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Merge pull request #86 from paddymul/master
Add Jupyterlite
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‎.github/workflows/deploy.yml

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name: Build and Deploy
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on:
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push:
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branches:
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- main
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- master
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pull_request:
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branches:
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- '*'
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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- name: Setup Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.12'
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- name: Install mamba
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uses: mamba-org/setup-micromamba@v1
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with:
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micromamba-version: '1.5.8-0'
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environment-file: build-environment.yml
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cache-environment: true
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- name: Build the JupyterLite site
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shell: bash -l {0}
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run: |
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cp README.md content
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jupyter lite build --contents cookbook --output-dir dist
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- name: Upload artifact
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uses: actions/upload-pages-artifact@v3
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with:
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path: ./dist
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deploy:
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needs: build
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if: github.ref == 'refs/heads/master'
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permissions:
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pages: write
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id-token: write
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environment:
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name: github-pages
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url: ${{ steps.deployment.outputs.page_url }}
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runs-on: ubuntu-latest
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steps:
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- name: Deploy to GitHub Pages
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id: deployment
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uses: actions/deploy-pages@v4

‎README.md

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11
Pandas cookbook
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===============
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[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/jvns/pandas-cookbook/master)
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Try it with Jupyter Lite
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[![lite-badge](https://jupyterlite.rtfd.io/en/latest/_static/badge.svg)](https://paddymul.github.io/pandas-cookbook/lab/index.html)
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[pandas](http://pandas.pydata.org/) is a Python library for doing
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data analysis. It's really fast and lets you do exploratory work
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Happy pandas!
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Running Jupyterlite locally
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===========================
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```bash
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#assuming you checked out into ~/code/pandas-cookbook
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cd ~/code/pandas-cookbook/ ; rm -rf _output ; time jupyter lite build --contents ./cookbook/ && cd _output && python -m http.server
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```
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Running the cookbook inside a Docker container.
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===============================================================
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This repository contains a Dockerfile and can be built into a docker container.

‎build-environment.yml

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name: build-env
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channels:
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- conda-forge
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dependencies:
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- python
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- pip
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- jupyter_server
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- jupyterlite-core >=0.4.1,<0.5.0
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- jupyterlite-xeus>=2.0.0,<3

‎cookbook/Chapter 1 - Reading from a CSV.ipynb

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‎cookbook/Chapter 2 - Selecting data & finding the most common complaint type.ipynb

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"outputs": [],
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"source": [
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"# because of mixed types we specify dtype to prevent any errors\n",
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"complaints = pd.read_csv('../data/311-service-requests.csv', dtype='unicode')"
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"complaints = pd.read_csv('./data/311-service-requests.csv', dtype='unicode')"
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]
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},
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.9.20"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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"nbformat_minor": 4
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}

‎cookbook/Chapter 3 - Which borough has the most noise complaints (or, more selecting data).ipynb

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"outputs": [],
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"source": [
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"# because of mixed types we specify dtype to prevent any errors\n",
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"complaints = pd.read_csv('../data/311-service-requests.csv', dtype='unicode')"
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"complaints = pd.read_csv('./data/311-service-requests.csv', dtype='unicode')"
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]
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},
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.9.20"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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"nbformat_minor": 4
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}

‎cookbook/Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb

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}
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],
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"source": [
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"bikes = pd.read_csv('../data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n",
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"bikes = pd.read_csv('./data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n",
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"bikes['Berri 1'].plot()"
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]
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},
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}
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],
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"source": [
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"bikes = pd.read_csv('../data/bikes.csv', \n",
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"bikes = pd.read_csv('./data/bikes.csv', \n",
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" sep=';', encoding='latin1', \n",
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" parse_dates=['Date'], dayfirst=True, \n",
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" index_col='Date')\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.9.20"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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"nbformat_minor": 4
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}

‎cookbook/Chapter 5 - Combining dataframes and scraping Canadian weather data.ipynb

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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}
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],
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"source": [
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"weather_2012_final = pd.read_csv('../data/weather_2012.csv', index_col='Date/Time')\n",
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"weather_2012_final = pd.read_csv('./data/weather_2012.csv', index_col='Date/Time')\n",
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"weather_2012_final['Temp (C)'].plot(figsize=(15, 6))"
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]
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},
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"Here's an URL template you can use to get data in Montreal. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#downloading via URLS doesn't work in Jupyterlite"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"#url = url_template.format(month=3, year=2012)\n",
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"#weather_mar2012 = pd.read_csv(url, skiprows=15, index_col='Date/Time', parse_dates=True, encoding='latin1', header=True)\n",
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"\n",
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"# because the url is broken, we use our saved dataframe for now\n",
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"weather_mar2012 = pd.read_csv('../data/weather_2012.csv')"
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"weather_mar2012 = pd.read_csv('./data/weather_2012.csv')"
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]
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},
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.9.20"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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"nbformat_minor": 4
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}

‎cookbook/Chapter 6 - String Operations- Which month was the snowiest.ipynb

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}
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],
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"source": [
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"weather_2012 = pd.read_csv('../data/weather_2012.csv', parse_dates=True, index_col='Date/Time')\n",
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"weather_2012 = pd.read_csv('./data/weather_2012.csv', parse_dates=True, index_col='Date/Time')\n",
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"weather_2012[:5]"
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],
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"metadata": {
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"display_name": "Python 3 (ipykernel)",
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"name": "python",
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"version": "3.9.20"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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}
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},
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"nbformat_minor": 4
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}

‎cookbook/Chapter 7 - Cleaning up messy data.ipynb

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"metadata": {},
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"source": [
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"requests = pd.read_csv('../data/311-service-requests.csv', dtype='unicode')"
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"requests = pd.read_csv('./data/311-service-requests.csv', dtype='unicode')"
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},
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{
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"outputs": [],
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"na_values = ['NO CLUE', 'N/A', '0']\n",
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"requests = pd.read_csv('../data/311-service-requests.csv', na_values=na_values, dtype={'Incident Zip': str})"
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"requests = pd.read_csv('./data/311-service-requests.csv', na_values=na_values, dtype={'Incident Zip': str})"
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{
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"outputs": [],
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"na_values = ['NO CLUE', 'N/A', '0']\n",
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"requests = pd.read_csv('../data/311-service-requests.csv', \n",
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"requests = pd.read_csv('./data/311-service-requests.csv', \n",
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" na_values=na_values, \n",
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" dtype={'Incident Zip': str})"
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],
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"metadata": {
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"version": "3.8.5"
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"version": "3.9.20"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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