数据指标波动假设检验统计

eda/recommended_indexs/hypothesis_test.py

@@ -170,7 +170,7 @@ def get_fivedate():
 	return someday
 five_days=get_fivedate()
 
-#获取最近5天的数据
+#获取最近5天的数据,此函数只适用于on_click_diary_card表格，具体原因可以查看数据代码
 def chi_DATA_recently(x,y,z,q,t1,t2):
     sql_cid = "select AVG({0}+{1}),AVG({2}) from {3} \
     where stat_date >= '{4}' and stat_date < '{5}' ".format(x,y,z,q,t1,t2)
@@ -180,6 +180,17 @@ def chi_DATA_yesterday(x,y,z,q,t1):
     sql_cid = "select {0}+{1},{2} from {3} where stat_date='{4}'  ".format(x,y,z,q,t1)
     CVR_DATA_yesterday = con_sql(sql_cid)[0]
     return CVR_DATA_yesterday
+#获取最近5天的数据
+def chi_DATA_recently_all(x,y,z,q,t1,t2):
+    sql_cid = "select AVG({0}),AVG({1}) from {2} \
+    where stat_date >= '{3}' and stat_date < '{4}' ".format(x,y,z,t1,t2)
+    CVR_DATA_recently = con_sql(sql_cid)[0]
+    return CVR_DATA_recently
+def chi_DATA_yesterday_all(x,y,z,q,t1):
+    sql_cid = "select {0},{1} from {2} where stat_date='{3}'  ".format(x,y,z,t1)
+    CVR_DATA_yesterday = con_sql(sql_cid)[0]
+    return CVR_DATA_yesterday
+
 
 #整理数据
 def data_cal(x,y):
@@ -227,8 +238,8 @@ temp1_old=[float(str(Decimal(chi_ctr_precise_old_recently[i]).quantize(Decimal('
 chi_ctr_precise_old_yesterday=chi_DATA_yesterday("clk_count_oldUser_all_a","clk_count_oldUser_all_b","imp_count_oldUser_all_precise","on_click_diary_card",yesterday)
 temp2_old=[float(chi_ctr_precise_old_yesterday[i]) for i in range(len(chi_ctr_precise_old_yesterday))]
 # print(temp2)
-tst_old=data_cal(temp1_old,temp2_old)
-chi_cal(tst_old)
+ctr_tst_old=data_cal(temp1_old,temp2_old)
+chi_cal(ctr_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：")
@@ -238,8 +249,51 @@ temp1_new=[float(str(Decimal(chi_ctr_precise_new_recently[i]).quantize(Decimal('
 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)
+ctr_tst_new=data_cal(temp1_new,temp2_new)
+chi_cal(ctr_tst_new)
+
+#老用户美购转化数据
+print("【3】老用户CVR数据波动假设检验结果：")
+chi_cvr_old_recently=chi_DATA_recently_all("diary_meigou_oldUser","diary_clk_oldUser","diary_meigou_crv",five_days,yesterday)
+cvr_old=[float(str(Decimal(chi_cvr_old_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_cvr_old_recently))]
+# print(temp1)
+chi_cvr_old_yesterday=chi_DATA_yesterday_all("diary_meigou_oldUser","diary_clk_oldUser","diary_meigou_crv",yesterday)
+cvr_old2=[float(chi_cvr_old_yesterday[i]) for i in range(len(chi_cvr_old_yesterday))]
+# print(temp2)
+cvr_tst_old=data_cal(cvr_old,cvr_old2)
+chi_cal(cvr_tst_old)
+#老用户美购转化数据
+print("【3】新用户CVR数据波动假设检验结果：")
+chi_cvr_new_recently=chi_DATA_recently_all("diary_meigou_newUser","diary_clk_newUser","diary_meigou_crv",five_days,yesterday)
+cvr_new=[float(str(Decimal(chi_cvr_new_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_cvr_new_recently))]
+# print(temp1)
+chi_cvr_new_yesterday=chi_DATA_yesterday_all("diary_meigou_newUser","diary_clk_newUser","diary_meigou_crv",yesterday)
+cvr_new2=[float(chi_cvr_new_yesterday[i]) for i in range(len(chi_cvr_new_yesterday))]
+# print(temp2)
+cvr_tst_new=data_cal(cvr_new,cvr_new2)
+chi_cal(cvr_tst_new)
+#老用户美购转化数据
+print("【3】老用户CT-CVR数据波动假设检验结果：")
+chi_ctcvr_old_recently=chi_DATA_recently_all("diary_meigou_oldUser","diary_exp_oldUser","diary_meigou_crv",five_days,yesterday)
+ctcvr_old=[float(str(Decimal(chi_ctcvr_old_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_ctcvr_old_recently))]
+# print(temp1)
+chi_ctcvr_old_yesterday=chi_DATA_yesterday_all("diary_meigou_oldUser","diary_exp_oldUser","diary_meigou_crv",yesterday)
+ctcvr_old2=[float(chi_ctcvr_old_yesterday[i]) for i in range(len(chi_ctcvr_old_yesterday))]
+# print(temp2)
+ctcvr_tst_old=data_cal(ctcvr_old,ctcvr_old2)
+chi_cal(ctcvr_tst_old)
+#老用户美购转化数据
+print("【3】新用户CT-CVR数据波动假设检验结果：")
+chi_ctcvr_new_recently=chi_DATA_recently_all("diary_meigou_newUser","diary_exp_newUser","diary_meigou_crv",five_days,yesterday)
+ctcvr_new=[float(str(Decimal(chi_ctcvr_new_recently[i]).quantize(Decimal('0.0')))) for i in range(len(chi_ctcvr_new_recently))]
+# print(temp1)
+chi_ctcvr_new_yesterday=chi_DATA_yesterday_all("diary_meigou_newUser","diary_exp_newUser","diary_meigou_crv",yesterday)
+ctcvr_new2=[float(chi_ctcvr_new_yesterday[i]) for i in range(len(chi_ctcvr_new_yesterday))]
+# print(temp2)
+ctcvr_tst_new=data_cal(ctcvr_new,ctcvr_new2)
+chi_cal(ctcvr_tst_new)
+
+
 
 
 # print(chi_ctr_precise_recently)