pizza chart

In 2016 my roommates and I decided it would be a good idea to count how many pizzas we ate over the course of a year. A tally was kept on our chalkboard wall in the kitchen recording how many pizzas we ate and where they came from. We did an excellent job collecting data eating pizza and I’m proud of us.

In total we ate 127 pizzas.

That's a pizza every 2.87 days.

We didn’t really bother tracking the size of pizza, date, or toppings, which all would have been great but also is a lot of effort when you can just draw a line on the wall and eat pizza. It’s probably for the best that we missed our target of 150 pizzas.


Here is a simple interactive chart of the pizza data made using Bokeh’s python library. The code is below if you want to take a look at it.

Most of them came from our favourite establishment, Domino’s, and the rest came from other places or were made in-house. The chart below shows the order we ate our pizzas in. Definitely had at least one or two Domino’s pizza parties.

pizza chart2

Here is the python code for the Bokeh chart:

from bokeh.charts import Bar, output_file, show
from bokeh.layouts import row

data = {
    'pizza': ["Domino's","Other","Homemade"],
    'quantity': [67,38,22]
}

tooltips=[
    ('Pizza', '@pizza'),
    ('Count', '@height'),
]

bar = Bar(data, values='quantity', label='pizza', title="Pizza Counts",
legend='top_right', color='pizza',plot_width=400, plot_height=400, 
tools='', tooltips=tooltips)

# styling the bar chart
bar.xaxis.axis_label = ''
bar.yaxis.axis_label = ''
bar.xaxis.major_tick_line_color = None
bar.yaxis.minor_tick_line_color = None

output_file("pizza_bar.html")
show(row(bar))

Heavily inspired by Michael Pecirno’s minimalist maps of the US, the Canada Below 60 project aims to display land use data in a minimalist map setting with no background layers, boundary features, or labels.

The raster data covers Canada below the 60th parallel and has a 30m cell size, coming in at a total (uncompressed) size of about 85GB. There are 7,772,100,116 individual cells in this dataset.

If you’re interested in downloading the data or reading the metadata to learn more about it, you can get it at Open Data Canada.