Python's MongoDB module Pymongo how-to collection

Before you start, of course, import modules:

>>> Import Pymongo

Next, the installation and startup of the local MongoDB server must be completed before you can continue.

Connections Built on Mongoclient:

Client = mongoclient (' localhost ', 27017) # or client = mongoclient (' mongodb://localhost:27017/')

Get the database:

>>> db = client.test_database# or >>> db = client[' test-database ']

Get a collection of data:

Collection = db.test_collection# or collection = db[' test-collection ')

The data in MongoDB uses a JSON-style document like this:

>>> Import datetime>>> post = {"Author": "Mike",...     " Text ":" My First blog post! ",...     " Tags ": [" MongoDB "," Python "," Pymongo "],...     " Date ": Datetime.datetime.utcnow ()}

Insert a document:

>>> posts = db.posts>>> post_id = Posts.insert_one (POST) .inserted_id>>> Post_idobjectid (' ...')

Find a piece of data:

>>> Posts.find_one () {u ' date ': Datetime.datetime (...), u ' text ': U ' My First blog post! ', U ' _id ': ObjectId (' ... ') , u ' author ': U ' Mike ', U ' tags ': [u ' MongoDB ', u ' python ', U ' Pymongo ']}>>> posts.find_one ({"Author": "Mike"}) { U ' Date ': Datetime.datetime (...), u ' text ': U ' My First blog post! ', U ' _id ': ObjectId (' ... '), U ' author ': U ' Mike ', U ' tags ': [u ' MongoDB ', u ' python ', U ' Pymongo ']}>>> posts.find_one ({"Author": "Eliot"}) >>>

To find by Objectid:

>>> Post_idobjectid (...) >>> Posts.find_one ({"_id": post_id}) {u ' date ': Datetime.datetime (...), u ' text ': U ' My First blog post! ', U ' _id ': ObjectId (' ... '), U ' author ': U ' Mike ', U ' tags ': [u ' MongoDB ', u ' python ', U ' Pymongo '}

Do not convert the Objectid type to string:

>>> post_id_as_str = str (post_id) >>> Posts.find_one ({"_id": Post_id_as_str}) # No result>>>

What if you have a post_id string?

From Bson.objectid import objectid# the web framework gets post_id from the URL and passes it as a stringdef get (post_id):  # Convert from string to ObjectId:  document = Client.db.collection.find_one ({' _id ': ObjectId (post_id)})

Multiple inserts:

>>> new_posts = [{"Author": "Mike",...        " Text ":" Another post! ",...        " Tags ": [" Bulk "," Insert "],...        " Date ": Datetime.datetime (one, one, one, one)},...       {"Author": "Eliot",...        " Title ":" MongoDB is Fun ",...        " Text ":" and Pretty Easy Too! ",...        " Date ": Datetime.datetime (one, ten, ten,}]>>>) result = Posts.insert_many (new_posts) >>> Result.inserted_ids[objectid (' ... '), ObjectId (' ... ')]

To find more than one piece of data:

>>> for post in Posts.find (): ...  Post ... {u ' date ': Datetime.datetime (...), u ' text ': U ' My First blog post! ', U ' _id ': ObjectId (' ... '), U ' author ': U ' Mike ', U ' tags ': [ U ' MongoDB ', u ' python ', U ' pymongo ']}{u ' Date ': Datetime.datetime (one, one, one, one, one), U ' text ': U ' another post! ', U ' _id ': O Bjectid (' ... '), U ' author ': U ' Mike ', U ' tags ': [u ' bulk ', u ' insert ']}{u ' date ': Datetime.datetime (one, one, ten, ten), U ' te XT ': U ' and pretty easy too! ', U ' _id ': ObjectId (' ... '), U ' author ': U ' Eliot ', U ' title ': U ' MongoDB is fun '}

Of course, you can also constrain the search criteria:

>>> for post in Posts.find ({"Author": "Mike"}):  ... Post ... {u ' date ': Datetime.datetime (...), u ' text ': U ' My First blog post! ', U ' _id ': ObjectId (' ... '), U ' author ': U ' Mike ', U ' tags ': [ U ' MongoDB ', u ' python ', U ' pymongo ']}{u ' Date ': Datetime.datetime (one, one, one, one, one), U ' text ': U ' another post! ', U ' _id ': O Bjectid (' ... '), U ' author ': U ' Mike ', U ' tags ': [u ' bulk ', u ' Insert ']}

Gets the number of data bars for the collection:

>>> Posts.count ()

or the number of data bars that satisfy some sort of search criteria:

>>> Posts.find ({"Author": "Mike"}). Count ()

Range lookup, such as time range:

>>> d = datetime.datetime (one, one, and one) >>> for post in Posts.find ({"date": {"$lt": D}}). Sort ("author "):...  Print Post ... {u ' date ': Datetime.datetime (All, one, ten, Ten,), U ' text ': U ' and pretty easy too! ', U ' _id ': ObjectId (' ... '), U ' author ': U  ' Eliot ', U ' title ': U ' MongoDB is fun '}{u ' date ': Datetime.datetime (one, one, one, one, one), U ' text ': U ' another post! ', U ' _id ': ObjectId (' ... '), U ' author ': U ' Mike ', U ' tags ': [u ' bulk ', u ' Insert ']}

$lt is less than the meaning.

How do I build an index? For example, the following search:

>>> Posts.find ({"date": {"$lt": D}}). Sort ("Author"). Explain () ["Cursor"]u ' basiccursor ' >>> Posts.find ({"date": {"$lt": D}}). Sort ("Author"). Explain () ["nscanned"]

To build an index:

>>> from Pymongo import Ascending, descending>>> posts.create_index (["Date", descending), ("Author" , ascending)]) u ' date_-1_author_1 ' >>> posts.find ({"date": {"$lt": D}}). Sort ("Author"). Explain () ["Cursor"] U ' btreecursor date_-1_author_1 ' >>> posts.find ({"date": {"$lt": D}}). Sort ("Author"). Explain () ["nscanned"]

Connection Aggregation

>>> account = db. account# or >>> account = db["Account"]

View all aggregation names

>>> Db.collection_names ()

View a single record of a cluster

>>> db. Account.find_one () >>> db. Account.find_one ({"UserName": "Keyword"})

To view a clustered field

>>> db. Account.find_one ({},{"UserName": 1, "Email": 1}) {u ' UserName ': U ' libing ', U ' _id ': ObjectId (' 4ded95c3b7780a774a099b7c ') ), U ' Email ': U ' libing@35.cn '} >>> db. Account.find_one ({},{"UserName": 1, "email": 1, "_id": 0}) {u ' UserName ': U ' libing ', u ' email ': U ' libing@35.cn '}

View multiple records in a cluster

>>> for item in DB. Account.find ():    item >>> for item in DB. Account.find ({"UserName": "Libing"}):    item["UserName"]

To view aggregated record statistics

>>> db. Account.find (). Count () >>> db. Account.find ({"UserName": "Keyword"}). Count ()

Sorting Clustered Query Results

>>> db. Account.find (). Sort ("UserName") #默认为升序 >>> db. Account.find (). Sort ("UserName", Pymongo. Ascending)  #升序 >>> db. Account.find (). Sort ("UserName", Pymongo. Descending) #降序

Clustered query results multi-column sorting

>>> db. Account.find (). Sort ([("UserName", Pymongo. Ascending), ("Email", Pymongo. Descending)])

Add a record

>>> db. Account.insert ({"AccountID": +, "UserName": "Libing"})

Modify a record

>>> db. Account.update ({"UserName": "Libing"},{"$set": {"Email": "Libing@126.com", "Password": "123"}})

Deleting records

>>> db. Account.remove ()  --Delete all >>> db. Test.remove ({"UserName": "Keyword"})

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