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.. to you under the Apache License, Version 2.0 (the
.. "License"); you may not use this file except in compliance
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.. specific language governing permissions and limitations
.. under the License.

HTML Rendering in Jupyter
=========================

When working in Jupyter notebooks or other environments that support rich HTML display, 
DataFusion DataFrames automatically render as nicely formatted HTML tables. This functionality
is provided by the ``_repr_html_`` method, which is automatically called by Jupyter to provide
a richer visualization than plain text output.

Basic HTML Rendering
--------------------

In a Jupyter environment, simply displaying a DataFrame object will trigger HTML rendering:

.. code-block:: python

    # Will display as HTML table in Jupyter
    df

    # Explicit display also uses HTML rendering
    display(df)

Customizing HTML Rendering
---------------------------

DataFusion provides extensive customization options for HTML table rendering through the
``datafusion.html_formatter`` module.

Configuring the HTML Formatter
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can customize how DataFrames are rendered by configuring the formatter:

.. code-block:: python

    from datafusion.html_formatter import configure_formatter
    
    # Change the default styling
    configure_formatter(
        max_cell_length=25,        # Maximum characters in a cell before truncation
        max_width=1000,            # Maximum width in pixels
        max_height=300,            # Maximum height in pixels
        max_memory_bytes=2097152,  # Maximum memory for rendering (2MB)
        min_rows_display=20,       # Minimum number of rows to display
        repr_rows=10,              # Number of rows to display in __repr__
        enable_cell_expansion=True,# Allow expanding truncated cells
        custom_css=None,           # Additional custom CSS
        show_truncation_message=True, # Show message when data is truncated
        style_provider=None,       # Custom styling provider
        use_shared_styles=True     # Share styles across tables
    )

The formatter settings affect all DataFrames displayed after configuration.

Custom Style Providers
-----------------------

For advanced styling needs, you can create a custom style provider:

.. code-block:: python

    from datafusion.html_formatter import StyleProvider, configure_formatter
    
    class MyStyleProvider(StyleProvider):
        def get_table_styles(self):
            return {
                "table": "border-collapse: collapse; width: 100%;",
                "th": "background-color: #007bff; color: white; padding: 8px; text-align: left;",
                "td": "border: 1px solid #ddd; padding: 8px;",
                "tr:nth-child(even)": "background-color: #f2f2f2;",
            }
            
        def get_value_styles(self, dtype, value):
            """Return custom styles for specific values"""
            if dtype == "float" and value < 0:
                return "color: red;"
            return None
    
    # Apply the custom style provider
    configure_formatter(style_provider=MyStyleProvider())

Performance Optimization with Shared Styles
--------------------------------------------

The ``use_shared_styles`` parameter (enabled by default) optimizes performance when displaying 
multiple DataFrames in notebook environments:

.. code-block:: python

    from datafusion.html_formatter import StyleProvider, configure_formatter
    # Default: Use shared styles (recommended for notebooks)
    configure_formatter(use_shared_styles=True)

    # Disable shared styles (each DataFrame includes its own styles)
    configure_formatter(use_shared_styles=False)

When ``use_shared_styles=True``:
- CSS styles and JavaScript are included only once per notebook session
- This reduces HTML output size and prevents style duplication
- Improves rendering performance with many DataFrames
- Applies consistent styling across all DataFrames

Creating a Custom Formatter
----------------------------

For complete control over rendering, you can implement a custom formatter:

.. code-block:: python

    from datafusion.html_formatter import Formatter, get_formatter
    
    class MyFormatter(Formatter):
        def format_html(self, batches, schema, has_more=False, table_uuid=None):
            # Create your custom HTML here
            html = "<div class='my-custom-table'>"
            # ... formatting logic ...
            html += "</div>"
            return html
    
    # Set as the global formatter
    configure_formatter(formatter_class=MyFormatter)
    
    # Or use the formatter just for specific operations
    formatter = get_formatter()
    custom_html = formatter.format_html(batches, schema)

Managing Formatters
-------------------

Reset to default formatting:

.. code-block:: python

    from datafusion.html_formatter import reset_formatter
    
    # Reset to default settings
    reset_formatter()

Get the current formatter settings:

.. code-block:: python

    from datafusion.html_formatter import get_formatter
    
    formatter = get_formatter()
    print(formatter.max_rows)
    print(formatter.theme)

Contextual Formatting
----------------------

You can also use a context manager to temporarily change formatting settings:

.. code-block:: python

    from datafusion.html_formatter import formatting_context
    
    # Default formatting
    df.show()
    
    # Temporarily use different formatting
    with formatting_context(max_rows=100, theme="dark"):
        df.show()  # Will use the temporary settings
    
    # Back to default formatting
    df.show()

Memory and Display Controls
---------------------------

You can control how much data is displayed and how much memory is used for rendering:

.. code-block:: python

    configure_formatter(
        max_memory_bytes=4 * 1024 * 1024,  # 4MB maximum memory for display
        min_rows_display=50,               # Always show at least 50 rows
        repr_rows=20                       # Show 20 rows in __repr__ output
    )

These parameters help balance comprehensive data display against performance considerations.

Best Practices
--------------

1. **Global Configuration**: Use ``configure_formatter()`` at the beginning of your notebook to set up consistent formatting for all DataFrames.

2. **Memory Management**: Set appropriate ``max_memory_bytes`` limits to prevent performance issues with large datasets.

3. **Shared Styles**: Keep ``use_shared_styles=True`` (default) for better performance in notebooks with multiple DataFrames.

4. **Reset When Needed**: Call ``reset_formatter()`` when you want to start fresh with default settings.

5. **Cell Expansion**: Use ``enable_cell_expansion=True`` when cells might contain longer content that users may want to see in full.

Additional Resources
--------------------

* :doc:`../dataframe/index` - Complete guide to using DataFrames
* :doc:`../io/index` - I/O Guide for reading data from various sources
* :doc:`../data-sources` - Comprehensive data sources guide
* :ref:`io_csv` - CSV file reading
* :ref:`io_parquet` - Parquet file reading  
* :ref:`io_json` - JSON file reading
* :ref:`io_avro` - Avro file reading
* :ref:`io_custom_table_provider` - Custom table providers
* `API Reference <https://arrow.apache.org/datafusion-python/api/index.html>`_ - Full API reference