The data was a mess. It lived in three different legacy databases: a PostgreSQL instance for customer records, a MySQL dump for sales, and a flat-file CSV the size of a small moon for web logs. His SQL was a scalpel, but this required a sledgehammer and a chemistry set.
at_risk = power_users[ (power_users['last_login'] < cutoff_date) & (power_users['plan_type'] == 'free') ] at_risk['churn_score'] = (at_risk['total_logins'] * 0.3) - (at_risk['pricing_page_views'] * 0.7) at_risk = at_risk.sort_values('churn_score', ascending=False) Write the result back to his beloved database at_risk[['user_id', 'churn_score']].to_sql('churn_predictions', postgres_conn, if_exists='replace') python programming and sql mark reed
Mark's old way: write a monstrous 15-line SQL query with nested subqueries, window functions, and a CASE statement that looked like a legal document. It would take 45 minutes to run, if it didn't time out first. The data was a mess
He opened his new Python script. He breathed. Then he wrote. He breathed
df_users = pd.read_sql(query, postgres_conn)
The real test came on a Tuesday night. The CEO wanted a report by morning: "Show me every customer who has logged in more than ten times, viewed the pricing page, but hasn't upgraded in the last 90 days. And rank them by likelihood to leave."
