Changes

**In querying for, instead, keywords such as "startup weekend," I use:the following.

**In querying for events parked under the category "science and technology", I use the following. However, this query also returns scientific seminars unrelated to entrepreneurship and is yet to be refined. **Note that the category ID of science and technology is 102.

**In each case, var <code>response</code> is a json object, that can be read/written in python using the requests method <code>response.json()</code>. Each endpoint used above are instances of e.g. GET events/search/ or GET categories/:id. There are different parameters each GET function can harness to get more specific results. To populate a comprehensive local database, I am thinking about looking into running the '''dream''' is to systematic queries from different endpoints and collecting all results, without repetition, in a centralized database. In order to do this, I'll have to familarize further with these GET functions and develop a systematic approach to automate queries to the eventbrite server. One way to do this isto import entrepreneurship buzzword libraries that are available on the web, and make queries by iterating through these search strings systematically.*Eventbrite event objects in json are well-organized and consistent. There are many interesting fields such as the longitude/latitude decimals, apart from name/location/organizer/start-time/end-time data which are data we want to amass initially. **For instance, the upcoming startup weekend event in Seville looks like the following.[[File:Capture_12.png]]**In the events object, organizer and venue are represented as ID's and have to be queried separately since they contain a multitude of string-value pairs such as "description", "logo", and "url" in the case of organizer data. Huge opportunity here for more data extraction. Kudos to eventbrite for documenting their stuff meticulously. Can you tell I'm impressed? **The dream is To produce a local database, I'm using the <code>import pandas as pd</code> library, the <code>pandas.DataFrame</code> object and the <code>pandas.DataFrame.to_csv()</code> method. Currently, I initialize a dataframe with columns of variables that I seek to extract, and iterate through event objects and venue/organizer objects within to systematically runpopulate the dataframe with rows of event data. '''Still debugging/writing at the moment'''.