Fastpages Notebook Blog Post¶

A tutorial of fastpages for Jupyter notebooks.

toc: true
badges: true
comments: true
categories: [jupyter]
image: images/chart-preview.png

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¶

The first cell in your Jupyter Notebook or markdown blog post contains front matter. Front matter is metadata that can turn on/off options in your Notebook. It is formatted like this:

# Title
> Awesome summary

- toc: true- branch: master- badges: true
- comments: true
- author: Hamel Husain & Jeremy Howard
- categories: [fastpages, jupyter]

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¶

A #hide comment at the top of any code cell will hide both the input and output of that cell in your blog post.

A #hide_input comment at the top of any code cell will only hide the input of that cell.

In [1]:

#hide_input
print('The comment #hide_input was used to hide the code that produced this.')

The comment #hide_input was used to hide the code that produced this.

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:

In [6]:

#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:

In [4]:

#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.

In [5]:

# hide
df = pd.read_json(movies) # load movies data
genres = df['Major_Genre'].unique() # get unique field values
genres = list(filter(lambda d: d is not None, genres)) # filter out None values
genres.sort() # sort alphabetically

In [4]:

#hide
mpaa = ['G', 'PG', 'PG-13', 'R', 'NC-17', 'Not Rated']

In [5]:

# 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))
)

Out[5]:

Example 2: Tooltips¶

In [6]:

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
)

Out[6]:

Example 3: More Tooltips¶

In [7]:

# 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
)

Out[7]:

Data Tables¶

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

In [11]:

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', 'Distributor', 'MPAA_Rating', 'IMDB_Rating', 'Rotten_Tomatoes_Rating']].head()

Out[11]:

	Title	Worldwide_Gross	Production_Budget	Distributor	MPAA_Rating	IMDB_Rating	Rotten_Tomatoes_Rating
0	The Land Girls	146083.0	8000000.0	Gramercy	R	6.1	NaN
1	First Love, Last Rites	10876.0	300000.0	Strand	R	6.9	NaN
2	I Married a Strange Person	203134.0	250000.0	Lionsgate	None	6.8	NaN
3	Let's Talk About Sex	373615.0	300000.0	Fine Line	None	NaN	13.0
4	Slam	1087521.0	1000000.0	Trimark	R	3.4	62.0

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:

twitter: https://twitter.com/jakevdp/status/1204765621767901185?s=20

Youtube Videos¶

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

youtube: https://youtu.be/XfoYk_Z5AkI

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, however the syntax is different compared to markdown documents. This guide provides more detail about this syntax, which looks like this:

{% raw %}For example, here is a footnote {% fn 1 %}.
And another {% fn 2 %}
{{ 'This is the footnote.' | fndetail: 1 }}
{{ 'This is the other footnote. You can even have a [link](www.github.com)!' | fndetail: 2 }}{% endraw %}

For example, here is a footnote {% fn 1 %}.

And another {% fn 2 %}

{{ 'This is the footnote.' | fndetail: 1 }} {{ 'This is the other footnote. You can even have a link!' | fndetail: 2 }}