flask操作

models.py

class CompanyGoodsModel(Base):
    id=Column(Integer, primary_key=True)
    company_id = Column(Integer)
    。。。
    #我们在model中定义一个to_json方法，来获取模型的数据
    def to_json(self):
        dict = self.__dict__
        if "_sa_instance_state" in dict:
            del dict["_sa_instance_state"]
            return dict
    def get_info_by_id(company_id, category_id):
        if company_id:
            data=CompanyGoodsModel.query.filter(CompanyGoodsModel.company_id == company_id).all()
            return data
        elif category_id:
            data = CompanyGoodsModel.query.filter(CompanyGoodsModel.category_id == category_id).all()
            return data

然后在视图层中

@api.route('/company_goods', methods=['GET', 'POST'])
def company_goods():
    if request.method == 'GET':
        company_id = request.args.get('company_id', '')
        category_id = request.args.get('category_id', '')
        data=CompanyGoodsModel.get_info_by_id(company_id, category_id)
        result=[]
        for d in data:
            d.goods_info=eval(d['goods_info'])
            result.append(d.to_json())
        if not result:
            return json_response('0001', {}, '数据获取失败 ')
        return json_response('0000', result, '数据获取成功')

 Flask更改和加密密码

from werkzeug.security import generate_password_hash, check_password_hash
class CompanyUser(Base):
        if change_db:
        __bind_key__ = 'normal_db'
    else:
        pass
    __tablename__ = 'partner_user'
     _password = Column('password', String(300))

    @property
    def password(self):
        return self._password

    @password.setter
    def password(self, raw):
        self._password = generate_password_hash(raw)

    def check_password(self, raw):
        return check_password_hash(self._password, raw)

在更改密码的时候，直接将明文存储在该字段上即可

sqlalchemy模糊查询

queryset = CompanyUser.query.filter(or_(CompanyUser.username.like('%{}%'.format(search)),
                                                    CompanyUser.phone_number.like('%{}%'.format(search)))).all()

 原生Flask分页

假设 page_index=1,page_size=10；所有分页查询不可以再跟first(),all()等

1.用offset()设置索引偏移量,limit()限制取出


#filter语句后面可以跟order_by语句
db.session.query(User.name).filter(User.email.like('%'+email+'%')).
limit(page_size).offset((page_index-1)*page_size)


2.用slice(偏移量，取出量)函数


#filter语句后面可以跟order_by语句
db.session.query(User.name).filter(User.email.like('%'+email+'%')).slice((page_index - 1) * 
page_size, page_index * page_size)
注释：此方法和第一种相同的效果。
因为：由一下内部方法可知，slice()函数第一个属性就是offset()函数值，第二个属性就是limit()函数值

@_generative(_no_statement_condition)
    def slice(self, start, stop):
        """apply LIMIT/OFFSET to the ``Query`` based on a "
        "range and return the newly resulting ``Query``."""

        if start is not None and stop is not None:
            self._offset = (self._offset or 0) + start
            self._limit = stop - start
        elif start is None and stop is not None:
            self._limit = stop
        elif start is not None and stop is None:
            self._offset = (self._offset or 0) + start

        if self._offset == 0:
            self._offset = None

    @_generative(_no_statement_condition)
    def limit(self, limit):
        """Apply a ``LIMIT`` to the query and return the newly resulting

        ``Query``.

        """
        self._limit = limit

    @_generative(_no_statement_condition)
    def offset(self, offset):
        """Apply an ``OFFSET`` to the query and return the newly resulting
        ``Query``.

        """
        self._offset = offset
3.用paginate(偏移量，取出量)函数,用于BaseQuery


user_obj=User.query.filter(User.email.like('%'+email+'%')).paginate(int(page_index), 
int(page_size),False)
#遍历时要加上items 
object_list =user_obj.items
4.filter中使用limit


#此处不能再跟order_by语句，否则报错
db.session.query(User.name).filter(User.email.like('%'+email+'%') and limit (page_index - 1) * 
page_size, page_size)

有关flask的教程博文

https://me.csdn.net/qq_38949193

 字符串时间和时间戳之间的互转

import time, datetime

tssl = '2019-11-9'


def date2timestamp(date):
    timeArray = time.strptime(date, "%Y-%m-%d")
    timeStamp = int(time.mktime(timeArray))
    return timeStamp


def timestamp2date(timestamp):
    timeStamp = 1381419600
    timeArray = time.localtime(timeStamp)
    otherStyleTime = time.strftime("%Y-%m-%d %H:%M:%S", timeArray)
    return otherStyleTime

print(date2timestamp(tssl))
print(date2timestamp('2019-11-10') - date2timestamp(tssl))

print(timestamp2date(tssl))

 sqlalchemy的简单增删查改

增加：

def add_data():
    p1 = Person(name='zhiliao1',age=19,country='china')
    p2 = Person(name='zhiliao2',age=20,country='china')
    session.add_all([p1,p2])
    session.commit()

# 查
def search_data():
    #获取所有的对象，query类型
    # all_person = session.query(Person).all()
    # for p in all_person:
    #     print(p)
    #     print(p.name)
    # all_person = session.query(Person).filter_by(name='zhiliao').all()
    # for x in all_person:
    #     print(x)
    # all_person = session.query(Person).filter(Person.name=='zhiliao').all()
    # print(all_person)  # [<__main__.Person object at 0x0000015BCE1B4630>]
    # for x in all_person:
    #     print(x)
    person = session.query(Person).first()
    print(person)


# 改
def update_data():
    person = session.query(Person).first()
    person.name = 'ketang'
    session.commit()

# 删
def delete_data():
    person = session.query(Person).first()
    session.delete(person)
    session.commit()

python将字符串类型改成日期类型

 

将字符串类型的'2019-03-14'改成date类型，如下：

import datetime

b = datetime.date(*map(int,'2019-03-14'.split('-')))
print(b,type(b))

 将datetime类型转成str

form datetime import datetime

str_date = datetime.now().strftime("%Y-%m-%d") 

将str类型转成datetime

form datetime import datetime

start_date = datetime.strptime("2019-04-15", "%Y-%m-%d") 

 连表查询

data = a.query.join(c, c.id == a.company_id).add_columns(
                        a.id, a.create_date, a.rating, a.is_display, a.comment, a.customer_id, a.company_id).filter(
                        a.rating < 3, a.is_display == display, or_(c.company_name.contains(search),
                        c.phone_number == search)).limit(page_size).offset((page_index - 1) * page_size)

将字符串的日期转化为datetime格式

start_date = datetime.date(*map(int, datas['start_date'].split('-')))

使用pandas生成excel表格

import pandas as pd
import os,time


file_path = os.path.abspath('.')+'/'+str(time.time()).split('.')[0] + '.xlsx'
print(file_path)
df=pd.DataFrame({'ID':[1,2,3],'Name':['tom','sim','victor']})
df.to_excel(file_path)

print('done!')

用xlsxwriter包写入Excel文件

data数据就是一个答列表，里面有一个个的字典，字典里面的value可能是字典，也可能是空字典，还有可能是字符串

"""
import xlsxwriter


# 写excel
def write_excel():
    workbook = xlsxwriter.Workbook('chat.xlsx')  # 创建一个excel文件
    worksheet = workbook.add_worksheet(u'sheet1')  # 在文件中创建一个名为TEST的sheet,不加名字默认为sheet1

    worksheet.set_column('A:A', 20)  # 设置第一列宽度为20像素
    bold = workbook.add_format({'bold': True})  # 设置一个加粗的格式对象

    worksheet.write('A1', 'HELLO')  # 在A1单元格写上HELLO
    worksheet.write('A2', 'WORLD', bold)  # 在A2上写上WORLD,并且设置为加粗
    worksheet.write('B2', U'中文测试', bold)  # 在B2上写上中文加粗

    worksheet.write(2, 0, 32)  # 使用行列的方式写上数字32,35,5
    worksheet.write(3, 0, 35.5)  # 使用行列的时候第一行起始为0,所以2,0代表着第三行的第一列,等价于A4
    worksheet.write(4, 0, '=SUM(A3:A4)')  # 写上excel公式
    workbook.close()


if __name__ == '__main__':
    # 写入Excel
    write_excel()
    print('写入成功')
"""

import xlsxwriter
import time
import os
import json
from test01 import data
import math


def write_excel():
    file_path = os.path.abspath('.') + '\' + str(time.time()).split('.')[0] + '.xlsx'
    work_book = xlsxwriter.Workbook(file_path)
    work_sheet = work_book.add_worksheet(u'sheet1')
    bold = work_book.add_format({'bold': True})
    columns = []
    for i in data[0].keys():
        columns.append(i)
    work_sheet.set_column(f'A:{chr(len(columns)+ ord("A"))}', 20)  # 空格的长度

    work_sheet.write(str(chr(ord('A')+math.ceil(len(columns)/2)))+'1', U'欧阳国勇的表格', bold)
    # print('A'+str(math.ceil(len(columns)/2)-1))
    # print(str(chr(ord('A')+math.ceil(len(columns)/2)))+'1',type(str(chr(ord('A')+math.ceil(len(columns)/2)))))
    datas = []
    for i in data:
        dd = []
        for j in i.values():
            dd.append(j)
        datas.append(dd)
    datas.insert(0, columns)
    for i in datas:
        if i == {}:
            continue
        for j in i:
            middle_value = j
            if j=={}:
                middle_value = 'fuck'
            if isinstance(j, dict):
                middle_value = str(j)

            work_sheet.write(datas.index(i)+2, i.index(j), middle_value)

    work_book.close()


if __name__ == '__main__':
    a = write_excel()
    print(a)

UUID

UUID是128位的全局唯一标识符，通常由32字节的字符串表示。它可以保证时间和空间的唯一性，也称为GUID，全称为：UUID —— Universally Unique IDentifier，Python 中叫 UUID。
它通过MAC地址、时间戳、命名空间、随机数、伪随机数来保证生成ID的唯一性。
UUID主要有五个算法，也就是五种方法来实现。

uuid1()——基于时间戳。由MAC地址、当前时间戳、随机数生成。可以保证全球范围内的唯一性，但MAC的使用同时带来安全性问题，局域网中可以使用IP来代替MAC。
uuid2()——基于分布式计算环境DCE（Python中没有这个函数）。算法与uuid1相同，不同的是把时间戳的前4位置换为POSIX的UID。实际中很少用到该方法。
uuid3()——基于名字的MD5散列值。通过计算名字和命名空间的MD5散列值得到，保证了同一命名空间中不同名字的唯一性，和不同命名空间的唯一性，但同一命名空间的同一名字生成相同的uuid。
uuid4()——基于随机数。由伪随机数得到，有一定的重复概率，该概率可以计算出来。
uuid5()——基于名字的SHA-1散列值。算法与uuid3相同，不同的是使用 Secure Hash Algorithm 1 算法。

postgresql时间自动生成

ALTER TABLE sys_backend_log OWNER TO postgres;
GRANT ALL ON TABLE sys_backend_log TO zexingj;
CREATE TRIGGER sys_backend_log_create_tri BEFORE INSERT ON sys_backend_log FOR EACH ROW EXECUTE PROCEDURE create_func();
CREATE TRIGGER sys_backend_log_update_tri BEFORE UPDATE ON sys_backend_log FOR EACH ROW EXECUTE PROCEDURE update_func();

python生成excel表格数据

    def download_excel(data):
        # 拼接文件路径
        file_path = os.path.abspath('.') + '\' + 'excel_file' + '\' + str(time.time()).split('.')[0] + '.xlsx'
        work_book = xlsxwriter.Workbook(file_path)  # 新建工作表
        work_sheet = work_book.add_worksheet(u'sheet1')  # 创建工作区
        bold = work_book.add_format({'bold': True})  # 字体加黑
        columns = [
            '操作人', '执行事件', '被操作事物名称', '操作缘由', '操作时间',
            '门店名', '手机号', '标题', '支付方式', '商品说明',
            '创建时间', '订单号', '支付金额', '审核结果']

        work_sheet.set_column(f'A:{chr(len(columns)+ ord("A"))}', 20)  # 设置数据覆盖到的第一行每格宽度
        # 写第一行的数据
        work_sheet.write(str(chr(ord('A') + len(columns) // 2)) + '1', U'审核-商户端-产品订单退款', bold)
        datas = []
        for i in data:
            try:
                sys_log_info = SysBackendLogModel.query.filter(SysBackendLogModel.create_id == str(i['key'])).first()
            except Exception as e:
                print(e)
                sys_log_info = ''
            print(sys_log_info)
            initiator = sys_log_info.initiator if sys_log_info is not None else '无'
            operation = sys_log_info.operation if sys_log_info is not None else '无'
            receiver = sys_log_info.receiver if sys_log_info is not None else '无'
            reason = sys_log_info.reason if sys_log_info is not None else '无'
            create_date = sys_log_info.create_date if sys_log_info  is not None else '无'
            datas.append([initiator, operation, receiver, reason, create_date,
                          i['buy_company_info']['company_name'] if i['buy_company_info'] else '无',
                          i['buy_company_info']['phone_number'] if i['buy_company_info'] else '无',
                          i['product_info']['title'] if i['product_info'] else '无',
                          i['product_order_info']['payment_method'] if i['product_order_info'] else '无',
                          i['product_info']['product_desc'] if i['product_info'] else '无',
                          i['product_order_info']['order_date'] if i['product_order_info'] else '无',
                          i['product_order_info']['trade_no'] if i['product_order_info'] else '无',
                          i['product_order_info']['paid_amount'] if i['product_order_info'] else '无',
                          '退款中' if i['status'] == 3 else '已退款'])

        print(datas)
        datas.insert(0, columns)

        for m, i in enumerate(datas):  # m就是 datas元素的索引
            print(m)
            if i == {}:
                continue
            for n, j in enumerate(i):  # n就是列表i元素的索引
                work_sheet.write(m + 1, n, str(j))

        work_book.close()
        return file_path

    def file_iterator(file_path, chunk_size=512):
        """
            文件读取迭代器
        :param file_path:文件路径
        :param chunk_size: 每次读取流大小
        :return:
        """
        with open(file_path, 'rb') as target_file:
            while True:
                chunk = target_file.read(chunk_size)
                if chunk:
                    yield chunk
                else:
                    break

 file_path = CompanyProductOrderModel.download_excel(data[0])
                data = CompanyProductOrderModel.file_iterator(file_path)
                filename = os.path.basename(file_path)
                response = Response(data)
                response.headers['Content-Type'] = 'application/octet-stream'
                response.headers["Content-Disposition"] = 'attachment;filename="{}"'.format(filename)
                # if os.path.exists(file_path):os.remove(file_path)  # 删除文件夹
                return response

 地图相关值坐标反差

def get_lati_and_longi(city, address):
    url = 'https://restapi.amap.com/v3/geocode/geo?key={}&address={}&city={}'
    KEY = '需要去高德地图去登陆获取'
    response = requests.get(url.format(KEY, address, city))
    result = response.content.decode("utf-8")
    result = json.loads(result)
    return result['geocodes'][0]['location']