s3-datakit 0.1.0

A Python toolkit to simplify common operations between S3 and Pandas.

S3 DataKit 🧰

A Python toolkit to simplify common operations between Amazon S3 and Pandas DataFrames.

Key Features

• List files in an S3 bucket.
• Upload local files to S3.
• Download files from S3 directly to a local path or a Pandas DataFrame.
• Supports CSV and Stata (.dta) when reading into DataFrames.

Installation

pip install s3-datakit

Credential Configuration

This package uses boto3 to interact with AWS. boto3 will automatically search for credentials in the following order:

1. Environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, etc.).
2. The AWS CLI credentials file (~/.aws/credentials).
3. IAM roles (if running on an EC2 instance or ECS container).

For local development, the easiest method is to use a .env file.

1. Install python-dotenv in your project (not as a library dependency):

pip install python-dotenv

2. Create a .env file in your project's root:

AWS_ACCESS_KEY_ID=YOUR_ACCESS_KEY
AWS_SECRET_ACCESS_KEY=YOUR_SECRET_KEY
AWS_DEFAULT_REGION=your-region # e.g., us-east-1

3. Load the variables in your script before using s3datakit:

from dotenv import load_dotenv
import s3datakit as s3dk

# Load environment variables from .env
load_dotenv()

# Now you can use the package's functions
s3dk.list_s3_files(bucket="my-bucket")

Usage

List Files

import s3datakit as s3dk

file_list = s3dk.list_s3_files(bucket="my-data-bucket")
if file_list:
    print(file_list)

Upload a File

import s3datakit as s3dk

s3dk.upload_s3_file(
    local_path="reports/report.csv",
    bucket="my-data-bucket",
    s3_path="final-reports/report_2025.csv"
)

Download a File

Option 1: Download to a local path

import s3datakit as s3dk

local_file = s3dk.download_s3_file(
    bucket="my-data-bucket",
    s3_path="final-reports/report_2025.csv",
    local_path="downloads/report.csv"
)
print(f"File downloaded to: {local_file}")

Option 2: Download directly to a Pandas DataFrame

import s3datakit as s3dk

df = s3dk.download_s3_file(
    bucket="my-data-bucket",
    s3_path="stata-data/survey.dta",
    local_path="temp/survey.dta",
    to_df=True
)
print(df.head())