pymongo

I have the following JSON stored in my MongoDB: { "_id" : ObjectId("54fed786265e7f01d66ca778"), "id" : "http://some.site.somewhere/entry-schema#", "schema" : "http://json-schema.org/draft-04/schema#", "description" : "schema for an fstab entry", "type" : "object", "required" : [ "storage" ], "properties" : { "storage" : { "type" : "object", "oneOf" : [ DBRef("#/definitions/diskDevice", 1), DBRef("#/definitions/diskUUID", 2), DBRef("#/definitions/nfs", 3), DBRef("#/definitions/tmpfs", 4) ] } }, "definitions" : { "diskDevice" : { }, "diskUUID" : { }, "nfs" : { }, "tmpfs" : { } } } I have written

pip install pymongo import pymongo # 建立连接 client = pymongo.MongoClient() # 指定数据库 （不存在则会新建） db = client['py_mongo'] # 删除数据库 # client.drop_database('py_mongo_temp') # 创建集合 # db.create_collection('col_temp') # 删除集合 # print(db.drop_collection('col_temp')) # 指定集合 （不存在则会新建） collection = db['mongo_col'] # collection = pymongo.MongoClient()['py_mongo']['mongo_col'] 基本使用 ：insert() remove() update() find() # 增 insert() # 如果不指定_id参数，MongoDB会为文档分配一个唯一的ObjectId # 增加一条 # collection.insert({'_id':1,'name':'JiYu','num':0}) # 增加多条 # collection.insert( [ # {'name':'jiyu','num':12}, # {'name':'jiyu','num':34}, #

Im trying to build a simple application in python, where I have tags that I associated to tags. Given the following data: Book: +-------------+--------------------------------+ | id | tags | +-------------+--------------------------------+ | 1 | [python, ruby, rails] | +-------------+--------------------------------+ | 2 | [fiction, fantasy] | +-------------+--------------------------------+ | 3 | [fiction, adventure] | +-------------+--------------------------------+ How would I (using pymongo) find: All books tagged as "fiction" All unique tags in the system 1. Find all books by tag: db

So I wanted to do some experiments with location data in mongodb, so I wrote some python code to generate some testing data. Unfortunately the documentation at http://docs.mongoengine.org/apireference.html#mongoengine.fields.PointField isn't explicit about how to format the input. class Location(db.Document): coord = db.PointField(required=True) # GeoJSON Trying to store a list containing the lng/lat fails: >>> a = Location(coord=[1,2]) >>> a.save() mongoengine.errors.OperationError: Could not save document (location object expected, location array not in correct format) Passing a geoJSON

I'm looking at mongodb tutorials . I want to make use of some javascript mongodb functions, but I can't see how I can do this with pymongo. I can fire up mongo shell, but this has to be done via pymongo as a part of my wsgi app. pymongo seems to have Code object - How can I 1)put some Code objects into my db, and 2)use them with pymongo? Asya Kamsky Any JavaScript functions you want to run on the server have to be executed with "eval" command . You can see how to invoke that from Python here . You can invoke functions you have stored on the server though it's not recommended. In general,

I am using PyMongo to simply iterate over a Mongo collection, but I'm struggling with handling large Mongodb date objects. For example, if I have some data in a collection that looks like this: "bad_data" : [ { "id" : "id01", "label" : "bad_data", "value" : "exist", "type" : "String", "lastModified" : ISODate("2018-06-01T10:04:35.000Z"), "expires" : Date(9223372036854775000) } ] I will do something like: from pymongo import MongoClient, database, cursor, collection client = MongoClient('localhost') db = client['db1'] db.authenticate('user', 'pass', source='admin') collection = db['collection']

I have two collections, a model and a papers collection. I need to be able to match fields from both of them. They have a field in common called reference which contains an identifier. I want to match documents that have the following 'authors' : 'Migliore M' from the papers collection 'celltypes' : 'Hippocampus CA3 pyramidal cell' from the models collection Here is what my code looks like: pipeline = [{'$lookup': {'from' : 'models', 'localField' : 'references', 'foreignField' : 'references', 'as' : 'cellmodels'}}, {'$match': {'authors' : 'Migliore M', 'cellmodels.celltypes' : 'Hippocampus CA3