feat: basic version of convert.py

convert.py

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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 > 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)

requirements.txt

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+qwikidata
+pyarrow
+pandas