Update README.md

README.md

@@ -180,51 +180,6 @@ This dataset can be used to:
 - Compare against previous metadata versions to track changes, tag evolution, and historical trends
 
 
-## Dataset Creation
-
-We scraped all post IDs on Danbooru from 1 up to the latest. Some restricted tags (e.g. `loli`) were hidden by the site and require a gold account to access, so they are not present.  
-For a more complete (but older) metadata reference, you may wish to combine this with Danbooru2021 or similar previous scrapes.
-
-The scraping process used a pool of roughly 400 IPs over six hours, ensuring consistent tag definitions. Below is a simplified example of the process used to convert the metadata into Parquet:
-
-```python
-import pandas as pd
-from pandarallel import pandarallel
-
-# Initialize pandarallel
-pandarallel.initialize(nb_workers=4, progress_bar=True)
-
-def flatten_dict(d, parent_key='', sep='_'):
-    """
-    Flattens a nested dictionary.
-    """
-    items = []
-    for k, v in d.items():
-        new_key = f"{parent_key}{sep}{k}" if parent_key else k
-        if isinstance(v, dict):
-            items.extend(flatten_dict(v, new_key, sep=sep).items())
-        elif isinstance(v, list):
-            items.append((new_key, ', '.join(map(str, v))))
-        else:
-            items.append((new_key, v))
-    return dict(items)
-
-def extract_all_illust_info(json_content):
-    """
-    Parses and flattens Danbooru JSON into a pandas Series.
-    """
-    flattened_data = flatten_dict(json_content)
-    return pd.Series(flattened_data)
-
-def dicts_to_dataframe_parallel(dicts):
-    """
-    Converts a list of dicts to a flattened DataFrame using pandarallel.
-    """
-    df = pd.DataFrame(dicts)
-    flattened_df = df.parallel_apply(lambda row: extract_all_illust_info(row.to_dict()), axis=1)
-    return flattened_df
-```
-
 ## Dataset Structure
 
 Below is a partial overview of the DataFrame columns, derived directly from the Danbooru JSONs:
@@ -376,6 +331,52 @@ Index(['approver_id', 'bit_flags', 'created_at', 'down_score', 'fav_count',
 </div>
 
 
+## Dataset Creation
+
+We scraped all post IDs on Danbooru from 1 up to the latest. Some restricted tags (e.g. `loli`) were hidden by the site and require a gold account to access, so they are not present.  
+For a more complete (but older) metadata reference, you may wish to combine this with Danbooru2021 or similar previous scrapes.
+
+The scraping process used a pool of roughly 400 IPs over six hours, ensuring consistent tag definitions. Below is a simplified example of the process used to convert the metadata into Parquet:
+
+```python
+import pandas as pd
+from pandarallel import pandarallel
+
+# Initialize pandarallel
+pandarallel.initialize(nb_workers=4, progress_bar=True)
+
+def flatten_dict(d, parent_key='', sep='_'):
+    """
+    Flattens a nested dictionary.
+    """
+    items = []
+    for k, v in d.items():
+        new_key = f"{parent_key}{sep}{k}" if parent_key else k
+        if isinstance(v, dict):
+            items.extend(flatten_dict(v, new_key, sep=sep).items())
+        elif isinstance(v, list):
+            items.append((new_key, ', '.join(map(str, v))))
+        else:
+            items.append((new_key, v))
+    return dict(items)
+
+def extract_all_illust_info(json_content):
+    """
+    Parses and flattens Danbooru JSON into a pandas Series.
+    """
+    flattened_data = flatten_dict(json_content)
+    return pd.Series(flattened_data)
+
+def dicts_to_dataframe_parallel(dicts):
+    """
+    Converts a list of dicts to a flattened DataFrame using pandarallel.
+    """
+    df = pd.DataFrame(dicts)
+    flattened_df = df.parallel_apply(lambda row: extract_all_illust_info(row.to_dict()), axis=1)
+    return flattened_df
+```
+
+
 ### Recommendations
 
 Users should be aware of potential biases and limitations, including the presence of adult content in some tags. More details and mitigations may be needed.