[Issue 1616] Fix examples numbering (skrub-data#1717)

Co-authored-by: Alice Goulley <alice.goulley@decathlon.com>

Author: Alispirale

CHANGES.rst

@@ -148,11 +148,11 @@ Highlights
 - :mod:`selectors`, :class:`ApplyToCols` and :class:`ApplyToFrame` are now available,
   providing utilities for selecting columns to which a transformer should be applied
   in a flexible way. For more details, see the :ref:`User guide <user_guide_selectors>`
-  and the :ref:`example <sphx_glr_auto_examples_09_apply_to_cols.py>`.
+  and the :ref:`example <sphx_glr_auto_examples_0090_apply_to_cols.py>`.
 
 - The :class:`SquashingScaler` has been added: it robustly rescales and smoothly
   clips numeric columns, enabling more robust handling of numeric columns
-  with neural networks. See the :ref:`example <sphx_glr_auto_examples_10_squashing_scaler.py>`
+  with neural networks. See the :ref:`example <sphx_glr_auto_examples_0100_squashing_scaler.py>`
 
 New features
 ------------

doc/modules/column_level_featurizing/robust_scaling.rst

@@ -34,5 +34,5 @@ array([[ 3.        ],
 
 More information about the theory behind the scaler is available in the
 |SquashingScaler| documentation, while this
-:ref:`working example <sphx_glr_auto_examples_10_squashing_scaler.py>` compares
+:ref:`working example <sphx_glr_auto_examples_0100_squashing_scaler.py>` compares
 different scalers when used on data that include outliers.

doc/modules/data_ops/basics/building_data_ops_plan.rst

@@ -90,5 +90,5 @@ By working only on Data Ops we ensure that all the operations done on the data
 are added correctly to the computational graph, which then allows the resulting
 learner to execute all steps as intended.
 
-See :ref:`sphx_glr_auto_examples_data_ops_11_data_ops_intro.py` for an introductory
+See :ref:`sphx_glr_auto_examples_data_ops_0110_data_ops_intro.py` for an introductory
 example on how to use skrub DataOps on a single dataframe.

doc/modules/data_ops/basics/control_flow.rst

@@ -145,9 +145,9 @@ Finally, there are other situations where using :func:`deferred` can be helpful:
 
 .. rubric:: Examples
 
-- See :ref:`sphx_glr_auto_examples_data_ops_11_data_ops_intro.py` for an introductory
+- See :ref:`sphx_glr_auto_examples_data_ops_0110_data_ops_intro.py` for an introductory
   example on how to use skrub DataOps on a single dataframe.
-- See :ref:`sphx_glr_auto_examples_data_ops_12_multiple_tables.py` for an example
+- See :ref:`sphx_glr_auto_examples_data_ops_0120_multiple_tables.py` for an example
   of how skrub DataOps can be used to process multiple tables using dataframe APIs.
-- See :ref:`sphx_glr_auto_examples_data_ops_13_choices.py` for an example of
+- See :ref:`sphx_glr_auto_examples_data_ops_0130_choices.py` for an example of
   hyper-parameter tuning using skrub DataOps.

doc/modules/data_ops/ml_pipeline/applying_different_transformers.rst

@@ -150,4 +150,4 @@ to obtain the final result:
 
 More info on advanced column selection and manipulation be found in
 :ref:`user_guide_selectors` and example
-:ref:`sphx_glr_auto_examples_09_apply_to_cols.py`.
+:ref:`sphx_glr_auto_examples_0090_apply_to_cols.py`.

doc/modules/data_ops/ml_pipeline/subsampling_data.rst

@@ -26,4 +26,4 @@ set to ``True`` to force using the subsampling when we call them. Note that
 even if we set ``keep_subsampling=True``, subsampling is not applied when using
 ``predict``.
 
-See more details in a :ref:`full example <sphx_glr_auto_examples_data_ops_14_subsampling.py>`.
+See more details in a :ref:`full example <sphx_glr_auto_examples_data_ops_0140_subsampling.py>`.

doc/modules/data_ops/validation/exporting_data_ops.rst

@@ -62,5 +62,5 @@ or in a different environment:
 >>> loaded_learner.fit({"orders": new_orders_df})
 SkrubLearner(data_op=<Apply TableVectorizer>)
 
-See :ref:`sphx_glr_auto_examples_data_ops_15_use_case.py` for an example of how
+See :ref:`sphx_glr_auto_examples_data_ops_0150_use_case.py` for an example of how
 to use the learner in a microservice.

doc/modules/data_ops/validation/hyperparameter_tuning.rst

@@ -141,7 +141,7 @@ It is also possible to use :meth:`ParamSearch.plot_results` to visualize the res
 of the search using a parallel coordinates plot.
 
 A full example of how to use hyperparameter search is available in
-:ref:`sphx_glr_auto_examples_data_ops_13_choices.py`.
+:ref:`sphx_glr_auto_examples_data_ops_0130_choices.py`.
 
 |
 

doc/modules/joining_tables/assembling.rst

@@ -58,4 +58,4 @@ in the right table (the table to be added). This is done by estimating the value
 that the missing rows would have by training a machine learning model on the data
 we have access to.
 
-This transformer is explored in more detail in :ref:`this example <sphx_glr_auto_examples_08_interpolation_join.py>`.
+This transformer is explored in more detail in :ref:`this example <sphx_glr_auto_examples_0080_interpolation_join.py>`.

examples/00_getting_started.py examples/0000_getting_started.pyexamples/00_getting_started.py renamed to examples/0000_getting_started.py

@@ -105,7 +105,7 @@
 # To handle rich tabular data and feed it to a machine learning model, the
 # pipeline returned by |tabular_pipeline| preprocesses and encodes
 # strings, categories and dates using the |TableVectorizer|.
-# See its documentation or :ref:`sphx_glr_auto_examples_01_encodings.py` for
+# See its documentation or :ref:`sphx_glr_auto_examples_0010_encodings.py` for
 # more details. An overview of the chosen defaults is available in
 # :ref:`user_guide_tabular_pipeline`.
 
@@ -124,7 +124,7 @@
 #
 # For **numerical features**, the |SquashingScaler| applies a robust
 # scaling technique that is less sensitive to outliers. Check the
-# :ref:`relative example <sphx_glr_auto_examples_10_squashing_scaler.py>`
+# :ref:`relative example <sphx_glr_auto_examples_0100_squashing_scaler.py>`
 # for more information on the feature.
 #
 # For **datetime columns**, skrub provides the |DatetimeEncoder|
@@ -183,7 +183,7 @@
 # which uses pre-trained language models retrieved from the HuggingFace hub to
 # create meaningful text embeddings.
 # See :ref:`user_guide_encoders_index` for more details on all the categorical encoders
-# provided by skrub, and :ref:`sphx_glr_auto_examples_01_encodings.py` for a
+# provided by skrub, and :ref:`sphx_glr_auto_examples_0010_encodings.py` for a
 # comparison between the different methods.
 #