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 pandas as pd import urllib # ---------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=cur.fetchall() except Exception as e: print("创建数据表失败:", e) else: print("创建数据表成功;") #data = pd.read_csv('car_sales.csv', index_col='Month', parse_dates=True) time=[] for i in range(len(data)): time.append(datetime.strptime(data[i][0], '%Y-%m')) print(time)哪里有错
时间: 2024-01-23 19:02:01 浏览: 31
在这段代码中,你没有导入 `datetime` 模块,所以不能使用 `datetime.strptime()` 函数。你可以在代码开头添加 `from datetime import datetime` 来导入 `datetime` 模块,并修正报错问题。此外,在读取 `data` 时,你可以直接使用 `pd.DataFrame()` 函数将其转化为 Pandas DataFrame 格式,而不需要手动解析时间和数据。修改后的代码如下:
```python
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)
```