How to stream a large gzipped .tsv file from s3, process it, and write back to a new file on s3?

I have a large file s3://my-bucket/in.tsv.gz that I would like to load and process, write back its processed version to an s3 output file s3://my-bucket/out.tsv.gz.

1. How do I streamline the in.tsv.gz directly from s3 without loading all the file to memory (it cannot fit the memory)
2. How do I write the processed gzipped stream directly to s3?

In the following code, I show how I was thinking to load the input gzipped dataframe from s3, and how I would write the .tsv if it were located locally bucket_dir_local = ./.

import pandas as pd
import s3fs
import os
import gzip
import csv
import io

bucket_dir = 's3://my-bucket/annotations/'
df = pd.read_csv(os.path.join(bucket_dir, 'in.tsv.gz'), sep='\t', compression="gzip")

bucket_dir_local='./'
# not sure how to do it with an s3 path
with gzip.open(os.path.join(bucket_dir_local, 'out.tsv.gz'), "w") as f:
    with io.TextIOWrapper(f, encoding='utf-8') as wrapper:
        w = csv.DictWriter(wrapper, fieldnames=['test', 'testing'], extrasaction="ignore")
        w.writeheader()
        for index, row in df.iterrows():
            my_dict = {"test": index, "testing": row[6]}
            w.writerow(my_dict)

Edit: smart_open looks like the way to go.