Are the id columns similar to primary keys in SQL?

Frequently Asked Questions Python FAQ
1

Question

in the context of this exercise, are id columns similar to primary keys in SQL?

Answer

They are similar, but their functionality is not the same as for SQL.

In SQL, primary keys provide a constraint so that the values in a column must be unique. It can throw an error if the values are not unique.

In Pandas however, there are not really any primary key constraints, as values under a column like product_id can still have duplicate values.

Instead, when working with dataframes, we would need to designate some column to act as a “primary key”, and make sure that any duplicates are removed from or prevented for the column.

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after renaming we can also add

reset_index()

as it will provide unique index values to each column… like this

orders_products = orders.merge(products.rename(columns = {‘id’ : ‘product_id’})).reset_index()

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just minor correction:

It will provide unique index values to each column row

and I think there is ambiguity in what you call index. Is it a column with the name index or the data DataFrame’s parameter index

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just a piece of additional information: if we need to designate a column as a “primary key” and ensure that there are no duplicates in a Pandas dataframe, we can follow these steps:

  1. Identify the Primary Key Column by choosing the column that we want to act as the primary key.

  2. Remove Duplicates using the drop_duplicates method to remove any duplicates in the primary key column.

  3. Check for duplicates in the primary key column before performing operations