mongodb 根据id排序 mongodb分组后排序

准备数据

```python
from pymongo import MongoClient
import datetimeclient=MongoClient('mongodb://localhost:27017')
table=client['db1']['emp']l=[
('张飞','male',18,'20170301','',7300.33,401,1), #以下是教学部
('张云','male',78,'20150302','teacher',1000000.31,401,1),
('刘备','male',81,'20130305','teacher',8300,401,1),
('关羽','male',73,'20140701','teacher',3500,401,1),
('曹操','male',28,'20121101','teacher',2100,401,1),
('诸葛亮','female',18,'20110211','teacher',9000,401,1),
('周瑜','male',18,'19000301','teacher',30000,401,1),
('司马懿','male',48,'20101111','teacher',10000,401,1),('袁绍','female',48,'20150311','sale',3000.13,402,2),#以下是销售部门
('张全蛋','female',38,'20101101','sale',2000.35,402,2),
('鹌鹑蛋','female',18,'20110312','sale',1000.37,402,2),
('王尼玛','female',18,'20160513','sale',3000.29,402,2),
('我尼玛','female',28,'20170127','sale',4000.33,402,2),('杨过','male',28,'20160311','operation',10000.13,403,3), #以下是运营部门
('小龙女','male',18,'19970312','operation',20000,403,3),
('郭靖','female',18,'20130311','operation',19000,403,3),
('黄蓉','male',18,'20150411','operation',18000,403,3),
('梅超风','female',18,'20140512','operation',17000,403,3)
]for n,item in enumerate(l):
 d={
 "_id":n,
 'name':item[0],
 'sex':item[1],
 'age':item[2],
 'hire_date':datetime.datetime.strptime(item[3],'%Y%m%d'),
 'post':item[4],
 'salary':item[5]
 }
 table.save(d)

# 准备数据
```

分组的概念与mysql相同,以某个字段作为依据进行归类,其目的是为了统计

## $match
```python
#match 用于对数据进行筛选
{"$match":{"字段":"条件"}},可以使用任何常用查询操作符$gt,$lt,$in等#例1、select * from db1.emp where post='teacher';
db.emp.aggregate({"$match":{"post":"teacher"}})#例2、select * from db1.emp where id > 3; 
db.emp.aggregate(
 {"$match":{"_id":{"$gt":3}}},
)
```

## $project

```python
# project翻译为投射 ,即将一个数据结果映射为另一个结果 过程中可以对某些数据进行修改 控制其最终显示的结果
{"$project":{"要保留的字段名":1,"要去掉的字段名":0,"新增的字段名":"表达式"}}

#1、select name,post,(age+1) as new_age from db1.emp;
db.emp.aggregate(
 {"$project":{
 "name":1,
 "post":1
 }})

#2、表达式之数学表达式
{"$add":[expr1,expr2,...,exprN]} #相加
{"$subtract":[expr1,expr2]} #第一个减第二个
{"$multiply":[expr1,expr2,...,exprN]} #相乘
{"$divide":[expr1,expr2]} #第一个表达式除以第二个表达式的商作为结果
{"$mod":[expr1,expr2]} #第一个表达式除以第二个表达式得到的余数作为结果
#例:所有人年龄加1显示
db.emp.aggregate(
 {"$project":{
 "name":1,
 "post":1,
 "new_age":{"$add":["$age",1]}
 }})
# 错误示范: 原因:参加运算的字段不能被影藏
db.emp.aggregate(
 {"$project":{
 "name":1,
 "salary":1,
 "age":0,
 "new_age":{"$add":["$age",1]}
 }})

#3、表达式之日期表达式:$year,$month,$week,$dayOfMonth,$dayOfWeek,$dayOfYear,$hour,$minute,$second
#例如：select name,date_format("%Y") as hire_year from db1.emp
db.emp.aggregate(
 {"$project":{"name":1,"hire_year":{"$year":"$hire_date"}}}
)#例如查看每个员工的工作多长时间
db.emp.aggregate(
 {"$project":{"name":1,"hire_period":{
 "$subtract":[
 {"$year":new Date()},
 {"$year":"$hire_date"}
 ]
 }}}
)

#4、字符串表达式
{"$substr":[字符串/$值为字符串的字段名,起始位置,截取几个字节]}
db.emp.aggregate({"$project":{"new_name":{"$substr":["$name",0,3]}}})
{"$concat":[expr1,expr2,...,exprN]} #指定的表达式或字符串连接在一起返回,只支持字符串拼接
db.emp.aggregate({"$project":{"new_name":{"$concat":["$name","$post"]}}}){"$toLower":expr}
{"$toUpper":expr}
db.emp.aggregate({"$project":{"new_name":{"$toUpper":"$post"}}})
db.emp.aggregate( {"$project":{"NAME":{"$toUpper":"$name"}}})

#5、逻辑表达式
$and
$or
$not
其他见Mongodb权威指南
```

 

## $group

```python
# $group用于分组
# 分组后具体信息被影藏 
db.emp.aggregate(
 {"$match":{"_id":{"$gt":3}}},
 {"$group":{"_id":"$post"}} 
)

# 通常我们要对分组后的内容进行统计这就需要对应的几个聚合函数
# select id,avg(salary) from db1.emp where id > 3 group by post; 
db.emp.aggregate(
 {"$match":{"_id":{"$gt":3}}},
 {"$group":{"_id":"$post",'avg_salary':{"$avg":"$salary"}}},
)
# math用于匹配 与mysql不同的是没有顺序限制 每一个操作像是一个管道接收上一个的数据进行处理再传给下一个# select id,avg(salary) from db1.emp where id > 3 group by post having avg(salary) > 10000; 
db.emp.aggregate(
 {"$match":{"_id":{"$gt":3}}},
 {"$group":{"_id":"$post",'avg_salary':{"$avg":"$salary"}}},
 {"$match":{"avg_salary":{"$gt":10000}}}
)# 对应的聚合函数 $sum、$avg、$max、$min、$first、$last
#1、将分组字段传给$group函数的_id字段即可
{"$group":{"_id":"$sex"}} #按照性别分组
{"$group":{"_id":"$post"}} #按照职位分组
{"$group":{"_id":{"state":"$state","city":"$city"}}} #按照多个字段分组，比如按照州市分组#2、分组后聚合得结果,类似于sql中聚合函数的聚合操作符：$sum、$avg、$max、$min、$first、$last
#例1：select post,max(salary) from db1.emp group by post; 
db.emp.aggregate({"$group":{"_id":"$post","max_salary":{"$max":"$salary"}}})#例2：去每个部门最大薪资与最低薪资
db.emp.aggregate({"$group":{"_id":"$post","max_salary":{"$max":"$salary"},"min_salary":{"$min":"$salary"}}})#例3：如果字段是排序后的，那么$first,$last会很有用,比用$max和$min效率高
db.emp.aggregate({"$group":{"_id":"$post","first_id":{"$first":"$_id"}}})#例4：求每个部门的总工资
db.emp.aggregate({"$group":{"_id":"$post","count":{"$sum":"$salary"}}})#例5：求每个部门的人数
db.emp.aggregate({"$group":{"_id":"$post","count":{"$sum":1}}})#3、数组操作符
{"$addToSet":expr}：不重复
{"$push":expr}：重复
# 等同于group_concat
#例：查询岗位名以及各岗位内的员工姓名:select post,group_concat(name) from db1.emp group by post;
db.emp.aggregate({"$group":{"_id":"$post","names":{"$push":"$name"}}})
db.emp.aggregate({"$group":{"_id":"$post","names":{"$addToSet":"$name"}}})
```

## $sort ,limit,skip

```python
{"$sort":{"字段名":1,"字段名":-1}} #1升序，-1降序
{"$limit":n} 
{"$skip":n} #跳过多少个文档
#例1、取平均工资最高的前两个部门db.emp.aggregate(
{
 "$group":{"_id":"$post","平均工资":{"$avg":"$salary"}}
},
{
 "$sort":{"平均工资":-1}
},
{
 "$limit":2
}
)
#例2、
db.emp.aggregate(
{
 "$group":{"_id":"$post","平均工资":{"$avg":"$salary"}}
},
{
 "$sort":{"平均工资":-1}
},
{
 "$limit":2
},
{
 "$skip":1
}
)
排序：$sort、限制：$limit、跳过：$skip
```

## $sample

```python
# 随机取出n条记录
#集合users包含的文档如下
{ "_id" : 1, "name" : "dave123", "q1" : true, "q2" : true }
{ "_id" : 2, "name" : "dave2", "q1" : false, "q2" : false }
{ "_id" : 3, "name" : "ahn", "q1" : true, "q2" : true }
{ "_id" : 4, "name" : "li", "q1" : true, "q2" : false }
{ "_id" : 5, "name" : "annT", "q1" : false, "q2" : true }
{ "_id" : 6, "name" : "li", "q1" : true, "q2" : true }
{ "_id" : 7, "name" : "ty", "q1" : false, "q2" : true }#下述操作时从users集合中随机选取3个文档
db.users.aggregate({"$sample":{"size":3}})
随机选取n个：$sample
```

 

# 可视化工具

https://robomongo.org

from selenium.webdriver import Chrome
from urllib.parse import urlencode
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
import time
import mongo




driver=Chrome()
name='黄金'
encode_dict=urlencode({"keyword":name,"enc":"utf-8","wq":name})
url='https://search.jd.com/Search?'+encode_dict
driver.get(url)

def spider():
    WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.CLASS_NAME, 'pn-next')))
    height = driver.execute_script("return document.body.clientHeight")
    driver.execute_script("""
                window.scrollTo({
                    top: %s,
                    behavior: "smooth"
                });""" % height)

    items = driver.find_elements_by_css_selector(".gl-item")
    if len(items)==60:
        for i in items:
            price=i.find_element_by_css_selector('.p-price i').text
            img=i.find_element_by_css_selector('.p-scroll img').get_attribute('src')
            url=i.find_element_by_css_selector('.p-img a').get_attribute('href')
            print(price)
            mongo_dict={'img':img,'price':price,'url':url}
            mongo.insert(mongo_dict)
        WebDriverWait(driver, 30).until(EC.element_to_be_clickable((By.CLASS_NAME, 'pn-next')))
        next_page = driver.find_element_by_css_selector('.pn-next')
        next_page.click()
        time.sleep(3)
        spider()

    else:

        return spider()



for i in range(5):
    time.sleep(1)
    spider()
time.sleep(6)
driver.close()

from pymongo import MongoClient
import datetime
c=MongoClient(host='127.0.0.1',port=27017)

db=c['admin']
db.authenticate('root','123')

db.c['db1']

def insert(data):
    c['db1']['jingdong'].insert(data)
if __name__=='__main__':
    insert({"url":"24qew"})