:py:mod:`ineqpy.utils`
======================

.. py:module:: ineqpy.utils

.. autoapi-nested-parse::

   Useful functions that make easier develop other functions.

   ..
       !! processed by numpydoc !!


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   ineqpy.utils._to_df
   ineqpy.utils._apply_to_df
   ineqpy.utils.not_empty_weights
   ineqpy.utils.not_null_condition
   ineqpy.utils._sort_values
   ineqpy.utils._clean_nans_values
   ineqpy.utils.normalize
   ineqpy.utils.extract_values
   ineqpy.utils.repeat_data_from_weighted
   ineqpy.utils.generate_data_to_test



.. py:function:: _to_df(*args, **kwargs) -> pandas.DataFrame


.. py:function:: _apply_to_df(func, df, x, weights, *args, **kwargs)

   
   Generalize main arguments as Series of a pd.Dataframe.


   :Parameters:

       **func** : function
           Function to convert his arguments in Series of an Dataframe.

       **df** : pandas.Dataframe
           DataFrame whats contains the Series `x_name` and `w_name`.

       **x_name** : str
           Name of the column in `df`.

       **weights_name** : str
           Name of the column in `df`.

   :Returns:

       **return** : func return
           It's depends of func output type.













   ..
       !! processed by numpydoc !!

.. py:function:: not_empty_weights(weights, like)

   
   Create weights.

   Create normalized weight if it's None use like to create it.

   :Parameters:

       **income, like** : array-like
           ..

   :Returns:

       **weights** : array-like
           Filtered array-like.







   .. seealso::

       
       :obj:`normalize`
           ..
       





   ..
       !! processed by numpydoc !!

.. py:function:: not_null_condition(income, weights)

   
   Filter not null condition.

   If a negative value is found in the incomes it will dropped.

   :Parameters:

       **income, weights** : array-like
           ..

   :Returns:

       **income, weights** : array-like
           Filtered array-like.













   ..
       !! processed by numpydoc !!

.. py:function:: _sort_values(values, partner)


.. py:function:: _clean_nans_values(this, pair)


.. py:function:: normalize(this)

   
   Normalize data by the sum.


   :Parameters:

       **this** : array-like
           ..

   :Returns:

       **out** : array-like
           ..













   ..
       !! processed by numpydoc !!

.. py:function:: extract_values(data, variable, weights)

   
   Extract values.


   :Parameters:

       **data** : pandas.DataFrame
           ..

       **variable** : str
           ..

       **weights** : str
           ..

   :Returns:

       **variable, weights** : array-like
           ..













   ..
       !! processed by numpydoc !!

.. py:function:: repeat_data_from_weighted(x, w)

   
   Generate data data (not sampled) from weights.


   :Parameters:

       **x, w** : array-like
           ..

   :Returns:

       **repeated_x, repeated_w** : np.array
           ..













   ..
       !! processed by numpydoc !!

.. py:function:: generate_data_to_test(n_sample_range=(20, 100))

   
   Generate sampled data for testing.


   :Parameters:

       **n_sample_range** : tuple[int, int]
           It's a shape, lenght and columns.

   :Returns:

       **income, weights** : np.array
           ..













   ..
       !! processed by numpydoc !!