Ciencia de datos

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-postgres",
        "postgresql://localhost/mibase"
      ]
    }
  }
}

-- Cursor sugiere índices, funciones de ventana y optimizaciones de consultas
SELECT
    user_id,
    event_type,
    COUNT(*) AS total_eventos,
    RANK() OVER (PARTITION BY user_id ORDER BY COUNT(*) DESC) AS ranking_frecuencia
FROM events
WHERE created_at >= NOW() - INTERVAL '7 days'
GROUP BY user_id, event_type;

import plotly.express as px
import pandas as pd

# La IA sugiere tipos de gráficos relevantes según las columnas del conjunto de datos
df = pd.read_csv('sales_data.csv')
fig = px.scatter(df, x='advertising_spend', y='revenue',
                 color='region', size='customer_count',
                 title='Ingresos vs gasto en publicidad por región')
fig.show()

// .devcontainer/devcontainer.json
{
  "name": "ds-env",
  "build": { "dockerfile": "Dockerfile" },
  "features": {
    "ghcr.io/devcontainers/features/python:1": { "version": "3.11" }
  },
  "postCreateCommand": "pip install -r requirements.txt"
}

# .devcontainer/Dockerfile
FROM mcr.microsoft.com/devcontainers/python:3.11
COPY requirements.txt .
RUN pip install --upgrade pip && \
    pip install -r requirements.txt

# requirements.txt
pandas==2.3.0
numpy
# agrega otras dependencias que necesites para tu proyecto