README.md

df_io

Python helpers for doing IO with Pandas DataFrames

Available methods

read_df

• bzip2/gzip/zstandard compression
• passing parameters to Pandas' readers
• reading from anything, which smart_open supports (local files, AWS S3 etc)
• most of the available formats, Pandas supports

write_df

This method supports:

• streaming writes
• chunked writes
• bzip2/gzip/zstandard compression
• passing parameters to Pandas' writers
• writing to anything, which smart_open supports (local files, AWS S3 etc)
• most of the available formats, Pandas supports

Documentation

API doc

Examples

Write a Pandas DataFrame (df) to an S3 path in CSV format (the default):

import df_io

df_io.write_df(df, 's3://bucket/dir/mydata.csv')

The same with gzip compression:

df_io.write_df(df, 's3://bucket/dir/mydata.csv.gz')

With zstandard compression using pickle:

df_io.write_df(df, 's3://bucket/dir/mydata.pickle.zstd', fmt='pickle')

Using JSON lines:

df_io.write_df(df, 's3://bucket/dir/mydata.json.gz', fmt='json')

Passing writer parameters:

df_io.write_df(df, 's3://bucket/dir/mydata.json.gz', fmt='json', writer_options={'lines': False})

Chunked write (splitting the df into equally sized parts and creating/writing outputs for them):

df_io.write_df(df, 's3://bucket/dir/mydata.json.gz', fmt='json', chunksize=10000)