First download this machine learning data file, before we get on with our tutorial to teach you how you can pare data with python. The above mentioned dataset provided in the link mimics exactly the way the data was when we visited the web pages at that point of time, but the interesting thing about this is we need not visit the page even.
We do in fact have the full HTML source code so it will exactly be like parsing the website without the annoying use of the bandwidth. Now the first thing to do when we start is to correspond the date to our data, and then we can pull the actual data.
This is the way we start:
import pandas as pd import os import time from datetime import datetime path ="X:/Backups/intraQuarter"
For a lot of quantitative corporate personnel, the raging debate between the tools of choice for analytics has been known to cause some rival enthusiasm instead of the age-old political debates on Thanksgiving!
The SAS vs. R debate was already hotly underway for the past couple of years, but recently many analytics professionals and aspiring analysts have requested us to include a comparison of Python in our debates. So, we decided to keep things light and simple and only asked a single question – “which analytics tool do your prefer to use: SAS, R Programming or Python?”
Read Also: Elementary Character Functions in SAS
Gradually our survey results have been showing a growing demand for open source tools over the past few years. In fact so much so, that this year almost 61.3% of respondents in a survey conducted by KDnuggets chose R and Python over 38.6 percent of people still opting for SAS. As it is SAS is a great tool for large companies to conduct their data analytics.
Are you keen on learning more about these numbers? So were we, so tallied a few survey results and opinions of analytics professionals to determine which is the better data analytics tool to learn first. And here is what we found…