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Aggregation Tutorials

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On this page

  • Introduction
  • Aggregation Task Summary
  • Before You Get Started
  • Tutorial
  • Add a match stage for people who are engineers
  • Add a sort stage to sort from youngest to oldest
  • Add a limit stage to see only three results
  • Add an unset stage to remove unneeded fields
  • Run the aggregation pipeline
  • Interpret results

Introduction

In this tutorial, you can learn how to use PyMongo to construct an aggregation pipeline, perform the aggregation on a collection, and print the results by completing and running a sample app. This aggregation performs the following operations:

  • Matches a subset of documents by a field value

  • Formats result documents

Aggregation Task Summary

This tutorial demonstrates how to query a collection for a specific subset of documents in a collection. The results contain documents that describe the three youngest people who are engineers.

This example uses one collection, persons, which contains documents describing people. Each document includes a person's name, date of birth, vocation, and other details.

Before You Get Started

Before you start this tutorial, complete the Aggregation Template App instructions to set up a working Python application.

After you set up the app, access the persons collection by adding the following code to the application:

person_coll = agg_db["persons"]

Delete any existing data in the collections and insert sample data into the persons collection as shown in the following code:

person_coll.delete_many({})
person_data = [
    {
        "person_id": "6392529400",
        "firstname": "Elise",
        "lastname": "Smith",
        "dateofbirth": datetime(1972, 1, 13, 9, 32, 7),
        "vocation": "ENGINEER",
        "address": {
            "number": 5625,
            "street": "Tipa Circle",
            "city": "Wojzinmoj",
        }
    },
    {
        "person_id": "1723338115",
        "firstname": "Olive",
        "lastname": "Ranieri",
        "dateofbirth": datetime(1985, 5, 12, 23, 14, 30),
        "gender": "FEMALE",
        "vocation": "ENGINEER",
        "address": {
            "number": 9303,
            "street": "Mele Circle",
            "city": "Tobihbo",
        }
    },
    {
        "person_id": "8732762874",
        "firstname": "Toni",
        "lastname": "Jones",
        "dateofbirth": datetime(1991, 11, 23, 16, 53, 56),
        "vocation": "POLITICIAN",
        "address": {
            "number": 1,
            "street": "High Street",
            "city": "Upper Abbeywoodington",
        }
    },
    {
        "person_id": "7363629563",
        "firstname": "Bert",
        "lastname": "Gooding",
        "dateofbirth": datetime(1941, 4, 7, 22, 11, 52),
        "vocation": "FLORIST",
        "address": {
            "number": 13,
            "street": "Upper Bold Road",
            "city": "Redringtonville",
        }
    },
    {
        "person_id": "1029648329",
        "firstname": "Sophie",
        "lastname": "Celements",
        "dateofbirth": datetime(1959, 7, 6, 17, 35, 45),
        "vocation": "ENGINEER",
        "address": {
            "number": 5,
            "street": "Innings Close",
            "city": "Basilbridge",
        }
    },
    {
        "person_id": "7363626383",
        "firstname": "Carl",
        "lastname": "Simmons",
        "dateofbirth": datetime(1998, 12, 26, 13, 13, 55),
        "vocation": "ENGINEER",
        "address": {
            "number": 187,
            "street": "Hillside Road",
            "city": "Kenningford",
        }
    }
]
person_coll.insert_many(person_data)

Tutorial

1

Add a match stage for people who are engineers

First, add a $match stage that finds documents in which the value of the vocation field is "ENGINEER":

pipeline.append({
    "$match": {
        "vocation": "ENGINEER"
    }
})
2

Add a sort stage to sort from youngest to oldest

Next, add a $sort stage that sorts the documents in descending order by the dateofbirth field to list the youngest people first. Because Python dictionaries don't maintain the order of their elements, use a SON``or ``OrderedDict object instead:

pipeline.append({
    "$sort": {
        "dateofbirth": -1
    }
})
3

Add a limit stage to see only three results

Next, add a $limit stage to the pipeline to output only the first three documents in the results.

pipeline.append({
    "$limit": 3
})
4

Add an unset stage to remove unneeded fields

Finally, add an $unset stage. The $unset stage removes unnecessary fields from the result documents:

pipeline.append({
    "$unset": [
        "_id",
        "address"
    ]
})

Tip

Use the $unset operator instead of $project to avoid modifying the aggregation pipeline if documents with different fields are added to the collection.

5

Run the aggregation pipeline

Add the following code to the end of your application to perform the aggregation on the persons collection:

aggregation_result = person_coll.aggregate(pipeline)

Finally, run the following command in your shell to start your application:

python3 agg_tutorial.py
6

Interpret results

The aggregated result contains three documents. The documents represent the three youngest people with the vocation of "ENGINEER", ordered from youngest to oldest. The results omit the _id and address fields.

{
  'person_id': '7363626383',
  'firstname': 'Carl',
  'lastname': 'Simmons',
  'dateofbirth': datetime.datetime(1998, 12, 26, 13, 13, 55),
  'vocation': 'ENGINEER'
}
{
  'person_id': '1723338115',
  'firstname': 'Olive',
  'lastname': 'Ranieri',
  'dateofbirth': datetime.datetime(1985, 5, 12, 23, 14, 30),
  'gender': 'FEMALE',
  'vocation': 'ENGINEER'
}
{
  'person_id': '6392529400',
  'firstname': 'Elise',
  'lastname': 'Smith',
  'dateofbirth': datetime.datetime(1972, 1, 13, 9, 32, 7),
  'vocation': 'ENGINEER'
}

To view the complete code for this tutorial, see the Completed Filtered Subset App on GitHub.

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