Verifying my one tailed setup for single sample hypothesis testing I have the following working code for 5% significance level:
from scipy.stats import ttest_1samp
import numpy as np
x =  [21.5, 24.5, 18.5, 17.2, 14.5, 23.2, 22.1, 20.5, 19.4, 18.1, 24.1, 18.5]
tscore, pvalue = ttest_1samp(x, popmean=-10)
print(np.mean(x))
print("t Statistic: ", tscore)
print("P Value: ", pvalue/2)

if (pvalue/2 < 0.05) and (tscore < 0):
    print("null H0: x̅ < µ accepted; alternative H1 : x̅ > µ rejected")

if (pvalue/2 > 0.05) and (tscore < 0):
    print("null H0: x̅ < µ rejected; alternative H1 : x̅ > µ accepted")

if (pvalue/2 < 0.05) and (tscore > 0):
    print("null H0: x̅ > µ accepted; alternative H1 : x̅ < µ rejected")

if (pvalue/2 > 0.05) and (tscore > 0):
    print("null H0: x̅ > µ rejected; alternative H1 : x̅ < µ accepted")

I am pretty sure about the code and null statements above. But I want to make sure that I am framing it correctly.
 A: The conclusions should be as follows:
from scipy.stats import ttest_1samp
import numpy as np
x = [21.5, 24.5, 18.5, 17.2, 14.5, 23.2, 22.1, 20.5, 19.4, 18.1, 24.1, 18.5]
tscore, pvalue = ttest_1samp(x, popmean=-10)
print(np.mean(x))
print("t Statistic: ", tscore)
print("P Value: ", pvalue/2)

if pvalue/2 < 0.05 and tscore < 0:
    print("null H0: x̅ = µ rejected; alternative H1 : x̅ < µ accepted")

if (pvalue/2 > 0.05) and (tscore < 0):
    print("null H0: x̅ = µ not rejected; alternative H1 : x̅ < µ not accepted")

if (pvalue/2 < 0.05) and (tscore > 0):
    print("null H0: x̅ = µ rejected; alternative H1 : x̅ > µ accepted")

if (pvalue/2 > 0.05) and (tscore > 0):
    print("null H0: x̅ = µ not rejected; alternative H1 : x̅ > µ not accepted")

When the p-value is below the chosen significance level, the null hypothesis ($H_0$) is always rejected.
One remark regarding the terminology: the null hypothesis is never "accepted", we either "reject it in favour of the alternative hypothesis" (when p-value < significance level) or "fail to reject it" (when p-value > significance level).
On a side note, in the most recent version of scipy (1.6.0) you can carry out a one-sided test directly with ttest_1samp by specifying either ttest_1samp(x, popmean=-10, alternative='less') or ttest_1samp(x, popmean=-10, alternative='greater').
