Data Vis

There are plenty of good libraries for charting data in Python, perhaps too many. Plotly is great, but a limit of 25 free charts is hardly a starting point. Sure, there's Matplotlib, but surely we find something a little less... well, lame. Where are all the simple-yet-powerful chart libraries at?

As you’ve probably guessed, this is where Seaborn comes in. Seaborn isn’t a third-party library, so you can get started without creating user accounts or worrying about API limits, etc. Seaborn is also built on top of Matplotlib, making it the logical next step up for anybody wanting

If you've been here before, you probably already know our affinity for Mapbox and the visualization tools it provides data scientists and analysts. In the past, we've covered encoding location data from raw addresses, as well as an exploration of Mapbox Studio for those getting acquainted with the tool. Today we're going a step further: drawing directions on a map.

It sounds simple enough: we already know how to geocode addresses, so all we need to do is literally go from point A to point B. That said, things always tend to get tricky, and if you've never worked with

Ahh, Plotly. Typing that name into a post headline triggers an emotional cocktail of both pride and embarrassment. Plotly has been at the core of some of the most influential products I’ve personally worked on over the years: a jumble of Fintech and humanitarian clients, all of which are still proudly waving their charts and dashboards around the world. Yet, my mind is boggled by a simple question: what the hell took us so long to write our first post about Plotly? We've been operating Hackers and Slackers for over a full year now... did I seriously write a

This wouldn't be a proper data blog unless we spend a vast majority of our time talking about cleaning data. Chances are if you're pursuing analysis that's groundbreaking (or worthwhile), we're probably starting with some ugly, untapped information. It turns out Mapbox has an API specifically for this purpose: the Mapbox Geocoding API.

Geocoding is a blanket term for turning vague information into specific Lat/Long coordinates. How vague, you ask? The API covers:

• Pinpointing exact location via street address.
• Locating regions or cities by recognizable name (ie: Rio de Janeiro).
• Locating cities by highly unspecific name (Geocoding for "Springfield"

There's a trend among those using Jupyter Notebooks (or equivalent) which leads me to believe humanity is coming to an important realization: Google Maps, as an API is expensive.

Regardless if Google maps is embedded as a consumer-facing widget, or part of a routine data-pipeline, a single surge of high-traffic can leave enterprises with price tags in the hundreds of thousands of dollars. In fact, I can hardly remember a product where this hadn't become the case. One can hardly blame the search engine; after all, our tendency to ignore the Terms and Service agreements (as well as payment policies)

The first part of understanding any type of software is taking a glance at its file structure. It may seem like an outlandish and redundant statement to make to a generation who grew up on GUIs. GitHub is essentially no more than a GUI for Git, so it’s unsurprisingly that one of the largest company to follow a similar business model recently bought Github for millions.

All that said, a question remains: how do we being to understand closed source applications? If we can’t see the structure behind an app, I suppose we’ll have to build this