数据指标波动假设检验统计

eda/recommended_indexs/hypothesis_test.py

@@ -196,30 +196,19 @@ def chi_cal(data):
     t1=data.iloc[0]
     t2=data.iloc[1]
     t11_count=t1[0]
-    print("t11:{}".format(t11_count))
     t12_count=t1[1]
-    print("t12:{}".format(t12_count))
     t21_count=t2[0]
-    print("t21:{}".format(t21_count))
     t22_count=t2[1]
-    print("t22:{}".format(t22_count))
 
     ###理论值计算
     temp1=data['共计']
-    print("共计：{}".format(temp1))
     rate1=temp1[0]/temp1[2]
-    print("rate1:{}".format(rate1))
     rate2=temp1[1]/temp1[2]
-    print("rate2:{}".format(rate2))
     temp2=data.iloc[2]
     t11_theory=temp2[0]*rate1
-    print("t11_theory:{}".format(t11_theory))
     t12_theory=temp2[1]*rate1
-    print("t12_theory:{}".format(t12_theory))
     t21_theory = temp2[0]*rate2
-    print("t21_theory:{}".format(t21_theory))
     t22_theory = temp2[1]*rate2
-    print("t22_theory:{}".format(t22_theory))
     #计算卡方值
     X=(((t11_count-t11_theory)**2)/t11_theory)+(((t12_count-t12_theory)**2)/t12_theory)+(((t21_count-t21_theory)**2)/t21_theory)+(((t22_count-t22_theory)**2)/t22_theory)
     print("卡方值为：{}".format(X))
@@ -231,7 +220,8 @@ def chi_cal(data):
     else:
         print("数据波动较小，95%可能性属于正常波动范围")
 
-#精准点击曝光数据（首页精选日记本列表on_click_diary_card）
+#老用户精准点击曝光数据（首页精选日记本列表on_click_diary_card）
+print("（精准曝光）首页精选日记本列表老用户ctr数据波动假设检验结果：")
 chi_ctr_precise_recently=chi_DATA_recently("clk_count_oldUser_all_a","clk_count_oldUser_all_b","imp_count_oldUser_all_precise","on_click_diary_card",five_days,yesterday)
 temp1=[float(str(Decimal(chi_ctr_precise_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_ctr_precise_recently))]
 # print(temp1)
@@ -239,9 +229,12 @@ chi_ctr_precise_yesterday=chi_DATA_yesterday("clk_count_oldUser_all_a","clk_coun
 temp2=[float(chi_ctr_precise_yesterday[i]) for i in range(len(chi_ctr_precise_yesterday))]
 # print(temp2)
 tst=data_cal(temp1,temp2)
-print(tst)
 chi_cal(tst)
 
+#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("（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：")
+
+
 
 # print(chi_ctr_precise_recently)
 # print(chi_ctr_precise_yesterday)