shanelynn

How often do you actually get wet going to work? Using pandas, python, and some graphs, we find out.

How wet is a cycling commute in Ireland? Pretty dry!… if you don’t live in Galway.

How often do you get wet cycling to work? Cycling in Ireland is taking off. The DublinBikes scheme is a massive success with over 10 million journeys, there’s large increases in people cycling in Irish cities, there’s a good cyclist community, and infrastructure is slowing improving around the country. However, Ireland is a rainy place! It turns out that …

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Cyclist in the rain. Blog about python scraping data from wunderground rainfall data.

Analysis of Weather data using Pandas, Python, and Seaborn

The most recent post on this site was an analysis of how often people cycling to work actually get rained on in different cities around the world. You can check it out here. The analysis was completed using data from the Wunderground weather website, Python, specifically the Pandas and Seaborn libraries. In this post, I will …

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Amazon Elastic Beanstalk – Logging to Logentries from Python Application

[Short version] The S3 ingestion script for Amazon applications provided by Logentries will not work for the gzip compressed log files produced by the Elastic Beanstalk log rotation system. A slightly edited script will work instead and can be found on Github here.[/Short Version]   Logentries is a brilliant startup originating here in Dublin for collecting …

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Fixing Office 2016 installation for Mac – error code 0xD0000006

This is a very quick post to help some people out on installation problems with Office for Mac 2016. On an excited day of installation of Excel 2016 on my Macbook, the following error threatened to ruin the day: “An unknown error has occurred, the error code is: 0xD0000006” Seemingly unfound on the internet, the solution, oddly enough …

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Summarising, Aggregating, and Grouping data in Python Pandas

Pandas – Python Data Analysis Library I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well. One aspect that I’ve recently been exploring is the task of …

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Parallel programming allows you to speed up your code execution - very useful for data science and data processing

Using Python Threading and Returning Multiple Results (Tutorial)

Threading in Python is simple. It allows you to manage concurrent threads doing work at the same time. The library is called “threading”, you create “Thread” objects, and they run target functions for you. You can start potentially hundreds of threads that will operate in parallel. Speed up long running tasks by parallelising and threading computation where you can.

Scraping Dublin City Bikes Data Using Python

FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post… SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in …

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Multiple heatmaps

Self-Organising Maps for Customer Segmentation using R

Self-Organising Maps Self-Organising Maps (SOMs) are an unsupervised data visualisation technique that can be used to visualise high-dimensional data sets in lower (typically 2) dimensional representations. In this post, we examine the use of R to create a SOM for customer segmentation. The figures shown here used use the 2011 Irish Census information for the …

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