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Python Vs. R for Data Analytics

python versus R for Data Analytics

Welcome to the age-old debate of Python versus R for data analytics. The two popular programming languages are commonly put up against each other by data analysts and scientists across the globe. In this article, we will delve into what each programming language is – as well as their strengths, weaknesses, and differences, specifically as they relate to data analysis.

What is the R Programming Language?

R is a programming language developed for statistical analysis and is primarily used by statisticians, data miners, and data analysts. R was developed solely for statistical analysis and visualization—therefore, that is its biggest strength. There are hundreds of well-established packages and libraries for these purposes within R. Another advantage of R is its integrated development environment (IDE), RStudio. There are some great Python IDE options to choose from like Spyder, Anaconda, or PyCharm – but it can be debated if they are on par with RStudio.

One of R’s biggest drawbacks, though, is that it requires you to learn a vast amount of packages and libraries, which can greatly increase its learning curve. For example, to manipulate data in R you may need dplyr, ggplot2, readr, and tidyr – among others, whereas in Python, all you would need is its pandas library. Another issue is that R cannot easily be embedded into web applications, while Python can.

Read: Data Analyst: Main Roles and Responsibilities

What is the Python Programming Language?

Python is a general purpose programming language that can do a variety of things such as build websites, automate tasks, and conduct data analysis. Python’s biggest strength is its flexibility to do multiple things. Although this article is focused on data analysis, it’s also a task that is accompanied by many other things, such as web development and machine learning. Having one tool like Python to do those things – and more – is convenient and powerful. In addition, Python has a growing number of libraries for data analysis and is quickly becoming the most popular programming language used today.

On the flipside, Python libraries are still being developed and are not as established as R’s libraries. Python also has notoriously slow processing speeds, depending on the package since it uses a large amount of memory.

Companies of all sizes use both Python and R, including some of the most prestigious in the world, such as Google, Facebook, Netflix, and Uber. In fact, it’s common for larger companies to simultaneously use both programming languages to capitalize on the strengths of each.

Read: Is the Google Data Analytics Professional Certificate Worth It?

Python or R for Data Analysis and Statistical Programming?

So which one is better for data analytics – Python or R? Well, it depends on what you are using each for. For pure statistical work, R is the better choice. It was built specifically by statisticians and, as such, is great at statistical computations. In fact, R is probably the most widely used language when it comes to developing statistical tools and software. R also supports a wide-range of data types, including arrays, matrices, vectors, and all sorts of data objects. Another feature of R is its ability to perform data cleansing and data wrangling tasks, making data easier to consume and more accurate.

However, Python is better for machine learning. In addition, Python is such a powerful and flexible programming language that it makes sense to learn it, as you won’t be limited in regards to the types of applications you can create. Python offers pretty solid data visualization, making it easier for data analysts to understand the information they are analyzing. Libraries like Matplotlib and APIs like Plotly make visualizing data in Python a snap. Another benefit of Python for data analytics is its ability to handle Big Data, thanks in part to is compatibility with Hadoop, via the package PyDoop, which provides an API for Hadoop.

There are other differences, of course, but in reality it will probably come down to what works best for you and your project. There is, of course, nothing that says you cannot simply learn both, as they are each highly-readable and easy to learn, with tons of community resources at hand if you have any trouble getting started and troubleshooting code.

Read more articles on Python development and Python programming tutorials.

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