s3iotools 0.0.3

Make S3 file object read/write easier, support raw file, csv, parquet, pandas.DataFrame.

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Welcome to s3iotools Documentation

Usage

Copy local file to s3 and download file object from s3 to local is easy:

from s3iotools import S3FileObject

s3obj = S3FileObject(bucket="my-bucket", key="hello.txt", path="hello.txt")

# get started, now we don't have file either on local or on s3
if s3obj.path_obj.exists():
    s3obj.path_obj.remove()
assert s3obj.exists_on_local() is False
assert s3obj.exists_on_s3() is False

s3obj.path_obj.write_text("hello world", encoding="utf-8)
assert s3obj.exists_on_local() is True

s3obj.copy_to_s3()
assert s3obj.exists_on_s3() is True

s3obj.path_obj.remove()
assert s3obj.exists_on_local() is False

s3obj.copy_to_local()
assert s3obj.exists_on_local() is True

You can manipulate s3 backed pandas.DataFrame easily:

import boto3
import pandas as pd
from s3iotools import S3Dataframe

session = boto3.Session(profile_name="xxx")
s3 = session.resource("s3")
bucket_name = "my-bucket"
s3df = S3Dataframe(s3_resource=s3, bucket_name=bucket_name)
s3df.df = pd.DataFrame(...)

s3df.to_csv(key="data.csv")
s3df.to_csv(key="data.csv.gz", gzip_compressed=True)

s3df_new = S3Dataframe(s3_resource=s3, bucket_name=bucket_name, key="data.csv")
s3df_new.read_csv()
s3df_new.df # access data

s3df_new = S3Dataframe(s3_resource=s3, bucket_name=bucket_name, key="data.csv.gz")
s3df_new.read_csv(gzip_compressed=True)
s3df_new.df # access data

json IO is similar.

s3df = S3Dataframe(s3_resource=s3, bucket_name=bucket_name)
s3df.df = pd.DataFrame(...)
s3df.to_json(key="data.json.gz", gzip_compressed=True)
s3df_new = S3Dataframe(s3_resource=s3, bucket_name=bucket_name, key="data.json.gz")
s3df_new.read_json(gzip_compressed=True)
s3df_new.df # access data

parquet is a columnar storage format, which is very efficient for OLAP query. You can just put data on S3, then use AWS Athena to query parquet files. parquet IO in s3iotools is easy:

s3df = S3Dataframe(s3_resource=s3, bucket_name=bucket_name)
s3df.df = pd.DataFrame(...)
s3df.to_parquet(key="data.parquet", compression="gzip")
s3df_new = S3Dataframe(s3_resource=s3, bucket_name=bucket_name, key="data.parquet")
s3df_new.read_parquet()
s3df_new.df # access data

s3iotools doesn’t automatically install pyarrow, you can install it with pip install pyarrow.

Install

s3iotools is released on PyPI, so all you need is:

$ pip install s3iotools

To upgrade to latest version:

$ pip install --upgrade s3iotools