python

Using Pandas iloc, loc, & ix to select rows and columns in DataFrames

Pandas Data Selection There are multiple ways to select and index rows and columns from Pandas DataFrames. I find tutorials online focusing on advanced selections of row and column choices a little complex for my requirements, but mastering the Pandas iloc, loc, and ix selectors can actually be made quite simple. Selection Options There’s three main options to …

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

Rainy cycling commutes in Ireland? Wunderground data in Python

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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AWS Elastic Beanstalk – Logging to Logentries & InsightOps from Python

[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 and …

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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.