product_list=[] a=product('test',0,0,) product_list.append(a)
时间: 2024-05-30 16:08:13 浏览: 14
这段代码中,首先定义了一个空列表product_list。然后创建了一个名为a的对象,这个对象是一个product类的实例,其中包含了三个属性:名称为test、价格为0、数量为0。接着使用append()方法将a对象添加到product_list列表中。这样,product_list列表中就有了一个元素,即a对象。
相关问题
import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_squared_error import pymysql import time,os import re import requests import urllib from datetime import datetime # ---------连接-------------- connect = pymysql.connect(host='localhost', # 本地数据库 user='root', password='123456', port=3306, charset='utf8') #服务器名,账户,密码,数据库名称 cur = connect.cursor() print(cur) # 读取数据 try: select_sqli = "SELECT time,xiaoliang FROM sheji.sale where chexing='海豚';" cur.execute(select_sqli) data = pd.DataFrame(cur.fetchall(), columns=['time', 'xiaoliang']) except Exception as e: print("读取数据失败:", e) else: print("读取数据成功") # 转换时间格式 data['time'] = pd.to_datetime(data['time'], format='%Y-%m') print(data)这个代码继续怎么写
接下来可以对数据进行预处理和可视化分析,例如:
1. 将时间列设置为数据的索引
```
data = data.set_index('time')
```
2. 对销量数据进行差分,以便将其转换为平稳的时间序列
```
diff_data = data.diff().dropna()
```
3. 对平稳时间序列进行自相关和偏自相关分析,以确定 ARIMA 模型的参数
```
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
plot_acf(diff_data)
plot_pacf(diff_data)
```
4. 通过交叉验证选择最佳的 ARIMA 模型
```
from sklearn.model_selection import TimeSeriesSplit
from itertools import product
p = range(0, 3)
d = range(0, 3)
q = range(0, 3)
params = product(p, d, q)
params_list = list(params)
tss = TimeSeriesSplit(n_splits=3)
best_model = None
best_mse = np.inf
for params in params_list:
mse_list = []
for train_index, test_index in tss.split(diff_data):
train_data = diff_data.iloc[train_index]
test_data = diff_data.iloc[test_index]
try:
model = ARIMA(train_data, order=params)
model_fit = model.fit(disp=False)
pred = model_fit.forecast(len(test_data))[0]
mse = mean_squared_error(test_data, pred)
mse_list.append(mse)
except:
continue
if len(mse_list) > 0:
mean_mse = np.mean(mse_list)
if mean_mse < best_mse:
best_mse = mean_mse
best_model = params
print(best_model)
```
5. 使用最佳的 ARIMA 模型进行预测,并将结果可视化
```
model = ARIMA(diff_data, order=best_model)
model_fit = model.fit(disp=False)
pred = model_fit.forecast(12)[0]
pred_cumsum = pred.cumsum()
forecast_data = pd.Series(data.iloc[-1, 0], index=pd.date_range(start=data.index[-1], periods=12, freq='M'))
forecast_data += pred_cumsum
plt.plot(data)
plt.plot(forecast_data)
```
object AreaTop3ProductStat { def main(args: Array[String]): Unit = { val conf = new SparkConf().setMaster("local[*]").setAppName("areaTop3ProductStat") val sc = new SparkContext(conf) val datas: RDD[UserVisitAction] = FileUtils.loadFile(sc, "data/user_visit_action") datas.cache()//缓存 //(城市id,产品id) val areaProductBasicRDD = datas.map(u => (u.city_id, u.click_product_id)) val top3ProductsByCityRDD = areaProductBasicRDD .groupByKey // 按城市进行分组 .mapValues(_.toList.distinct.sorted.takeRight(3)) // 打印输出 top3ProductsByCityRDD.collect.foreach { case (city, products) => println(s"Top 3 products for city $city: ${products.mkString(", ")}") } sc.stop() } }这段代码怎么改将数据持久化到MySQL
要将数据持久化到MySQL,可以按照以下步骤进行修改:
1. 导入相关依赖包,包括MySQL JDBC驱动、Spark SQL的依赖等。
```scala
import java.sql.DriverManager
import org.apache.spark.sql.{Row, SparkSession}
```
2. 创建SparkSession对象,用于操作Spark SQL。
```scala
val spark = SparkSession.builder()
.appName("areaTop3ProductStat")
.master("local[*]")
.getOrCreate()
```
3. 将数据转换为DataFrame,方便后续操作。
```scala
import spark.implicits._
val df = areaProductBasicRDD.toDF("city_id", "product_id")
```
4. 创建MySQL连接,并将DataFrame中的数据存储到MySQL表中。
```scala
val url = "jdbc:mysql://localhost:3306/test"
val prop = new java.util.Properties
prop.setProperty("user", "root")
prop.setProperty("password", "123456")
df.write.mode("append").jdbc(url, "area_top3_products", prop)
```
完整代码如下:
```scala
import java.sql.DriverManager
import org.apache.spark.sql.{Row, SparkSession}
object AreaTop3ProductStat {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
.appName("areaTop3ProductStat")
.master("local[*]")
.getOrCreate()
val datas = spark.read.textFile("data/user_visit_action")
val areaProductBasicRDD = datas.map(line => {
val fields = line.split("_")
(fields(2), fields(6))
})
val top3ProductsByCityRDD = areaProductBasicRDD
.groupByKey
.mapValues(_.toList.distinct.sorted.takeRight(3))
top3ProductsByCityRDD.foreachPartition(partitionOfRecords => {
val url = "jdbc:mysql://localhost:3306/test"
val prop = new java.util.Properties
prop.setProperty("user", "root")
prop.setProperty("password", "123456")
val conn = DriverManager.getConnection(url, prop)
conn.setAutoCommit(false)
val stmt = conn.prepareStatement("INSERT INTO area_top3_products VALUES (?, ?, ?)")
partitionOfRecords.foreach(record => {
val city = record._1
val products = record._2
products.foreach(product => {
stmt.setString(1, city)
stmt.setString(2, product)
stmt.setInt(3, 1)
stmt.addBatch()
})
})
stmt.executeBatch()
conn.commit()
stmt.close()
conn.close()
})
spark.stop()
}
}
```
在这个例子中,我们使用了JDBC来将数据写入到MySQL中。具体来说,我们将RDD转换为DataFrame,然后使用DataFrame的write方法将数据写入到MySQL表中。需要注意的是,在实际生产环境中,我们需要根据实际情况进行优化,比如批量插入数据、使用连接池等。
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