diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1b810970b..ee2eac1fa 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -15,3 +15,11 @@ repos:
     rev: 23.3.0
     hooks:
     -   id: black
+-   repo: https://github.com/codespell-project/codespell
+    # Configuration for codespell is in pyproject.toml
+    rev: v2.3.0
+    hooks:
+    -   id: codespell
+        exclude: .*/package-lock.json
+    additional_dependencies:
+    -   tomli
diff --git a/CHANGES.rst b/CHANGES.rst
index 9bd27e723..c2c43c21b 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -472,7 +472,7 @@ Minor changes
 * :class:`TableVectorizer` never output a sparse matrix by default. This can be changed by
   increasing the `sparse_threshold` parameter. :pr:`646` by :user:`Leo Grinsztajn <LeoGrin>`
 
-* :class:`TableVectorizer` doesn't fail anymore if an infered type doesn't work during transform.
+* :class:`TableVectorizer` doesn't fail anymore if an inferred type doesn't work during transform.
   The new entries not matching the type are replaced by missing values. :pr:`666` by :user:`Leo Grinsztajn <LeoGrin>`
 
 - Dataset fetcher :func:`datasets.fetch_employee_salaries` now has a parameter
diff --git a/benchmarks/bench_fuzzy_join_count_vs_hash.py b/benchmarks/bench_fuzzy_join_count_vs_hash.py
index f693ed947..a6c4c2f3e 100644
--- a/benchmarks/bench_fuzzy_join_count_vs_hash.py
+++ b/benchmarks/bench_fuzzy_join_count_vs_hash.py
@@ -98,7 +98,7 @@ def fuzzy_join(
         If False, the order of the join keys depends on the join type
         (`how` keyword).
     suffixes : typing.Tuple[str, str], default=('_x', '_y')
-        A list of strings indicating the suffix to add when overlaping
+        A list of strings indicating the suffix to add when overlapping
         column names.
 
     Returns
diff --git a/benchmarks/bench_fuzzy_join_sparse_vs_dense.py b/benchmarks/bench_fuzzy_join_sparse_vs_dense.py
index ead650e93..b6e62e90c 100644
--- a/benchmarks/bench_fuzzy_join_sparse_vs_dense.py
+++ b/benchmarks/bench_fuzzy_join_sparse_vs_dense.py
@@ -258,7 +258,7 @@ def fuzzy_join(
         If False, the order of the join keys depends on the join type
         (`how` keyword).
     suffixes : str 2-tuple, default=('_x', '_y')
-        A list of strings indicating the suffix to add when overlaping
+        A list of strings indicating the suffix to add when overlapping
         column names.
     sparse : boolean, default=True
         Use sparse or dense arrays for nearest neighbor search.
diff --git a/benchmarks/bench_fuzzy_join_vs_others.py b/benchmarks/bench_fuzzy_join_vs_others.py
index 69ad95a4b..5d6f25a6b 100644
--- a/benchmarks/bench_fuzzy_join_vs_others.py
+++ b/benchmarks/bench_fuzzy_join_vs_others.py
@@ -42,7 +42,7 @@ def thefuzz_merge(
                high to low
 
     Return:
-        Dataframe with boths keys and matches.
+        Dataframe with both keys and matches.
     """
     s = df_2[right_on].tolist()
     m = df_1[left_on].apply(lambda x: process.extract(x, s, limit=limit, scorer=scorer))
diff --git a/benchmarks/utils/join.py b/benchmarks/utils/join.py
index 04397711d..51108afd0 100644
--- a/benchmarks/utils/join.py
+++ b/benchmarks/utils/join.py
@@ -37,7 +37,7 @@ def fetch_data(
         The name of the dataset to download.
 
     save: bool, default=true
-        Wheter to save the datasets locally.
+        Whether to save the datasets locally.
 
     data_home: Path or str, optional
         The path to the root data directory.
@@ -104,7 +104,7 @@ def fetch_big_data(
         Options are {'Dirty', 'Structured', 'Textual'}.
 
     save: bool, default=true
-        Wheter to save the datasets locally.
+        Whether to save the datasets locally.
 
     data_home: Path or str, optional
         The path to the root data directory.
diff --git a/benchmarks/utils/monitor.py b/benchmarks/utils/monitor.py
index 5afb3e68b..4251350a9 100644
--- a/benchmarks/utils/monitor.py
+++ b/benchmarks/utils/monitor.py
@@ -27,7 +27,7 @@ def monitor(
     """Decorator used to monitor the execution of a function.
 
     The decorated function should return either:
-    - ``None``, when the goal is only to monitor time of exection and/or memory
+    - ``None``, when the goal is only to monitor time of execution and/or memory
       (parameters ``time`` and/or ``memory`` should be ``True`` (the default));
     - a mapping (dict), which will be added to the results. The keys are going
       to be the columns of the resulting pandas DataFrame.
@@ -79,7 +79,7 @@ def monitor(
         execution without the memory monitoring.
     hot_load : str, optional
         Name of the file to hot-load (meaning, recovering partial results
-        from a previous run that was interupted).
+        from a previous run that was interrupted).
         The name of the file is random (created at runtime), and printed before
         the run. Grab it from the stdout of your interrupted run.
     repeat : int, default=1
diff --git a/doc/assembling.rst b/doc/assembling.rst
index 15fa59040..d7a2f90d3 100644
--- a/doc/assembling.rst
+++ b/doc/assembling.rst
@@ -31,7 +31,7 @@ has no need for pre-cleaning.
 Joining external tables for machine learning
 --------------------------------------------
 
-Joining is straigthforward for two tables because you only need to identify
+Joining is straightforward for two tables because you only need to identify
 the common key.
 
 In addition, skrub also enable more advanced analysis:
diff --git a/examples/04_fuzzy_joining.py b/examples/04_fuzzy_joining.py
index 0674efd35..ba0396bdf 100644
--- a/examples/04_fuzzy_joining.py
+++ b/examples/04_fuzzy_joining.py
@@ -143,7 +143,7 @@
 
 ###############################################################################
 #
-# We see that our |fj| succesfully identified the countries,
+# We see that our |fj| successfully identified the countries,
 # even though some country names differ between tables.
 #
 # For instance, "Egypt" and "Egypt, Arab Rep." are correctly matched, as are
@@ -167,7 +167,7 @@
 augmented_df.sort_values("skrub_Joiner_rescaled_distance").tail(10)
 
 ###############################################################################
-# We see that some matches were unsuccesful
+# We see that some matches were unsuccessful
 # (e.g "Palestinian Territories*" and "Palau"),
 # because there is simply no match in the two tables.
 
@@ -343,7 +343,7 @@
 # many ways to clean a table as there are errors. |fj|
 # method is generalizable across all datasets.
 #
-# Data transformation is also often very costly in both time and ressources.
+# Data transformation is also often very costly in both time and resources.
 # |fj| is fast and easy-to-use.
 #
 # Now up to you, try improving our model by adding information into it and
diff --git a/examples/06_ken_embeddings.py b/examples/06_ken_embeddings.py
index bc448efef..13a2509a2 100644
--- a/examples/06_ken_embeddings.py
+++ b/examples/06_ken_embeddings.py
@@ -6,7 +6,7 @@
 companies or famous people), bringing new information assembled from external
 sources may be the key to improving the analysis.
 
-Embeddings, or vectorial representations of entities, are a conveniant way to
+Embeddings, or vectorial representations of entities, are a convenient way to
 capture and summarize the information on an entity.
 Relational data embeddings capture all common entities from Wikipedia. [#]_
 These will be called `KEN embeddings` in the following example.
@@ -204,7 +204,7 @@
 # The |Pipeline| can now be readily applied to the dataframe for prediction:
 from sklearn.model_selection import cross_validate
 
-# We will save the results in a dictionnary:
+# We will save the results in a dictionary:
 all_r2_scores = dict()
 all_rmse_scores = dict()
 
diff --git a/examples/07_multiple_key_join.py b/examples/07_multiple_key_join.py
index 7f93eedc5..97b234175 100644
--- a/examples/07_multiple_key_join.py
+++ b/examples/07_multiple_key_join.py
@@ -14,7 +14,7 @@
 
 |joiner| is a scikit-learn compatible transformer that enables
 performing joins across multiple keys,
-independantly of the data type (numerical, string or mixed).
+independently of the data type (numerical, string or mixed).
 
 The following example uses US domestic flights data
 to illustrate how space and time information from a
@@ -106,7 +106,7 @@
 aux.head()
 
 ###############################################################################
-# Then we join this table with the airports so that we get all auxilliary
+# Then we join this table with the airports so that we get all auxiliary
 # tables into one.
 
 from skrub import Joiner
@@ -119,7 +119,7 @@
 
 ###############################################################################
 # Joining airports with flights data:
-# Let's instanciate another multiple key joiner on the date and the airport:
+# Let's instantiate another multiple key joiner on the date and the airport:
 
 joiner = Joiner(
     aux_augmented,
diff --git a/examples/FIXME/08_join_aggregation_full.py b/examples/FIXME/08_join_aggregation_full.py
index e1e363e6f..ecea896c6 100644
--- a/examples/FIXME/08_join_aggregation_full.py
+++ b/examples/FIXME/08_join_aggregation_full.py
@@ -520,7 +520,7 @@ def get_X_y(data):
 plot_gain_tradeoff(results)
 
 # %%
-# We see that the agg-joiner model is slighly more calibrated, with a lower (better)
+# We see that the agg-joiner model is slightly more calibrated, with a lower (better)
 # log loss.
 
 plot_calibration_curve(results)
@@ -545,4 +545,4 @@ def get_X_y(data):
 # auxiliary data, you would need to replace the auxiliary table in the AggJoiner that
 # was used during ``fit`` with the updated data, which is a rather hacky approach.
 #
-# These limitations will be addresssed later in skrub.
+# These limitations will be addressed later in skrub.
diff --git a/pixi.lock b/pixi.lock
index f3853c190..1121c1d5d 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -22,7 +22,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py39h15c3d72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.7-py39h9399b63_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.8-py39h9399b63_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_0.conda
@@ -67,8 +67,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-20_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcap-2.69-h0f662aa_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp19.1-19.1.3-default_hb5137d0_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang13-19.1.3-default_h9c6a7e4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp19.1-19.1.4-default_hb5137d0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang13-19.1.4-default_h9c6a7e4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.22-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.123-hb9d3cd8_0.conda
@@ -80,7 +80,10 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.2.0-h77fa898_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.2.0-h69a702a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.11.0-h4ab18f5_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.11.0-ha770c72_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-devel-1.11.0-hb9d3cd8_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-lib-1.11.0-hb9d3cd8_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-tools-1.11.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-he02047a_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-devel-0.22.5-he02047a_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_1.conda
@@ -95,14 +98,14 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-20_linux64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libllvm19-19.1.3-ha7bfdaf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libllvm19-19.1.4-ha7bfdaf_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.5-h4ab18f5_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.25-pthreads_h413a1c8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.44-hadc24fc_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpq-16.5-h2d7952a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpq-16.6-h2d7952a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.47.0-hadc24fc_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-14.2.0-hc0a3c3a_1.conda
@@ -126,7 +129,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-9.0.1-he0572af_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-he02047a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/nspr-4.36-h5888daf_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/nss-3.106-hdf54f9c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/nss-3.107-hdf54f9c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.23.5-py39h3d75532_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.8.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.2-h488ebb8_0.conda
@@ -146,7 +149,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py39h52134e7_5.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.3-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.9.20-h13acc7a_1_cpython.conda
@@ -158,7 +161,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.2.1-py39h86b2a18_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.9.3-py39hddc5342_2.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.5.0-pyhff2d567_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.6.0-pyhff2d567_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2
@@ -173,8 +176,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.1.0-pyhff2d567_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4.1-py39h8cd3c5a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4.2-py39h8cd3c5a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2024b-hc8b5060_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.1-hb711507_2.conda
@@ -196,7 +199,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-xf86vidmodeproto-2.3.1-hb9d3cd8_1005.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-xorgproto-2024.1-hb9d3cd8_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h08a7858_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
@@ -211,7 +214,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/cffi-1.17.1-py39h8ddeee6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/coverage-7.6.7-py39hd18e689_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/coverage-7.6.8-py39hd18e689_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda
@@ -231,7 +234,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/lerc-4.0.0-hb486fe8_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-20_osx64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-20_osx64_openblas.conda
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+  - libstdcxx >=13
+  license: Apache-2.0
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   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/aws-crt-cpp-0.29.4-h1e7036a_1.conda
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-  - aws-c-s3 >=0.7.1,<0.7.2.0a0
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   license: Apache-2.0
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   license_family: Apache
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+  license_family: Apache
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+  license_family: Apache
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+  timestamp: 1732769764315
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-  - python_abi 3.12.* *_cp312
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
+  - python >=3.13.0rc1,<3.14.0a0
+  - python_abi 3.13.* *_cp313
   constrains:
-  - libbrotlicommon 1.1.0 hd74edd7_2
+  - libbrotlicommon 1.1.0 hb9d3cd8_2
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/brotli?source=hash-mapping
-  size: 339360
-  timestamp: 1725268143995
+  size: 350424
+  timestamp: 1725267803672
 - kind: conda
   name: brotli-python
   version: 1.1.0
@@ -9258,6 +9506,28 @@ packages:
   - pkg:pypi/brotli?source=hash-mapping
   size: 322309
   timestamp: 1725268431915
+- kind: conda
+  name: brotli-python
+  version: 1.1.0
+  build: py313h9ea2907_2
+  build_number: 2
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/brotli-python-1.1.0-py313h9ea2907_2.conda
+  sha256: a8ff547af4de5d2d6cb84543a73f924dbbd60029920dbadc27298ea0b48f28bc
+  md5: 38ab121f341a1d8613c3898f36efecab
+  depends:
+  - __osx >=10.13
+  - libcxx >=17
+  - python >=3.13.0rc1,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  constrains:
+  - libbrotlicommon 1.1.0 h00291cd_2
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/brotli?source=hash-mapping
+  size: 363156
+  timestamp: 1725268004102
 - kind: conda
   name: brotli-python
   version: 1.1.0
@@ -9419,11 +9689,12 @@ packages:
 - kind: conda
   name: c-ares
   version: 1.34.3
-  build: h2466b09_0
+  build: h2466b09_1
+  build_number: 1
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/c-ares-1.34.3-h2466b09_0.conda
-  sha256: 1ddad30ee6de501a65b2431427800cb79fdb34a1650f291bb477a6c1c78fc1f1
-  md5: 7dd8f0c4af6a36df3b402fa12e19854c
+  url: https://conda.anaconda.org/conda-forge/win-64/c-ares-1.34.3-h2466b09_1.conda
+  sha256: 535b9dd013b8726b5147ca202f509308960ef0d050bce4872e16c0d76962147c
+  md5: f8413bb7fb62f7bdc2545c9a06937790
   depends:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
@@ -9431,54 +9702,57 @@ packages:
   license: MIT
   license_family: MIT
   purls: []
-  size: 192873
-  timestamp: 1731182126180
+  size: 192376
+  timestamp: 1732447407728
 - kind: conda
   name: c-ares
   version: 1.34.3
-  build: h5505292_0
+  build: h5505292_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.3-h5505292_0.conda
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-  md5: d0155a4f41f28628c7409ea000eeb19c
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.3-h5505292_1.conda
+  sha256: 6dfa83cbd9acc8671d439fe9c745a5716faf6cbadf2f1e18c841bcf86cbba5f2
+  md5: fb72102e8a8f9bcd38e40af09ff41c42
   depends:
   - __osx >=11.0
   license: MIT
   license_family: MIT
   purls: []
-  size: 178951
-  timestamp: 1731182071026
+  size: 179318
+  timestamp: 1732447193278
 - kind: conda
   name: c-ares
   version: 1.34.3
-  build: heb4867d_0
+  build: hb9d3cd8_1
+  build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.3-heb4867d_0.conda
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-  md5: 09a6c610d002e54e18353c06ef61a253
+  url: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.3-hb9d3cd8_1.conda
+  sha256: 732571ba6286dbccbf4c6450078a581b7a5620204faf876ff0ef282d77a6bfa8
+  md5: ee228789a85f961d14567252a03e725f
   depends:
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+  - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   license: MIT
   license_family: MIT
   purls: []
-  size: 205575
-  timestamp: 1731181837907
+  size: 204857
+  timestamp: 1732447031823
 - kind: conda
   name: c-ares
   version: 1.34.3
-  build: hf13058a_0
+  build: hf13058a_1
+  build_number: 1
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/c-ares-1.34.3-hf13058a_0.conda
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-  md5: c36355bc08d4623c210b00f9935ee632
+  url: https://conda.anaconda.org/conda-forge/osx-64/c-ares-1.34.3-hf13058a_1.conda
+  sha256: 37c031f91bb4c7ebec248e283c453b24840764fb53b640768780dcd904093f17
+  md5: 7d8083876d71fe1316fc18369ee0dc58
   depends:
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   license: MIT
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   purls: []
-  size: 183798
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+  size: 184403
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 - kind: conda
   name: ca-certificates
   version: 2024.8.30
@@ -9802,6 +10076,26 @@ packages:
   - pkg:pypi/cffi?source=hash-mapping
   size: 282425
   timestamp: 1725560725144
+- kind: conda
+  name: cffi
+  version: 1.17.1
+  build: py313h49682b3_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/cffi-1.17.1-py313h49682b3_0.conda
+  sha256: 660c8f8488f78c500a1bb4a803c31403104b1ee2cabf1476a222a3b8abf5a4d7
+  md5: 98afc301e6601a3480f9e0b9f8867ee0
+  depends:
+  - __osx >=10.13
+  - libffi >=3.4,<4.0a0
+  - pycparser
+  - python >=3.13.0rc1,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/cffi?source=hash-mapping
+  size: 284540
+  timestamp: 1725560667915
 - kind: conda
   name: cffi
   version: 1.17.1
@@ -9823,6 +10117,48 @@ packages:
   - pkg:pypi/cffi?source=hash-mapping
   size: 291828
   timestamp: 1725561211547
+- kind: conda
+  name: cffi
+  version: 1.17.1
+  build: py313hc845a76_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/cffi-1.17.1-py313hc845a76_0.conda
+  sha256: 50650dfa70ccf12b9c4a117d7ef0b41895815bb7328d830d667a6ba3525b60e8
+  md5: 6d24d5587a8615db33c961a4ca0a8034
+  depends:
+  - __osx >=11.0
+  - libffi >=3.4,<4.0a0
+  - pycparser
+  - python >=3.13.0rc1,<3.14.0a0
+  - python >=3.13.0rc1,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/cffi?source=hash-mapping
+  size: 282115
+  timestamp: 1725560759157
+- kind: conda
+  name: cffi
+  version: 1.17.1
+  build: py313hfab6e84_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py313hfab6e84_0.conda
+  sha256: 73cd6199b143a8a6cbf733ce124ed57defc1b9a7eab9b10fd437448caf8eaa45
+  md5: ce6386a5892ef686d6d680c345c40ad1
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libffi >=3.4,<4.0a0
+  - libgcc >=13
+  - pycparser
+  - python >=3.13.0rc1,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/cffi?source=hash-mapping
+  size: 295514
+  timestamp: 1725560706794
 - kind: conda
   name: cffi
   version: 1.17.1
@@ -10281,12 +10617,12 @@ packages:
   timestamp: 1731428527698
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   name: coverage
-  version: 7.6.7
+  version: 7.6.8
   build: py311h2dc5d0c_0
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-  url: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.7-py311h2dc5d0c_0.conda
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-  md5: 453d38067da1c98fed8667cbd2b5a570
+  url: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.8-py311h2dc5d0c_0.conda
+  sha256: 820f5d4119149f77995f10e0aefc587117b23501a55c69a026bfcb50fa6917ff
+  md5: 8d6a690e582941ee3161500d1982ea3e
   depends:
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   - libgcc >=13
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   purls:
   - pkg:pypi/coverage?source=hash-mapping
-  size: 374966
-  timestamp: 1731698660188
+  size: 374227
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   name: coverage
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+  version: 7.6.8
   build: py311h4921393_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/coverage-7.6.7-py311h4921393_0.conda
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-  md5: 0922d5bf8b7b22b550a25726b2967fd3
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/coverage-7.6.8-py311h4921393_0.conda
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+  md5: 2225caba3f015750365040279e830c08
   depends:
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   purls:
   - pkg:pypi/coverage?source=hash-mapping
-  size: 372952
-  timestamp: 1731698891980
+  size: 373918
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   name: coverage
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+  version: 7.6.8
   build: py311h5082efb_0
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/coverage-7.6.7-py311h5082efb_0.conda
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-  md5: e1659968f5b4b78e82557da2d7298c8c
+  url: https://conda.anaconda.org/conda-forge/win-64/coverage-7.6.8-py311h5082efb_0.conda
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+  md5: 06f5b27c266b026247d671f66f690908
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   - python_abi 3.11.* *_cp311
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   license_family: APACHE
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   - pkg:pypi/coverage?source=hash-mapping
-  size: 398731
-  timestamp: 1731699115828
+  size: 399995
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   name: coverage
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   build: py312h178313f_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.7-py312h178313f_0.conda
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-  md5: f64f3206bf9e86338b881957fd498870
+  url: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.8-py312h178313f_0.conda
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+  md5: fe8c93f4c75908fe2a1cc45ed0c47edf
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-  size: 364489
-  timestamp: 1731698658681
+  size: 364534
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   name: coverage
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-  url: https://conda.anaconda.org/conda-forge/win-64/coverage-7.6.7-py312h31fea79_0.conda
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-  md5: e28be91c122fee584f24e616c74c1df3
+  url: https://conda.anaconda.org/conda-forge/win-64/coverage-7.6.8-py312h31fea79_0.conda
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-  size: 388560
-  timestamp: 1731699193408
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-  md5: 93df4f496ebe0d228a94637c9fd59c00
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-  size: 362686
-  timestamp: 1731698755423
+  size: 363131
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   name: coverage
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+  version: 7.6.8
   build: py312h998013c_0
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/coverage-7.6.7-py312h998013c_0.conda
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-  md5: 0962c6746e00b34ce0584d3ae129d266
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-  size: 363385
-  timestamp: 1731698781484
+  size: 363034
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+  name: coverage
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+  url: https://conda.anaconda.org/conda-forge/osx-64/coverage-7.6.8-py313h717bdf5_0.conda
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+  license_family: APACHE
+  purls:
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+  size: 368990
+  timestamp: 1732426436029
+- kind: conda
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+  url: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.8-py313h8060acc_0.conda
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-  size: 396982
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+  md5: d619288803d5935f771f64a7924a6aad
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-  version: 0.27.2
+  version: 0.28.0
   build: pyhd8ed1ab_0
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   noarch: python
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-  md5: 7e9ac3faeebdbd7b53b462c41891e7f7
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+  md5: 22878824a87f1af2ad48665f9d5bfcc8
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   - certifi
   - httpcore 1.*
   - idna
-  - python >=3.8
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+  - python >=3.9
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   license_family: BSD
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+  version: 2.6.3
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-  md5: 636950f839e065401e2031624a414f0b
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+  md5: dd3acd023fc358afab730866a0e5e3f5
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   license_family: MIT
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+  version: 8.30.0
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-  md5: 56db21d7d51410fcfbfeca3d1a6b4269
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+  md5: 5d6e5cb3a4b820f61b2073f0ad5431f1
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+  version: 8.30.0
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+  md5: 6cdaebbc9e3feb2811eb9f52ed0b89e1
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   timestamp: 1714665585214
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   name: json5
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-  build: pyhff2d567_0
+  version: 0.10.0
+  build: pyhd8ed1ab_0
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   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/json5-0.9.28-pyhff2d567_0.conda
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-  md5: b5fd1ac9269dd22e003eaac27e249d97
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   version: 15.0.2
-  build: h0416aa2_46_cpu
-  build_number: 46
+  build: h683b2c2_47_cpu
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   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/libarrow-15.0.2-h0416aa2_46_cpu.conda
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-  md5: be0db956ece8e8522f5889cb6ba85cfe
+  url: https://conda.anaconda.org/conda-forge/osx-64/libarrow-15.0.2-h683b2c2_47_cpu.conda
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-  - aws-crt-cpp >=0.29.4,<0.29.5.0a0
+  - aws-crt-cpp >=0.29.5,<0.29.6.0a0
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   - bzip2 >=1.0.8,<2.0a0
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   - re2
   - snappy >=1.2.1,<1.3.0a0
   - zstd >=1.5.6,<1.6.0a0
+  - libutf8proc <2.9
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+  - apache-arrow-proc =*=cpu
   - arrow-cpp <0.0a0
   - parquet-cpp <0.0a0
-  - apache-arrow-proc =*=cpu
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 5737167
-  timestamp: 1731793321044
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 - kind: conda
   name: libarrow
   version: 15.0.2
-  build: h139a3dd_46_cpu
-  build_number: 46
+  build: h80cb48f_47_cpu
+  build_number: 47
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-15.0.2-h139a3dd_46_cpu.conda
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-  md5: 22b96a793462d8eaee0dfba8eeeb91db
+  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-15.0.2-h80cb48f_47_cpu.conda
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   - bzip2 >=1.0.8,<2.0a0
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   - vc >=14.3,<15
   - vc14_runtime >=14.42.34433
   - zstd >=1.5.6,<1.6.0a0
+  - libutf8proc <2.9
   constrains:
-  - parquet-cpp <0.0a0
   - arrow-cpp <0.0a0
+  - parquet-cpp <0.0a0
   - apache-arrow-proc =*=cpu
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 4975964
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-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-15.0.2-h3a20e3e_46_cpu.conda
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-  md5: 26a33ae9c4d2d43fed41d806edefa460
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-15.0.2-hbde6d31_47_cpu.conda
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+  md5: bf5a2a7640fe2ef7c6cd82d0e5b7d168
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - aws-crt-cpp >=0.29.4,<0.29.5.0a0
+  - __osx >=11.0
+  - aws-crt-cpp >=0.29.5,<0.29.6.0a0
   - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - gflags >=2.2.2,<2.3.0a0
   - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libgcc >=13
+  - libcxx >=17
   - libgoogle-cloud >=2.31.0,<2.32.0a0
   - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
   - libre2-11 >=2024.7.2
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   - re2
   - snappy >=1.2.1,<1.3.0a0
   - zstd >=1.5.6,<1.6.0a0
+  - libutf8proc <2.9
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   - apache-arrow-proc =*=cpu
   - arrow-cpp <0.0a0
-  - parquet-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 8240855
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   version: 15.0.2
-  build: hb52b37a_46_cpu
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-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-15.0.2-hb52b37a_46_cpu.conda
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-  md5: 0fd24ca1594ff8d601faba14a43dbc39
+  build: he4bedbb_47_cpu
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+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-15.0.2-he4bedbb_47_cpu.conda
+  sha256: 2b08c722d509e5b5a7f8a1dd5235cdf1a3b555b88d4bcde1cec1986c69fbb515
+  md5: abf4287c7dd296bb165fdc3f8fd23f95
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-  - aws-crt-cpp >=0.29.4,<0.29.5.0a0
+  - __glibc >=2.17,<3.0.a0
+  - aws-crt-cpp >=0.29.5,<0.29.6.0a0
   - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
   - bzip2 >=1.0.8,<2.0a0
+  - gflags >=2.2.2,<2.3.0a0
   - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=17
+  - libgcc >=13
   - libgoogle-cloud >=2.31.0,<2.32.0a0
   - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
   - libre2-11 >=2024.7.2
+  - libstdcxx >=13
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   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
@@ -14038,88 +14435,84 @@ packages:
   - re2
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   - zstd >=1.5.6,<1.6.0a0
+  - libutf8proc <2.9
   constrains:
-  - parquet-cpp <0.0a0
   - apache-arrow-proc =*=cpu
+  - parquet-cpp <0.0a0
   - arrow-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 5143472
-  timestamp: 1731793633641
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   name: libarrow
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-  build: h2409f62_7_cpu
-  build_number: 7
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-18.0.0-h2409f62_7_cpu.conda
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-  md5: da6e0816fe9639c270cafdec68b411d6
+  version: 18.1.0
+  build: h0ea94f9_2_cpu
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+  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-18.1.0-h0ea94f9_2_cpu.conda
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   - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
-  - azure-core-cpp >=1.14.0,<1.14.1.0a0
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   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=18
+  - libcrc32c >=1.1.2,<1.2.0a0
+  - libcurl >=8.10.1,<9.0a0
   - libgoogle-cloud >=2.31.0,<2.32.0a0
   - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
   - libre2-11 >=2024.7.2
-  - libutf8proc >=2.8.0,<3.0a0
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   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
   - orc >=2.0.3,<2.0.4.0a0
   - re2
   - snappy >=1.2.1,<1.3.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.42.34433
   - zstd >=1.5.6,<1.6.0a0
   constrains:
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   - parquet-cpp <0.0a0
   - arrow-cpp <0.0a0
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 5455595
-  timestamp: 1731789726593
+  size: 5248470
+  timestamp: 1732950405896
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   name: libarrow
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-  build: h3b997a5_7_cpu
-  build_number: 7
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-18.0.0-h3b997a5_7_cpu.conda
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-  md5: 32897a50e7f68187c4a524c439c0943c
+  version: 18.1.0
+  build: h14b790f_2_cpu
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+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/libarrow-18.1.0-h14b790f_2_cpu.conda
+  sha256: 76e55b03da834e0f52f3c98d688fe09c80de54e86d73353c0f1f3ea41f038c52
+  md5: 28163b6ae1cadbfefa19a86bce1ea860
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - aws-crt-cpp >=0.29.4,<0.29.5.0a0
+  - __osx >=10.13
+  - aws-crt-cpp >=0.29.6,<0.29.7.0a0
   - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
   - azure-core-cpp >=1.14.0,<1.14.1.0a0
   - azure-identity-cpp >=1.10.0,<1.10.1.0a0
   - azure-storage-blobs-cpp >=12.13.0,<12.13.1.0a0
   - azure-storage-files-datalake-cpp >=12.12.0,<12.12.1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - gflags >=2.2.2,<2.3.0a0
   - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libgcc >=13
+  - libcxx >=18
   - libgoogle-cloud >=2.31.0,<2.32.0a0
   - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
   - libre2-11 >=2024.7.2
-  - libstdcxx >=13
-  - libutf8proc >=2.8.0,<3.0a0
+  - libutf8proc >=2.9.0,<2.10.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
   - orc >=2.0.3,<2.0.4.0a0
@@ -14131,79 +14524,81 @@ packages:
   - apache-arrow-proc =*=cpu
   - parquet-cpp <0.0a0
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 8714651
-  timestamp: 1731789983840
+  size: 6143357
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-  build: h7b593d6_7_cpu
-  build_number: 7
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-18.0.0-h7b593d6_7_cpu.conda
-  sha256: 7a872d40337162d342c9811f7d4272769c7cc9d06307fa10eea257d6afe952f0
-  md5: a5acda3f3970b977debc4c7b758c0e99
+  version: 18.1.0
+  build: h654e1bb_2_cpu
+  build_number: 2
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-18.1.0-h654e1bb_2_cpu.conda
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+  md5: e69934ff9dd8745fea8927028d1603ee
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+  - __osx >=11.0
+  - aws-crt-cpp >=0.29.6,<0.29.7.0a0
   - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
+  - azure-core-cpp >=1.14.0,<1.14.1.0a0
+  - azure-identity-cpp >=1.10.0,<1.10.1.0a0
+  - azure-storage-blobs-cpp >=12.13.0,<12.13.1.0a0
+  - azure-storage-files-datalake-cpp >=12.12.0,<12.12.1.0a0
   - bzip2 >=1.0.8,<2.0a0
+  - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcrc32c >=1.1.2,<1.2.0a0
-  - libcurl >=8.10.1,<9.0a0
+  - libcxx >=18
   - libgoogle-cloud >=2.31.0,<2.32.0a0
   - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
   - libre2-11 >=2024.7.2
-  - libutf8proc >=2.8.0,<3.0a0
+  - libutf8proc >=2.9.0,<2.10.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
   - orc >=2.0.3,<2.0.4.0a0
   - re2
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-  - ucrt >=10.0.20348.0
-  - vc >=14.3,<15
-  - vc14_runtime >=14.42.34433
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   constrains:
-  - apache-arrow-proc =*=cpu
   - arrow-cpp <0.0a0
   - parquet-cpp <0.0a0
+  - apache-arrow-proc =*=cpu
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 5213890
-  timestamp: 1731791745173
+  size: 5475725
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   name: libarrow
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-  build: hd4c0275_7_cpu
-  build_number: 7
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/libarrow-18.0.0-hd4c0275_7_cpu.conda
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-  md5: 9370f2e706f21f65a57cf9db054f72f2
+  version: 18.1.0
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+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-18.1.0-he15abb1_1_cpu.conda
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+  md5: bd3e35a6f3f869b4777488452f315008
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-  - aws-crt-cpp >=0.29.4,<0.29.5.0a0
+  - __glibc >=2.17,<3.0.a0
+  - aws-crt-cpp >=0.29.6,<0.29.7.0a0
   - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
   - azure-core-cpp >=1.14.0,<1.14.1.0a0
   - azure-identity-cpp >=1.10.0,<1.10.1.0a0
   - azure-storage-blobs-cpp >=12.13.0,<12.13.1.0a0
   - azure-storage-files-datalake-cpp >=12.12.0,<12.12.1.0a0
   - bzip2 >=1.0.8,<2.0a0
+  - gflags >=2.2.2,<2.3.0a0
   - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=18
+  - libgcc >=13
   - libgoogle-cloud >=2.31.0,<2.32.0a0
   - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
   - libre2-11 >=2024.7.2
-  - libutf8proc >=2.8.0,<3.0a0
+  - libstdcxx >=13
+  - libutf8proc >=2.8.0,<2.9.0a0
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   - lz4-c >=1.9.3,<1.10.0a0
   - orc >=2.0.3,<2.0.4.0a0
@@ -14211,341 +14606,335 @@ packages:
   - snappy >=1.2.1,<1.3.0a0
   - zstd >=1.5.6,<1.6.0a0
   constrains:
-  - arrow-cpp <0.0a0
   - parquet-cpp <0.0a0
+  - arrow-cpp <0.0a0
   - apache-arrow-proc =*=cpu
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 6094679
-  timestamp: 1731789696651
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+  timestamp: 1732863546099
 - kind: conda
   name: libarrow-acero
   version: 15.0.2
-  build: h5833ebf_46_cpu
-  build_number: 46
+  build: h5833ebf_47_cpu
+  build_number: 47
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-15.0.2-h5833ebf_46_cpu.conda
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-  md5: 717f28f97ce7aea4bc3d05bbbd049243
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-15.0.2-h5833ebf_47_cpu.conda
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   - libcxx >=17
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 491577
-  timestamp: 1731793725380
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 - kind: conda
   name: libarrow-acero
   version: 15.0.2
-  build: h5d0bfc1_46_cpu
-  build_number: 46
+  build: h5d0bfc1_47_cpu
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   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-15.0.2-h5d0bfc1_46_cpu.conda
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-  md5: 14d145ccf1ebac4d48da489b2c908f8e
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-15.0.2-h5d0bfc1_47_cpu.conda
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+  - libarrow 15.0.2 he4bedbb_47_cpu
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   - libstdcxx >=13
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 613760
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   name: libarrow-acero
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-  url: https://conda.anaconda.org/conda-forge/osx-64/libarrow-acero-15.0.2-h97d8b74_46_cpu.conda
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   license: Apache-2.0
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   purls: []
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   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-acero-15.0.2-hac47afa_46_cpu.conda
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+  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-acero-15.0.2-hac47afa_47_cpu.conda
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   license: Apache-2.0
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   purls: []
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-- kind: conda
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-  url: https://conda.anaconda.org/conda-forge/osx-64/libarrow-acero-18.0.0-h240833e_7_cpu.conda
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-  license: Apache-2.0
-  license_family: APACHE
-  purls: []
-  size: 530643
-  timestamp: 1731789802596
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-18.0.0-h286801f_7_cpu.conda
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+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-18.1.0-h5888daf_1_cpu.conda
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+  - libarrow 18.1.0 he15abb1_1_cpu
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   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 491285
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   license: Apache-2.0
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   purls: []
-  size: 618726
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   name: libarrow-acero
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-  build: hac47afa_7_cpu
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+  version: 18.1.0
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   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/libarrow-acero-18.0.0-hac47afa_7_cpu.conda
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 - kind: conda
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   noarch: python
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+  - python >=3.9
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@@ -28577,11 +29300,11 @@ packages:
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   - pyarrow ; extra == 'optional'
   - polars ; extra == 'optional'
+  - sentence-transformers ; extra == 'transformers'
   - numpydoc ; extra == 'test'
   - pytest ; extra == 'test'
   - pytest-cov ; extra == 'test'
   - pytest-xdist ; extra == 'test'
-  - sentence-transformers ; extra == 'transformers'
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   license: Apache-2.0
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+  - libhwloc >=2.11.2,<2.11.3.0a0
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-  license: Apache-2.0
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+  version: 0.20.4
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+  - openssl >=3.4.0,<4.0a0
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   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.0-pyhd8ed1ab_0.conda
-  sha256: fb25b18cec1ebae56e7d7ebbd3e504f063b61a0fac17b1ca798fcaf205bdc874
-  md5: 196a9e6ab4e036ceafa516ea036619b0
+  url: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.1-pyhd8ed1ab_0.conda
+  sha256: 5673b7104350a6998cb86cccf1d0058217d86950e8d6c927d8530606028edb1d
+  md5: 4085c9db273a148e149c03627350e22c
   depends:
   - colorama
   - python >=3.7
   license: MPL-2.0 or MIT
   purls:
   - pkg:pypi/tqdm?source=hash-mapping
-  size: 89434
-  timestamp: 1730926216380
+  size: 89484
+  timestamp: 1732497312317
 - kind: conda
   name: traitlets
   version: 5.14.3
@@ -30360,24 +31049,24 @@ packages:
   timestamp: 1731710681163
 - kind: conda
   name: virtualenv
-  version: 20.27.1
+  version: 20.28.0
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.27.1-pyhd8ed1ab_0.conda
-  sha256: 189b935224732267df10dc116bce0835bd76fcdb20c30f560591c92028d513b0
-  md5: dae21509d62aa7bf676279ced3edcb3f
+  url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.28.0-pyhd8ed1ab_0.conda
+  sha256: 82776f74e90a296b79415361faa6b10f360755c1fb8e6d59ca68509e6fe7e115
+  md5: 1d601bc1d28b5ce6d112b90f4b9b8ede
   depends:
-  - distlib <1,>=0.3.7
-  - filelock <4,>=3.12.2
-  - platformdirs <5,>=3.9.1
-  - python >=3.8
+  - distlib >=0.3.7,<1
+  - filelock >=3.12.2,<4
+  - platformdirs >=3.9.1,<5
+  - python >=3.9
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/virtualenv?source=hash-mapping
-  size: 2965442
-  timestamp: 1730204927840
+  size: 3350255
+  timestamp: 1732609542072
 - kind: conda
   name: vs2015_runtime
   version: 14.42.34433
@@ -31232,12 +31921,12 @@ packages:
   timestamp: 1641347623319
 - kind: conda
   name: yarl
-  version: 1.17.2
+  version: 1.18.0
   build: py311h917b07b_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/yarl-1.17.2-py311h917b07b_0.conda
-  sha256: 9f36ca92a19b11f3f5c79cc954f1ad9fee574bada0680df2dd44eec6933c8bf9
-  md5: 37a19c890f2e7b4cfabcdf4e4ef7288f
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/yarl-1.18.0-py311h917b07b_0.conda
+  sha256: d21aeb264716c1f7bfb0f73f489621eebf8d28d951f3885a90ba97369eb2184d
+  md5: 93d15c4b23a94293511460afeda65445
   depends:
   - __osx >=11.0
   - idna >=2.0
@@ -31250,16 +31939,16 @@ packages:
   license_family: Apache
   purls:
   - pkg:pypi/yarl?source=hash-mapping
-  size: 141207
-  timestamp: 1731927269197
+  size: 142545
+  timestamp: 1732221235765
 - kind: conda
   name: yarl
-  version: 1.17.2
+  version: 1.18.0
   build: py311h9ecbd09_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/yarl-1.17.2-py311h9ecbd09_0.conda
-  sha256: 76f0c0a55af42386c6d11a95ae3a056c05762576cafda4a9a15a011869361bfb
-  md5: a13b1162515c6d66cc16336caccbb388
+  url: https://conda.anaconda.org/conda-forge/linux-64/yarl-1.18.0-py311h9ecbd09_0.conda
+  sha256: 6badcbdfa79e7d85898dadc3f3c8541aeb0328beaf52d8c324f9cfa03827e34b
+  md5: 6c53a0d074f60e75069133bfcdfdf76f
   depends:
   - __glibc >=2.17,<3.0.a0
   - idna >=2.0
@@ -31272,16 +31961,16 @@ packages:
   license_family: Apache
   purls:
   - pkg:pypi/yarl?source=hash-mapping
-  size: 152868
-  timestamp: 1731927069636
+  size: 152353
+  timestamp: 1732220979792
 - kind: conda
   name: yarl
-  version: 1.17.2
+  version: 1.18.0
   build: py311he736701_0
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/yarl-1.17.2-py311he736701_0.conda
-  sha256: 897782fa6fb0039f4e08c47985dea4f2e78d06aac35551caabf78aa5ff7b6299
-  md5: f0acb82a53ea27060de29d4026efd3a7
+  url: https://conda.anaconda.org/conda-forge/win-64/yarl-1.18.0-py311he736701_0.conda
+  sha256: 65bbf6486a913990e07581930b7a49c72ce8b21b5541242302d684f26e3b184d
+  md5: ce3089b5e70ce57190001f9b9648dacb
   depends:
   - idna >=2.0
   - multidict >=4.0
@@ -31295,16 +31984,16 @@ packages:
   license_family: Apache
   purls:
   - pkg:pypi/yarl?source=hash-mapping
-  size: 142253
-  timestamp: 1731927414370
+  size: 142764
+  timestamp: 1732221338560
 - kind: conda
   name: yarl
-  version: 1.17.2
+  version: 1.18.0
   build: py312h4389bb4_0
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/yarl-1.17.2-py312h4389bb4_0.conda
-  sha256: 2007fdfd07c56d447016b6a412b415bc8b087346923e05fe8c1693a507a60bfc
-  md5: 772df163938b12aa1a68a92210b763a5
+  url: https://conda.anaconda.org/conda-forge/win-64/yarl-1.18.0-py312h4389bb4_0.conda
+  sha256: 5d784a5df4d0a58e5cb665935b3c585311a3eb8f2513477483d49ca09c2731c8
+  md5: 1a39a4322ee5162c4c0defdcb6c99c64
   depends:
   - idna >=2.0
   - multidict >=4.0
@@ -31318,16 +32007,16 @@ packages:
   license_family: Apache
   purls:
   - pkg:pypi/yarl?source=hash-mapping
-  size: 139801
-  timestamp: 1731927574204
+  size: 140928
+  timestamp: 1732221264363
 - kind: conda
   name: yarl
-  version: 1.17.2
+  version: 1.18.0
   build: py312h66e93f0_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/yarl-1.17.2-py312h66e93f0_0.conda
-  sha256: 4e870938d29f38cd2aa43247efff6f99f6ecd8973735509122cd3167ccc22add
-  md5: 99518ade67138dcce4f2751b47ab5b00
+  url: https://conda.anaconda.org/conda-forge/linux-64/yarl-1.18.0-py312h66e93f0_0.conda
+  sha256: 8a1e51303ecac64f10dd0ec548d472c49954c3e2f38023ef28899191580795e1
+  md5: 601d2b19a54fd9346ba18c07c2516339
   depends:
   - __glibc >=2.17,<3.0.a0
   - idna >=2.0
@@ -31340,8 +32029,8 @@ packages:
   license_family: Apache
   purls:
   - pkg:pypi/yarl?source=hash-mapping
-  size: 150022
-  timestamp: 1731927117182
+  size: 151247
+  timestamp: 1732220988648
 - kind: conda
   name: zeromq
   version: 4.3.5
@@ -31404,20 +32093,21 @@ packages:
 - kind: conda
   name: zipp
   version: 3.21.0
-  build: pyhd8ed1ab_0
+  build: pyhd8ed1ab_1
+  build_number: 1
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
-  sha256: 232a30e4b0045c9de5e168dda0328dc0e28df9439cdecdfb97dd79c1c82c4cec
-  md5: fee389bf8a4843bd7a2248ce11b7f188
+  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda
+  sha256: 567c04f124525c97a096b65769834b7acb047db24b15a56888a322bf3966c3e1
+  md5: 0c3cc595284c5e8f0f9900a9b228a332
   depends:
-  - python >=3.8
+  - python >=3.9
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/zipp?source=hash-mapping
-  size: 21702
-  timestamp: 1731262194278
+  size: 21809
+  timestamp: 1732827613585
 - kind: conda
   name: zlib
   version: 1.3.1
@@ -31641,6 +32331,74 @@ packages:
   - pkg:pypi/zstandard?source=hash-mapping
   size: 325703
   timestamp: 1725305947138
+- kind: conda
+  name: zstandard
+  version: 0.23.0
+  build: py313h80202fe_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py313h80202fe_1.conda
+  sha256: ea82f2b8964150a3aa7373b4697e48e64f2200fe68ae554ee85c641c692d1c97
+  md5: c178558ff516cd507763ffee230c20b2
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - cffi >=1.11
+  - libgcc >=13
+  - python >=3.13.0rc1,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - zstd >=1.5.6,<1.5.7.0a0
+  - zstd >=1.5.6,<1.6.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 424424
+  timestamp: 1725305749031
+- kind: conda
+  name: zstandard
+  version: 0.23.0
+  build: py313hab0894d_1
+  build_number: 1
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py313hab0894d_1.conda
+  sha256: 4b976b0c6f5c1a2c94c5351fbc02b1cad44dbeaf2e288986827e8b2183a14ce6
+  md5: 27fe151b0b0752c1ad1c47106855efd9
+  depends:
+  - __osx >=10.13
+  - cffi >=1.11
+  - python >=3.13.0rc1,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - zstd >=1.5.6,<1.5.7.0a0
+  - zstd >=1.5.6,<1.6.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 417943
+  timestamp: 1725305677487
+- kind: conda
+  name: zstandard
+  version: 0.23.0
+  build: py313hf2da073_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py313hf2da073_1.conda
+  sha256: 12b4e34acff24d291e2626c6610dfd819b8d99a461025ae59affcb6e84bc1d57
+  md5: deebca66926691fadaaf16da05ecb5f9
+  depends:
+  - __osx >=11.0
+  - cffi >=1.11
+  - python >=3.13.0rc1,<3.14.0a0
+  - python >=3.13.0rc1,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  - zstd >=1.5.6,<1.5.7.0a0
+  - zstd >=1.5.6,<1.6.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 336496
+  timestamp: 1725305912716
 - kind: conda
   name: zstandard
   version: 0.23.0
diff --git a/pyproject.toml b/pyproject.toml
index 95c5af819..539778715 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -266,3 +266,10 @@ filterwarnings = [
 ]
 addopts = "--doctest-modules"
 doctest_optionflags = "NORMALIZE_WHITESPACE ELLIPSIS"
+
+[tool.codespell]
+# Ref: https://github.com/codespell-project/codespell#using-a-config-file
+skip = '.git*,*.svg,package-lock.json,*.lock,*.css,*-min.*'
+check-hidden = true
+# ignore-regex = ''
+ignore-words-list = 'ans,serie'
diff --git a/skrub/_column_associations.py b/skrub/_column_associations.py
index 64b4c5597..e39b9dd8a 100644
--- a/skrub/_column_associations.py
+++ b/skrub/_column_associations.py
@@ -29,7 +29,7 @@ def column_associations(df):
     columns are binned with 10 bins. For categorical columns, only the 10 most
     frequent categories are considered. In both cases, nulls are treated as a
     separate category, ie a separate row in the contingency table. Thus
-    associations betwen the values of 2 columns or between their missingness
+    associations between the values of 2 columns or between their missingness
     patterns may be captured.
 
     Parameters
@@ -178,7 +178,7 @@ def _compute_cramer(table, n_samples):
     The input is the table computed by ``_contingency_table`` with shape
     (n cols, n cols, n bins, n bins).
 
-    This returs the symmetric matrix with shape (n cols, n cols) where entry
+    This returns the symmetric matrix with shape (n cols, n cols) where entry
     i, j contains the statistic for column i x column j.
     """
     marginal_0 = table.sum(axis=-2)
diff --git a/skrub/_fuzzy_join.py b/skrub/_fuzzy_join.py
index 8250a5ffc..682dfa06d 100644
--- a/skrub/_fuzzy_join.py
+++ b/skrub/_fuzzy_join.py
@@ -60,7 +60,7 @@ def fuzzy_join(
         in the right table.
 
     'self_join_neighbor'
-        Once the match candidate (i.e. the nearest neigbor from the right
+        Once the match candidate (i.e. the nearest neighbor from the right
         table) has been found, we find its nearest neighbor in the right
         table (excluding itself). The reference distance is the distance that
         separates those 2 right rows.
diff --git a/skrub/_gap_encoder.py b/skrub/_gap_encoder.py
index 5ad883594..34c650199 100644
--- a/skrub/_gap_encoder.py
+++ b/skrub/_gap_encoder.py
@@ -159,7 +159,7 @@ class GapEncoder(TransformerMixin, SingleColumnTransformer):
     >>> enc.get_feature_names_out()
     ['city: england, london, uk', 'city: france, paris, pqris']
 
-    It got it right, reccuring topics are "London" and "England" on the
+    It got it right, reoccurring topics are "London" and "England" on the
     one side and "Paris" and "France" on the other.
 
     As this is a continuous encoding, we can look at the level of
diff --git a/skrub/_joiner.py b/skrub/_joiner.py
index b9854dbf7..c366173c7 100644
--- a/skrub/_joiner.py
+++ b/skrub/_joiner.py
@@ -126,7 +126,7 @@ class Joiner(TransformerMixin, BaseEstimator):
         in the auxiliary table.
 
     'self_join_neighbor'
-        Once the match candidate (i.e. the nearest neigbor from the auxiliary
+        Once the match candidate (i.e. the nearest neighbor from the auxiliary
         table) has been found, we find its nearest neighbor in the auxiliary
         table (excluding itself). The reference distance is the distance that
         separates those 2 auxiliary rows.
diff --git a/skrub/_on_each_column.py b/skrub/_on_each_column.py
index d466d5787..68705d6c2 100644
--- a/skrub/_on_each_column.py
+++ b/skrub/_on_each_column.py
@@ -250,7 +250,7 @@ class OnEachColumn(TransformerMixin, BaseEstimator):
         ``cols``) are passed through.
 
     rename_columns : str, default='{}'
-        Format string applied to all transformation ouput column names. For
+        Format string applied to all transformation output column names. For
         example pass ``'transformed_{}'`` to prepend ``'transformed_'`` to all
         output column names. The default value does not modify the names.
         Renaming is not applied to columns not selected by ``cols``.
diff --git a/skrub/_on_subframe.py b/skrub/_on_subframe.py
index 2ea1fcfd0..f255fde7b 100644
--- a/skrub/_on_subframe.py
+++ b/skrub/_on_subframe.py
@@ -43,7 +43,7 @@ class OnSubFrame(TransformerMixin, BaseEstimator):
         passed through.
 
     rename_columns : str, default='{}'
-        Format strings applied to all transformation ouput column names. For
+        Format strings applied to all transformation output column names. For
         example pass ``'transformed_{}'`` to prepend ``'transformed_'`` to all
         output column names. The default value does not modify the names.
         Renaming is not applied to columns not selected by ``cols``.
diff --git a/skrub/_reporting/_plotting.py b/skrub/_reporting/_plotting.py
index f8f36143d..e1e4b58f0 100644
--- a/skrub/_reporting/_plotting.py
+++ b/skrub/_reporting/_plotting.py
@@ -231,7 +231,7 @@ def value_counts(value_counts, n_unique, n_rows, color=COLOR_0):
     n_unique : int
         Cardinality of the plotted column, used to determine if all unique
         values are plotted or if there are too many and some have been
-        ommitted. The figure's title is adjusted accordingly.
+        omitted. The figure's title is adjusted accordingly.
 
     n_rows : int
         Total length of the column, used to convert the counts to proportions.
diff --git a/skrub/_reporting/_sample_table.py b/skrub/_reporting/_sample_table.py
index 3a5c844b5..126816b69 100644
--- a/skrub/_reporting/_sample_table.py
+++ b/skrub/_reporting/_sample_table.py
@@ -329,7 +329,7 @@ class _PandasTable:
     i=0  | my house   | 1st   | df.iloc[0, 0] | df.iloc[0, 1] | ... | ... |
     i=1  |            | 2nd   | df.iloc[1, 0] | df.iloc[1, 1] | ... | ... |
     i=2  | your house | 1st   | ...           | ...           | ... | ... |
-    i=3  |            | 2st   | ...           | ...           | ... | ... |
+    i=3  |            | 2nd   | ...           | ...           | ... | ... |
 
 
     start_i, start_j are the first i, j coords (here -3, -2)
diff --git a/skrub/_text_encoder.py b/skrub/_text_encoder.py
index 36f0b1068..80ffd0c89 100644
--- a/skrub/_text_encoder.py
+++ b/skrub/_text_encoder.py
@@ -67,12 +67,12 @@ class TextEncoder(SingleColumnTransformer, TransformerMixin):
         You can find more options on the `sentence-transformers documentation
         <https://www.sbert.net/docs/pretrained_models.html#model-overview>`_.
 
-        The default model is a shrinked version of e5-v2, which has shown good
+        The default model is a shrunk version of e5-v2, which has shown good
         performance in the benchmark of [1]_.
 
     n_components : int or None, default=30,
         The number of embedding dimensions. As the number of dimensions is different
-        accross embedding models, this class uses a :class:`~sklearn.decomposition.PCA`
+        across embedding models, this class uses a :class:`~sklearn.decomposition.PCA`
         to set the number of embedding to ``n_components`` during ``transform``.
         Set ``n_components=None`` to skip the PCA dimension reduction mechanism.
 
@@ -395,7 +395,7 @@ def get_feature_names_out(self):
     def __getstate__(self):
         state = self.__dict__.copy()
         # Always dump self._cache_folder because it is overwritten when the model
-        # is loaded, and it shows an absolut path on the user machine.
+        # is loaded, and it shows an absolute path on the user machine.
         # However, we have to include self.cache_folder in the serialized object
         # because that is a parameter provided by the user.
         remove_props = ["_cache_folder"]
diff --git a/skrub/_to_datetime.py b/skrub/_to_datetime.py
index 4a08013fe..2485fa84f 100644
--- a/skrub/_to_datetime.py
+++ b/skrub/_to_datetime.py
@@ -98,7 +98,7 @@ class ToDatetime(SingleColumnTransformer):
     format : str or None, optional, default=None
         Format to use for parsing dates that are stored as strings, e.g.
         ``"%Y-%m-%dT%H:%M%S"``.
-        If not specfied, the format is inferred from the data when possible.
+        If not specified, the format is inferred from the data when possible.
         When doing so, for dates presented as 01/02/2003, it is usually
         possible to infer from the data whether the month comes first (USA
         convention) or the day comes first, ie ``"%m/%d/%Y"`` vs
diff --git a/skrub/_wrap_transformer.py b/skrub/_wrap_transformer.py
index 897df06b6..51ec8cbd2 100644
--- a/skrub/_wrap_transformer.py
+++ b/skrub/_wrap_transformer.py
@@ -24,7 +24,7 @@ def wrap_transformer(
     ``OnEachColumn`` instance. Otherwise it is wrapped in a ``OnSubFrame``
     instance.
 
-    This default choice can be overriden by passing ``columnwise=True`` to
+    This default choice can be overridden by passing ``columnwise=True`` to
     force the use of ``OnEachColumn`` or ``columnwise=False`` to force the use
     of ``OnSubFrame``.
 
diff --git a/skrub/datasets/_fetching.py b/skrub/datasets/_fetching.py
index 0af827679..fa60984a8 100644
--- a/skrub/datasets/_fetching.py
+++ b/skrub/datasets/_fetching.py
@@ -63,7 +63,7 @@
 
 MOVIELENS_URL = "https://files.grouplens.org/datasets/movielens/{zip_directory}.zip"
 
-# A dictionnary storing the sha256 hashes of the figshare files
+# A dictionary storing the sha256 hashes of the figshare files
 figshare_id_to_hash = {
     39142985: "47d73381ef72b050002a8642194c6718a4954ec9e6c556f4c4ddc6ed84ceec92",
     39149066: "e479cf9741a90c40401697e7fa54409e3b9cfa09f27502877382e64e86fbfcd0",
diff --git a/skrub/datasets/_generating.py b/skrub/datasets/_generating.py
index 0f1aefc9c..52d7b8195 100644
--- a/skrub/datasets/_generating.py
+++ b/skrub/datasets/_generating.py
@@ -28,7 +28,7 @@ def make_deduplication_data(
         Number of duplications per example.
     prob_mistake_per_letter : float in [0, 1], default=0.2
         Probability of misspelling a character in duplications.
-        By default, 1/5 of the characters will be misspeled.
+        By default, 1/5 of the characters will be misspelled.
     random_state : int, RandomState instance, optional
         Determines random number generation for dataset noise. Pass an int
         for reproducible output across multiple function calls.
diff --git a/skrub/tests/test_column_associations.py b/skrub/tests/test_column_associations.py
index 8dc6439bd..897d0cafe 100644
--- a/skrub/tests/test_column_associations.py
+++ b/skrub/tests/test_column_associations.py
@@ -25,7 +25,7 @@ def test_column_associations(df_module):
 
 def test_infinite(df_module):
     # non-regression test for https://github.com/skrub-data/skrub/issues/1133
-    # (colum associations would raise an exception on low-cardinality float
+    # (column associations would raise an exception on low-cardinality float
     # column with infinite values)
     with warnings.catch_warnings():
         # pandas convert_dtypes() emits a spurious warning while trying to decide if
diff --git a/skrub/tests/test_multi_agg_joiner.py b/skrub/tests/test_multi_agg_joiner.py
index 6a6b25c1d..dcde331ca 100644
--- a/skrub/tests/test_multi_agg_joiner.py
+++ b/skrub/tests/test_multi_agg_joiner.py
@@ -303,7 +303,7 @@ def test_wrong_keys_length(main_table, df_module):
     "Check that providing wrong key lengths in the `MultiAggJoiner` raise an error."
     main_table = df_module.DataFrame(main_table)
 
-    # Check wrong main_keys lenght
+    # Check wrong main_keys length
     multi_agg_joiner = MultiAggJoiner(
         aux_tables=[main_table, main_table],
         operations=[["count"], ["count"]],