Python operates mongodb in nine steps

1 the import pymongo

[

from pymongo import MongoClient

]

Connect to server port number 27017

Connection MongoDB

To connect to MongoDB, we need to use MongoClient in the PyMongo library. Generally, we can pass in IP and port of MongoDB. The first parameter is address host, and the second parameter is port port.

[

conn = MongoClient("localhost")

MongoClient(host='127.0.0.1',port=27017)

]

3 Connect to the database

db = conn. database name

Connection collection

[

collection = db[collection_name]

or

collection = db.collection_name

]

View all aggregate names

[

db.collection_names()

]

4 Inserting Data
(1) Insert 1 piece of data

[

db. user. insert ({" name ":" xiali just ", "age" : 18, "hobby" : "learning"})

]

(2) Insert multiple pieces of data

[

db. user. insert ([{" name ":" xiali just ", "age" : 18, "hobby" : "learning"}, {" name ":" xxxoo ", "age" : 48, "hobby" : "learning"}]

]

(3) Above x is recommended

[

insert_one inserts 1 data

insert_many() inserts multiple pieces of data

]

(4) Return id use insert_one()

[

data.inserted_id

data.inserted_ids

]

5 Query data
(1) Query all

db.user.find()

[

# Conditional queries
# data = ES126en.user.find ({"name":" Sunday "})
# print(data) # returns result similar to 1 iterator which can be pulled out one by one using the next method
# print(next(data)) # extract 1 piece of data

]

2) Query 1 item

[

db.user.find_one()

]

(3) Conditional query

[

db. user. find ({" name ":" 3 "})

]

(4) query id

[

from bson.objectid import ObjectId*# for ID queries

data = db.user.find({"_id":ObjectId("59a2d304b961661b209f8da1")})

]

(5) Fuzzy query

[

(1) {"name":{'$regex':" Zhang "}}

(2) import re {'xxx':re.compile('xxx')}

]

6 sort limit count skip
(1) sort sorting

The & # 8203; Age > 10

[

data = db. user.find({"age":{"$gt":10}).sort("age",-1) # age ascending query pymongo. ASCENDING -- ascending

data = db.user.find({"age":{"$gt":10}).sort("age",1) # age descending order query pymongo.DESCENDING -- descending order

]

(2) limit values

The & # 8203; Take 3 pieces of data

[

db.user.find().limit(3)

data = db.user.find({"age":{"$gt":10}}).sort("age",-1).limit(3)

]

(3) Number of count statistics

db.user.find().count()

(4) skip starts from the number of data

db.user.find().skip(2)

7 update modify
The & # 8203; The update() method is also officially not recommended. The update_one() method and update_many() method are also divided here. The usage is more strict.

(1) update()

[

db. user. update ({" name ":" 3 "}, {" $set ": {" age" : 25}})

db. user. update ({" name ":" 3 "}, {" $inc ": {" age" : 25}})

]

(2) update_one() Update the data that meets the conditions in article 1

[

The & # 8203; db. user. update_one ({" name ":" 3 "}, {" $set ": {" age" : 99}})

]

(3) update_many() updates all eligible data

[

db. user. update_many ({" name ":" 3 "}, {" $set ": {" age" : 91}})

]

(4) The returned result is type UpdateResult, and then call the attributes matched_count and modified_count to get the number of matching data and the number of affected data, respectively.

print (result matched_count, result modified_count)

8 remove delete

The deletion operation is relatively simple, just call remove() method to specify the deletion condition, and all data that meets the criteria will be deleted.

(1) Delete sheet 3

[

collection.remove({"name":"lilei"})

]

(2) Delete all

[

collection.remove()

]

(3) There are still two new recommended methods, delete_one() and delete_many(). Examples are as follows:

[

delete_one() deletes data that meets the criteria in article 1

collection. delete_one ({" name ":" Kevin "})

delete_many() deletes all eligible data and returns a result of type DeleteResult

collection. delete_many ({" age ": {$lt: 25}})

]

(4) The number of deleted data bars can be obtained by calling deleted_count attribute.

[

result.deleted_count

]

9 Close connection

[

conn.close()

]