Streaming pandas DataFrame to/from S3 with on-the-fly processing and GZIP compression

pandas_s3_streaming.py

def s3_to_pandas(client, bucket, key, header=None):

    # get key using boto3 client
    obj = client.get_object(Bucket=bucket, Key=key)
    gz = gzip.GzipFile(fileobj=obj['Body'])
    
    # load stream directly to DF
    return pd.read_csv(gz, header=header, dtype=str)

def s3_to_pandas_with_processing(client, bucket, key, header=None):
    
    # get key using boto3 client
    obj = client.get_object(Bucket=bucket, Key=key)
    gz = gzip.GzipFile(fileobj=obj['Body'])

    # replace some characters in incomming stream and load it to DF
    lines = "\n".join([line.replace('?', ' ') for line in gz.read().decode('utf-8').split('\n')])
    return pd.read_csv(io.StringIO(lines), header=None, dtype=str)

def pandas_to_s3(df, client, bucket, key):

    # write DF to string stream
    csv_buffer = io.StringIO()
    df.to_csv(csv_buffer, index=False)

    # reset stream position
    csv_buffer.seek(0)
    # create binary stream
    gz_buffer = io.BytesIO()

    # compress string stream using gzip
    with gzip.GzipFile(mode='w', fileobj=gz_buffer) as gz_file:
        gz_file.write(bytes(csv_buffer.getvalue(), 'utf-8'))
        
    # write stream to S3
    obj = client.put_object(Bucket=bucket, Key=key, Body=gz_buffer.getvalue())

Nice piece of code.
Any thoughts on whether / how we can make sure the dtypes can be maintained in this process?

Update - this is how we solved this for now (leaving out some Class definitions, just showing the general idea):

obj = pd.DataFrame({'id': [1, 2, 3], 'country': ['BE', 'FR', 'NL']})
contents = bytearray(source=obj.to_csv(index=False), encoding='utf-8')
# Persist on S3, using different approach depending on object size
if sys.getsizeof(contents) < 1e9:
    session.resource('s3').Bucket(bucket).put_object(Key=path, Body=contents)
else:
    local_store.persist(path=path, contents=contents)
    session.client('s3').upload_file(
        Filename=local_store.directory + path, Bucket=bucket, Key=path
    )
    if remove_local:
         os.remove(local_store.directory + path)
# Load from S3 bucket
response = session.client('s3').get_object(Bucket=bucket, Key=path)
obj_returned = response['Body'].read()
# Validate
obj_returned.equals(obj)

Seems like we really need the bytearray to maintain the dtypes...
All feedback is more than welcome.

do you know how to write csv to s3 with utf-8 encoding. I keep seeing symbols like √ in my csv reports. So annoying. I'm using StringIO() and boto3.client.put_object()

thanks

do you know how to write csv to s3 with utf-8 encoding. I keep seeing symbols like √ in my csv reports. So annoying. I'm using StringIO() and boto3.client.put_object()

thanks

import boto3
from io import StringIO

BUCKET_NAME = 'YOUR_BUCKET_NAME'
DATA_KEY_CSV = 'here/my/file.csv'

s3 = boto3.resource('s3')
bucket = s3.Bucket(BUCKET_NAME)

csv_buffer = StringIO()
schedule_events.to_csv(csv_buffer, encoding="utf-8")  # here is encoding you need
bucket.put_object(Body=csv_buffer.getvalue(), Key=DATA_KEY_CSV)

btw, no need to use gzip when you read a file.

import s3fs

df = pd.read_json('s3://bucket/path/to/file.json.gz', orient="columns")

works well.

btw, no need to use gzip when you read a file.

import s3fs

df = pd.read_json('s3://bucket/path/to/file.json.gz', orient="columns")

works well.

s3fs comes with a lot of dependencies and it is hard to install maintain.

This gist is awesome! thank you!