:beers: cheers

sqllitetest.py

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+"""A dataset script that will hit SQLite file and return the results."""
+
+
+import sqlite3
+from pathlib import Path
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    # VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    # BUILDER_CONFIGS = [
+    #     datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
+    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
+    # ]
+
+    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "ssn": datasets.Value("int32"),
+                    "first_name": datasets.Value("string"),
+                    "last_name": datasets.Value("string"),
+                    "department": datasets.Value("int32"),
+                    "salary": datasets.Value("int32"),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        
+        # NOTE - Here is where you would put actual connection details if you're connecting to a database
+        # For the sake of this example, we use a local path to a sqllite file.
+        conn = sqlite3.connect("test.db")
+        
+        # Execute a query and get the cursor object back, which we'll use to iterate over
+        curr = conn.execute("SELECT * FROM Employees")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"cursor": curr},
+            )
+        ]
+    
+    def _generate_examples(self, cursor):
+        for i, ex in enumerate(cursor):
+            yield str(i), {
+                "ssn": ex[0],
+                "first_name": ex[1],
+                "last_name": ex[2],
+                "department": ex[3],
+                "salary": ex[4],
+            }
+        # Probably not necessary but just in case...we close the connection which we can find within the cursor object
+        cursor.connection.close()

test.db

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+version https://git-lfs.github.com/spec/v1
+oid sha256:66a75a4d7d23f92c66dc98752ce3bf3a886a1c50ae071d13996810552e5e6d37
+size 12288