Create Collection - PyMongo - Examples

PyMongo - Create a Collection in MongoDB Database

To create a collection in a MongoDB database using PyMongo in Python, follow these steps:

  1. Create a client to the MongoDB instance.
  2. Provide the name of the database to the client. This returns a reference to the database.
  3. Provide the name of the new collection by indexing the database reference with the collection name.
    This returns a reference to the collection. The collection is created when the first document is inserted into it.

Examples

1. Create a MongoDB Collection Named "testers"

The following Python program creates a collection named testers.

Python Program

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

# Use the database named "organisation"
mydb = myclient["organisation"]

# Create a new collection named "testers"
mycol = mydb["testers"]

Explanation:

  1. We import the pymongo library and establish a connection to the MongoDB instance using MongoClient.
  2. We select the organisation database using myclient["organisation"].
  3. We create a new collection named testers by referencing the database with the collection name.
  4. Note that the collection is created only when a document is inserted into it, so it won't show up in list_collection_names() until content is added.

Output

No output yet. The collection "testers" is created only after inserting data.

Note: The collection is not physically created until at least one document is inserted. You can check this by listing the collections after inserting a document into the collection.

2. Insert a Document and Verify Collection Creation

In this example, we create the collection and insert a document to confirm its creation.

Python Program

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

# Use the database named "organisation"
mydb = myclient["organisation"]

# Use the collection named "testers"
mycol = mydb["testers"]

# A document to insert
tester = { "name": "Ram", "address": "India" }

# Insert the document into the collection
x = mycol.insert_one(tester)

# List the collections after inserting the document
print("List of collections\n--------------------")
for coll in mydb.list_collection_names():
    print(coll)

Explanation:

  1. We select the organisation database and the testers collection as before.
  2. We define a tester dictionary with a name and address.
  3. The document is inserted into the collection using insert_one().
  4. Finally, we list the collections in the database. The testers collection should now appear in the list since the document was inserted.

Output

PyMongo Create Collection in MongoDB Database

3. Create Collection with Specific Options

In this example, we create a collection with specific options such as creating an index on the collection.

Python Program

import pymongo

myclient = pymongo.MongoClient("mongodb://localhost:27017/")

# Use the database named "organisation"
mydb = myclient["organisation"]

# Create a collection named "employees" with an index on the "name" field
mycol = mydb.create_collection("employees", codec_options=pymongo.codec_options.CodecOptions(tz_aware=True))

# Create an index on the "name" field
mycol.create_index([("name", pymongo.ASCENDING)])

Explanation:

  1. We create a collection named employees with specific options, such as enabling timezone-aware datetime support using codec_options.
  2. We then create an index on the name field of the collection using create_index() with ASCENDING order.
  3. This is useful for optimizing query performance when filtering or sorting by the name field.

Output:

No output yet. The collection "employees" with an index on "name" is now created.

Summary

In this PyMongo Tutorial, we learned how to create a collection in a MongoDB database using the following approaches:

  • Creating a basic collection when a document is inserted for the first time.
  • Inserting a document to verify collection creation and list collections in the database.
  • Creating a collection with specific options, such as setting up indexes for performance optimization.

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