In [1]:

    

import pymongo


    

In [2]:

    

pymongo.version


    

    
Out[2]:





'3.6.1'

In [3]:

    

from pymongo import MongoClient


    

In [9]:

    

client = MongoClient('localhost')


    

In [5]:

    

db = client.packt


    

In [6]:

    

testCollection = db.testCollection


    

In [7]:

    

testCollection.insert_one({'a': 1})


    

    
Out[7]:





<pymongo.results.InsertOneResult at 0x11137e3c8>

In [8]:

    

testCollection.find_one()


    

    
Out[8]:





{'_id': ObjectId('5b3512b4de17baeac545c57c'), 'a': 1}

In [10]:

    

testCollection.count()


    

    
Out[10]:





1

In [14]:

    

# Find Matching Collection
res = testCollection.insert_one({'b':2})


    

In [12]:

    

res.inserted_id


    

    
Out[12]:





ObjectId('5b3513b9de17baeac545c57e')

In [13]:

    

testCollection.find_one({'b':2})


    

    
Out[13]:





{'_id': ObjectId('5b3513b9de17baeac545c57e'), 'b': 2}

In [15]:

    

# Find record but do not return the _id as part of the result(projection operation)
testCollection.find_one({'b':2},{'_id':0})


    

    
Out[15]:





{'b': 2}

In [17]:

    

# Drop Collection and Database
db.drop_collection('testCollection')


    

    
Out[17]:





{'ns': 'packt.testCollection', 'nIndexesWas': 1, 'ok': 1.0}

In [18]:

    

db.drop_collection('testCollection')


    

    
Out[18]:





{'ok': 0.0,
 'errmsg': 'ns not found',
 'code': 26,
 'codeName': 'NamespaceNotFound'}

In [19]:

    

client.drop_database('packt')


    

In [20]:

    

# xrabne only retrieves values when needed --> GENERATORS


    

In [21]:

    

# Mongo Cursors
cur = db.collection.find()
for doc in cur:
    print(doc)


    

In [26]:

    

import random, string


    

In [28]:

    

letters = list(string.ascii_lowercase)


    

In [29]:

    

letters[:5]


    

    
Out[29]:





['a', 'b', 'c', 'd', 'e']

In [30]:

    

rs = testCollection.insert_many([{random.choice(letters):random.randint(1,10)} for i in range(10)])


    

In [31]:

    

rs.acknowledged


    

    
Out[31]:





True

In [32]:

    

rs.inserted_ids


    

    
Out[32]:





[ObjectId('5b3516adde17baeac545c580'),
 ObjectId('5b3516adde17baeac545c581'),
 ObjectId('5b3516adde17baeac545c582'),
 ObjectId('5b3516adde17baeac545c583'),
 ObjectId('5b3516adde17baeac545c584'),
 ObjectId('5b3516adde17baeac545c585'),
 ObjectId('5b3516adde17baeac545c586'),
 ObjectId('5b3516adde17baeac545c587'),
 ObjectId('5b3516adde17baeac545c588'),
 ObjectId('5b3516adde17baeac545c589')]

In [33]:

    

cur = testCollection.find()


    

In [34]:

    

type(cur)


    

    
Out[34]:





pymongo.cursor.Cursor

In [35]:

    

cur.count()


    

    
Out[35]:





10

In [36]:

    

for doc in cur:
    print(doc)


    

    




{'_id': ObjectId('5b3516adde17baeac545c580'), 'e': 1}
{'_id': ObjectId('5b3516adde17baeac545c581'), 'k': 6}
{'_id': ObjectId('5b3516adde17baeac545c582'), 'm': 3}
{'_id': ObjectId('5b3516adde17baeac545c583'), 'f': 5}
{'_id': ObjectId('5b3516adde17baeac545c584'), 'f': 5}
{'_id': ObjectId('5b3516adde17baeac545c585'), 'q': 3}
{'_id': ObjectId('5b3516adde17baeac545c586'), 's': 2}
{'_id': ObjectId('5b3516adde17baeac545c587'), 'c': 4}
{'_id': ObjectId('5b3516adde17baeac545c588'), 'i': 4}
{'_id': ObjectId('5b3516adde17baeac545c589'), 'x': 2}

In [37]:

    

cur.alive


    

    
Out[37]:





False

In [38]:

    

# cur is a connectio to database hence CLOSE it
cur.close()


    

In [39]:

    

db.drop_collection('testCollection')


    

    
Out[39]:





{'ns': 'packt.testCollection', 'nIndexesWas': 1, 'ok': 1.0}

In [40]:

    

# find(query, projection) -> query: return only conditions that match, projection: return only fields[values: 0,1]


    

In [42]:

    

db = client.packt


    

In [43]:

    

coll = db.embedded


    

In [44]:

    

res = coll.insert_one({'ages': [30,42,12,19]})


    

In [52]:

    

coll.insert_one({'ages': [18,34,56,23,11]})


    

    
Out[52]:





<pymongo.results.InsertOneResult at 0x111485b88>

In [53]:

    

coll.find_one({'ages': 30}, {'_id': 0})


    

    
Out[53]:





{'ages': [30, 42, 12, 19]}

In [56]:

    

# Match arrayw ith conditinos on any element of array
coll.find_one({'ages': {'$gt': 42, '$lt': 20}}, {'_id': 0})


    

    
Out[56]:





{'ages': [18, 34, 56, 23, 11]}

In [58]:

    

# Match array with conditions on any single element of array
coll.find_one({'ages': {'$elemMatch': {'$lt': 22, '$gt': 15}}}, {'_id': 0})


    

    
Out[58]:





{'ages': [30, 42, 12, 19]}

In [62]:

    

# Match by array index
coll.find_one({'ages.1':42}, {'_id':0})


    

    
Out[62]:





{'ages': [30, 42, 12, 19]}

In [63]:

    

coll.find_one({'ages.1': {'$gt':40}}, {'_id': 0})


    

    
Out[63]:





{'ages': [30, 42, 12, 19]}

In [64]:

    

res = coll.insert_one({'personInfo': {'name': 'Alice', 'age':30}})
res = coll.insert_one({'personInfo': {'name': 'Chuks', 'age':53}})
res = coll.insert_one({'personInfo': {'name': 'Bob', 'age':42}})
res = coll.insert_one({'personInfo': {'name': 'Charlie', 'age':12}})
res = coll.insert_one({'personInfo': {'name': 'Davids', 'age':19}})


    

In [72]:

    

# Match by Age
coll.find_one({'personInfo.age': {'$gt': 40}}, {'_id':0})


    

    
Out[72]:





{'personInfo': {'name': 'Chuks', 'age': 53}}

In [73]:

    

# Match by Name
coll.find_one({'personInfo.name': 'Alice'}, {'_id': 0})


    

    
Out[73]:





{'personInfo': {'name': 'Alice', 'age': 30}}

In [74]:

    

# Updates: update_one() requires an operator e.g. $set, $inc, $unset, /$rename
# update_one({key: {'$inc': 10}})


    

In [75]:

    

# Comparison Operators: $gt, $lt, $neq, $lte


    

In [76]:

    

# Aggregate Commands: Computes stats e.g. sums, means
# Format: aggregate(pipeline = [<pipeline steps>])


    

In [78]:

    

# Example:
# aggregate([
#     {key: value},{"$group": 
#                  {"_id": null, 
#                   "total": {"$sum": value}}}
# ])


    

In [79]:

    

salaries = db.salaries


    

In [80]:

    

type(salaries)


    

    
Out[80]:





pymongo.collection.Collection

In [81]:

    

salaries.insert_one({'name': 'Alice', 'salary': 50000})
salaries.insert_one({'name': 'Bob', 'salary': 40000})
salaries.insert_one({'name': 'Charlie', 'salary': 60000})


    

    
Out[81]:





<pymongo.results.InsertOneResult at 0x11149b608>

In [82]:

    

# Update One record
salaries.update_one({'name': 'Alice'}, {'$set': {'salary': 55000}})


    

    
Out[82]:





<pymongo.results.UpdateResult at 0x1114ad208>

In [84]:

    

# Update a field value
salaries.find_one({'name': 'Alice'})


    

    
Out[84]:





{'_id': ObjectId('5b3542e3de17baeac545c591'), 'name': 'Alice', 'salary': 55000}

In [85]:

    

# Remove a field from Document
alice = salaries.update_one({'name': 'Alice'}, {'$unset': {'salary': ''}})


    

In [89]:

    

salaries.find_one({'name': 'Alice'}, {'_id': 0})


    

    
Out[89]:





{'name': 'Alice'}

In [105]:

    

salaries.find().count()


    

    
Out[105]:





3

In [123]:

    

pipeline = []


    

In [124]:

    

pipeline.append({"$match": {"salary": {'$exists': "True"}}})


    

In [125]:

    

pipeline.append({'$group': {"_id":None, "avSalary": {"$avg": "$salary"}, "totalSalary": {"$sum":"$salary"}}})


    

In [127]:

    

cursor = salaries.aggregate(pipeline=pipeline)


    

In [128]:

    

cursor.next()


    

    
Out[128]:





{'_id': None, 'avSalary': 50000.0, 'totalSalary': 100000}

In [129]:

    

for d in salaries.find():
    print(d)


    

    




{'_id': ObjectId('5b3542e3de17baeac545c591'), 'name': 'Alice'}
{'_id': ObjectId('5b3542e3de17baeac545c592'), 'name': 'Bob', 'salary': 40000}
{'_id': ObjectId('5b3542e3de17baeac545c593'), 'name': 'Charlie', 'salary': 60000}

In [130]:

    

depts = db.sals


    

In [131]:

    

type(depts)


    

    
Out[131]:





pymongo.collection.Collection

In [132]:

    

depts.insert_one({'name':'Alice', 'salary': 50000, 'unit': 'legal'})
depts.insert_one({'name':'Bob', 'salary': 40000, 'unit': 'marketing'})
depts.insert_one({'name':'Charlie', 'salary': 60000, 'unit': 'communications'})
depts.insert_one({'name':'David', 'salary': 70000, 'unit': 'legal'})
depts.insert_one({'name':'Edwina', 'salary': 90000, 'unit': 'communications'})


    

    
Out[132]:





<pymongo.results.InsertOneResult at 0x1114d2148>

In [136]:

    

pipeline = []
# Aggregate based on unit
pipeline.append({'$group': {"_id": "$unit", "avSalary": {"$avg": "$salary"}, "totalSalary": {"$sum": "$salary"}}})


    

In [134]:

    

cur_depts = depts.aggregate(pipeline=pipeline)


    

In [135]:

    

for c in cur_depts:
    print(c)


    

    




{'_id': 'communications', 'avSalary': 75000.0, 'totalSalary': 150000}
{'_id': 'marketing', 'avSalary': 40000.0, 'totalSalary': 40000}
{'_id': 'legal', 'avSalary': 60000.0, 'totalSalary': 120000}

In [ ]: