How to Quickly Generate Wordclouds in Python

February 9, 2023 2 minute read

I’m working on a machine learning project that will model auction prices for paintings. Based on some domain knowledge, I have a solid intuition that certain keywords included in the artwork title and medium features will be useful in predicting price outcomes. My eventual plan is to use sklearn.feature_extraction.text.TfidfVectorizer to create features from the words in each of these fields, but before doing that I thought it would be helpful to get a better sense of what the most frequently-occurring words even were.

Hence, a wordcloud. Turns out it’s super easy to generate these using the wordcloud module.

Andy Warhol

Let’s take the titles of Andy Warhol works as an example. As a reminder, we are not considering all Warhol works here, but only those works that appear in the auction data I collected, so any trends we notice should not be imputed to Warhol the artist.

Here’s a sample of the data:

warhol.head()

	artist_name	title	date	medium	dims	auction_date	auction_house	auction_sale	auction_lot	price_realized	...	auction_year	price_realized_USD_constant_2022	area_cm_sq	volume_cm_cu	years_after_death_of_auction	artist_age_at_auction	artist_age_at_artwork_completion	artwork_age_at_auction	years_ago_of_auction
2013	Andy Warhol	The Shadow	1981	unique screenprint on Lenox Museum Board	96.5 x 96.5 cm	Dec 14, 2022	Christie's	First Open \| Post-War & Contemporary Art	Lot19	US\$52,920	...	2022	52920.0	9312.25	NaN	35.0	NaN	53.0	41.0	1
2014	Andy Warhol	Nervous System	1985	synthetic polymer on canvas	50.8 x 58.4 cm	Dec 14, 2022	Christie's	First Open \| Post-War & Contemporary Art	Lot39	US\$40,320	...	2022	40320.0	2966.72	NaN	35.0	NaN	57.0	37.0	1
2015	Andy Warhol	Portrait of Anselmino	1974	Acrylic and silkscreen on canvas	101.5 x 101.5 cm	Dec 9, 2022	Ketterer Kunst	Evening Sale with Collection Hermann Gerlinger	Lot60	€375,000• US\$395,839	...	2022	395839.0	10302.25	NaN	35.0	NaN	46.0	48.0	1
2016	Andy Warhol	Vanishing Animals: Okapi	1986	synthetic polymer paint on paper	59.2 x 80 cm (23 1/4 x 31 1/2 in.)	Dec 8, 2022	Phillips• London	New Now	Lot100	£15,120• US\$18,498	...	2022	18498.0	4736.00	NaN	35.0	NaN	58.0	36.0	1
2017	Andy Warhol	Tie	1979	acrylic on cut canvas	5.0 x 137.2 cm	Dec 7, 2022	Sotheby's	Contemporary Discoveries	Lot159	NaN	...	2022	NaN	686.00	NaN	35.0	NaN	51.0	43.0	1

5 rows × 45 columns

At this point, we’re ready to create a wordcloud. The only slightly tricky bit is that the WordCloud.generate method takes a string, but the title feature of this DataFrame is a series, so i’ve used the str.join method to join the list items into one very long string.

# Import wordcloud module
from wordcloud import WordCloud

plt.subplots(figsize=(10, 8))

# Instantiate WordCloud object
wordcloud = (
    WordCloud(max_words=100, background_color='white', colormap='magma', width=800, height=800)
    .generate(' '.join(warhol['title'].dropna()))
)

# Show image
plt.imshow(wordcloud, interpolation='bilinear', )
plt.axis('off');

Warhol Titles Wordcloud

This is helpful to get a sense of how frequently various words (and pairs of words, too) appear in the dataset.

Out of curiosity, let’s see if there are noticeable auction price differences (in constant 2022 dollars) for some of these key words.

Warhol Title Keywods Price Correlation

Looks like Jackie O works fetch a higher median price than, for instance, Campbell’s Soup works.

I’m also curious to generate a wordcloud for the medium feature.

plt.subplots(figsize=(10, 8))

wordcloud = (
    WordCloud(max_words=100, background_color='white', colormap='magma', width=800, height=800)
    .generate(' '.join(warhol['medium'].dropna()))
)

plt.imshow(wordcloud, interpolation='bilinear', )
plt.axis('off');