Python MongoDB Operations Encyclopedia

Database
Increase
Use DB1 #有则切换, none added

Check
Show DBS #查看所有
DB #当前

By deleting
Db.dropdatabase ()

Collection:
Increase:
Db.user
Db.user.info
Db.user.auth

View
Show collections
Show tables

By deleting
Db.user.info.drop ()

Document:
Increase
Db.user.insert ({"_id": 1, "name": "Egon"})
user0={
"Name": "Egon",
"Age": 10,
' Hobbies ': [' music ', ' read ', ' Dancing '],
' Addr ': {
' Country ': ' China ',
' City ': ' BJ '
}
}

Db.user.insert (USER0)
Db.user.insertMany ([User1,user2,user3,user4,user5])

Db.t1.insert ({"_id": 1, "a": 1, "B": 2, "C": 3})
#有相同的_id则覆盖, no same _id is added, you must specify _ID
Db.t1.save ({"_id": 1, "Z": 6})
Db.t1.save ({"_id": 2, "Z": 6})
Db.t1.save ({"Z": 6})

The difference between save and insert:
If there is a primary key in the new data, insert () prompts for an error, and save () changes the original content to the new content.
Such as:
Data already exists: {_id:1, ' name ': ' N1 '}, when the insert operation is performed again,
Insert ({_id:1, "name": "N2"}) will report a duplicate error prompt for the primary key
Save ({_id:1, "name": "N2"}) will change N1 to N2.
Same point:
If there is no primary key in the new data, a record is added.
Data already exists: {_id:1, ' name ': ' N1 '}, when the insert operation is performed again,
Insert ({"name": "N2"}) inserted data because there is no primary key, so it will add a piece of data
Save ({"name": "N2"}) adds a single piece of data.
Check

Comparison operation: =,!=,>,<,>=,<=
#1, select * from db1.user where id = 3
Db.user.find ({"_id": 3})

#2, select * from Db1.user where id! = 3
Db.user.find ({"_id": {"$ne": 3}})

#3, select * from Db1.user where ID > 3
Db.user.find ({"_id": {"$GT": 3}})

#4, select * from Db1.user where < 3
Db.user.find ({"Age": {"$lt": 3}})

#5, select * from Db1.user where ID >= 3
Db.user.find ({"_id": {"$gte": 3}})

#6, select * from Db1.user where ID <= 3
Db.user.find ({"_id": {"$lte": 3}})

#逻辑运算: $and, $or, $not
#1 SELECT * from Db1.user where ID >=3 and ID <=4;
Db.user.find ({"_id": {"$gte": 3, "$lte": 4}})

#2 SELECT * from Db1.user where ID >=3 and ID <=4 and >=40;
Db.user.find ({
"_id": {"$gte": 3, "$lte": 4},
"Age": {"$gte": 40}
})

Db.user.find ({"$and": [
{"_id": {"$gte": 3, "$lte": 4}},
{"Age": {"$gte": 40}}
]})

#3 SELECT * from Db1.user where ID >=0 and ID <=1 or id >=4 or name = "Yuanhao";
Db.user.find ({"$or": [
{"_id": {"$lte": 1, "$gte": 0}},
{"_id": {"$gte": 4}},
{"Name": "Yuanhao"}
]})

#4 SELECT * from Db1.user where id% 2 = 1;
Db.user.find ({"_id": {"$mod": [2,1]}})

Db.user.find ({
"_id": {"$not": {"$mod": [2,1]}}
})

#成员运算: $in, $nin
Db.user.find ({"name": {"$in": ["Alex", "Egon"]}})
Db.user.find ({"name": {"$nin": ["Alex", "Egon"]}})

#正则匹配
SELECT * from Db1.user where name regexp "^jin.*? (g|n) $ ";
Db.user.find ({
"Name":/^jin.*? (g|n) $/i
})

#查看指定字段
Select Name,age from Db1.user where name regexp "^jin.*? (g|n) $ ";
Db.user.find ({
"Name":/^jin.*? (g|n) $/i
},
{
"_id": 0,
"Name": 1,
"Age": 1
}
)

#查询数组相关
Db.user.find ({
"Hobbies": "Dancing"
})

Db.user.find ({
"Hobbies": {"$all": ["Dancing", "Tea"]}
})

Db.user.find ({
"Hobbies.2": "Dancing"
})

Db.user.find (
{},
{
"_id": 0,
"Name": 0,
"Age": 0,
"Addr": 0,
"Hobbies": {"$slice": [+]},
}
)

Db.user.find (
{},
{
"_id": 0,
"Name": 0,
"Age": 0,
"Addr": 0,
"Hobbies": {"$slice": 2},
}
)

Db.user.find (
{
"Addr.country": "China"
}
)

Db.user.find (). Sort ({"_id": 1, "Age":-1})

Db.user.find (). Limit (2). Skip (0)
Db.user.find (). Limit (2). Skip (2)
Db.user.find (). Limit (2). Skip (4)
Db.user.find (). DISTINCT ()

Change
One syntax:
Db.table.update (
Conditions
Modify Fields,
Other parameters
)

Update db1.t1 set id=10 where name= "Egon";

Db.table.update (
{},
{"Age": 11},
{
"Multi": true,
"Upsert": True
}
)

1, update db1.user set age=23,name= "Wuhan University Lang" where name= "Wupeiqi";
#覆盖式
Db.user.update (
{"Name": "Wupeiqi"},
{"Age": +, "name": "Wuhan University Lang"}
)
#局部修改: $set
Db.user.update (
{"Name": "Alex"},
{"$set": {"age": Pan Jinlian, "name": "-alex"}}
)

#改多条
Db.user.update (
{"_id": {"$gte": 1, "$lte": 2}},
{"$set": {"age": 53,}},
{"Multi": true}
)
#有则修改, none added
Db.user.update (
{"Name": "EGON"},
{"$set": {"name": "EGON", "Age": 28,}},
{"Multi": true, "Upsert": true}
)

#修改嵌套文档
Db.user.update (
{"Name": "Pan Jinlian-alex"},
{"$set": {"addr.country": "Japan"}}
)

#修改数组
Db.user.update (
{"Name": "Pan Jinlian-alex"},
{"$set": {"hobbies.1": "Piao"}}
)

#删除字段
Db.user.update (
{"Name": "Pan Jinlian-alex"},
{"$unset": {"Hobbies": ""}}
)

2. $inc
Db.user.update (
{},
{"$inc": {"Age": 1}},
{"Multi": true}
)

Db.user.update (
{},
{"$inc": {"Age":-10}},
{"Multi": true}
)

3, $push, $pop $pull
Db.user.update (
{"Name": "Yuanhao"},
{"$push": {"Hobbies": "Tangtou"}},
{"Multi": true}
)

Db.user.update (
{"Name": "Yuanhao"},
{"$push": {"Hobbies": {"$each": ["Tattoo", "Smoking"]}},
{"Multi": true}
)

#从头删-1, delete 1 from the tail
Db.user.update (
{"Name": "Yuanhao"},
{"$pop": {"Hobbies":-1}},
{"Multi": true}
)

Db.user.update (
{"Name": "Yuanhao"},
{"$pop": {"Hobbies": 1}},
{"Multi": true}
)

#按条件删
Db.user.update (
{"Name": "Yuanhao"},
{"$pull": {"Hobbies": "Tattoo"}},
{"Multi": true}
)

#3, $addToSet
Db.t3.insert ({"URLs": []})
Db.t3.update (
{},
{"$addToSet": {"URLs": {"$each": [
"Http://www.baidu.com",
"Http://www.baidu.com",
"Http://www.baidu.com",
"Http://www.baidu.com",
"Http://www.baidu.com"
]}}},
{"Multi": true}
)

By deleting
Db.user.deleteOne ({"_id": {"$gte": 3}})
Db.user.deleteMany ({"_id": {"$gte": 3}})
Db.user.deleteMany ({})

Polymerization
One: $match
Cases:
Select post from Db1.emp where the > GROUP by post has AVG (salary) > 10000;

# $match
#1, select * from db1.emp where > 20
Db.emp.aggregate ({"$match": {"age": {"$GT": 20}})

# $group
#2, select post from db1.emp where Age > Group by post;
Db.emp.aggregate (
{"$match": {"age": {"$GT": 20}},
{"$group": {"_id": "$post"}}
)

#3, select Post,avg (Salary) as avg_salary from Db1.emp where the Age > group by post;
Db.emp.aggregate (
{"$match": {"age": {"$GT": 20}},
{"$group": {"_id": "$post", "avg_salary": {"$avg": "$salary"}}}
)

#select post from Db1.emp where the Age > GROUP by post has AVG (salary) > 10000;
Db.emp.aggregate (
{"$match": {"age": {"$GT": 20}},
{"$group": {"_id": "$post", "avg_salary": {"$avg": "$salary"}},
{"$match": {"avg_salary": {"$GT": 10000}}
)

Two: Projection
{"$project": {"field name to keep": 1, "Field name to remove": 0, "new field name": "Expression"}}
Example 1:
Db.emp.aggregate (
{"$project": {"_id": 0, "name": 1, "POST": 1, "annual_salary": {"$multiply": [[], "$salary"]}},
{"$group": {"_id": "$post", "average yearly salary": {"$avg": "$annual _salary"}}},
{"$match": {"Average yearly salary": {"$GT": 1000000}},
{"$project": {"department name": "$_id", "average yearly salary": 1, "_id": 0}}
)

Example 2:
Db.emp.aggregate (
{"$project": {"_id": 0, "name": 1, "Hire_period": {"$subtract": [New Date (), "$hire _date"]}}}
)

Db.emp.aggregate (
{"$project": {"_id": 0, "name": 1, "Hire_year": {"$year": "$hire _date"}}}
)

Db.emp.aggregate (
{"$project": {"_id": 0, "name": 1, "Hire_period": {"$subtract": [{"$year": New Date ()},{"$year": "$hire _date"}]}}}
)

Example 3:
Db.emp.aggregate (
{"$project": {"_id": 0, "new_name": {"$toUpper": "$name"},}}
)

Db.emp.aggregate (
{"$match": {"name": {"$ne": "Egon"}}},
{"$project": {"_id": 0, "new_name": {"$concat": ["$name", "_SB"]},}}
)

Db.emp.aggregate (
{"$match": {"name": {"$ne": "Egon"}}},
{"$project": {"_id": 0, "new_name": {"$substr": ["$name", 0,3]},}}
)

Three: {"$group": {"_id": Group field, "new field name": Aggregation operator}}

#select Post,max,min,sum,avg,count,group_concat from Db1.emp group by post;

Db.emp.aggregate (
{"$group": {
"_id": "$post",
"Max_age": {"$max": "$age"},
"min_id": {"$min": "$_id"},
"Avg_salary": {"$avg": "$salary"},
"Sum_salary": {"$sum": "$salary"},
"Count": {"$sum": 1},
"Names": {"$push": "$name"}
}
}
)

Four: Sort: $sort, limit: $limit, skip: $skip
Db.emp.aggregate (
{"$match": {"name": {"$ne": "Egon"}}},
{"$project": {"_id": 1, "new_name": {"$substr": ["$name", 0,3]}, "Age": 1}},
{"$sort": {"Age": 1, "_id":-1}},
{"$skip": 5},
{"$limit": 5}
)

# add
Db.emp.aggregate ({"$sample": {"Size": 3}}) #随机选取3个文档

Python MongoDB Operations Encyclopedia