https://github.com/debnsuma/pycon_polars101
https://github.com/debnsuma/pycon_polars101
Last synced: 8 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/debnsuma/pycon_polars101
- Owner: debnsuma
- Created: 2023-05-23T22:31:20.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-04T18:18:27.000Z (over 2 years ago)
- Last Synced: 2025-03-24T08:22:22.943Z (9 months ago)
- Language: Jupyter Notebook
- Size: 15.6 MB
- Stars: 13
- Watchers: 2
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-polars - Polars Workshop on AWS - A comprehensive workshop comparing Polars to Pandas, exploring a wide range of functions and features by [@debnsuma](https://github.com/debnsuma). (Resources / Tutorials & workshops)
README
#
🐻❄️ Welcome to Polars Workshop on AWS 🐻❄️
## Introduction
In this workshop we will start with [**Polars**](https://pola-rs.github.io/polars-book/) basics and shall compare with Pandas DataFrame, and shall walk through code exploring functions and features of Polars, for example load and transform data from CSV, Excel, or Parquet, perform data analysis in parallel and prepare your data for machine learning pipelines and shall compare with **Pandas** and **Spark**.
We focus more on the following, which makes Polars special:
- parallel hashing
- lazy execution
- expresive API
We are going to use **Amazon SageMaker Notebook** as our working environment, and you may like to use any environment of your choice.
## Getting started
If you are using local environment, please make sure you perform the following steps before getting started
```bash
git clone https://github.com/debnsuma/pycon_polars101.git
cd pycon_polars101
chmod +x pip-deploy.sh
./pip-deploy.sh
source polar_env/bin/activate
cd notebooks
jupyter lab
```
Follow all the sections one after another under the **`notebooks`** folder