I had a colleague approach me the other day asking:
“Can you teach me some Python?”
Naturally, I said:
“Sure, no problem”, and I sent him an hour meeting request.
The meeting came, and I found myself uneasy. Where does one start explaining Python in 1 hour? Data types? For loops? Anaconda? Capital letter conventions? Pandas? Numpy? Jupyter notebooks? Machine Learning?
The guy was interested in data ingestion, manipulation and visualisation, but showing him that you can read in a csv with pandas and you can create visualisations with seaborn easily didn’t give him or me a warm fuzzy feeling.
It felt as if I was skipping over the basics, how do you know that you have to use matplotlib to save your seaborn figures? How do you explain the difference between a Jupyter notebook and a script? Where does anaconda (a bigger snake) fit into the whole picture? Going on a tangent and explaining each of these topics seemed to cause more confusion.
So here we are, with a basic layout of tools and tips used in Python in general and focusing a bit on data related tasks. If you are already a seasoned Pythoner, you might not gain that much from this blog post, as this is more targetted at the basics. However, if you’ve also pondered about some of the topics mentioned above — keep reading.
The Language
First released in 1991, Python is an object orientated, interpreted, high-level, general-purpose programming language or as Jake VanderPlas, author of The Python Data Science Handbook once tweeted:
Python is the second-best language for everything.
As a side note, all those underlined concepts are hyperlinks, if you don’t know any of them, or you are unsure, take the time to click them and read up a bit. However, I digress.
Being the second best language for everything makes Python an excellent all-purpose swiss knife to have in your back pocket that can ease 90% of the problems and tasks that you’ve got in your life. From…
