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Key Takeaways: SQL

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Key Takeaways: SQL#

  1. Relational Databases Structure

    • Data is organized into tables, consisting of rows (records) and columns (attributes).

    • Primary Keys (PK) uniquely identify rows, and Foreign Keys (FK) link tables to maintain referential integrity.

  2. Core SQL Commands

    • CREATE TABLE – define a new table.

    • INSERT INTO – add rows of data.

    • SELECT – retrieve data.

    • WHERE – filter rows.

    • UPDATE / DELETE – modify or remove data.

    • DROP TABLE – remove a table permanently.

  3. The “Big 6” Elements of a SELECT Statement

    • SELECT: Choose columns.

    • FROM: Specify tables.

    • WHERE: Filter rows.

    • GROUP BY: Aggregate rows.

    • HAVING: Filter aggregated results.

    • ORDER BY: Sort results.

    • LIMIT: Restrict the number of rows returned.

  4. JOINs for Combining Tables

    • INNER JOIN: Only matching rows.

    • LEFT JOIN: All left table rows, matched right table rows.

    • RIGHT JOIN: All right table rows, matched left table rows.

    • FULL OUTER JOIN: All rows from both tables, NULL for missing matches.

  5. Aggregations and Filtering

    • Use SUM(), COUNT(), AVG(), MIN(), MAX() for aggregation.

    • Use GROUP BY to summarize data per category.

    • Use HAVING to filter after aggregation (cannot use WHERE for aggregated results).

  6. SQL in Python with Pandas

    • sqlite3 allows creating a lightweight database in Colab.

    • Use pd.read_sql_query() to load SQL query results directly into a DataFrame for analysis.

    • Combining SQL + Pandas enables powerful data workflows in Python.

  7. Best Practices

    • Always use WHERE when updating or deleting rows.

    • Use table aliases for readability in JOINs.

    • Use LIMIT when exploring large datasets to preview data efficiently.

    • Test queries on sample data before running on full datasets.

Conclusion:
By mastering table creation, data insertion, SELECT statements, JOINs, aggregation, and integration with Pandas, you can perform complex data analysis efficiently in SQL and Python. This chapter lays the foundation for building real-world data pipelines and analytical workflows.

Knowledge Check#

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