refactor: initial version of 1000 row w/ schema

convert.py

@@ -9,17 +9,46 @@ wjd_dump_path = "wikidata-20240304-all.json.bz2"
 wjd = WikidataJsonDump(wjd_dump_path)
 
 # Create an empty list to store the dictionaries
-# data = []
+data = []
 
 # # Iterate over the entities in wjd and add them to the list
-# for ii, entity_dict in enumerate(wjd):
-#     if ii > 1:
-#         break
+for ii, entity_dict in enumerate(wjd):
+    if ii > 1000:
+        break
     
-#     if entity_dict["type"] == "item":
-#         data.append(entity_dict)
+    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",
@@ -32,115 +61,103 @@ wjd = WikidataJsonDump(wjd_dump_path)
 #  "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())
-# ])
+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))
+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"

wikidata-20240304-all-1000.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:40316d762768104615584aa6df5b2809846ebd9e26ec9d220a3110fd3c7c3948
-size 69861490
+oid sha256:3f7e27f98a954aec271bd3742091275da5be259741e40fa0157ef115089c3563
+size 2496325