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# DataFrame Rendering

DataFusion provides configurable rendering for DataFrames in both plain text and HTML
formats. The `datafusion.dataframe_formatter` module controls how DataFrames are
displayed in Jupyter notebooks (via `_repr_html_`), in the terminal (via `__repr__`),
and anywhere else a string or HTML representation is needed.

## Basic Rendering

In a Jupyter environment, displaying a DataFrame triggers HTML rendering:

```python
# Will display as HTML table in Jupyter
df

# Explicit display also uses HTML rendering
display(df)
```

In a terminal or when converting to string, plain text rendering is used:

```python
# Plain text table output
print(df)
```

## Configuring the Formatter

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

```python
from datafusion.dataframe_formatter import configure_formatter

configure_formatter(
    max_cell_length=25,           # Maximum characters in a cell before truncation
    max_width=1000,               # Maximum width in pixels (HTML only)
    max_height=300,               # Maximum height in pixels (HTML only)
    max_memory_bytes=2097152,     # Maximum memory for rendering (2MB)
    min_rows=10,                  # Minimum number of rows to display
    max_rows=10,                  # Maximum rows to display
    enable_cell_expansion=True,   # Allow expanding truncated cells (HTML only)
    custom_css=None,              # Additional custom CSS (HTML only)
    show_truncation_message=True, # Show message when data is truncated
    style_provider=None,          # Custom styling provider (HTML only)
    use_shared_styles=True,       # Share styles across tables (HTML only)
)
```

The formatter settings affect all DataFrames displayed after configuration.

## Custom Style Providers

For HTML styling, you can create a custom style provider that implements the
`StyleProvider` protocol:

```python
from datafusion.dataframe_formatter import configure_formatter

class MyStyleProvider:
    def get_cell_style(self):
        """Return CSS style string for table data cells."""
        return "border: 1px solid #ddd; padding: 8px; text-align: left;"

    def get_header_style(self):
        """Return CSS style string for table header cells."""
        return (
            "background-color: #007bff; color: white; "
            "padding: 8px; text-align: left;"
        )

# Apply the custom style provider
configure_formatter(style_provider=MyStyleProvider())
```

## Custom Cell Formatters

You can register custom formatters for specific Python types. A cell formatter is any
callable that takes a value and returns a string:

```python
from datafusion.dataframe_formatter import get_formatter

formatter = get_formatter()

# Format floats to 2 decimal places
formatter.register_formatter(float, lambda v: f"{v:.2f}")

# Format dates in a custom way
from datetime import date
formatter.register_formatter(date, lambda v: v.strftime("%B %d, %Y"))
```

## Custom Cell and Header Builders

For full control over the HTML of individual cells or headers, you can set custom
builder functions:

```python
from datafusion.dataframe_formatter import get_formatter

formatter = get_formatter()

# Custom cell builder receives (value, row, col, table_id) and returns HTML
def my_cell_builder(value, row, col, table_id):
    color = "red" if isinstance(value, (int, float)) and value < 0 else "black"
    return f"<td style='color: {color}; padding: 8px;'>{value}</td>"

formatter.set_custom_cell_builder(my_cell_builder)

# Custom header builder receives a schema field and returns HTML
def my_header_builder(field):
    return f"<th style='background: #333; color: white; padding: 8px;'>{field.name}</th>"

formatter.set_custom_header_builder(my_header_builder)
```

## Performance Optimization with Shared Styles

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

```python
from datafusion.dataframe_formatter import 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

## Working with the Formatter Directly

You can use `get_formatter()` and `set_formatter()` for direct access to the global
formatter instance:

```python
from datafusion.dataframe_formatter import (
    DataFrameHtmlFormatter,
    get_formatter,
    set_formatter,
)

# Get and modify the current formatter
formatter = get_formatter()
print(formatter.max_rows)
print(formatter.max_cell_length)

# Create and set a fully custom formatter
custom_formatter = DataFrameHtmlFormatter(
    max_cell_length=50,
    max_rows=20,
    enable_cell_expansion=False,
)
set_formatter(custom_formatter)
```

Reset to default formatting:

```python
from datafusion.dataframe_formatter import reset_formatter

# Reset to default settings
reset_formatter()
```

## Memory and Display Controls

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

```python
from datafusion.dataframe_formatter import configure_formatter

configure_formatter(
    max_memory_bytes=4 * 1024 * 1024,  # 4MB maximum memory for display
    min_rows=20,                       # Always show at least 20 rows
    max_rows=50,                       # Show up to 50 rows in 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