histogrammar


Namehistogrammar JSON
Version 1.1.0 PyPI version JSON
download
home_pageNone
SummaryComposable histogram primitives for distributed data reduction
upload_time2025-02-10 15:41:06
maintainerNone
docs_urlNone
authorNone
requires_python>=3.9
licenseApache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
keywords pandas spark data-science data-analysis statistics python jupyter ipython
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            ==================================
histogrammar Python implementation
==================================

histogrammar is a Python package for creating histograms. histogrammar has multiple histogram types,
supports numeric and categorical features, and works with Numpy arrays and Pandas and Spark dataframes.
Once a histogram is filled, it's easy to plot it, store it in JSON format (and retrieve it), or convert
it to Numpy arrays for further analysis.

At its core histogrammar is a suite of data aggregation primitives designed for use in parallel processing.
In the simplest case, you can use this to compute histograms, but the generality of the primitives
allows much more.

Several common histogram types can be plotted in Matplotlib and Bokeh with a single method call.
If Numpy or Pandas is available, histograms and other aggregators can be filled from arrays ten to a hundred times
more quickly via Numpy commands, rather than Python for loops.

This Python implementation of histogrammar been tested to guarantee compatibility with its Scala implementation.

Latest Python release: v1.1.0 (Feb 2025).
Latest update: Feb 2025.

References
==========

Histogrammar is a core component of `popmon <https://github.com/ing-bank/popmon>`_, a package by ING bank
that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets,
largely thanks to Histogrammar.



Announcements
=============

Changes
-------

See Changes log `here <https://github.com/histogrammar/histogrammar-python/blob/master/CHANGES.rst>`_.


Spark 3.X
---------

With Spark 3.X, based on Scala 2.12 or 2.13, make sure to pick up the correct histogrammar jar files:

.. code-block:: python

  spark = SparkSession.builder.config("spark.jars.packages", "io.github.histogrammar:histogrammar_2.12:1.0.30,io.github.histogrammar:histogrammar-sparksql_2.12:1.0.30").getOrCreate()


For Scala 2.13, in the string above simply replace "2.12" with "2.13".

December, 2023


Example notebooks
=================

.. list-table::
   :widths: 80 20
   :header-rows: 1

   * - Tutorial
     - Colab link
   * - `Basic tutorial <https://nbviewer.jupyter.org/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_basic.ipynb>`_
     - |notebook_basic_colab|
   * - `Detailed example (featuring configuration, Apache Spark and more) <https://nbviewer.jupyter.org/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_advanced.ipynb>`_
     - |notebook_advanced_colab|
   * - `Exercises <https://nbviewer.jupyter.org/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_exercises.ipynb>`_
     - |notebook_exercises_colab|

Documentation
=============

See `histogrammar-docs <https://histogrammar.github.io/histogrammar-docs/>`_ for a complete introduction to `histogrammar`.
(A bit old but still good.) There you can also find documentation about the Scala implementation of `histogrammar`.

Check it out
============

The `historgrammar` library requires Python 3.8+ and is pip friendly. To get started, simply do:

.. code-block:: bash

  $ pip install histogrammar

or check out the code from our GitHub repository:

.. code-block:: bash

  $ git clone https://github.com/histogrammar/histogrammar-python
  $ pip install -e histogrammar-python

where in this example the code is installed in edit mode (option -e).

You can now use the package in Python with:

.. code-block:: python

  import histogrammar

**Congratulations, you are now ready to use the histogrammar library!**

Quick run
=========

As a quick example, you can do:

.. code-block:: python

  import pandas as pd
  import histogrammar as hg
  from histogrammar import resources

  # open synthetic data
  df = pd.read_csv(resources.data('test.csv.gz'), parse_dates=['date'])
  df.head()

  # create a histogram, tell it to look for column 'age'
  # fill the histogram with column 'age' and plot it
  hist = hg.Histogram(num=100, low=0, high=100, quantity='age')
  hist.fill.numpy(df)
  hist.plot.matplotlib()

  # generate histograms of all features in the dataframe using automatic binning
  # (importing histogrammar automatically adds this functionality to a pandas or spark dataframe)
  hists = df.hg_make_histograms()
  print(hists.keys())

  # multi-dimensional histograms are also supported. e.g. features longitude vs latitude
  hists = df.hg_make_histograms(features=['longitude:latitude'])
  ll = hists['longitude:latitude']
  ll.plot.matplotlib()

  # store histogram and retrieve it again
  ll.toJsonFile('longitude_latitude.json')
  ll2 = hg.Factory().fromJsonFile('longitude_latitude.json')

These examples also work with Spark dataframes (sdf):

.. code-block:: python

  from pyspark.sql.functions import col
  hist = hg.Histogram(num=100, low=0, high=100, quantity=col('age'))
  hist.fill.sparksql(sdf)

For more examples please see the example notebooks and tutorials.


Project contributors
====================

This package was originally authored by DIANA-HEP and is now maintained by volunteers.

Contact and support
===================

* Issues & Ideas & Support: https://github.com/histogrammar/histogrammar-python/issues

Please note that `histogrammar` is supported only on a best-effort basis.

License
=======
`histogrammar` is completely free, open-source and licensed under the `Apache-2.0 license <https://en.wikipedia.org/wiki/Apache_License>`_.

.. |notebook_basic_colab| image:: https://colab.research.google.com/assets/colab-badge.svg
    :alt: Open in Colab
    :target: https://colab.research.google.com/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_basic.ipynb
.. |notebook_advanced_colab| image:: https://colab.research.google.com/assets/colab-badge.svg
    :alt: Open in Colab
    :target: https://colab.research.google.com/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_advanced.ipynb
.. |notebook_exercises_colab| image:: https://colab.research.google.com/assets/colab-badge.svg
    :alt: Open in Colab
    :target: https://colab.research.google.com/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_exercises.ipynb

            

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "histogrammar",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.9",
    "maintainer_email": "Max Baak <maxbaak@gmail.com>",
    "keywords": "pandas, spark, data-science, data-analysis, statistics, python, jupyter, ipython",
    "author": null,
    "author_email": "\"Jim Pivarski (DIANA-HEP)\" <pivarski@fnal.gov>, Max Baak <maxbaak@gmail.com>",
    "download_url": "https://files.pythonhosted.org/packages/97/70/491990f1b95b1e14cacdd02bb29d64a2c2a916fab9d2338f16e15e7b84de/histogrammar-1.1.0.tar.gz",
    "platform": null,
    "description": "==================================\nhistogrammar Python implementation\n==================================\n\nhistogrammar is a Python package for creating histograms. histogrammar has multiple histogram types,\nsupports numeric and categorical features, and works with Numpy arrays and Pandas and Spark dataframes.\nOnce a histogram is filled, it's easy to plot it, store it in JSON format (and retrieve it), or convert\nit to Numpy arrays for further analysis.\n\nAt its core histogrammar is a suite of data aggregation primitives designed for use in parallel processing.\nIn the simplest case, you can use this to compute histograms, but the generality of the primitives\nallows much more.\n\nSeveral common histogram types can be plotted in Matplotlib and Bokeh with a single method call.\nIf Numpy or Pandas is available, histograms and other aggregators can be filled from arrays ten to a hundred times\nmore quickly via Numpy commands, rather than Python for loops.\n\nThis Python implementation of histogrammar been tested to guarantee compatibility with its Scala implementation.\n\nLatest Python release: v1.1.0 (Feb 2025).\nLatest update: Feb 2025.\n\nReferences\n==========\n\nHistogrammar is a core component of `popmon <https://github.com/ing-bank/popmon>`_, a package by ING bank\nthat allows one to check the stability of a dataset. popmon works with both pandas and spark datasets,\nlargely thanks to Histogrammar.\n\n\n\nAnnouncements\n=============\n\nChanges\n-------\n\nSee Changes log `here <https://github.com/histogrammar/histogrammar-python/blob/master/CHANGES.rst>`_.\n\n\nSpark 3.X\n---------\n\nWith Spark 3.X, based on Scala 2.12 or 2.13, make sure to pick up the correct histogrammar jar files:\n\n.. code-block:: python\n\n  spark = SparkSession.builder.config(\"spark.jars.packages\", \"io.github.histogrammar:histogrammar_2.12:1.0.30,io.github.histogrammar:histogrammar-sparksql_2.12:1.0.30\").getOrCreate()\n\n\nFor Scala 2.13, in the string above simply replace \"2.12\" with \"2.13\".\n\nDecember, 2023\n\n\nExample notebooks\n=================\n\n.. list-table::\n   :widths: 80 20\n   :header-rows: 1\n\n   * - Tutorial\n     - Colab link\n   * - `Basic tutorial <https://nbviewer.jupyter.org/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_basic.ipynb>`_\n     - |notebook_basic_colab|\n   * - `Detailed example (featuring configuration, Apache Spark and more) <https://nbviewer.jupyter.org/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_advanced.ipynb>`_\n     - |notebook_advanced_colab|\n   * - `Exercises <https://nbviewer.jupyter.org/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_exercises.ipynb>`_\n     - |notebook_exercises_colab|\n\nDocumentation\n=============\n\nSee `histogrammar-docs <https://histogrammar.github.io/histogrammar-docs/>`_ for a complete introduction to `histogrammar`.\n(A bit old but still good.) There you can also find documentation about the Scala implementation of `histogrammar`.\n\nCheck it out\n============\n\nThe `historgrammar` library requires Python 3.8+ and is pip friendly. To get started, simply do:\n\n.. code-block:: bash\n\n  $ pip install histogrammar\n\nor check out the code from our GitHub repository:\n\n.. code-block:: bash\n\n  $ git clone https://github.com/histogrammar/histogrammar-python\n  $ pip install -e histogrammar-python\n\nwhere in this example the code is installed in edit mode (option -e).\n\nYou can now use the package in Python with:\n\n.. code-block:: python\n\n  import histogrammar\n\n**Congratulations, you are now ready to use the histogrammar library!**\n\nQuick run\n=========\n\nAs a quick example, you can do:\n\n.. code-block:: python\n\n  import pandas as pd\n  import histogrammar as hg\n  from histogrammar import resources\n\n  # open synthetic data\n  df = pd.read_csv(resources.data('test.csv.gz'), parse_dates=['date'])\n  df.head()\n\n  # create a histogram, tell it to look for column 'age'\n  # fill the histogram with column 'age' and plot it\n  hist = hg.Histogram(num=100, low=0, high=100, quantity='age')\n  hist.fill.numpy(df)\n  hist.plot.matplotlib()\n\n  # generate histograms of all features in the dataframe using automatic binning\n  # (importing histogrammar automatically adds this functionality to a pandas or spark dataframe)\n  hists = df.hg_make_histograms()\n  print(hists.keys())\n\n  # multi-dimensional histograms are also supported. e.g. features longitude vs latitude\n  hists = df.hg_make_histograms(features=['longitude:latitude'])\n  ll = hists['longitude:latitude']\n  ll.plot.matplotlib()\n\n  # store histogram and retrieve it again\n  ll.toJsonFile('longitude_latitude.json')\n  ll2 = hg.Factory().fromJsonFile('longitude_latitude.json')\n\nThese examples also work with Spark dataframes (sdf):\n\n.. code-block:: python\n\n  from pyspark.sql.functions import col\n  hist = hg.Histogram(num=100, low=0, high=100, quantity=col('age'))\n  hist.fill.sparksql(sdf)\n\nFor more examples please see the example notebooks and tutorials.\n\n\nProject contributors\n====================\n\nThis package was originally authored by DIANA-HEP and is now maintained by volunteers.\n\nContact and support\n===================\n\n* Issues & Ideas & Support: https://github.com/histogrammar/histogrammar-python/issues\n\nPlease note that `histogrammar` is supported only on a best-effort basis.\n\nLicense\n=======\n`histogrammar` is completely free, open-source and licensed under the `Apache-2.0 license <https://en.wikipedia.org/wiki/Apache_License>`_.\n\n.. |notebook_basic_colab| image:: https://colab.research.google.com/assets/colab-badge.svg\n    :alt: Open in Colab\n    :target: https://colab.research.google.com/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_basic.ipynb\n.. |notebook_advanced_colab| image:: https://colab.research.google.com/assets/colab-badge.svg\n    :alt: Open in Colab\n    :target: https://colab.research.google.com/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_advanced.ipynb\n.. |notebook_exercises_colab| image:: https://colab.research.google.com/assets/colab-badge.svg\n    :alt: Open in Colab\n    :target: https://colab.research.google.com/github/histogrammar/histogrammar-python/blob/master/histogrammar/notebooks/histogrammar_tutorial_exercises.ipynb\n",
    "bugtrack_url": null,
    "license": "Apache License\n                                   Version 2.0, January 2004\n                                http://www.apache.org/licenses/\n        \n           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n        \n           1. Definitions.\n        \n              \"License\" shall mean the terms and conditions for use, reproduction,\n              and distribution as defined by Sections 1 through 9 of this document.\n        \n              \"Licensor\" shall mean the copyright owner or entity authorized by\n              the copyright owner that is granting the License.\n        \n              \"Legal Entity\" shall mean the union of the acting entity and all\n              other entities that control, are controlled by, or are under common\n              control with that entity. For the purposes of this definition,\n              \"control\" means (i) the power, direct or indirect, to cause the\n              direction or management of such entity, whether by contract or\n              otherwise, or (ii) ownership of fifty percent (50%) or more of the\n              outstanding shares, or (iii) beneficial ownership of such entity.\n        \n              \"You\" (or \"Your\") shall mean an individual or Legal Entity\n              exercising permissions granted by this License.\n        \n              \"Source\" form shall mean the preferred form for making modifications,\n              including but not limited to software source code, documentation\n              source, and configuration files.\n        \n              \"Object\" form shall mean any form resulting from mechanical\n              transformation or translation of a Source form, including but\n              not limited to compiled object code, generated documentation,\n              and conversions to other media types.\n        \n              \"Work\" shall mean the work of authorship, whether in Source or\n              Object form, made available under the License, as indicated by a\n              copyright notice that is included in or attached to the work\n              (an example is provided in the Appendix below).\n        \n              \"Derivative Works\" shall mean any work, whether in Source or Object\n              form, that is based on (or derived from) the Work and for which the\n              editorial revisions, annotations, elaborations, or other modifications\n              represent, as a whole, an original work of authorship. For the purposes\n              of this License, Derivative Works shall not include works that remain\n              separable from, or merely link (or bind by name) to the interfaces of,\n              the Work and Derivative Works thereof.\n        \n              \"Contribution\" shall mean any work of authorship, including\n              the original version of the Work and any modifications or additions\n              to that Work or Derivative Works thereof, that is intentionally\n              submitted to Licensor for inclusion in the Work by the copyright owner\n              or by an individual or Legal Entity authorized to submit on behalf of\n              the copyright owner. For the purposes of this definition, \"submitted\"\n              means any form of electronic, verbal, or written communication sent\n              to the Licensor or its representatives, including but not limited to\n              communication on electronic mailing lists, source code control systems,\n              and issue tracking systems that are managed by, or on behalf of, the\n              Licensor for the purpose of discussing and improving the Work, but\n              excluding communication that is conspicuously marked or otherwise\n              designated in writing by the copyright owner as \"Not a Contribution.\"\n        \n              \"Contributor\" shall mean Licensor and any individual or Legal Entity\n              on behalf of whom a Contribution has been received by Licensor and\n              subsequently incorporated within the Work.\n        \n           2. Grant of Copyright License. Subject to the terms and conditions of\n              this License, each Contributor hereby grants to You a perpetual,\n              worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n              copyright license to reproduce, prepare Derivative Works of,\n              publicly display, publicly perform, sublicense, and distribute the\n              Work and such Derivative Works in Source or Object form.\n        \n           3. Grant of Patent License. Subject to the terms and conditions of\n              this License, each Contributor hereby grants to You a perpetual,\n              worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n              (except as stated in this section) patent license to make, have made,\n              use, offer to sell, sell, import, and otherwise transfer the Work,\n              where such license applies only to those patent claims licensable\n              by such Contributor that are necessarily infringed by their\n              Contribution(s) alone or by combination of their Contribution(s)\n              with the Work to which such Contribution(s) was submitted. If You\n              institute patent litigation against any entity (including a\n              cross-claim or counterclaim in a lawsuit) alleging that the Work\n              or a Contribution incorporated within the Work constitutes direct\n              or contributory patent infringement, then any patent licenses\n              granted to You under this License for that Work shall terminate\n              as of the date such litigation is filed.\n        \n           4. Redistribution. You may reproduce and distribute copies of the\n              Work or Derivative Works thereof in any medium, with or without\n              modifications, and in Source or Object form, provided that You\n              meet the following conditions:\n        \n              (a) You must give any other recipients of the Work or\n                  Derivative Works a copy of this License; and\n        \n              (b) You must cause any modified files to carry prominent notices\n                  stating that You changed the files; and\n        \n              (c) You must retain, in the Source form of any Derivative Works\n                  that You distribute, all copyright, patent, trademark, and\n                  attribution notices from the Source form of the Work,\n                  excluding those notices that do not pertain to any part of\n                  the Derivative Works; and\n        \n              (d) If the Work includes a \"NOTICE\" text file as part of its\n                  distribution, then any Derivative Works that You distribute must\n                  include a readable copy of the attribution notices contained\n                  within such NOTICE file, excluding those notices that do not\n                  pertain to any part of the Derivative Works, in at least one\n                  of the following places: within a NOTICE text file distributed\n                  as part of the Derivative Works; within the Source form or\n                  documentation, if provided along with the Derivative Works; or,\n                  within a display generated by the Derivative Works, if and\n                  wherever such third-party notices normally appear. The contents\n                  of the NOTICE file are for informational purposes only and\n                  do not modify the License. You may add Your own attribution\n                  notices within Derivative Works that You distribute, alongside\n                  or as an addendum to the NOTICE text from the Work, provided\n                  that such additional attribution notices cannot be construed\n                  as modifying the License.\n        \n              You may add Your own copyright statement to Your modifications and\n              may provide additional or different license terms and conditions\n              for use, reproduction, or distribution of Your modifications, or\n              for any such Derivative Works as a whole, provided Your use,\n              reproduction, and distribution of the Work otherwise complies with\n              the conditions stated in this License.\n        \n           5. Submission of Contributions. Unless You explicitly state otherwise,\n              any Contribution intentionally submitted for inclusion in the Work\n              by You to the Licensor shall be under the terms and conditions of\n              this License, without any additional terms or conditions.\n              Notwithstanding the above, nothing herein shall supersede or modify\n              the terms of any separate license agreement you may have executed\n              with Licensor regarding such Contributions.\n        \n           6. Trademarks. This License does not grant permission to use the trade\n              names, trademarks, service marks, or product names of the Licensor,\n              except as required for reasonable and customary use in describing the\n              origin of the Work and reproducing the content of the NOTICE file.\n        \n           7. Disclaimer of Warranty. Unless required by applicable law or\n              agreed to in writing, Licensor provides the Work (and each\n              Contributor provides its Contributions) on an \"AS IS\" BASIS,\n              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n              implied, including, without limitation, any warranties or conditions\n              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n              PARTICULAR PURPOSE. You are solely responsible for determining the\n              appropriateness of using or redistributing the Work and assume any\n              risks associated with Your exercise of permissions under this License.\n        \n           8. Limitation of Liability. In no event and under no legal theory,\n              whether in tort (including negligence), contract, or otherwise,\n              unless required by applicable law (such as deliberate and grossly\n              negligent acts) or agreed to in writing, shall any Contributor be\n              liable to You for damages, including any direct, indirect, special,\n              incidental, or consequential damages of any character arising as a\n              result of this License or out of the use or inability to use the\n              Work (including but not limited to damages for loss of goodwill,\n              work stoppage, computer failure or malfunction, or any and all\n              other commercial damages or losses), even if such Contributor\n              has been advised of the possibility of such damages.\n        \n           9. Accepting Warranty or Additional Liability. While redistributing\n              the Work or Derivative Works thereof, You may choose to offer,\n              and charge a fee for, acceptance of support, warranty, indemnity,\n              or other liability obligations and/or rights consistent with this\n              License. However, in accepting such obligations, You may act only\n              on Your own behalf and on Your sole responsibility, not on behalf\n              of any other Contributor, and only if You agree to indemnify,\n              defend, and hold each Contributor harmless for any liability\n              incurred by, or claims asserted against, such Contributor by reason\n              of your accepting any such warranty or additional liability.\n        ",
    "summary": "Composable histogram primitives for distributed data reduction",
    "version": "1.1.0",
    "project_urls": {
        "repository": "https://github.com/histogrammar/histogrammar-python"
    },
    "split_keywords": [
        "pandas",
        " spark",
        " data-science",
        " data-analysis",
        " statistics",
        " python",
        " jupyter",
        " ipython"
    ],
    "urls": [
        {
            "comment_text": null,
            "digests": {
                "blake2b_256": "098f3515ded54283da5613f5fc25929a68886e5ae0cdd63cabb8c9239eb53e5e",
                "md5": "ad80bec993eaff7e3798ea40276baa6e",
                "sha256": "8170d4b128abc0dfe056ddc92ead6731cdbf8db8f4ce38120513f2f538e12831"
            },
            "downloads": -1,
            "filename": "histogrammar-1.1.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "ad80bec993eaff7e3798ea40276baa6e",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.9",
            "size": 200725,
            "upload_time": "2025-02-10T15:41:03",
            "upload_time_iso_8601": "2025-02-10T15:41:03.415015Z",
            "url": "https://files.pythonhosted.org/packages/09/8f/3515ded54283da5613f5fc25929a68886e5ae0cdd63cabb8c9239eb53e5e/histogrammar-1.1.0-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": null,
            "digests": {
                "blake2b_256": "9770491990f1b95b1e14cacdd02bb29d64a2c2a916fab9d2338f16e15e7b84de",
                "md5": "76d9ed3c3ce878b394e3ae16463817ed",
                "sha256": "3b47ed3a1336bbc6458d6b9680287621a2b3db4ac6607cb23e988e27cec0e8bc"
            },
            "downloads": -1,
            "filename": "histogrammar-1.1.0.tar.gz",
            "has_sig": false,
            "md5_digest": "76d9ed3c3ce878b394e3ae16463817ed",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.9",
            "size": 3993023,
            "upload_time": "2025-02-10T15:41:06",
            "upload_time_iso_8601": "2025-02-10T15:41:06.464411Z",
            "url": "https://files.pythonhosted.org/packages/97/70/491990f1b95b1e14cacdd02bb29d64a2c2a916fab9d2338f16e15e7b84de/histogrammar-1.1.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-02-10 15:41:06",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "histogrammar",
    "github_project": "histogrammar-python",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "lcname": "histogrammar"
}
        
Elapsed time: 0.90284s