Loading Data from SQL to Python

It's **Day 104**, and we're connecting the dots.

The Old Way (Slow)

1. Run SQL in a tool like DBeaver.

2. Export as CSV.

3. Load CSV into Python.

The Professional Way (Automated)

We use a library like `sqlalchemy` or `psycopg2` to let Python talk to the database directly.

import pandas as pd

from sqlalchemy import create_engine

# 1. Connect to the DB

engine = create_engine('postgresql://user:pass@localhost:5432/mydb')

# 2. Run your SQL directly into a DataFrame!

query = "SELECT * FROM sales WHERE amount > 1000"

df = pd.read_sql(query, engine)

print(df.head())

Why this is a game changer

You can now automate your reports. Imagine a script that runs every Monday morning, pulls the latest sales, builds a chart, and emails it to your boss. This is the foundation of **Data Engineering**.

Your Task for Today

Research "SQLAlchemy connection strings" for the specific database you use (Postgres, MySQL, SQLite, etc).

*Day 105: Filtering Data with Boolean Indexing.*