Every sector has a grand debate going on, like, who is a better captain, Virat Kohli or Sourav Ganguly? Or Who is a better chef, Gordon Ramsay or Jamie Oliver? In the field of data science, a similar debate is about Python and R. Both of them are popular languages used for a variety of tasks in this sector. They each have their pros and cons as well.
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They are similar in some respects (they both are open-source and free), but they have some stark differences too. In this article, we’ll be discussing the main differences between Python and R, and figure out which one is the best among the two.
What is Python?
Python is one of the most popular programming languages. It was released in 1989, and since then, it has become a household name in the coding sector. Although it’s been available since the 90s, Python entered the field of data science only a few years back. But in a small span, it has evolved into a powerful language with lot of advantages for data science.
It has multiple specialized libraries for machine learning and deep learning, which enable data scientists to deploy powerful data models quickly.
Its popular libraries are Scipy, Pandas, Seaborn, and Numpy. You can use Python for deploying machine learning at a larger scale. Data scientists use Python for web scraping, data wrangling, and plenty of other tasks.
What is R?
For doing statistical analysis, many people would choose R. It was developed around 20 years ago. R has libraries for almost all kinds of analysis a person can perform.
Many data scientists preferred R over others (and many still do). R supports compelling data visualization, so generating reports is much better.
R lets you create fantastic web applications through its frameworks. This programming language makes building data models relatively more comfortable as it breaks down complex procedures in multiple steps.
Even with all these advantages, R has some drawbacks in the form of slow performance and lack of web frameworks.
Differences in Data Collection
Python lets you take data directly from the web. You can use the request library for this purpose. Through requests and beautiful soup, you can use data even from the tables present on Wikipedia.
Python also lets you source data from JSON or CSVs.
R, on the other hand, lets you import data from Excel and CSVs. It is not as effective in web scraping as Python, but through Rvest and magrittr, it resolves that issue to some extent. They are similar to requests and beautiful soap.
You can convert files in SPSS or Minitab into R data frames too.
Differences in Data Exploration
Python lets you uncover data by using Pandas, a data analysis library. It organizes data into data frames. You can clean data frames easily (such as removing the NaN value with 0).
Pandas lets you hold a vast amount of data and offers you multiple features to display the data efficiently.
R is more potent in data exploration because it was made for this purpose. You can use R to apply statistical tests, build probability distributions, and use data mining techniques.
R is great for optimization, signal processing, analytics, and random number generation.
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Differences in Data Visualization
For data visualization through Python, you’ll have to use the IPython Notebook or the Matplotlib library. This library can create graphs for the data you have.
If you’re interested in developing advanced graphs, you can use Plot.ly. R is much better than Python in terms of data visualization. It has many packages that let you develop compelling visuals for your data.
It has a graphics module that enables you to create basic plots for all the data matrices. You can use ggplot2 for making more advanced plots in R as well.
Python is quite more popular than R in the data science sector. In 2017, Python was the most popular programming language, while R was in 6th place at that time.
So we can say that Python is more popular than R. However, the popularity of R has risen substantially over these years.
Well, in terms of demand, both R and Python show a positive trend. However, the number of data science jobs requiring Python is nearly 1.5x more than the number of jobs requiring R.
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Python has been present in the market before R, and it has many other uses apart from data science. The demand for R in data analytics is higher than Python, and it is the most in-demand skill for that role.
The percentage of data analysts using R in 2014 was 58%, while it was 42% for the users of Python. In terms of offering job opportunities, the best data science language would be SQL.
While R is more prevalent in academics, Python is popular in production. Because Python is already a full-fledged programming language, many companies prefer it over R.
However, R was developed by scholars for academic purposes. So, if you want to enter the academics field, you will need to learn R. R has been the favorite in academia for a long time, and it has just recently entered the corporate industry.
R vs. Python: What’s Better for Beginners?
Both R and Python are popular in the field of data science. And they are gaining popularity with each passing day. They are different in terms of ease of learning, as well. While R has a steep learning curve, in the beginning, Python is simple, and one can learn it much faster. Learning Python is linear, but if you complete the basics, learning R no longer remains a problem.
- If you don’t know anything about programming, you should start with Python
- If you are experienced in programming, you should start with R
Learning both of these languages would be fun. Programmers choose Python for multiple reasons but R will help you in data analysis and modeling.
Both Python and R have their quirks. While R is better for visualization, Python is better for scraping. It all depends on your skill level and purpose.
For machine learning, you’ll have to study Python, but for statistical learning, R would be a better choice.