Exporters

Overview

The exporters module implements the Factory pattern to provide flexible data export capabilities. It supports exporting job analysis data to both JSON and Excel formats.

Architecture

The module uses three key design patterns:

Abstract Base Class: DataExporter defines the export interface
Concrete Implementations: JSONExporter and ExcelExporter
Factory Pattern: ExporterFactory instantiates the appropriate exporter

DataExporter

Abstract base class that defines the export interface.

Class Definition

from abc import ABC, abstractmethod
from typing import Dict

class DataExporter(ABC):
    pass

Methods

exportar()

Abstract method that all exporters must implement.

@abstractmethod
def exportar(self, datos: Dict, ruta_salida: str) -> str:
    pass

datos

Dict

required

Dictionary containing job data to export. Expected structure:

termino_busqueda (str): Search term used
titulo_oferta (str): Job title
url (str): Job posting URL
habilidades (List[str]): Cleaned skills list
fecha_extraccion (str): Extraction timestamp

ruta_salida

str

required

File path where the exported data should be saved (including file extension)

return

str

The file path where data was successfully saved

ExporterFactory

Factory class that instantiates the appropriate exporter based on format.

Class Definition

class ExporterFactory:
    """Fábrica que decide qué exportador instanciar."""

Methods

obtener_exportador()

Static factory method that returns an exporter instance for the specified format.

@staticmethod
def obtener_exportador(formato: str):

formato

str

required

Export format identifier. Supported values:

"json" - Returns a JSONExporter instance
"excel" - Returns an ExcelExporter instance

return

DataExporter

Instance of the appropriate exporter (JSONExporter or ExcelExporter)

raises

ValueError

Raised if the formato parameter is not “json” or “excel”

Example Usage:

from exportadores.exporter_factory import ExporterFactory

# Get JSON exporter
json_exporter = ExporterFactory.obtener_exportador('json')

# Get Excel exporter
excel_exporter = ExporterFactory.obtener_exportador('excel')

# Invalid format raises ValueError
try:
    csv_exporter = ExporterFactory.obtener_exportador('csv')
except ValueError as e:
    print(f"Error: {e}")  # "Formato csv no soportado"

JSONExporter

Exporter implementation for JSON format using Python’s built-in json module.

Class Definition

import json
from .base_exporter import DataExporter

class JSONExporter(DataExporter):
    """Exportador responsable de guardar los diccionarios de datos en formato .json."""

Methods

exportar()

Saves job data to a JSON file with proper UTF-8 encoding and human-readable formatting.

def exportar(self, datos: Dict, ruta_salida: str) -> str:

datos

Dict

required

Dictionary containing job analysis data

ruta_salida

str

required

Output file path (should end with .json)

return

str

The file path where data was saved

Implementation Details:

Uses UTF-8 encoding to properly handle Spanish characters (á, é, í, ó, ú, ñ)
ensure_ascii=False preserves accented characters
indent=4 creates human-readable JSON with 4-space indentation
Prints confirmation message to console

Example Usage:

from exportadores.json_exporter import JSONExporter

exporter = JSONExporter()

datos = {
    'termino_busqueda': 'Python Developer',
    'titulo_oferta': 'Senior Python Engineer',
    'url': 'https://linkedin.com/jobs/...',
    'habilidades': ['Python', 'Django', 'PostgreSQL'],
    'fecha_extraccion': '2026-03-07 14:30:00'
}

ruta = exporter.exportar(datos, 'datos_extraidos/job_20260307.json')
print(f"Saved to: {ruta}")

Output File Example:

{
    "termino_busqueda": "Python Developer",
    "titulo_oferta": "Senior Python Engineer",
    "url": "https://linkedin.com/jobs/...",
    "habilidades": [
        "Python",
        "Django",
        "PostgreSQL"
    ],
    "fecha_extraccion": "2026-03-07 14:30:00"
}

ExcelExporter

Exporter implementation for Excel format using pandas and openpyxl.

Class Definition

import pandas as pd
from .base_exporter import DataExporter

class ExcelExporter(DataExporter):
    """Exportador responsable de guardar los diccionarios de datos en formato .xlsx usando Pandas."""

Methods

exportar()

Creates a multi-sheet Excel workbook with organized job data.

def exportar(self, datos: Dict, ruta_salida: str) -> str:

datos

Dict

required

Dictionary containing job analysis data

ruta_salida

str

required

Output file path (should end with .xlsx)

return

str

The file path where the Excel file was saved

Excel Structure: The generated Excel file contains two sheets:

Sheet 1: Información General

Contains metadata about the job posting:

Campo	Valor
Término de Búsqueda	User’s search term
Título de la Oferta	Job title
URL	Job posting URL
Fecha de Extracción	Timestamp
Total de Habilidades	Count of skills

Sheet 2: Habilidades

Numbered list of all extracted skills:

Número	Habilidad
1	Python
2	Django
3	PostgreSQL
…	…

Implementation Details:

Uses pandas.DataFrame for data structuring
Uses openpyxl engine for writing .xlsx files
Creates professional-looking tables with headers
Handles missing data with 'N/A' fallback values

Example Usage:

from exportadores.excel_exporter import ExcelExporter

exporter = ExcelExporter()

datos = {
    'termino_busqueda': 'Data Scientist',
    'titulo_oferta': 'Senior Data Scientist',
    'url': 'https://linkedin.com/jobs/view/123456',
    'habilidades': [
        'Python',
        'Machine Learning',
        'TensorFlow',
        'SQL',
        'Statistics'
    ],
    'fecha_extraccion': '2026-03-07 14:30:00'
}

ruta = exporter.exportar(datos, 'datos_extraidos/job_20260307.xlsx')
print(f"Excel saved to: {ruta}")

Complete Usage Example

Here’s how to use the factory pattern to export data in multiple formats:

from datetime import datetime
from exportadores.exporter_factory import ExporterFactory

# Prepare job data
datos_completos = {
    'termino_busqueda': 'Full Stack Developer',
    'titulo_oferta': 'Full Stack Engineer - Remote',
    'url': 'https://linkedin.com/jobs/view/789012',
    'habilidades': [
        'React',
        'Node.js',
        'TypeScript',
        'MongoDB',
        'Docker',
        'AWS'
    ],
    'fecha_extraccion': datetime.now().strftime("%Y-%m-%d %H:%M:%S")
}

# Generate timestamp for filenames
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
base_path = f"datos_extraidos/linkedin_{timestamp}"

# Export to JSON
json_exporter = ExporterFactory.obtener_exportador('json')
json_path = json_exporter.exportar(datos_completos, f"{base_path}.json")
print(f"JSON exported: {json_path}")

# Export to Excel
excel_exporter = ExporterFactory.obtener_exportador('excel')
excel_path = excel_exporter.exportar(datos_completos, f"{base_path}.xlsx")
print(f"Excel exported: {excel_path}")

Dependencies

import json
from typing import Dict

The Excel exporter requires pandas and openpyxl. Install with:

pip install pandas openpyxl

Best Practices

Use the Factory

Always use ExporterFactory.obtener_exportador() instead of directly instantiating exporters. This maintains flexibility and follows the Factory pattern.

Consistent File Naming

Use timestamps in filenames to prevent overwriting previous exports:

timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")

Create Output Directory

Ensure the output directory exists before exporting:

import os
os.makedirs('datos_extraidos', exist_ok=True)

Handle Exceptions

Wrap export calls in try-except blocks to handle file permission errors and disk space issues gracefully.

Core Modules

Utilities

Overview

Architecture

DataExporter

Class Definition

Methods

exportar()

ExporterFactory

Class Definition

Methods

obtener_exportador()

JSONExporter

Class Definition

Methods

exportar()

ExcelExporter

Class Definition

Methods

exportar()

Complete Usage Example

Dependencies

Best Practices

Use the Factory

Consistent File Naming

Create Output Directory

Handle Exceptions

Build docs developers (and LLMs) love

Core Modules

Utilities

​Overview

​Architecture

​DataExporter

​Class Definition

​Methods

​exportar()

​ExporterFactory

​Class Definition

​Methods

​obtener_exportador()

​JSONExporter

​Class Definition

​Methods

​exportar()

​ExcelExporter

​Class Definition

​Methods

​exportar()

​Complete Usage Example

​Dependencies

​Best Practices

Use the Factory

Consistent File Naming

Create Output Directory

Handle Exceptions

Build docs developers (and LLMs) love

Overview

Architecture

DataExporter

Class Definition

Methods

exportar()

ExporterFactory

Class Definition

Methods

obtener_exportador()

JSONExporter

Class Definition

Methods

exportar()

ExcelExporter

Class Definition

Methods

exportar()

Complete Usage Example

Dependencies

Best Practices