Is Python better than R for data analysis and why?

Clearly, R is better since it is professionally for data mining. 

In the battle of "best" data science tools, python and R both have their pros and cons. Both Python and R are popular programming languages for statistics.  While R’s functionality is developed with statisticians in mind (think of R's strong data visualization capabilities!), Python is often praised for its easy-to-understand syntax.

Right now R is one of the fastest growing statistical languages in the corporate world. One of the main strengths of R is its huge community that provides support through mailing lists, user-contributed documentation and a very active Stack Overflow group. There is also CRAN, a huge repository of curated R packages to which users can easily contribute.  These packages are a collection of R functions and data that make it easy to immediately get access to the latest techniques and functionalities without needing to develop everything from scratch yourself.

On the other hand, python is full fledge programming language, means it is not only for analysis rather it is a tool of engineers who want to delve with data analysis. It’s a flexible language that is great to do something novel, and given its focus on readability and simplicity, its learning curve is relatively low. Just like R, Python has a great community but it is a bit more scattered, since it’s a general purpose language. Nevertheless, Python for data science is rapidly claiming a more dominant position in the Python universe.

- R has a long and trusted history and a robust supporting community in the data industry. 

-Python is a general-purpose programming language that can pretty much do anything you need it to: data munging data engineering, data wrangling, website scraping, web app building, and more. 

**In general, you can’t err whether you choose to learn Python first or R first for data analysis. Each language has its pros and cons for different scenarios and tasks. In addition, there are actually libraries to use Python with R, and vice versa—so learning one won’t preclude you from being able to learn and use the other. Perhaps the best solution is to use the above guidelines to decide which of the two languages to begin with, then fortify your skill set by learning the other one.