Medium posts and some general notes

I've been trying out Medium as a blogging platform a bit, and while I've only written two things over there I thought it would be a good idea to point anyone who ends up here in that direction.

How much Does Carbon Cost? is an exploration into what my local green power program ultimately prices a tonne of CO2 at. It was a fun exercise in following a thread backwards.

Generalized Python Graphing for EIA-860 is a short and (I hope) simple for getting up and running with some graphing capabilities in Python. I specifically look at EIA Form-860, and how you can start to pull data out and visualize it without ever opening Excel.

In my drafts folder I have a part 2 to the EIA-860 graphing post that gets a bit more energy specific, and something on a giant energy infrastructure project in Chicago that never happened and no one's ever heard of. I'm pretty excited about that one, I only stumbled across it when methodically cataloging decades-old FERC applications.

Finally, with all the recent talk around the Iranian deal, it might be time for me to re-read and update my piece on the cost of Iranian sanctions at the pump. That story has certainly changed considerably since the price of oil has collapsed.


USA Monthly Trade Balance, 2000-2014

I nearly didn't get to a post today, so it's going to be a brief one. It's another Census Bureau dataset, this time from their Foreign Trade section. Specifically, I grabbed the U.S. International Trade in Goods and Services data, and plotted the monthly balance over the last 14 years. There's a lot more that could be done with this, but it's Friday, and I'm pretty sleepy. Like many charts that track the economic status of America in some way, the results of the recession appear to be visible here. However, another possibility in this case is that the glut of much cheaper natural gas pulled some energy-intensive manufacturing back to American industrial areas that had either been operating below capacity or simply idle. A third factor is the rise of consumer culture in emerging economies, China in particular, which could have served to stabilize the monthly imbalance in recent years between $30,000 and $50,000 (in millions).



2012 Public School Funding and Expenditures (Elementary-Secondary)

The US Census Bureau publishes a set of public school funding and expenditure data, breaking it down by source (federal, state, local), as well as type for spending (current spending and capital outlays). The numbers are broken down by state, so here are a few descriptive graphics on the published numbers.

First, the top ten revenue states, along with their expenditures. Dollars are in thousands, so that $80 million at the top of the y-axis is actually $80 billion.

top revenue states

Next, I found the ratio of Revenue/Expenditure for each state, and the United States as a whole. I also grabbed the % difference for each state's ratio from the USA as a whole, which is plotted on the secondary y-axis.

top ten highest ratio top ten lowest ratio

Finally, here's the highest and lowest per capita revenue, with a line showing their % difference from the national average. Dollars here are dollars, not thousands of dollars.

per capita revenue highest per capita revenue lowest

Decadal Histograms of Monthly Yosemite Temperatures

Following up on the look at Yosemite's precipitation trends on Monday, here's a glance into the Park's temperature trends. I created 3 histograms each for four decades. The 70s, 80s, 90s, and 2000s. Together, these give a small glimpse into a changing temperature regime. As always, these are quick and dirty graphs. Today, they're produced via ggplot2 in R. All bins are set to a range of 3 degrees.

Throughout all of these histograms, it's important to watch the x axis along with the shape. It's pretty easy to miss a shift because the eye is drawn to the shape, but even a smaller peak on the right could indicate higher temperatures overall if the entire set has shifted in that direction.

First, a set of monthly minimum temperature histograms.

tmin_70s tmin_80s tmin_90s


Next, a set of monthly mean temperatures.

tmean_70s tmean_80s tmean_90stmean_00sAnd finally, a set of monthly max temperatures.

70s 80s 90s00s

Consumer Complaints about Financial Products

The Consumer Financial Protection Bureau (CFPB) has been collecting consumer complaints for 3 years, and has apparently been collecting them into one of the most usable government databases I've ever seen. This is particularly neat given the qualitative nature of many of these complaints. The CFPB was only created in 2011 after the 2010 passage of the Dodd–Frank Wall Street Reform and Consumer Protection Act, which might explain why its website appears far more modern than most federal endeavors. The data has a lot of potential, but here are a few tidbits I grabbed right away.

First, I noticed that each complaint was entered with a zipcode, and I wondered which had seen the most complaints. Here's the top ten, along with their actual location (first town name result based on a quick google search).


Florida and California are both well-represented, although I was surprised to see a Michigan zip code take the top spot. Perhaps these are areas with high concentrations of retired individuals?

In a similar vein, here are the top ten "Issues" reported in the database.