["python","mongodb","pandas","pymongo"]
How to import data from mongodb to pandas?

I have a large amount of data in a collection in mongodb which I need to analyze. How do i import that data to pandas? I am new to pandas and numpy. EDIT: The mongodb collection contains sensor values tagged with date and time. The sensor values are of float datatype. Sample Data: { "_cls" : "SensorReport", "_id" : ObjectId("515a963b78f6a035d9fa531b"), "_types" : [ "SensorReport" ], "Readings" : [ { "a" : 0.958069536790466, "_types" : [ "Reading" ], "ReadingUpdatedDate" : ISODate("2013-04-02T08:26:35.297Z"), "b" : 6.296118156595, "_cls" : "Reading" }, { "a" : 0.95574014778624, "_types" : [ "Reading" ], "ReadingUpdatedDate" : ISODate("2013-04-02T08:27:09.963Z"), "b" : 6.29651468650064, "_cls" : "Reading" }, { "a" : 0.953648289182713, "_types" : [ "Reading" ], "ReadingUpdatedDate" : ISODate("2013-04-02T08:27:37.545Z"), "b" : 7.29679823731148, "_cls" : "Reading" }, { "a" : 0.955931884300997, "_types" : [ "Reading" ], "ReadingUpdatedDate" : ISODate("2013-04-02T08:28:21.369Z"), "b" : 6.29642922525632, "_cls" : "Reading" }, { "a" : 0.95821381, "_types" : [ "Reading" ], "ReadingUpdatedDate" : ISODate("2013-04-02T08:41:20.801Z"), "b" : 7.28956613, "_cls" : "Reading" }, { "a" : 4.95821335, "_types" : [ "Reading" ], "ReadingUpdatedDate" : ISODate("2013-04-02T08:41:36.931Z"), "b" : 6.28956574, "_cls" : "Reading" }, { "a" : 9.95821341, "_types" : [ "Reading" ], "ReadingUpdatedDate" : ISODate("2013-04-02T08:42:09.971Z"), "b" : 0.28956488, "_cls" : "Reading" }, { "a" : 1.95667927, "_types" : [ "Reading" ], "ReadingUpdatedDate" : ISODate("2013-04-02T08:43:55.463Z"), "b" : 0.29115237, "_cls" : "Reading" } ], "latestReportTime" : ISODate("2013-04-02T08:43:55.463Z"), "sensorName" : "56847890-0", "reportCount" : 8 }
pymongo might give you a hand, followings is some code I'm using: import pandas as pd from pymongo import MongoClient def _connect_mongo(host, port, username, password, db): """ A util for making a connection to mongo """ if username and password: mongo_uri = 'mongodb://%s:%s@%s:%s/%s' % (username, password, host, port, db) conn = MongoClient(mongo_uri) else: conn = MongoClient(host, port) return conn[db] def read_mongo(db, collection, query={}, host='localhost', port=27017, username=None, password=None, no_id=True): """ Read from Mongo and Store into DataFrame """ # Connect to MongoDB db = _connect_mongo(host=host, port=port, username=username, password=password, db=db) # Make a query to the specific DB and Collection cursor = db[collection].find(query) # Expand the cursor and construct the DataFrame df = pd.DataFrame(list(cursor)) # Delete the _id if no_id: del df['_id'] return df