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Multiple Tables in Pandas

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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, and timestamp
  • products would contain the information to describe each product: product_id, product_description and product_price
  • customers would contain the information for each customer: customer_id, customer_name, customer_address, and customer_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.

Pandas DataFrame Inner Merge

In Pandas the .merge() function uses an inner merge by default. An inner merge can be thought of as the intersection between two (or more) DataFrames. This is similar to a Venn diagram. In other words, an inner merge only returns rows both tables have in common. Any rows in one DataFrame that are not in the other, will not be in the result.

A Venn diagram of the intersection of two sets. The RED area is the intersection. This is what we get from an INNER MERGE.

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    Data Scientist: Analytics Specialist

    Data Analysts and Analytics Data Scientists use Python and SQL to query, analyze, and visualize data — and communicate findings.
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    Data Scientist: Natural Language Processing Specialist

    NLP Data Scientists find meaning in language, analyze text and speech, and create chatbots. They use Python, SQL, & NLP to answer questions.
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