Last Updated on by Kumar Raja

Data Collection With Python & R

For most of the years, Data Scientists have used R programming for their Data Science operations. R is an open-source programming language which has specially designed keeping in view of statistics & has thousands of public packages. Many Data Scientists still use R for the data modelling operations in Data Science & is considered as the best alternative to Matlab and SAS.

Python is one of the simplest programming languages which is developed over the agile approach. Having a very short learning curve, Python is considered as the best programming language for Data Science operations. The use of Python isn’t just limited to Data Science; Python implementation can be seen in several other technologies. It is also being used to make graphics easily with large data which was earlier considered to be a challenging task. Get a clear idea of the Data Collection process in & the relevant tools by joining for Kelly Technologies Data Science Training In Hyderabad program.   

Now let’s take a look at the Data Collection with both Python & R.

Data Collection With Python-

As we are clearly aware of the fact that data comes in several types & sizes. With the help of Python handling data of any format is seems to be a lot easier. Using Python, we can also import SQL tables directly into the programs source code.

Having a number of libraries, Python lets you access data from different websites & Wikipedia tables are not exception. Also data of any size can be easily centralized with just a single click by using Python. Having organized the data, it becomes easy to handle the data analysis operations.

Data Collection With R-

R supports importing data from only text files, CSV and Excel. It can also import data from SPSS or Minitab. Compare to Python, R is isn’t much versatile & is not capable of mining data from websites as seen in the case of Python.

To address the data collection issues in R, developers are working round the clock & in the process they have integrated several new packages into R. Packages like Rvest helps in performing basic web scraping task, magrittr helps the data preparation process.

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