The Database Toolkit for Python
As seen in the code above, we will use SQLAlchemy to connect to our Redshift instance using the connection credentials. Then, we use the read_sql method to make a SQL query on the database. Finally, we can load the results directly into a DataFrame and use it for our analysis.
PostgreSQL database adapter for the Python programming language
Since Redshift is compatible with other databases such as PostgreSQL, we use the Python psycopg library to access and query the data from Redshift. We will then store the query results as a dataframe in pandas using the SQLAlchemy library.
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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pandas is a widely-used data analysis library in Python. It provides a high-performance data structure called DataFrame for working with table-like structures.
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