From 05586e0f424282f13f17c0abd3768367393030f9 Mon Sep 17 00:00:00 2001
From: PredictiveManish <manish.tiwari.09@zohomail.in>
Date: Wed, 28 Jan 2026 00:55:32 +0530
Subject: [PATCH 1/4] Added database example using SQLite and Ibis for DataOps

---
 .../basics/data_ops_vs_alternatives.rst       |  10 ++
 doc/vision.rst                                |   3 +-
 examples/data_ops/1160_database_example.py    | 163 ++++++++++++++++++
 3 files changed, 175 insertions(+), 1 deletion(-)
 create mode 100644 examples/data_ops/1160_database_example.py

diff --git a/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst b/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst
index 38803c4ea..421a18c79 100644
--- a/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst
+++ b/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst
@@ -65,3 +65,13 @@ All DataOps-specific operations are available through the ``.skb`` attribute,
 which provides access to the DataOps namespace. Other library-specific methods
 are available directly from the DataOp object, as if it were a regular object
 (like a Pandas or Polars DataFrame or Series).
+
+Working with databases
+======================
+
+Skrub DataOps can work with databases by converting database tables to pandas
+DataFrames. For example, you can use `Ibis <https://ibis-project.org>`__ to
+connect to SQL databases (such as SQLite, PostgreSQL, MySQL, etc.), execute
+queries, and then convert the results to pandas DataFrames for use with skrub
+DataOps. See :ref:`sphx_glr_auto_examples_data_ops_1160_database_example.py` for
+a complete example showing how to integrate SQLite and Ibis with skrub DataOps.
diff --git a/doc/vision.rst b/doc/vision.rst
index 55da9b861..fe0773b67 100644
--- a/doc/vision.rst
+++ b/doc/vision.rst
@@ -22,7 +22,8 @@ data assembly).
 
 In the long term, as skrub is built on higher-level APIs, it will make it
 easier for data-scientists to use efficient database patterns and
-backends.
+backends. See :ref:`sphx_glr_auto_examples_data_ops_1160_database_example.py` for
+an example of working with databases using SQLite and Ibis.
 
 Skrub seeks tradeoffs in terms of flexibility: its high-level APIs are by
 construction restrictive compared to directly manipulating dataframes.
diff --git a/examples/data_ops/1160_database_example.py b/examples/data_ops/1160_database_example.py
new file mode 100644
index 000000000..1816f8dbf
--- /dev/null
+++ b/examples/data_ops/1160_database_example.py
@@ -0,0 +1,163 @@
+"""
+Working with databases using skrub: example SQLite and Ibis integration
+=======================================================================
+"""
+
+# %%
+# This example focuses on integrating database queries into a skrub DataOps plan.
+# We do not cover model evaluation or deployment.
+#
+# In many production settings, tabular data lives in SQL databases rather than
+# in-memory pandas DataFrames. skrub is designed to integrate naturally with
+# expression-based systems such as Ibis, allowing database-side computation
+# before materializing data for machine learning pipelines.
+#
+# In this example, we demonstrate how to:
+#   - connect to a SQLite database using Ibis
+#   - perform joins and filtering at the database level
+#   - materialize the result once into pandas
+#   - build a skrub DataOps plan on top of the database-backed data
+
+# %%
+# Setting up a SQLite database
+# -----------------------------
+# For demonstration purposes, we create a small SQLite database locally.
+# In practice, this would typically be an existing production database.
+
+import os
+import sqlite3
+import tempfile
+
+import numpy as np
+import pandas as pd
+
+# Create a temporary SQLite database
+db_file = tempfile.NamedTemporaryFile(delete=False, suffix=".db")
+db_path = db_file.name
+db_file.close()
+
+# Generate example data
+np.random.seed(42)
+n_customers = 100
+n_orders = 500
+
+customers_df = pd.DataFrame(
+    {
+        "customer_id": range(1, n_customers + 1),
+        "age": np.random.randint(18, 80, n_customers),
+        "city": np.random.choice(["New York", "London", "Paris", "Tokyo"], n_customers),
+    }
+)
+
+orders_df = pd.DataFrame(
+    {
+        "order_id": range(1, n_orders + 1),
+        "customer_id": np.random.randint(1, n_customers + 1, n_orders),
+        "product": np.random.choice(
+            ["Laptop", "Phone", "Tablet", "Monitor", "Keyboard"], n_orders
+        ),
+        "quantity": np.random.randint(1, 5, n_orders),
+        "price": np.random.uniform(100, 2000, n_orders).round(2),
+    }
+)
+
+# Write tables to SQLite
+conn = sqlite3.connect(db_path)
+customers_df.to_sql("customers", conn, index=False, if_exists="replace")
+orders_df.to_sql("orders", conn, index=False, if_exists="replace")
+conn.close()
+
+# %%
+# Connecting to the database with Ibis
+# -----------------------------------
+# We use Ibis to define database-side expressions instead of writing raw SQL.
+
+import ibis
+
+con = ibis.sqlite.connect(db_path)
+
+customers = con.table("customers")
+orders = con.table("orders")
+
+# %%
+# Database-side transformations
+# -----------------------------
+# In production workflows, joins and filtering are typically executed in the
+# database to avoid unnecessary data transfer.
+
+# Join orders with customers
+joined = orders.join(
+    customers,
+    orders.customer_id == customers.customer_id,
+)
+
+# Select only the columns needed for downstream processing
+joined = joined.select(
+    orders.price,
+    orders.quantity,
+    orders.product,
+    customers.age,
+    customers.city,
+)
+
+# Optionally, apply database-side filtering
+joined = joined.filter(joined.price > 200)
+
+# %%
+# Materializing the data
+# ---------------------
+# Once the relevant transformations have been expressed in Ibis, we
+# materialize the result as a pandas DataFrame.
+
+joined_df = joined.execute()
+
+# %%
+# Building a skrub DataOps plan
+# -----------------------------
+# We now construct a DataOps plan using the materialized data.
+
+import skrub
+
+X = skrub.X(joined_df)
+y = skrub.y(joined_df["price"])
+
+# %%
+# Applying a tabular pipeline
+# ---------------------------
+# For this example, we use skrub's built-in tabular pipeline.
+# The goal here is to illustrate integration with database-backed data,
+# not to optimize model performance.
+
+tab_pipeline = skrub.tabular_pipeline("regression")
+
+predictions = X.skb.apply(tab_pipeline, y=y)
+
+# %%
+# Inspecting the DataOps graph
+# ----------------------------
+# We can visualize the full DataOps plan to understand how data flows
+# from the database materialization to model predictions.
+
+predictions.skb.draw_graph()
+
+# %%
+# Conclusion
+# ----------
+#
+# This example demonstrates how skrub can be used alongside database
+# backends by:
+#
+# 1. Defining joins and filters using Ibis expressions
+# 2. Materializing the transformed data only once
+# 3. Building a skrub DataOps plan on top of the resulting DataFrame
+#
+# This workflow allows teams to leverage database engines for data
+# preparation while keeping a single, reusable machine learning
+# pipeline defined with skrub.
+
+# %%
+# Cleanup
+# -------
+# Remove the temporary database file.
+
+os.unlink(db_path)

From 0fa0d4d052ab97010c06c43acbfc960d0fa351c0 Mon Sep 17 00:00:00 2001
From: PredictiveManish <manish.tiwari.09@zohomail.in>
Date: Wed, 28 Jan 2026 00:58:48 +0530
Subject: [PATCH 2/4] Slight docs lang alignment

---
 doc/modules/data_ops/basics/data_ops_vs_alternatives.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst b/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst
index 421a18c79..d27751d78 100644
--- a/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst
+++ b/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst
@@ -71,7 +71,7 @@ Working with databases
 
 Skrub DataOps can work with databases by converting database tables to pandas
 DataFrames. For example, you can use `Ibis <https://ibis-project.org>`__ to
-connect to SQL databases (such as SQLite, PostgreSQL, MySQL, etc.), execute
-queries, and then convert the results to pandas DataFrames for use with skrub
+connect to SQL databases (such as SQLite, PostgreSQL, MySQL, etc.), express and execute
+queries, and then materialize the results as pandas DataFrames for use with skrub
 DataOps. See :ref:`sphx_glr_auto_examples_data_ops_1160_database_example.py` for
 a complete example showing how to integrate SQLite and Ibis with skrub DataOps.

From 6c284bd36479dc13ecbf2c2d674e37342bfea5c5 Mon Sep 17 00:00:00 2001
From: Manish Tiwari <manish.tiwari.09@zohomail.in>
Date: Mon, 2 Feb 2026 22:16:33 +0530
Subject: [PATCH 3/4] commiting for restart testing

---
 doc/modules/data_ops/basics/data_ops_vs_alternatives.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst b/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst
index d27751d78..970a13ebe 100644
--- a/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst
+++ b/doc/modules/data_ops/basics/data_ops_vs_alternatives.rst
@@ -70,7 +70,7 @@ Working with databases
 ======================
 
 Skrub DataOps can work with databases by converting database tables to pandas
-DataFrames. For example, you can use `Ibis <https://ibis-project.org>`__ to
+DataFrames. For example, you can use `Ibis <https://ibis-project.org>`_ to
 connect to SQL databases (such as SQLite, PostgreSQL, MySQL, etc.), express and execute
 queries, and then materialize the results as pandas DataFrames for use with skrub
 DataOps. See :ref:`sphx_glr_auto_examples_data_ops_1160_database_example.py` for

From e9f2d31d454fb72eaa4f6c8a69f282b3609cd1b7 Mon Sep 17 00:00:00 2001
From: Riccardo Cappuzzo <riccardo.cappuzzo@gmail.com>
Date: Mon, 16 Feb 2026 16:14:44 +0100
Subject: [PATCH 4/4] adding missing dependency

---
 pixi.lock      | 2955 ++++++++++++++++++++++++++++++++++++++++++++++--
 pyproject.toml |    1 +
 2 files changed, 2871 insertions(+), 85 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 2dd6b7177..782adcf0a 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -6,6 +6,8 @@ environments:
     - url: https://conda.anaconda.org/pytorch/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
@@ -545,6 +547,8 @@ environments:
     - https://pypi.org/simple
     - https://pypi.anaconda.org/scientific-python-nightly-wheels/simple
     - https://pypi.fury.io/arrow-nightlies
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
@@ -886,6 +890,8 @@ environments:
     - url: https://conda.anaconda.org/pytorch/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
@@ -1508,6 +1514,8 @@ environments:
     - url: https://conda.anaconda.org/pytorch/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
@@ -2374,6 +2382,8 @@ environments:
     - url: https://conda.anaconda.org/pytorch/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda
@@ -3154,6 +3164,8 @@ environments:
     - url: https://conda.anaconda.org/pytorch/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
@@ -3800,6 +3812,8 @@ environments:
     - url: https://conda.anaconda.org/pytorch/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
@@ -4729,6 +4743,8 @@ environments:
     - url: https://conda.anaconda.org/pytorch/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
@@ -5451,6 +5467,8 @@ environments:
     - url: https://conda.anaconda.org/pytorch/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
@@ -5988,6 +6006,8 @@ environments:
     - url: https://conda.anaconda.org/pytorch/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda
@@ -6009,6 +6029,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-atk-2.38.0-h0630a04_3.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/atpublic-7.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyhcf101f3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.9.3-hef928c7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.13-h2c9d079_1.conda
@@ -6031,8 +6052,13 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/backports.zstd-1.3.0-py313h18e8e13_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/bidict-0.23.1-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/bigquery-magics-0.12.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/blinker-1.9.0-pyhff2d567_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-he440d0b_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/branca-0.8.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py313hf159716_1.conda
@@ -6046,19 +6072,23 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py313hf46b229_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.5.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.4-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorlog-6.10.1-pyh707e725_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py313hc8edb43_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.13.4-py313h3dea7bd_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.12-py313hd8ed1ab_100.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cryptography-46.0.5-py313heb322e3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.28-hd9c7081_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/datasets-4.0.0-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/db-dtypes-1.4.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h24cb091_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py313h5d5ffb9_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.2.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/deprecated-1.3.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/dill-0.3.8-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda
@@ -6068,8 +6098,10 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fastavro-1.12.1-py313h07c4f96_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.24.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fmt-12.1.0-hff5e90c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/folium-0.20.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
@@ -6080,18 +6112,37 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.61.1-py313h3dea7bd_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.1-ha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/freexl-2.0.0-h9dce30a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.16-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.7.0-py313h6b9daa2_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2025.3.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.44.5-h2b0a6b4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.1.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-base-1.1.2-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/geos-3.14.1-h480dda7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda
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+  - markdown-it-py >=2.2.0
+  - pygments >=2.13.0,<3.0.0
+  - python >=3.10
+  - typing_extensions >=4.0.0,<5.0.0
+  - python
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/rich?source=compressed-mapping
+  size: 208269
+  timestamp: 1769971520792
 - conda: https://conda.anaconda.org/conda-forge/noarch/roman-numerals-4.1.0-pyhd8ed1ab_0.conda
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   md5: 0dc48b4b570931adc8641e55c6c17fe4
@@ -30622,6 +33155,18 @@ packages:
   - pkg:pypi/rpds-py?source=hash-mapping
   size: 243419
   timestamp: 1764543047271
+- conda: https://conda.anaconda.org/conda-forge/noarch/rsa-4.9.1-pyhd8ed1ab_0.conda
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+  md5: 58958bb50f986ac0c46f73b6e290d5fe
+  depends:
+  - pyasn1 >=0.1.3
+  - python >=3.9
+  license: Apache-2.0
+  license_family: APACHE
+  purls:
+  - pkg:pypi/rsa?source=hash-mapping
+  size: 31709
+  timestamp: 1744825527634
 - conda: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.15.0-h40fa522_0.conda
   noarch: python
   sha256: fc456645570586c798d2da12fe723b38ea0d0901373fd9959cab914cbb19518b
@@ -31947,6 +34492,55 @@ packages:
   - pkg:pypi/setuptools?source=compressed-mapping
   size: 637506
   timestamp: 1770634745653
+- conda: https://conda.anaconda.org/conda-forge/linux-64/shapely-2.1.2-py313had47c43_2.conda
+  sha256: 0bf96349dd2cccba4faf6b98f2f3e02767cdc8b78a6bc1a0ee4f88bddee84917
+  md5: 6e550dd748e9ac9b2925411684e076a1
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - geos >=3.14.1,<3.14.2.0a0
+  - libgcc >=14
+  - numpy >=1.23,<3
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/shapely?source=hash-mapping
+  size: 648024
+  timestamp: 1762523698473
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/shapely-2.1.2-py313h10b2fc2_2.conda
+  sha256: 6b1132016ba3752867981eacd28045d51c671e7818e3e9bcdf34ef275fb90032
+  md5: 7dc5b3a207a5c0af5fb7dacca24587a7
+  depends:
+  - __osx >=11.0
+  - geos >=3.14.1,<3.14.2.0a0
+  - numpy >=1.23,<3
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/shapely?source=hash-mapping
+  size: 612190
+  timestamp: 1762524161011
+- conda: https://conda.anaconda.org/conda-forge/win-64/shapely-2.1.2-py313h64ccc5a_2.conda
+  sha256: 7cc45e575e5bcb0596b57f3821ef0d4cbc437fde06f413fae46a2826f6eb68bf
+  md5: 89e833ece06dd9d0c0a46d74d1125bf6
+  depends:
+  - geos >=3.14.1,<3.14.2.0a0
+  - numpy >=1.23,<3
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/shapely?source=hash-mapping
+  size: 613015
+  timestamp: 1762523741425
 - conda: https://conda.anaconda.org/conda-forge/linux-64/sip-6.10.0-py310hea6c23e_1.conda
   sha256: ddc1fdcd47f3157951a17330d863a9bb81ae6e9fe67c60b52af6ff9750f36bc4
   md5: 1a395a5ab0bf1d6f1e4757e1d9ec9168
@@ -32022,7 +34616,7 @@ packages:
 - pypi: ./
   name: skrub
   version: 0.8.dev0
-  sha256: 50a4146be5fa4bad875bb654cd51b6d581b55e0af3ca781833d78e50d3a37703
+  sha256: 9c63637aa470abf0236726fa078a1d6da21152c7540d184f38d84e22b70fca95
   requires_dist:
   - numpy>=1.23.5
   - pandas>=1.5.3
@@ -32032,32 +34626,7 @@ packages:
   - matplotlib>=3.4.3
   - requests>=2.27.1
   - pydot
-  - ipykernel ; extra == 'dev'
-  - ipython ; extra == 'dev'
-  - jupyterlab ; extra == 'dev'
-  - jupyterlite-sphinx ; extra == 'dev'
-  - jupyterlite-pyodide-kernel ; extra == 'dev'
-  - numpydoc ; extra == 'dev'
-  - pydata-sphinx-theme ; extra == 'dev'
-  - seaborn ; extra == 'dev'
-  - sphinx<9 ; extra == 'dev'
-  - sphinx-copybutton ; extra == 'dev'
-  - sphinx-gallery ; extra == 'dev'
-  - sphinxext-opengraph ; extra == 'dev'
-  - sphinx-autosummary-accessors ; extra == 'dev'
-  - statsmodels ; extra == 'dev'
-  - ruff==0.15.0 ; extra == 'dev'
-  - pre-commit ; extra == 'dev'
-  - pytest ; extra == 'dev'
-  - pytest-cov ; extra == 'dev'
-  - pytest-xdist ; extra == 'dev'
-  - pyarrow ; extra == 'dev'
-  - polars ; extra == 'dev'
-  - plotly ; extra == 'dev'
-  - optuna ; extra == 'dev'
-  - sentence-transformers ; extra == 'transformers'
   requires_python: '>=3.10'
-  editable: true
 - conda: https://conda.anaconda.org/conda-forge/linux-64/sleef-3.9.0-ha0421bc_0.conda
   sha256: 57afc2ab5bdb24cf979964018dddbc5dfaee130b415e6863765e45aed2175ee4
   md5: e8a0b4f5e82ecacffaa5e805020473cb
@@ -32630,6 +35199,61 @@ packages:
   - pkg:pypi/sqlalchemy?source=hash-mapping
   size: 3973576
   timestamp: 1769087100467
+- conda: https://conda.anaconda.org/conda-forge/noarch/sqlglot-28.10.1-pyhcf101f3_0.conda
+  sha256: 68d740708bb7c1830357da3a7c86e9a03f30d2a8420123b391fbc9a5706416e2
+  md5: 1fac4456406ddeeb995a33f7391893d5
+  depends:
+  - python >=3.10
+  - python
+  constrains:
+  - sqlglotrs ==0.12.0
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/sqlglot?source=hash-mapping
+  size: 5241124
+  timestamp: 1771197038061
+- conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.51.2-h04a0ce9_0.conda
+  sha256: ccce47d8fe3a817eac5b95f34ca0fcb12423b0c7c5eee249ffb32ac8013e9692
+  md5: bb88d9335d09e54c7e6b5529d1856917
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - icu >=78.2,<79.0a0
+  - libgcc >=14
+  - libsqlite 3.51.2 hf4e2dac_0
+  - libzlib >=1.3.1,<2.0a0
+  - ncurses >=6.5,<7.0a0
+  - readline >=8.3,<9.0a0
+  license: blessing
+  purls: []
+  size: 183298
+  timestamp: 1768147986603
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/sqlite-3.51.2-h77b7338_0.conda
+  sha256: a13c798ad921da0c84c441c390ace3b53e5c40fe6b11d00e07641d985021c4d1
+  md5: 93796186d49d0b09243fb5a8f83e53b6
+  depends:
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+  - icu >=78.2,<79.0a0
+  - libsqlite 3.51.2 h1ae2325_0
+  - libzlib >=1.3.1,<2.0a0
+  - ncurses >=6.5,<7.0a0
+  - readline >=8.3,<9.0a0
+  license: blessing
+  purls: []
+  size: 165840
+  timestamp: 1768148351309
+- conda: https://conda.anaconda.org/conda-forge/win-64/sqlite-3.51.2-hdb435a2_0.conda
+  sha256: 8194c1326f052852dd827f5277ba381228a968e841d410eb18c622cf851b11ba
+  md5: bc9265bd9f30f9ded263cb762a4fc847
+  depends:
+  - libsqlite 3.51.2 hf5d6505_0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: blessing
+  purls: []
+  size: 400812
+  timestamp: 1768148302390
 - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda
   sha256: 570da295d421661af487f1595045760526964f41471021056e993e73089e9c41
   md5: b1b505328da7a6b246787df4b5a49fbc
@@ -33019,6 +35643,17 @@ packages:
   - pkg:pypi/tomli?source=compressed-mapping
   size: 21453
   timestamp: 1768146676791
+- conda: https://conda.anaconda.org/conda-forge/noarch/toolz-0.12.1-pyhd8ed1ab_0.conda
+  sha256: 22b0a9790317526e08609d5dfdd828210ae89e6d444a9e954855fc29012e90c6
+  md5: 2fcb582444635e2c402e8569bb94e039
+  depends:
+  - python >=3.7
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/toolz?source=hash-mapping
+  size: 52358
+  timestamp: 1706112720607
 - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.3-py310h7c4b9e2_0.conda
   sha256: c27c28d19f8ba8ef6efd35dc47951c985db8a828db38444e1fad3f93f8cedb8d
   md5: 30b9d5c1bc99ffbc45a63ab8d1725b93
@@ -33566,6 +36201,40 @@ packages:
   - pkg:pypi/uri-template?source=hash-mapping
   size: 23990
   timestamp: 1733323714454
+- conda: https://conda.anaconda.org/conda-forge/linux-64/uriparser-0.9.8-hac33072_0.conda
+  sha256: 2aad2aeff7c69a2d7eecd7b662eef756b27d6a6b96f3e2c2a7071340ce14543e
+  md5: d71d3a66528853c0a1ac2c02d79a0284
+  depends:
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 48270
+  timestamp: 1715010035325
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/uriparser-0.9.8-h00cdb27_0.conda
+  sha256: fa0bcbfb20a508ca9bf482236fe799581cbd0eab016e47a865e9fa44dbe3c512
+  md5: e8ff9e11babbc8cd77af5a4258dc2802
+  depends:
+  - __osx >=11.0
+  - libcxx >=16
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 40625
+  timestamp: 1715010029254
+- conda: https://conda.anaconda.org/conda-forge/win-64/uriparser-0.9.8-h5a68840_0.conda
+  sha256: ed0eed8ed0343d29cdbfaeb1bfd141f090af696547d69f91c18f46350299f00d
+  md5: 28b4cf9065681f43cc567410edf8243d
+  depends:
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 49181
+  timestamp: 1715010467661
 - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-1.26.20-pyhd8ed1ab_0.conda
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   md5: 0511ede4b6dd034d77fa80c6d09794e1
@@ -33714,6 +36383,17 @@ packages:
   - pkg:pypi/websocket-client?source=hash-mapping
   size: 61391
   timestamp: 1759928175142
+- conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.15-pyhd8ed1ab_0.conda
+  sha256: 826af5e2c09e5e45361fa19168f46ff524e7a766022615678c3a670c45895d9a
+  md5: dc257b7e7cad9b79c1dfba194e92297b
+  depends:
+  - python >=3.10
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/widgetsnbextension?source=hash-mapping
+  size: 889195
+  timestamp: 1762040404362
 - conda: https://conda.anaconda.org/conda-forge/noarch/win_inet_pton-1.1.0-pyh7428d3b_8.conda
   sha256: 93807369ab91f230cf9e6e2a237eaa812492fe00face5b38068735858fba954f
   md5: 46e441ba871f524e2b067929da3051c2
@@ -33732,6 +36412,49 @@ packages:
   license_family: MIT
   purls: []
   size: 1176306
+- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.17.3-py313h07c4f96_1.conda
+  sha256: 3688598866224e3fbeed8a74f12fd0a3c19dadcb931ce778bdc6cc2e04621b3b
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+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=14
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  license: BSD-2-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/wrapt?source=hash-mapping
+  size: 64865
+  timestamp: 1756851811052
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-1.17.3-py313hcdf3177_1.conda
+  sha256: 5919f7142db9344116760b797e4a5d28ca3961f927a2ba1c4a61d3f0f3282dd2
+  md5: cd6b5084444b0b4ed22dde20355d4c4b
+  depends:
+  - __osx >=11.0
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  license: BSD-2-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/wrapt?source=hash-mapping
+  size: 62577
+  timestamp: 1756851972334
+- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-1.17.3-py313h5ea7bf4_1.conda
+  sha256: 260a3295f39565c28be9232a11ca7ee435af6e9366ffd2569ff29a63e7c144a0
+  md5: 3e199c8db04833fe628867462aeaca24
+  depends:
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: BSD-2-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/wrapt?source=hash-mapping
+  size: 63385
+  timestamp: 1756851987645
 - conda: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.1-h4f16b4b_2.conda
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@@ -33804,6 +36527,44 @@ packages:
   purls: []
   size: 51689
   timestamp: 1718844051451
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.3.0-hd9031aa_1.conda
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+  md5: 66a1db55ecdb7377d2b91f54cd56eafa
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - icu >=78.1,<79.0a0
+  - libgcc >=14
+  - libnsl >=2.0.1,<2.1.0a0
+  - libstdcxx >=14
+  license: Apache-2.0
+  license_family: Apache
+  purls: []
+  size: 1660075
+  timestamp: 1766327494699
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/xerces-c-3.3.0-h25f632f_1.conda
+  sha256: 89152175f45b5e84e0f1575848f607e305ffc122ab59d9704ea77ce699b1bd2b
+  md5: 0b886d06130b774f086d3b2ce0b7277a
+  depends:
+  - __osx >=11.0
+  - icu >=78.1,<79.0a0
+  - libcxx >=19
+  license: Apache-2.0
+  license_family: Apache
+  purls: []
+  size: 1283088
+  timestamp: 1766327630028
+- conda: https://conda.anaconda.org/conda-forge/win-64/xerces-c-3.3.0-hac47afa_1.conda
+  sha256: 9583a8fcf01c59b26a4285bc151b6315fd0bd504e1628f004519dc871eb17073
+  md5: d1097e01041cfed41c81f1e3d1f52572
+  depends:
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: Apache-2.0
+  license_family: Apache
+  purls: []
+  size: 3598939
+  timestamp: 1766327729418
 - conda: https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.46-hb03c661_0.conda
   sha256: aa03b49f402959751ccc6e21932d69db96a65a67343765672f7862332aa32834
   md5: 71ae752a748962161b4740eaff510258
@@ -34355,6 +37116,17 @@ packages:
   purls: []
   size: 105768
   timestamp: 1746458183583
+- conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2025.11.0-pyhd8ed1ab_0.conda
+  sha256: b194a1fbc38f29c563b102ece9d006f7a165bf9074cdfe50563d3bce8cae9f84
+  md5: 16933322051fa260285f1a44aae91dd6
+  depends:
+  - python >=3.8
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/xyzservices?source=hash-mapping
+  size: 51128
+  timestamp: 1763813786075
 - conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h280c20c_3.conda
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   md5: a77f85f77be52ff59391544bfe73390a
@@ -34607,6 +37379,19 @@ packages:
   purls: []
   size: 77606
   timestamp: 1727963209370
+- conda: https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.1-h2466b09_2.conda
+  sha256: 8c688797ba23b9ab50cef404eca4d004a948941b6ee533ead0ff3bf52012528c
+  md5: be60c4e8efa55fddc17b4131aa47acbd
+  depends:
+  - libzlib 1.3.1 h2466b09_2
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: Zlib
+  license_family: Other
+  purls: []
+  size: 107439
+  timestamp: 1727963788936
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda
   sha256: ea4e50c465d70236408cb0bfe0115609fd14db1adcd8bd30d8918e0291f8a75f
   md5: 2aadb0d17215603a82a2a6b0afd9a4cb
diff --git a/pyproject.toml b/pyproject.toml
index b0b046794..212ac684b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -125,6 +125,7 @@ sphinx-sitemap = "*"
 statsmodels = "*"
 optuna = "*"
 skorch = "*"
+ibis-bigquery = "*"
 
 [tool.pixi.feature.lint.dependencies]
 ruff = "==0.15.0"