Archives

2024

• 11 12 Getting to know the Polars GPU engine
• 21 11 Reading and writing files on S3 with Polars
• 21 11 Running a Jeckyll blog locally with Docker
• 11 11 Optimising Parquet reads with Polars
• 07 11 Polars, Altair and Vegafusion
• 07 11 Cheatsheet for Pandas to Polars
• 18 06 Fast python package installs with uv in Docker
• 03 04 Fitting linear models within Polars
• 26 02 Reading from S3 with Polars (or DeltaLake) using AWS SSO
• 16 02 What is a Polars expression?
• 30 01 Pandas to Polars: what to know for time series analysis
• 22 01 Which operations work in streaming mode in Polars?
• 16 01 Streaming large datasets in Polars

2023

• 03 12 AWS Lambda with Polars
• 19 10 What does ChatGPT's Advanced Data Analysis have installed?
• 05 10 Maybe they should just call it Regular Data Analysis
• 12 09 Understanding the Polars nested column types
• 27 07 Comparison of Matplotlib and Plotly in Polars
• 28 06 Filling time series gaps in lazy mode
• 24 04 Crucial parameters for streaming in Polars
• 04 04 Exploding a Polars pivot for feature engineering
• 01 04 Ordering of groupby and unique in Polars
• 18 03 Concat, extend or vstack?
• 20 01 Filtering one df by another
• 20 01 Embrace streaming mode in Polars
• 20 01 Lazy mode's hidden timesaver in Polars
• 20 01 Polars 🤝 Seaborn
• 18 01 Nested dtypes in Polars 1: the `pl.List` dtype
• 17 01 Talking Polars on the Real Python podcast
• 06 01 Sinking larger-than-memory Parquet files

2022

• 16 12 Polars ❤️ sorted data 2: groupby
• 08 12 To go big you must be lazy
• 23 11 AWS Lambda with Polars II: PyArrow
• 23 11 Quickstart with Polars
• 21 11 Inspecting a Polars query
• 10 11 Profiling a Polars query
• 08 11 ML pre-processing with Polars
• 07 11 Data science 2025
• 03 11 Polars 🤝 Matplotlib
• 01 11 Polars & Huggingface datasets
• 31 10 Polars on a diet
• 25 10 Polars up and running
• 25 10 Polars ❤️ sorted data 1: statistics
• 11 10 Polars doesn't have an index but what if you want one? Or many?
• 11 10 Fit Scikit-learn and XGBoost models directly from Polars
• 06 10 Don't fear the List dtype in Polars
• 04 10 Breaking your bad habits with Polars
• 03 10 Getting the largest values with Polars
• 29 09 Combining data with different schemas
• 13 09 Polars can help if your data is sorted
• 13 09 Using Spacy with Polars?
• 12 09 Flexible time series aggregations in Polars
• 06 09 Doing ML pre-processing in Polars
• 05 09 Setting an attribute shortcut in python
• 05 09 Don't loop over columns in Polars
• 05 09 IPC files in Polars
• 05 09 Loading from a database with Polars
• 05 09 Boolean filters in Polars?
• 07 06 Testing for data science 3: testing with Pandas
• 07 06 Testing for data science 2: testing in practice
• 06 06 Testing for data science 1: why test?
• 02 01 Parallel loop the loop in numpy
• 02 01 Attaining flow while writing software

2021

• 15 09 What software skills do oceanographers want to learn
• 05 08 Dev in docker without going insane
• 10 01 JPO at 50: Part II
• 01 01 JPO at 50: Part I

2020

• 26 07 Dev in docker
• 26 06 Career progression