1. Python Resources for QGIS Users

    Tue 18 March 2014

    There’s a discussion thread on the QGIS LinkedIn Group page about Python tutorials and resources. There were a few good suggestions, so I thought I’d share these with others. It starts with a very common question from a GIS (or any software that supports scripting) user:

    I’m a real ‘end-user’ of qgis and I want to improve my skills a little… I’ve found many python tutorials online but I don’t know which are any good. Can anyone point me to some good resources?

    The responses were useful, but not exhaustive:

    • The PyQGIS Programmer’s Guide ...

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  2. A quick bookmarklet for off-campus library access

    Tue 17 December 2013

    I have been doing a fair bit of research off-campus lately, and as usual, have been having trouble accessing research materials (mainly academic publications) from home. Fortunately, Hunter College provides off-campus access to all electronic resources available to Hunter students, faculty and staff via their Library proxy server. Unfortunately, it turns out to be a huge pain to use anything other than the library search facilities (like Google Scholar) through the proxy server*. In fact, when working off-campus, you actually have to preface each URL address to licensed resources with http://proxy.wexler.hunter.cuny.edu/login?url= in order ...

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  3. Describing Variation

    Fri 22 November 2013

    The 3rd in a series of tutorials on using Python for introductory statistical analysis, this tutorial covers methods for describing data via simple statistical calculations and statistical graphics. As always, the notebook for this tutorial is available here.

    In the 1880s, Sir Francis Galton, one of the pioneers of statistics, collected data on the heights of approximately 900 adult children and their parents in London. Galton was interested in studying the relationship between a full-grown child’s height and his or her mother’s and father’s height. In order to do so, Galton collected height measurements from about 200 ...

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  4. Data: Cases, Variables, Samples

    Sat 09 November 2013

    The second in a series of tutorials on using Python for introductory statistical analysis, this tutorial covers data, including cases, variables, samples, and a whole lot more. As always, the iPython Notebook associated with this tutorial is available here on github.

    Data used in statistical modeling are usually organized into tables, often created using spreadsheet software. Most people presume that the same software used to create a table of data should be used to display and analyze it. This is part of the reason for the popularity of spreadsheet programs such as ‘Excel’ and ‘Google Spreadsheets’.

    For serious statistical work ...

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  5. A Fresh Approach using Python: Introduction

    Fri 01 November 2013

    Welcome to the first in a series of tutorials on using Python for introductory statistical analysis. As I put more of these tutorials online, you should be able to access them easily by clicking or searching for the relevant category: “Statistical Modeling for Python”.

    This series of tutorials is based on the ‘Computational Technique’ sections of each chapter from ‘Statistical Modeling: A Fresh Approach (2nd Edition)’. The goal of this series of tutorials is to show how all of the R analysis and commands used in the book can be done just as easily using the Python programming language. This ...

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  6. Essential Python Geospatial Libraries

    Fri 12 July 2013

    Just so I don’t forget, here is a list of really awesome Python libraries that I’m using these days to do lots of fun things with spatial data [UPDATE: I’ve added a few more]:

    • pandas - For data handling and munging
    • shapely - For geometry handling
    • cartopy - For plotting spatial data
    • rtree - For efficiently querying spatial data
    • nodebox-opengl - For playing around with animations
    • statsmodels - For models and stats in Python (otherwise I’d use R)
    • numpy - For pretty much anything that involves arrays
    • geopy - For geolocating and things like that
    • ipython - For a wondering interactive environment in which to play
    • freetype-py - For converting font glyphs to polygons (odd I know…)
    • ogr/gdal - For reading, writing, and transforming geospatial data formats
    • pyqgis - For anything and everything GIS
    • fiona - For making it easy to read/write geospatial data formats
    • matplotlib - For all my plotting needs
    • networkx - For working with networks (duh!)
    • pelican - For blogging about all this stuff…
    • pysal - For all your spatial econometrics needs (and more)
    • descartes - For plotting geometries in matplotlib

    Based on Twitter and some of the comments below, I should also add:

    • geographiclib - For solving geodesic problems
    • pyshp - For reading and writing shapefiles (in pure Python)
    • pyproj - For conversions between projections

    Any others I’ve missed?

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  7. Will it Python?

    Tue 12 February 2013

    Over the past few weeks, I’ve been following a really great blog by Carl Vogel. This blog has an excellent (growing) collection of Python examples based on porting code and examples from R to Python. In general, it is useful for those “interested in the Python data analysis toolkit and its viability as an alternative to R”. Carl draws on examples from Machine Learning for Hackers by Drew Conway and John Miles White, as well as Gelman and Hill’s Data Analysis Using Regression and Multilevel/Hierarchical Models.

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  8. It’s about time…

    Wed 09 November 2011

    Well its been a long time since my last post, but I do have a relatively good reason: I was finishing up my PhD thesis. The good news is that I’m now done and graduated! I’m hoping I’ll have a bit more time to blog and continue working on side-projects that I had to put on-hold while finishing up. My plan for the next few months is to finish up here in Maynooth, (unofficially) start some post-doc work, and finish/get going on several papers on my PhD research. I’m also going to try to learn Bayesian statistics, fiddle about with some visualizations I’ve been working on, and start getting back into QGIS and Python development again

    In the mean time, I’ve put together a fun little visualization of my PhD thesis in the form of a word-cloud.

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