Multiple Tables in Pandas
Learn how to combine information from multiple DataFrames.
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Efficient Data Storage with Multiple Tables
Efficient Data Storage with Multiple Tables
For efficient data storage, related information is often spread across multiple tables of a database.
Consider an e-commerce business that tracks the products that have been ordered from its website. Business data for the company could be split into three tables:
orders
would contain the information necessary to describe an order:order_id
,customer_id
,product_id
,quantity
, andtimestamp
products
would contain the information to describe each product:product_id
,product_description
andproduct_price
customers
would contain the information for each customer:customer_id
,customer_name
,customer_address
, andcustomer_phone_number
This table structure prevents the storage of redundant information, given that each customer’s and product’s information is only stored once, rather than each time a customer places an order for another item.
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