red-panda 0.1.6

Pandas and AWS interoperability for data science.

Data science on AWS without frustration.

Features

• DataFrame/files to and from S3 and Redshift.

• Run queries on Redshift in Python.

• Manage files on S3.

Installation

$ pip install red-panda

Using red-panda

Import red-panda and create an instance of RedPanda. If you create the instance with debug on (i.e. rp = RedPanda(redshift_conf, s3_conf, debug=True)), red-panda will print the planned queries instead of executing them.

from red_panda import RedPanda

redshift_conf = {
    'user': 'awesome-developer',
    'password': 'strong-password',
    'host': 'awesome-domain.us-east-1.redshift.amazonaws.com',
    'port': 5432,
    'dbname': 'awesome-db',
}

s3_conf = {
    'aws_access_key_id': 'your-aws-access-key-id',
    'aws_secret_access_key': 'your-aws-secret-access-key',
    # 'aws_session_token': 'temporary-token-if-you-have-one',
}

rp = RedPanda(redshift_conf, s3_conf)

Load your Pandas DataFrame into Redshift as a new table.

import pandas as pd

df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})

s3_bucket = 's3-bucket-name'
s3_path = 'parent-folder/child-folder' # optional, if you don't have any sub folders
s3_file_name = 'test.csv' # optional, randomly generated if not provided
rp.df_to_redshift(df, 'test_table', bucket=s3_bucket, path=s3_path, append=False)

It is also possible to:

• Upload a DataFrame or flat file to S3

• Delete files from S3

• Load S3 data into Redshift

s3_key = s3_path + '/' + s3_file_name
rp.df_to_s3(df, s3_bucket, s3_key)

rp.delete_from_s3(s3_bucket, s3_key)

pd.to_csv(df, 'test_data.csv', index=False)
rp.file_to_s3('test_data.csv', s3_bucket, s3_key)


redshift_column_datatype = {
    'col1': 'int',
    'col2': 'int',
}
rp.s3_to_redshift(
    s3_bucket, s3_key, 'test_table', column_definition=redshift_column_datatype
)

For API documentation, visit https://red-panda.readthedocs.io/en/latest/.

TODO

In no particular order:

• Support more data formats for copy. Currently only support delimited files.

• Improve tests and docs.

• Better ways of inferring data types from dataframe to Redshift.

• Explore using S3 Transfer Manager’s upload_fileobj for df_to_s3 to take advantage of automatic multipart upload.

• Add COPY from S3 manifest file, in addition to COPY from S3 source path.

• Build cli to manage data outside of Python.

• Support GCP?

In progress:

• Take advantage of Redshift slices for parallel processing. Split files for COPY.

Done:

• Unload from Redshift to S3.

• Handle when user does have implicit column that is the index in a DataFrame. Currently index is automatically dropped.

• Add encryption options for files uploaded to S3. By adding support for all kwargs for s3 put_object/upload_file methods.