As of the writing of this tutorial, before match week 30+, I’m ranked #3,919 in the world in Fantasy Premier League Soccer (team: Yin Aubameyang), which equates to the top 0.05% in the world.
It didn’t happen by accident, and it wasn’t all luck.
It’s taken several years of playing this game to learn the patience, skill, and strategy required to succeed. From #1,181,262 in 2011/12 to #39,804 in 2018/19, and now #3,919 in 2019/20 with 8 GWs left:
With digital art NFT phenomenons like Cryptopunks selling for millions of dollars, and the NFT boom in full swing, digital artwork is capturing the attention of millions.
Today, we will learn how to take one small step towards participating in this digital gold-rush: digitizing hand-drawn artwork.
In this short tutorial, I’ll teach you how to import a hand-drawn image into Figma, and create a duplicate, vector image of this hand-drawn image — a trace — using the Image Tracer Plug-in.
Let’s jump in.
I use .loc on a daily basis. It’s like using the filter function on a spreadsheet.
It’s an effortless way to filter down a Pandas Dataframe into a smaller chunk of data.
It typically works like this:
new_df = df.loc[df.column == 'value']
Sometimes, you’ll want to filter by a couple of conditions. Let’s pretend you want to filter down where this is true and that is true. You might write:
new_df = df.loc[(df.this == True) & (df.that == True)]
Now let’s pretend that list of filter attributes grows a little bit. …
So I was minding my own business one day when out of the blue — wham! I decided I needed to learn how to scrape YouTube.
I was curious about the popularity of different guests on the popular JRE podcast. Using Joe Rogan’s YouTube views as a proxy for podcast episode popularity, I figured I could scrape youtube views from each video page and then answer some of these interesting little questions:
So, with this in mind, I whipped out my trusty web-scraping package, BeautifulSoup, and started…
Howdy folks. Seems my first FPL API tutorial was a hit, so I’m back with another Python/Pandas Fantasy Premier League API tutorial for you all.
This time, we’ll be creating two different DataFrames: (1) a DataFrame that contains the current season’s gameweek histories for each player and (2) a DataFrame that contains all past season histories for each player.
This will be an excellent introduction into using the Requests package to hit an API endpoint and build a DataFrame, and an equally excellent introduction into using a Python “For Loop” to iterate over a sequence of player id’s and hit…
Hi, my name is David. And I’m really bad at fantasy soccer.
I used to be pretty good, actually. In 2018/19, I finished #39,804 overall. Then, in 2019/20, I finished #1,144 overall. Pretty good stuff by many standards!
Now, I’m really, really bad. #5,132,692 bad.
If you want to be this bad, you can be. It’s not easy. But lucky for you, I’ve recently collected the experience, and I’ll be happy to share it with you!
In this short tutorial, I’ll share with you THE FIVE STEPS TO BECOMING REALLY, REALLY BAD AT FANTASY SOCCER.
Let’s dive in!
It’s 9am and Brandon’s is texting me again about his latest grab. He’s just made $80, flipping a free item he discovered on Craigslist for cold, hard cash. It’s only 9am, but he’s already made $80, and for only 2 hours of his time.
The day before, he was texting me about a set of faux leather chairs and an ottoman he found on Facebook Marketplace. He sold the whole set an hour after he picked it up for $100.
Then there was the pouf ottoman he found on a curb while picking up a used Cabela’s cooler. He flipped…
Working with Python in a Jupyter notebook is — atleast to me — the fastest and most rewarding way to get started with programming. The combination of Python, Pandas, and Jupyter will open up a new world of data analysis, visualization, and exploration into the great wide world of data and programming.
In my opinion — as a person who still sees my own history as a beginner in the rear-view mirror — it’s the PERFECT onboarding ramp for someone new to programming.
It’s even more perfect if you are coming into programming in Pandas/Python/Jupyter by way of Excel. …
Documentation for how to create a common visualization using Python, Jupyter Notebook, and Pandas.
This documentation assumes some familiarity with these tools, but not too much. You really just need to be able to get a notebook open and use a keyboard to succeed here.
Let’s dive in!
Translating JSON structured data from and API into a Pandas Dataframe is one of the first skills you’ll need to expand your fledging Jupyter/Pandas skillsets. It’s an exciting skill to learn because it opens up a world of new data to explore and analyze. How fun. What are you waiting for? Just do it.