
4) Productivity - Python is extremely productive to the programmer and also in the development area. Due to its integration feature, framework and increased control abilities, it speeds up the development process.
**1) Difficulty in going to other languages - **If you work with Python for a span of time, I would warn you not to fall in blind love. Declaring values and variables would stand as insecurity thereafter.
**2) Weak computation in mobile - **Though Python has made its name in most of desktop and server platforms, mobile computation is still a dream.
3) Speed reduction - Since Python executes using an interpreter rather than a compiler, the time needed for execution and compilation is a bit higher than expected.
**4) Run-time errors - **Testing time, run-time errors and design restrictions are some common problems since Python was initially dynamically typed.
**1) Data and visualization - **R would be your choice if data analytics and data visualization are priorities for your project.
**2) Wealthy with libraries and tools - **R has a rich ecosystem of statistical libraries which makes it a better tool for statistical computations.
Caret is a machine learning library which is capable of creating effective prediction models.
R contains advanced data analysis packages which can control the pre-modeling, modeling and post-modeling phases and can also perform particular tasks like data visualization and model validation.
3) Good Explorations - If you are work is about statistical models and you are just in phase 1 of your exploratory project, consider R to be that friend of yours who explains concepts in simple and brief just before the exam.
**1) Steep learning curve - **R is definitely a challenging programming language and few developers work with it for building projects.
**2) Inconsistency - **The pace of development of R is decreased due to the inconsistency of the language because most algorithms in R are provided by third parties.
Every time you have a new algorithm in hand, it needs to learn new ways to model it.
Here’s a brief summary of all the important aspects of comparison between the two most important languages for Data Science and Machine Learning - Python and R.
After understanding the whole scenario, we can draw a conclusion that the entire decision whether R is better than Python, is up to us. It is the users’ requirement which makes a programming language like R and Python popular than the other. It is our choice, based on the features, to select the programming language to work on Data Science or Machine learning or Predictive models or data manipulation and so on. On the other hand, it might be possible for a third language as a conjunction of both R and Python. Till then let us merge our creativity and the machine and develop models that could nearly be a betterment for the human race.
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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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