Python MongoDB - $group (aggregation)

In PyMongo, the aggregate() method processes data through a pipeline of stages to produce aggregated results. One key stage is $group, which groups documents based on a specified identifier like a field name and applies accumulator operations e.g., $sum, $avg, $max. It then outputs a new set of documents representing the grouped and processed data.

Syntax

{
$group: {
_id: <grouping field>,
<newField>: { <accumulator>: <expression> }
}
}

Parameters:

• _id: The field to group the documents by (e.g., group by "product name" or "category").
• <newField>: The result field where you store the output (like total, average, etc.).
• <accumulator>: The operation to perform on each group.
• <expression>: The value to apply the operation on (e.g., the "amount" field).

Here is our sample data.

Output

Data inserted.

Explanation:

• MongoClient() connects to the local server and selects grpDB.sales.
• insert_many(data) loads sample orders into the sales collection.
• delete_many({}) clears previous data to avoid duplicates.

Examples

Example 1: Average amount per user

Output

Explanation:

• $group groups documents by user.
• $avg calculates average amount spent by each user.

Example 2: Total amount per product

Output

Explanation:

• $group organizes documents by product.
• $sum computes the total sales amount for each product.

Example 3: Max amount spent per user

Output

Explanation:

• $group groups orders by user.
• $max finds the highest amount spent by each user in a single transaction.

Related Articles

• Insert and Update Data Query
• MongoDB Aggregation Pipeline
• How to Setup MongoDB with Python on Localhost