"""
Implements variance() function.
"""

from .mean import mean
from .decimalize import decimalize

def variance(data, sample=True):
    """
    Variance_ is the sum of squared deviations from the mean. It is a general measurement of
    how far from the mean the values are.

    .. _Variance: http://en.wikipedia.org/wiki/Variance

    Args:
        data: A list of numeric objects.
        sample: A boolean value. If True, calculates sample variance. If False, calculates
            population variance.

    Returns:
        A float object.

    Examples:
        >>> variance([1, 2, 3, 4])
        1.6666666666666667
        >>> variance([1, 2, 3, 4], sample = False)
        1.25
        >>> variance([1, 2, 3, 4, 5, 6])
        3.5
        >>> variance([-2, -1, 0, 1, 2])
        2.5
        >>> variance([1]) # variance of one value is not defined
        >>> variance([4]) # variance of one value is not defined
    """
    if len(data) < 2:
        return(None)

    data = decimalize(data)
    m = decimalize(mean(data))
    if sample: # pylint: disable=no-else-return
        return(float(sum([pow(x - m, 2) for x in data]) / (len(data) - 1)))
    else:
        return(float(mean([pow(x - m, 2) for x in data])))

"""
Implements variance() function.
"""

from .mean import mean

def variance(data, sample=True):
    """
    Variance_ is the sum of squared deviations from the mean. It is a general measurement of
    how far from the mean the values are.

    .. _Variance: http://en.wikipedia.org/wiki/Variance

    Args:
        data: A list of numeric objects.
        sample: A boolean value. If True, calculates sample variance. If False, calculates
            population variance.

    Returns:
        A float object.

    Examples:
        >>> variance([1, 2, 3, 4])
        1.6666666666666667
        >>> variance([1, 2, 3, 4], sample = False)
        1.25
        >>> variance([1, 2, 3, 4, 5, 6])
        3.5
        >>> variance([-2, -1, 0, 1, 2])
        2.5
        >>> variance([1]) # variance of one value is not defined
        >>> variance([4]) # variance of one value is not defined
    """
    if len(data) < 2:
        return(None)

    m = mean(data)
    if sample: # pylint: disable=no-else-return
        return(float(sum([pow(x - m, 2) for x in data]) / (len(data) - 1)))
    else:
        return(float(mean([pow(x - m, 2) for x in data])))

Remove unnecessary decimalize from variance().