<!---
Copyright 2018-2020 H2O.ai
Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
IN THE SOFTWARE.
-->
# datatable
[](https://gitter.im/h2oai/datatable)
[](https://pypi.org/project/datatable/)
[](https://github.com/h2oai/datatable/blob/main/LICENSE)
[](https://travis-ci.org/h2oai/datatable)
[](https://datatable.readthedocs.io/en/latest/?badge=latest)
[](https://www.codacy.com/gh/semmjon/datatable/dashboard?utm_source=github.com&utm_medium=referral&utm_content=semmjon/datatable&utm_campaign=Badge_Grade)
This is an automate Fork of datatable.
Because there is no automated build / publish pipeline for the datatable package, and it is not optimal to always install from GitHub.
Fork-URL: https://github.com/semmjon/datatable.git
This is a Python package for manipulating 2-dimensional tabular data structures
(aka data frames). It is close in spirit to [pandas][] or [SFrame][]; however we
put specific emphasis on speed and big data support. As the name suggests, the
package is closely related to R's [data.table][] and attempts to mimic its core
algorithms and API.
Requirements: Python 3.6+ (64 bit) and pip 20.3+.
## Project goals
`datatable` started in 2017 as a toolkit for performing big data (up to 100GB)
operations on a single-node machine, at the maximum speed possible. Such
requirements are dictated by modern machine-learning applications, which need
to process large volumes of data and generate many features in order to
achieve the best model accuracy. The first user of `datatable` was
[Driverless.ai][].
The set of features that we want to implement with `datatable` is at least
the following:
* Column-oriented data storage.
* Native-C implementation for all datatypes, including strings. Packages such
as pandas and numpy already do that for numeric columns, but not for
strings.
* Support for date-time and categorical types. Object type is also supported,
but promotion into object discouraged.
* All types should support null values, with as little overhead as possible.
* Data should be stored on disk in the same format as in memory. This will
allow us to memory-map data on disk and work on out-of-memory datasets
transparently.
* Work with memory-mapped datasets to avoid loading into memory more data than
necessary for each particular operation.
* Fast data reading from CSV and other formats.
* Multi-threaded data processing: time-consuming operations should attempt to
utilize all cores for maximum efficiency.
* Efficient algorithms for sorting/grouping/joining.
* Expressive query syntax (similar to [data.table][]).
* Minimal amount of data copying, copy-on-write semantics for shared data.
* Use "rowindex" views in filtering/sorting/grouping/joining operators to
avoid unnecessary data copying.
* Interoperability with pandas / numpy / pyarrow / pure python: the users
should have the ability to convert to another data-processing framework
with ease.
## Installation
On macOS, Linux and Windows systems installing datatable is as easy as
```sh
pip install datatable
```
On all other platforms a source distribution will be needed. For more
information see [Build instructions](https://datatable.readthedocs.io/en/latest/install.html).
## See also
* [Build instructions](https://datatable.readthedocs.io/en/latest/install.html)
* [Documentation](https://datatable.readthedocs.io/en/latest/?badge=latest)
[pandas]: https://github.com/pandas-dev/pandas
[sframe]: https://github.com/turi-code/SFrame
[data.table]: https://github.com/Rdatatable/data.table
[driverless.ai]: https://www.h2o.ai/driverless-ai/
Raw data
{
"_id": null,
"home_page": "https://github.com/h2oai/datatable",
"name": "python-datatable",
"maintainer": "Oleksiy Kononenko",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "oleksiy.kononenko@h2o.ai",
"keywords": "datatable,data,dataframe,frame,data.table,munging,numpy,pandas,data processing,ETL",
"author": "Pasha Stetsenko",
"author_email": "pasha.stetsenko@h2o.ai",
"download_url": "https://files.pythonhosted.org/packages/8d/45/2115a7d65483be25906e2c574111e924fec10aa4e8f37f6f4845019c06d6/python_datatable-1.1.3.tar.gz",
"platform": null,
"description": "<!---\n Copyright 2018-2020 H2O.ai\n\n Permission is hereby granted, free of charge, to any person obtaining a\n copy of this software and associated documentation files (the \"Software\"),\n to deal in the Software without restriction, including without limitation\n the rights to use, copy, modify, merge, publish, distribute, sublicense,\n and/or sell copies of the Software, and to permit persons to whom the\n Software is furnished to do so, subject to the following conditions:\n\n The above copyright notice and this permission notice shall be included in\n all copies or substantial portions of the Software.\n\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n IN THE SOFTWARE.\n-->\n\n# datatable\n\n[](https://gitter.im/h2oai/datatable)\n[](https://pypi.org/project/datatable/)\n[](https://github.com/h2oai/datatable/blob/main/LICENSE)\n[](https://travis-ci.org/h2oai/datatable)\n[](https://datatable.readthedocs.io/en/latest/?badge=latest)\n[](https://www.codacy.com/gh/semmjon/datatable/dashboard?utm_source=github.com&utm_medium=referral&utm_content=semmjon/datatable&utm_campaign=Badge_Grade)\n\nThis is an automate Fork of datatable.\nBecause there is no automated build / publish pipeline for the datatable package, and it is not optimal to always install from GitHub.\nFork-URL: https://github.com/semmjon/datatable.git\n\nThis is a Python package for manipulating 2-dimensional tabular data structures\n(aka data frames). It is close in spirit to [pandas][] or [SFrame][]; however we\nput specific emphasis on speed and big data support. As the name suggests, the\npackage is closely related to R's [data.table][] and attempts to mimic its core\nalgorithms and API.\n\nRequirements: Python 3.6+ (64 bit) and pip 20.3+.\n\n\n## Project goals\n\n`datatable` started in 2017 as a toolkit for performing big data (up to 100GB)\noperations on a single-node machine, at the maximum speed possible. Such\nrequirements are dictated by modern machine-learning applications, which need\nto process large volumes of data and generate many features in order to\nachieve the best model accuracy. The first user of `datatable` was\n[Driverless.ai][].\n\nThe set of features that we want to implement with `datatable` is at least\nthe following:\n\n* Column-oriented data storage.\n\n* Native-C implementation for all datatypes, including strings. Packages such\n as pandas and numpy already do that for numeric columns, but not for\n strings.\n\n* Support for date-time and categorical types. Object type is also supported,\n but promotion into object discouraged.\n\n* All types should support null values, with as little overhead as possible.\n\n* Data should be stored on disk in the same format as in memory. This will\n allow us to memory-map data on disk and work on out-of-memory datasets\n transparently.\n\n* Work with memory-mapped datasets to avoid loading into memory more data than\n necessary for each particular operation.\n\n* Fast data reading from CSV and other formats.\n\n* Multi-threaded data processing: time-consuming operations should attempt to\n utilize all cores for maximum efficiency.\n\n* Efficient algorithms for sorting/grouping/joining.\n\n* Expressive query syntax (similar to [data.table][]).\n\n* Minimal amount of data copying, copy-on-write semantics for shared data.\n\n* Use \"rowindex\" views in filtering/sorting/grouping/joining operators to\n avoid unnecessary data copying.\n\n* Interoperability with pandas / numpy / pyarrow / pure python: the users\n should have the ability to convert to another data-processing framework\n with ease.\n\n\n## Installation\n\nOn macOS, Linux and Windows systems installing datatable is as easy as\n```sh\npip install datatable\n```\n\nOn all other platforms a source distribution will be needed. For more\ninformation see [Build instructions](https://datatable.readthedocs.io/en/latest/install.html).\n\n\n## See also\n\n* [Build instructions](https://datatable.readthedocs.io/en/latest/install.html)\n* [Documentation](https://datatable.readthedocs.io/en/latest/?badge=latest)\n\n\n [pandas]: https://github.com/pandas-dev/pandas\n [sframe]: https://github.com/turi-code/SFrame\n [data.table]: https://github.com/Rdatatable/data.table\n [driverless.ai]: https://www.h2o.ai/driverless-ai/\n",
"bugtrack_url": null,
"license": "Mozilla Public License v2.0",
"summary": "Python library for fast multi-threaded data manipulation and munging.",
"version": "1.1.3",
"split_keywords": [
"datatable",
"data",
"dataframe",
"frame",
"data.table",
"munging",
"numpy",
"pandas",
"data processing",
"etl"
],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "b0fd7a90c0e5028e9f2fb43667fa659d",
"sha256": "e97bc8d898d06ab750a31a9aba38e1f585b4465c03170ea81d489301fe8d91c4"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp310-cp310-macosx_10_15_x86_64.whl",
"has_sig": false,
"md5_digest": "b0fd7a90c0e5028e9f2fb43667fa659d",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.6",
"size": 8151431,
"upload_time": "2022-12-13T20:24:59",
"upload_time_iso_8601": "2022-12-13T20:24:59.763209Z",
"url": "https://files.pythonhosted.org/packages/e4/6a/552fef6dabd649f153b8446284724c9687580fc1eb0dfa0af86ed68c366c/python_datatable-1.1.3-cp310-cp310-macosx_10_15_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "04ddeeabca8d2dd6653790109160d6ef",
"sha256": "b9d30c536079ee3a31ae052006efbf8169fc684f399fd12052c32abaafe18b7a"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp310-cp310-manylinux_2_35_x86_64.whl",
"has_sig": false,
"md5_digest": "04ddeeabca8d2dd6653790109160d6ef",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.6",
"size": 28529570,
"upload_time": "2022-12-13T20:21:35",
"upload_time_iso_8601": "2022-12-13T20:21:35.548667Z",
"url": "https://files.pythonhosted.org/packages/f0/c3/c8652c702fbea435ca181565291bf77b6bfe13f9fcbf13f5faea9d0f6349/python_datatable-1.1.3-cp310-cp310-manylinux_2_35_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "3a1bbfdc4659fd74a089319fd5e2204b",
"sha256": "b8dbb1bd51e89ed86010cc14f56eac49216aad7aae409d5e3f6bcab67e626360"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp310-cp310-win_amd64.whl",
"has_sig": false,
"md5_digest": "3a1bbfdc4659fd74a089319fd5e2204b",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.6",
"size": 4265587,
"upload_time": "2022-12-13T20:22:17",
"upload_time_iso_8601": "2022-12-13T20:22:17.087783Z",
"url": "https://files.pythonhosted.org/packages/e3/e7/d9a24a48ba7c31da9b3f5b24a5180d591758970440212fdf0f106031ea7d/python_datatable-1.1.3-cp310-cp310-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "ecf3e85604c92e62d9f91cfb173c8d59",
"sha256": "a445dd9794a27c965b1727a7c7353629855503ec42b7f2429d5228650dc0d0b0"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp311-cp311-macosx_10_9_universal2.whl",
"has_sig": false,
"md5_digest": "ecf3e85604c92e62d9f91cfb173c8d59",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.6",
"size": 8170418,
"upload_time": "2022-12-13T20:27:27",
"upload_time_iso_8601": "2022-12-13T20:27:27.768076Z",
"url": "https://files.pythonhosted.org/packages/96/e5/4f8f1ca4e5e616cadf2f080bc6ef895c740106f891a42bdbaa86ce65d441/python_datatable-1.1.3-cp311-cp311-macosx_10_9_universal2.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "a6fec622ef3866245724ca7217827467",
"sha256": "56a3bd7935cdeddaba7f380ab865f5ef7f8ed48e47c62cf16fc396657a1b55e5"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp311-cp311-manylinux_2_35_x86_64.whl",
"has_sig": false,
"md5_digest": "a6fec622ef3866245724ca7217827467",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.6",
"size": 28542055,
"upload_time": "2022-12-13T20:21:46",
"upload_time_iso_8601": "2022-12-13T20:21:46.223052Z",
"url": "https://files.pythonhosted.org/packages/58/fe/43183744b99fa42b347be46186da900524fdcb56320b6786e994720e093a/python_datatable-1.1.3-cp311-cp311-manylinux_2_35_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "0e940e33eb784328a27f692a3fb83970",
"sha256": "9802c9bbb874df9097d475533a3706667769bc1b446d76daff3130a7829a663d"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp311-cp311-win_amd64.whl",
"has_sig": false,
"md5_digest": "0e940e33eb784328a27f692a3fb83970",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.6",
"size": 4285555,
"upload_time": "2022-12-13T20:23:53",
"upload_time_iso_8601": "2022-12-13T20:23:53.733137Z",
"url": "https://files.pythonhosted.org/packages/18/91/82e0299f6011a1dddd2d392dd69c371d6f1e77acb85751b58e2d98cde8c3/python_datatable-1.1.3-cp311-cp311-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "0995d9feeeb5464088e30365e7e37879",
"sha256": "bfcddba8a1528782277617bf865416a41e45c2252f92a58991c5afefa6fbcb74"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp38-cp38-macosx_10_15_x86_64.whl",
"has_sig": false,
"md5_digest": "0995d9feeeb5464088e30365e7e37879",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.6",
"size": 8151306,
"upload_time": "2022-12-13T20:24:32",
"upload_time_iso_8601": "2022-12-13T20:24:32.910822Z",
"url": "https://files.pythonhosted.org/packages/bf/47/1e5c1239ae802cc6f06aa692d3ec4706c525091268a889a6734323113c81/python_datatable-1.1.3-cp38-cp38-macosx_10_15_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "c939da765ab16672eb4160d1ff183e57",
"sha256": "639936dd14a3a0f2f517b9bc96e4a52693416600c35b00fb71ff7d5e40b9f54a"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp38-cp38-manylinux_2_35_x86_64.whl",
"has_sig": false,
"md5_digest": "c939da765ab16672eb4160d1ff183e57",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.6",
"size": 28512835,
"upload_time": "2022-12-13T20:21:56",
"upload_time_iso_8601": "2022-12-13T20:21:56.401155Z",
"url": "https://files.pythonhosted.org/packages/cf/0f/3c760377bfaef07685ec6d7ef6b330b1f6fe4ee0ff592a0548f300a51d9b/python_datatable-1.1.3-cp38-cp38-manylinux_2_35_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "63a6c1dd2e2de5e55ce7142c3ae36b58",
"sha256": "3a43ac1832091921707d4c5fc5f5fd36be7c59f72b5e4b8e89a00971028452a5"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp38-cp38-win_amd64.whl",
"has_sig": false,
"md5_digest": "63a6c1dd2e2de5e55ce7142c3ae36b58",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.6",
"size": 4265582,
"upload_time": "2022-12-13T20:23:10",
"upload_time_iso_8601": "2022-12-13T20:23:10.313511Z",
"url": "https://files.pythonhosted.org/packages/70/95/23e3dad323fce766cd9e5bbbf85cfde5ceffb9e63c2e1a9988586e1d19d0/python_datatable-1.1.3-cp38-cp38-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "a9ef60222fb72d73623b818d0ec37ba3",
"sha256": "31207ace0134ca9038431093d9b8c07a7d437961118c84cab8fbe23f53b3543e"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp39-cp39-macosx_10_15_x86_64.whl",
"has_sig": false,
"md5_digest": "a9ef60222fb72d73623b818d0ec37ba3",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.6",
"size": 8151426,
"upload_time": "2022-12-13T20:26:55",
"upload_time_iso_8601": "2022-12-13T20:26:55.696279Z",
"url": "https://files.pythonhosted.org/packages/17/b8/27b9f1d2dcf96894777ada6421926f37a666990b94a4249901ceb4dac64a/python_datatable-1.1.3-cp39-cp39-macosx_10_15_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "8bfc32ce2f91d255f860184dcfdd9c0a",
"sha256": "7adc63734ca11a37d41c6ae3b642d083f3971815469a2e0fe3240a71af707574"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp39-cp39-manylinux_2_35_x86_64.whl",
"has_sig": false,
"md5_digest": "8bfc32ce2f91d255f860184dcfdd9c0a",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.6",
"size": 28456921,
"upload_time": "2022-12-13T20:21:35",
"upload_time_iso_8601": "2022-12-13T20:21:35.865856Z",
"url": "https://files.pythonhosted.org/packages/d0/d5/762bb71bc71913b33c918eff48a01078b74079479483ba8d3ce62426d38e/python_datatable-1.1.3-cp39-cp39-manylinux_2_35_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "c89c1d95bb0d9280e32897c175b6dea9",
"sha256": "b612410099fd210dafd1d0e59ac6a2c447b9b90973afcabb2c01f2355b38a214"
},
"downloads": -1,
"filename": "python_datatable-1.1.3-cp39-cp39-win_amd64.whl",
"has_sig": false,
"md5_digest": "c89c1d95bb0d9280e32897c175b6dea9",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.6",
"size": 4265582,
"upload_time": "2022-12-13T20:23:00",
"upload_time_iso_8601": "2022-12-13T20:23:00.760491Z",
"url": "https://files.pythonhosted.org/packages/fc/45/5ef1a431f1e6f5ef475e436f97e7e627e897682e119db2120194859750a5/python_datatable-1.1.3-cp39-cp39-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "e3560e1cbbe322efd52549e95fcb5c51",
"sha256": "7a102d5892ed627f8f389261aa219a3107f2e5a3a32865bc06963cb2d2d991b5"
},
"downloads": -1,
"filename": "python_datatable-1.1.3.tar.gz",
"has_sig": false,
"md5_digest": "e3560e1cbbe322efd52549e95fcb5c51",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 1310974,
"upload_time": "2022-12-13T20:21:38",
"upload_time_iso_8601": "2022-12-13T20:21:38.404120Z",
"url": "https://files.pythonhosted.org/packages/8d/45/2115a7d65483be25906e2c574111e924fec10aa4e8f37f6f4845019c06d6/python_datatable-1.1.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-12-13 20:21:38",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "h2oai",
"github_project": "datatable",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "python-datatable"
}
JSON
