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

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

This version

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

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-1.4.39.tar.gz (8.2 MB view details)

Uploaded Source

Built Distributions

SQLAlchemy-1.4.39-cp310-cp310-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

SQLAlchemy-1.4.39-cp310-cp310-win32.whl (1.6 MB view details)

Uploaded CPython 3.10 Windows x86

SQLAlchemy-1.4.39-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.39-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-1.4.39-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-1.4.39-cp310-cp310-macosx_10_15_universal2.whl (1.6 MB view details)

Uploaded CPython 3.10 macOS 10.15+ universal2 (ARM64, x86-64)

SQLAlchemy-1.4.39-cp39-cp39-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

SQLAlchemy-1.4.39-cp39-cp39-win32.whl (1.6 MB view details)

Uploaded CPython 3.9 Windows x86

SQLAlchemy-1.4.39-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.39-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-1.4.39-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-1.4.39-cp39-cp39-macosx_10_15_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.9 macOS 10.15+ x86-64

SQLAlchemy-1.4.39-cp38-cp38-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

SQLAlchemy-1.4.39-cp38-cp38-win32.whl (1.6 MB view details)

Uploaded CPython 3.8 Windows x86

SQLAlchemy-1.4.39-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.39-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-1.4.39-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-1.4.39-cp38-cp38-macosx_10_15_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.8 macOS 10.15+ x86-64

SQLAlchemy-1.4.39-cp37-cp37m-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.7m Windows x86-64

SQLAlchemy-1.4.39-cp37-cp37m-win32.whl (1.6 MB view details)

Uploaded CPython 3.7m Windows x86

SQLAlchemy-1.4.39-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.39-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-1.4.39-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-1.4.39-cp37-cp37m-macosx_10_15_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.7m macOS 10.15+ x86-64

SQLAlchemy-1.4.39-cp36-cp36m-win_amd64.whl (1.6 MB view details)

Uploaded CPython 3.6m Windows x86-64

SQLAlchemy-1.4.39-cp36-cp36m-win32.whl (1.6 MB view details)

Uploaded CPython 3.6m Windows x86

SQLAlchemy-1.4.39-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.39-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-1.4.39-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-1.4.39-cp36-cp36m-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.6m macOS 10.14+ x86-64

SQLAlchemy-1.4.39-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-1.4.39-cp27-cp27m-win_amd64.whl (1.6 MB view details)

Uploaded CPython 2.7m Windows x86-64

SQLAlchemy-1.4.39-cp27-cp27m-win32.whl (1.6 MB view details)

Uploaded CPython 2.7m Windows x86

SQLAlchemy-1.4.39-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-1.4.39-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-1.4.39.tar.gz.

File metadata

  • Download URL: SQLAlchemy-1.4.39.tar.gz
  • Upload date:
  • Size: 8.2 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-1.4.39.tar.gz
Algorithm Hash digest
SHA256 8194896038753b46b08a0b0ae89a5d80c897fb601dd51e243ed5720f1f155d27
MD5 2ed55104ccd08e503cc138b3fc041457
BLAKE2b-256 1f93e5211e989324793487efb45405343d81b554886e278234066e20f77d434d

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 50e7569637e2e02253295527ff34666706dbb2bc5f6c61a5a7f44b9610c9bb09
MD5 bc855304b2606b394dfd4e5084b6671b
BLAKE2b-256 04e94b313778b1f16d1eea96a1e657f04011ca9d51c28c42c070e7718e7c1892

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp310-cp310-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.39-cp310-cp310-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.39-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 91d2b89bb0c302f89e753bea008936acfa4e18c156fb264fe41eb6bbb2bbcdeb
MD5 a037ddcd183baed4ecdbe06bd975cf51
BLAKE2b-256 fe8e399663bac6946b52d7206d96c82d6350d8f31d536a781ceff4f1082a016b

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ede13a472caa85a13abe5095e71676af985d7690eaa8461aeac5c74f6600b6c0
MD5 5e46361a60d02201d107abae1cf6e9b5
BLAKE2b-256 44c382386d127ff87aa5751ff35052d057ffe0d8d0ca674349f5006b86fe0296

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-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-1.4.39-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 26146c59576dfe9c546c9f45397a7c7c4a90c25679492ff610a7500afc7d03a6
MD5 ca0aed9b509f0c22814f86229d269cb3
BLAKE2b-256 3c781caf5af666e20468cb34961db31da6881520d931c9b61fa316fda4c21395

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-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-1.4.39-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 7f13644b15665f7322f9e0635129e0ef2098409484df67fcd225d954c5861559
MD5 7af9c881070a41056967061c593a7ecb
BLAKE2b-256 d81c0f698cae8f02e1b67b987ab2b5522f4b00cc5c9db180748ea090cfba0b49

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp310-cp310-macosx_10_15_universal2.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp310-cp310-macosx_10_15_universal2.whl
Algorithm Hash digest
SHA256 1745987ada1890b0e7978abdb22c133eca2e89ab98dc17939042240063e1ef21
MD5 53feeb3be29cb4bc66b4f6bdda9ffbb4
BLAKE2b-256 9de71b6a6c46445605ee86663e12bfb741f5adf985a30f9e96a6a2130bb34065

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 8b773c9974c272aae0fa7e95b576d98d17ee65f69d8644f9b6ffc90ee96b4d19
MD5 d2a6ccf50f8e77ee837bc74318d601f5
BLAKE2b-256 5c39b985e3d715228cb380defdcfb92de89f7e3ff36018b66af6b623124b2281

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp39-cp39-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.39-cp39-cp39-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.39-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 d1f665e50592caf4cad3caed3ed86f93227bffe0680218ccbb293bd5a6734ca8
MD5 cbda7de1c0f8c6b54316dcf707a1c066
BLAKE2b-256 88c0d74b3598fff4fc96e64ea2b227aa4dd202af36a6cc094f40ff6689b226ad

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e7a7667d928ba6ee361a3176e1bef6847c1062b37726b33505cc84136f657e0d
MD5 b06e445ab6a6366298ce26f45aa946b8
BLAKE2b-256 0e4f9e6ac4e6579fe381921a0122da544e108c8ea70eadc2f8d1c69b199f9193

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-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-1.4.39-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b0538b66f959771c56ff996d828081908a6a52a47c5548faed4a3d0a027a5368
MD5 d3daf6381cbee0dd1456b83a1c663dd1
BLAKE2b-256 563d6d1bddd32e8a991c76f6ee220150d7608428e1c56c932d974cc1e0808217

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-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-1.4.39-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 c6d00cb9da8d0cbfaba18cad046e94b06de6d4d0ffd9d4095a3ad1838af22528
MD5 713996e8b529886bddf3c78cdb2e94ab
BLAKE2b-256 fcf2ba0396792be7ef7f656375e6055a754268a102599731394201898e3a750e

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp39-cp39-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 365b75938049ae31cf2176efd3d598213ddb9eb883fbc82086efa019a5f649df
MD5 d032b4a80bea06267c3867b29f81cdf7
BLAKE2b-256 f45c893ee2f6ddfbb3229234d854d57d68743323c88f4d05a00da340e4d8b55a

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 b71be98ef6e180217d1797185c75507060a57ab9cd835653e0112db16a710f0d
MD5 ef48f7a18e618339cea478af68203b2a
BLAKE2b-256 7fe53ce69f727f79011b1dd90efe44447ef9cf17bf50e083bbe3fd6dbaae48d0

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp38-cp38-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.39-cp38-cp38-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.39-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 047ef5ccd8860f6147b8ac6c45a4bc573d4e030267b45d9a1c47b55962ff0e6f
MD5 1f37aaf7f8d351de405be0e4c6bc06fa
BLAKE2b-256 c8f0aa93404323b93b430b738b79c4b8b0e17c1940959bda038cb401936f8b74

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 14ea8ff2d33c48f8e6c3c472111d893b9e356284d1482102da9678195e5a8eac
MD5 7b36aa25bf72d111a443fd654f372134
BLAKE2b-256 85a53eadde4773838efd1f5aaaaae3a1cc75320bc39cc8a5018441fa6a99c1c6

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-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-1.4.39-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1962dfee37b7fb17d3d4889bf84c4ea08b1c36707194c578f61e6e06d12ab90f
MD5 c25f1cbc5fe1b62c670580672e3cab92
BLAKE2b-256 d2cba1fbda580859c2639aa92ff9274026eac3c67d0d4ce993a0b78d11228189

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-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-1.4.39-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 ec3985c883d6d217cf2013028afc6e3c82b8907192ba6195d6e49885bfc4b19d
MD5 3bee3dd511e220df6041bb86c9c1b518
BLAKE2b-256 e73b3725ab2dbaa5c98c9d8a6ee91f9ee12e52b5172426a268c8288059b8eecd

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 5c2d19bfb33262bf987ef0062345efd0f54c4189c2d95159c72995457bf4a359
MD5 338bc260428a6d9b8c9c9c79c071ec28
BLAKE2b-256 f2e4f8da25afc3c4e1b6925a7ce8be606865fb5ae5e4053bf1b6334208c88a45

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 6d81de54e45f1d756785405c9d06cd17918c2eecc2d4262dc2d276ca612c2f61
MD5 e83e0269d215f28f873d59b94a0b51c6
BLAKE2b-256 c0886d3eb3397cf0001dfc070287ffeca3e071368689fbc1f7dfbdd5730f1f95

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp37-cp37m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.39-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.39-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 f2a42acc01568b9701665e85562bbff78ec3e21981c7d51d56717c22e5d3d58b
MD5 81e783244da216f5f7ab5e9782de87b3
BLAKE2b-256 9db9808dc1bf61713425ba6988ab4771f0e329bc4f6a67ff403b864d25d1785c

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 621f050e72cc7dfd9ad4594ff0abeaad954d6e4a2891545e8f1a53dcdfbef445
MD5 907dde021b8158b96b15b4118aee20b4
BLAKE2b-256 a3f002cd82861ab9642e8d577094f8d49cabf54ad6dc6423eb56ee7b378fc1ef

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-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-1.4.39-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 20bf65bcce65c538e68d5df27402b39341fabeecf01de7e0e72b9d9836c13c52
MD5 0dc6f10d280e7cea4e9686385a42f289
BLAKE2b-256 bb5042221a5b923b0035d8b26430cb045f5aafa8ec3bc3419414ddd31eaa9ed7

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-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-1.4.39-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 05a05771617bfa723ba4cef58d5b25ac028b0d68f28f403edebed5b8243b3a87
MD5 77149ee167a9f0ce9df5a2e4d0fdf172
BLAKE2b-256 68bffd77f59f26c36598d25d5b370c69fccc991e5754e19b31a7301d2bb7c650

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp37-cp37m-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp37-cp37m-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 6f95706da857e6e79b54c33c1214f5467aab10600aa508ddd1239d5df271986e
MD5 a9f907e219fceb4c077f96149fe012dc
BLAKE2b-256 48b39940e05cc37199afd58c91f564bba18e56acb585257c64c86cb586889a6c

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 0ec54460475f0c42512895c99c63d90dd2d9cbd0c13491a184182e85074b04c5
MD5 b24da5947e3f1dca83b446f0236e6731
BLAKE2b-256 df023bb091422c291a9172399ebce6774c324c4caf74e32f6b6f560d1c9b0739

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp36-cp36m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.39-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.39-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 fbc076f79d830ae4c9d49926180a1140b49fa675d0f0d555b44c9a15b29f4c80
MD5 96a877d10a17f533f967cb0a3c89e671
BLAKE2b-256 5dc3b83f8299a3046e60409673dbf7ca97e81a9ffe60a48b31a2ee99fba49865

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f24d4d6ec301688c59b0c4bb1c1c94c5d0bff4ecad33bb8f5d9efdfb8d8bc925
MD5 130b23de44cfcaa987a37fe99ba8c0a6
BLAKE2b-256 85c3c8227ff584ecf9b00fd74113625b834890d24d12fb5a76700790ed2c14de

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-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-1.4.39-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e6e2c8581c6620136b9530137954a8376efffd57fe19802182c7561b0ab48b48
MD5 0bc56279de9833affb5e5e4ff470a040
BLAKE2b-256 96727709549881c871c06d78a05e64a8679fc73964b9d32b2d85188983d5de40

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-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-1.4.39-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 7b2785dd2a0c044a36836857ac27310dc7a99166253551ee8f5408930958cc60
MD5 dfd083ef8881bb99f09808f079d1904a
BLAKE2b-256 ebb6b8579f5a39712fee884db2bdb9e726437b0cc2f2cb57430613651282f3eb

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp36-cp36m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp36-cp36m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 107df519eb33d7f8e0d0d052128af2f25066c1a0f6b648fd1a9612ab66800b86
MD5 6da88483665194079641d0478dc66047
BLAKE2b-256 c94af76a21662a4b18b4f27934fd899b2f3ec9fe8998cfedb1aecc526bc73d0a

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 8f901be74f00a13bf375241a778455ee864c2c21c79154aad196b7a994e1144f
MD5 b8d6c74979acea3e6abf3e5d4826df18
BLAKE2b-256 3c4cb38590be407c7898b009af0cdee876c56c40adee04db90089c637e6888fc

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp27-cp27m-win_amd64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 864d4f89f054819cb95e93100b7d251e4d114d1c60bc7576db07b046432af280
MD5 2c1bffcb7d133acbe1ba7c3bdc2d15ad
BLAKE2b-256 25d16a2e52e0925c335f22878eec3531796cf49b420c52608f557093f74a0e18

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp27-cp27m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.39-cp27-cp27m-win32.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: CPython 2.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.39-cp27-cp27m-win32.whl
Algorithm Hash digest
SHA256 b30e70f1594ee3c8902978fd71900d7312453922827c4ce0012fa6a8278d6df4
MD5 cbcb895334bea984ad57f4425efa5730
BLAKE2b-256 a76bf2e6b17c74c25acff155c6b71acf504a816f7b85fff0a8135feb2852235a

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 752ef2e8dbaa3c5d419f322e3632f00ba6b1c3230f65bc97c2ff5c5c6c08f441
MD5 4adfab6cb1ef388ac0de2c3fc5af122a
BLAKE2b-256 5b816a20d83a3872a1ae68bd4d8c3ce56c5a0663dbe499161f874d7865edff6a

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.39-cp27-cp27m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.39-cp27-cp27m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 4770eb3ba69ec5fa41c681a75e53e0e342ac24c1f9220d883458b5596888e43a
MD5 4e3ba9205c83b3443e624c9e92f76384
BLAKE2b-256 2c71099c1e06e79f2e130ae0e9901bcb0d69759d47c0d719fafd52788fb0a8d6

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