Convert Z-score (Z-value, standard score) to p-value for normal distribution in Python

Starting Python 3.8, the standard library provides the NormalDist object as part of the statistics module.

It can be used to apply the inverse cumulative distribution function (inv_cdf, also known as the quantile function or the percent-point function) and the cumulative distribution function (cdf):

NormalDist().inv_cdf(0.95)
# 1.6448536269514715
NormalDist().cdf(1.64)
# 0.9494974165258963

p_value = scipy.stats.norm.pdf(abs(z_score_max)) #one-sided test 
p_value = scipy.stats.norm.pdf(abs(z_score_max))*2 # two - sided test

The probability density function (pdf) function in python yields values p-values that are drawn from a z-score table in a intro/AP stats book.

For Scipy lovers, Tough this is old question but relevant, and we can have not only normal but other distributions as well so here is solution for few more distributions:

def get_p_value_normal(z_score: float) -> float:
    """get p value for normal(Gaussian) distribution 

    Args:
        z_score (float): z score

    Returns:
        float: p value
    """
    return round(norm.sf(z_score), decimal_limit)


def get_p_value_t(z_score: float) -> float:
    """get p value for t distribution 

    Args:
        z_score (float): z score

    Returns:
        float: p value
    """
    return round(t.sf(z_score), decimal_limit)


def get_p_value_chi2(z_score: float) -> float:
    """get p value for chi2 distribution 

    Args:
        z_score (float): z score

    Returns:
        float: p value
    """
    return round(chi2.ppf(z_score, df), decimal_limit)