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 >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*
Classifiers

Project description

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 >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*
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

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

This version

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-1.3.16.tar.gz (6.1 MB view details)

Uploaded Source

Built Distributions

SQLAlchemy-1.3.16-cp38-cp38-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.8 Windows x86-64

SQLAlchemy-1.3.16-cp38-cp38-win32.whl (1.2 MB view details)

Uploaded CPython 3.8 Windows x86

SQLAlchemy-1.3.16-cp38-cp38-manylinux2010_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

SQLAlchemy-1.3.16-cp38-cp38-manylinux1_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.8

SQLAlchemy-1.3.16-cp38-cp38-macosx_10_13_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.8 macOS 10.13+ x86-64

SQLAlchemy-1.3.16-cp37-cp37m-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.7m Windows x86-64

SQLAlchemy-1.3.16-cp37-cp37m-win32.whl (1.2 MB view details)

Uploaded CPython 3.7m Windows x86

SQLAlchemy-1.3.16-cp37-cp37m-manylinux2010_x86_64.whl (1.2 MB view details)

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

SQLAlchemy-1.3.16-cp37-cp37m-manylinux1_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.7m

SQLAlchemy-1.3.16-cp37-cp37m-macosx_10_13_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.7m macOS 10.13+ x86-64

SQLAlchemy-1.3.16-cp36-cp36m-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.6m Windows x86-64

SQLAlchemy-1.3.16-cp36-cp36m-win32.whl (1.2 MB view details)

Uploaded CPython 3.6m Windows x86

SQLAlchemy-1.3.16-cp36-cp36m-manylinux2010_x86_64.whl (1.2 MB view details)

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

SQLAlchemy-1.3.16-cp36-cp36m-manylinux1_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.6m

SQLAlchemy-1.3.16-cp36-cp36m-macosx_10_13_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.6m macOS 10.13+ x86-64

SQLAlchemy-1.3.16-cp27-cp27m-win_amd64.whl (1.2 MB view details)

Uploaded CPython 2.7m Windows x86-64

SQLAlchemy-1.3.16-cp27-cp27m-win32.whl (1.2 MB view details)

Uploaded CPython 2.7m Windows x86

SQLAlchemy-1.3.16-cp27-cp27m-macosx_10_13_x86_64.whl (1.2 MB view details)

Uploaded CPython 2.7m macOS 10.13+ x86-64

File details

Details for the file SQLAlchemy-1.3.16.tar.gz.

File metadata

  • Download URL: SQLAlchemy-1.3.16.tar.gz
  • Upload date:
  • Size: 6.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/43.0.0 requests-toolbelt/0.9.1 tqdm/4.38.0 CPython/3.7.6

File hashes

Hashes for SQLAlchemy-1.3.16.tar.gz
Algorithm Hash digest
SHA256 7224e126c00b8178dfd227bc337ba5e754b197a3867d33b9f30dc0208f773d70
MD5 1a3e113859ecb55900467595a04ce000
BLAKE2b-256 7f4badfb1f03da7f50db054a5b728d32dbfae8937754cfa159efa0216a3758d1

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 7d98e0785c4cd7ae30b4a451416db71f5724a1839025544b4edbd92e00b91f0f
MD5 c5ce8c51909653e2d16603fb7a8df705
BLAKE2b-256 ff651a9ff3fb4629c1dcd601115263f2a1fda306f4a1a37fad510a2605ddd203

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp38-cp38-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp38-cp38-win32.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 3e625e283eecc15aee5b1ef77203bfb542563fa4a9aa622c7643c7b55438ff49
MD5 bb3830f3e58ce00f32cdc224c10f88dd
BLAKE2b-256 41d9a539367beadb855697f4a8d3a16964fc10ca717a1d0b89a232e620716f9d

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp38-cp38-manylinux2010_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp38-cp38-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.8, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp38-cp38-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 5b1bf3c2c2dca738235ce08079783ef04f1a7fc5b21cf24adaae77f2da4e73c3
MD5 4fbc311efe8da9538e78f101f702e5f5
BLAKE2b-256 2b1a65673e3d242dd557576c538b72e8ae85224d698cd08b7b60e822d203fab4

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp38-cp38-manylinux1_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp38-cp38-manylinux1_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp38-cp38-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 bbb545da054e6297242a1bb1ba88e7a8ffb679f518258d66798ec712b82e4e07
MD5 c950ddbe671bc34f916eb6cdfd7cc3f2
BLAKE2b-256 593d9f09ce17e86c8d46428ffae3f822726ef66cf7854fb76b5eeb577e95185b

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp38-cp38-macosx_10_13_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp38-cp38-macosx_10_13_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.8, macOS 10.13+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp38-cp38-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 43078c7ec0457387c79b8d52fff90a7ad352ca4c7aa841c366238c3e2cf52fdf
MD5 72e52c71976398eff9253f1bc9b3c6ae
BLAKE2b-256 fe81d42e132793cc33d943eeca8c97fd3b4dd5f875202d52d8b0169797be2e80

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 2dc57ee80b76813759cccd1a7affedf9c4dbe5b065a91fb6092c9d8151d66078
MD5 20e2f343fc288b8e5bd8f3a0ef7f0906
BLAKE2b-256 d4095f989050393a2164139d3fe791e7d7087123ea5281ef62f48cf6ad7a95b7

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp37-cp37m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 68d78cf4a9dfade2e6cf57c4be19f7b82ed66e67dacf93b32bb390c9bed12749
MD5 962cc8b39c9e520e53edd1bcdf81c1b1
BLAKE2b-256 abd6c8c255dbbf426e962c6ed28b459bf7a7be25cdb1385e9a6cc50221069002

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp37-cp37m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp37-cp37m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 211a1ce7e825f7142121144bac76f53ac28b12172716a710f4bf3eab477e730b
MD5 73b3a103b37dd3b10e58c93de875560c
BLAKE2b-256 caafab3197ff3c3eb3521eb6627ccbba36e504993acf164a8e8bf82a5d1239fb

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 13d48cd8b925b6893a4e59b2dfb3e59a5204fd8c98289aad353af78bd214db49
MD5 aaf8a242b6ee28f6e6a637107dedfeb5
BLAKE2b-256 3040e88a14a333c7b982454f32c00ada09fc8d8c29d9f4c0cd9999d7637699c4

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp37-cp37m-macosx_10_13_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp37-cp37m-macosx_10_13_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.7m, macOS 10.13+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp37-cp37m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 114b6ace30001f056e944cebd46daef38fdb41ebb98f5e5940241a03ed6cad43
MD5 d14457c979dc18867d3f7d60decaee04
BLAKE2b-256 a7234981d1d5bf1575a01e4680a4662141844f94218f88b55a918b483cc81c7f

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 d00b393f05dbd4ecd65c989b7f5a81110eae4baea7a6a4cdd94c20a908d1456e
MD5 0a690a1848efb88dd929c63a992005c9
BLAKE2b-256 95aa7b05b5aa7baef1d4f9b3e5ecacf495098c7a0ef6541e74db3c802eff71f2

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp36-cp36m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 0a690a6486658d03cc6a73536d46e796b6570ac1f8a7ec133f9e28c448b69828
MD5 118d293f348731a2c3bf3d626b49da01
BLAKE2b-256 5ea451fcedea62d430c310e77d4a70177af1fdf862a8edeb09e3519d68d7964f

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp36-cp36m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp36-cp36m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.6m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 e18752cecaef61031252ca72031d4d6247b3212ebb84748fc5d1a0d2029c23ea
MD5 348b4caf834a41f9bd0cf30dea20db40
BLAKE2b-256 013d876b2c4e862de63dd25e53adddb9f53796d82758e81e25c057bc49ac94e7

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 7025c639ce7e170db845e94006cf5f404e243e6fc00d6c86fa19e8ad8d411880
MD5 99181a839394e43c4b1922222582e5d9
BLAKE2b-256 964c35fccaa5a642f7f4f887798662fefb21b8366c429f3ce4c59c7b077811b4

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp36-cp36m-macosx_10_13_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp36-cp36m-macosx_10_13_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.6m, macOS 10.13+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp36-cp36m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 6056b671aeda3fc451382e52ab8a753c0d5f66ef2a5ccc8fa5ba7abd20988b4d
MD5 fcf90d5bd16250d4bad7760fa3a387e3
BLAKE2b-256 d031ac4c21281373a49a826ecbede21f0e6cf29b1d9a129b81794164566fa705

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp27-cp27m-win_amd64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp27-cp27m-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 2.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 128f6179325f7597a46403dde0bf148478f868df44841348dfc8d158e00db1f9
MD5 0c13fad3ca5abad320abbb1be8341b07
BLAKE2b-256 a45e75b476880b27ef57b0a376ae3b5a906bff3b89694d61e89b93d6c8b9a5e3

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp27-cp27m-win32.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp27-cp27m-win32.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 2.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp27-cp27m-win32.whl
Algorithm Hash digest
SHA256 083e383a1dca8384d0ea6378bd182d83c600ed4ff4ec8247d3b2442cf70db1ad
MD5 943c7e2d5da457d0f548eccc61824872
BLAKE2b-256 da4979e45eb65a06bd5e03fca8fab161b7447be4345e04d80dba5be274fd0d5c

See more details on using hashes here.

File details

Details for the file SQLAlchemy-1.3.16-cp27-cp27m-macosx_10_13_x86_64.whl.

File metadata

  • Download URL: SQLAlchemy-1.3.16-cp27-cp27m-macosx_10_13_x86_64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 2.7m, macOS 10.13+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.7

File hashes

Hashes for SQLAlchemy-1.3.16-cp27-cp27m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 8d8c21e9d4efef01351bf28513648ceb988031be4159745a7ad1b3e28c8ff68a
MD5 63f6aa36e79f3005ac41bb2af6c04deb
BLAKE2b-256 756a25db5c553cc45718752886fa849d6f8f828374c3c480ec0b18fdb0a31df6

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