My last post about my favourite ‘new’ (well, new to me) Python packages seemed to be very well received. I’ll post a ‘debrief’ post within the next few weeks, reflecting on the various comments that were made on Hacker News, Reddit and so on, but before that I want to post a slightly more personal […]
As I’ve been blogging a lot more about Python over the last year, I thought I’d list a few of my favourite ‘new’ Python modules from 2015. These aren’t necessarily modules that were newly released in 2015, but modules that were ‘new to me’ this year – and may be new to you too! tqdm […]
I wrote a post a few months ago about a couple of useful Jupyter (formerly known as IPython) notebook extensions, and commented that they were a bit of a pain to install. Well, I’ve found a great way to get around that problem – an extension called nbextensions that will help you manage your notebook […]
Slightly boringly, this very similar to my last post – but it’s also something useful that you may want to know, and that I’ll probably forget if I don’t write it down somewhere. Basically, scipy.ndimage.filters.convolve is about twice as fast as scipy.signal.convolve2d. I run convolutions a lot on satellite images, and Landsat images are around 8000 […]
This entry in my series covers manifestoclouds: my code for producing word clouds from political party manifestos. This is very simple, generic code that just ties together a few libraries – and is by no means restricted to just political party manifestos – but I keep it around because I find it useful occasionally. I use […]
Recently I’ve been investigating a key dataset in my research, and really seeking to understand what is causing the patterns that I see. I realised that it would be really useful if I could plot an interactive scatter plot in Python, and then hover over points to find out further information in them. Putting this into […]
As a fellow of the Software Sustainability Institute, I have talked a lot about how important software is in science, and how we need to make sure we recognise its contribution. Writing good scientific software takes a lot of work, and can have a lot of impact on the community, but it tends not to […]
This is just a brief public service announcement reporting something that I’ve just found: np.percentile is a lot faster than scipy.stats.scoreatpercentile – almost an order of magnitude faster in some cases. Someone recently asked me why on earth I was using scoreatpercentile anyway – and it turns out that np.percentile was only added in numpy 1.7, which […]
I was going to post this as one of my ‘previously unpublicised code’ posts, but that would be stretching the title a bit, as I have previously blogged about my implementation of the van Heuklon (1979) ozone model. This is just a brief update (in the spirit of my ‘previously unpublicised code’ posts) to say that […]
Linear regression is often used to estimate the relationship between two variables – basically by drawing the ‘line of best fit’ on a graph. The mathematical method that is used for this is known as Least Squares, and aims to minimise the sum of the squared error for each point. The key question here is how do […]