.. _sphx_glr_auto_examples_stats:

Binning & Statistics
--------------------

Learn how to perform data binning and compute descriptive statistics using
pyinterp’s built-in tools.



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    <div class="sphx-glr-thumbnails">

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    <div class="sphx-glr-thumbcontainer" tooltip="While NumPy provides a wide range of statistical functions, calculating multiple statistical variables from the same array often requires multiple passes over the data. The :pypyinterp.DescriptiveStatistics class offers a more efficient solution by computing several statistical variables in a single pass. This approach is not only faster but also more numerically stable, thanks to its incremental calculation algorithm.">

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  .. image:: /auto_examples/stats/images/thumb/sphx_glr_ex_descriptive_statistics_thumb.png
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  :ref:`sphx_glr_auto_examples_stats_ex_descriptive_statistics.py`

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      <div class="sphx-glr-thumbnail-title">Descriptive Statistics</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Binning is a technique used to group continuous values into a smaller number of bins. This is particularly useful when you have irregularly distributed data and want to analyze it on a regular grid. In this example, we will use pyinterp&#x27;s 2D binning functionality to calculate drifter velocity statistics in the Black Sea over a 9-year period.">

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  .. image:: /auto_examples/stats/images/thumb/sphx_glr_ex_binning_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_stats_ex_binning.py`

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      <div class="sphx-glr-thumbnail-title">Binning</div>
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.. toctree::
   :hidden:

   /auto_examples/stats/ex_descriptive_statistics
   /auto_examples/stats/ex_binning