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from qwikidata.entity import WikidataItem
from qwikidata.json_dump import WikidataJsonDump
import pyarrow as pa
import pyarrow.parquet as pq
import pandas as pd
# create an instance of WikidataJsonDump
wjd_dump_path = "wikidata-20240304-all.json.bz2"
wjd = WikidataJsonDump(wjd_dump_path)
# Create an empty list to store the dictionaries
data = []
# # Iterate over the entities in wjd and add them to the list
for ii, entity_dict in enumerate(wjd):
if ii > 1000:
break
if entity_dict["type"] == "item":
data.append(entity_dict)
# Create a Parquet schema for the [Wikidata Snak Format](https://doc.wikimedia.org/Wikibase/master/php/docs_topics_json.html#json_snaks)
# {
# "snaktype": "value",
# "property": "P17",
# "datatype": "wikibase-item",
# "datavalue": {
# "value": {
# "entity-type": "item",
# "id": "Q30",
# "numeric-id": 30
# },
# "type": "wikibase-entityid"
# }
snak = pa.struct([
("snaktype", pa.string()),
("property", pa.string()),
("datatype", pa.string()),
("datavalue", pa.struct([
("value", pa.struct([
("entity-type", pa.string()),
("id", pa.string()),
("numeric-id", pa.int64())
])),
("type", pa.string())
]))
])
# TODO: Schema for Data Set
# Based on the [Wikidata JSON Format Docs](https://doc.wikimedia.org/Wikibase/master/php/docs_topics_json.html)
# Create a schema for the table
# {
# "id": "Q60",
# "type": "item",
# "labels": {},
# "descriptions": {},
# "aliases": {},
# "claims": {},
# "sitelinks": {},
# "lastrevid": 195301613,
# "modified": "2020-02-10T12:42:02Z"
#}
schema = pa.schema([
("id", pa.string()),
("type", pa.string()),
# {
# "labels": {
# "en": {
# "language": "en",
# "value": "New York City"
# },
# "ar": {
# "language": "ar",
# "value": "\u0645\u062f\u064a\u0646\u0629 \u0646\u064a\u0648 \u064a\u0648\u0631\u0643"
# }
# }
("labels", pa.map_(pa.string(), pa.struct([
("language", pa.string()),
("value", pa.string())
]))),
# "descriptions": {
# "en": {
# "language": "en",
# "value": "largest city in New York and the United States of America"
# },
# "it": {
# "language": "it",
# "value": "citt\u00e0 degli Stati Uniti d'America"
# }
# }
("descriptions", pa.map_(pa.string(), pa.struct([
("language", pa.string()),
("value", pa.string())
]))),
# "aliases": {
# "en": [
# {
# "language": "en",pa.string
# "value": "New York"
# }
# ],
# "fr": [
# {
# "language": "fr",
# "value": "New York City"
# },
# {
# "language": "fr",
# "value": "NYC"
# },
# {
# "language": "fr",
# "value": "The City"
# },
# {
# "language": "fr",
# "value": "La grosse pomme"
# }
# ]
# }
# }
("aliases", pa.map_(pa.string(), pa.list_(pa.struct([
("language", pa.string()),
("value", pa.string())
])))),
# {
# "claims": {
# "P17": [
# {
# "id": "q60$5083E43C-228B-4E3E-B82A-4CB20A22A3FB",
# "mainsnak": {},
# "type": "statement",
# "rank": "normal",
# "qualifiers": {
# "P580": [],
# "P5436": []
# },
# "references": [
# {
# "hash": "d103e3541cc531fa54adcaffebde6bef28d87d32",
# "snaks": []
# }
# ]
# }
# ]
# }
# }
("claims", pa.map_(pa.string(), pa.list_(snak))),
("sitelinks", pa.struct([
("site", pa.string()),
("title", pa.string())
])),
("lastrevid", pa.int64()),
("modified", pa.string())
])
# Create a table from the list of dictionaries and the schema
table = pa.Table.from_pandas(pd.DataFrame(data), schema=schema)
# table = pa.Table.from_pandas(pd.DataFrame(wjd))
# Write the table to disk as parquet
parquet_path = "wikidata-20240304-all.parquet"
pq.write_table(table, parquet_path)
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