Data Vis

Visualize your data with charting tools like Matplotlib, Plotly, D3, Chart.js, Muze, Seaborn, and countless others. Primarily focused on programmatic visualization as opposed to Business Intelligence software.
Data Science
29 Apr 2019

Plotting Data With Seaborn and Pandas

Create beautiful data visualizations out-of-the-box with Python’s Seaborn.
Plotting Data With Seaborn and Pandas

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

Continue Reading
Data Vis
28 Feb 2019

Drawing Mapbox Route Objects via the Directions API

Using the Mapbox Directions API to visually draw routes.
Drawing Mapbox Route Objects via the Directions API

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

Continue Reading
Plotly
20 Dec 2018

Integrate Plotly Dash Into Your Flask App

Crack full control over Plotly Dash by hacking an integration with Flask.
Integrate Plotly Dash Into Your Flask App

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

Continue Reading
Data Vis
18 Dec 2018

Geocoding Raw Datasets for Mapbox

Use the Mapbox Python SDK to transform a collection of addresses into lat/long coordinates.
Geocoding Raw Datasets for Mapbox

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"
Continue Reading
Data Vis
11 Dec 2018

Geographic Data Visualization with Mapbox

Visualizing Geodata with Mapbox's API and Tools.
Geographic Data Visualization with Mapbox

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)

Continue Reading
Statistics
17 Jul 2018

Lynx Roundup, July 17th

Scaling a Graph db, presenting survey data, badly presenting data.
Lynx Roundup, July 17th Continue Reading
Roundup
12 Jul 2018

Lynx Roundup, July 12th

Daily roundup of Data Science news around the industry, 7/12/2018.
Lynx Roundup, July 12th Continue Reading
Roundup
06 Jul 2018

Lynx Roundup, July 6th

Daily roundup of Data Science news around the industry, 7/6/2018.
Lynx Roundup, July 6th Continue Reading
Roundup
19 Jun 2018

Lynx Roundup, June 19th

Daily roundup of Data Science news around the industry, 6/19/2018.
Lynx Roundup, June 19th

https://www.cybertec-postgresql.com/en/index-decreases-select-performance/

https://dsweb.siam.org/The-Magazine/Article/topological-data-analysis-1

https://github.com/DistrictDataLabs/yellowbrick

Continue Reading
Roundup
17 Jun 2018

Lynx Roundup, June 17th

Daily roundup of Data Science news around the industry, 6/17/2018.
Lynx Roundup, June 17th Continue Reading
Python
17 Nov 2017

Generating Tree Hierarchies with Treelib

Using Python to visualize file hierarchies as trees.
Generating Tree Hierarchies with Treelib

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

Continue Reading