When doing Statistical Analysis, curiosity and intuition are two of a Data Scientist’s most powerful tools. The third one may be Pandas.
Today we’ll leverage Python’s Pandas framework for Data Analysis, and Seaborn for Data Visualization. Sometimes when facing a Data problem, we must first dive into the Dataset and learn about it. Its properties, its variables’ distributions — we need to immerse in the domain.
To some, Vim is a beautiful relic from the past. To others, it’s that annoying thing you have to escape whenever you need to write a message for a merge commit. Let me introduce you to this picturesque text editor and its wonders, and show you why we’re still using it 26 years after its first release.
In this shell tutorial, we’ll deal with tasks that require interaction with files or strings.
As developers, there are lots of repetitive things we do every day that take away our precious time. Finding ways to automate and optimize those processes is usually very lucrative.
As developers, the terminal can be our second home.
Web Scraping with ScraPy comes into the scene whenever you need to generate your own dataset. Sometimes Kaggle is not enough.
Sometimes you open a big Dataset with Python’s Pandas, try to get a few metrics, and the whole thing just freezes horribly. Dask Dataframes may solve your problem.