add

make_data.py

@@ -4,14 +4,14 @@ import pandas as pd
 
 
 def exp():
-    date_str = "20200101"
+    date_str = "20200222"
 
     sql = "select b.merchant_id " \
           "from statistic_doctor_rank_factor d " \
     "left join hippo_merchantrelevance b on d.doctor_id = b.doctor_id " \
     "where d.partition_date = '{}';".format(date_str)
 
-    db = pymysql.connect(host='172.16.30.143', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
+    db = pymysql.connect(host='172.16.30.141', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
     cursor = db.cursor()
     cursor.execute(sql)
     result = cursor.fetchall()
@@ -49,7 +49,7 @@ def doctor():
           "left join hippo_merchantrelevance b on d.doctor_id = b.doctor_id " \
           "where d.partition_date = '{}';".format(date_str)
 
-    db = pymysql.connect(host='172.16.30.143', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
+    db = pymysql.connect(host='172.16.30.141', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
     cursor = db.cursor()
     cursor.execute(sql)
     result = cursor.fetchall()
@@ -151,7 +151,7 @@ def hospital():
           "left join hippo_merchantrelevance b on api.id = b.doctor_id " \
           "where api.doctor_type = 1 and h.date = '{}';".format(date_str)
 
-    db = pymysql.connect(host='172.16.30.143', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
+    db = pymysql.connect(host='172.16.30.141', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
     cursor = db.cursor()
     cursor.execute(sql)
     result = cursor.fetchall()
@@ -248,7 +248,7 @@ def old():
     "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')
+    db = pymysql.connect(host='172.16.30.141', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
     cursor = db.cursor()
     cursor.execute(sql)
     result = cursor.fetchall()
@@ -318,10 +318,111 @@ def old():
     print("doctor end")
 
 
+def new_doctor():
+    date_str = "20200101"
+    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,m.doctor_discount_30_days from statistic_doctor_rank_factor d " \
+          "left join hippo_merchantrelevance b on d.doctor_id = b.doctor_id " \
+          "left join statistic_merchant_rank_factor m on b.merchant_id = m.merchant_id" \
+          "where d.partition_date = '{}' and m.partition_date = '{}';".format(date_str,date_str)
+
+    db = pymysql.connect(host='172.16.30.141', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
+    cursor = db.cursor()
+    cursor.execute(sql)
+    result = cursor.fetchall()
+    df = pd.DataFrame(list(result))
+    print(df.shape)
+    print(df.head(6))
+    # name = ["doctor_id", "service_exposure_pv_30", "service_ctr_30", "expert_exposure_pv_30", "expert_pv_30",
+    #         "merchant_id"]
+    #
+    # df = df.rename(columns=dict(zip(list(range(len(name))), name)))
+    # print(df.shape)
+    #
+    # df = df.dropna(subset=["merchant_id"])
+    # print("drop")
+    # print(df.shape)
+    # print(df.head(6))
+    #
+    # 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)))
+    # print("tmp")
+    # print(tmp.shape)
+    # print(tmp.head(6))
+    #
+    # df["merchant_id"] = df["merchant_id"].astype('int64')
+    # df["merchant_id"] = df["merchant_id"].astype("str")
+    # tmp["merchant_id"] = tmp["merchant_id"].astype("str")
+    # df = pd.merge(df, tmp, on='merchant_id')
+    #
+    # print("merge")
+    # print(df.shape)
+    #
+    # 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"]:
+    #     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])]
+    # print("expert_exposure_pv_30")
+    # print(df.shape)
+    # df = df[~df["all_exposure"].isin([0.0])]
+    # print("all_exposure")
+    # print(df.shape)
+    # df["tmp"] = df["service_pv_30"] + df["mexpert_pv_30"] +df["organization_pv_30"]
+    # df = df[~df["tmp"].isin([0.0])]
+    # print("tmp")
+    # print(df.shape)
+    # 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["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"])
+    # print(df.shape)
+    #
+    # 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"]
+    # data = df.loc[:, columns]
+    #
+    # data = data.drop_duplicates()
+    # print(data.shape)
+    # data.to_csv('/tmp/1_doctor.csv',index=False)
+    # print("doctor end")
+
+
 if __name__ == "__main__":
-    doctor()
+    # doctor()
     # hospital()
     # old()
 
+    new_doctor()
+