About

This notebook is a demonstration of some of capabilities of fastpages with notebooks.

With fastpages you can save your jupyter notebooks into the _notebooks folder at the root of your repository, and they will be automatically be converted to Jekyll compliant blog posts!

Front Matter

Front Matter is a markdown cell at the beginning of your notebook that allows you to inject metadata into your notebook. For example:

  • Setting toc: true will automatically generate a table of contents
  • Setting badges: true will automatically include GitHub and Google Colab links to your notebook.
  • Setting comments: true will enable commenting on your blog post, powered by utterances.

More details and options for front matter can be viewed on the front matter section of the README.

Markdown Shortcuts

put a #hide flag at the top of any cell you want to completely hide in the docs

put a #collapse-hide flag at the top of any cell if you want to hide that cell by default, but give the reader the option to show it:

#collapse-hide
import pandas as pd
import altair as alt

put a #collapse-show flag at the top of any cell if you want to show that cell by default, but give the reader the option to hide it:

#collapse-show
cars = 'https://vega.github.io/vega-datasets/data/cars.json'
movies = 'https://vega.github.io/vega-datasets/data/movies.json'
sp500 = 'https://vega.github.io/vega-datasets/data/sp500.csv'
stocks = 'https://vega.github.io/vega-datasets/data/stocks.csv'
flights = 'https://vega.github.io/vega-datasets/data/flights-5k.json'

Interactive Charts With Altair

Charts made with Altair remain interactive. Example charts taken from this repo, specifically this notebook.

Example 1: DropDown 

# single-value selection over [Major_Genre, MPAA_Rating] pairs
# use specific hard-wired values as the initial selected values
selection = alt.selection_single(
    name='Select',
    fields=['Major_Genre', 'MPAA_Rating'],
    init={'Major_Genre': 'Drama', 'MPAA_Rating': 'R'},
    bind={'Major_Genre': alt.binding_select(options=genres), 'MPAA_Rating': alt.binding_radio(options=mpaa)}
)
  
# scatter plot, modify opacity based on selection
alt.Chart(movies).mark_circle().add_selection(
    selection
).encode(
    x='Rotten_Tomatoes_Rating:Q',
    y='IMDB_Rating:Q',
    tooltip='Title:N',
    opacity=alt.condition(selection, alt.value(0.75), alt.value(0.05))
)

Example 2: Tooltips 

alt.Chart(movies).mark_circle().add_selection(
    alt.selection_interval(bind='scales', encodings=['x'])
).encode(
    x='Rotten_Tomatoes_Rating:Q',
    y=alt.Y('IMDB_Rating:Q', axis=alt.Axis(minExtent=30)), # use min extent to stabilize axis title placement
    tooltip=['Title:N', 'Release_Date:N', 'IMDB_Rating:Q', 'Rotten_Tomatoes_Rating:Q']
).properties(
    width=600,
    height=400
)

Example 3: More Tooltips 

# select a point for which to provide details-on-demand
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('date:T'),
    alt.Y('price:Q', scale=alt.Scale(type='log')),
    alt.Color('symbol:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='date:T'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='price:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='price:Q'
    ).transform_filter(label),
    
    data=stocks
).properties(
    width=700,
    height=400
)

Data Tables

You can display tables per the usual way in your blog:

movies = 'https://vega.github.io/vega-datasets/data/movies.json'
df = pd.read_json(movies)
# display table with pandas
df[['Title', 'Worldwide_Gross', 
    'Production_Budget', 'IMDB_Rating']].head()
Title Worldwide_Gross Production_Budget IMDB_Rating
0 The Land Girls 146083.0 8000000.0 6.1
1 First Love, Last Rites 10876.0 300000.0 6.9
2 I Married a Strange Person 203134.0 250000.0 6.8
3 Let's Talk About Sex 373615.0 300000.0 NaN
4 Slam 1087521.0 1000000.0 3.4

Images

Local Images

You can reference local images and they will be copied and rendered on your blog automatically. You can include these with the following markdown syntax:

![](my_icons/fastai_logo.png)

Remote Images

Remote images can be included with the following markdown syntax:

![](https://image.flaticon.com/icons/svg/36/36686.svg)

Animated Gifs

Animated Gifs work, too!

![](https://upload.wikimedia.org/wikipedia/commons/7/71/ChessPawnSpecialMoves.gif)

Captions

You can include captions with markdown images like this:

![](https://www.fast.ai/images/fastai_paper/show_batch.png "Credit: https://www.fast.ai/2020/02/13/fastai-A-Layered-API-for-Deep-Learning/")

Other Elements

Tweetcards

Typing > twitter: https://twitter.com/jakevdp/status/1204765621767901185?s=20 will render this:

Youtube Videos

Typing > youtube: https://youtu.be/XfoYk_Z5AkI will render this:

Boxes / Callouts

Typing > Warning: There will be no second warning! will render this:

Warning: There will be no second warning!

Typing > Important: Pay attention! It's important. will render this:

Important: Pay attention! It’s important.

Typing > Tip: This is my tip. will render this:

Tip: This is my tip.

Typing > Note: Take note of this. will render this:

Note: Take note of this.

Typing > Note: A doc link to [an example website: fast.ai](https://www.fast.ai/) should also work fine. will render in the docs:

Note: A doc link to an example website: fast.ai should also work fine.

Footnotes

You can have footnotes in notebooks just like you can with markdown.

For example, here is a footnote 1.

  1. This is the footnote.↩