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

This version

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-1.4.17.tar.gz (7.6 MB view details)

Uploaded Source

Built Distributions

SQLAlchemy-1.4.17-cp39-cp39-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.9 Windows x86-64

SQLAlchemy-1.4.17-cp39-cp39-win32.whl (1.5 MB view details)

Uploaded CPython 3.9 Windows x86

SQLAlchemy-1.4.17-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.17-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-1.4.17-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-1.4.17-cp39-cp39-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.9 macOS 10.14+ x86-64

SQLAlchemy-1.4.17-cp38-cp38-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.8 Windows x86-64

SQLAlchemy-1.4.17-cp38-cp38-win32.whl (1.5 MB view details)

Uploaded CPython 3.8 Windows x86

SQLAlchemy-1.4.17-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.17-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-1.4.17-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-1.4.17-cp38-cp38-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.8 macOS 10.14+ x86-64

SQLAlchemy-1.4.17-cp37-cp37m-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.7m Windows x86-64

SQLAlchemy-1.4.17-cp37-cp37m-win32.whl (1.5 MB view details)

Uploaded CPython 3.7m Windows x86

SQLAlchemy-1.4.17-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.17-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-1.4.17-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-1.4.17-cp37-cp37m-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.7m macOS 10.14+ x86-64

SQLAlchemy-1.4.17-cp36-cp36m-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.6m Windows x86-64

SQLAlchemy-1.4.17-cp36-cp36m-win32.whl (1.5 MB view details)

Uploaded CPython 3.6m Windows x86

SQLAlchemy-1.4.17-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ ARM64

SQLAlchemy-1.4.17-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-1.4.17-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-1.4.17-cp36-cp36m-macosx_10_14_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.6m macOS 10.14+ x86-64

SQLAlchemy-1.4.17-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-1.4.17-cp27-cp27m-win_amd64.whl (1.5 MB view details)

Uploaded CPython 2.7m Windows x86-64

SQLAlchemy-1.4.17-cp27-cp27m-win32.whl (1.5 MB view details)

Uploaded CPython 2.7m Windows x86

SQLAlchemy-1.4.17-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-1.4.17-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-1.4.17.tar.gz.

File metadata

  • Download URL: SQLAlchemy-1.4.17.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/3.9.5

File hashes

Hashes for SQLAlchemy-1.4.17.tar.gz
Algorithm Hash digest
SHA256 651cdb3adcee13624ba22d5ff3e96f91e16a115d2ca489ddc16a8e4c217e8509
MD5 8ef9a98841797e815ddc8e9c002dece7
BLAKE2b-256 2318a93bbb6d361febc3df077dd4258ef552722d86969ea238821142d5123643

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 7eb55d5583076c03aaf1510473fad2a61288490809049cb31028af56af7068ee
MD5 8deb178865cbfa768a024f3aad80838f
BLAKE2b-256 33648237d761a2a62a398b6410f4b27d9aac7508e56b852df5bc76534771d18e

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp39-cp39-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 6fe1c8dc26bc0005439cb78ebc78772a22cccc773f5a0e67cb3002d791f53f0f
MD5 c983bba70c43c395e044befa08e36ce2
BLAKE2b-256 b0e76d0228cf4863ce61119c559eba1b5d62576ed8b33e163b6c85d0aa2fab3c

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.17-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f63e1f531a8bf52184e2afb53648511f3f8534decb7575b483a583d3cd8d13ed
MD5 115c21c3ec2a01b32dc8b077c2fc9c83
BLAKE2b-256 0fe7c2cb21e8459fbc17d67a88a3a6af7e7b0afff3ea9bda3c0a608faf249865

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-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-1.4.17-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 58c02d1771bb0e61bc9ced8f3b36b5714d9ece8fd4bdbe2a44a892574c3bbc3c
MD5 bb37ad09dc5952d8d9d50bf7f3777137
BLAKE2b-256 4cd9e44f687a32a406fd44d99dc6d0233c27727a2c5adc4855f11180a88ebc81

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-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-1.4.17-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 7dc3d3285fb682316d580d84e6e0840fdd8ffdc05cb696db74b9dd746c729908
MD5 42b77ea59a0230dd438467932d855789
BLAKE2b-256 a3e9b5f5fb12581c74c4f452c6cb3304f173004faed984e7cbe99f5fc8b66ca7

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 4c5e20666b33b03bf7f14953f0deb93007bf8c1342e985bd7c7cf25f46fac579
MD5 e63bfd76a55929a133adb72fc430d8d7
BLAKE2b-256 38f294c10d175825e872c07815fd431fa593de6abc749a7452a5206904f7c5e9

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 bc89e37c359dcd4d75b744e5e81af128ba678aa2ecea4be957e80e6e958a1612
MD5 ebb60655b57755e42e2b221e0126def0
BLAKE2b-256 1681ab7035d3e285c90d43184e0c73020d545d0f5c412fad7fe233867ed3baf4

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp38-cp38-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 dde05ae0987e43ec84e64d6722ce66305eda2a5e2b7d6fda004b37aabdfbb909
MD5 505ba1e38a90bde71abe466bfc239bc8
BLAKE2b-256 dc4e30ee5be45fff24d9279c8bf62964c3e63a53259479208f9aaaa55dbe353a

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.17-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 7222f3236c280fab3a2d76f903b493171f0ffc29667538cc388a5d5dd0216a88
MD5 1cd3a3d2573a184c79f7a9205f63db70
BLAKE2b-256 61decd3fcb6527dd200aab910ed5d245de0f3bcba1a55db594d074d96140404c

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-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-1.4.17-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 216ff28fe803885ceb5b131dcee6507d28d255808dd5bcffcb3b5fa75be2e102
MD5 0142b397396bc23a54cefaf89ec8f30a
BLAKE2b-256 e85a21cffff888b71a61b212af2e01b4b103d95a4080d22d06e5f0f0639f049d

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-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-1.4.17-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 4b09191ed22af149c07a880f309b7740f3f782ff13325bae5c6168a6aa57e715
MD5 6290247962dd43f8d1180dc3313daa62
BLAKE2b-256 7c32d98edafa7362e045e12b732773325089e3ade3e1f0e02a9616cb80945322

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 b59b2c0a3b1d93027f6b6b8379a50c354483fe1ebe796c6740e157bb2e06d39a
MD5 1e4bfc8fc1125c1c8b8d60c6ef67f427
BLAKE2b-256 90baf085e17fa92bdc23ed34c77f3261e2c5e7a555ab490e5f80ecc0ed3c9e6f

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 a2d225c8863a76d15468896dc5af36f1e196b403eb9c7e0151e77ffab9e7df57
MD5 5c065153e3e7355dc46703978370839f
BLAKE2b-256 0b4244fdc853f397657b59e359e78b70ef24c853d3c480cd43d29867f09f916e

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp37-cp37m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 4d93b62e98248e3e1ac1e91c2e6ee1e7316f704be1f734338b350b6951e6c175
MD5 f0b57d39d6761e8ebaa8345c1e4e5490
BLAKE2b-256 de3149c19348169db42dc2643e788821e828e1549a722277079a5c312fa6bfa7

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.17-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8e133e2551fa99c75849848a4ac08efb79930561eb629dd7d2dc9b7ee05256e6
MD5 6c07d34e65989d8a67ff8d43aabd4ffd
BLAKE2b-256 f4cbfe5ff007e26e2b2b5fbd0998a36c6dd6ecfd10ad9591083c5328a3ea8dac

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-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-1.4.17-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 461a4ea803ce0834822f372617a68ac97f9fa1281f2a984624554c651d7c3ae1
MD5 cae055405c8a8af8e8a4f6fdfeea86fe
BLAKE2b-256 3630be03c3d4d439e9f4c996eff39ec023206d325280317108af3412588bd86a

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-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-1.4.17-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 7e45043fe11d503e1c3f9dcf5b42f92d122a814237cd9af68a11dae46ecfcae1
MD5 4f20ea2c566d94744266ca71ed92b6ce
BLAKE2b-256 a7b371600d96d6f1b2a640e315407f5b0294ae1c230ec267e75c9e076f9174b7

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 82922a320d38d7d6aa3a8130523ec7e8c70fa95f7ca7d0fd6ec114b626e4b10b
MD5 ce1233145e7ca7a430f6350fd2a948e0
BLAKE2b-256 2a0d8bbf67a331d9ebcc513a64fa0a129976181b307c234c6903678693d9b26d

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 b0ad951a6e590bbcfbfeadc5748ef5ec8ede505a8119a71b235f7481cc08371c
MD5 53e7978b1e3064d68898c8cbc67e90fc
BLAKE2b-256 48c7c0f0eeffd6854e737747d490ab323c6a28fd3f239b0e5643db37a283ec76

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp36-cp36m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 bde055c019e6e449ebc4ec61abd3e08690abeb028c7ada2a3b95d8e352b7b514
MD5 c4bcc2e13be179543428296e18e32fc5
BLAKE2b-256 25170decce2b63724f2ea9f5ba3aee72d00086c584cc68ae7ab452c9920d7991

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.17-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5732858e56d32fa7e02468f4fd2d8f01ddf709e5b93d035c637762890f8ed8b6
MD5 8a88c6f49ceffab0ceaf8f665d8fde55
BLAKE2b-256 c0340d60a55a1e3986b254b57b90b6a2ac8a75ec6feb1ac41cd309461ee1b983

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-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-1.4.17-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 196fb6bb2733834e506c925d7532f8eabad9d2304deef738a40846e54c31e236
MD5 4ea39985a6e035ddcdbdb641216b33c0
BLAKE2b-256 e583a06107e9f08d6df22e759f07a43cf7f88e904fb799898bedcb578f739980

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-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-1.4.17-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 949ac299903d2ed8419086f81847381184e2264f3431a33af4679546dcc87f01
MD5 58ee9bbd00d2eb2a7d507e0e50ce043a
BLAKE2b-256 720cabd3bd19298cd3fc0a6f2f0ac05c369e7272472f578397043929ed743c79

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp36-cp36m-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp36-cp36m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 1dd77acbc19bee9c0ba858ff5e4e5d5c60895495c83b4df9bcdf4ad5e9b74f21
MD5 3318dfca7f8b0b717a136e5a00e11b8a
BLAKE2b-256 660be1b2b76b8d83462fb4b2ab12bf9e0f124a76f7a940118f394998b141658f

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.17-cp27-cp27mu-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 a5f00a2be7d777119e15ccfb5ba0b2a92e8a193959281089d79821a001095f80
MD5 ff00782f0eb74e1c80c742069adcbf86
BLAKE2b-256 d63a657003dd3b2713648742def6f3fa8553591d18071d0ec8ef6a11d35c5584

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp27-cp27m-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 ee6e7ca09ff274c55d19a1e15ee6f884fa0230c0d9b8d22a456e249d08dee5bf
MD5 8282b58ffbfdc3d3ad9afca5e34206bd
BLAKE2b-256 6d2d8203231fca77eefc98af10dc56a74a22389b7b2f53ae9eac399d887fabe9

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp27-cp27m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp27-cp27m-win32.whl
Algorithm Hash digest
SHA256 f1c68f7bd4a57ffdb85eab489362828dddf6cd565a4c18eda4c446c1d5d3059d
MD5 1877ddbb86d8c383ea136a8e6785cfeb
BLAKE2b-256 f2e912b6f67c756d7c81e5b1b9f5b87ac71288f89929cc00aa1d2b47fa2af7b1

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for SQLAlchemy-1.4.17-cp27-cp27m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 fdad4a33140b77df61d456922b7974c1f1bb2c35238f6809f078003a620c4734
MD5 b80e650b73a85687fd7c1323f4817ec5
BLAKE2b-256 d7e18fd33d6d0c986507642df4b49d1d2e9a7d53ed954b913ea361252a9c8527

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.4.17-cp27-cp27m-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.4.17-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/4.3.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10

File hashes

Hashes for SQLAlchemy-1.4.17-cp27-cp27m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 c367ed95d41df584f412a9419b5ece85b0d6c2a08a51ae13ae47ef74ff9a9349
MD5 f6e7a377b799e73771e94002256e1c13
BLAKE2b-256 b6958d949545c660286d1892ab16e0fc03517da17cd6dedc68ff52f2ed67f20a

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