Will take Python R over
You may have remembered a few times when you said thank you for the technology used but couldn't fully understand why these things were happening. Almost all of us these days spend most of our time on ecommerce websites or browsing Google.
It has often happened that someone made a mistake while searching in Google and the message “Did you mean that seriously ...? This is none other than Google's machine learning algorithms, a system that detects what you were looking for a particular search a few years ago.
Let's take another scenario to make it clearer: Amazon is a globally known e-commerce platform. People are looking for products that they need. For example, suppose Mr Paul is looking for a Motorola cellular device, he does a search and finds the cell phone (made by Motorola), but the website also suggests some relevant product details along with the cell phone, such as screen protectors, headphones that work best with this cell phone specific cell phone are compatible. Again, this is the machine learning algorithm used by Amazon. The intent is to clarify these companies working on this technology by reducing the complexity to make it easier to use with customer satisfaction.
Why should companies care about the analysis of their data, which areas of application are there and which tools are suitable for this? Find out
What is python
Python is a multi-paradigm language created by Guido van Rossum in 1991. It can be used in web development, software development, system scripting, and so on. It works on different platforms. Python was designed for readability, so it bears some resemblance to the English language. Python focuses on simple, less cluttered syntax and grammar. In Python, spaces mark the indentations to delimit the block. It uses dynamic typing and late binding which bind the methods and variables at runtime. With a large number of libraries, Python can be used for many purposes. It was included in the top ten most popular programming languages.
What is r?
R is a statistical language. It is used for statistical software development and data analysis. Since data mining and the study of data have become popular, R has also gained its popularity. In addition to statistical techniques, R also offers a variety of libraries for graphic techniques. It can create static charts that are used for publication quality charts. Dynamic and interactive charts are also available. R has a package archive network (CRAN- Comprehensive R Archive Network) for all packages it supports. It contains more than 10,000 packages. R is a command line language, but there are several interfaces that provide an interactive GUI to make the developer's job easier.
Differences between R and Python
Although R vs. Python is popular for similar purposes, i.e. data analysis and machine learning, the two languages have different properties. Each language has different advantages and disadvantages. R programming and Python are popular alternatives out there.
Main differences between R programming & Python
R was developed by Ross Ihaka and Robert Gentleman in 1995, while Python was developed by Guido Van Rossum in 1991. R focuses on a coding language that was developed exclusively for statistics and data analysis, while Python and its packages are flexibly tailored to the data.
R is great for complex visualizations with easy customization, while Python is not that good for print-ready visualizations. R is difficult to integrate into the production process. Most often this is a statistical analysis and graphics tool, while Python can be easily integrated into a production workflow and become a real part of the product.
R has a stable version (current) of 3.5.0 from April 23, 2018, whereas Python 3.6.5 (current) is available from March 28, 2018. R has the filename extensions .r, .R, .R Data, .rds, and .rda, while Python has the filename extensions .py, .pyc, .pyd, .pyo, .pwy, .pyz.
Speed and Performance: Although both languages are used for big data analysis. But in terms of performance, Python is a better option for building critical, yet fast, applications. R is a bit slower than Python, but still fast enough to handle large data operations.
Graphics and visualization
Data can be easily understood if it can be visualized. R offers various packages for the graphical interpretation of data. Ggplot2 offers customized graphics. Python also has libraries for visualization, but it's a little more complex than R. R has a nicely printed library that helps create publication quality graphs.
Both R vs. Python languages have gotten their popularity with the rising popularity of data science and machine learning. While Python offers a lot of finely tuned libraries, R has received an interface to Python's deep learning package with KerasR. Thus, both languages now have a very good collection of deep learning packages. But Python stands out in the case of deep learning and AI.
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Because R was designed for data statistics, it provides better support and libraries for statistics. Python is best used for application development and deployment. But R and its libraries implement a variety of statistical and graphical techniques for data analysis.
80% of the data worldwide is unstructured. Data generated from social media is mostly unstructured. Python offers packages such as NLTK, scikit-image, PyPI for analyzing unstructured data. R also offers libraries for analyzing unstructured data, but the support is not as good as with Python. However, both languages can be used to analyze unstructured data.
Both R vs. Python have good community support. Both languages have a user mailing list, StackOverflow groups, user contributed documents, and code. So here is a connection between the two languages. But neither language has customer service support. This means that only online communities and developer documents are available to users.
Here is an overview of the differences between R and Python in a table:
|R codes require more maintenance||Python codes are more robust and easier to maintain|
|R is more of a static language and is also used for graphic techniques||Python is used as a general purpose language for development and deployment|
|R is better used for data visualization||Python is better suited for deep learning|
|R has hundreds of packages or ways to accomplish the same task. It has several packages for one task||Python was designed on the philosophy that "there should only be one obvious way." Hence, there are few main packages to accomplish the task|
|R is easy to start. It has simpler libraries and plots||Learning Python libraries can be a little complex|
|R only supports procedural programming for some functions and object-oriented programming for other functions||Python is a multi-paradigm language. this means that Python supports multiple paradigms such as object-oriented, structured, functional and aspect-oriented programming|
|R is a command line interpreted language||Python strives for a simple syntax. It bears a resemblance to the English language|
|R was developed for data analysis, so it has more powerful statics packages||Python's statistics packages are less powerful|
|R is slower than Python, but not by much||Python is faster|
|R makes it easy to use complicated math calculations and static tests||Python is good for building something from scratch. it is also used for application development|
|R is less popular, but still has a lot of users||Python is more popular than R.|
Both R and. Python have their pros and cons, it's an uphill battle between the two. Python seems a little more popular with data scientists, but R isn't a complete failure either. R was designed for statistical analysis and is very good at it. Python, on the other hand, is a general-purpose language for application development. Both languages offer a wide variety of libraries and packages, and in some cases cross-library support is also available. Therefore, which of the two languages is chosen depends entirely on the requirements of the user. They can also be used together in a mixed scenario! If you are still unsure which programming language suits you best, download our infographic or contact us directly.
My name is Swen Deobald and I am an enthusiastic SAP Analytics consultant. As Compamind's head of department, my team and I support you in all matters relating to SAP Analytics, Business Warehouse, BusinessObjects and the SAP Analytics Cloud.
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