A Romp through MongoDB Atlas & Python 4

Now let’s extract all the people in team blue whose last name is “Einstein”.

Here is the code:

########### THIS PART OF THE CODE REMAINS STATIC ################

#pymongo is the PyPI library we use to interface with MongoDB 
from pymongo import MongoClient

#   IMPORTANT +++ THESE YOU ALWAYS NEED +++
# (1) Connect to mongodb cluster using the connection string (ie. set up a "client")
# (2) Give mongodb the name of the database you want to access
# (3) Give mongodb the name of the collection within the database you want to access

#(1) This is the connection string
CONNECTION_STRING = "mongodb+srv://mongodbeducation:akRYE9B9ukLHaw8Z@cluster0.rikur45.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"

#(2) this is the database 
this_database = 'people_db'

#(3) This is the collection
this_collection = 'people'

#Pass the connection string to MongoDB
client = MongoClient(CONNECTION_STRING)

#Pass the name of the database
dbname = client[this_database]

#Pass collection name
collection_name = dbname[this_collection]

########### END OF STATIC CODE ########################

# +++++++++++  HERE IS THE CHANGED CODE  +++++++++++

#Let's find all the people who are members of the blue team
query01 = {"team":"blue"}
#And all the people whose name is Einstein
query02 = {"last_name":"Einstein"}

#Combine query01 & query02 using the $and operator 
this_query = {"$and":[query01, query02]}
cursor = collection_name.find(this_query)

results_list = list(cursor)

# +++++++++++  END CHANGED CODE  +++++++++++++++++

print(f'number of documents extracted: {len(results_list)}')

print('Extract from first 10 results')

for i, x in enumerate(results_list):
    print(f'i = {i}')
    print(f'{x['first_name']} {x['last_name']} is in team {x['team']}')
    if i >= 9:
        break

We combined two queries of the form:

{field name: value}

Using the $and operator

And the results:

number of documents extracted: 3
Extract from first 10 results
i = 0
Ulysses Einstein is in team blue
i = 1
Eugene Einstein is in team blue
i = 2
Martha Einstein is in team blue

Notice how we combined two queries using the “$and” operator. The $and operator takes a list. All conditions in the list must be met (all “true”) for the document to be included in the cursor list.

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In our next we’re going to discuss a different way of achieving the same result using “aggregation” instead of find.

Now let’s find all people located in London, Berlin or Wagga Wagga. We’ll uses the $or operator.

##### Static part of code goes here ###############

#People who are located in London
query01 = {"city":"London"}

#People who are located in Berlin
query02 = {"city":"Berlin"}

#People who are located in Wagga Wagga
query03 = {"city":"Wagga Wagga"}


#Combine all queries with $or operator 
this_query = {"$or":[query01, query02, query03]}
cursor = collection_name.find(this_query)

results_list = list(cursor)

# +++++++++++  END CHANGED CODE  +++++++++++++++++

print(f'number of documents extracted: {len(results_list)}')

print('Extract from first 20 results')

for i, x in enumerate(results_list):
    print(f'i = {i}')
    print(f'{x['first_name']} {x['last_name']} lives in {x['city']}')
    if i >= 19:
        break

And here is the output:

number of documents extracted: 127
Extract from first 20 results
i = 0
Solomon Xavier lives in Wagga Wagga
i = 1
Alice Maxwell lives in London
i = 2
Una Benson lives in Berlin
i = 3
Tariq Feynman lives in Berlin
i = 4
Patricia Unger lives in London
i = 5
Deborah Johnson lives in Wagga Wagga
i = 6
Evelyn Evans lives in Berlin
i = 7
Felicity Garfield lives in Wagga Wagga
i = 8
Ruth Patterson lives in Berlin
i = 9
Qunicy Deng lives in Wagga Wagga
i = 10
Yolanda Tchaikovsky lives in Wagga Wagga
i = 11
Solomon Karloff lives in London
i = 12
Fred Morton lives in Berlin
i = 13
Amelia Evans lives in Berlin
i = 14
Una Du Toit lives in London
i = 15
Reuben Dawson lives in Berlin
i = 16
Yvette Quillan lives in Wagga Wagga
i = 17
Philip Lincoln lives in Wagga Wagga
i = 18
Eugene Kahn lives in Wagga Wagga
i = 19
Lynda Du Toit lives in London

Next we extract the people who have a bonus greater than 200000, a salary less than 100000 and were born in or after 1975.

Code:

##### Static part of code goes here ###############

#People with bonus > 200000
query01 = {"bonus":{"$gt":200000}}

#salary < 100000
query02 = {"salary":{"$lt":100000}}

#born in or after 1975
query03 = {"dob.year":{"$gte":1975}}  #Note use of dot notation


this_query = {"$and":[query01, query02, query03]} #all 3 conditions must be satisfied
cursor = collection_name.find(this_query)

results_list = list(cursor)

# +++++++++++  END CHANGED CODE  +++++++++++++++++

print(f'number of documents extracted: {len(results_list)}')

print('Extract from first 10 results')

for i, x in enumerate(results_list):
    print(f'i = {i}')
    print(f'{x['first_name']} {x['last_name']}:')
    print(f'bonus: {x['bonus']} salary: {x['salary']}, Year of Birth: {x['dob']['year']}')
    print()
    if i >= 9:
        break

And output:

number of documents extracted: 113
Extract from first 10 results
i = 0
Solomon Xavier:
bonus: 365000 salary: 99000, Year of Birth: 1979

i = 1
Solomon Dawson:
bonus: 334000 salary: 77000, Year of Birth: 2002

i = 2
Albert Nixon:
bonus: 272000 salary: 84000, Year of Birth: 2003

i = 3
Tariq Einstein:
bonus: 292000 salary: 93000, Year of Birth: 1981

i = 4
Una Cohen:
bonus: 360000 salary: 76000, Year of Birth: 2000

i = 5
Kenneth Potter:
bonus: 216000 salary: 76000, Year of Birth: 1986

i = 6
Felix Sayers:
bonus: 242000 salary: 85000, Year of Birth: 1998

i = 7
Jane Gavascar:
bonus: 333000 salary: 81000, Year of Birth: 1984

i = 8
Zelde Anderson:
bonus: 315000 salary: 80000, Year of Birth: 1979

i = 9
Theresa Dawson:
bonus: 237000 salary: 91000, Year of Birth: 1983

Next we shall consider the aggregate method. It has many advantages over find.