boto3 Archives - Radish Logic

How to load a JSON file from S3 to a Python dictionary using boto3

If you want to get a JSON file from an S3 Bucket and load it into a Python Dictionary then you can use the example codes below.

There are 4 scenarios for the examples scripts below.

Basic JSON file from S3 to Python Dictionary
With Try/Except block
With datetime, date, and time conversions
Running the code in a Lambda Function

AWS boto3 provides 2 ways to access S3 files, the boto3.client('s3') and boto3.resource('s3'). For each of the example scenarios above, a code will be provided for the two methods.

Since both method will function the same, you can choose whichever method you like.

How to read a file in S3 and store it in a String using Python and boto3

If you want to get a file from an S3 Bucket and then put it in a Python string, try the examples below.

boto3, the AWS SDK for Python, offers two distinct methods for accessing files or objects in Amazon S3: client method and the resource method.

Option 1 uses the boto3.client('s3') method, while options 2 and 3 use the boto3.resource('s3') method.

All 3 options do the exact same thing so get the one that you feel comfortable with or the one that will fit your use case.

How to write a Dictionary to JSON file in S3 Bucket using boto3 and Python

If you want to write a python dictionary to a JSON file in S3 then you can use the code examples below.

There are two code examples doing the same thing below because boto3 provides a client method and a resource method to edit and access AWS S3.

Writing Python Dictionary to an S3 Object using boto3 Client

import boto3
import json
from datetime import date

data_dict = {
    'Name': 'Daikon Retek',
    'Birthdate': date(2000, 4, 7),
    'Subjects': ['Math', 'Science', 'History']
}

# Convert Dictionary to JSON String
data_string = json.dumps(data_dict, indent=2, default=str)


# Upload JSON String to an S3 Object
client = boto3.client('s3')

client.put_object(
    Bucket='radishlogic-bucket', 
    Key='s3_folder/client_data.json',
    Body=data_string
)

How to upload a file to S3 Bucket using boto3 and Python

There are 3 ways to upload or copy a file from your local computer to an Amazon Web Services (AWS) S3 Bucket using boto3. All of these will be discussed in this post including multipart uploads.

The codes below will work if you are Windows, Mac, and Linux. Will also work if you working in a Lambda function using Python.

Each method will have an example using boto3 S3 client and S3 resource so you can use whatever method you are comfortable with.

upload_file method
upload_fileobj method (supports multipart upload)
put_object method

How to write Python string to a file in S3 Bucket using boto3

To write a file from a Python string directly to an S3 bucket we need to use the boto3 package.

There are 2 ways to write a file in S3 using boto3. The first is via the boto3 client, and the second is via the boto3 resource. Both of these methods will be shown below.

S3 objects and keys

If you are new to AWS S3, you might be confused with some of the terms. So we’ll define some of them here. If you already know what objects and keys are then you can skip this section.

S3 objects are the same as files. When we run the method put_object what it means is that we are putting a file into S3.

S3 keys are the same as the filename with its full path. So if we want to create an object in S3 with the name of filename.txt within the foobar folder then the key is foobar/filename.txt.

Now that we have clarified some of the AWS S3 terms, follow the details below to start writing Python strings directly to objects in S3.

boto3: Convert AMI Creation Date from string to Python datetime

When retrieving the AMI Creation Date from boto3 it returns a string data type. Visually, this is okay but it is challenging to do operations and comparisons to the AMI Creation Date in this format.

To solve the issue we need to convert the AMI Creation Date from type string to datetime before we could do some operations.

The AMI Creation Date string looks like 2019-09-18T07:34:34.000Z. To convert this we need to use the strptime function from the datetime.datetime library.