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build(datasets): Add official support for python 3.13 #938

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2 changes: 1 addition & 1 deletion .github/workflows/kedro-datasets.yml
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ jobs:
strategy:
matrix:
os: [ ubuntu-latest, windows-latest ]
python-version: [ "3.10", "3.11", "3.12" ]
python-version: [ "3.13" ]
uses: ./.github/workflows/unit-tests.yml
with:
plugin: kedro-datasets
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1 change: 1 addition & 0 deletions kedro-datasets/RELEASE.md
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

## Major features and improvements

- Added support for Python 3.13
- Supported passing `database` to `ibis.TableDataset` for load and save operations.
- Added functionality to save pandas DataFrames directly to Snowflake, facilitating seamless `.csv` ingestion.
- Added Python 3.9, 3.10 and 3.11 support for `snowflake.SnowflakeTableDataset`.
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17 changes: 9 additions & 8 deletions kedro-datasets/pyproject.toml
Original file line number Diff line number Diff line change
Expand Up @@ -98,7 +98,7 @@ pandas-jsondataset = ["kedro-datasets[pandas-base]"]
pandas-parquetdataset = ["kedro-datasets[pandas-base]", "pyarrow>=6.0"]
pandas-sqltabledataset = ["kedro-datasets[pandas-base]", "SQLAlchemy>=1.4, <3.0"]
pandas-sqlquerydataset = ["kedro-datasets[pandas-base]", "SQLAlchemy>=1.4, <3.0", "pyodbc>=4.0"]
pandas-xmldataset = ["kedro-datasets[pandas-base]", "lxml~=4.6"]
pandas-xmldataset = ["kedro-datasets[pandas-base]", "lxml"]
pandas = [
"""kedro-datasets[pandas-csvdataset,\
pandas-deltatabledataset,\
Expand All @@ -115,7 +115,7 @@ pandas = [
pandas-xmldataset]"""
]

pickle-pickledataset = ["compress-pickle[lz4]~=2.1.0"]
pickle-pickledataset = ["compress-pickle[lz4]>=2.1.0"]
pickle = ["kedro-datasets[pickle-pickledataset]"]

pillow-imagedataset = ["Pillow>=9.0"]
Expand Down Expand Up @@ -212,7 +212,7 @@ test = [
"compress-pickle[lz4]~=2.1.0",
"coverage>=7.2.0",
"dask[complete]>=2021.10",
"delta-spark>=1.0, <3.0",
"delta-spark>=1.0, <4.0",
"deltalake>=0.10.0",
"dill~=0.3.1",
"filelock>=3.4.0, <4.0",
Expand All @@ -227,7 +227,7 @@ test = [
"joblib>=0.14",
"jupyterlab>=3.0",
"jupyter~=1.0",
"lxml~=4.6",
"lxml~=5.3",
"matplotlib>=3.5, <4.0",
"memory_profiler>=0.50.0, <1.0",
"moto==5.0.0",
Expand All @@ -239,7 +239,8 @@ test = [
"plotly>=4.8.0, <6.0",
"polars[deltalake,xlsx2csv]>=1.0",
"pyarrow>=1.0; python_version < '3.11'",
"pyarrow>=7.0; python_version >= '3.11'", # Adding to avoid numpy build errors
"pyarrow>=7.0; python_version >= '3.11' and python_version < '3.13'", # Adding to avoid numpy build errors
"pyarrow>=18.0; python_version >= '3.13'",
"pyodbc~=5.0",
"pyspark>=3.0; python_version < '3.11'",
"pyspark>=3.4; python_version >= '3.11'",
Expand All @@ -258,8 +259,8 @@ test = [
"pyOpenSSL>=22.1.0", # Workaround for incorrect pin in the snowflake-connector-python package; see https://github.com/snowflakedb/snowflake-connector-python/issues/2109
"SQLAlchemy>=1.2",
"tables>=3.6",
"tensorflow-macos~=2.0; platform_system == 'Darwin' and platform_machine == 'arm64'",
"tensorflow~=2.0; platform_system != 'Darwin' or platform_machine != 'arm64'",
"tensorflow-macos~=2.12; platform_system == 'Darwin' and platform_machine == 'arm64' and python_version < '3.13'",
"tensorflow~=2.12; (platform_system != 'Darwin' or platform_machine != 'arm64') and python_version < '3.13'",
"triad>=0.6.7, <1.0",
"xarray>=2023.1.0",
"xlsxwriter~=1.0",
Expand All @@ -274,7 +275,7 @@ lint = [
"bandit>=1.6.2, <2.0",
"blacken-docs==1.9.2",
"black~=22.0",
"import-linter[toml]==1.2.6",
"import-linter[toml]>=1.2.6",
"mypy~=1.0",
"pre-commit>=2.9.2",
"ruff~=0.0.290",
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Original file line number Diff line number Diff line change
@@ -1,6 +1,5 @@
from io import StringIO
from pathlib import PurePosixPath

import pytest
from Bio import SeqIO
from fsspec.implementations.http import HTTPFileSystem
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,9 @@
allow_module_level=True,
)

if sys.version.minor >= 13:
pytest.skip("Tensorflow is not available in Python 3.13 yet")


# In this test module, we wrap tensorflow and TensorFlowModelDataset imports into a module-scoped
# fixtures to avoid them being evaluated immediately when a new test process is spawned.
Expand Down
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