Use descriptive dataset table names and update loading examples

README.md

@@ -16,6 +16,24 @@ task_categories:
   - time-series-forecasting
 size_categories:
   - 10K<n<100K
+configs:
+- config_name: ethereum_swap_quotes
+  data_files:
+  - split: train
+    path: e10_quote_benchmark/**/*.parquet
+  default: true
+- config_name: ethereum_swap_routes
+  data_files:
+  - split: train
+    path: e16_dex_routes/**/*.parquet
+- config_name: perpetual_order_book_depth
+  data_files:
+  - split: train
+    path: e17_perp_depth/**/*.parquet
+- config_name: collection_runs
+  data_files:
+  - split: train
+    path: e0_run_manifest/**/*.parquet
 ---
 
 # Crypto execution quotes and order-book depth
@@ -26,9 +44,9 @@ Ethereum swap quotes, route details and selected perpetual-futures order-book sn
 
 | Table | Record |
 |---|---|
-| `e10_quote_benchmark` | A swap quote at a specified size, with available provider-fee details |
-| `e16_dex_routes` | A route leg, with venue, pool and amount where supplied |
-| `e17_perp_depth` | An order-book snapshot summarising spread and resting notional within bands around the midpoint |
+| `ethereum_swap_quotes` | A swap quote at a specified size, with available provider-fee details |
+| `ethereum_swap_routes` | A route leg, with venue, pool and amount where supplied |
+| `perpetual_order_book_depth` | An order-book snapshot summarising spread and resting notional within bands around the midpoint |
 
 ## Using the data
 
@@ -51,20 +69,34 @@ Data is stored as Parquet files under `dataset/YYYY/MM/`, with partitions for co
 
 ### Load a table
 
+Install `datasets` and `pandas` to run this example. The `train` split contains all observations in the selected table; it is not a predefined modelling split.
+
 ```python
-from huggingface_hub import snapshot_download
-import pandas as pd, glob
+from datasets import load_dataset
 
-path = snapshot_download("dataforge-labs/crypto-execution-costs", repo_type="dataset",
-                         allow_patterns="e10_quote_benchmark/**")
-df = pd.concat(map(pd.read_parquet,
-                   glob.glob(f"{path}/e10_quote_benchmark/**/*.parquet", recursive=True)))
+data = load_dataset("dataforge-labs/crypto-execution-costs",
+                    "ethereum_swap_quotes", split="train")
+df = data.to_pandas()
 ```
 
 ## Coverage
 
-`e0_run_manifest` records collection windows, poll counts and failures. It is published in full and may cover dates beyond the fixed data sample. Collection gaps are not interpolated. Use the manifest together with table timestamps and error fields to assess coverage.
+`collection_runs` records collection windows, poll counts and failures. It is published in full and may cover dates beyond the fixed data sample. Collection gaps are not interpolated. Use this table together with measurement timestamps and error fields to assess coverage.
 
 ## License and contact
 
 The public sample is published under ODC-BY. Attribute it to "DataForge (dataforge-labs)". For questions about the data or access to additional history, open a discussion in this repository.
+
+<details>
+<summary>File paths and compatibility</summary>
+
+The table names above are Hugging Face dataset configurations. Each configuration reads the original Parquet files, whose paths remain unchanged for existing downloads and scripts. No records are copied, moved or renamed.
+
+| Table name | Storage directory |
+|---|---|
+| `ethereum_swap_quotes` | `e10_quote_benchmark/` |
+| `ethereum_swap_routes` | `e16_dex_routes/` |
+| `perpetual_order_book_depth` | `e17_perp_depth/` |
+| `collection_runs` | `e0_run_manifest/` |
+
+</details>