Extend preview to custom datasets

When creating a custom dataset, if you wish to enable data preview for that dataset, you must implement a preview() function within the custom dataset class. Kedro-Viz currently supports previewing tables, Plotly charts, images, and JSON objects.

The return type of the preview() function should match one of the following types, as defined in the kedro-datasets source code (_typing.py file):

TablePreview = NewType("TablePreview", dict)
ImagePreview = NewType("ImagePreview", bytes)
PlotlyPreview = NewType("PlotlyPreview", dict)
JSONPreview = NewType("JSONPreview", dict)

Arbitrary arguments can be included in the preview() function, which can be later specified in the catalog.yml file.

Below is an example demonstrating how to implement the preview() function with user-specified arguments for a CustomDataset class that utilizes TablePreview to enable previewing tabular data on Kedro-Viz:

  type: CustomDataset
  filepath: ${_base_location}/01_raw/companies.csv
      layer: raw
        nrows: 5
        ncolumns: 2 
        filters: {
          gender: male 

from kedro_datasets._typing import TablePreview

class CustomDataset:
  def preview(self, nrows, ncolumns, filters) -> TablePreview:
    filtered_data = self.data
    for column, value in filters.items():
        filtered_data = filtered_data[filtered_data[column] == value]
    subset = filtered_data.iloc[:nrows, :ncolumns]
    df_dict = {}
    for column in subset.columns:
        df_dict[column] = subset[column]
    return df_dict

Examples of Previews

  1. TablePreview

  1. ImagePreview

  1. PlotlyPreview