¿Cómo empiezo en datos si solo sé SQL y Python?

# 1) Levantar Postgres en Docker (persistente)
docker run -d --name pg-local \
  -e POSTGRES_USER=admin \
  -e POSTGRES_PASSWORD=admin \
  -e POSTGRES_DB=demo \
  -p 5432:5432 \
  -v pgdata:/var/lib/postgresql/data \
  postgres:16

# 2) (opcional) UI simple para revisar tablas
docker run -d --name adminer -p 8080:8080 adminer

volumen persistente (pgdata) para no perder datos.

# docker-compose.yml
services:
  postgres:
    image: postgres:16
    environment:
      POSTGRES_USER: admin
      POSTGRES_PASSWORD: admin
      POSTGRES_DB: demo
    ports: ["5432:5432"]
    volumes: ["pgdata:/var/lib/postgresql/data"]
  adminer:
    image: adminer
    ports: ["8080:8080"]
volumes:
  pgdata: {}

`docker compose up -d` y queda andando + Adminer en :8080.

-- init.sql
CREATE TABLE IF NOT EXISTS orders (
  order_id   SERIAL PRIMARY KEY,
  user_id    INT,
  total      NUMERIC,
  status     TEXT,
  order_ts   TIMESTAMP DEFAULT NOW()
);

útil para tus primeros inserts desde Python.

# csv_to_pg.py
import pandas as pd
from sqlalchemy import create_engine

csv_path = "orders.csv"  # crea un CSV de prueba con columnas: user_id,total,status
df = pd.read_csv(csv_path)

engine = create_engine("postgresql+psycopg2://admin:admin@localhost:5432/demo")
# inserta/append en la tabla 'orders'
df.to_sql("orders", engine, if_exists="append", index=False)
print("Cargado CSV → Postgres (tabla orders)")

# rick_and_morty.py
import requests, pandas as pd

base = "https://rickandmortyapi.com/api"
r = requests.get(f"{base}/character", timeout=30)
r.raise_for_status()
data = r.json()
chars = pd.json_normalize(data["results"])[["id","name","status","species","gender","origin.name"]]
print(chars.head())  # pruébalo, luego puedes cargarlo a Postgres igual que el CSV

# insert_pg.py
import psycopg2

conn = psycopg2.connect("dbname=demo user=admin password=admin host=localhost port=5432")
cur = conn.cursor()
cur.execute("INSERT INTO orders (user_id,total,status) VALUES (%s,%s,%s)", (123, 45.6, "PAID"))
conn.commit()
cur.close(); conn.close()
print("Fila insertada")

# (opción rápida) entorno local
python -m venv .venv && source .venv/bin/activate
pip install "apache-airflow==2.9.*" --constraint \
  https://raw.githubusercontent.com/apache/airflow/constraints-2.9.3/constraints-3.11.txt

export AIRFLOW_HOME=$(pwd)/airflow_home
airflow db init
airflow users create -u admin -p admin -r Admin -e a@a.com -f A -l Admin
airflow webserver -p 8081  # en otra terminal: airflow scheduler

abre 2 terminales: webserver y scheduler.

# ${AIRFLOW_HOME}/dags/local_etl.py
from datetime import datetime
from airflow import DAG
from airflow.operators.python import PythonOperator
import psycopg2, requests

DB_DSN = "dbname=demo user=admin password=admin host=host.docker.internal port=5432"  # si Airflow en Docker
# si Airflow sin Docker y Postgres local nativo usa host=localhost

def extract_characters(**_):
    url = "https://rickandmortyapi.com/api/character"
    data = requests.get(url, timeout=30).json()["results"]
    return [(c["id"], c["name"], c["status"]) for c in data[:10]]

def load_to_pg(**context):
    rows = context["ti"].xcom_pull(task_ids="extract")
    conn = psycopg2.connect(DB_DSN)
    cur = conn.cursor()
    cur.execute("CREATE TABLE IF NOT EXISTS characters (id INT PRIMARY KEY, name TEXT, status TEXT)")
    cur.executemany("INSERT INTO characters (id,name,status) VALUES (%s,%s,%s) ON CONFLICT (id) DO NOTHING", rows)
    conn.commit(); cur.close(); conn.close()

with DAG(
    "local_etl",
    start_date=datetime(2025,8,1),
    schedule="@daily",
    catchup=False,
) as dag:
    extract = PythonOperator(task_id="extract", python_callable=extract_characters)
    load    = PythonOperator(task_id="load",    python_callable=load_to_pg, provide_context=True)
    extract >> load

si corres Airflow en Docker, usa host.docker.internal para hablar con Postgres del host.

# report.py — top estados de characters
import pandas as pd
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

engine = create_engine("postgresql+psycopg2://admin:admin@localhost:5432/demo")
df = pd.read_sql("SELECT status, COUNT(*) c FROM characters GROUP BY 1 ORDER BY c DESC", engine)
print(df)

# gráfico rápido
ax = df.plot(kind="bar", x="status", y="c", title="Personajes por estado")
plt.tight_layout()
plt.savefig("characters_by_status.png")

# HTML rápido
html = df.to_html(index=False)
open("report.html","w").write(f"<h1>Personajes por estado</h1>{html}")
print("Generados: characters_by_status.png y report.html")

produce characters_by_status.png y report.html