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 …
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.