added tryout script

test_parquet.py

@@ -0,0 +1,56 @@
+
+import datasets
+import pyarrow as pa
+import pyarrow.parquet as pq
+logger = datasets.utils.logging.get_logger(__name__)
+
+
+
+_URLS = { "train": "https://huggingface.co/datasets/moska/test_parquet/resolve/main/data/example.parquet" }
+
+
+
+class ParquetDataset(datasets.ArrowBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            version=datasets.Version(version="0.0.1"),
+            description=f"test_parquet dataset.",
+        )
+
+    ]
+
+
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description="reading parquet format.",
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {   "pop_est": datasets.Value(dtype="float64"),
+                    "continent": datasets.Value(dtype="string"),
+                    "name": datasets.Value(dtype="string"),
+                    "iso_a3": datasets.Value(dtype="string"),
+                    "gdp_md_est": datasets.Value(dtype="int64"),
+                    "geometry": datasets.Value("binary"),
+                    # These are the features of your dataset like images, labels ...
+                }
+            ),
+        )
+
+
+    def _split_generators(self, dl_manager: datasets.download.DownloadManager):
+        files = _URLS[self.config.name]
+        downloaded_files = dl_manager.download(files)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepath': downloaded_files['train']})
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_tables(self, filepaths: list[str]):
+        for file_idx, filepath in enumerate(filepaths):
+            with open(filepath, mode="rb") as f:
+                parquet_file = pq.ParquetFile(source=filepath)
+                for batch_idx, record_batch in enumerate(parquet_file.iter_batches()):
+                    pa_table = pa.Table.from_batches([record_batch])
+                    yield f"{file_idx}_{batch_idx}", pa_table