Script to interact with MongoDB using pymongo driver

mongo_test_restaurants.py

# mongo_test_restaurants.py
# Python 2.7.6

"""
Test script to connect to MongoDB collections using pymongo library
Connects to an already imported connection named "restaurants"
source - https://docs.mongodb.org/getting-started/python/
"""

from pymongo import MongoClient, ASCENDING, DESCENDING
from datetime import datetime


# create a connection
# If no parameters passed assumes localhost and port 27017
client = MongoClient()

# connect to MongoDB database
db = client.test
# db = client["test"]  # This is also valid

# Connect to a collection
restaurants = db.restaurants
# coll = db["restaurants"]  # This is also valid

""" QUERYING DATA """

# Query all the documents in collection
cursor1 = restaurants.find()  # returns pymongo.cursor.Cursor type
# cursor = db.restaurants.find()  # This is also valid

# Query documents with filter criteria
cursor2 = restaurants.find({"borough": "Manhattan"})

# Query documents by a field in embedded document
cursor3 = restaurants.find({"address.zipcode": "10075"})


""" Conditional Operators
greater than - $gt
less than - $lt
OR - key as "$or" and value as List of dicts
AND - One dict with multiple keys
"""

# Querying using an operator
cursor4 = restaurants.find({"grades.score": {"$gt": 30}})

# Logical AND - one dict with multiple keys
cursor5 = restaurants.find({"cuisine": "Italian", "address.zipcode": "10075"})

# Logical OR - key as "$or" and value as List of dicts
cursor6 = restaurants.find({"$or": [{"cuisine": "Italian"},
                                    {"address.zipcode": "10075"}]})


""" Sorting queried data
use sort() method
pass sort parameters as list of tuples
ascending - pymongo.ASCENDING
descending - pymongo.DESCENDING
"""

cursor7 = restaurants.find({"cuisine": "Indian"}).sort([
    ("borough", ASCENDING),  # pymongo.ASCENDING
    ("grades.score", DESCENDING)])  # pymongo.DESCENDING


# Iterate the cursor and print all the documents
for document in cursor7:
    print (document)  # returns a dict variable


""" INSERTING DATA """
"""
insert one document - insert_one()
insert many documents - insert_many()
"""

record = {
    "address": {
        "street": "2 Avenue",
        "zipcode": "10075",
        "building": "1480",
        "coord": [-73.9557413, 40.7720266]
    },
    "borough": "Manhattan",
    "cuisine": "Italian",
    "grades": [
        {
            "date": datetime.strptime("2014-10-01", "%Y-%m-%d"),
            "grade": "A",
            "score": 11
        },
        {
            "date": datetime.strptime("2014-01-16", "%Y-%m-%d"),
            "grade": "B",
            "score": 17
        }
    ],
    "name": "Vella",
    "restaurant_id": "41704620"
}

result = restaurants.insert_one(record)
print type(result)  # <class 'pymongo.results.InsertOneResult'>
print result.inserted_id


""" UPDATING DATA """
"""
update one document - update_one()
update many documents - update_many()
Replace an entire document, except for _id - replace_one()

Update parameters
 - filter document (similar to query data)
 - update document (with update operators)
 - Optional upsert parameter
update operators - https://docs.mongodb.org/manual/reference/operator/update/)
"""

# To update an embedded field from the first matched document
update_result = restaurants.update_one(
    {"restaurant_id": "41156888"},  # Filter document
    {"$set": {"address.street": "East 32nd street"}}  # update document
)

print update_result.matched_count
print update_result.modified_count
print type(update_result)  # <class 'pymongo.results.UpdateResult'>


# Update multiple documents
update_result2 = restaurants.update_many(
    {"address.zipcode": "10016", "cuisine": "Other"},  # Filter documet
    {
        "$set": {"cuisine": "Category to be determined"},
        "$currentDate": {"lastModified": True}
    }  # update document
)

print update_result2.matched_count
print update_result2.modified_count
print type(update_result2)  # <class 'pymongo.results.UpdateResult'>


""" DELETING DATA """
"""
delete single document - delete_one()
delete mulitple documents - delete_many()
to drop a collection - restaurants.drop()
"""

delete_result = restaurants.delete_many({"borough": "Manhattan"})

print delete_result.deleted_count
print type(delete_result)  # <class 'pymongo.results.DeleteResult'>


""" DATA AGGREGATION """
"""
To aggregate data (such as grouping or total or count) - aggregate()
db.collection.aggregate([<stage1>,<stage2>,..])
each stage is processed sequentially
"""

# Group documents and calculate count
agg_cursor = restaurants.aggregate(
    [
        {"$group": {"_id": "$borough",  # Group by "borough" field
                    "count": {"$sum": 1}}}  # count by "$sum" accumulator
    ]
)

for document in agg_cursor:
    print (document)


# Filter and group documents
agg_cursor2 = restaurants.aggregate(
    [
        {"$match": {"borough": "Queens", "cuisine": "Brazilian"}},
        {"$group": {"_id": "$address.zipcode", "count": {"$sum": 1}}}
    ]
)

for document in agg_cursor2:
    print (document)


""" Indexing Data """
"""
to create single index or compound index - create_index()
index is automatically created for _id
only creates index if index does not exist
"""

# To create a single index
idx = restaurants.create_index([("cuisine", ASCENDING)])

print idx  # cuisine_1
print type(idx)  # <type 'unicode'>

# To create a compound index
idx2 = restaurants.create_index([
    ("cuisine", ASCENDING),
    ("address.zipcode", DESCENDING)
])

print idx2  # cuisine_1_address.zipcode_-1
print type(idx2)  # <type 'unicode'>