Use native timestamps, booleans, integers, and nulls for subreddit metadata

README.md

@@ -27,19 +27,19 @@ dataset_info:
   - name: display_name
     dtype: string
   - name: created_utc
-    dtype: string
+    dtype: timestamp[us, tz=UTC]
   - name: over18
-    dtype: string
+    dtype: bool
   - name: url
     dtype: string
   - name: subscribers
-    dtype: string
+    dtype: int64
   - name: public_description
     dtype: string
   - name: subreddit
     dtype: string
   - name: retrieved_on
-    dtype: string
+    dtype: timestamp[us, tz=UTC]
   - name: rules_markdown
     dtype: string
   - name: descp
@@ -47,10 +47,10 @@ dataset_info:
   config_name: default
   splits:
   - name: data
-    num_bytes: 409798485
+    num_bytes: 400524343
     num_examples: 395018
-  download_size: 183178103
-  dataset_size: 409798485
+  download_size: 184146598
+  dataset_size: 400524343
 ---
 
 # Subreddits Directory
@@ -61,30 +61,26 @@ The dataset contains **395,018 rows and 12 columns** of subreddit names, descrip
 
 **This dataset contains historical metadata.** The publication date of this repository is not the collection date. Names, descriptions, member counts, rules, availability, and other values reflect previously collected records. Where available, `retrieved_on` provides a record's retrieval timestamp. This is not a verified directory of every currently existing subreddit.
 
-## Data and preservation
+## Dataset contents
 
-The single file `data/data.parquet` uses Zstandard compression and is 183,178,103 bytes (approximately 183 MB). The `data` split contains the entire table. It is a general-purpose directory of subreddit metadata, with no predefined task or evaluation partition.
+Each record describes a Reddit community through its name, title, descriptions, member count, creation and retrieval dates, adult-content flag, and available rules. The `data` split contains the complete directory.
 
-All original column names, parsed cell values, row order, and column order are preserved. No rows were filtered, deduplicated, added, or reordered. No descriptions were rewritten, combined, trimmed, or filled in. Existing rules and other auxiliary columns are retained.
-
-Every column is stored as a **string**, including counts, booleans, and timestamps. This deliberately avoids changing numeric spellings, boolean spellings, or timestamp representations during CSV type inference. Empty CSV cells remain empty strings, not nulls. CSV quoting and delimiters are serialization details and are replaced by Parquet encoding.
-
-The conversion was verified by comparing **every cell of every row** with the original records. `conversion-manifest.json` records the validation result, file SHA-256 checksums, schema, and empty-string counts. The Parquet file contains the complete published table and can be used independently.
+All original records and columns are retained in their original order. Descriptions and rules have not been rewritten, and no metadata has been refreshed. Missing values and any duplicate records remain part of the dataset.
 
 ## Columns
 
-| Column | Meaning | Empty cells |
+| Column | Meaning | Missing values |
 |---|---|---:|
 | `title` | Subreddit title. | 18 |
 | `description` | Longer subreddit description/sidebar text. | 161,178 |
 | `display_name` | Subreddit display name. | 1 |
-| `created_utc` | Community creation-time value in its original representation. | 0 |
-| `over18` | Adult-content/NSFW flag, retained as text. | 0 |
+| `created_utc` | When the community was created (UTC). | 0 |
+| `over18` | Whether the community is marked as adult content/NSFW. | 0 |
 | `url` | Community URL or relative Reddit path. | 0 |
-| `subscribers` | Subscriber/member count at collection time, retained as text. | 0 |
+| `subscribers` | Subscriber/member count at collection time. | 0 |
 | `public_description` | Short public subreddit description. | 54,126 |
 | `subreddit` | Subreddit name. | 0 |
-| `retrieved_on` | Retrieval-time value, when provided; retained verbatim. | 259,611 |
+| `retrieved_on` | When the metadata was retrieved (UTC), where available. | 259,611 |
 | `rules_markdown` | Community rules in Markdown, when provided. | 259,611 |
 | `descp` | Auxiliary description field. Its original construction is undocumented; it is preserved without reinterpretation. | 0 |
 
@@ -107,8 +103,6 @@ dataset = load_dataset(
 row = next(iter(dataset))
 ```
 
-Consumers can explicitly parse numeric or date columns for their own analysis. No such coercion is applied to the published data.
-
 ## Download the Parquet file
 
 Download directly from this repository with the Hugging Face CLI:
@@ -117,8 +111,6 @@ Download directly from this repository with the Hugging Face CLI:
 hf download semvec/subreddits-directory data/data.parquet --repo-type dataset --local-dir subreddits-directory
 ```
 
-The repository also includes `convert.py`, the CSV-to-Parquet conversion and validation script used to prepare this edition. It is not needed to load or download the dataset.
-
 ## Usage terms and limitations
 
 The full original collection methodology is not documented. Community flags and metadata have not been independently validated, and completeness is not guaranteed.

conversion-manifest.json

@@ -5,8 +5,8 @@
   "source_sha256": "a449ece09a2115e8eba670a0d00cc0cca734b4b3d85d289dcd5f6f2becd833b1",
   "source_license_identifier": "reddit-api",
   "parquet_file": "data/data.parquet",
-  "parquet_bytes": 183178103,
-  "parquet_sha256": "20cd09b4fed1c12bd316574c43a0ebaae0beb27b75c74d58c1057eb2104a0746",
+  "parquet_bytes": 184146598,
+  "parquet_sha256": "c8a520b2d2785cf79c9baa23ba98f904e5bee2e146acb6ff9b20aa345ddddb68",
   "row_count": 395018,
   "column_count": 12,
   "columns": [
@@ -27,17 +27,31 @@
     "title": "string",
     "description": "string",
     "display_name": "string",
-    "created_utc": "string",
-    "over18": "string",
+    "created_utc": "timestamp[us, tz=UTC]",
+    "over18": "bool",
     "url": "string",
-    "subscribers": "string",
+    "subscribers": "int64",
     "public_description": "string",
     "subreddit": "string",
-    "retrieved_on": "string",
+    "retrieved_on": "timestamp[us, tz=UTC]",
     "rules_markdown": "string",
     "descp": "string"
   },
-  "empty_string_counts": {
+  "source_empty_string_counts": {
+    "title": 18,
+    "description": 161178,
+    "display_name": 1,
+    "created_utc": 0,
+    "over18": 0,
+    "url": 0,
+    "subscribers": 0,
+    "public_description": 54126,
+    "subreddit": 0,
+    "retrieved_on": 259611,
+    "rules_markdown": 259611,
+    "descp": 0
+  },
+  "null_counts": {
     "title": 18,
     "description": 161178,
     "display_name": 1,
@@ -52,9 +66,11 @@
     "descp": 0
   },
   "validation": {
-    "method": "Full row-by-row and cell-by-cell comparison with parsed source CSV",
+    "method": "Full cell-by-cell comparison: exact nonempty text, inverse semantic comparisons for typed values, empty cells mapped to null",
     "verified_rows": 395018,
-    "all_values_identical": true,
+    "all_semantic_values_match": true,
+    "nonempty_text_unchanged": true,
+    "null_counts_match_source_empty_cells": true,
     "row_order_preserved": true,
     "column_order_preserved": true
   },
@@ -63,7 +79,12 @@
     "compression": "zstd",
     "csv_encoding": "utf-8",
     "type_inference": false,
-    "empty_fields": "Preserved as empty strings; no null inference",
+    "empty_fields": "Only exact empty strings become null; literal null-like text is preserved",
+    "created_utc": "ISO 8601 UTC to timestamp[us, tz=UTC]",
+    "retrieved_on": "Unix seconds to timestamp[us, tz=UTC] using exact decimal arithmetic",
+    "over18": "True/False text to boolean",
+    "subscribers": "Nonnegative integer text to int64",
+    "write_page_index": true,
     "metadata_refreshed": false
   }
 }

convert.py

@@ -1,7 +1,7 @@
-"""Losslessly convert the source CSV's parsed cells to Parquet.
+"""Convert subreddit metadata to Parquet with explicit semantic types.
 
 Usage: python convert.py path/to/subreddits_with_details.csv output_directory
-Requires pyarrow. No Reddit requests, cleanup, filtering, or type inference.
+Requires pyarrow. No Reddit requests, filtering, or metadata refresh.
 """
 
 import csv
@@ -9,11 +9,70 @@ import hashlib
 import itertools
 import json
 import sys
+from datetime import datetime, timedelta, timezone
+from decimal import Decimal
 from pathlib import Path
 
 import pyarrow as pa
 import pyarrow.parquet as pq
 
+EPOCH = datetime(1970, 1, 1, tzinfo=timezone.utc)
+TIMESTAMP = pa.timestamp("us", tz="UTC")
+TYPES = {
+    "created_utc": TIMESTAMP,
+    "retrieved_on": TIMESTAMP,
+    "over18": pa.bool_(),
+    "subscribers": pa.int64(),
+}
+
+
+def parse_cell(name, value):
+    if value == "":
+        return None
+    if name == "created_utc":
+        parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
+        if parsed.tzinfo is None:
+            raise ValueError(f"Creation timestamp lacks timezone: {value!r}")
+        return parsed.astimezone(timezone.utc)
+    if name == "retrieved_on":
+        micros = Decimal(value) * 1_000_000
+        if not micros.is_finite() or micros != micros.to_integral_value():
+            raise ValueError(f"Invalid or sub-microsecond retrieval timestamp: {value!r}")
+        return EPOCH + timedelta(microseconds=int(micros))
+    if name == "over18":
+        if value not in ("True", "False"):
+            raise ValueError(f"Unexpected boolean: {value!r}")
+        return value == "True"
+    if name == "subscribers":
+        if not value.isascii() or not value.isdecimal():
+            raise ValueError(f"Invalid subscriber count: {value!r}")
+        return int(value)
+    return value
+
+
+def verify_cell(name, original, converted):
+    """Check output against source using inverse semantic comparisons."""
+    if original == "":
+        return converted is None
+    if converted is None:
+        return False
+    if name == "created_utc":
+        # The source uses either minute or second precision, always UTC.
+        return original in (
+            converted.strftime("%Y-%m-%dT%H:%M:%SZ"),
+            converted.strftime("%Y-%m-%dT%H:%MZ")
+            if converted.second == converted.microsecond == 0 else "",
+        ) and converted.utcoffset() == timedelta(0)
+    if name == "retrieved_on":
+        delta = converted - EPOCH
+        micros = (delta.days * 86400 + delta.seconds) * 1_000_000 + delta.microseconds
+        return Decimal(micros) / 1_000_000 == Decimal(original)
+    if name == "over18":
+        return type(converted) is bool and str(converted) == original
+    if name == "subscribers":
+        return type(converted) is int and Decimal(converted) == Decimal(original)
+    return converted == original
+
 
 def sha256(path):
     with path.open("rb") as stream:
@@ -30,31 +89,44 @@ def convert(source, output):
         reader = csv.reader(stream, strict=True)
         columns = next(reader)
         assert len(set(columns)) == len(columns), "Duplicate source column names"
-        schema = pa.schema([(name, pa.string()) for name in columns])
+        schema = pa.schema([(name, TYPES.get(name, pa.string())) for name in columns])
         empty_counts = dict.fromkeys(columns, 0)
-        with pq.ParquetWriter(target, schema, compression="zstd") as writer:
+        with pq.ParquetWriter(target, schema, compression="zstd", write_page_index=True) as writer:
             while rows := list(itertools.islice(reader, 5000)):
                 assert all(len(row) == len(columns) for row in rows), "Ragged CSV row"
                 arrays = []
                 for index, name in enumerate(columns):
                     values = [row[index] for row in rows]
                     empty_counts[name] += values.count("")
-                    arrays.append(pa.array(values, type=pa.string()))
+                    arrays.append(pa.array(
+                        [parse_cell(name, value) for value in values],
+                        type=schema.field(name).type,
+                    ))
                 writer.write_table(pa.Table.from_arrays(arrays, schema=schema))
                 row_count += len(rows)
 
     # Independently read the original CSV again and compare every output cell.
     verified_rows = 0
+    null_counts = dict.fromkeys(columns, 0)
+    parquet = pq.ParquetFile(target)
+    assert parquet.schema_arrow.equals(schema), "Parquet schema mismatch"
     with source.open(encoding="utf-8", newline="") as stream:
         reader = csv.reader(stream, strict=True)
         assert next(reader) == columns
-        for batch in pq.ParquetFile(target).iter_batches(batch_size=5000):
+        for batch in parquet.iter_batches(batch_size=5000):
             assert batch.schema.names == columns
+            for name, array in zip(columns, batch.columns):
+                null_counts[name] += array.null_count
             for row in zip(*(column.to_pylist() for column in batch.columns)):
-                assert list(row) == next(reader), f"Mismatch at row {verified_rows}"
+                original = next(reader)
+                assert all(
+                    verify_cell(name, old, new)
+                    for name, old, new in zip(columns, original, row)
+                ), f"Mismatch at row {verified_rows}"
                 verified_rows += 1
         assert next(reader, None) is None, "Source has additional rows"
     assert verified_rows == row_count
+    assert null_counts == empty_counts, "Null counts differ from source empty cells"
 
     manifest = {
         "source": "https://www.kaggle.com/datasets/hashiromer/subreddits-directory",
@@ -68,12 +140,15 @@ def convert(source, output):
         "row_count": row_count,
         "column_count": len(columns),
         "columns": columns,
-        "column_types": dict.fromkeys(columns, "string"),
-        "empty_string_counts": empty_counts,
+        "column_types": {field.name: str(field.type) for field in schema},
+        "source_empty_string_counts": empty_counts,
+        "null_counts": null_counts,
         "validation": {
-            "method": "Full row-by-row and cell-by-cell comparison with parsed source CSV",
+            "method": "Full cell-by-cell comparison: exact nonempty text, inverse semantic comparisons for typed values, empty cells mapped to null",
             "verified_rows": verified_rows,
-            "all_values_identical": True,
+            "all_semantic_values_match": True,
+            "nonempty_text_unchanged": True,
+            "null_counts_match_source_empty_cells": True,
             "row_order_preserved": True,
             "column_order_preserved": True,
         },
@@ -82,7 +157,12 @@ def convert(source, output):
             "compression": "zstd",
             "csv_encoding": "utf-8",
             "type_inference": False,
-            "empty_fields": "Preserved as empty strings; no null inference",
+            "empty_fields": "Only exact empty strings become null; literal null-like text is preserved",
+            "created_utc": "ISO 8601 UTC to timestamp[us, tz=UTC]",
+            "retrieved_on": "Unix seconds to timestamp[us, tz=UTC] using exact decimal arithmetic",
+            "over18": "True/False text to boolean",
+            "subscribers": "Nonnegative integer text to int64",
+            "write_page_index": True,
             "metadata_refreshed": False,
         },
     }

data/data.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:20cd09b4fed1c12bd316574c43a0ebaae0beb27b75c74d58c1057eb2104a0746
-size 183178103
+oid sha256:c8a520b2d2785cf79c9baa23ba98f904e5bee2e146acb6ff9b20aa345ddddb68
+size 184146598