gamsapi: powerful Python toolkit to manage GAMS (i.e., sparse) data and control GAMS solves

What is it?

gamsapi is a Python package that includes submodules to control GAMS, manipulate and transfer data to/from the GAMS modeling system (through GDX files or in-memory objects). This functionality is available from a variety of different Python interfaces including standard Python scripts and Jupyter Notebooks. We strive to make it as simple as possible for users to generate, debug, customize, and ultimately use data to solve optimization problems -- all while maintaining high performance.

Main Features

Here are just a few of the things that gamsapi does well:

Seamlessly integrates GAMS data requirements into standard data pipelines (i.e., Pandas, Numpy)
Link and harmonize data sets across different symbols
Clean/debug data before it enters the modeling environment
Customize the look and feel of the data (i.e., labeling conventions)
Bring data to GAMS from a variety of different starting points
Send model output to a variety of different data endpoints (SQL, CSV, Excel, etc.)
Automatic data reshaping and standardization -- will work to translate your data formats into the Pandas DataFrame standard
Control GAMS model solves and model specification

Where to get it

The source code is currently available with any typical GAMS system. No license is needed in order to use gamsapi. A license is necessary in order to solve GAMS models.

GAMS has a robust Academic Program that includes a full-featured GAMSPy License (w/ commercial solvers -- no model size limitations)!

Dependencies

Installing gamsapi will not install any third-party dependencies, as such, it only contains basic functionality. Users should modify this base installation by choosing extras to install -- extras are described in the documentation.

# from PyPI (with extra "transfer")
pip install gamsapi[transfer]

# from PyPI (with extras "transfer" and "magic")
pip install gamsapi[transfer,magic]

# from PyPI (include all dependencies)
pip install gamsapi[all]

Documentation

The official documentation is hosted on gams.com.

Getting Help

For usage questions, the best place to go to is GAMS. General questions and discussions can also take place on the GAMS World Forum.

Discussion and Development

If you have a design request or concern, please write to support@gams.com.

Download files

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

Source Distribution

gamsapi-54.3.1.tar.gz (940.9 kB view details)

Uploaded Aug 22, 2026 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.

gamsapi-54.3.1-cp314-cp314-win_amd64.whl (1.7 MB view details)

Uploaded Aug 22, 2026 CPython 3.14Windows x86-64

gamsapi-54.3.1-cp314-cp314-manylinux_2_28_x86_64.whl (1.3 MB view details)

Uploaded Aug 22, 2026 CPython 3.14manylinux: glibc 2.28+ x86-64

gamsapi-54.3.1-cp314-cp314-manylinux_2_28_aarch64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.14manylinux: glibc 2.28+ ARM64

gamsapi-54.3.1-cp314-cp314-macosx_13_0_x86_64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.14macOS 13.0+ x86-64

gamsapi-54.3.1-cp314-cp314-macosx_13_0_arm64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.14macOS 13.0+ ARM64

gamsapi-54.3.1-cp313-cp313-win_amd64.whl (1.7 MB view details)

Uploaded Aug 22, 2026 CPython 3.13Windows x86-64

gamsapi-54.3.1-cp313-cp313-manylinux_2_28_x86_64.whl (1.3 MB view details)

Uploaded Aug 22, 2026 CPython 3.13manylinux: glibc 2.28+ x86-64

gamsapi-54.3.1-cp313-cp313-manylinux_2_28_aarch64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.13manylinux: glibc 2.28+ ARM64

gamsapi-54.3.1-cp313-cp313-macosx_13_0_x86_64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.13macOS 13.0+ x86-64

gamsapi-54.3.1-cp313-cp313-macosx_13_0_arm64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.13macOS 13.0+ ARM64

gamsapi-54.3.1-cp312-cp312-win_amd64.whl (1.7 MB view details)

Uploaded Aug 22, 2026 CPython 3.12Windows x86-64

gamsapi-54.3.1-cp312-cp312-manylinux_2_28_x86_64.whl (1.3 MB view details)

Uploaded Aug 22, 2026 CPython 3.12manylinux: glibc 2.28+ x86-64

gamsapi-54.3.1-cp312-cp312-manylinux_2_28_aarch64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.12manylinux: glibc 2.28+ ARM64

gamsapi-54.3.1-cp312-cp312-macosx_13_0_x86_64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.12macOS 13.0+ x86-64

gamsapi-54.3.1-cp312-cp312-macosx_13_0_arm64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.12macOS 13.0+ ARM64

gamsapi-54.3.1-cp311-cp311-win_amd64.whl (1.7 MB view details)

Uploaded Aug 22, 2026 CPython 3.11Windows x86-64

gamsapi-54.3.1-cp311-cp311-manylinux_2_28_x86_64.whl (1.3 MB view details)

Uploaded Aug 22, 2026 CPython 3.11manylinux: glibc 2.28+ x86-64

gamsapi-54.3.1-cp311-cp311-manylinux_2_28_aarch64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.11manylinux: glibc 2.28+ ARM64

gamsapi-54.3.1-cp311-cp311-macosx_13_0_x86_64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.11macOS 13.0+ x86-64

gamsapi-54.3.1-cp311-cp311-macosx_13_0_arm64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.11macOS 13.0+ ARM64

gamsapi-54.3.1-cp310-cp310-win_amd64.whl (1.7 MB view details)

Uploaded Aug 22, 2026 CPython 3.10Windows x86-64

gamsapi-54.3.1-cp310-cp310-manylinux_2_28_x86_64.whl (1.3 MB view details)

Uploaded Aug 22, 2026 CPython 3.10manylinux: glibc 2.28+ x86-64

gamsapi-54.3.1-cp310-cp310-manylinux_2_28_aarch64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.10manylinux: glibc 2.28+ ARM64

gamsapi-54.3.1-cp310-cp310-macosx_13_0_x86_64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.10macOS 13.0+ x86-64

gamsapi-54.3.1-cp310-cp310-macosx_13_0_arm64.whl (1.2 MB view details)

Uploaded Aug 22, 2026 CPython 3.10macOS 13.0+ ARM64

File details

Details for the file gamsapi-54.3.1.tar.gz.

File metadata

Download URL: gamsapi-54.3.1.tar.gz
Upload date: Aug 22, 2026
Size: 940.9 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/7.0.0 CPython/3.12.12

File hashes

Hashes for gamsapi-54.3.1.tar.gz
Algorithm	Hash digest
SHA256	`0c5a1567589cdd3ca0f5c735d8d787299f733d6486c145bfe635ef210a028ef4`
MD5	`f8683d5557c617bc23dfaf7b50002d56`
BLAKE2b-256	`d3d4da800ad9068b98800db79362eb0600a0aa51c29d227c670e2cb78e5f7d5a`

Algorithm	Hash digest
SHA256	`51b5cc7a72ebd9eb536f81ba0995429e9da6e500d68f32b1ef72b836a9210c18`
MD5	`481b2c74190695794d3e014b62ef3da7`
BLAKE2b-256	`3075a58a7e65d65de5ee83f63e7e005fa755876b0da9240c7889196031b5591f`

Algorithm	Hash digest
SHA256	`b8cac59802a6ab055d5f3afe62bb071b7fd7f8753ee9d14aa5ecb5dfb9e09b07`
MD5	`f6e2be402007ea8e15986f9ed393231e`
BLAKE2b-256	`8d4b3920133130d3df2d02989eb1347c9ef5f48141b8efaa7b006110677fff34`

Algorithm	Hash digest
SHA256	`debd86e80fe8e09e823c517b7a5698ed405c204237d0b394922b79363c9a4f3c`
MD5	`2490c6c5459b15684c5b300d28fc9697`
BLAKE2b-256	`44bb1711e3d8eed3368830a7749bf5ec39055282905e9e273903d9bf6513dff6`

Algorithm	Hash digest
SHA256	`91aad05f1b68a8c3d3a012ce3e4986d74f187f62868408ca16ce262f2ef08ead`
MD5	`c80c10af804b789f811384f8cdb1968b`
BLAKE2b-256	`ad844d1cc0fb3735f25271b4b95d288fb7c6ab7a92a3bc43a7e1cc47a780ecfe`

Algorithm	Hash digest
SHA256	`dc4f735746a1e37933a523adf5d2052035d91653ce32edf59c04128a3d2ee9a8`
MD5	`82596113c023121b0aec64ecb508b9b4`
BLAKE2b-256	`365a760ea9dbebc48222f74df4c4cb0bcfb58dc2d197a7afd1df1e1e2fe32e53`

Algorithm	Hash digest
SHA256	`588ad93a062f0c36cbe4867c4d706f9a99300d200766952999edb808c7b21658`
MD5	`e7d15221d724d7064b7eef60036bcaf7`
BLAKE2b-256	`a8eca4319c4ad2268f6549663a11aca45d23eb04f86c8d0cc552fa930173144e`

Algorithm	Hash digest
SHA256	`403409531e68dd19c0af2805de391fe680b97034233a8437cdaecc3060a2551f`
MD5	`702562f54c518c634e581a051623acc6`
BLAKE2b-256	`28dc3d25178ec9b5ccff01bd0bfc337135cf33f95b150d14de2a83a005a63518`

Algorithm	Hash digest
SHA256	`fdc2ccc3235b64a09fb005fb72af5beae1785e7418935abef40a94d67cf7c5f9`
MD5	`fc01ad3f18f25633f9cc79724d90a55e`
BLAKE2b-256	`9fafc2b7aa7241eac141e0826e3544b44779a322b9748a28f49a6b20117ee455`

Algorithm	Hash digest
SHA256	`8fb90c52323ff2bbd1fb0a53d2404f1168b2d2e73d651b26dce3c2bedc2f8152`
MD5	`65e45768941bf30c2853fd3df766ca93`
BLAKE2b-256	`9c933e1ee65f75d6862f4ab200060f0844e9a9e49436244a7b4e02ad37880210`

Algorithm	Hash digest
SHA256	`b7049f16ad05f7d8a7fed180475eddf8f5c480afb0a5cec3e2d4b6b7c824106f`
MD5	`9fcba5a9ac6bc10b218eb41fec6bbb7b`
BLAKE2b-256	`57bdbd9f770920202a5541ac72b31d277150093b17c4c65d6bec55faf501105c`

Algorithm	Hash digest
SHA256	`f10ca776d9ee50593de4cd1ef62ff77f8c95b9fd9dd615b7af9aec36e14dd1d0`
MD5	`abbc455cf588fe99c5d91b49702e5314`
BLAKE2b-256	`660b324742ea7f792908f04fa0ce91976a757300fafa23031eba43c64746fb5b`

Algorithm	Hash digest
SHA256	`71e9702f9c452073bc7fb61f67c9030c8ef4d878d327b5908762c2512bc4676e`
MD5	`a9488070a97d2cd972aebeae455ed0b3`
BLAKE2b-256	`54913af177cfe6907c95afa432bd49f4b28bd9384c4d372075dd5cbf7ed70310`

Algorithm	Hash digest
SHA256	`b05f70ff0e594f0e14623c957f8b13f06f1322120cb710599bdbb2436bb4fd3f`
MD5	`af03959eed51f3171f450f9d109ccbd4`
BLAKE2b-256	`9549e08c1dcb61bb4d8f5040c2aef99540a826c64e0ac34b70de30d748caea13`

Algorithm	Hash digest
SHA256	`0479c232b2f3c585314f38e23cd5ee511fa3bb341a2c346b5dd02895ea7069ed`
MD5	`ac114df625d60218d269c64b96f974b5`
BLAKE2b-256	`84786425f20ddc67137b420855a2973f988be3c5e6a46fd61ac5a81a054a4d97`

Algorithm	Hash digest
SHA256	`d538593fa845ca3ece6ebc47520b0e07a5a095bfa75c43f7616c5c8009135add`
MD5	`e1299d8bcc346a956922fae5d0cb1e84`
BLAKE2b-256	`60c1c9ad076edd6a753d0dc264e8864b51fcf1465bf8672f6cec130675bbd68e`

Algorithm	Hash digest
SHA256	`6091e5cea1ff2e5036843ae8b6d16b117eac552ac097e2f28f5095d7e88b5ba6`
MD5	`9f5af82f01b8931afd7d1f7adc905920`
BLAKE2b-256	`54cb411938c10e3ea06feb8cc1c7e6a43405060aded064805abda8d9b25398c4`

Algorithm	Hash digest
SHA256	`4a07ed082ccc6ab41498169566b8ee8f2f9874e6f2c21e1730b534c428bc339c`
MD5	`5549a073a820bd1d7788f93fd43f9226`
BLAKE2b-256	`d77c8dd1126e7425515781e108a3fe36edc9ad91634ce48caddf0282e5812536`

Algorithm	Hash digest
SHA256	`dc8e50765344281740879e7906c2ee8bfa209b43ec4b0db7336bdbc1140475ab`
MD5	`2e0a55f47bd787378ec20baf621d52ea`
BLAKE2b-256	`92fc7dde7a086720c016b947f761f3f0fe0b9b32087dbdd35e60a4d3d408b29a`

Algorithm	Hash digest
SHA256	`16c5f8e61165aae6be9fc393cdd70aecab1d94096e01e91d724362d9808e9288`
MD5	`4f80d7f669a03f6dcfb7c35964fb0503`
BLAKE2b-256	`c5332b39e485afd7ad81839ebd7b4c2ad3100695b61cfaef14a3e0ae557b94f4`

Algorithm	Hash digest
SHA256	`9235f001f69f26403cb36800673865e9c15cd7ed8f96a4086e29faf075d704e3`
MD5	`71e6441d1dd0c496e99d96235032c0dc`
BLAKE2b-256	`12499b2bf7699775c3a867e3e5ff53ae54c9c4c2fd02ced6f7c8c60a7331448d`

gamsapi 54.3.1

gamsapi: powerful Python toolkit to manage GAMS (i.e., sparse) data and control GAMS solves

What is it?

Main Features

Where to get it

Dependencies

Documentation

Getting Help

Discussion and Development

Project links

Key dates

1 maintainer

Credits

License

Requires

Provides Extra

Classifiers

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

File metadata

File hashes

Algorithm	Hash digest
SHA256	`f0a951a6bc534748fa9810b4ff82e58b18f8bdb4515c458a8d24538ab7a6b435`
MD5	`d9723e7d05669ed87f3742a3c4a98cb4`
BLAKE2b-256	`40a67ee56a1e76278be54b53ca9812bb5d30bc8896171374975c45599b86153d`

Algorithm	Hash digest
SHA256	`5187d5276c8d67f646f2d99bea60049aefcf93bad05faf6a544919d15945c8ba`
MD5	`ad2669aa0646bc00f019412fd5ce2c04`
BLAKE2b-256	`854f2f499007d3180dd215e3d0228b3889aafd89213acc90156fbc1a56072776`

Algorithm	Hash digest
SHA256	`909311f7f33e60637d9e2273b15af42842dcdf61118413042aa20feb695fd18c`
MD5	`7c081b0ac50f76844ad71b14d432a698`
BLAKE2b-256	`d13cd75635ac60d144a1f660c848074d5a7d1246144f41abc846265c64e9eb22`

Algorithm	Hash digest
SHA256	`c3ec5ce9073248f32f3fca9735ab8883381e3cf1e34c83736b50886c11da8a75`
MD5	`1593ccc9537ffda570b0e00254a41886`
BLAKE2b-256	`b969712c512a1463d44aafa6ecd9f892a3630923bfd703d7fb2d1d19e55f0fbe`

Algorithm	Hash digest
SHA256	`887dde361c06e8eb91721ad9016662d25347232539c91eb17e4eadb8aad60598`
MD5	`c98c90db9327e1e56ac36a61f2ef721f`
BLAKE2b-256	`e76059e2677ba8e8366d67d4bd12f8800dc7387b687d8c48bcc9d8c083d7d1aa`