# Matplotlib & Datetimes – Tutorial 02: Bar Plot

To add some interesting information to the previous tutorial, I’ve downloaded the number of licence plates given for new cars in Belgium for the same time span: 2005 587764 2006 633570 2007 644313 2008 652590 2009 571001 2010 642086 2011 679619 Load them in the same fashion: plates, number = np.loadtxt(‘newplates.txt’,skiprows=1,unpack=True) xdates2 = [datetime.datetime.strptime(str(int(date)),’%Y’) for…

# Matplotlib & Datetimes – Tutorial 01: Fuel Prices

Anyone who has played a little with dates know how painful it can be… Even more when you want to plot this data !! Matplotlib provides (link) a dates API, but to be honnest, even if the documentation is well maintained, I find it confusing. Maybe because they made the choice of a Gregorian-based calendar,…

# Matplotlib Basemap tutorial 07: Shapefiles unleached

New version here Following a question in the matplotlib mailing list, I dug inside the code of readshapefile, in order to gain power : The goal: The data: http://www.gadm.org/ saved inside a new “borders/” folder ! The idea: Opening a GADM shapefile, get region names, and plot filled regions with random color ! The process:…

# Matplotlib Fonts (plots, basemaps, etc.)

Here is the trick (well documented on the matplotlib webpage) to define the font family and size of what appears on your matplotlib plot:

# Numpy Trick 01

I usually forget how much Numpy makes life easy : Say, you have a 101 element array, e.g.: import numpy as np a = np.linspace(0,100,101) and you want to take every 4th item in that array, that’s as easy as : print a[::4] will output: array([0., 4., … , 96., 100.]) I love Numpy !