Using MongoDB Aggregation Stages with Python: $match and $group

Review the following code, which demonstrates how to use the $match and $group stages in a MongoDB aggregation pipeline by using PyMongo.

Using $match

When we build queries by using the aggregation framework, each stage transforms or organizes data in a specific way. In this lesson, we used the $match and $group stages.

Use the $match operator to select documents that match the specified query condition(s) and pass the matching documents to the next stage. $match takes a document that specifies the query.

$match should be placed early in a pipeline to reduce the number of documents that will be processed later in the pipeline.

Here's an example of the $match stage:

# Select accounts with balances of less than $1000.
select_by_balance = {"$match": {"balance": {"$lt": 1000}}}

Using $group

Use the $group stage to separate documents into groups. The $group stage must have an _id field that specifies the group key. The group key is preceded by a $ and enclosed in quotation marks.

A $group stage can include additional field(s) that are computed by using accumulator operators, such as $avg.

Here's an example of the $group stage:

# Separate documents by account type and calculate the average balance for each account type.
separate_by_account_calculate_avg_balance = {
    "$group": {"_id": "$account_type", "avg_balance": {"$avg": "$balance"}}
}

Aggregation Example That Uses $match and $group

The following is an example of an aggregation pipeline that uses $match and $group.

# Connect to MongoDB cluster with MongoClient
client = MongoClient(MONGODB_URI)

# Get reference to 'bank' database
db = client.bank

# Get reference to 'accounts' collection
accounts_collection = db.accounts

# Calculate the average balance of checking and savings accounts with balances of less than $1000.

# Select accounts with balances of less than $1000.
select_by_balance = {"$match": {"balance": {"$lt": 1000}}}

# Separate documents by account type and calculate the average balance for each account type.
separate_by_account_calculate_avg_balance = {
    "$group": {"_id": "$account_type", "avg_balance": {"$avg": "$balance"}}
}

# Create an aggegation pipeline using 'stage_match_balance' and 'stage_group_account_type'.
pipeline = [
    select_by_balance,
    separate_by_account_calculate_avg_balance,
]

# Perform an aggregation on 'pipeline'.
results = accounts_collection.aggregate(pipeline)

print()
print(
    "Average balance of checking and savings accounts with balances of less than $1000:", "\n"
)

for item in results:
    pprint.pprint(item)

client.close()