DOC - improving howto section on tablereport

doc/guides/table_report/alter_appearance.rst → doc/guides/table_report/01_alter_appearance.rst

@@ -7,7 +7,7 @@
 How to tweak the Appearance of the |TableReport|
 ------------------------------------------------
 
-The skrub global configuration includes various parameters that allow to tweak
+The skrub global configuration includes various parameters that let you tweak
 the HTML representation of the |TableReport|.
 
 For performance reasons, the |TableReport| disables the computation of

doc/guides/table_report/exporting.rst → doc/guides/table_report/02_exporting.rst

@@ -29,7 +29,7 @@ It is also possible to export the raw HTML, or a HTML fragment to embed in a pag
 with :func:`~skrub.TableReport.html` and  :func:`~skrub.TableReport.html_snippet`
 respectively.
 
-Finally, it is possible to export the data in JSON format, which allows structured
+The report can be exported in JSON format, which allows structured
 access to the data and statistics used to build the report with
 :func:`~skrub.TableReport.json`.
 
@@ -46,3 +46,16 @@ disabled directly when generating the table report.
 
     tr = TableReport(df, plot_distributions=False)
     json_data = tr.json()
+
+Finally, :func:`~skrub.TableReport.markdown` produces a shortened summary of the
+report in Markdown format. This summary contains the measured statistics and the
+associations (if measured): plots and table preview are skipped from this view.
+This format can be shared easily in text form, or fed to an AI agent to obtain
+insight about a given table.
+
+.. warning::
+
+  No sanitization of the input data is performed, and the report includes raw data
+  (column names and cell values). Therefore, it should not be used on untrusted data,
+  or when the resulting summary may be too large as it could lead to security risks
+  or performance problems.

doc/guides/table_report/finding_correlated_columns.rst → doc/guides/table_report/03_finding_correlated_columns.rst

@@ -1,9 +1,10 @@
 .. |TableReport| replace:: :class:`~skrub.TableReport`
+.. |DropSimilar| replace:: :class:`~skrub.DropSimilar`
 .. |column_associations| replace:: :func:`~skrub.column_associations`
 
 .. _user_guide_table_report_associations:
 
-How to find correlated columns in a datarame
+How to find correlated columns in a dataframe
 ============================================
 
 In addition to |TableReport|'s **Associations** tab, you can compute associations
@@ -13,11 +14,12 @@ associations.
 Reported metrics include `Cramer’s V statistic <https://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V>`_
 and `Pearson’s Correlation Coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_.
 The result is returned as a dataframe that contains the column name and idx for the
-left and right table and both associations; results are sorted in descending order
+left and the right table, and both associations; results are sorted in descending order
 by Cramer’s V association.
 
 This can be useful to have access to the information used in the |TableReport|
-for later use (e.g., to select which columns to drop).
+for later use (e.g., to select which columns to drop). These associations are
+also used by the |DropSimilar| transformer to select which columns should be dropped.
 
 .. code-block::
 

doc/guides/table_report/04_custom_filters.rst

@@ -0,0 +1,32 @@
+.. |TableReport| replace:: :class:`~skrub.TableReport`
+
+
+How to define custom filters for the TableReport
+================================================
+
+It is possible to define custom filters for the |TableReport| using either column
+names, or :ref:`skrub selectors <user_guide_selectors>`.
+
+By defining a custom filter, it becomes easier to show and work directly on a given
+subset of columns.
+
+For example, we might want to select only the columns whose name follows a certain
+pattern (here, starting with "metric"):
+
+>>> import pandas as pd
+>>> from skrub import TableReport
+>>> from skrub import selectors as s
+>>> df = pd.DataFrame(
+...     {"id": [1, 2, 3], "metric1": [1, 2, 3], "metric2": [4, 5, 6], "metric3": [7, 8, 9]}
+... )
+
+Custom filters should be defined as a dictionary where the key is the name of the
+filter that should be displayed in the generated report, and the value is either
+a list of columns, the indices of the columns (first column has index 0 etc.), or
+a skrub selector, as shown in this example:
+
+>>> filters = {"only_metrics": s.glob("metric*")}
+>>> report = TableReport(df, column_filters=filters)
+
+Custom filters are placed at the top of the list of filters, in the "Filter columns"
+drop-down menu.

doc/howto.rst

@@ -17,9 +17,10 @@ the :ref:`API Reference <api_ref>`.
 .. toctree::
    :maxdepth: 2
 
-   guides/table_report/alter_appearance.rst
-   guides/table_report/exporting.rst
-   guides/table_report/finding_correlated_columns.rst
+   guides/table_report/01_alter_appearance.rst
+   guides/table_report/02_exporting.rst
+   guides/table_report/03_finding_correlated_columns.rst
+   guides/table_report/04_custom_filters.rst
    guides/utilities/customizing_configuration.rst
    guides/utilities/deduplicate_categorical_data.rst
    guides/utilities/fetching_datasets.rst