Creating wordclouds with Altair - word-cloud

Question

How do I create a wordcloud with Altair?
Vega and vega-lite provide wordcloud functionality which I have used succesfully in the past.
Therefore it should be possible to access it from Altair if I understand correctly and
I would prefer to prefer to express the visualizations in Python rather than embedded JSON.
All the examples for Altair I have seen involve standard chart types like
scatter plots and bar graphs.
I have not seen any involving wordclouds, networks, treemaps, etc.
More specifically how would I express or at least approximate the following Vega visualization in Altair?
def wc(pages, width=2**10.5, height=2**9.5):
return {
"$schema": "https://vega.github.io/schema/vega/v3.json",
"name": "wordcloud",
"width": width,
"height": height,
"padding": 0,
"data" : [
{
'name' : 'table',
'values' : [{'text': pg.title, 'definition': pg.defn, 'count': pg.count} for pg in pages)]
}
],
"scales": [
{
"name": "color",
"type": "ordinal",
"range": ["#d5a928", "#652c90", "#939597"]
}
],
"marks": [
{
"type": "text",
"from": {"data": "table"},
"encode": {
"enter": {
"text": {"field": "text"},
"align": {"value": "center"},
"baseline": {"value": "alphabetic"},
"fill": {"scale": "color", "field": "text"},
"tooltip": {"field": "definition", "type": "nominal", 'fontSize': 32}
},
"update": {
"fillOpacity": {"value": 1}
},
},
"transform": [
{
"type": "wordcloud",
"size": [width, height],
"text": {"field": "text"},
#"rotate": {"field": "datum.angle"},
"font": "Helvetica Neue, Arial",
"fontSize": {"field": "datum.count"},
#"fontWeight": {"field": "datum.weight"},
"fontSizeRange": [2**4, 2**6],
"padding": 2**4
}
]
}
],
}
Vega(wc(pages))

Answer 1

Altair's API is built on the Vega-Lite grammar, which includes only a subset of the plot types available in Vega. Word clouds cannot be created in Vega-Lite, so they cannot be created in Altair.

Answer 2

With mad respect to #jakevdp, you can construct a word cloud (or something word cloud-like) in altair by recognizing that the elements of a word cloud chart involve:
a dataset of words and their respective quantities
text_marks encoded with each word, and optionally size and or color based on quantity
"randomly" distributing the text_marks in 2d space.
One simple option to distribute marks is to add an additional 'x' and 'y' column to data, each element being a random sample from the range of your chosen x and y domain:
import random
def shuffled_range(n): return random.sample(range(n), k=n)
n = len(words_and_counts) # words_and_counts: a pandas data frame
x = shuffled_range(n)
y = shuffled_range(n)
data = words_and_counts.assign(x=x, y=y)
This isn't perfect as it doesn't explicitly prevent word overlap, but you can play with n and do a few runs of random number generation until you find a layout that's pleasing.
Having thus prepared your data you may specify the word cloud elements like so:
base = alt.Chart(data).encode(
x=alt.X('x:O', axis=None),
y=alt.Y('y:O', axis=None)
).configure_view(strokeWidth=0) # remove border
word_cloud = base.mark_text(baseline='middle').encode(
text='word:N',
color=alt.Color('count:Q', scale=alt.Scale(scheme='goldred')),
size=alt.Size('count:Q', legend=None)
)
Here's the result applied to the same dataset used in the Vega docs: