convert.py

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)