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#List the objects in our bucket
response = s3.list_objects(Bucket=bucketname)
for item in response['Contents']:
    print(item['Key'])

If you'd want to list more than 1000 objects in a bucket, you can use paginator:

#List objects with paginator (not constrained to a 1000 objects)
paginator = s3.get_paginator('list_objects_v2')
pages = paginator.paginate(Bucket=bucketname)

objects = []
for page in pages:
    for obj in page['Contents']:
        objects.append(obj["Key"])

print('Number of objects: ', len(objects))

Where an obj looks like this: 

{'Key': 'clouds.yaml', 'LastModified': datetime.datetime(2021, 11, 18, 0, 39, 23, 320000, tzinfo=tzlocal()), 'ETag': '"0e2f62675cea45f7eee2418a4f6ba0d6-1"', 'Size': 1023, 'StorageClass': 'STANDARD'}

Now lets try to read a file from a bucket into Python's memory, so we can work with it inside Python without ever saving the file to our local computer:

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If you're interested in more, I recommend taking a look at this article, which gives you a more detailed view into boto3's functionality (although it does emphasize on Amazon Web Services specifically, you can take a look at the Python code involved):

https://dashbird.io/blog/boto3-aws-python/

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