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playground(label: str, query: str, source: str = None, size: float = 1.0) -> pd.DataFrame
The playground function allows you to create an interactive SQL playground within your application. It provides a dynamic interface for querying connected data sources and visualizing results directly.

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Parameters

  • label (str): The display label for the playground component. This is used for identification and UI rendering.
  • query (str): The SQL query string to execute. This supports standard SQL syntax and can include SELECT, JOIN, WHERE, etc.
  • source (str, optional): (Optional) The name of the specific data source to query. If not provided, the function attempts to auto-detect the data source from the SQL query itself. (See “Default Behavior for source” below)
  • size (float): (Optional) The width of the component in a row. Defaults to 1.0 (full row).
Playground UI Example
For example: 
playground(
    label="User Data",
    query="SELECT * FROM users WHERE age > 30"
)
In this case, source is auto-detected as users. Note: Auto-detection works best when the query has a clear FROM or JOIN clause.

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Returns

  • pd.DataFrame: The resulting DataFrame after executing the query. You can use it for further processing or visualization.

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Usage Example

Here’s how to create a simple playground: 
from preswald import playground

# Create the playground with auto-detection of the data source
df = playground(
    label="User Age Filter", 
    query="SELECT name, age FROM users WHERE age > 25"
)
Or, explicitly pass the data source: 
df = playground(
    label="User Age Filter",
    query="SELECT name, age FROM users WHERE age > 25",
    source="users"
)

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Query Execution

The playground component:
  1. Auto-loads any saved query state (if user modified the query previously).
  2. Runs the SQL query against the provided or detected data source.
  3. Renders the result interactively in the frontend.

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Error Handling

  • Handles invalid queries and missing data sources gracefully
  • Captures and displays query errors in the UI
  • Logs detailed debug information for troubleshooting

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Best Practices

  1. Use meaningful labels to distinguish multiple playgrounds.
  2. Prefer explicit source when working with complex queries or joins.
  3. Always review auto-detected source for correctness.
  4. Use appropriate WHERE clauses to limit large datasets.
  5. Handle exceptions gracefully to improve user experience.
  • query(): Executes SQL queries directly
  • get_df(): Retrieves full datasets
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