Let's delve into the most important R and Python libraries for visualizing data and creating different graphs, and what their advantages and disadvantages are.
Being a professional in certain programming languages is the goal of every aspiring data professional. Reaching a certain level in one of the countless languages is a fundamental milestone for everyone.
For data engineers, SQL is probably the most important language. As a web developer, you need to know JavaScript, HTML, CSS, and PHP while you sleep. For data scientists, on the other hand, Python and R are the tools of choice. Both languages have their strengths and weaknesses, and both offer powerful tools and a large community for analyzing and visualizing data.
If you're at the beginning of your data science journey, the choice between R and Python can be overwhelming. But if you want to advance in this field in the long term, sooner or later you will come into contact with both languages. Plus, if you're already in college, you probably have courses in both languages.
But let's dive into the most important R and Python libraries for visualizing data, how creating graphs in R and Python is different (with code examples), and what the pros and cons of the two languages are.
table of Contents
1 – What makes R essential? (And essential libraries for visualizations)
2—…
