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Add to preview() documentation
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pointblank/validate.py

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@@ -1147,6 +1147,69 @@ def preview(
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columns_subset=pb.col(pb.starts_with("item") | pb.matches("player"))
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)
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```
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### Working with CSV Files
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The `preview()` function can directly accept CSV file paths, making it easy to preview data stored
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in CSV files without manual loading:
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```{python}
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# Get a path to a CSV file from the package data
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csv_path = pb.get_data_path("global_sales", "csv")
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pb.preview(csv_path)
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```
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You can also use a Path object to specify the CSV file:
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```{python}
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from pathlib import Path
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csv_file = Path(pb.get_data_path("game_revenue", "csv"))
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pb.preview(csv_file, n_head=3, n_tail=3)
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```
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### Working with Parquet Files
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The `preview()` function can directly accept Parquet files and datasets in various formats:
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```{python}
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# Single Parquet file from package data
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parquet_path = pb.get_data_path("nycflights", "parquet")
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pb.preview(parquet_path)
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```
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You can also use glob patterns and directories:
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```python
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# Multiple Parquet files with glob patterns
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pb.preview("data/sales_*.parquet")
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# Directory containing Parquet files
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pb.preview("parquet_data/")
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# Partitioned Parquet dataset
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pb.preview("sales_data/") # Auto-discovers partition columns
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```
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### Working with Database Connection Strings
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The `preview()` function supports database connection strings for direct preview of database
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tables. Connection strings must specify a table using the `::table_name` suffix:
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```{python}
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# Get path to a DuckDB database file from package data
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duckdb_path = pb.get_data_path("game_revenue", "duckdb")
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pb.preview(f"duckdb:///{duckdb_path}::game_revenue")
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```
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For comprehensive documentation on supported connection string formats, error handling, and
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installation requirements, see the [`connect_to_table()`](`pointblank.connect_to_table`)
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function.
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```
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"""
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# Process input data to handle different data source types

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