Getting all your standard data analysis done in less than 30 seconds. The wonders of Pandas Profiling.
Thank you.
This is a really short one, but before we get started, I just want to voice a giant thank you to everyone who read and shared my last article: Python trick 101, what every new programmer should know. The reception was absolutely bonkers. The amount of claps and views completely dwarfing my other articles. So thanks and lets get on with it!
The vanilla pandas way (the boring way)
Want to show off your portfolio? Maybe you need a page for your online resumé or CV? Maybe you want to demo a web development project?
Look no further!
This is what we’ll be doing:
- Setting up a GitHub Pages website from scratch.
- Adding a template to make it look pretty.
This is mine: https://nistrup.github.io/
Before starting we do have a few prerequisites:
- A GitHub account.
- A terminal.
- Very limited experience with Git and general HTML editing. (I refuse to call it “HTML programming”)
The reason we need a GitHub account is that we’ll be using GitHub Pages so if you don’t have that GitHub profile just yet, now would be the time to create it!
Now get ready for some blatantly copy/pasted step-by-step instruction directly from GitHub.
Step 1, Create a repository
Head over to GitHub and create a new repository named username.github.io, …
Time and estimate the progress of your functions in Python (and pandas!)
In this article I’ll try to break my own record for the shortest, most concise article ever, so without further ado, let’s go!
Introducing tqdm
Take your Data Analysis to the next level!
Thank you (…again)
Turning a blind eye to the completely obvious risk of sounding like a broken record I just want to voice yet another giant thank you to everyone who’s been reading and sharing my last two articles: Python tricks 101, what every new programmer should know and Exploring your data with just 1 line of Python. Here I was, thinking that the “Python tricks 101…” article had been successful and then you go ahead and blow any expectations away once more.
So, to quote myself:
“Thanks and lets get on with it!”
For this article I thought it would be nice to create a list of things I’ve learned that have sped up or improved my average day-to-day data analysis. So without any further ado, here’s a list of what we’ll be covering in the…
Python is more popular than ever and people are proving on a daily basis that Python is a very powerful and easy-to-pick-up language.
I’ve been coding in Python for a few years, the last 6 months professionally, and here’s some of the things I wish I knew when I first started out:
- String manipulation
- List comprehension
- lambda & map()
- if, elif and else condition one-liners
- zip()
# 1: String manipulation
A single function to read, update, save and gather data.
Disclaimer: I know this isn’t what I typically post but since it’s an interest of mine — just like traditional finance, machine learning, and data science — I think some of you might find this interesting as well.
I’ve recently been investigating the relationship between data science and the cryptocurrency market for a pretty long article I’m writing. During the process, I needed to retrieve price history and other data, so I decided to write a supplementary piece about how I accomplished that.
A non-exhaustive list of my favourite sources of Data Science content, enjoy!
In this article I’ve split the sources into three “distinct” categories:
- Podcasts
- Blogs
- General
Please enjoy. I hope you find it useful. Make sure to follow my profile 🔍 if you enjoy this article and want to see more!
Podcasts
A single function to read, update, save, and gather data
Disclaimer: I know this isn’t what I typically post but since it’s an interest of mine — just like traditional finance, machine learning, and data science — I think some of you might find this interesting as well.
I’ve recently been investigating the relationship between data science and the cryptocurrency market for a pretty long article I’m writing. During the process, I needed to retrieve price history and other data, so I decided to write a supplementary piece about how I accomplished that.
What you’ll learn from this article: Setting up a full Python environment in a Linux system using WSL in Windows with fully customizable Jupyter Notebooks!
What we’ll be doing:
- Setting up WSL (with optional ZSH and Oh-my-zsh)
- Setting up Anaconda.
- Setting up a customizable Jupyter Notebook environment.
Make sure to follow my profile if you enjoy this article and want to see more!
Setting up WSL
Why you should always save your data as .npy instead of .csv
Introduction
First of all thanks a lot to my followers for sticking with me these last few months, I’ve been terribly busy and haven’t had a lot of time to pump out articles. I’ve decided that a partial remedy for this is to make some shorter and easier to digest articles which will be easier to produce! Therefore this is my first attempt at making a short-and-to-the-point article.
I hope you find it useful!
Make sure to follow my profile if you enjoy this article and want to see more!
The TL;DR:
Reading a 10 million data-point file from storage:
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