wrangle_in_py.column_drop_threshold
===================================

.. py:module:: wrangle_in_py.column_drop_threshold


Functions
---------

.. autoapisummary::

   wrangle_in_py.column_drop_threshold.column_drop_threshold


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

.. py:function:: column_drop_threshold(df, threshold, variance=None)

   Returns a copy of the dataframe inputted with columns removed if they did not meet the threshold specified,
   and with columns removed if they had a coefficient of variance lower than specified.

   :param df: The input pandas dataframe whose missingness threshold and coefficient of variance needs to be checked
   :type df: pd.DataFrame
   :param threshold: Must be 0 <= threshold <= 1
                     The threshold for the proportion of missing values to allow in each column of the dataframe,
                     Columns with a larger proportion of missing observations than the threshold will be removed from the dataframe
   :type threshold: float
   :param variance: Default is None
                    The lowest coefficient of variance to allow in any one column of the dataframe
                    Columns with a lower variance than specified will be removed from the dataframe
                    A column must have at least 2 numbers for coefficient of variance to be calculated
                      because the coefficient of variance cannot be calculated with 1 or 0 numbers.
   :type variance: float

   :raises TypeError :: If the input for df is not a pandas DataFrame.
   :raises ValueError :: If the input for threshold is not a float and in the inclusive range 0 and 1.
       Or if the input for variance is not a float >=0.

   :returns: A new dataframe where each column meets or exceeds the specified allowable missingness threshold, and the variance threshold.
             Any columns previously not meeting the thresholds have been removed.
   :rtype: pd.DataFrame

   .. rubric:: Examples

   >>> data = {'apple': [1, 2, NaN], 'banana': [3, 4, 5], 'kiwi': [NaN, 30, NaN], 'peach': [2, 2, 2]}
   >>> df = pd.DataFrame(data)
   >>> column_drop_threshold(df, 0.35, 0.1)
       apple banana
   0   1     3
   1   2     4
   2   NaN   5