The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides from that talk, along with a video recording of same.
Anyone familiar with the use of Python for data science and analysis projects has googled some combination of “plotting in python”, “data visualisation in python”, “barcharts in python” at some point. It’s not uncommon to end up lost in a sea of competing libraries, confused and alone, and just to go home again! The purpose …
Plotting with Python and Pandas – Libraries for Data Visualisation Read More »
CSV (comma-separated value) files are a common file format for transferring and storing data. The ability to read, manipulate, and write data to and from CSV files using Python is a key skill to master for any data scientist or business analysis. In this post, we’ll go over what CSV files are, how to read CSV files into Pandas DataFrames, and how to write DataFrames back to CSV files post analysis.
This post shows how to load, use, and make your own word embeddings using Python. Use the Gensim and Spacy libraries to load pre-trained word vector models from Google and Facebook, or train custom models using your own data and the Word2Vec algorithm. This post is a direct follow-on from the introductory Word Embeddings post, and will show you how to get started using word vectors with your own models and systems.
This post provides an introduction to “word embeddings” or “word vectors”. Word embeddings are real-number vectors that represent words from a vocabulary, and have broad applications in the area of natural language processing (NLP). We examine training, use, and properties of word embeddings models, and look at how and why you should look to use word embeddings over older bag-of-words techniques in your data science and language modelling tasks.
The Pandas DataFrame – this blog post covers the basics of loading, editing, and viewing data in Python, and getting to grips with the all-important data structure in Python – the Pandas Dataframe. Learn by example to load CSV files, rename columns, extract statistics, and select rows and columns.
In this post, geocoded data for all property price sales in Ireland from 2012-2017 is available. Data is sourced on the Irish Property Price Register and geocoded using the Google geocoding script in Python. All of the GPS latitude/longitude coordinates are further tied to census small area and electoral division boundaries.
Many precious hours have been lost to Character encoding errors and EOF character errors in CSV files being read by the Pandas read_csv file. This is an incredibly frustrating start to any analysis! Hopefully this post will save some people from the same fate!
A lot of modern-day games, especially the ones being developed for mobile, are built on business models revolving around data. Understanding how the audience thinks and responds with a product, as well as knowing how retention works in gaming, are both important in paving the way for the future of gaming.
Merging and Joining data sets are key activities of any data scientist or analyst. In this tutorial, we explore the process of combining datasets based on common columns quickly and easily with the Python Pandas library and it’s fast merge() functionality. Finally conquer merging and become a master with this 2-part tutorial.