SQL Challenge: E-commerce data analysis

The 11 .sql files contain the queries from a Kaggle notebook I wrote to showcase the capabilities of SQL for data analysis. To run these queries, download the SQLite database file from:

Kaggle dataset: E-commerce dataset by Olist (SQLite)

For more information on how to use the database and some context on the SQL queries on each file, have a look at the notebook. The goal of the notebook is to perform all data manipulation using only SQL, and use python only to visualize the results:

Kaggle notebook: SQL Challenge - E-commerce data analysis

Each SQL statement in the .sql files is preceded by a comment that indicates the name of the python variable that contains the query in the notebook. For example, the following SQL query from this repository is preceded by the comment "orders_per_day":

-- orders_per_day
SELECT
    DATE(order_purchase_timestamp) AS day,
    COUNT(*) AS order_count
FROM orders
GROUP BY day;

The same query is stored in a string in the Kaggle notebook, named "orders_per_day":

orders_per_day = """
    SELECT
        DATE(order_purchase_timestamp) AS day,
        COUNT(*) AS order_count
    FROM orders
    GROUP BY day;