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:

http://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

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

This version

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-1.4.14.tar.gz (7.6 MB view details)

Uploaded Source

Built Distributions

SQLAlchemy-1.4.14-cp39-cp39-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.9 Windows x86-64

SQLAlchemy-1.4.14-cp39-cp39-win32.whl (1.5 MB view details)

Uploaded CPython 3.9 Windows x86

SQLAlchemy-1.4.14-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.14-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

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

SQLAlchemy-1.4.14-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.5 MB view details)

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

SQLAlchemy-1.4.14-cp39-cp39-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.9 macOS 10.14+ x86-64

SQLAlchemy-1.4.14-cp38-cp38-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.8 Windows x86-64

SQLAlchemy-1.4.14-cp38-cp38-win32.whl (1.5 MB view details)

Uploaded CPython 3.8 Windows x86

SQLAlchemy-1.4.14-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.14-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

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

SQLAlchemy-1.4.14-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.5 MB view details)

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

SQLAlchemy-1.4.14-cp38-cp38-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.8 macOS 10.14+ x86-64

SQLAlchemy-1.4.14-cp37-cp37m-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.7m Windows x86-64

SQLAlchemy-1.4.14-cp37-cp37m-win32.whl (1.5 MB view details)

Uploaded CPython 3.7m Windows x86

SQLAlchemy-1.4.14-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.14-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

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

SQLAlchemy-1.4.14-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.5 MB view details)

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

SQLAlchemy-1.4.14-cp37-cp37m-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.7m macOS 10.14+ x86-64

SQLAlchemy-1.4.14-cp36-cp36m-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.6m Windows x86-64

SQLAlchemy-1.4.14-cp36-cp36m-win32.whl (1.5 MB view details)

Uploaded CPython 3.6m Windows x86

SQLAlchemy-1.4.14-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.14-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

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

SQLAlchemy-1.4.14-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.5 MB view details)

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

SQLAlchemy-1.4.14-cp36-cp36m-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.6m macOS 10.14+ x86-64

SQLAlchemy-1.4.14-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.5 MB view details)

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

SQLAlchemy-1.4.14-cp27-cp27m-win_amd64.whl (1.5 MB view details)

Uploaded CPython 2.7m Windows x86-64

SQLAlchemy-1.4.14-cp27-cp27m-win32.whl (1.5 MB view details)

Uploaded CPython 2.7m Windows x86

SQLAlchemy-1.4.14-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.5 MB view details)

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

SQLAlchemy-1.4.14-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-1.4.14.tar.gz.

File metadata

  • Download URL: SQLAlchemy-1.4.14.tar.gz
  • Upload date:
  • Size: 7.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.9.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.2

File hashes

Hashes for SQLAlchemy-1.4.14.tar.gz
Algorithm Hash digest
SHA256 fcea0af70a356fdff9efa917d33ec5a8538fe7bde3004ceded7377f1a97d8ef3
MD5 ec577c9618b9d98efeeb7c5a7db7ecc2
BLAKE2b-256 0759982b812faae46237f59cc37c88ea61c2000e23cbc736928483bff1738568

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 296a3b53e50c3d61ea22969b9d57937714e62f4bd7b5db7bb0afe80e8fbbe44c
MD5 76f1b880b36009b47ca990128fcb00bf
BLAKE2b-256 905d3143213edf4c1c6f1b74e558e536b83ad9a407c786f1be5cdd79eeeee7c0

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp39-cp39-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp39-cp39-win32.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 a2c6e766410eebb0e7c4a839acce5534619f5d03bd2aabf44e0caf8b813613b9
MD5 296360e2014a6811fdfc7a25a87c3fc2
BLAKE2b-256 1ac838a16b5d3a4d8731fc010e8a793d404e3d1fb309c103a2853daf0545db27

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.14-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 bd718612457ac34f6f19ff9b00c80c68ec0f46dae1334671c653a0dc960df9ce
MD5 bb2d1161be28ad6de6b9358e524047ad
BLAKE2b-256 ec3dbda1eb0406537964b785d50c269688856702d6678785c955220ed0a699cf

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-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-1.4.14-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 79957deee6bb1e546dee806c66d3effeac3b2e33233663011550dbfe60cbfecd
MD5 83a21806d737b007c7b777605156249e
BLAKE2b-256 f1fe4e4af74ccb67a52bf8d6f3e355774c54874003bb18de0843a62eb1f0c8ed

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-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-1.4.14-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 a365e616462e9d4409e87226a9da55d2cf2aa1b17c53208519ba3f8582edd529
MD5 9a765ce7962c02c4de991be033711561
BLAKE2b-256 3ee2d96b3d7d61e7dc1066f6b706f95956d7c3d7697c32f0643950847ebff756

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp39-cp39-macosx_10_14_x86_64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.9, macOS 10.14+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 51b5f73e4a38d07bd883aec6c4ce3292309bdf9e9ce2b21653b92c7ce9bc6f5a
MD5 f78dce97beb65a70c6175e69d7ca6542
BLAKE2b-256 9e0785e28c5b092c16ed31e5bb424ff4b79df95e1dbddeee787830b218c5a1db

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 38fce422c8553b14145e4d5ada3cc7643bb720d2761976ab03177b65dc14fb4d
MD5 b699ec4374776717575644a51d8e4bc2
BLAKE2b-256 d5d5cd7c190f31da1499af903bac2f28cb2ac66b930f5bd09d7283151aa93210

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp38-cp38-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp38-cp38-win32.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 6fd1bd8f4c134aa3a2fd43abe0f83245a3537001ea3c84dc0e28768a40a1a53c
MD5 5a82b13e8f8e5d28b5d794ec8138cf3c
BLAKE2b-256 4774437d45551d9d9e19f4ca63a4ff3cf4ac3793ef86a56c98f40e65be294e0d

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.14-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5c115aaadbb555c787a14218d94185a0929d4a625e6f150fe86a9f7fca0f5d30
MD5 4b330ff7f17a2403713b51fd9d187b76
BLAKE2b-256 35b7990fe9e760769bc9827b3e12410f4e179721391eebcbb7ed41553c263909

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-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-1.4.14-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b7e1570f54585247526e05cb642c46ee9be2eabe7f04945665c0a36b86bf1ee6
MD5 6e426a98981d2c70188fdbebabadb38a
BLAKE2b-256 8d08cb372ede5901c3f920caf63d76fa9b9244757c725f7ca15c9f92cb414009

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-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-1.4.14-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 2048c3f7934c589b2a8521da9a3c8f77b413a8e21b015be96355c6968028351e
MD5 8d1455d7a439a0e4ee690fe1c8c1258d
BLAKE2b-256 9cc00890ea56d81c3b69d00051fd09cea3360c473ee839da4a5fb4451363bcc2

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp38-cp38-macosx_10_14_x86_64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.8, macOS 10.14+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 bcd85d7a71e2de3567c5c4b26b1a90c57432b3032ff9132dafae1c52a4edeea0
MD5 82a73ff0d31a2150a0b841c250ace298
BLAKE2b-256 abf10627a9094620c6d34be84c93b27ecee6c36d07070dbad558935d416818f9

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 e0b75b7712f25d8d51a793cbbffb26688d2f655b97c9dfad02aad67b09d121a6
MD5 0ce83709fa27e34dbabaa25dd5681e57
BLAKE2b-256 760750f53e882bb62c402ee8f01d759fe34bc944b1f616938086ae9d980f005d

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp37-cp37m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 5129af3c0cd879e37d3f7db417f7c6500c992b6e5d1cae42ba05e6dac97e3e60
MD5 c6fb886633ac36937a51de8b6374b758
BLAKE2b-256 62102dcee96726f9a16d05cef66925c4cc14c25f4fb96754d3081d7b7284a72d

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.14-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 238545b8e769c7415395ea2d3716ee116511c2816ae75e69f4148e56bc16c978
MD5 89f8400999cd667775124542311e2622
BLAKE2b-256 7f7b9738e646c96d7450b092f85e3071fed3dd4395dccac0a67ad38344db0bc8

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-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-1.4.14-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a6c40fce57d12d5b4d99d091b6c33c8a8087cb753402218fd7a2b3be72de7e20
MD5 e93b5993760a4963b13143c35db66978
BLAKE2b-256 b4f7dd1ffac491bf8bf48ce6a16a19b80a20a6a7192477740897243f115f81e3

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-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-1.4.14-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 3fa861a9211d284430a6669c3debf9cfac7cd738e1c50844575751900ebe16bf
MD5 7f0adcd3b65a660a0e56e4b47dc29628
BLAKE2b-256 c97088585cdd487579a389b150fb98ac137baf4e4fb0036a364841fce75c311d

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp37-cp37m-macosx_10_14_x86_64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.7m, macOS 10.14+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 dff5c33036a493c00e252a7067ecaa09ef5bbbeea53337083aba013be903c43a
MD5 636ac75b0fdd7fde58a91b0449470ee1
BLAKE2b-256 27cac7b42794b6a6f9db4fdfb972870fa9f933f01c067634c087c8835f097daa

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 1508b5a58046ca9ec4c4d2a3f1dcd6bc2eb1b04e0e5ef7b85fe73fc1805b504a
MD5 9f4e8ac5baadd0f7e82896d0edd3d3e0
BLAKE2b-256 4bbda33ff44fcb9c47006c61c848e2c51c4a7ab6a4c30f869fc3831b580f325f

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp36-cp36m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 10176150fb4f7bef87e58b496e11ec8c09a06d7aaddec858aa3afe68ecf44be1
MD5 c8db81edd2f91684c86e1925faecfa70
BLAKE2b-256 adecd59ff75d3a3c86f097a6f2624730c34618266300c2e3330431fd11abeef3

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.14-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 99d4c701fe3d17f2f842b32633573c6e9ec43b75fe0e1140b1c079d2df7d73e6
MD5 8fa12e1607d32f2651d661d84cbc6053
BLAKE2b-256 ac3afd7391d0413e0f2f1ba7dbd76feae1d42f490ae82387a15623a8ada1efa5

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-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-1.4.14-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9934f1065bfce44dbd6c8dc68ce8f35492357d7cac5d83ddb585dbec5f3ff6ad
MD5 38360c030c963457d07c2aaa1ebad4a7
BLAKE2b-256 47afcaed01d153a3ef8daa0e5e36691e5f9e633c6e43578b645284772395e5c5

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-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-1.4.14-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 907fc5a1db1bf9a3a26a205eba6529250ba5aced16f2bddb4a59763826294609
MD5 8547ef13d5d768b80a194b4a40988770
BLAKE2b-256 af5b9ce66e638a7bd318049eee38396997a5d881966ea70702beb6c14343b5cd

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp36-cp36m-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp36-cp36m-macosx_10_14_x86_64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.6m, macOS 10.14+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp36-cp36m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 b3e88738f82871a35a7c5995075100ddad74543e8697e683f655183860649024
MD5 b72f048fb67b036fff66ff5a6668908e
BLAKE2b-256 e0ae68dc1a224349cd3114d99cbd2725c0a0b13710ef8496533cf1d90dfa8fc9

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.14-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 ab659472962815ea945e643166ab67299a1415c05d0fb6d9f9d915f9f4540cd9
MD5 625686550da515d8995cd3f06de5d19b
BLAKE2b-256 7a9698b88d3fdc8637239c4dfa82715ad7511491e1df5fb1021f61ecc3216e54

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp27-cp27m-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp27-cp27m-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 2.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 b8d6aab7f021ffb25d34671a48dc18007a12fad07c920be81401b6bf35ecf2d9
MD5 ad2cb2023ef9e50e033d645442da3814
BLAKE2b-256 08f50e201074ea4b9b1cdda3e68afe62671b82f9baff58505b29f901ae038c29

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp27-cp27m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp27-cp27m-win32.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 2.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp27-cp27m-win32.whl
Algorithm Hash digest
SHA256 cf3e10181d4b96bafa693a03948b0da2136c977682a902b601b9fa0f35934cd0
MD5 80d0f6b88b2713e6fac8721f7af8053d
BLAKE2b-256 7853341c9afeea563cdad19c0d73b2ac102eb9601c9c9d6b9487428756395a14

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.14-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 32412b74f8920b91b1412a8574167bdfe5dd189fdc9b7676f898ea49892fabca
MD5 fc3bc8cd37fe897637639c8c4472604b
BLAKE2b-256 0b7b4ab7fc5c3b6f6f8f036c200674ef0bd501621456fe4f5dcb10c412306844

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.14-cp27-cp27m-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.14-cp27-cp27m-macosx_10_14_x86_64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 2.7m, macOS 10.14+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.9

File hashes

Hashes for SQLAlchemy-1.4.14-cp27-cp27m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 98d977df2854be582f83b1d02e32febdd72a93b6396417889448a0c0c20fd856
MD5 204c6ba79ca51188a144124e686b58f4
BLAKE2b-256 d1bb6d29524b875e81829982dabf371748b30bb6aa78bf27f3e7b2499fa52612

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