from .standard_deviation import standard_deviation
from .mean import mean

def coefficient_of_variation(data):
    """
    The `coefficient_of_variation`_ is the ratio of the standard deviation to the mean
    .. _`coefficient of variation`: https://en.wikipedia.org/wiki/Coefficient_of_variation

    Args:
        data: A list of numerical objects.
    Returns:
        A float object.
    Examples:
        >>> coefficient_of_variation([1, 2, 3])
        0.5
        >>> coefficient_of_variation([1, 2, 3, 4])
        0.5163977794943222
        >>> coefficient_of_variation([-1, 0, 1, 2, 3, 4])
        1.247219128924647
    """
    return standard_deviation(data) / mean(data)
    
from .standard_deviation import standard_deviation
from .mean import mean

def coefficient_of_variation(data, sample = True):
    """
    The `coefficient of variation`_ is the ratio of the standard deviation to the mean.

    .. _`coefficient of variation`: https://en.wikipedia.org/wiki/Coefficient_of_variation

    Args:
        data: A list of numerical objects.
    Returns:
        A float object.
    Examples:
        >>> coefficient_of_variation([1, 2, 3])
        0.5
        >>> ss.coefficient_of_variation([1, 2, 3], False)
        0.408248290463863
        >>> coefficient_of_variation([1, 2, 3, 4])
        0.5163977794943222
        >>> coefficient_of_variation([-1, 0, 1, 2, 3, 4])
        1.247219128924647
    """
    return standard_deviation(data, sample) / mean(data)

Add sample param to CV function
Add sample param to CV function

Boolean param to make possible to calculate coefficient of variation
for population (default is sample).

  from .standard_deviation import standard_deviation
  from .mean import mean
  
- def coefficient_of_variation(data):
+ def coefficient_of_variation(data, sample = True):
      """
-     The `coefficient_of_variation`_ is the ratio of the standard deviation to the mean
+     The `coefficient of variation`_ is the ratio of the standard deviation to the mean.
+ 
      .. _`coefficient of variation`: https://en.wikipedia.org/wiki/Coefficient_of_variation
  
      Args:
          data: A list of numerical objects.
      Returns:
          A float object.
      Examples:
          >>> coefficient_of_variation([1, 2, 3])
          0.5
+         >>> ss.coefficient_of_variation([1, 2, 3], False)
+         0.408248290463863
          >>> coefficient_of_variation([1, 2, 3, 4])
          0.5163977794943222
          >>> coefficient_of_variation([-1, 0, 1, 2, 3, 4])
          1.247219128924647
      """
-     return standard_deviation(data) / mean(data)
+     return standard_deviation(data, sample) / mean(data)
-     
+ 
Add sample param to CV function
## Code Before:
from .standard_deviation import standard_deviation
from .mean import mean

def coefficient_of_variation(data):
    """
    The `coefficient_of_variation`_ is the ratio of the standard deviation to the mean
    .. _`coefficient of variation`: https://en.wikipedia.org/wiki/Coefficient_of_variation

    Args:
        data: A list of numerical objects.
    Returns:
        A float object.
    Examples:
        >>> coefficient_of_variation([1, 2, 3])
        0.5
        >>> coefficient_of_variation([1, 2, 3, 4])
        0.5163977794943222
        >>> coefficient_of_variation([-1, 0, 1, 2, 3, 4])
        1.247219128924647
    """
    return standard_deviation(data) / mean(data)
    
## Instruction:
Add sample param to CV function
## Code After:
from .standard_deviation import standard_deviation
from .mean import mean

def coefficient_of_variation(data, sample = True):
    """
    The `coefficient of variation`_ is the ratio of the standard deviation to the mean.

    .. _`coefficient of variation`: https://en.wikipedia.org/wiki/Coefficient_of_variation

    Args:
        data: A list of numerical objects.
    Returns:
        A float object.
    Examples:
        >>> coefficient_of_variation([1, 2, 3])
        0.5
        >>> ss.coefficient_of_variation([1, 2, 3], False)
        0.408248290463863
        >>> coefficient_of_variation([1, 2, 3, 4])
        0.5163977794943222
        >>> coefficient_of_variation([-1, 0, 1, 2, 3, 4])
        1.247219128924647
    """
    return standard_deviation(data, sample) / mean(data)
