TableReport#

Usage examples at the bottom of this page.

class skrub.TableReport(dataframe, order_by=None, title=None, column_filters=None)[source]#

Summarize the contents of a dataframe.

This class summarizes a dataframe, providing information such as the type and summary statistics (mean, number of missing values, etc.) for each column.

Parameters:

dataframepandas or polars DataFrame: The dataframe to summarize.
order_bystr: Column name to use for sorting. Other numerical columns will be plotted as function of the sorting column. Must be of numerical or datetime type.
titlestr: Title for the report.
column_filtersdict: A dict for adding custom entries to the column filter dropdown menu. Each key is an id for the filter (e.g. "first_10") and the value is a mapping with the keys display_name (the name shown in the menu, e.g. "First 10 columns") and columns (a list of column names). See the end of the “Examples” section below for details.

Attributes:

htmlstr: Get the report as a full HTML page.
html_snippetstr: Get the report as an HTML fragment that can be inserted in a page.
jsonstr: Get the report data in JSON format.

Notes

You can see some example reports for a few datasets online. We also provide an experimental online demo that allows you to select a CSV or parquet file and generate a report directly in your web browser.

Examples

>>> import pandas as pd
>>> from skrub import TableReport
>>> df = pd.DataFrame(dict(a=[1, 2], b=['one', 'two'], c=[11.1, 11.1]))
>>> report = TableReport(df)

If you are in a Jupyter notebook, to display the report just have it be the last expression evaluated in a cell so that it is displayed in the cell’s output.

>>> report
<TableReport: use .open() to display>

(Note that above we only see the string represention, not the report itself, because we are not in a notebook.)

Whether you are using a notebook or not, you can always open the report as a full page in a separate browser tab with its open method: report.open().

You can also get the HTML report as a string. For a full, standalone web page:

>>> report.html()
Processing...
'<!DOCTYPE html>\n<html lang="en-US">\n\n<head>\n    <meta charset="utf-8"...'

For an HTML fragment that can be inserted into a page:

>>> report.html_snippet()
'\n<div id="report_...-wrapper" hidden>\n    <template id="report_...'

Advanced configuration: you can add custom column filters that will appear in the report’s dropdown menu.

>>> filters = {
...     "at_least_2": {
...         "display_name": "Columns with at least 2 unique values",
...         "columns": ["a", "b"],
...     }
... }
>>> report = TableReport(df, column_filters=filters)

With the code above, in addition to the default filters such as “All columns”, “Numeric columns”, etc., the added “Columns with at least 2 unique values” will be available in the report, selecting columns “a” and “b”.

Methods

`html`()	Get the report as a full HTML page.
`html_snippet`()	Get the report as an HTML fragment that can be inserted in a page.
`json`()	Get the report data in JSON format.
`open`()	Open the HTML report in a web browser.

html()[source]#

Get the report as a full HTML page.

Returns:

str: The HTML page.

html_snippet()[source]#

Get the report as an HTML fragment that can be inserted in a page.

Returns:

str: The HTML snippet.

json()[source]#

Get the report data in JSON format.

Returns:

str: The JSON data.

open()[source]#: Open the HTML report in a web browser.