Alx assignment: add SQL exercise solutions for filtering queries in Northwind database #21
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Pull Request Description
Summary
This pull request introduces solutions to several SQL query exercises using the Northwind SQLite database. The work focuses on practicing filtering data using WHERE, IN, logical operators (AND/OR), and conditions on numeric and boolean-like fields.
Changes Included
Added SQL queries to retrieve:
Names of customers located in Germany.
Products with a Unit Price greater than 30 and marked as discontinued.
Contact names and phone numbers of customers from USA or Canada.
Orders placed by VINET, QUICK, or SUPRD, shipped via shipper 1 or 2.
Challenge query: Employees with the job title Sales Representative or Sales Manager based in London.
Motivation
This pull request contributes to improving SQL querying proficiency, specifically using filtering, logical operators, and pattern matching techniques in analytics workflows.
Testing / Validation
All queries were executed successfully against the Northwind SQLite database
Query results verified manually to ensure correct filtering logic
Execution environment: Jupyter Notebook using SQL Magic (%sql and %%sql)