add device_read

tensnsorflow/device_read_time_normal.py

+import pandas as pd
+import pymysql
+from sklearn.preprocessing import MinMaxScaler
+from sqlalchemy import create_engine
+
+
+def con_sql(db,sql):
+    cursor = db.cursor()
+    try:
+        cursor.execute(sql)
+        result = cursor.fetchall()
+        df = pd.DataFrame(list(result))
+    except Exception:
+        print("发生异常", Exception)
+        df = pd.DataFrame()
+    finally:
+        db.close()
+    return df
+
+
+def normal():
+    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select * from device_read_time"
+    df = con_sql(db, sql)
+    df = df.rename(columns={0:"device_id",1:"0",2:"1",3:"10",4:"1024",5:"1080",6:"11",
+                            7:"12",8:"13",9:"2",10:"2054",11:"2214",12:"3",13:"4",14:"5",16:"6933",
+                            17:"7",18:"9",19:"922",20:"929",21:"971",22:"992"})
+    device_id = df[["device_id"]]
+    df = df.drop("device_id",axis=1)
+    minMax = MinMaxScaler()
+    result = minMax.fit_transform(df)
+    result = device_id.join(result)
+    yconnect = create_engine('mysql+pymysql://root:3SYz54LS9#^9sBvC@10.66.157.22:4000/jerry_test?charset=utf8')
+    pd.io.sql.to_sql(result, "device_read_time_normal", yconnect, schema='jerry_test', if_exists='fail', index=False)
+
+
+if __name__ == "__main__":
+    normal()
\ No newline at end of file

tensnsorflow/test.py

@@ -67,7 +67,7 @@ def click():
 
 def get_cid():
     db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
-    sql = "select distinct cid_id from esmm_train_data where device_id = '358035085192742' " \
+    sql = "select cid_id from esmm_train_data where device_id = '358035085192742' " \
           "and stat_date >= '2018-12-03'"
     df = con_sql(db, sql)[0].values.tolist()
     print(",".join(df))
@@ -75,6 +75,20 @@ def get_cid():
 
 
 if __name__ == "__main__":
-   get_cid()
+
+    import pandas as pd
+    from sklearn.preprocessing import MinMaxScaler
+
+
+    # 读取葡萄酒数据集
+    data = pd.read_csv("G:/dataset/wine.csv")
+    # 获取第二列Alcohol
+    x = data["Alcohol"]
+    # 获取数据的基本情况
+    print(x.describe())
+    minMax = MinMaxScaler()
+    # 将数据进行归一化
+    x_std = minMax.fit_transform(x)
+    pd.DataFrame()