If you run a PostgreSQL database, use pg_stat_activity to find and identify slow and blocked processes and queries, with the query text and responsible user quickly. pg_blocking_pids and pg_locks will give you everything you need to know about database locks.
Introduction Sleeping, and python. Two of my favourite things, when combined with the the Python Fitbit library, Matplotlib, and Pandas, can generate informative plots of your sleeping habits! This post explores how we can pull date from the Fitbit API, create a Pandas Dataframe, and then plot the results. In this tutorial, I’ve used Python …
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.