Database Abstraction Library

Navigation

Verified details

These details have been verified by PyPI
Owner
organization icon SQLAlchemy
Maintainers
Avatar for CaselIT from gravatar.com CaselIT

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Mike Bayer
  • Requires: Python !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, >=2.7
Classifiers

Reason this release was yanked:

Regression when inserting orm models using insert. See https://github.com/sqlalchemy/sqlalchemy/issues/7878

Project description

PyPI PyPI - Python Version PyPI - Downloads

The Python SQL Toolkit and Object Relational Mapper

Introduction

SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. SQLAlchemy provides a full suite of well known enterprise-level persistence patterns, designed for efficient and high-performing database access, adapted into a simple and Pythonic domain language.

Major SQLAlchemy features include:

  • An industrial strength ORM, built from the core on the identity map, unit of work, and data mapper patterns. These patterns allow transparent persistence of objects using a declarative configuration system. Domain models can be constructed and manipulated naturally, and changes are synchronized with the current transaction automatically.

  • A relationally-oriented query system, exposing the full range of SQL’s capabilities explicitly, including joins, subqueries, correlation, and most everything else, in terms of the object model. Writing queries with the ORM uses the same techniques of relational composition you use when writing SQL. While you can drop into literal SQL at any time, it’s virtually never needed.

  • A comprehensive and flexible system of eager loading for related collections and objects. Collections are cached within a session, and can be loaded on individual access, all at once using joins, or by query per collection across the full result set.

  • A Core SQL construction system and DBAPI interaction layer. The SQLAlchemy Core is separate from the ORM and is a full database abstraction layer in its own right, and includes an extensible Python-based SQL expression language, schema metadata, connection pooling, type coercion, and custom types.

  • All primary and foreign key constraints are assumed to be composite and natural. Surrogate integer primary keys are of course still the norm, but SQLAlchemy never assumes or hardcodes to this model.

  • Database introspection and generation. Database schemas can be “reflected” in one step into Python structures representing database metadata; those same structures can then generate CREATE statements right back out - all within the Core, independent of the ORM.

SQLAlchemy’s philosophy:

  • SQL databases behave less and less like object collections the more size and performance start to matter; object collections behave less and less like tables and rows the more abstraction starts to matter. SQLAlchemy aims to accommodate both of these principles.

  • An ORM doesn’t need to hide the “R”. A relational database provides rich, set-based functionality that should be fully exposed. SQLAlchemy’s ORM provides an open-ended set of patterns that allow a developer to construct a custom mediation layer between a domain model and a relational schema, turning the so-called “object relational impedance” issue into a distant memory.

  • The developer, in all cases, makes all decisions regarding the design, structure, and naming conventions of both the object model as well as the relational schema. SQLAlchemy only provides the means to automate the execution of these decisions.

  • With SQLAlchemy, there’s no such thing as “the ORM generated a bad query” - you retain full control over the structure of queries, including how joins are organized, how subqueries and correlation is used, what columns are requested. Everything SQLAlchemy does is ultimately the result of a developer- initiated decision.

  • Don’t use an ORM if the problem doesn’t need one. SQLAlchemy consists of a Core and separate ORM component. The Core offers a full SQL expression language that allows Pythonic construction of SQL constructs that render directly to SQL strings for a target database, returning result sets that are essentially enhanced DBAPI cursors.

  • Transactions should be the norm. With SQLAlchemy’s ORM, nothing goes to permanent storage until commit() is called. SQLAlchemy encourages applications to create a consistent means of delineating the start and end of a series of operations.

  • Never render a literal value in a SQL statement. Bound parameters are used to the greatest degree possible, allowing query optimizers to cache query plans effectively and making SQL injection attacks a non-issue.

Documentation

Latest documentation is at:

https://www.sqlalchemy.org/docs/

Installation / Requirements

Full documentation for installation is at Installation.

Getting Help / Development / Bug reporting

Please refer to the SQLAlchemy Community Guide.

Code of Conduct

Above all, SQLAlchemy places great emphasis on polite, thoughtful, and constructive communication between users and developers. Please see our current Code of Conduct at Code of Conduct.

License

SQLAlchemy is distributed under the MIT license.

Project details

Verified details

These details have been verified by PyPI
Owner
organization icon SQLAlchemy
Maintainers
Avatar for CaselIT from gravatar.com CaselIT

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Mike Bayer
  • Requires: Python !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, >=2.7
Classifiers

Release history Release notifications | RSS feed

2.0.36

2.0.35

2.0.34

2.0.33

2.0.32

2.0.31

2.0.30

2.0.29

2.0.28

2.0.27

2.0.26

2.0.25

2.0.24

2.0.23

2.0.22

2.0.21

2.0.20

2.0.19

2.0.18

2.0.17

2.0.16

2.0.15

2.0.14

2.0.13

2.0.12

2.0.11

2.0.10

2.0.9

2.0.8

2.0.7

2.0.6

2.0.5.post1

2.0.5 yanked

Reason this release was yanked:

wheel files failed to build due to a test issue

2.0.4

2.0.3

2.0.2

2.0.1

2.0.0

2.0.0rc3 pre-release

2.0.0rc2 pre-release

2.0.0rc1 pre-release

2.0.0b4 pre-release

2.0.0b3 pre-release

2.0.0b2 pre-release

2.0.0b1 pre-release

1.4.54

1.4.53

1.4.52

1.4.51

1.4.50

1.4.49

1.4.48

1.4.47

1.4.46

1.4.45

1.4.44

1.4.43

1.4.42

1.4.41

1.4.40

1.4.39

1.4.38

1.4.37

1.4.36

1.4.35

1.4.34 yanked

Reason this release was yanked:

critical caching issue identified, 1.4.35 resolves
This version

1.4.33 yanked

Reason this release was yanked:

Regression when inserting orm models using insert. See https://github.com/sqlalchemy/sqlalchemy/issues/7878

1.4.32

1.4.31

1.4.30

1.4.29

1.4.28

1.4.27

1.4.26

1.4.25

1.4.24

1.4.23

1.4.22

1.4.21

1.4.20

1.4.19

1.4.18

1.4.17

1.4.16

1.4.15

1.4.14

1.4.13

1.4.12

1.4.11

1.4.10

1.4.9

1.4.8

1.4.7

1.4.6

1.4.5

1.4.4

1.4.3

1.4.2

1.4.1

1.4.0

1.4.0b3 pre-release

1.4.0b2 pre-release

1.4.0b1 pre-release

1.3.24

1.3.23

1.3.22

1.3.21

1.3.20

1.3.19

1.3.18

1.3.17

1.3.16

1.3.15

1.3.14

1.3.13

1.3.12

1.3.11

1.3.10

1.3.9

1.3.8

1.3.7

1.3.6

1.3.5

1.3.4

1.3.3

1.3.2

1.3.1

1.3.0

1.3.0b3 pre-release

1.3.0b2 pre-release

1.3.0b1 pre-release

1.2.19

1.2.18

1.2.17

1.2.16

1.2.15

1.2.14

1.2.13

1.2.12

1.2.11

1.2.10

1.2.9

1.2.8

1.2.7

1.2.6

1.2.5

1.2.4

1.2.3

1.2.2

1.2.1

1.2.0

1.2.0b3 pre-release

1.2.0b2 pre-release

1.2.0b1 pre-release

1.1.18

1.1.17

1.1.16

1.1.15

1.1.14

1.1.13

1.1.12

1.1.11

1.1.10

1.1.9

1.1.8

1.1.7

1.1.6

1.1.5

1.1.4

1.1.3

1.1.2

1.1.1

1.1.0

1.1.0b3 pre-release

1.1.0b2 pre-release

1.1.0b1 pre-release

1.0.19

1.0.18

1.0.17

1.0.16

1.0.15

1.0.14

1.0.13

1.0.12

1.0.11

1.0.10

1.0.9

1.0.8

1.0.7

1.0.6

1.0.5

1.0.4

1.0.3

1.0.2

1.0.1

1.0.0

1.0.0b5 pre-release

1.0.0b4 pre-release

1.0.0b3 pre-release

1.0.0b2 pre-release

1.0.0b1 pre-release

0.9.10

0.9.9

0.9.8

0.9.7

0.9.6

0.9.5

0.9.4

0.9.3

0.9.2

0.9.1

0.9.0

0.8.7

0.8.6

0.8.5

0.8.4

0.8.3

0.8.2

0.8.1

0.8.0

0.8.0b2 pre-release

0.7.10

0.7.9

0.7.8

0.7.7

0.7.6

0.7.5

0.7.4

0.7.3

0.7.2

0.7.1

0.7.0

0.6.9

0.6.8

0.6.7

0.6.6

0.6.5

0.6.4

0.6.3

0.6.2

0.6.1

0.6.0

0.6beta3 pre-release

0.6beta2 pre-release

0.6beta1 pre-release

0.5.8

0.5.7

0.5.6

0.5.5

0.5.4

0.5.3

0.5.2

0.5.1

0.5.0

0.5.0rc4 pre-release

0.5.0rc3 pre-release

0.5.0rc2 pre-release

0.5.0rc1 pre-release

0.5.0beta3 pre-release

0.5.0beta2 pre-release

0.5.0beta1 pre-release

0.4.8

0.4.7

0.4.6

0.4.5

0.4.4

0.4.3

0.4.2

0.4.2b pre-release

0.4.2a pre-release

0.4.1

0.4.0

0.4.0beta6 pre-release

0.4.0beta5 pre-release

0.4.0beta4 pre-release

0.4.0beta3 pre-release

0.4.0beta2 pre-release

0.4.0beta1 pre-release

0.3.11

0.3.10

0.3.9

0.3.8

0.3.7

0.3.6

0.3.5

0.3.4

0.3.3

0.3.2

0.3.1

0.3.0

0.2.8

0.2.7

0.2.6

0.2.5

0.2.4

0.2.3

0.2.2

0.2.1

0.2.0

0.1.7

0.1.6

0.1.5

0.1.4

0.1.3

0.1.2

0.1.1

0.1.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

SQLAlchemy-1.4.33.tar.gz (8.1 MB view details)

Uploaded Source

Built Distributions

SQLAlchemy-1.4.33-cp310-cp310-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

SQLAlchemy-1.4.33-cp310-cp310-win32.whl (1.6 MB view details)

Uploaded CPython 3.10 Windows x86

SQLAlchemy-1.4.33-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.33-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.12+ x86-64 manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp310-cp310-macosx_10_15_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.10 macOS 10.15+ x86-64

SQLAlchemy-1.4.33-cp39-cp39-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

SQLAlchemy-1.4.33-cp39-cp39-win32.whl (1.6 MB view details)

Uploaded CPython 3.9 Windows x86

SQLAlchemy-1.4.33-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.33-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ x86-64 manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp39-cp39-macosx_10_15_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.9 macOS 10.15+ x86-64

SQLAlchemy-1.4.33-cp38-cp38-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

SQLAlchemy-1.4.33-cp38-cp38-win32.whl (1.6 MB view details)

Uploaded CPython 3.8 Windows x86

SQLAlchemy-1.4.33-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.33-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64 manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp38-cp38-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.8 macOS 10.14+ x86-64

SQLAlchemy-1.4.33-cp37-cp37m-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.7m Windows x86-64

SQLAlchemy-1.4.33-cp37-cp37m-win32.whl (1.6 MB view details)

Uploaded CPython 3.7m Windows x86

SQLAlchemy-1.4.33-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.33-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64 manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp37-cp37m-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.7m macOS 10.14+ x86-64

SQLAlchemy-1.4.33-cp36-cp36m-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.6m Windows x86-64

SQLAlchemy-1.4.33-cp36-cp36m-win32.whl (1.6 MB view details)

Uploaded CPython 3.6m Windows x86

SQLAlchemy-1.4.33-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.33-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.12+ x86-64 manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp36-cp36m-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.6m macOS 10.14+ x86-64

SQLAlchemy-1.4.33-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.6 MB view details)

Uploaded CPython 2.7mu manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp27-cp27m-win_amd64.whl (1.5 MB view details)

Uploaded CPython 2.7m Windows x86-64

SQLAlchemy-1.4.33-cp27-cp27m-win32.whl (1.5 MB view details)

Uploaded CPython 2.7m Windows x86

SQLAlchemy-1.4.33-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.6 MB view details)

Uploaded CPython 2.7m manylinux: glibc 2.5+ x86-64

SQLAlchemy-1.4.33-cp27-cp27m-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 2.7m macOS 10.14+ x86-64

File details

Details for the file SQLAlchemy-1.4.33.tar.gz.

File metadata

  • Download URL: SQLAlchemy-1.4.33.tar.gz
  • Upload date:
  • Size: 8.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.5.0 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0

File hashes

Hashes for SQLAlchemy-1.4.33.tar.gz
Algorithm Hash digest
SHA256 84747d1cc4823285b8253a34513162a664d4989217461e111097446b98803bfc
MD5 8e52702a492bd60c7d09936c8821c788
BLAKE2b-256 febfbf9ab5f5af71c21539ad81225b35df1dcbf00b360d0230a179a519b2228d

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 0d19eed02bf1225889e6f91cd7c2f89618919cb283846e7ed8aae1b0fe5f0403
MD5 e8b92e70057f1d1d4b21b48783467663
BLAKE2b-256 e9445c96a2e7032263aab70b4620446852dce75968eb2b08257eb9d8960cd7ee

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp310-cp310-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.33-cp310-cp310-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.33-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 4443237f1f87d460453f6b33683f25135f3fba9dffbf2a053caae15bf838cfc2
MD5 a30239c133fce21f16bf637ddc158b39
BLAKE2b-256 41943dd32b362a06562831ec554f339594ce59ecb1a180565b16120fdc2f1941

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ea76d3f258c7b221a536fea200b64fc1b9272b48de4d1695bef616b7e5269183
MD5 2681b01219833abdd71304ec43870772
BLAKE2b-256 9d66bd45c97227b32173a03173e498c363dc6c1abcadbddb4d69708a8846ccbe

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3081f61a86d0c2b6928fa7f4666bdeba0b9c7cc19619255454a71bfa60b55978
MD5 10b6a25c6987845815e6418c40bcde16
BLAKE2b-256 2fe0b4dd0654ed40b7cfc5ee3c4042b712ff07e3b5d9b6630081b6232b0370bf

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 bda76918f8c6da01278a97365bc17fa97d902be7f6d7596aad2bd7d9b52adbba
MD5 296920474b11df1826f7a99aed0950e5
BLAKE2b-256 f48be8a2952d90665075b27ee167e8e6039e6b2ab9f1cbe74383445603b66945

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp310-cp310-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp310-cp310-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 f39fb329a53043c10814fee68e123f02addc6000ed96994aedf24afe6fc30e9b
MD5 fec25212e3dfddf8084b28ce1dfd2fd7
BLAKE2b-256 80d565a1629c654f2756b176cfbf8da0b12ea8cad3c81e6e14cda951f9cc8b3a

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 c7c667579800445f390b56c37ee383639465766ebc2041a2d1f1279cda01d4a0
MD5 f45838d5d1bf7aac966d736a58012a7c
BLAKE2b-256 685b786c074b301798162b37c071fb3a99710f401a256171f53dde9ebd33f5a4

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp39-cp39-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.33-cp39-cp39-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.33-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 f409a1a44e3da766445600714e2ca70ddf735898382c11c5c250eb88a7b8b0d1
MD5 e040aeb6f866961bef2780c181d227f4
BLAKE2b-256 18c7f49e294341c41416eab097887355822178056af23a01015ff325dad7ed4c

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c3bbcfbaa33d5998698ed84eba0807a58023c86d0fa540ff4da96637815a4d92
MD5 bbb119061e7fa1dce1f7bff9ee4251ba
BLAKE2b-256 7b02ff314d3b973f4256a42ec275e2ced6d9187080579523a6ba4a6336e764b2

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5ee1ce25cc21db5553f607511fb41b85b7dc5eade15536c5c238f898996987de
MD5 0a1ab43e0550ed535ef9c32c2856d0e4
BLAKE2b-256 d4f88abba67e19ef439865f8cc8be42df4db4a23d729bfe43ff03b2799a62fb3

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 eab54f6ec81c12b6184ebeacccd89567cee8fc94b2f9fba23aec30ca25fc287c
MD5 27cdb4d70191fb71a4d0c0f9507a8cef
BLAKE2b-256 b3dccedc9ddf87ce28461ef673b48a4b1fc7803af603dc340a70138060139345

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp39-cp39-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 89d51682716135e0d93584c3ca625c40805a014aaddb3961074a04895793d4d3
MD5 e2cb35fbc011cd9868348543584f9eac
BLAKE2b-256 54fb67bfe1522c38a4ff41d5178bc52e821512b130ccc46996adcfbf97bb55f9

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 18a965490cc0f83f3e867078096e78e97d81bf370f45debae6027331904d3348
MD5 0f9f5e9cf20f4b9b92deaccffd978758
BLAKE2b-256 2e70db25a84e055bcfc78782bca116dc438f3b225a4db75120f908aa857d3558

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp38-cp38-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.33-cp38-cp38-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.33-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 c8f671575256dddf1c334b2052aff88ac1c913e5430300057f1b0f2f12495019
MD5 a0f37ecc6c8d61197324115ae95d58c4
BLAKE2b-256 51bf10190843d2a68e04b2e391df9a68529cd14d588c11b6ce33cef87738d5ad

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 44f0301e246c4d35d84e70192410d01509aea03a99cb963451aa0b652b7529f5
MD5 6e419fbe83257f6f85e0710acea77c87
BLAKE2b-256 ece2a10a03ee2e16f87f969128428acaf0a5cd11da780773ea3657bdd3c5c34f

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1ee7c85f27b1ae59c3fe8fd715ed38e73ba8c885c90f74bcbae609c0fdb0ada3
MD5 55c1660eb2159da353b5a1fcdd786dfa
BLAKE2b-256 b983712a5f28675279a71a0be32fac872880f97f8e6422b1649a299cda835841

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 03b5dc8b6399a51187e220ab351dfa8f81c310ce59442d047b74cd91ac4e859a
MD5 60cb641c1d35f9e07f2c02735257a34d
BLAKE2b-256 01c99da103adc1c57274364e24dc15966b79a143d8324221b9db56be44eb4cc4

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 cd4954eff9e9b9a294f632e7bdb0a4c41e23c89609c6b2f742f1321740566477
MD5 5bede2155efedfafb37fab7c24709208
BLAKE2b-256 8a87907daac17f61c7a2041bca2cfd8553538b6c14b1c391dd31b5e575c785ac

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 d258091ba28615ae133bf6a9975a24b9ed0bcc47f48bb1f57fa13cbe2bd4033c
MD5 d5061c64f6cc34e0cf1c8929973a3525
BLAKE2b-256 c073f64331ef4b5b3f4dfd88b51cf74c671c55633c4d983379ed4f654c8238be

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp37-cp37m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.33-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.33-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 a720cef2bcd4e645ae1fa01a143a31c04e095f26ff925f6090cb1ef7f1859e5d
MD5 aee9409a8bf38c170428f836f66ee077
BLAKE2b-256 494f0ac8eb319012e2d91c8e8355a5f18979c9a03d09d543749ea18777697fe6

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 1a6ae212067856bf2236708cb434554851cbe8099027953e94241a1743afb2b1
MD5 1372d2d2166923d802e6432c7dbbb7bc
BLAKE2b-256 6b3997e7e39bc102bb88c018aaa5ba0a27b58e79796133471a9dfb4644dd5179

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c6545f832267bfea780c8fc6235f7a1fc87778e3e2629ddddeb88a94f9181292
MD5 a614d2168f6ffb0126cb2838bbbb5197
BLAKE2b-256 33edc1f5769595d1b9619bd4254d275e16fdafb00360ed598d687732d035ea0f

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 dd51b09d540e757dd6377f02950a80c0dc63aec6188582afdc21bf0db66efa0a
MD5 5154cd5c4d9e25575a51746e60c1501b
BLAKE2b-256 4589a3bd59b15319a4383152e411c2487647a2548b4ebad8a512c804299ed549

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 34d2e697115cab4a66d1b8ea60050004ce62f0598c6cf146ee66a4025c7cb7fc
MD5 13a4cd68c4d902a4d1e141ba5e92c7d6
BLAKE2b-256 fbaa75960c60fbba41980cfd01edc41f3997ef5913a51a4110c162dc7ac62e7e

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 a11d8e12ff761101aa44404ce2df15e32f061d5559b862a847976c2efff014a1
MD5 64d7cf456211bc6f2bafe096d60b23fe
BLAKE2b-256 397a39f0b345835048539e046bee2186b983abd14222b446e17b976d815a5b0a

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp36-cp36m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.33-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.33-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 36a3535bf9dd5f42a42d2cb6305f992888bbcb5789c615d35e34368853ae46f5
MD5 a84c4cdb61a6718a8e17a4be57bbd3fa
BLAKE2b-256 ceb3b65fa1f5a933a127e6202881dcda4812e9afde9da1b7e2499989f5349704

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 9473d5dadee7a81d003cf18b1f5266b0fb29a9dff985bc205d71ca8da037e18b
MD5 f5903f18d4e3d51033a085cf1b752d1d
BLAKE2b-256 cc317df0a14348df7ae6f92573a7c11973ecf8b8e7072a65063dc4c154e51b50

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4b7371495c91319bfaa010e257eb8d3cd2d3eae14b256412d3294247ea7f0d78
MD5 cbd8e93fbfcd1fa02c286a0c589fe2f8
BLAKE2b-256 9b2e910de73e376175355a3d2e285f3d0c59a36caf47191c7250fd79586274ad

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 e403e1cfe7789eddba83bc7677dd8ffdaab56fb2f14eb3b6f014037b09cd8096
MD5 8bd7346421c4f03bdd86dc08f9f85b3f
BLAKE2b-256 fa5636dc3217d5981896ad4c43b026effb6093a9d805bee084526ff9bcd141bc

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp36-cp36m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp36-cp36m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 5a43870fc272cf6dabf6ce7ad297a08e7f31672ea9ccd217a305c5bce8eafa9f
MD5 1d53abc4e9a80da361c15fa11d9c4655
BLAKE2b-256 e97c32fe17463d2503c3647c14aae6506170c1fdfd8fbe46d344cb8987a1a97f

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 0539328f70f0c2bbaa0137be7a0787ceea7eda29e41e3a9d575c52030d4251aa
MD5 3fca34d395e3b624a9a82e2c7b75aea1
BLAKE2b-256 26c42459aa6ef086ca3a99b123967752037ef47ca462124370ff538b89d05a97

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp27-cp27m-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 5b2718cb9e2bcf0053a83a6d0c491476b7f3832c59d82b53ccc78bb869e4027f
MD5 f82e941c836b588b421ff442b158f6a9
BLAKE2b-256 e47e54a4b5addd92aec14fadeb9434e97f95f1fffa981993bf05384d9d4f2604

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp27-cp27m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.33-cp27-cp27m-win32.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 2.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.33-cp27-cp27m-win32.whl
Algorithm Hash digest
SHA256 5a5f798fc3f3ca9b5f1bf66b50a58439d558d5df132e12cfed22e2bc167c411c
MD5 876a853e82a25397cd7e5527ff5ccda6
BLAKE2b-256 93fba0fd8f796e194a94a22ceaf50305915e18fc555486c8e85ae39f09cbbc8b

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 d29a8c0e8c2b77f2f548acdf948543a661f6414282598147e094bab091f37af6
MD5 f95b2f72b9bb7d0b09315308fa1f3812
BLAKE2b-256 cf7b1c18d99c077ef2a4531b9b60e412ea0acd9488099f2f5c76ce9c161a1f4f

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.33-cp27-cp27m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.33-cp27-cp27m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 8b4cf1effee1ef6db3f6c5185d32c0e6518bcf06212861875779676a22f68370
MD5 e712755a3329aafe738ed6e25e4b6686
BLAKE2b-256 45e38ac853202d75d15b7406b539b63b9876ac628aedaff790b0915205d5871e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page