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Showing posts with the label Martin Hawksey

EarthQuake

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Earthquake Start of a project to bring in information from a website with an Application Programming Interface (API). We are going to use a modified version of "Project: Fetching Current Weather Data" from "Automate the boring stuff with Python" by Al Sweigart What is going on below? We import libraries to dela with json, reuest from the server and the pandas library. In [1]: import json , requests import pandas as pd from pandas import json_normalize In this section we creating a string made up of the URL. Requesting the information from the site with the URL we created and pass back the information. Data comes from the US Geological survey  https://www.usgs.gov/about/about-us/who-we-are and one of their earthquake feeds. Then print out what was returned. In [2]: url = 'https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_hour.geojson' response = requests . get ( url ) response . raise_for_status () Now we need load the data which i...
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Social Visualisation using TAGS

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In a previous post I discussed using a twitter social analysis tool . In this post I am going to discuss another fantastic tool TAGS. Developed by Martin Hawksey ( @mhawksey ) this is a great free tool for visualising tweets; using a Google  Spreadsheet to be the  front end to link into Google Visualization API. I am not going to go into a description of how to set-up it here; I don't need to the best place to go for this is to follow the link here . In this post, I am mainly going through my (probably bit random) thoughts, an example and experiences with it. So the example was a twitter chat #caschat or #CASchat held on the 13th December 2016. The conversation is between teachers of computing as well as those who support or have an interest in supporting computing in schools.  Figure 1 I find the visualisation of twitter chats interesting; seeing the links between people, groups that form, but also seeing the way people engage....
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