Mailman 3 May 2026 - Python-announce-list

ANN: Python Meeting Düsseldorf - 03.06.2026
by eGenix Team May 25, 2026

May 25, 2026

/This announcement is in German since it targets a local user group//meeting in Düsseldorf, Germany / Über uns Python Meeting Düsseldorf - Ein Treffen von Python Enthusiasten und Interessierten in ungezwungener Atmosphäre *A special note to our English speaking friends: Presentations and questions can be in English but the meetings are also a chance to improve your German.* Einleitung Das Python Meeting Düsseldorf <https://pyddf.de/> ist eine regelmäßige Veranstaltung in Düsseldorf, die sich an Python-Begeisterte aus der Region wendet. Einen guten Überblick über die Vorträge bietet unser PyDDF YouTube-Kanal <http://www.youtube.com/pyddf/>, auf dem wir Videos der Vorträge nach den Meetings veröffentlichen. Veranstaltet wird das Meeting von der eGenix.com GmbH <https://www.egenix.com/>, Langenfeld, in Zusammenarbeit mit Clark Consulting & Research <http://www.clark-consulting.eu/>, Düsseldorf. Ankündigung Das nächste Python Meeting Düsseldorf <https://pyddf.de/> findet an folgendem Termin statt: *03.06.2026, 18:00 Uhr Raum 1, 2.OG im* *Bürgerhaus Stadtteilzentrum Bilk* <http://www.duesseldorf.de/jugendamt/fam/famfoe/bueh/ac/index.shtml> *Bachstr. 145* <https://maps.app.goo.gl/VWiQKvJpTNy6gern9>*,**40217 Düsseldorf* <https://maps.app.goo.gl/VWiQKvJpTNy6gern9> Programm Geplante Vorträge: * Jochen Wersdörfer: *Using Agents to Create Desktop Apps from Websites* * Marc-André Lemburg: *Let's make friends: OSS and AI - An open minded approach* * Charlie Clark: *Was ich im letzten Jahr gelernt habe (LT)* * Marc-André Lemburg: *Bericht von der PyCon Italia 2026 (LT)* * Jens Diemer: *nab - uv Resolver-Geschwindigkeit in reinem Python (LT)* Startzeit und Dauer Wir treffen uns um 18:00 Uhr im Bürgerhaus in den Düsseldorf Arcaden. Die Anfahrt und Ortsbeschreibung findet ihr auf unserer Seite "Tagungsort <https://pyddf.de/venue/>". Wir brauchen für die Vorträge und Diskussionen meistens bis ca. 20:30 Uhr. Format Das Python Meeting Düsseldorf <https://pyddf.de/> nutzt eine Mischung aus (Lightning) Talks und offener Diskussion (Open Space). Vorträge können vorher angemeldet werden, oder auch spontan während des Treffens eingebracht werden. Ein Beamer mit HDMI und Full-HD Auflösung steht zur Verfügung. *(Lightning) Talk Anmeldung* bitte formlos per EMail an info(a)pyddf.de <mailto:info@pyddf.de?subject=Anmeldung Lightning Talk Python Meeting Düsseldorf> Kostenbeteiligung Das Python Meeting Düsseldorf <https://pyddf.de/> wird von Python Nutzern für Python Nutzer veranstaltet. Tagungsraum, Beamer und Getränke produzieren Kosten. Daher bitten wir die Teilnehmer um eine Kostenbeteiligung in Höhe von *EUR 10,00* inkl. 19% Mwst. Schüler und Studenten zahlen *EUR 5,00* inkl. 19% Mwst. Wir möchten alle Teilnehmer bitten, den Betrag in bar mitzubringen. Anmeldung Da wir nur 25 Personen in dem angemieteten Raum empfangen können, möchten wir bitten, sich vorher anzumelden: >>> *Anmeldung bitte über den hierfür angelegten**LinkedIn Event* <https://www.linkedin.com/events/7463565579895853056>** *⚠️ Wichtig*: Bitte nur dann anmelden, wenn ihr absolut sicher seid, dass ihr auch kommt. Angesichts der begrenzten Anzahl Plätze, haben wir kein Verständnis für kurzfristige Absagen oder No-Shows. Viel Spaß ! -- /Marc-André Lemburg/ eGenix.com <https://egenix.com/?ref=pyddf.de> -- Marc-Andre Lemburg eGenix.com Professional Python Services directly from the Experts (#1, May 25 2026) >>> Python Projects, Coaching and Support ... https://www.egenix.com/ >>> Python Product Development ... https://consulting.egenix.com/ ________________________________________________________________________ ::: We implement business ideas - efficiently in both time and costs ::: eGenix.com Software, Skills and Services GmbH Pastor-Loeh-Str.48 D-40764 Langenfeld, Germany. CEO Dipl.-Math. Marc-Andre Lemburg Registered at Amtsgericht Duesseldorf: HRB 46611 https://www.egenix.com/company/contact/ https://www.malemburg.com/

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TatSu v5.20.0
by Juancarlo Añez May 23, 2026

May 23, 2026

v5.20.0 Update ============== https://pypi.org/project/TatSu/5.20.0/ Sibling Projects ---------------- There are ports of TatSu to Go and Rust. They are functionally complete with except for features (like synthetic classes) that rely on the dynamic nature of Python. 铁修 TieXiu ........... 铁修 TieXiu is the port of TatSu to Rust. It features a PyO3 interface os it’s also a Python library, but the benchmarks show that the pure-Python parsers generated by TatSu are still more performant when hosting from Python. See the TieXiu README for a discussion of the performance limits of PEG parsers. ⻰OGoPEGo .......... ⻰OGoPEGo is the port of TatSu to Go. The implementation, being the most mature, is beautifully concise, and using the generated parsers has a simplicity closer to the Python style allowed by TatSu. Internals --------- - The algorithm for left-recursion analysis went over another round of simplification and optimization. Then the analysis done in pegen, a more efficient and theoretically-sound approach, was evaluated. All tests pass with the pegen’s SCC (Strongly Connected Components) algorithm, so the old-and-tried algorithm in TatSu was replaced. Although left-recursion analysis is performed once per Grammar, before any parsing, a simpler implementation makes this core part of TatSu easier to maintain. g2e (ANTLR to TatSu) -------------------- - The g2e (ANTLR grammar to TatSu) translator has been revived and significantly simplified. A working example (python3.tatsu, 551 lines) is generated from Python 3’s full ANTLR grammar and passes tatsu.compile(). - Removed the regex conversion approach for ANTLR token rules. ANTLR lexer patterns (notably \uXXXX escapes) are not viable as Python regex patterns. Non-trivial token rules now emit Fail() instead of Pattern. - g2e substitutes simple token definitions (like OPEN_PAREN : '(' {opened++;};) for their right hand side (just '(') for better looking grammars. For complex token definitions ANTLR uses a special syntax which is not that of Python-compatible (PCRE2) regular expressions, so g2e omits them, leaving it to the user to decide how to handle those tokens. In many cases a single pattern match is enough for the grammar of interest, and a semantic rule may be added to validate additional conditions that the parsed token should meet. - Streamlined generated grammar output — removed unnecessary parenthesization: - Single token references in alternatives no longer wrapped in extra parens: (NEWLINE) → NEWLINE. - Groups inside [...], {...}, {...}+ unwrapped: [('as' NAME)] → ['as' NAME], {('.' NAME)} → {'.' NAME}. - Rule deduplication by name handles tokens {} declarations that collide with defined rules (e.g. INDENT/DEDENT). - Token name resolution now uses uppercase names consistently. - The g2e example (examples/g2e) uses the old, LL(1) Python grammar. Now, since Python’s PEG parser the actual grammar is a much simpler one. The example is kept as it was to demonstrate g2e’s behavior over a complex grammar. Tools ----- - A new --recursion-limit (-R1) option was added to the tatsu CLI tool so it can handle large and deeply recursive input grammars. When used as a library, the host program should call sys.setrecursionlimit() when required by the grammar complexity. - Added better rendering to FailedParse.__str__(). Now a code fragment and line numbers are shown, as in many modern tools. error: expecting 'world' --> example:1:7 | 1 | hello missing | ^ expecting 'world' -> start JSON ---- - tatsu.ebnf define rules for JSON literals, so true, false, and null, may be used where previously only True, False, and None were recognized. The Python literals are still honored as before, as well as the boolean rule resolving to True for non-falsy values. These literals are only used in grammar directives, as parsing is only interested in the strings that match a Token or Pattern. - Now a Grammar can be imported from the JSON produced by model.asjson(). Roundtrip has been tested and it works. New methods Grammar.load(value: Any) -> Grammar and Grammar.loads(json: str) -> Grammar make the functionality available. class Grammar: @staticmethod def load(value: Any) -> Grammar: from .json import load_grammar return load_grammar(value) @staticmethod def loads(value: str) -> Grammar: from .json import loads_grammar return loads_grammar(value) Grammar Syntax -------------- - The definition of the DEDENT rule in the TatSu grammar is used to support EBNF notations with no rule-terminatiors and grammars with no blank lines * rules. The pattern used in the rule was incorrectly consuming the first non-space character starting the next rule. Fixed. Now this is a valid EBNF definition: grammar = r""" @@grammar :: MiniJSON @@nameguard :: False @@whitespace :: /\s+/ start: value $ value: object | array | string | number | 'true' | 'false' | 'null' object: '{' members? '}' array: '[' elements? ']' members: pair (',' pair)* elements: value (',' value)* pair: string ':' value string: '"' CONTENT '"' CONTENT: /[^"]*/ number: /-?\d+(\.\d+)?/ """ --- -- Juancarlo Añez mailto:apalala@gmail.com

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python-oracledb 4.0.1
by Anthony Tuininga May 19, 2026

May 19, 2026

What is python-oracledb? python-oracledb is a Python extension module that enables access to Oracle Database for Python and conforms to the Python database API 2.0 specifications with a number of enhancements. This module replaces cx_Oracle. Where do I get it? https://pypi.org/project/oracledb/4.0.1/ The easiest method to install/upgrade python-oracledb is via pip as in python -m pip install oracledb --upgrade What's new? This release addresses a number of issues. See the full release notes for all of the details: https://python-oracledb.readthedocs.io/en/latest/release_notes.html#oracled… Please provide any feedback via GitHub issues: https://github.com/oracle/ python-oracledb/issues or discussions: https://github.com/oracle/python- oracledb/discussions

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NumPy 2.4.6 released.
by Charles R Harris May 18, 2026

May 18, 2026

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.4.6. NumPy 2.4.6 is a quick release that fixes a regression discovered in the 2.4.5 release. This release supports Python versions 3.11-3.14. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.4.6>; release notes and the changelog are available on Github <https://github.com/numpy/numpy/releases/tag/v2.4.6>. *Contributors* A total of 4 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - !EarlMilktea - Charles Harris - Sebastian Berg - Warren Weckesser *Pull requests merged* A total of 4 pull requests were merged for this release. - #31444: MAINT: Prepare 2.4.x for further development - #31453: BUG: Fix regression in ``arr.conj()`` - #31459: BUG: ``np.linalg.svd(..., hermitian=True)`` returns non-unitary... - #31460: BUG: Don't call INCREF/DECREF on descr in NpyStringAcquireAllocator... Cheers, Charles Harris

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NumPy 2.4.5 Released
by Charles R Harris May 15, 2026

May 15, 2026

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.4.5. NumPy 2.4.5 is a patch release that fixes bugs discovered after the 2.4.4 release, has some typing improvements, and maintains infrastructure. This release supports Python versions 3.11-3.14. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.4.5>; release notes and the changelog are available on Github <https://github.com/numpy/numpy/releases/tag/v2.4.5>. *Contributors* A total of 17 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Aleksei Nikiforov - Anarion Zuo + - Ankit Ahlawat - Breno Favaretto + - Charles Harris - Igor Krivenko + - Ijtihed Kilani + - Joren Hammudoglu - Maarten Baert + - Matti Picus - Nathan Goldbaum - Praneeth Kodumagulla + - Ralf Gommers - RoomWithOutRoof + - Sebastian Berg - Warren Weckesser - div + *Pull requests merged* A total of 28 pull requests were merged for this release. - #31093: MAINT: Prepare 2.4.x for further development - #31182: TYP: fix ``np.shape`` assignability issue for python lists (#31171) - #31197: ENH: Return rank 0 for empty matrices in matrix_rank (#30422) - #31198: CI/BUG: add native jobs for s390x, fix bug in ``pack_inner``... - #31199: BUG: f2py map complex_long_double to NPY_CLONGDOUBLE - #31205: MAINT: f2py: Stop setting re._MAXCACHE to 50. - #31206: BUG: fix heap buffer overflow in timedelta to string casts - #31207: MAINT: Rename ppc64le and s390x workflow (#31121) - #31208: BUG: Fix matvec/vecmat in-place aliasing (out=input produces... - #31209: TYP: ``tile``\ : accept numpy scalars and arrays as second argument... - #31211: DEP: Undo deprecation for np.dtype() signature used by old pickles... - #31212: REV: Manual revert of float16 svml use (#31178) - #31222: TYP: ``ix_`` fix for boolean and non-1d input (#31218) - #31329: BUG: incorrect temp elision for new-style (NEP 43) user-defined... - #31330: TYP: fix sliding_window_view axis parameter typing - #31335: BUG: Prevent deadlock due to downstream importing NumPy in dlopen... - #31336: BUG: Fix segfault in nditer.multi_index when __getitem__ raises... - #31338: TYP: Fix ruff lint error - #31357: BUG: fix memory leak in np.zeros when fill-zero loop raises (#31320) - #31358: BUG: np.einsum() fails with a 0-dimensional out argument and... - #31379: BUG: Fix signed overflow issue in npy_gcd for INT_MIN on s390x... - #31383: CI: remove Cirrus CI FreeBSD job (#31380) - #31390: BUILD: newer MKL uses so.3 - #31391: BLD/MAINT: improve support for Intel LLVM compilers - #31401: BUG: Avoid UB in safe_[add,sub,mul] helpers (#31396) - #31402: BUG: exclude __pycache__ directories from wheels (#31397) - #31404: TYP: ``_NestedSequence`` type parameter default to work around... - #31426: TYP: Fix ``DTypeLike`` runtime type-checker support (#31425) Cheers, Charles Harris

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Guppy 3/Heapy 3.1.7
by YiFei Zhu May 11, 2026

May 11, 2026

I am happy to announce Guppy 3 3.1.7 Guppy 3 is a library and programming environment for Python, currently providing in particular the Heapy subsystem, which supports object and heap memory sizing, profiling and debugging. It also includes a prototypical specification language, the Guppy Specification Language (GSL), which can be used to formally specify aspects of Python programs and generate tests and documentation from a common source. Guppy 3 is a fork of Guppy-PE, created by Sverker Nilsson for Python 2. This release unbreaks wheel builds for Python 3.14.4 and Python 3.14.5, due to modified ABI in PyInterpreterState. Additionally, many bugs has been found by Daniel Diniz with LLM and has been fixed in this release. (This was featured in LWN! https://lwn.net/Articles/1067234/). This includes several segfaults, error propagation bugs, and refcounting bugs. Support for free-threaded mode is still in development. Existing unit tests are passing but many edge cases are still being worked on. Other notable changes: o Both C extension modules are now using multi-phase init. o A new implementation for Horizon using PyRefTracer is added for Python 3.13+. o Internal tuples (NYTUPLELIKE) now account for PyTupleObject having an additional field Py_hash_t ob_hash in Python 3.14 License: MIT The project homepage is on GitHub: https://github.com/zhuyifei1999/guppy3 Enjoy, YiFei Zhu

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TatSu v5.17.0
by Juancarlo Añez May 6, 2026

May 6, 2026

竜 TatSu is a tool that takes grammars in a superset of EBNF as input, and outputs memoizing (Packrat) PEG parsers in Python. The classic variations of EBNF (Tomassetti, EasyExtend, Wirth) and ISO EBNF are also supported as input grammar format. The Overdue Major Refactoring ------------------------------------------ Maintenance and contributions to TatSu have been more difficult than necessary because of the way the code evolved through its lifetime. - Very long modules and classes that try to do too much - Algorithms difficult to understand or with incorrect semantics - Basic features missing, because the above made them hard to implement This release is a major refactoring of the code in TatSu. - Complex modules were partitioned into sub-modules and classes with well-defined purpose - Several algorithms were rewritten to make their semantics clear and evident, and their implementation more efficient - Many unit tests were added to assert the semantics of complex algorithms - Several user-facing features were added as they became easier to implement For the details about the many changes please take a look at the commit log. - pypi: https://pypi.org/project/TatSu/ - docs: https://tatsu.readthedocs.io/ - repo: https://github.com/neogeny/TatSu Every effort has been made to preserve backwards compatibility by keeping most unit tests intact and testing with projects with large grammars and complex processing. If something escaped those tests, there will be a bugfix release with the fixes soon enough. User-Facing Changes - The TatSu documentation has been improved and expanded, and it has a better look&feel with improved navigation. - TatSu doesn’t care about file names, but the default extension used in unit tests, examples, and documentation for grammars is now .tatsu - EBNF, both ISO and the classic variations, is fully supported as grammar input format - Now tatsu.parse(...., asmodel=True) produces a model that matches the ::Type declarations in their grammar (see the models documentation for a thorough review of the features). - walkers.NodeWalker now handles all known types of input. Also: - DepthFirstWalker was reimplemented to ensure DFS semantics - PostOrderDepthFirstWalker walks children before parents - PreOrderWalker was broken and crazy. It was rewritten as a BreadthFirstWalker with the correct semantics - Constant expressions in a grammar are now evaluated deeply with multiple passes of eval() as to produce results that are intuitively correct: def test_constant_math(): grammar = r""" start = a:`7` b:`2` @:```{a} / {b}``` $ ; """ result = parse(grammar, '', trace=True) assert result == 3.5 - Evaluation of Python expressions by the parsing engine now use safe_eval(), a hardened firewall around most security attacks targeting eval() (see the safeeval module for details) - Because None is a valid initial value for attributes and a frequent return value for callables, the required logic for undefined values was moved to the notnone module, which declares Undefined as an alias for notnone.NotNone In [1]: from tatsu.util.undefined import Undefined In [2]: u = Undefined In [3]: u is None Out[3]: False In [4]: u is Undefined Out[4]: True In [5]: Undefined is None Out[5]: False In [6]: d = u or 'OK' In [7]: d Out[7]: 'OK' - objectmodel.Node was rewritten to give it clear semantics and efficiency - New attributes to Node after initialization generate a warning if the name of a method is being shadowed. This change avoids confusing @dataclass, which is used in generated object models. - Node equality is explicitly defined as object identity. No attempts are made at comparing Node structurally. - Node.children() has the expected semantics, and is much more efficient. - Node.parseinfo is now honored by the parsing engine (previously, only results of type AST could have a parseinfo). Generation of parseinfo is disabled by default, and is enabled by passing pareseinfo=True to the API entry points. def test_node_parseinfo(self): grammar = """ @@grammar :: Test start::Test = true | false ; true = "test" @:`True` $; false = "test" @:`False` $; """ text = 'test' node = tatsu.parse(grammar, text, asmodel=True, parseinfo=True, ) assert type(node).__name__ == 'Test' assert node.ast is True assert node.parseinfo is not None assert node.parseinfo.pos == 0 assert node.parseinfo.endpos == len(text) - Synthetic classes created by synth.synthetize() during parsing with ModelBuilderSemantics behave more consistently, and now have a base class of class SynthNode(BaseNode) - Now ast.AST has consistent semantics of a dict that allows access to contents using the attribute interface - asjson() and friends now cover all known cases with improved consistency and efficiency, so there are less demands over clients of the API - Entry points no longer list a large subset of the configuration options defined in ParserConfig, but still accept them through **settings keyword arguments. Now ParserConfig verifies that the settings passed to are valid, eliminating the frustration of passing an incorrect setting name (a typo) and hoping it has the intended effect. - TatSu still has no library dependencies for its core functionality, but several libraries are used during its development and testing. The TatSu development configuration uses uv and hatch. Several requirements-xyz.txt files are generated in favor of those using pip with pyenv, virtualenvwrapper, or virtualenv - All attempts at recovering comments from parsed input were removed. It never worked, so it had no use. Comment recovery may be attempted in the future. - All pre-existing grammars are compatible with this version of TatSu. - Previously generated Python parsers and models, work with this version of TatSu, yet you should consider generating them anew to take advantage of the improved speed, layout, and features. - CAVEAT: Several functions, methods, and argument names were deprecated. They can still be used, but warnings will be issued at runtime. - CAVEAT: If there are invalid strings or regex patterns in your grammars YOU MUST fix them because now the grammar parser validates strings and patterns. - Many of the functions that TatSu defines for its own use are useful in other contexts. Some examples are: from tatsu.safeeval import is_eval_safe from tatsu.safeeval import hasshable from tatsu.safeeval import make_hashable from tatsu.util import safe_name from tatsu.util.misc import find_from_rematch from tatsu.util.misc import topsort from tatsu.util.undefined import Undefined # ... ----------------------------------------------------------------------------------------------- -- Juancarlo Añez mailto:apalala@gmail.com

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ANN: DIPY 1.12.1 Released
by Serge K. May 5, 2026

May 5, 2026

Hello all, We are excited to announce a new release of Diffusion Imaging in PYthon (DIPY): DIPY 1.12.1 ! DIPY is a the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging. DIPY 1.12.1 (Friday, 23 April 2026) The full release notes are at: https://docs.dipy.org/stable/release_notes/release1.12.html. Thank you all for your contributions and feedback! Please click here <https://docs.dipy.org/stable/api_changes.html#dipy-1-12-0-changes> to check 1.12.1 API changes. Highlights of 1.12.1 release include: - NF: FORCE reconstruction model - NF: New BiasField correction method. - NF: Parallelization of EuDX tractography. - NF: Intermediate map for symmetric diffeomorphic registration. - NF: StatefulSurface class to handle surfaces. - NF: Multiple new workflows (dipy_fit_force, dipy_fit_msmtcsd, dipy_brain_mask, dipy_cluster_streamlines, dipy_fit_powermap, dipy_fit_fwdti). - ENH: Cythonization of multiple algorithms to improve performance. - RF: TRX as default file format for tractography outputs. - RF: Adoption of pathlib across workflows. - RF: Refactoring of NLmeans denoising (classic and blockwise variants). - Added support for Python 3.14 and drop support for Python 3.10. - Documentation update. - Closed 324 issues and merged 173 pull requests. To upgrade or install <http://dipy.org/release0.10.html> DIPY Run the following command in your terminal: <http://dipy.org/release0.10.html> pip install --upgrade dipy This version of DIPY depends on nibabel (3.0.0+). For visualization you need FURY (0.12.0+). Questions or suggestions? For any questions go to https://dipy.org, or send an e-mail to dipy(a)python.org Finally, a new forum is available at https://github.com/dipy/dipy/discussions Have a wonderful time using the new version. Please support us by citing DIPY in your papers using the following DOI: 10.3389/fninf.2014.00008 <https://www.ncbi.nlm.nih.gov/pubmed/24600385> On behalf of the DIPY developers, Eleftherios Garyfallidis, Ariel Rokem, Serge Koudoro https://dipy.org/contributors

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PyCA cryptography 48.0.0 released
by Paul Kehrer May 4, 2026

May 4, 2026

PyCA cryptography 48.0.0 has been released to PyPI. cryptography includes both high level recipes and low level interfaces to common cryptographic algorithms such as symmetric ciphers, asymmetric algorithms, message digests, X.509, key derivation functions, and much more. We support Python 3.9+, and PyPy3 3.11. Changelog (https://cryptography.io/en/latest/changelog/#v48-0-0) * BACKWARDS INCOMPATIBLE: Support for Python 3.8 has been removed. cryptography now requires Python 3.9 or later. * BACKWARDS INCOMPATIBLE: Loading an X.509 CRL whose inner TBSCertList.signature algorithm does not match the outer signatureAlgorithm now raises ValueError. Previously, such CRLs were parsed successfully and only rejected during signature validation. * Added support for ML-KEM key encapsulation and ML-DSA signing when using OpenSSL 3.5.0 or later, in addition to the existing AWS-LC and BoringSSL support. This means post-quantum algorithms are now available to users of our wheels. * Note: Going forward, we do not guarantee that all functionality in cryptography will be available when building against OpenSSL. See ( https://cryptography.io/en/latest/statements/state-of-openssl/) for more information. -Paul Kehrer (reaperhulk)

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python-oracledb 4.0.0
by Anthony Tuininga May 4, 2026

May 4, 2026

What is python-oracledb? python-oracledb is a Python extension module that enables access to Oracle Database for Python and conforms to the Python database API 2.0 specifications with a number of enhancements. This module replaces cx_Oracle. Where do I get it? https://pypi.org/project/oracledb/4.0.0/ The easiest method to install/upgrade python-oracledb is via pip as in python -m pip install oracledb --upgrade What's new? This release has the following highlights: - support for Oracle Database 26ai Deep Data Security in thin mode - support for Continuous Query Notification (CQN) and Advanced Queuing (AQ) notifications in thin mode - new functions to help developers prevent SQL injection when processing user input to create SQL statements - improved cloud native authentication - improved support for data frames (binary views "vb" and string views "vu" formats now accepted) See the full release notes for all of the details: https://python-oracledb.readthedocs.io/en/latest/release_notes.html#oracled… Please provide any feedback via GitHub issues: https://github.com/oracle/ python-oracledb/issues or discussions: https://github.com/oracle/python- oracledb/discussions

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