t-test of two distributions null vs alternative

t-test

import seaborn as sns, numpy as np
from scipy import stats
import numpy as np
import statistics

Null_Hypothesis = "YieldA = YieldB"
Alternate_Hypothesis = "YieldA != YieldB"

Vx1_1 = [4618, 3094, 158, 4701, 2692, 2012, 2569, 1233, 1895, 181]
Vx2_1 = [3352, 174, 1661, 1984, 1518, 3594, 3346, 1834, 3300, 3816]

print("The r stat = ", r.statistic)
print("The r pvalue = ", r.pvalue)
print(r.statistic, r.pvalue)

A_mean_pts = statistics.mean(Vx1_1)
B_mean_pts = statistics.mean(Vx2_1)

print("Mean Points for A =", A_mean_pts)
print("Mean Points for B =", B_mean_pts)

tstat, pval = stats.ttest_ind(YieldA['pts'], 110)

print("The T Stat = %.2f, and the P Value = %.4f" % (tstat, pval))
print("T Stat = %.2f, P Value = %.4f" % (tstat, pval))
if pval < 0.03:
    print("Reject the null hypothesis")
else:
    print("Accept the null hypothesis")