pandashift 1.0.2

Module for quick and easy access to redshift using pandas

pandashift

Wrapper for working with Amazon Redshift using pandas without the use of S3

Installing

pip install pandashift

Usage

There are 2 ways work with this package

1. Setting up environment variables
2. Passing credentials on every call

Below are examples of both approaches

With environment variables set up

from pandashift import read_query, execute_query, load_db

# Read Data
df = read_query('SELECT * FROM public.test')

# Execute statement (aka table create/drop/etc)
execute_query('TRUNCATE public.test')

# Loading dataframe
load_df(df, table_name ='public.test')

No environment variables

from pandashift import read_query, execute_query, load_db

creds = {
        "host":"YOUR HOST",
        "port":"YOUR PORT",
        "dbname":"YOUR DATABASE",
        "user":"YOUR USER",
        "password":"YOUR PASSWORD"
        }

# Read Data
df = read_query('SELECT * FROM public.test',credentials = creds)

# Execute statement (aka table create/drop/etc)
execute_query('TRUNCATE public.test',credentials = creds)

# Loading dataframe
load_df(df, table_name = 'public.test',credentials = creds)

Functions

load_df

Parameter	Usage
init_df	The dataframe for loading
table_name	The table that you want to load the df to
credentials	Credentials to use for connection if any
verify_column_names	The checks that the dataframe column order matches the table column order, by default True
empty_str_as_null	This option will interpret empty string '' as NULL, by default True
maximum_insert_length	Maximum length of the insert statement, alter this if you get error exceeding the max statement length, by default 16000000
perform_analyze	If this is true at the end of loading will run ANALYZE table, by default False