GroupBy
=======

.. currentmodule:: pandasCore

GroupBy operations allow you to split data into groups based on some criteria,
apply a function to each group independently, and combine the results.

Example
-------

.. code-block:: python

   import pandasCore as pd

   df = pd.DataFrame({
       'A': ['foo', 'bar', 'foo', 'bar'],
       'B': [1, 2, 3, 4],
       'C': [2.0, 4.0, 6.0, 8.0]
   })

   # Group by single column
   grouped = df.groupby('A')

   # Aggregation
   df.groupby('A').sum()
   df.groupby('A').mean()
   df.groupby('A').agg(['sum', 'mean'])

Parameters
----------

.. list-table::
   :widths: 15 10 10 65
   :header-rows: 1

   * - Parameter
     - Type
     - Default
     - Description
   * - by
     - str/list
     - required
     - Column(s) to group by
   * - axis
     - int
     - 0
     - Split along rows (0) or columns (1)
   * - level
     - int/str
     - None
     - Group by index level
   * - as_index
     - bool
     - True
     - Return with group labels as index
   * - sort
     - bool
     - True
     - Sort group keys
   * - group_keys
     - bool
     - True
     - Add group keys to index
   * - observed
     - bool
     - False
     - Only show observed values for categoricals
   * - dropna
     - bool
     - True
     - Drop groups with NA values

Aggregation Methods
-------------------

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

   * - Method
     - Description
   * - count()
     - Count of values
   * - sum()
     - Sum of values
   * - mean()
     - Mean of values
   * - median()
     - Median of values
   * - std()
     - Standard deviation
   * - var()
     - Variance
   * - min()
     - Minimum
   * - max()
     - Maximum
   * - first()
     - First value
   * - last()
     - Last value
   * - prod()
     - Product
   * - size()
     - Group sizes
   * - sem()
     - Standard error of mean
   * - describe()
     - Descriptive statistics
   * - nunique()
     - Count unique values

Transformation Methods
----------------------

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

   * - Method
     - Description
   * - apply(func)
     - Apply function to each group
   * - transform(func)
     - Transform with function
   * - filter(func)
     - Filter groups with function
   * - agg(func)
     - Aggregate with function(s)

Iteration
---------

.. code-block:: python

   # Iterate over groups
   for name, group in df.groupby('A'):
       print(name)
       print(group)