代码上传

1.csv

+SKU,product_type,product_band,NAME
+8146008,18K,梦金园,梦金园玫瑰18K金五角星芒吊坠不含链 定价
+7887835,0.8,六福珠宝（LUKFOOK JEWELLERY）,六福珠宝 18K金玫瑰色心形吊坠女款链坠不含项链 定价 L18TBKP0051R 总重约0.80克
+4146484,0.60-0.79,周六福（ZLF）,周六福珠宝 18K金女款玫瑰金吊坠心形蝴蝶 不含链KI040668 约0.60-0.79g
+8999787,40cm,潮宏基（CHJ JEWELLERY）,潮宏基 CHJ JEWELLERY 悦己-宝心 玛瑙贝壳18K金彩金项链 XQK33900010 约40cm加尾链
+8788926,40cm,潮宏基（CHJ JEWELLERY）,潮宏基 CHJ JEWELLERY 悦己-缘心 18K金彩金项链 XQK30000303 约40cm加尾链
+8788920,42cm,潮宏基（CHJ JEWELLERY）,潮宏基 CHJ JEWELLERY 哆啦A梦-站立 贝壳18K金彩金项链 XQK34200012 约42cm加尾链
+8429247,90373N,周生生（CHOW SANG SANG）,周生生CHOW SANG SANG 18K红色黄金Love Decode爱情密语星星项链 女款 90373N 47厘米
+8352133,40cm,潮宏基（CHJ JEWELLERY）,潮宏基 CHJ JEWELLERY 悦己-小幸运love 18K金彩金项链 XQK30000299 约40cm加尾链
+8217452,89865N,周生生（CHOW SANG SANG）,周生生CHOW SANG SANG 18K红色黄金Choker心形钻石项链89865N 43厘米
+5605336,40-42cm,赛菲尔（Sunfeel）,赛菲尔 18k金项链女款 K黄水波纹锁骨链 金链子细 彩金 约40-42cm
+5483710,04800N18KY,周生生（CHOW SANG SANG）,周生生CHOW SANG SANG 18K金黄色黄金项链百搭素链女款  04800N18KY 45厘米
+5264080,E121035,周大福（CHOW TAI FOOK）,周大福（CHOW TAI FOOK）七夕情人节礼物 时尚水波链 18K金项链 E121035 980 45cm
+5113397,04800N18KY,周生生（CHOW SANG SANG）,周生生CHOW SANG SANG 18K金黄色黄金项链百搭素链女款  04800N18KY 40厘米
+4030285,18k,赛菲尔（Sunfeel）,赛菲尔 18k金项链 女款O字十字K金项链 锁骨链女 金链子
\ No newline at end of file

2.csv

+sku,productcode
+8146008,na
+7887835,匹配5项以上。
+4146484,na
+8999787,匹配5项以上。
+8788926,匹配5项以上。
+8788920,匹配5项以上。
+8429247,na
+8352133,匹配5项以上。
+8217452,na
+5605336,na
+5483710,na
+5264080,na
+5113397,na
+4030285,na

brand_name.py

+import jieba
+import re
+import xlrd
+import pandas as pd
+import numpy as np
+
+
+class brandd:
+    """提取品牌名"""
+    def __init__(self, addr):
+        self.addr = addr
+
+    def contain_let(self, string):
+        """判断字符串string中包含字母"""
+        return bool(re.search('[A-z]', string))
+
+    def contain_chin(self, string):
+        """判断字符串string中是否包含汉字"""
+        zhmodel = re.compile(u'[\u4e00-\u9fa5]')
+        match = zhmodel.search(string)
+        if match:
+            boolValue = True
+        else:
+            boolValue = False
+        return boolValue
+
+    def read_in_1(self, addr_in):
+        """从excel文件中读取数据
+        addr_in为缺失的csv文件地址"""
+        datafile = xlrd.open_workbook(addr_in)
+        table = datafile.sheets()[0]
+        matrix_text = pd.DataFrame([])
+        for i in range(table.ncols):
+            matrix_text[i] = table.col_values(i)
+        matrix_text.rename(columns=matrix_text.iloc[0, :], inplace=True)
+        matrix_text.drop([0], axis=0, inplace=True)
+        type_nonstand = matrix_text[['BAND_NAME']]  # 匹配的非标准数据
+        BrandName_data = []
+        for i in range(len(type_nonstand)):
+            string_sin = jieba.cut(type_nonstand.iloc[i, 0])
+            for string in string_sin:
+                if self.contain_let(string) or self.contain_chin(string):
+                    BrandName_data.append(string)
+        return list(set(BrandName_data))
+
+    def runing(self):
+        brandname = self.read_in_1(self.addr)
+        return brandname

database.py

+from moudules import *
+import pandas as pd
+
+
+sqlserver = sql_find()
+similar = similar()
+
+def logic_function(table, price_name='jd_price'):
+    sku_lyst = []
+    price_lyst = []
+    m = 0
+    for productcode, sku, name, product_price in zip(table['库内已有产品'], table['SKU'], table['NAME'], table[price_name]):
+        try:
+            if productcode != 'NULL':
+                sku_label, symbol = left(productcode, sku, product_price)          #symbol为0为没有修改的，为1为修改了的，为6为放入新品库的。
+            else:
+                sku_label, symbol = right(sku, str(name), product_price)
+            sku_lyst.append(sku_label)
+            if symbol == 1:
+                price_lyst.append(m)
+            m += 1
+
+            f=open('断点记录.txt','w', encoding="utf-8",errors='ignore')
+            f.write(f'{m}\n')           #该文件‘断点记录.txt’第一项保存当前行数，第二项保存需要录入价格系统的行号。
+            for price in price_lyst:    #如果中途中断，请对表提取第二项的行进入价格系统，并且删除前第一项的行。
+                f.write(f'{price}, ')
+            f.close()
+        except:
+            print(f'在第{m}条出错！')
+    print('操作完成！')
+    return sku_lyst, price_lyst
+            
+            
+def left(productcode, sku, product_price):        
+    state = sqlserver.state_extract(productcode)
+    if state == '1' or state == '2' or state == '4':
+        sub_class = True     #此处添加判断子类是否正确的函数。
+        if sub_class:
+            symbol = sqlserver.state_update(productcode, state_num='1')      #此处为在数据库中把状态置'1'。
+            price = sqlserver.price_get(productcode)
+            if price == '0' or price == 'NULL' or price == '' or price == 'None':
+                if product_price == '0' or product_price == 'NULL' or product_price == 'None' or product_price == '':
+                    #price = get_price_by_url(sku)       #此函数为利用sku爬取网站价格数据。
+                    sqlserver.price_update(productcode, price)
+                else :
+                    sqlserver.price_update(productcode, product_price)
+            return f'{sku}_补齐重要参数', symbol
+        else: 
+            symbol = sqlserver.state_update(productcode, state_num='6')
+            sqlserver.sku_delete(sku)
+            return f'{sku}_放入新品建库', symbol
+    elif state == '6':
+        symbol = 6
+        return f'{sku}_放入新品建库', symbol
+
+
+def right(sku, name, product_price):
+    
+    if similar.istrue(name):        #此处为similar类找到数据库中匹配的型号，返回True。
+        productcode = similar.productcode_get()          #此处为提取与该型号所匹配产品的productcode。
+        left(productcode, sku, product_price)
+    else:
+        return f'{sku}_放入新品建库'
+
+
+def table_save(table, lyst):
+    table = table[['库内已有产品', 'NAME', 'jd_price', 'sku_id', 'create_date']]
+    table = pd.concat([table, pd.DataFrame(columns=['商户编码'])])
+    table = table.reindex(columns=['商户编码', '库内已有产品', 'NAME', 'jd_price', 'sku_id', 'create_date'])
+
+    for i in range(len(table)):
+        table.iloc[i, 0] = 'DS-JD'
+        table.iloc[i, 4] = f"https://item.jd.com/{table.iloc[i, 4]}.html"
+
+    table = table.rename(columns={'库内已有产品': '产品编码', 'NAME': '产品名称', 'jd_price': '产品价格', 'sku_id': '产品链接', 'create_date': '创建时间'})
+    new_table = table.loc[lyst].reset_index(drop = True)
+    pre(new_table, '价格录入.csv')
+
+
+def main():
+    table = data_load('new.xlsx').reset_index(drop = True)
+    data_list, price_list = logic_function(table)
+    try:
+        table_save(table, price_list)
+
+        data_list_buqi = []
+        data_list_xinping = []
+        for val in data_list:
+            if val.split('_')[-1] == '补齐重要参数':
+                data_list_buqi.append(val.split('_')[-2])
+            elif val.split('_')[-1] == '放入新品建库':
+                data_list_xinping.append(val.split('_')[-2])
+
+        print('补齐重要参数\n', data_list_buqi)
+        print('新品建库\n', data_list_xinping)
+        f=open('补齐重要参数.txt','a+', encoding="utf-8",errors='ignore')
+        for val in data_list_buqi:
+            f.write(val+"\n")
+        f.close()
+        f=open('新品建库.txt','a+', encoding="utf-8",errors='ignore')
+        for val in data_list_xinping:
+            f.write(val+"\n")
+        f.close()
+        print('完成！')
+    except:
+        print('出错，打印出sku标签列表和价格录入的行号！')
+        print(data_list)
+        print(price_list)
+    
+
+
+if __name__ == "__main__":
+    main()

moudules.py

+# -*- coding:utf-8 -*-
+import pymssql
+import pandas as pd
+import xlrd
+from product_type_extract import *
+from brand_name import brandd
+import numpy as np
+import re
+
+def BN(str):
+	res = re.findall(r'[\u4E00-\u9FA5]', str)
+	new_res = ''.join(res)
+	#print(len(new_res))
+	if len(new_res) == 0:
+		res1 = re.findall(r'[a-zA-Z0-9]', str)
+		new_res = ''.join(res1)
+	return new_res
+
+def data_load(path):
+        data = xlrd.open_workbook(path)
+        table = data.sheets()[0]
+        ncols = table.ncols
+        data2 = pd.DataFrame([])
+        for i in range(ncols):
+            data2[i] = table.col_values(i)
+        data2.rename(columns=data2.iloc[0, :], inplace=True)
+        data2.drop([0], axis=0, inplace=True)
+        return data2
+
+#def judge_subclass()
+def pre(matrix_text, addr):
+    """将数据导出
+    matrix_text：导出数据
+    addr：导出地址
+    """
+    matrix_text.to_csv(addr, sep=',', index=0, encoding='utf_8_sig', columns=matrix_text.columns)
+
+class similar():
+    '''
+    查询44w条型号速度太慢，应该对'名称提取型号.csv'的查询优化算法。
+    '''
+    def __init__(self, brand_table='平安新品分类.9.17未匹配.xlsx'):
+        self.model1 = brandd(brand_table)
+        self.brandname = self.model1.runing()
+
+    
+    def productcode_get(self):
+        return self.productcode
+
+
+    def istrue_database(self, type_name):
+        model = Extract(type_name, self.brandname)
+        type_single = model.runing()
+        judge = False
+        if type_single != 'na': 
+            sql_find_1 = sql_find() 
+            self.productcode = sql_find_1.type_search(type_single)
+            if self.productcode != None:
+                judge = True
+        return judge
+ 
+class sql_find():
+    
+    def __init__ (self):
+        self.conn = pymssql.connect(host='123.56.115.207', user='zgcprice3311', password='admin@2018@)!*', database='ZI_DataBase', autocommit=True)
+        self.cursor = self.conn.cursor()
+    
+    
+    def state_extract(self, productcode, database_name='Info_Product', state_name='State'):
+        '''
+        根据pcode提取状态。
+        '''
+        self.cursor.execute(f"select {state_name} from {database_name} where ProductCode = '{str(productcode)}'")
+        try : n = self.cursor.fetchone()[0]
+        except TypeError:
+            n = self.cursor.fetchone()
+        return n
+
+    
+    def price_get(self, productcode, database_name='ZI_Price_Quote', name='MaximumPrice'):
+        '''
+        根据pcode提取价格。
+        '''
+        self.cursor.execute(f"select {name} from {database_name} where ProductCode = '{str(productcode)}'")
+        try: n = str(self.cursor.fetchone()[0])
+        except TypeError:
+            n = self.cursor.fetchone()
+        return n
+
+    
+    def state_update(self, productcode, state_num, database_name='Info_Product', state_name='State'):
+        '''
+        状态字更新。
+        '''
+        self.cursor.execute(f"select {state_name} from {database_name} where ProductCode ='{str(productcode)}'")
+        n = self.cursor.fetchone()[0]
+        if n != state_num:
+            self.cursor.execute(f"update {database_name} set {state_name}='{state_num}' where ProductCode ='{str(productcode)}'")
+            print(f'{productcode} 状态已由 {n} 更改为 {state_num} 。')
+            if state_num == '1':
+                return 1
+            elif state_num == '6':
+                return 6
+        return 0
+
+    
+    def price_update(self, productcode, price, database_name='ZI_Price_Quote', state_name='MaximumPrice'):
+        '''
+        价格更新。
+        '''
+        self.cursor.execute(f"update {database_name} set {MaximumPrice}='{price}' where ProductCode ='{str(productcode)}'")
+        print(f'{productcode} 价格已经更改为 {price} 。')
+
+
+    def sku_delete(self, sku, database_name='productcode_sku'):
+        '''
+        删除sku。  
+        '''
+        self.cursor.execute(f"delete from {database_name} where sku ='{sku}'")
+
+
+    def type_search(self, product_type, database_name='product_type', state_name='productcode'):
+        self.cursor.execute(f"select {state_name} from {database_name} where product_type = '{str(product_type)}'")
+        n = self.cursor.fetchall()
+        return n
+    
+    def brand_search(self, productcode):
+        self.cursor.execute(f"select BrandCode from info_product where productcode = '{str(productcode)}'")
+        n = self.cursor.fetchone()[0][4:]
+        self.cursor.execute(f"select brandname from zi_brand_list where BrandID = '{str(n)}'")
+        n = self.cursor.fetchone()[0]
+        return n

product_type_extract.py

+import jieba
+import re
+import xlrd
+import pandas as pd
+import numpy as np
+
+
+class Extract:
+    def __init__(self, string_non, BrandName):
+        self.string_non = string_non
+        self.BrandName = BrandName
+
+    def contain_num(self, string):
+        """判断字符串string中包含数字"""
+        return bool(re.search('[0-9]', string))
+
+    def contain_let(self, string):
+        """判断字符串string中包含字母"""
+        return bool(re.search('[A-z]', string))
+
+    def contain_chin(self, string):
+        """判断字符串string中是否包含汉字"""
+        zhmodel = re.compile(u'[\u4e00-\u9fa5]')
+        match = zhmodel.search(string)
+        if match:
+            boolValue = True
+        else:
+            boolValue = False
+        return boolValue
+
+    def contain_sym(self, string):
+        """判断字符串string中包含'+'和'-'"""
+        return bool(re.search('[-+]', string))
+
+    def select_num(self, string):
+        """挑选字符串中的数字"""
+        num = re.findall(r"\d+\.?\d*", string)
+        return num[0]
+
+    def seek_type_maybe(self, str_single):
+        """挑选可能的型号字符串"""
+        str_goal = jieba.cut(str_single)
+        word_goal = ""
+        for word in str_goal:
+            word_goal += word + " "
+        sym = ('＋', '；', '：', '（', '(', ')', '）', "，", ',', '【', '】')
+        word_goal = ''.join(c for c in word_goal if c not in sym)  # 去掉标点符号
+        vec = word_goal.split()
+        type_maybe = {}
+        for i in range(len(vec)):
+            if vec[i] == '-':
+                type_maybe[i] = vec[i - 1] + '-' + vec[i + 1]
+            elif vec[i] == '+':
+                type_maybe[i] = vec[i - 1] + '+' + vec[i + 1]
+            else:
+                type_maybe[i] = vec[i]
+        return type_maybe
+
+    def select_one(self, type_in):
+        """挑选含数字的字符串
+        type_in数据类型为字典"""
+        type_out = {}
+        for key in type_in:
+            if self.contain_num(type_in[key]):
+                type_out[key] = type_in[key]
+        return type_out
+
+    def select_two(self, type_in):
+        """挑选含字符串长度在三到十
+        type_in数据类型为字典"""
+        type_out = {}
+        for key in type_in:
+            if (len(type_in[key]) >= 3)and(len(type_in[key]) <= 10):
+                type_out[key] = type_in[key]
+        return type_out
+
+    def select_three(self, type_in, type_st):
+        """挑选品牌名后面的字符串
+        返回的是一个值"""
+        type_out = []
+        for key in type_in:
+            index = key
+            if index == 1:
+                if type_st[index-1] in self.BrandName:
+                    type_out = type_in[key]
+                    break
+            elif index >= 2:
+                if (type_st[index - 1] in self.BrandName)and(type_st[index - 2] in self.BrandName):
+                    type_out = type_in[key]
+                    break
+        return type_out
+
+    def select_four(self, type_in, type_st):
+        """挑选型号后面的字符串"""
+        type_product = []
+        for i in range(1, len(type_in)):
+            index = list(type_in.items())[i][0]
+            if (type_st[index - 1] == '型号')and(type_st[index - 2] == '型号'):
+                type_product.append(type_st[i])
+        return list(set(type_product))
+
+    def select_five(self, type_mul):
+        """挑选带有'+'或者'-'的字符串"""
+        type_sin = []
+        for key in type_mul:
+            if self.contain_sym(type_mul[key]):
+                type_sin.append(type_mul[key])  # 可能有多个带有'+'或者'-'的可能型号
+                break
+        return type_sin
+
+    def select_six(self, type_mul):
+        """根据数字挑选字符串（重复的数字或者数字较多的字符串）"""
+        type_select = []
+        if len(type_mul) >= 2:
+            for key_i in type_mul:
+                for key_j in type_mul:
+                    num_i = self.select_num(type_mul[key_i])
+                    num_j = self.select_num(type_mul[key_j])
+                    if num_i == num_j and key_i != key_j and len(type_mul[key_i]) >= len(type_mul[key_j]):
+                        type_select = type_mul[key_j]
+                        # break
+                    else:
+                        type_len = {}
+                        for key in type_mul:
+                            type_len[key] = self.select_num(type_mul[key])
+                        index_max = max(type_len, key=type_len.get)
+                        type_select = type_mul[index_max]
+                        # type_select = type_mul
+        return type_select
+
+    def runing(self):
+        global type_final_out
+        output1 = self.seek_type_maybe(self.string_non)           # 字符串分词，挑选可能的型号
+        output2 = self.select_one(output1)               # 挑选含数字的字符串
+        output3 = self.select_two(output2)               # 挑选长度在三到十的字符串
+        if len(output3) == 1:
+            type_final = list(output3.items())[0][1]
+        else:
+            output4 = self.select_three(output3, output1)  # 挑选品牌名后面的字符串
+            if len(output4) == 1:
+                type_final = output4
+            else:
+                output5 = self.select_five(output3)          # 挑选带有'+'或者'-'的字符串
+                if len(output5) == 1:
+                    type_final = output5
+                else:
+                    output6 = self.select_four(output3, output1)      # 挑选型号后面的字符串
+                    if len(output6) == 1:
+                        type_final = output6
+                    else:
+                        output7 = self.select_six(output3)
+                        if len(output7) == 1:
+                            type_final = output7
+                        else:
+                            type_final =output3
+        if len(type_final) == 0:
+            type_final_out = 'na'
+        elif type(type_final) == str:
+            type_final_out = type_final
+        elif len(type_final) == 1 and type(type_final) == list:
+            type_final_out = type_final[0]
+        elif len(type_final) == 1 and type(type_final) == dict:
+            type_final_out = list(type_final.items())[0][1]
+        elif len(type_final) >= 2 and type(type_final) == dict:
+            type_len = {}
+            for key in type_final:
+                type_len[key] = len(type_final[key])
+            index = max(type_len, key=type_len.get)
+            type_final_out = type_final[index]
+        return type_final_out
+
+
+def read_in(addr_in):
+    """从excel文件中读取数据
+    addr_in为缺失的csv文件地址"""
+    datafile = xlrd.open_workbook(addr_in)
+    table = datafile.sheets()[0]
+    matrix_text = pd.DataFrame([])
+    for i in range(table.ncols):
+        matrix_text[i] = table.col_values(i)
+    matrix_text.rename(columns=matrix_text.iloc[0, :], inplace=True)
+    matrix_text.drop([0], axis=0, inplace=True)
+    type_nonstand = matrix_text[['productname']]  # 匹配的非标准数据
+    return list(np.array(type_nonstand))
+
+
+def out_data(data, addr):
+    columns = 'product_type'
+    data.insert(0, columns)
+    pd.DataFrame(data).to_csv(addr, index=0)

requirements.txt

+xlrd
+pandas
+jieba
+numpy
+pymssql

type_lyst.py

+from moudules import *
+'''
+table = pd.read_csv('名称提取型号_test.csv').reset_index(drop = True)
+print(table.iloc[0][1])
+'''
+
+abc = '佑游泳镜泳帽男女防水防雾游泳耳塞鼻夹眼镜高清泳镜套装Z6615电镀粉色平光'
+similar = similar()
+sqlserver = sql_find()
+print(sqlserver.brand_search(6612086630086))
+'''
+if similar.istrue_database(abc):
+    print(similar.productcode_get())
+else: print('na')
+'''

type_match.py

+from moudules import *
+import pandas as pd
+
+class Index(object):
+    def __init__(self, number=50, decimal=2):
+        """
+        :param decimal: 你保留的保留小数位
+        :param number: # 号的 个数
+        """
+        self.decimal = decimal
+        self.number = number
+        self.a = 100/number   # 在百分比 为几时增加一个 # 号
+ 
+    def __call__(self, now, total):
+        # 1. 获取当前的百分比数
+        percentage = self.percentage_number(now, total)
+ 
+        # 2. 根据 现在百分比计算
+        well_num = int(percentage / self.a)
+        # print("well_num: ", well_num, percentage)
+ 
+        # 3. 打印字符进度条
+        progress_bar_num = self.progress_bar(well_num)
+ 
+        # 4. 完成的进度条
+        result = "\r%s %s" % (progress_bar_num, percentage)
+        return result
+ 
+    def percentage_number(self, now, total):
+        """
+        计算百分比
+        :param now:  现在的数
+        :param total:  总数
+        :return: 百分
+        """
+        return round(now / total * 100, self.decimal)
+ 
+    def progress_bar(self, num):
+        """
+        显示进度条位置
+        :param num:  拼接的  “#” 号的
+        :return: 返回的结果当前的进度条
+        """
+        # 1. "#" 号个数
+        well_num = "#" * num
+ 
+        # 2. 空格的个数
+        space_num = " " * (self.number - num)
+ 
+        return '[%s%s]' % (well_num, space_num)
+
+def flip90_right(arr):
+    new_arr = arr.reshape(arr.size)
+    new_arr = new_arr[::-1]
+    new_arr = new_arr.reshape(arr.shape)
+    new_arr = np.transpose(new_arr)[::-1]
+    return new_arr
+
+
+def out_data(sku, pcode, addr):
+    columns = ['productcode', 'sku']
+    pcode.insert(0, columns[0])
+    sku.insert(0, columns[1])
+    data = np.array([pcode, sku])
+    data = flip90_right(data)
+    pd.DataFrame(data).to_csv(addr, header=0, index=0, encoding='utf_8_sig')
+
+print('请保证型号待匹配表表头包含:‘SKU’，‘product_type’，‘NAME’。')
+table_name = input('请输入型号待匹配表的路径：')
+save_name = input('请输入保存匹配后的表的路径:')
+index = Index()
+similar = similar()
+sqlserver = sql_find()
+table = pd.read_csv(table_name)
+SKU_lyst = list(table['SKU'])
+l = len(SKU_lyst)
+m = 0
+sku2pcode_lyst = []
+for sku, NAME, brand in zip(table['SKU'], table['NAME'], table['product_band']):
+    try:    
+        if similar.istrue_database(NAME):        #此处为similar类找到数据库中匹配的型号，返回True。
+            pcodelyst = []
+            productcode_lyst = similar.productcode_get()          #此处为提取与该型号所匹配产品的productcode。
+            #print(brand)
+            #print(sqlserver.brand_search(productcode_lyst[0][0]))
+            if len(productcode_lyst) < 10:
+                for i in range(len(productcode_lyst)):
+                    if BN(brand) == BN(sqlserver.brand_search(productcode_lyst[i][0])):
+                        pcodelyst.append(productcode_lyst[i][0])
+                if len(pcodelyst) == 0:
+                    sku2pcode = 'na' 
+                elif len(pcodelyst) < 5:
+                    sku2pcode = str(','.join(pcodelyst))
+                    print(sku2pcode)
+                elif len(pcodelyst) > 5:
+                    sku2pcode = '匹配5项以上。'
+            else:
+                sku2pcode = '匹配5项以上。'
+        else:
+            sku2pcode = 'na'
+    except:
+        print(f'\n数据库连接不稳定。SKU{sku}产品出错,请人工核实此条。')
+        sku2pcode = 'error'
+    print(index(m, l-1), end='%')
+    m += 1
+    sku2pcode_lyst.append(sku2pcode)
+
+out_data(SKU_lyst, sku2pcode_lyst, '2.csv')
+print('\n完成！')

数据库名称.py

+import pymssql
+import pandas as pd
+def pre(matrix_text, addr):
+    """将数据导出
+    matrix_text：导出数据
+    addr：导出地址
+    """
+    matrix_text.to_csv(addr, sep=',', index=0, encoding='utf_8_sig', columns=matrix_text.columns)
+
+
+conn = pymssql.connect(host='123.56.115.207', user='zgcprice3311',password='admin@2018@)!*',database='ZI_DataBase')
+cur = conn.cursor()
+sql_1 = "select productcode,productname,categoryid,subcategorycode from info_product where state <> '6' "
+cur.execute(sql_1)
+data = (cur.fetchall())
+columns = [desc[0] for desc in cur.description]
+data_1 = pd.DataFrame(data, columns=columns)
+pre(data_1, '产品名称.csv')
+