add

make_data.py

@@ -129,7 +129,7 @@ def hospital():
           "left join hippo_merchantrelevance b on api.id = b.doctor_id " \
           "left join al_meigou_service_smart_rank_budan_payment budan on b.merchant_id = budan.merchant_id " \
           "where api.doctor_type = 1 and h.date = '{}' " \
-          "and budan.stat_date = '{}' limit 6;".format(date_str, date_tmp)
+          "and budan.stat_date = '{}'; limit 6".format(date_str, date_tmp)
 
     db = pymysql.connect(host='172.16.30.143', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
     cursor = db.cursor()
@@ -144,45 +144,48 @@ def hospital():
 
     print(df.head(6))
 
-    # sql = "select merchant_id,doctor_ad_money_30_days,expand_rechange_amount_30," \
-    #       "service_pv_30,expert_pv_30,organization_pv_30 from statistic_merchant_rank_factor " \
-    #       "where partition_date = '{}';".format(date_str)
-    #
-    # cursor = db.cursor()
-    # cursor.execute(sql)
-    # result = cursor.fetchall()
-    # db.close()
-    # tmp = pd.DataFrame(list(result))
-    # name = ["merchant_id", "doctor_ad_money_30_days", "expand_rechange_amount_30", "service_pv_30",
-    #         "mexpert_pv_30", "organization_pv_30"]
-    # tmp = tmp.rename(columns=dict(zip(list(range(len(name))), name)))
-    #
+    sql = "select merchant_id,doctor_ad_money_30_days," \
+          "service_pv_30,expert_pv_30,organization_pv_30,doctor_discount_30_days from statistic_merchant_rank_factor " \
+          "where partition_date = '{}' limit 6;".format(date_str)
+
+    cursor = db.cursor()
+    cursor.execute(sql)
+    result = cursor.fetchall()
+    db.close()
+    tmp = pd.DataFrame(list(result))
+    name = ["merchant_id", "doctor_ad_money_30_days", "expand_rechange_amount_30", "service_pv_30",
+            "mexpert_pv_30", "organization_pv_30","doctor_discount_30_days"]
+    tmp = tmp.rename(columns=dict(zip(list(range(len(name))), name)))
+    print(tmp.head(6))
+
     # df["merchant_id"] = df["merchant_id"].astype("str")
     # tmp["merchant_id"] = tmp["merchant_id"].astype("str")
     # df = pd.merge(df, tmp, on='merchant_id')
     #
-    # for i in ["service_exposure_pv_30", "service_ctr_30", "expert_exposure_pv_30", "expert_pv_30",
-    #   "doctor_ad_money_30_days", "expand_rechange_amount_30", "service_pv_30",
-    #  "mexpert_pv_30", "organization_pv_30", "budan_payment_30_days"]:
+    # for i in ["hospital_exposure_pv_30","service_exposure_pv_30","expert_exposure_pv_30",
+    #           "service_ctr_30","hospital_ctr_30","expert_ctr_30",
+    #           "doctor_ad_money_30_days", "service_pv_30",
+    #           "mexpert_pv_30", "organization_pv_30", "budan_payment_30_days","doctor_discount_30_days"]:
     #     df[i] = df[i].astype("float")
     #
-    # df["all_exposure"] = df["service_exposure_pv_30"] + df["expert_exposure_pv_30"]
-    # df = df[~df["expert_exposure_pv_30"].isin([0.0])]
+    # df["all_exposure"] = df["hospital_exposure_pv_30"] + df["service_exposure_pv_30"] + df["expert_exposure_pv_30"]
     # df = df[~df["all_exposure"].isin([0.0])]
     # df["tmp"] = df["service_pv_30"] + df["mexpert_pv_30"] +df["organization_pv_30"]
     # df = df[~df["tmp"].isin([0.0])]
     # print("aaaaaaaa")
     # df["ctr"] = df["service_exposure_pv_30"] / df["all_exposure"] * df["service_ctr_30"] + \
-    #             df["expert_exposure_pv_30"]/df["all_exposure"] * (df["expert_pv_30"] / df["expert_exposure_pv_30"])
+    #             df["hospital_exposure_pv_30"]/ df["all_exposure"] * df["hospital_ctr_30"] + \
+    #             df["expert_exposure_pv_30"]/df["all_exposure"] * df["expert_ctr_30"]
     # df["commission"] = (df["doctor_ad_money_30_days"] + df["budan_payment_30_days"])/df["tmp"]
-    # df["pv_ad"] = df["expand_rechange_amount_30"]/df["tmp"]
-    # df["score"] = df["ctr"]**0.5 * (df["commission"] + df["pv_ad"])
-    # columns = ["score","ctr","commission","pv_ad","service_exposure_pv_30","service_ctr_30","expert_exposure_pv_30","expert_pv_30",
-    #         "merchant_id","doctor_ad_money_30_days","expand_rechange_amount_30","service_pv_30",
-    #         "mexpert_pv_30","organization_pv_30","budan_payment_30_days"]
+    # df["cpt"] = df["doctor_discount_30_days"]/df["tmp"]
+    # df["score"] = df["ctr"]**0.5 * (df["commission"] + df["cpt"])
+    # columns = ["score","ctr","commission","cpt","hospital_exposure_pv_30","service_exposure_pv_30",
+    #            "expert_exposure_pv_30",
+    #           "service_ctr_30","hospital_ctr_30","expert_ctr_30", "service_pv_30",
+    #           "mexpert_pv_30", "organization_pv_30"]
     # data = df.loc[:, columns]
     # print(data.head(6))
-    # data.to_csv('/home/gmuser/doctor.csv',index=False)
+    # data.to_csv('/home/gmuser/hospital.csv',index=False)
 
 if __name__ == "__main__":
     hospital()