psycopg2

psycopg2 - Python-PostgreSQL Database Adapter

These details have not been verified by PyPI

Project links

Project description

Psycopg is the most popular PostgreSQL database adapter for the Python programming language. Its main features are the complete implementation of the Python DB API 2.0 specification and the thread safety (several threads can share the same connection). It was designed for heavily multi-threaded applications that create and destroy lots of cursors and make a large number of concurrent “INSERT”s or “UPDATE”s.

Psycopg 2 is mostly implemented in C as a libpq wrapper, resulting in being both efficient and secure. It features client-side and server-side cursors, asynchronous communication and notifications, “COPY TO/COPY FROM” support. Many Python types are supported out-of-the-box and adapted to matching PostgreSQL data types; adaptation can be extended and customized thanks to a flexible objects adaptation system.

Psycopg 2 is both Unicode and Python 3 friendly.

Documentation

Documentation is included in the doc directory and is available online.

Installation

If your pip version supports wheel packages it should be possible to install a binary version of Psycopg including all the dependencies from PyPI. Just run:

$ pip install -U pip      # make sure your pip is up-to-date
$ pip install psycopg2

If you want to build Psycopg from source you will need some prerequisites (a C compiler, development packages): please check the install and the faq documents in the doc dir for the details.

For any other resource (source code repository, bug tracker, mailing list) please check the project homepage.

Linux/OSX:
Windows:

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

2.9.11

Oct 10, 2025

2.9.10

Oct 16, 2024

2.9.9

Oct 3, 2023

2.9.8

Sep 28, 2023

2.9.7

Aug 5, 2023

2.9.6

Apr 3, 2023

2.9.5

Oct 25, 2022

2.9.4

Oct 6, 2022

2.9.3

Dec 29, 2021

2.9.2

Nov 12, 2021

2.9.1

Jun 17, 2021

2.9

Jun 16, 2021

2.8.6

Sep 6, 2020

2.8.5

Apr 6, 2020

2.8.4

Oct 20, 2019

2.8.3

Jun 13, 2019

2.8.2

Apr 14, 2019

2.8.1

Apr 6, 2019

2.8

Apr 4, 2019

2.7.7

Jan 22, 2019

2.7.6.1

Nov 11, 2018

2.7.6

Nov 9, 2018

2.7.5

Jun 17, 2018

2.7.4

Feb 8, 2018

2.7.3.2

Oct 24, 2017

This version

2.7.3.1

Aug 26, 2017

2.7.3

Jul 24, 2017

2.7.2

Jul 22, 2017

2.7.1

Mar 13, 2017

2.7

Mar 1, 2017

2.6.2

Jul 7, 2016

2.6.1

Jun 15, 2015

2.6

Feb 9, 2015

2.5.5

Feb 9, 2015

2.5.4

Aug 30, 2014

2.5.3

May 13, 2014

2.5.2

Jan 7, 2014

2.5.1

Jun 23, 2013

2.5

Apr 7, 2013

2.4.6

Dec 12, 2012

2.4.5

Mar 29, 2012

2.4.4

Dec 19, 2011

2.4.3

Dec 12, 2011

2.4.2

Jun 17, 2011

2.4.1

May 11, 2011

2.4

Feb 27, 2011

2.3.2

Feb 18, 2011

2.3.1

Dec 4, 2010

2.3.0

Dec 1, 2010

2.2.2

Jul 26, 2010

2.2.1

Jul 26, 2010

2.2.0

Jul 26, 2010

2.0.14

Apr 16, 2010

2.0.13

Apr 16, 2010

2.0.12

Aug 16, 2009

2.0.11

Jul 7, 2009

2.0.10

Jul 7, 2009

2.0.8

Feb 15, 2009

2.0.7

Feb 15, 2009

2.0.6

Feb 15, 2009

2.0.5.1

Dec 2, 2006

2.0.4

Sep 1, 2006

2.0.3

Sep 1, 2006

2.0.2

Jun 12, 2006

Download files

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

Source Distribution

psycopg2-2.7.3.1.tar.gz (425.1 kB view details)

Uploaded Aug 26, 2017 Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

psycopg2-2.7.3.1-cp36-cp36m-win_amd64.whl (943.4 kB view details)

Uploaded Aug 26, 2017 CPython 3.6mWindows x86-64

psycopg2-2.7.3.1-cp36-cp36m-win32.whl (804.0 kB view details)

Uploaded Aug 26, 2017 CPython 3.6mWindows x86

psycopg2-2.7.3.1-cp36-cp36m-manylinux1_x86_64.whl (2.6 MB view details)

Uploaded Aug 26, 2017 CPython 3.6m

psycopg2-2.7.3.1-cp36-cp36m-manylinux1_i686.whl (2.4 MB view details)

Uploaded Aug 26, 2017 CPython 3.6m

psycopg2-2.7.3.1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (1.5 MB view details)

Uploaded Aug 26, 2017 CPython 3.6mmacOS 10.10+ Intel (x86-64, i386)macOS 10.10+ x86-64macOS 10.6+ Intel (x86-64, i386)macOS 10.9+ Intel (x86-64, i386)macOS 10.9+ x86-64

psycopg2-2.7.3.1-cp35-cp35m-win_amd64.whl (943.4 kB view details)

Uploaded Aug 26, 2017 CPython 3.5mWindows x86-64

psycopg2-2.7.3.1-cp35-cp35m-win32.whl (804.0 kB view details)

Uploaded Aug 26, 2017 CPython 3.5mWindows x86

psycopg2-2.7.3.1-cp35-cp35m-manylinux1_x86_64.whl (2.6 MB view details)

Uploaded Aug 26, 2017 CPython 3.5m

psycopg2-2.7.3.1-cp35-cp35m-manylinux1_i686.whl (2.3 MB view details)

Uploaded Aug 26, 2017 CPython 3.5m

psycopg2-2.7.3.1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (1.5 MB view details)

Uploaded Aug 26, 2017 CPython 3.5mmacOS 10.10+ Intel (x86-64, i386)macOS 10.10+ x86-64macOS 10.6+ Intel (x86-64, i386)macOS 10.9+ Intel (x86-64, i386)macOS 10.9+ x86-64

psycopg2-2.7.3.1-cp34-cp34m-win_amd64.whl (932.1 kB view details)

Uploaded Aug 26, 2017 CPython 3.4mWindows x86-64

psycopg2-2.7.3.1-cp34-cp34m-win32.whl (812.6 kB view details)

Uploaded Aug 26, 2017 CPython 3.4mWindows x86

psycopg2-2.7.3.1-cp34-cp34m-manylinux1_x86_64.whl (2.6 MB view details)

Uploaded Aug 26, 2017 CPython 3.4m

psycopg2-2.7.3.1-cp34-cp34m-manylinux1_i686.whl (2.3 MB view details)

Uploaded Aug 26, 2017 CPython 3.4m

psycopg2-2.7.3.1-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (1.5 MB view details)

Uploaded Aug 26, 2017 CPython 3.4mmacOS 10.10+ Intel (x86-64, i386)macOS 10.10+ x86-64macOS 10.6+ Intel (x86-64, i386)macOS 10.9+ Intel (x86-64, i386)macOS 10.9+ x86-64

psycopg2-2.7.3.1-cp33-cp33m-win_amd64.whl (932.1 kB view details)

Uploaded Aug 26, 2017 CPython 3.3mWindows x86-64

psycopg2-2.7.3.1-cp33-cp33m-win32.whl (812.7 kB view details)

Uploaded Aug 26, 2017 CPython 3.3mWindows x86

psycopg2-2.7.3.1-cp33-cp33m-manylinux1_x86_64.whl (2.6 MB view details)

Uploaded Aug 26, 2017 CPython 3.3m

psycopg2-2.7.3.1-cp33-cp33m-manylinux1_i686.whl (2.3 MB view details)

Uploaded Aug 26, 2017 CPython 3.3m

psycopg2-2.7.3.1-cp27-cp27mu-manylinux1_x86_64.whl (2.6 MB view details)

Uploaded Aug 26, 2017 CPython 2.7mu

psycopg2-2.7.3.1-cp27-cp27mu-manylinux1_i686.whl (2.3 MB view details)

Uploaded Aug 26, 2017 CPython 2.7mu

psycopg2-2.7.3.1-cp27-cp27m-win_amd64.whl (934.2 kB view details)

Uploaded Aug 26, 2017 CPython 2.7mWindows x86-64

psycopg2-2.7.3.1-cp27-cp27m-win32.whl (808.8 kB view details)

Uploaded Aug 26, 2017 CPython 2.7mWindows x86

psycopg2-2.7.3.1-cp27-cp27m-manylinux1_x86_64.whl (2.6 MB view details)

Uploaded Aug 26, 2017 CPython 2.7m

psycopg2-2.7.3.1-cp27-cp27m-manylinux1_i686.whl (2.3 MB view details)

Uploaded Aug 26, 2017 CPython 2.7m

psycopg2-2.7.3.1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (1.5 MB view details)

Uploaded Aug 26, 2017 CPython 2.7mmacOS 10.10+ Intel (x86-64, i386)macOS 10.10+ x86-64macOS 10.6+ Intel (x86-64, i386)macOS 10.9+ Intel (x86-64, i386)macOS 10.9+ x86-64

psycopg2-2.7.3.1-cp26-cp26mu-manylinux1_x86_64.whl (2.6 MB view details)

Uploaded Aug 26, 2017 CPython 2.6mu

psycopg2-2.7.3.1-cp26-cp26mu-manylinux1_i686.whl (2.3 MB view details)

Uploaded Aug 26, 2017 CPython 2.6mu

psycopg2-2.7.3.1-cp26-cp26m-manylinux1_x86_64.whl (2.6 MB view details)

Uploaded Aug 26, 2017 CPython 2.6m

psycopg2-2.7.3.1-cp26-cp26m-manylinux1_i686.whl (2.3 MB view details)

Uploaded Aug 26, 2017 CPython 2.6m

File details

Details for the file psycopg2-2.7.3.1.tar.gz.

File metadata

Download URL: psycopg2-2.7.3.1.tar.gz
Upload date: Aug 26, 2017
Size: 425.1 kB
Tags: Source
Uploaded using Trusted Publishing? No

File hashes

Hashes for psycopg2-2.7.3.1.tar.gz
Algorithm	Hash digest
SHA256	`9b7b16e26448b43cf167f785d8b5345007731ebf153a510e12dae826800caa65`
MD5	`903e6324bd456c0e3a95aa89b8a78d72`
BLAKE2b-256	`6bfb15c687eda2f925f0ff59373063fdb408471b4284714a7761daaa65c01f15`

Algorithm	Hash digest
SHA256	`a4d7134058e8869d785c0be386cd702fe2a4be14b678d7571a51045e1bbad862`
MD5	`7b7f27512db9ad4e9e979548b7fd4045`
BLAKE2b-256	`afe087f4a4e49467e19ab2248c534b73982fb2e3d86b0c2d7232e2ff937c8c4d`

Algorithm	Hash digest
SHA256	`279dbf220c94c9f73aefac719ea3b9550ca791389bc9184c15e835516bc8428d`
MD5	`185d04c1b0062aec1e04a38ba411c2f7`
BLAKE2b-256	`3e20e5b2326bad5bebbb733d29d2fe05845c7e6865f3c4ef629b333362d21d28`

Algorithm	Hash digest
SHA256	`471e3140e1cb241ecb53602cdc98fe305b82c854cfa53c3e343d642683dc96c7`
MD5	`84946bbe9f24b4a22728efed69d25ac2`
BLAKE2b-256	`2144159fe55329b6b94dd3077080fed6c2f77c0d6b858bc415a5784e9e885235`

Algorithm	Hash digest
SHA256	`4fbe2c29f6d4c8d9eac5fc3c42c42036e7cc58e225036130b7713afa72489aac`
MD5	`33ef5eefe1cdeb4900f65f4f906848a2`
BLAKE2b-256	`37eb462c0feffb78e541e8b3bb76beedca0880960bb560041378e253bfa8d817`

Algorithm	Hash digest
SHA256	`04a5b2805c620ddecdff33e015631cc8d7ea8dd01e1fcc930bfe002fdc26b9e0`
MD5	`ec2d31b566b3c4ac82a55213d2181931`
BLAKE2b-256	`ac3bbff673f2b9a5fc40231a99e70a13b077a7b9f532e8ab87543010fc000a96`

Algorithm	Hash digest
SHA256	`3fbba0dd7f3ac458f355dcfc4d7d9cd082c19748e453bcd81bf9d8bc14260c01`
MD5	`ea5bc1258abe01e8fd696e1ac45c2e84`
BLAKE2b-256	`4cc17f730fb09df5e37dee2d09677806763546ffa1a9cf3b9a37626c54ba2738`

Algorithm	Hash digest
SHA256	`4cca242df228364b4de6241c54553301856bc253d7f21e15009b11c812eb7b5c`
MD5	`ead0ada3aa684ebee15e72b2141a1289`
BLAKE2b-256	`d70a1c6a01660d244aab7351606dea38287c67f51c07ca2ce21a7adb0da5be8e`

Algorithm	Hash digest
SHA256	`891a70235c202158780fc0ab98d7ca2a7ed1625c26725b15119d47b2d852a5c6`
MD5	`5f762891361de3416ea2befbc9f05678`
BLAKE2b-256	`443cee27949f3b1db38b515b206e47048142f0bf6ca89b2871edcef58d202b58`

Algorithm	Hash digest
SHA256	`35d1fec112337c2607c8ca17dee3c2f8455e07dd69d140ff8e86ef1240dace3d`
MD5	`2097ba3590535ee9a669a739b893bfd9`
BLAKE2b-256	`d8767cf9e21c6697e028e85e4d54fcee21f2afc4c6b1834b13e7a07551738a77`

Algorithm	Hash digest
SHA256	`e26aca5eb272869fdf8e55111f36026517c1c0799eb7ef1ddf67d4412affe1ef`
MD5	`906e3b1f312f74c3075d8b4aeb45e81e`
BLAKE2b-256	`7cc89c9ac41bf1100e37700da62cb691be179cb4d120d0bdeb64159c7c86f818`

Algorithm	Hash digest
SHA256	`6194e81d8839b636118f5275c53be3c70eb3c3abcf836de675c34b20c06025ea`
MD5	`13149cef2bd4d7024f6a05673a79fab8`
BLAKE2b-256	`19f853f229047bbe73af0a3c21008a9d11535c02ceb92a9371a118a34563a555`

Algorithm	Hash digest
SHA256	`604c49bf196c654c417ade1dc765bd23fe9bc3392d9a8c7184a7077142d621b3`
MD5	`e31b60dad44f91c8bd5e33ac4ea182da`
BLAKE2b-256	`11624a4d0ebd94d9c7b9700596034fe6fc94c1220216a6bbe563ce328a924f23`

Algorithm	Hash digest
SHA256	`4db0de7d6236acbf7d020926489b6c4b98e845ba98df11057f22d54866e26b20`
MD5	`b7f0cd4a46c99ec35164645f6f2dbe21`
BLAKE2b-256	`7c2d08ee1cad880f21cbd46797b4267f6e3979214788705fa8eeaba6328de32f`

Algorithm	Hash digest
SHA256	`0bd0b1cf81eb6d74a77199570d5ce097fb3c6b8e57acc1edd328cef5c552f98a`
MD5	`cb0ed84c91600d861650294752b3a432`
BLAKE2b-256	`ca7ef29537a442d22596553605f87ae5d1da2f6467b9beb83b894c2289166b71`

Algorithm	Hash digest
SHA256	`f07791ee5793621bfaaa844f731529cd72321280f94e8dc3bd4ef524d20f58f2`
MD5	`110357c01074cd5e1aaec23e6c261cdb`
BLAKE2b-256	`614e0c6b002179de00fd8aebf6ded404a45dc21ecc360cd101ad605b0aeb7a40`

Algorithm	Hash digest
SHA256	`e80a1ae04218e854a5d71085f9498c8c979033ca855cedeed3afc5d976c348ce`
MD5	`740f2b47452e39eb1ac63eb493d53172`
BLAKE2b-256	`31341c56a92982a7ed443455e73d4a23142f523ba5fd6df803f1fd87a7941692`

Algorithm	Hash digest
SHA256	`f74e50341f45fb9bcd3598c11b5774c3e4349ee38cf15c342cc7075c73ef1f95`
MD5	`abea9a2e1b768c60c65111bb3b1ae08d`
BLAKE2b-256	`8a75a6d51f5f76a4d65c851bb4efb6575873bf320dae2c72cfb96738bc679f2d`

Algorithm	Hash digest
SHA256	`16482d050db68503abbb693795d75e9fca287a00f662376759825404533ca87c`
MD5	`cd9acf120c330a42d7baf3e4c0bf3bb7`
BLAKE2b-256	`68ccc8e18585b6a907a341f6e553424d0f8b886c63a6a5d3a7587f22641e558e`

Algorithm	Hash digest
SHA256	`9a0a74a6f20d82c389095c88d4d281e66eb3ffbc09adf543dcec4bec22436569`
MD5	`c3c34e3395f3b7710dfdd247d9590d87`
BLAKE2b-256	`6d2869cac9dff0e2bfa863edc2c289dc8e4651cd5d3bc90aed0a4877a7af59d2`

Algorithm	Hash digest
SHA256	`b7646f7bdb42ba3cf7ef9f500f6514b5e413d25c5b3093d70e6ba52df417a83c`
MD5	`a9e46cdca916fe64a82aadf8aaab8e7e`
BLAKE2b-256	`2e8f0dcae97762633916470a41b808a59cad7455d06b296f95f18adba606748f`

psycopg2 2.7.3.1

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

Documentation

Installation

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

Algorithm	Hash digest
SHA256	`99a298b9030f8fd36f885c5d0661e4d5a059136a541bb6c4d7d1100e38da3cd1`
MD5	`db3e64a924efc095720f21913ea23741`
BLAKE2b-256	`893e54120d228fa0808fc078c814b1d0f3c1cacda07c115756fbb9ac871e27a5`

Algorithm	Hash digest
SHA256	`c939b238cbc18e786909d20277c9f051241696ebb03ca9e3490094f526ce69a7`
MD5	`3c4dcfa2ceb7daeddf7d455411048cf0`
BLAKE2b-256	`910c2748bafb709121e537e5dd26d64d0564f99ff5347d572b19a360814fe017`

Algorithm	Hash digest
SHA256	`39369e40bc3e062da5da93ce5f1e7f9bed95e9a60cb6f003d316f2a834722e2d`
MD5	`e5bed2318506849ac74012daa354066a`
BLAKE2b-256	`7423ad45eec052bcab9004e62cf028189582541c7274a4012aabda1ec4b84c7a`

Algorithm	Hash digest
SHA256	`4d7c872d9c85745964cf8efabba3fb1a6010b345c18d0e2b620509f0444aa729`
MD5	`9807023d44378d088d9470e591b08df8`
BLAKE2b-256	`3f873a4568b0f7ffdf32b211a5c16f08ad31815bd505b6abb9006ae8b2412812`

Algorithm	Hash digest
SHA256	`ebee4f59803eda1ed4035649469a713818dbf5b6de2e12396edd9fa228727c20`
MD5	`6f73900765555097844bb832562c9980`
BLAKE2b-256	`1aacbe5d6bde73b332429f2bdd7eceafa673253600758ddc6e7c75722ca0a20b`

Algorithm	Hash digest
SHA256	`face605eea5826fa36600c04447b61674992bbd5eb177f66f86f61a04436dbd9`
MD5	`4a2df463aadd3a38734b19a924d5b573`
BLAKE2b-256	`c8bc8c641f6d5d886d210826fe0625ab0ce9bb731a50e264896a05b77a79ebf8`

Algorithm	Hash digest
SHA256	`8d2003d23d8fc59f0f0a2e73c13baa41581006b8227a9d82bbdc1aa233285ba4`
MD5	`8a2f0f9fea3e40545403a05cb84c60f6`
BLAKE2b-256	`323ffa18851319e70d4ee3935d2832158d88d85366e232bb4280564635fc6d3d`

Algorithm	Hash digest
SHA256	`df70e0a387b0145a7a80de67d43d84e1c5a24a33bbfaedb96271876d573e5fb6`
MD5	`a91660d9468e94a95b86f35bba355620`
BLAKE2b-256	`969aba8bc2220fb3a36dd1f8d2562e6c87b624f65efcea7cd7c3c20f29f8cad3`

Algorithm	Hash digest
SHA256	`3c99c88216e0fc62113a1177aae9db18a533274370e99a4537b433b87b319360`
MD5	`ffb30b9ac6a7b1c4cb849e3f0ef098eb`
BLAKE2b-256	`865e9903b074e3c09e3e566e9bbeefb660e08c45e817d897526b05f18edc1e61`

Algorithm	Hash digest
SHA256	`3ab693b907b2c7a34c1dca198cfc454516cfd75440fd913bb372da6f70d6db55`
MD5	`0e794fdc949b0ac9e2c92ad48a5060e9`
BLAKE2b-256	`0637482f7bd821564497fb81841a8869ebeb83cc6f765f9d7d979fb09a2e311a`