| Name | histogrammar JSON |
| Version |
1.1.0
JSON |
| download |
| home_page | None |
| Summary | Composable histogram primitives for distributed data reduction |
| upload_time | 2025-02-10 15:41:06 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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|
| keywords |
pandas
spark
data-science
data-analysis
statistics
python
jupyter
ipython
|
| VCS |
 |
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| requirements |
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|
==================================
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
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"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. 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