add

make_data.py

@@ -18,13 +18,35 @@ def doctor():
     "left join al_meigou_service_smart_rank_budan_payment budan on b.merchant_id = budan.merchant_id " \
     "where d.partition_date = '{}' and m.partition_date = '{}' " \
     "and budan.stat_date = '{}' limit 6;".format(date_str,date_str,date_tmp)
-    print(sql)
+
     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))
-    print(df.head(6))
+
+    name = ["doctor_id","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 = df.rename(columns=dict(zip(list(range(len(name))),name)))
+    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"]:
+        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])]
+
+    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["commission"] = (df["doctor_ad_money_30_days"] + df["budan_fund"])/df["tmp"]
+    df["pv_ad"] = df["expand_rechange_amount_30"]/df["tmp"]
+    df["score"] = df["ctr"]**0.5 * (df["commission"] + df["pv_ad"])
+    print(df)
 
 
 if __name__ == "__main__":