python-2 sample T test , boxplot 그리기

import pandas as pd
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns

filename_Single ='D:/a.xlsx'
df_Single= pd.read_excel(filename_Single, sheet_name=2, engine='openpyxl')

filename_Dual ='D:/b.xlsx'
df_Dual= pd.read_excel(filename_Dual, sheet_name=2, engine='openpyxl')

# ------- 평균/ 표준편차 구하기 --------------------------------
Single_R = df_Single.loc[df_Single['ResponseTime'] != '-']
a_Single = Single_R['ResponseTime'].astype('float')  # 통계를 위해 사용 변수

Dual_R = df_Dual.loc[df_Dual['ResponseTime'] != '-']
a_Dual = Dual_R['ResponseTime'].astype('float')      # 통계를 위해 사용 변수

average_Single = np.mean(a_Single)
std_Single = np.std(a_Single)

average_Dual = np.mean(a_Dual)
std_Dual = np.std(a_Dual)

print("Single Average : ", average_Single, "            Dual Average : ", average_Dual)
print("Single STD     :", std_Single, "     Dual STD : ", std_Dual)

#-----------------2 sample t test ------------------------------
#-----------------2.1 정규성 검정 --------------------------------
# p value > 0.05 귀무가설채택( 귀무가설: 정규분포 한다)
print("---------Nornality test---------")
Normal_test = stats.ranksums(a_Single, a_Dual)
print(Normal_test)

#-----------------2.2 등분산 검정 --------------------------------
# 귀무가설 : 등분산 한다, P value > 0.05 채택, 0.05< pvalue-> 등분산 하지 않으므로, 2sample t진행(이분산)
print("---------equality test---------")
equal_test = stats.levene(a_Single, a_Dual)
print(equal_test)

#-----------------2.3 2 sample T 검정 ---------------------------
# 귀무가설 : a=b, P value > 0.05 채택, 0.05< pvalue-> 같지 않다
print("---------2 sample t test---------")
two_sample = stats.ttest_ind(a_Single, a_Dual, equal_var= False)
print(two_sample)

# 귀무가설 H0: a=b /  대립가설 H1: a=/b
# P vale > 0.05  귀무가설 채택 / p value < 0.05 대립가설 채택

#-----------------3. Graphic ---------------------------
#----1) 1개 데이타 box plot 그리기 -----------------------
# plt.boxplot(a_Single, sym='r+')
# plt.title("Response time")
# plt.show()
#----2) 여러개 데이타 box plot그리기 ----------------------
fig, ax = plt.subplots()

ax.boxplot([a_Single, a_Dual])
ax.set_ylim(0.0, 4.0)
ax.set_xlabel("Camera count")
ax.set_ylabel("value")

plt.show()