Update README.md

README.md

@@ -39,10 +39,10 @@ The transformation was performed using the following DuckDB query:
 ```python
 import duckdb
 
-Connect to a new DuckDB database
+#Connect to a new DuckDB database
 new_db = duckdb.connect('merged_notebooks.db')
 
-Query to concatenate markdown, code, and output
+#Query to concatenate markdown, code, and output
 query = """
 SELECT path,
 STRING_AGG(CONCAT('###Markdown\n', markdown, '\n###Code\n', code, '\n###Output\n', output), '\n') AS concatenated_notebook
@@ -50,5 +50,19 @@ FROM read_parquet('jupyter-code-text-pairs/data/*.parquet')
 GROUP BY path
 """
 
-Execute the query and create a new table
-new_db.execute(f"CREATE TABLE concatenated_notebooks AS {query}")
+#Execute the query and create a new table
+new_db.execute(f"CREATE TABLE concatenated_notebooks AS {query}")
+```
+## Usage
+
+To replicate the transformation or explore the original dataset, you can download it using the following command:
+
+```bash
+git clone https://huggingface.co/datasets/bigcode/jupyter-code-text-pairs
+
+```
+Once downloaded, you can use the provided DuckDB query to process the data as needed.
+
+## Conclusion
+
+This dataset provides a more integrated view of Jupyter notebooks by merging markdown, code, and output into a single format. The use of DuckDB demonstrates its capability to handle large datasets efficiently, making it an excellent tool for data transformation tasks.