# New Tutorial Series: Pandas

In the coming months, I’ll prepare some tutorials over an excellent data analysis package called pandas ! To show you the power of pandas, just take a look at this old tutorial, where I exploited the power of itertools to group sparse data into 5 seconds bins. The magic of pandas is that, when you…

# Matplotlib & Datetimes – Tutorial 04: Grouping & Analysing Sparse Data

To extend the previous tutorial (see here), we define a data array that has some information about the event that occurred for each datetime. The plot of data vs time now looks like: The data array is constructed with numpy.random: data = np.random.randint(10000,size=len(times)) Now, we will modify the example from tutorial 03: def group(di): return…

# Matplotlib & Datetimes – Tutorial 03: Grouping Sparse Data

New tutorial, more advanced this time ! Let’s say we have a number of observations, like occurrences of earthquakes, or visitors connecting to a webserver, etc. These observations don’t occur every second, they are sparse on the time axis. To prepare an example, I’ve created a set of random datetimes like this : N =…

# Matplotlib Basemap tutorial 09: Drawing circles

In the previous tutorial, I defined a “shoot” method to compute the landing point of a shoot from one point, to a given azimuth and distance. Using this logic, it’s possible to find the points situated at a given distance from a “centre” point, a circle. The goal: Drawing circles of a given radius around…

# 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 !