数据指标波动假设检验统计

eda/recommended_indexs/hypothesis_test.py

@@ -233,10 +233,10 @@ chi_cal(tst_old)
 #新用户精准点击曝光数据（首页精选日记本列表on_click_diary_card）
 print("【2】（精准曝光）首页精选日记本列表新用户ctr数据波动假设检验结果：")
 chi_ctr_precise_new_recently=chi_DATA_recently("clk_count_newUser_all_a","clk_count_newUser_all_b","imp_count_newUser_all_precise","on_click_diary_card",five_days,yesterday)
-temp1_new=[float(str(Decimal(chi_ctr_precise_old_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_ctr_precise_old_recently))]
+temp1_new=[float(str(Decimal(chi_ctr_precise_new_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_ctr_precise_new_recently))]
 # print(temp1)
-chi_ctr_precise_old_yesterday=chi_DATA_yesterday("clk_count_newUser_all_a","clk_count_newUser_all_b","imp_count_newUser_all_precise","on_click_diary_card",yesterday)
-temp2_new=[float(chi_ctr_precise_old_yesterday[i]) for i in range(len(chi_ctr_precise_old_yesterday))]
+chi_ctr_precise_new_yesterday=chi_DATA_yesterday("clk_count_newUser_all_a","clk_count_newUser_all_b","imp_count_newUser_all_precise","on_click_diary_card",yesterday)
+temp2_new=[float(chi_ctr_precise_new_yesterday[i]) for i in range(len(chi_ctr_precise_new_yesterday))]
 # print(temp2)
 tst_new=data_cal(temp1_new,temp2_new)
 chi_cal(tst_new)