Using our pre-trained and fine-tuned language models that can generate lyrics in the style of Leonard or Bob, we'll built out a simple frontend web app. Things I'll be using (and learning) include Google Cloud services like Cloud Functions and Cloud Run, as well as frontend staples like React.

How to build a lyrics generator in the style of Bob Dylan, Leonard Cohen, and a Leonard-Bob hydrid! In this first of two parts I go through how to prepare the data and fine-tune a language model so that it is ready to start generating some (almost incomprehensible - exactly as Bob would prefer!) lyrics.

Why are defaults for chart styles always so boring? Some might even argue in many progams they are actively harmful to gaining insight. Here are some tricks for communicating between matplotlib and seaborn for making your charts look elegant, clean, and informative.

Creating a table of summary statistics? Getting some year-on-year financial data? In this post we'll arm you with all the tools you need to reshape your data into aggregated or condensed forms through the powerful groupby and aggregate methods provided in pandas. Both numerical and categorical (string) column examples are shown.

Wide-to-long. Long-to-wide. Tidy. Normalized. Messy! Data comes in lots of shapes and sizes. Fortunately the Python library Pandas has a wide and flexible range of tools to help us massage data into not just the shape we want, but the shape we need. This post will look at some key dataframe methods for converting the shape of your data to suit your need.

Calculating shooting streaks for the key 3-pt shooters going into the GSW-POR Western Conference Finals. Using some tricks in Pandas we can get the number of consecutive makes or misses and visualise them with emojis - fun!