
Querying documents is at the core of working with databases. In MongoDB, queries are powerful and flexible, and when combined with Python using the PyMongo
library, they become both simple and efficient to use.
In this tutorial, you'll learn how to perform various types of queries on a MongoDB collection using Python.
Table of Contents
-
Introduction to MongoDB Queries
-
Prerequisites
-
Installing PyMongo
-
Connecting to MongoDB
-
Inserting Sample Data
-
Basic Queries
-
Query Operators
-
Sorting Query Results
-
Limiting Query Results
-
Projecting Fields
-
Complete Working Example
-
Tips and Common Pitfalls
1. Introduction to MongoDB Queries
MongoDB uses JSON-like syntax for queries. You use key-value pairs and operators to filter documents. PyMongo allows you to express these queries using Python dictionaries.
⚙️ 2. Prerequisites
-
Python 3.x installed
-
MongoDB running locally or on MongoDB Atlas
-
Basic understanding of Python dictionaries and JSON
3. Installing PyMongo
Install the MongoDB driver for Python using pip:
pip install pymongo
4. Connecting to MongoDB
Local MongoDB server:
import pymongo
client = pymongo.MongoClient("mongodb://localhost:27017/")
MongoDB Atlas:
client = pymongo.MongoClient("mongodb+srv://<username>:<password>@<cluster>.mongodb.net/?retryWrites=true&w=majority")
5. Inserting Sample Data
Create a database and insert sample documents:
db = client["mydatabase"]
collection = db["users"]
collection.insert_many([
{"name": "Alice", "age": 25, "email": "[email protected]", "active": True},
{"name": "Bob", "age": 30, "email": "[email protected]", "active": False},
{"name": "Charlie", "age": 28, "email": "[email protected]", "active": True},
{"name": "Diana", "age": 35, "email": "[email protected]", "active": True}
])
6. Basic Queries
Find a user named "Alice":
user = collection.find_one({"name": "Alice"})
print(user)
Find all users aged 30:
results = collection.find({"age": 30})
for user in results:
print(user)
⚙️ 7. Query Operators
MongoDB supports a wide range of operators:
Operator | Description | Example |
---|---|---|
$gt |
Greater than | {"age": {"$gt": 25}} |
$lt |
Less than | {"age": {"$lt": 30}} |
$gte |
Greater than or equal | {"age": {"$gte": 30}} |
$lte |
Less than or equal | {"age": {"$lte": 25}} |
$ne |
Not equal | {"name": {"$ne": "Alice"}} |
$in |
Matches any in array | {"age": {"$in": [25, 30]}} |
$and |
Logical AND | {"$and": [{"age": {"$gt": 25}}, {"active": True}]} |
$or |
Logical OR | {"$or": [{"age": 25}, {"active": False}]} |
Example: Users over 25 and active
query = {"$and": [{"age": {"$gt": 25}}, {"active": True}]}
for user in collection.find(query):
print(user)
8. Sorting Query Results
Sort users by age ascending:
for user in collection.find().sort("age", 1):
print(user)
Sort by age descending:
for user in collection.find().sort("age", -1):
print(user)
9. Limiting Query Results
Limit the number of returned documents:
for user in collection.find().limit(2):
print(user)
10. Projecting Fields
Show only specific fields in results:
for user in collection.find({}, {"_id": 0, "name": 1, "email": 1}):
print(user)
This will exclude _id
and show only name
and email
.
11. Complete Working Example
import pymongo
# Connect to MongoDB
client = pymongo.MongoClient("mongodb://localhost:27017/")
db = client["mydatabase"]
collection = db["users"]
# Insert sample data (if not already present)
if collection.count_documents({}) == 0:
collection.insert_many([
{"name": "Alice", "age": 25, "email": "[email protected]", "active": True},
{"name": "Bob", "age": 30, "email": "[email protected]", "active": False},
{"name": "Charlie", "age": 28, "email": "[email protected]", "active": True},
{"name": "Diana", "age": 35, "email": "[email protected]", "active": True}
])
# Query with filter and projection
query = {"age": {"$gt": 25}}
projection = {"_id": 0, "name": 1, "age": 1}
print("Users older than 25:")
for user in collection.find(query, projection).sort("age", 1).limit(3):
print(user)
12. Tips and Common Pitfalls
Tip / Pitfall | Solution / Advice |
---|---|
Case-sensitive field names | Ensure exact field name matches |
Querying for non-existing fields | Returns no results instead of errors |
$in works only on lists |
Provide an array of values |
Use projection to improve performance | Especially when documents are large |
Don't forget to iterate over cursors | find() returns a cursor, not a list |
Compound queries require $and , $or |
Combine multiple conditions properly |
✅ Conclusion
MongoDB queries with Python and PyMongo are intuitive, flexible, and powerful. From basic filtering to advanced logical operators and projections, you can easily manipulate and retrieve the exact data you need.
Understanding MongoDB queries is key to leveraging its power in real-world Python applications, whether for analytics, web development, or automation.
Tips and Tricks
What is pass in Python?
Python | Pass Statement
The pass statement is used as a placeholder for future code. It represents a null operation in Python. It is generally used for the purpose of filling up empty blocks of code which may execute during runtime but has yet to be written.
def myfunction():
pass
How can you generate random numbers?
Python | Generate random numbers
Python provides a module called random using which we can generate random numbers. e.g: print(random.random())
We have to import a random module and call the random() method as shown below:
import random
print(random.random())
The random() method generates float values lying between 0 and 1 randomly.
To generate customized random numbers between specified ranges, we can use the randrange() method
Syntax: randrange(beginning, end, step)
import random
print(random.randrange(5,100,2))
What is lambda in Python?
Python | Lambda function
A lambda function is a small anonymous function. This function can have any number of parameters but, can have just one statement.
Syntex:
lambda arguments : expression
a = lambda x,y : x+y
print(a(5, 6))
It also provides a nice way to write closures. With that power, you can do things like this.
def adder(x):
return lambda y: x + y
add5 = adder(5)
add5(1) #6
As you can see from the snippet of Python, the function adder takes in an argument x and returns an anonymous function, or lambda, that takes another argument y. That anonymous function allows you to create functions from functions. This is a simple example, but it should convey the power lambdas and closures have.
What is swapcase() function in the Python?
Python | swapcase() Function
It is a string's function that converts all uppercase characters into lowercase and vice versa. It automatically ignores all the non-alphabetic characters.
string = "IT IS IN LOWERCASE."
print(string.swapcase())
How to remove whitespaces from a string in Python?
Python | strip() Function | Remove whitespaces from a string
To remove the whitespaces and trailing spaces from the string, Python provides a strip([str]) built-in function. This function returns a copy of the string after removing whitespaces if present. Otherwise returns the original string.
string = " Python "
print(string.strip())
What is the usage of enumerate() function in Python?
Python | enumerate() Function
The enumerate() function is used to iterate through the sequence and retrieve the index position and its corresponding value at the same time.
lst = ["A","B","C"]
print (list(enumerate(lst)))
#[(0, 'A'), (1, 'B'), (2, 'C')]
Can you explain the filter(), map(), and reduce() functions?
Python | filter(), map(), and reduce() Functions
- filter() function accepts two arguments, a function and an iterable, where each element of the iterable is filtered through the function to test if the item is accepted or not.
>>> set(filter(lambda x:x>4, range(7))) # {5, 6}
-
map() function calls the specified function for each item of an iterable and returns a list of result
>>> set(map(lambda x:x**3, range(7))) # {0, 1, 64, 8, 216, 27, 125}
-
reduce() function reduces a sequence pair-wise, repeatedly until we arrive at a single value..
>>> reduce(lambda x,y:y-x, [1,2,3,4,5]) # 3
Let’s understand this:
2-1=1
3-1=2
4-2=2
5-2=3Hence, 3.
What is a namedtuple?
Python | namedtuple
A namedtuple will let us access a tuple’s elements using a name/label. We use the function namedtuple() for this, and import it from collections.
>>> from collections import namedtuple
#format
>>> result=namedtuple('result','Physics Chemistry Maths')
#declaring the tuple
>>> Chris=result(Physics=86,Chemistry=92,Maths=80)
>>> Chris.Chemistry
# 92
Write a code to add the values of same keys in two different dictionaries and return a new dictionary.
We can use the Counter method from the collections module
from collections import Counter
dict1 = {'a': 5, 'b': 3, 'c': 2}
dict2 = {'a': 2, 'b': 4, 'c': 3}
new_dict = Counter(dict1) + Counter(dict2)
print(new_dict)
# Print: Counter({'a': 7, 'b': 7, 'c': 5})
Python In-place swapping of two numbers
Python | In-place swapping of two numbers
>>> a, b = 10, 20
>>> print(a, b)
10 20
>>> a, b = b, a
>>> print(a, b)
20 10
Reversing a String in Python
Python | Reversing a String
>>> x = 'PythonWorld'
>>> print(x[: : -1])
dlroWnohtyP
Python join all items of a list to convert into a single string
Python | Join all items of a list to convert into a single string
>>> x = ["Python", "Online", "Training"]
>>> print(" ".join(x))
Python Online Training
python return multiple values from functions
Python | Return multiple values from functions
>>> def A():
return 2, 3, 4
>>> a, b, c = A()
>>> print(a, b, c)
2 3 4
Python Print String N times
Python | Print String N times
>>> s = 'Python'
>>> n = 5
>>> print(s * n)
PythonPythonPythonPythonPython
Python check the memory usage of an object
Python | Check the memory usage of an object
>>> import sys
>>> x = 100
>>> print(sys.getsizeof(x))
28