| 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 > 1: | |
| # break | |
| # if entity_dict["type"] == "item": | |
| # data.append(entity_dict) | |
| # TODO: Schema for Data Set | |
| # 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.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.array(pa.struct([ | |
| # ("id", pa.string()), | |
| # ("mainsnak", pa.struct([])), | |
| # ("type", pa.string()), | |
| # ("rank", pa.string()), | |
| # ("qualifiers", pa.map_(pa.string(), pa.array(pa.struct([ | |
| # ])))), | |
| # ("references", pa.array(pa.struct([ | |
| # ("hash", pa.string()), | |
| # ("snaks", pa.array(pa.struct([]))) | |
| # ]))) | |
| # ])))), | |
| # ("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) | |