add

make_data.py

@@ -234,9 +234,92 @@ def hospital():
     data.to_csv('/tmp/1_hospital.csv',index=False)
 
 
+def old():
+    date_str = (datetime.datetime.now() - datetime.timedelta(days=4)).strftime("%Y%m%d")
+    date_tmp = (datetime.datetime.now() - datetime.timedelta(days=4)).strftime("%Y-%m-%d")
+    print(date_str)
+
+    sql = "select d.doctor_id,d.service_exposure_pv_30,d.service_ctr_30,d.expert_exposure_pv_30,d.expert_pv_30," \
+    "b.merchant_id,budan.budan_payment_30_days " \
+          "from statistic_doctor_rank_factor d " \
+    "left join hippo_merchantrelevance b on d.doctor_id = b.doctor_id " \
+    "left join al_meigou_service_smart_rank_budan_payment budan on b.merchant_id = budan.merchant_id " \
+    "where d.partition_date = '{}' and budan.stat_date = '{}';".format(date_str, date_tmp)
+
+    db = pymysql.connect(host='172.16.30.143', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
+    cursor = db.cursor()
+    cursor.execute(sql)
+    result = cursor.fetchall()
+    df = pd.DataFrame(list(result))
+    name = ["doctor_id", "service_exposure_pv_30", "service_ctr_30", "expert_exposure_pv_30", "expert_pv_30",
+            "merchant_id","budan_payment_30_days"]
+
+    df = df.rename(columns=dict(zip(list(range(len(name))), name)))
+
+    sql = "select merchant_id,doctor_discount_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_discount_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)))
+
+    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_discount_30_days", "expand_rechange_amount_30", "service_pv_30",
+     "mexpert_pv_30", "organization_pv_30", "budan_payment_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 = 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.loc[df["doctor_discount_30_days"] < 0, ["doctor_discount_30_days"]] = 0
+    df.loc[df["budan_payment_30_days"] < 0, ["budan_payment_30_days"]] = 0
+    df.loc[df["expand_rechange_amount_30"] < 0, ["expand_rechange_amount_30"]] = 0
+
+    df["commission"] = (df["doctor_discount_30_days"] + df["budan_payment_30_days"])/df["tmp"]
+    df["pv_ad"] = df["expand_rechange_amount_30"]/df["tmp"]
+
+    df.loc[df["all_exposure"] <= 1500, ["ctr"]] = 0.01
+    df.loc[df["ctr"] < 0.01, ["ctr"]] = 0.01
+    df.loc[df["ctr"] > 0.2, ["ctr"]] = 0.2
+    df.loc[df["commission"] > 20, ["commission"]] = 20
+    df.loc[df["commission"] < 0.01, ["commission"]] = 0.01
+    df.loc[df["pv_ad"] > 20, ["pv_ad"]] = 20
+    df.loc[df["pv_ad"] < 0.01, ["pv_ad"]] = 0.01
+
+    df["score"] = df["ctr"] ** 0.5 * (df["commission"] + df["pv_ad"])
+
+    columns = ["doctor_id","score","ctr","commission","pv_ad","service_exposure_pv_30",
+               "service_ctr_30","expert_exposure_pv_30","expert_pv_30",
+            "merchant_id","doctor_discount_30_days","expand_rechange_amount_30","service_pv_30",
+            "mexpert_pv_30","organization_pv_30","budan_payment_30_days"]
+    data = df.loc[:, columns]
+
+    data = data.drop_duplicates()
+    print(data.shape)
+    data.to_csv('/tmp/6_doctor.csv',index=False)
+    print("doctor end")
+
+
 if __name__ == "__main__":
-    doctor()
-    hospital()
+    # doctor()
+    # hospital()
+    old()