import numpy as np

def perf_metrics_2X2(yobs, yhat):
    """
    Returns the specificity, sensitivity, positive predictive value, and negeative predictive value 
    of a 2X2 table.

    where:
    0 = negative case
    1 = positive case

    Parameters
    ----------
    yobs :  array of positive and negative ``observed`` cases
    yhat : array of positive and negative ``predicted`` cases

    Returns
    -------
    sensitivity  = TP / (TP+FN)
    specificity  = TN / (TN+FP)
    pos_pred_val = TP/ (TP+FP)
    neg_pred_val = TN/ (TN+FN)

    Author: Julio Cardenas-Rodriguez
    """
    TP = np.sum(  yobs[yobs==1] == yhat[yobs==1] )
    TN = np.sum(  yobs[yobs==0] == yhat[yobs==0] )
    FP = np.sum(  yobs[yobs==1] == yhat[yobs==0] )
    FN = np.sum(  yobs[yobs==0] == yhat[yobs==1] )

    sensitivity  = TP / (TP+FN)
    specificity  = TN / (TN+FP)
    pos_pred_val = TP/ (TP+FP)
    neg_pred_val = TN/ (TN+FN)

    return sensitivity, specificity, pos_pred_val, neg_pred_val

import pandas as pd
y     =    np.array([0, 1, 1, 1, 0, 1, 0,0,1,0])
y_hat = np.array([1, 1, 0, 1, 0, 1,0, 1,1,0])

metrics  =  perf_metrics_2X2(y, y_hat)

print(pd.DataFrame( dict( Metric = ['Specificity', 'Sensitivity', 'PPV','NPV'], 
                          Performance = np.round(metrics,3))))

        Metric  Performance
0  Specificity        0.800
1  Sensitivity        0.600
2          PPV        0.667
3          NPV        0.750