Load S3 Data into AWS SageMaker Notebook

I’ve just started to experiment with AWS SageMaker and would like to load data from an S3 bucket into a pandas dataframe in my SageMaker python jupyter notebook for analysis.

I could use boto to grab the data from S3, but I’m wondering whether there is a more elegant method as part of the SageMaker framework to do this in my python code?

Thanks in advance for any advice.

Answers:

Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.

Method 1

import boto3
import pandas as pd
from sagemaker import get_execution_role

role = get_execution_role()
bucket='my-bucket'
data_key = 'train.csv'
data_location = 's3://{}/{}'.format(bucket, data_key)

pd.read_csv(data_location)

Method 2

In the simplest case you don’t need boto3, because you just read resources.
Then it’s even simpler:

import pandas as pd

bucket='my-bucket'
data_key = 'train.csv'
data_location = 's3://{}/{}'.format(bucket, data_key)

pd.read_csv(data_location)

But as Prateek stated make sure to configure your SageMaker notebook instance to have access to s3. This is done at configuration step in Permissions > IAM role

Method 3

If you have a look here it seems you can specify this in the InputDataConfig. Search for “S3DataSource” (ref) in the document. The first hit is even in Python, on page 25/26.

Method 4

You could also access your bucket as your file system using s3fs

import s3fs
fs = s3fs.S3FileSystem()

# To List 5 files in your accessible bucket
fs.ls('s3://bucket-name/data/')[:5]

# open it directly
with fs.open(f's3://bucket-name/data/image.png') as f:
    display(Image.open(f))

Method 5

Do make sure the Amazon SageMaker role has policy attached to it to have access to S3. It can be done in IAM.

Method 6

You can also use AWS Data Wrangler https://github.com/awslabs/aws-data-wrangler:

import awswrangler as wr

df = wr.s3.read_csv(path="s3://...")

Method 7

A similar answer with the f-string.

import pandas as pd
bucket = 'your-bucket-name'
file = 'file.csv'
df = pd.read_csv(f"s3://{bucket}/{file}")
len(df) # print row counts

Method 8

This code sample to import csv file from S3, tested at SageMaker notebook.

Use pip or conda to install s3fs. !pip install s3fs

import pandas as pd

my_bucket = '' #declare bucket name
my_file = 'aa/bb.csv' #declare file path

import boto3 # AWS Python SDK
from sagemaker import get_execution_role
role = get_execution_role()

data_location = 's3://{}/{}'.format(my_bucket,my_file)
data=pd.read_csv(data_location)
data.head(2)

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0