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make release-tag: Merge branch 'main' into stable
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‎HISTORY.md‎

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# History
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## v0.27.1 - 2026-02-13
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### Bugs Fixed
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* `Meets Threshold?` column in QualityReport details is showing numerical values instead of booleans - Issue [#843](https://github.com/sdv-dev/SDMetrics/issues/843) by @fealho
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### Maintenance
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* Update README and other meta information to point to the DataCebo forum - Issue [#845](https://github.com/sdv-dev/SDMetrics/issues/845) by @pvk-developer
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* Update license information in pyproject.toml to use new format - Issue [#814](https://github.com/sdv-dev/SDMetrics/issues/814) by @pvk-developer
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## v0.27.0 - 2026-01-29
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### New Features

‎README.md‎

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[![Downloads](https://pepy.tech/badge/sdmetrics)](https://pepy.tech/project/sdmetrics)
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[![Tests](https://github.com/sdv-dev/SDMetrics/workflows/Run%20Tests/badge.svg)](https://github.com/sdv-dev/SDMetrics/actions?query=workflow%3A%22Run+Tests%22+branch%3Amain)
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[![Coverage Status](https://codecov.io/gh/sdv-dev/SDMetrics/branch/main/graph/badge.svg)](https://codecov.io/gh/sdv-dev/SDMetrics)
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[![Slack](https://img.shields.io/badge/Community-Slack-blue?style=plastic&logo=slack)](https://bit.ly/sdv-slack-invite)
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[![Forum](https://img.shields.io/badge/Forum-Join%20now!-36C5F0)](https://forum.datacebo.com)
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[![Tutorial](https://img.shields.io/badge/Demo-Get%20started-orange?style=plastic&logo=googlecolab)](https://bit.ly/sdmetrics-demo)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14279167.svg)](https://doi.org/10.5281/zenodo.14279167)
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‎latest_requirements.txt‎

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copulas==0.14.0
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copulas==0.14.1
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numpy==2.3.5
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pandas==2.3.3
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plotly==6.5.2
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scikit-learn==1.8.0
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scipy==1.17.0
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tqdm==4.67.1
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tqdm==4.67.3

‎pyproject.toml‎

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@@ -5,7 +5,6 @@ authors = [{ name = "MIT Data To AI Lab", email = "dailabmit@gmail.com" }]
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classifiers = [
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'Development Status :: 2 - Pre-Alpha',
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'Intended Audience :: Developers',
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'License :: OSI Approved :: MIT License',
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'Natural Language :: English',
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'Programming Language :: Python :: 3',
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'Programming Language :: Python :: 3.9',
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]
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keywords = ['sdmetrics', 'SDMetrics']
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dynamic = ['version']
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license = { text = 'MIT license' }
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license = 'MIT'
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license-files = ['LICENSE']
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requires-python = ">=3.9,<3.15"
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readme = 'README.md'
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dependencies = [
@@ -53,7 +53,7 @@ dependencies = [
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"Issue Tracker" = "https://github.com/sdv-dev/SDMetrics/issues"
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"Changes" = "https://github.com/sdv-dev/SDMetrics/blob/main/HISTORY.md"
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"Twitter" = "https://twitter.com/sdv_dev"
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"Chat" = "https://bit.ly/sdv-slack-invite"
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"Chat" = "https://forum.datacebo.com/"
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[project.entry-points]
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sdmetrics = { main = 'sdmetrics.cli.__main__:main' }
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[tool.setuptools]
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include-package-data = true
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license-files = ['LICENSE']
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[tool.setuptools.packages.find]
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include = ['sdmetrics', 'sdmetrics.*']
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add-ignore = ['D107', 'D407', 'D417']
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[tool.bumpversion]
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current_version = "0.27.0"
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current_version = "0.27.1.dev1"
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parse = '(?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\.(?P<release>[a-z]+)(?P<candidate>\d+))?'
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serialize = [
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'{major}.{minor}.{patch}.{release}{candidate}',

‎sdmetrics/__init__.py‎

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__author__ = 'MIT Data To AI Lab'
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__email__ = 'dailabmit@gmail.com'
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__version__ = '0.27.0'
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__version__ = '0.27.1.dev1'
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import sys
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import warnings as python_warnings

‎sdmetrics/reports/single_table/_properties/column_pair_trends.py‎

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'Meets Threshold?': meets_threshold,
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'Error': error_messages,
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})
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result['Meets Threshold?'] = result['Meets Threshold?'].astype('boolean')
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if result['Error'].isna().all():
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result = result.drop('Error', axis=1)

‎static_code_analysis.txt‎

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Run started:2026-01-29 21:43:48.344046+00:00
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Run started:2026-02-13 15:55:31.917050+00:00
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Test results:
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>> Issue: [B101:assert_used] Use of assert detected. The enclosed code will be removed when compiling to optimised byte code.
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--------------------------------------------------
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Code scanned:
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Total lines of code: 12188
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Total lines of code: 12189
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Total lines skipped (#nosec): 0
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Total potential issues skipped due to specifically being disabled (e.g., #nosec BXXX): 0
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‎tests/integration/reports/multi_table/test_quality_report.py‎

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'Real Correlation': [np.nan],
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'Synthetic Correlation': [np.nan],
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'Real Association': [np.nan],
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'Meets Threshold?': [True],
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'Meets Threshold?': pd.Series([True], dtype='boolean'),
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})
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pd.testing.assert_frame_equal(details[1], expected_df_1)
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'Real Correlation': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
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'Synthetic Correlation': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
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'Real Association': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
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'Meets Threshold?': [True, True, True, True, True, True],
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'Meets Threshold?': pd.Series([True, True, True, True, True, True], dtype='boolean'),
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})
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pd.testing.assert_frame_equal(details[3], expected_df_3)
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pd.testing.assert_frame_equal(details[7], expected_df_3)
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'Real Correlation': [np.nan, 0.946664, 0.966247, 0.862622],
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'Synthetic Correlation': [np.nan, 0.926925, 0.936853, 0.798384],
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'Real Association': [np.nan, np.nan, np.nan, np.nan],
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'Meets Threshold?': [True, True, True, True],
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'Meets Threshold?': pd.Series([True, True, True, True], dtype='boolean'),
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})
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pd.testing.assert_frame_equal(details[5], expected_df_4)
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‎tests/integration/reports/single_table/_properties/test_column_pair_trends.py‎

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'Real Correlation': [0.04735340044317632, np.nan, np.nan, np.nan, np.nan, np.nan],
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'Synthetic Correlation': [-0.11506297326956302, np.nan, np.nan, np.nan, np.nan, np.nan],
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'Real Association': [np.nan] * 6,
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'Meets Threshold?': [True] * 6,
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'Meets Threshold?': pd.Series([True] * 6, dtype='boolean'),
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}
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expected_details = pd.DataFrame(expected_details_dict)
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pd.testing.assert_frame_equal(column_pair_trends.details, expected_details)
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'Real Correlation': [np.nan] * 6,
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'Synthetic Correlation': [np.nan] * 6,
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'Real Association': [np.nan] * 6,
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'Meets Threshold?': [True] * 6,
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'Meets Threshold?': pd.Series([True] * 6, dtype='boolean'),
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}
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expected_details = pd.DataFrame(expected_details_dict)
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pd.testing.assert_frame_equal(column_pair_trends.details, expected_details)
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'Real Correlation': [np.nan, np.nan, np.nan],
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'Synthetic Correlation': [np.nan, np.nan, np.nan],
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'Real Association': [0.0, 1.0, 0.0],
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'Meets Threshold?': [False, True, False],
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'Meets Threshold?': pd.Series([False, True, False], dtype='boolean'),
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})
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pd.testing.assert_frame_equal(column_pair_trends.details, expected_details)
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assert score == 1.0

‎tests/integration/reports/single_table/test_quality_report.py‎

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np.nan,
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],
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'Real Association': [np.nan] * 6,
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'Meets Threshold?': [True] * 6,
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'Meets Threshold?': pd.Series([True] * 6, dtype='boolean'),
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}
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expected_details_column_shapes = pd.DataFrame(expected_details_column_shapes_dict)
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expected_details_cpt = pd.DataFrame(expected_details_cpt__dict)
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np.nan,
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'Real Association': [np.nan] * 6,
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'Meets Threshold?': [True] * 6,
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'Meets Threshold?': pd.Series([True] * 6, dtype='boolean'),
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}
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expected_details_column_shapes = pd.DataFrame(expected_details_column_shapes_dict)
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expected_details_cpt = pd.DataFrame(expected_details_cpt__dict)
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'Real Correlation': [np.nan] * 6,
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'Synthetic Correlation': [np.nan] * 6,
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'Real Association': [np.nan] * 6,
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'Meets Threshold?': [np.nan, True, True, np.nan, np.nan, True],
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'Meets Threshold?': pd.Series(
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[np.nan, True, True, np.nan, np.nan, True], dtype='boolean'
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),
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'Error': [
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'AttributeError', # This can be either ValueError or AttributeError
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None,
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pd.testing.assert_frame_equal(col_pair_report[1:], expected_details_cpt[1:])
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assert report.get_score() == 0.8204378797402054
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def test_meets_threshold_column_has_boolean_dtype_with_errors(self):
423+
"""Test that 'Meets Threshold?' column contains booleans when errors occur."""
424+
# Setup
425+
real_data, synthetic_data, metadata = load_demo(modality='single_table')
426+
real_data.loc[2, 'second_perc'] = 'a' # Corrupt data to trigger errors
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# Run
429+
report = QualityReport()
430+
report.generate(real_data, synthetic_data, metadata, verbose=False)
431+
details = report.get_details('Column Pair Trends')
432+
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# Assert
434+
meets_threshold_col = details['Meets Threshold?']
435+
assert meets_threshold_col.dtype == 'boolean'
436+
437+
# Ensure non-NA values are actual booleans, not integers
438+
non_na_values = meets_threshold_col.dropna()
439+
assert len(non_na_values) > 0
440+
for val in non_na_values:
441+
assert isinstance(val, np.bool_)
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420443
def test_report_with_column_nan(self):
421444
"""Test the report with column full of NaNs."""
422445
# Setup
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np.nan,
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],
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'Real Association': [np.nan] * 10,
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'Meets Threshold?': [True, True, True, np.nan, True, True, np.nan, True, True, True],
564+
'Meets Threshold?': pd.array(
565+
[True, True, True, pd.NA, True, True, pd.NA, True, True, True], dtype='boolean'
566+
),
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'Error': [
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None,
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None,

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