<jupyter_start><jupyter_text>OmniArt Dataset Insights

---<jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Get OmniArt csv dump
# !wget -c https://isis-data.science.uva.nl/strezoski/omniart/omniart_v3/data/csv/omniart_v3_datadump.csv<jupyter_output><empty_output><jupyter_text>Data Exploration<jupyter_code>dataset = pd.read_csv("omniart_v3_datadump.csv", delimiter=",")
dataset.info()
dataset.head()<jupyter_output><empty_output><jupyter_text>Unique Entries<jupyter_code>dataset.apply(pd.Series.nunique).to_frame()<jupyter_output><empty_output><jupyter_text>Artists<jupyter_code># First 20
dataset['artist_full_name'].value_counts().to_frame().head(20)<jupyter_output><empty_output><jupyter_text>Sources<jupyter_code>dataset['collection_origins'].value_counts().to_frame()
dataset['collection_origins'].value_counts().plot.barh(xlabel="collection", ylabel="size").set_title("Collection sizes")<jupyter_output><empty_output><jupyter_text>Artwork type<jupyter_code># First 20
dataset['artwork_type'].value_counts().to_frame().head(20)<jupyter_output><empty_output><jupyter_text>School<jupyter_code># First 20
dataset['school'].value_counts().to_frame().head(20)
for i in dataset['school'].unique():
    print(i)<jupyter_output>modern
Italia
Great Britain, Richmondshire, North Yorkshire, Yorkshire and the Humber, England, UK
France métropolitaine, France
unknown
Nederland
flemish
Deutschland, Europe
netherlandish
italian
french
english
german
dutch
spanish
austrian
hungarian
danish
american
nan
japanese
catalan
chinese
london
british
roman
greek
irish
philadelphia
printed for <NAME>; and m. tonson
turkey,
balkan,
colonial
persian
hindi
india
tibet
philippines
burma
america
leipzig
new york
korean
chicago
lubeck
european
russian
portuguese
south american
swiss
breslau
roma
cypriot
indonesia
etruscan
minoan
cretan
calcutta
stockport; champante & whitrow
bombay
tlatilco
veracruz
colima
wari
moche
vicús
chimú
valdivia
maya
el
akan
nayarit
tairona
glasgow
parthian
achaemenid
iran
assyrian
sumerian
phrygian
sasanian
babylonian
westminster
boulogne
xxu
madrid
bruxelles
amsterdam
scottish
paris
colombia
frankfurt am main
orvieto
paris, poissy [printed]
burlington [vt.]
cheltenham
newcastle
philadelphia, [printed] [...]<jupyter_text>general_type<jupyter_code>dataset['general_type'].value_counts().to_frame()
dataset['general_type'].value_counts().drop(index=['unknown']).plot.barh(xlabel="Type", ylabel="size").set_title("General Type sizes")<jupyter_output><empty_output>
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{'chain_length': '8', 'dir': 'jupyter-structured-clean-dedup', 'id': '295452', 'license': 'MIT', 'n_tokens_mistral': 1005, 'n_tokens_neox': 972, 'n_words': 176, 'path': 'notebooks/omniart-dataset.ipynb', 'repo_name': 'uai-art-image-processing/sketch-to-artwork', 'size': '2531'}