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Interactive Table VIS Mixin

What is Interactive Table VIS Mixin?

The Interactive Table VIS mixin is responsible to allow the user to vis. and interact with any dataset of interest within your Jupyter Notebook much better than Pandas Dataframe visualisation. Showing off statistics on top of each of your dataset's attributes, etc.

A mixin, in this very instance, is nothing more than a class that connects external libraries for their use directly adapted towards the UrbanMapper workflow.

Documentation Under Alpha Construction

This documentation is in its early stages and still being developed. The API may therefore change, and some parts might be incomplete or inaccurate.

Use at your own risk, and please report anything that seems incorrect / outdated you find.

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interactive_table_vis

TableVisMixin

Mixin for creating interactive data table visualisations in notebooks.

This mixin provides methods for displaying dataframes as interactive tables with filtering and sorting capabilities. It enhances the data exploration experience in Jupyter notebooks by offering richer visualisations compared to standard DataFrame displays.

All this thanks to Skrub's TableReport class. Find out more about Skrub, via their official doc.

Examples:

>>> from urban_mapper import UrbanMapper
>>> import geopandas as gpd
>>>
>>> # Initialise UrbanMapper
>>> mapper = UrbanMapper()
>>>
>>> # Load sample data
>>> data = gpd.read_file("nyc_taxi_trips.geojson")
>>>
>>> # Display as an interactive table
>>> mapper.table_vis.interactive_display(
...     dataframe=data,
...     n_rows=15,
...     order_by="trip_distance",
...     title="NYC Taxi Trips"
... )
Source code in src/urban_mapper/mixins/interactive_table_vis.py 
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@beartype
class TableVisMixin:
    """Mixin for creating interactive data table visualisations in notebooks.

    This mixin provides methods for displaying dataframes as interactive tables
    with filtering and sorting capabilities. It enhances the data exploration
    experience in Jupyter notebooks by offering richer visualisations compared
    to standard DataFrame displays.

    All this thanks to Skrub's `TableReport` class. Find out more about [Skrub, via their official doc](https://skrub-data.org/stable/).

    Examples:
        >>> from urban_mapper import UrbanMapper
        >>> import geopandas as gpd
        >>>
        >>> # Initialise UrbanMapper
        >>> mapper = UrbanMapper()
        >>>
        >>> # Load sample data
        >>> data = gpd.read_file("nyc_taxi_trips.geojson")
        >>>
        >>> # Display as an interactive table
        >>> mapper.table_vis.interactive_display(
        ...     dataframe=data,
        ...     n_rows=15,
        ...     order_by="trip_distance",
        ...     title="NYC Taxi Trips"
        ... )
    """

    def interactive_display(
        self,
        dataframe: Union[pd.DataFrame, gpd.GeoDataFrame],
        n_rows: int = 10,
        order_by: Optional[Union[str, List[str]]] = None,
        title: Optional[str] = "Table Report",
        column_filters: Optional[Dict[str, Dict[str, Union[str, List[str]]]]] = None,
        verbose: int = 1,
    ) -> None:
        """Display a `dataframe` as an `interactive HTML` table with sorting and filtering capabilities.

        This method generates an enhanced table visualisation, enabling users to
        explore data more effectively than with standard DataFrame displays. It
        supports sorting by columns, applying filters, and customising the number
        of rows displayed.

        Find out more about the `TableReport` class in the [Skrub documentation](https://skrub-data.org/stable/reference/generated/skrub.TableReport.html#skrub.TableReport).

        Args:
            dataframe (Union[pd.DataFrame, gpd.GeoDataFrame]): The dataframe to display.
            n_rows (int, optional): Number of rows to show in the table. Defaults to 10.
            order_by (Optional[Union[str, List[str]]], optional): Column(s) to sort the data by. Defaults to None.
            title (Optional[str], optional): Title to display above the table. Defaults to "Table Report".
            column_filters (Optional[Dict[str, Dict[str, Union[str, List[str]]]]], optional): Filters to apply to specific columns.
                Format: {column_name: {filter_type: filter_value}}, e.g., {"fare_amount": {"greater_than": 50.0}}. Defaults to None.
            verbose (int, optional): Verbosity level for report generation. Defaults to 1.

        Returns:
            None: Displays the table directly in the notebook.

        Examples:
            >>> # Basic display with default settings
            >>> mapper.table_vis.interactive_display(dataframe=data)
            >>>
            >>> # Advanced display with sorting and filtering
            >>> mapper.table_vis.interactive_display(
            ...     dataframe=taxi_data,
            ...     n_rows=20,
            ...     order_by=["trip_distance", "fare_amount"],
            ...     title="High-Value Taxi Trips",
            ...     column_filters={
            ...         "payment_type": {"equals": "credit card"},
            ...         "fare_amount": {"greater_than": 50.0}
            ...     },
            ...     verbose=2
            ... )
        """
        if dataframe is not None and 0 < n_rows < len(dataframe):
            report = TableReport(
                dataframe=dataframe,
                n_rows=n_rows,
                order_by=order_by,
                title=title,
                column_filters=column_filters,
                verbose=verbose,
            )
            display(HTML(report.html()))

interactive_display(dataframe, n_rows=10, order_by=None, title='Table Report', column_filters=None, verbose=1)

Display a dataframe as an interactive HTML table with sorting and filtering capabilities.

This method generates an enhanced table visualisation, enabling users to explore data more effectively than with standard DataFrame displays. It supports sorting by columns, applying filters, and customising the number of rows displayed.

Find out more about the TableReport class in the Skrub documentation.

Parameters:

Name Type Description Default
dataframe Union[DataFrame, GeoDataFrame]

The dataframe to display.

 required
n_rows int

Number of rows to show in the table. Defaults to 10.

 10
order_by Optional[Union[str, List[str]]]

Column(s) to sort the data by. Defaults to None.

 None
title Optional[str]

Title to display above the table. Defaults to "Table Report".

 'Table Report'
column_filters Optional[Dict[str, Dict[str, Union[str, List[str]]]]]

Filters to apply to specific columns. Format: {column_name: {filter_type: filter_value}}, e.g., {"fare_amount": {"greater_than": 50.0}}. Defaults to None.

 None
verbose int

Verbosity level for report generation. Defaults to 1.

 1

Returns:

Name Type Description
None None

Displays the table directly in the notebook.

Examples:

>>> # Basic display with default settings
>>> mapper.table_vis.interactive_display(dataframe=data)
>>>
>>> # Advanced display with sorting and filtering
>>> mapper.table_vis.interactive_display(
...     dataframe=taxi_data,
...     n_rows=20,
...     order_by=["trip_distance", "fare_amount"],
...     title="High-Value Taxi Trips",
...     column_filters={
...         "payment_type": {"equals": "credit card"},
...         "fare_amount": {"greater_than": 50.0}
...     },
...     verbose=2
... )
Source code in src/urban_mapper/mixins/interactive_table_vis.py 
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def interactive_display(
    self,
    dataframe: Union[pd.DataFrame, gpd.GeoDataFrame],
    n_rows: int = 10,
    order_by: Optional[Union[str, List[str]]] = None,
    title: Optional[str] = "Table Report",
    column_filters: Optional[Dict[str, Dict[str, Union[str, List[str]]]]] = None,
    verbose: int = 1,
) -> None:
    """Display a `dataframe` as an `interactive HTML` table with sorting and filtering capabilities.

    This method generates an enhanced table visualisation, enabling users to
    explore data more effectively than with standard DataFrame displays. It
    supports sorting by columns, applying filters, and customising the number
    of rows displayed.

    Find out more about the `TableReport` class in the [Skrub documentation](https://skrub-data.org/stable/reference/generated/skrub.TableReport.html#skrub.TableReport).

    Args:
        dataframe (Union[pd.DataFrame, gpd.GeoDataFrame]): The dataframe to display.
        n_rows (int, optional): Number of rows to show in the table. Defaults to 10.
        order_by (Optional[Union[str, List[str]]], optional): Column(s) to sort the data by. Defaults to None.
        title (Optional[str], optional): Title to display above the table. Defaults to "Table Report".
        column_filters (Optional[Dict[str, Dict[str, Union[str, List[str]]]]], optional): Filters to apply to specific columns.
            Format: {column_name: {filter_type: filter_value}}, e.g., {"fare_amount": {"greater_than": 50.0}}. Defaults to None.
        verbose (int, optional): Verbosity level for report generation. Defaults to 1.

    Returns:
        None: Displays the table directly in the notebook.

    Examples:
        >>> # Basic display with default settings
        >>> mapper.table_vis.interactive_display(dataframe=data)
        >>>
        >>> # Advanced display with sorting and filtering
        >>> mapper.table_vis.interactive_display(
        ...     dataframe=taxi_data,
        ...     n_rows=20,
        ...     order_by=["trip_distance", "fare_amount"],
        ...     title="High-Value Taxi Trips",
        ...     column_filters={
        ...         "payment_type": {"equals": "credit card"},
        ...         "fare_amount": {"greater_than": 50.0}
        ...     },
        ...     verbose=2
        ... )
    """
    if dataframe is not None and 0 < n_rows < len(dataframe):
        report = TableReport(
            dataframe=dataframe,
            n_rows=n_rows,
            order_by=order_by,
            title=title,
            column_filters=column_filters,
            verbose=verbose,
        )
        display(HTML(report.html()))
Provost Simon