n_date = {"itemId":"743931293309","bizCode":"ali.china.tmall","channel":"pc_detail","pageSize":20,"pageNum":1} pageNum后面的值是变量，怎么写

你可以使用字符串的格式化方法来将变量的值插入到字符串中。在Python中，可以使用f-string或者.format()方法来实现。下面是两种写法的示例：

1. 使用f-string：

pageNum = 1
n_date = f'{{"itemId":"743931293309","bizCode":"ali.china.tmall","channel":"pc_detail","pageSize":20,"pageNum":{pageNum}}}'

2. 使用.format()方法：

pageNum = 1
n_date = '{{"itemId":"743931293309","bizCode":"ali.china.tmall","channel":"pc_detail","pageSize":20,"pageNum":{}}}'.format(pageNum)

这样，你就可以根据变量的值来动态生成n_date字符串了。

相关问题

改进代码func TracingList(ctx *gin.Context) { resp := models.Response{ Code: 0, Msg: "success", } t, _ := strconv.Atoi(ctx.Query("t")) label, _ := strconv.Atoi(ctx.Query("label")) page, _ := strconv.Atoi(ctx.DefaultQuery("page", "1")) pageSize, _ := strconv.Atoi(ctx.DefaultQuery("pageSize", "10")) code := ctx.Query("code") //设施类型 if len(code) == 0 { resp.Code = 400 resp.Msg = "请输入code值" ctx.JSON(400, resp) return } type Total struct { gorm.Model Type int16 Source int16 Explanation string Label int16 FacilityID string } db := common.DB.Session(&gorm.Session{}) if t < 0 || t > 5 { var req interface{} switch t { case 0: req = []*Total{} case 1: req = []*models.Flaw{} case 2: req = []*models.Issue{} case 3: req = []*models.Record{} case 4: req = []*models.Quality{} case 5: req = []*models.Inspection{} } //查询并分页 if t < 1 || t > 5 { res := db.Scopes(models.Paginate(page, pageSize)).Where("facility_id=?", code).Find(&req) if res.RowsAffected == 0 { resp.Code = 400 resp.Msg = "记录不存在" ctx.JSON(400, resp) return } } else { res := db.Scopes(models.Paginate(page, pageSize)).Raw("SELECT * FROM flaws WHERE facility_id = ? "+ "UNION SELECT * FROM issues WHERE facility_id = ? "+ "UNION SELECT * FROM records WHERE facility_id = ? "+ "UNION SELECT * FROM qualities WHERE facility_id = ? "+ "UNION SELECT * FROM inspections WHERE facility_id = ?", "code", "code", "code", "code", "code").Find(&req) if res.RowsAffected == 0 { resp.Code = 400 resp.Msg = "记录不存在" ctx.JSON(400, resp) return } } resp.Data = req ctx.JSON(200, resp) } else if label < 1 || label > 4 { var req []*Total res := db.Scopes(models.Paginate(page, pageSize)).Raw("SELECT * FROM flaws WHERE label = ? "+ "UNION SELECT * FROM issues WHERE label = ? "+ "UNION SELECT * FROM records WHERE label = ? "+ "UNION SELECT * FROM qualities WHERE label = ? "+ "UNION SELECT * FROM inspections WHERE label = ?", "label", "label", "label", "label", "label").Find(&req) if res.RowsAffected == 0 { resp.Code = 400 resp.Msg = "记录不存在" ctx.JSON(400, resp) return } resp.Data = req ctx.JSON(200, resp) } else { ctx.JSON(400, models.Response{ Code: 400, Msg: "参数有误", }) return } }

以下是改进的代码：

func TracingList(ctx *gin.Context) {
    resp := models.Response{
        Code: 0,
        Msg:  "success",
    }

    code := ctx.Query("code")
    if len(code) == 0 {
        resp.Code = 400
        resp.Msg = "请输入code值"
        ctx.JSON(400, resp)
        return
    }

    t, err := strconv.Atoi(ctx.Query("t"))
    if err != nil || t < 0 || t > 5 {
        resp.Code = 400
        resp.Msg = "无效的参数t"
        ctx.JSON(400, resp)
        return
    }

    label, err := strconv.Atoi(ctx.Query("label"))
    if err != nil || label < 1 || label > 4 {
        resp.Code = 400
        resp.Msg = "无效的参数label"
        ctx.JSON(400, resp)
        return
    }

    page, err := strconv.Atoi(ctx.DefaultQuery("page", "1"))
    if err != nil || page < 1 {
        resp.Code = 400
        resp.Msg = "无效的参数page"
        ctx.JSON(400, resp)
        return
    }

    pageSize, err := strconv.Atoi(ctx.DefaultQuery("pageSize", "10"))
    if err != nil || pageSize < 1 {
        resp.Code = 400
        resp.Msg = "无效的参数pageSize"
        ctx.JSON(400, resp)
        return
    }

    var req interface{}
    switch t {
    case 0:
        req = []*Total{}
    case 1:
        req = []*models.Flaw{}
    case 2:
        req = []*models.Issue{}
    case 3:
        req = []*models.Record{}
    case 4:
        req = []*models.Quality{}
    case 5:
        req = []*models.Inspection{}
    }

    db := common.DB.Session(&gorm.Session{})
    res := db.Scopes(models.Paginate(page, pageSize)).Where("facility_id=?", code)
    if t >= 1 && t <= 5 {
        res = res.Table("flaws").Where("facility_id=?", code).
            Or("facility_id=?", code).
            Or("facility_id=?", code).
            Or("facility_id=?", code).
            Or("facility_id=?", code)
    }
    res.Find(req)
    if res.RowsAffected == 0 {
        resp.Code = 400
        resp.Msg = "记录不存在"
        ctx.JSON(400, resp)
        return
    }

    resp.Data = req
    ctx.JSON(200, resp)
}

主要改进包括：

1. 对输入的参数进行了有效性检查，如 t、label、page 和 pageSize 都需要为正整数，code 不能为空。
2. 使用了 Go 语言内置的错误处理机制，当参数无效时，返回错误信息。
3. 将 SQL 查询语句拆分为两段，根据 t 的值判断使用哪一段，避免了使用 Raw() 方法的安全隐患。
4. 代码结构更加清晰，易于阅读和维护。

2、为下面的程序的每一行标上注释； import requests import re import pandas as pd import time import datetime url = 'http://datacenter-web.eastmoney.com/api/data/v1/get?' name_list = [] code_list = [] trader_date_list = [] close_list = [] change_rate_list = [] buy_num_list = [] result_list = [] result_df = pd.DataFrame() for page in range(1, 4): params = ( ('callback', 'jQuery112305930880286224138_1632364981303'), ('sortColumns', 'NET_BUY_AMT,TRADE_DATE,SECURITY_CODE'), ('sortTypes', '-1,-1,1'), ('pageSize', '50'), ('pageNumber', str(page)), ('reportName', 'RPT_ORGANIZATION_TRADE_DETAILS'), ('columns', 'ALL'), ('source', 'WEB'), ('clientl', 'WE'), ('filter', "(TRADE_DATE>='2021-09-17')") ) response = requests.get(url, params=params) text = response.text print(text) # re准则查找数据 name = re.findall('"SECURITY_NAME_ABBR":"(.*?)"', text) # 名称 code = re.findall('"SECURITY_CODE":"(.*?)"', text) # 股票代码 trader_date = re.findall('"TRADE_DATE":"(.*?)"', text) # 交易日期 close = re.findall('"CLOSE_PRICE":(.*?)\,', text) # 收盘价 change_rate = re.findall('"CHANGE_RATE":(.*?)\,', text) # 涨幅 buy_num = re.findall('"BUY_TIMES":(.*?)\,', text) # 买入机构数量 # 将对应的列表里的数据全部加起来 name_list = name_list + name code_list = code_list + code trader_date_list = trader_date_list + trader_date close_list = close_list + close change_rate_list = change_rate_list + change_rate buy_num_list = buy_num_list + buy_num time.sleep(2) # 将所有列表合并成二维数组 result_list = [trader_date_list, code_list, name_list, close_list, change_rate_list, buy_num_list] # 将数据转为DataFrame格式 result_df = pd.DataFrame(result_list).T.rename( columns={0: '交易日期', 1: '股票代码', 2: '股票名称', 3: '收盘价', 4: '涨幅', 5: '买入机构'}) result_df['交易日期'] =pd.to_datetime(result_df['交易日期']) # 时间只取年月日 result_df = result_df.sort_values(by='交易日期', ascending=True) print(result_df)

# 导入需要的库
import requests
import re
import pandas as pd
import time
import datetime

# 定义请求的url
url = 'http://datacenter-web.eastmoney.com/api/data/v1/get?'

# 定义空列表用于存储数据
name_list = []
code_list = []
trader_date_list = []
close_list = []
change_rate_list = []
buy_num_list = []
result_list = []
result_df = pd.DataFrame()

# 循环请求数据
for page in range(1, 4):
params = (
('callback', 'jQuery112305930880286224138_1632364981303'),
('sortColumns', 'NET_BUY_AMT,TRADE_DATE,SECURITY_CODE'),
('sortTypes', '-1,-1,1'),
('pageSize', '50'),
('pageNumber', str(page)),
('reportName', 'RPT_ORGANIZATION_TRADE_DETAILS'),
('columns', 'ALL'),
('source', 'WEB'),
('clientl', 'WE'),
('filter', "(TRADE_DATE>='2021-09-17')")
)

# 发送请求，并获取响应数据
response = requests.get(url, params=params)
text = response.text
print(text)

# 使用正则表达式查找数据
name = re.findall('"SECURITY_NAME_ABBR":"(.*?)"', text) # 名称
code = re.findall('"SECURITY_CODE":"(.*?)"', text) # 股票代码
trader_date = re.findall('"TRADE_DATE":"(.*?)"', text) # 交易日期
close = re.findall('"CLOSE_PRICE":(.*?)\,', text) # 收盘价
change_rate = re.findall('"CHANGE_RATE":(.*?)\,', text) # 涨幅
buy_num = re.findall('"BUY_TIMES":(.*?)\,', text) # 买入机构数量

# 将对应的列表里的数据全部加起来
name_list = name_list + name
code_list = code_list + code
trader_date_list = trader_date_list + trader_date
close_list = close_list + close
change_rate_list = change_rate_list + change_rate
buy_num_list = buy_num_list + buy_num

# 暂停2秒钟
time.sleep(2)

# 将所有列表合并成二维数组
result_list = [trader_date_list, code_list, name_list, close_list, change_rate_list, buy_num_list]

# 将数据转为DataFrame格式
result_df = pd.DataFrame(result_list).T.rename(columns={0: '交易日期', 1: '股票代码', 2: '股票名称', 3: '收盘价', 4: '涨幅', 5: '买入机构'})

# 将时间列转换为日期格式，并只取年月日
result_df['交易日期'] = pd.to_datetime(result_df['交易日期'])

# 根据交易日期排序
result_df = result_df.sort_values(by='交易日期', ascending=True)

# 打印结果
print(result_df)