Data Export

Overview

The LinkedIn Job Analyzer provides flexible data export functionality, allowing users to save extracted job data and AI insights in multiple formats. The export system uses the Factory Pattern to instantiate the appropriate exporter based on user preference.

Supported formats: JSON and Excel (.xlsx)

Architecture

The export system consists of three main components:

Base Exporter Interface

base_exporter.py

from abc import ABC, abstractmethod
from typing import Dict

class DataExporter(ABC):
    """Interfaz base para todos los exportadores de datos."""
    
    @abstractmethod
    def exportar(self, datos: Dict, ruta_salida: str) -> str:
        """Debe retornar la ruta donde se guardó el archivo."""
        pass

Factory Implementation

The ExporterFactory dynamically selects the correct exporter:

exporter_factory.py:4-14

class ExporterFactory:
    """Fábrica que decide qué exportador instanciar."""
    
    @staticmethod
    def obtener_exportador(formato: str):
        if formato == 'json':
            return JSONExporter()
        elif formato == 'excel':
            return ExcelExporter()
        else:
            raise ValueError(f"Formato {formato} no soportado")

The factory pattern makes it easy to add new export formats without modifying existing code

Export Formats

JSON
Excel

JSON Export

The JSONExporter saves data in a human-readable JSON format with proper UTF-8 encoding for international characters.

json_exporter.py:5-21

class JSONExporter(DataExporter):
    """
    Exportador responsable de guardar los diccionarios de datos en formato .json.
    """
    
    def exportar(self, datos: Dict, ruta_salida: str) -> str:
        """
        Guarda los datos en un archivo JSON usando la ruta proporcionada.
        """
        # Abrimos el archivo en modo escritura ('w') con soporte para caracteres latinos (utf-8)
        with open(ruta_salida, 'w', encoding='utf-8') as f:
            # ensure_ascii=False permite guardar acentos y ñ correctamente
            # indent=4 le da un formato legible para humanos
            json.dump(datos, f, ensure_ascii=False, indent=4)
        
        print(f"[DATOS] Archivo JSON guardado con éxito: {ruta_salida}")
        return ruta_salida

Features

UTF-8 Encoding

Preserves accented characters and special symbols

Pretty Printing

4-space indentation for human readability

ensure_ascii=False

Allows native storage of non-ASCII characters

Compact

Single file with all data

Example Output

resultado_python_developer.json

{
    "termino_busqueda": "Python Developer",
    "titulo_oferta": "Senior Python Backend Developer",
    "url": "https://www.linkedin.com/jobs/view/123456789",
    "fecha_extraccion": "2026-03-07 14:23:15",
    "habilidades": [
        "Python 3.10+",
        "FastAPI",
        "PostgreSQL",
        "Docker",
        "AWS",
        "5+ años de experiencia"
    ],
    "resumen_ia": "**Objetivo del Rol**: Desarrollador backend especializado en Python..."
}

Excel Export

The ExcelExporter uses pandas to create multi-sheet Excel workbooks with structured data.

excel_exporter.py:5-45

class ExcelExporter(DataExporter):
    """
    Exportador responsable de guardar los diccionarios de datos en formato .xlsx usando Pandas.
    """
    
    def exportar(self, datos: Dict, ruta_salida: str) -> str:
        """
        Crea DataFrames de Pandas y los guarda en múltiples hojas de un Excel.
        """
        # 1. Crear DataFrame para la lista de habilidades
        df_habilidades = pd.DataFrame({
            'Número': range(1, len(datos['habilidades']) + 1),
            'Habilidad': datos['habilidades']
        })
        
        # 2. Crear DataFrame para el resumen general de la oferta
        df_info = pd.DataFrame({
            'Campo': [
                'Término de Búsqueda', 
                'Título de la Oferta', 
                'URL', 
                'Fecha de Extracción', 
                'Total de Habilidades'
            ],
            'Valor': [
                datos.get('termino_busqueda', 'N/A'),
                datos.get('titulo_oferta', 'N/A'),
                datos.get('url', 'N/A'),
                datos.get('fecha_extraccion', 'N/A'),
                len(datos.get('habilidades', []))
            ]
        })
        
        # 3. Guardar en Excel creando diferentes pestañas (hojas)
        # Usamos openpyxl como motor (engine) para escribir el .xlsx
        with pd.ExcelWriter(ruta_salida, engine='openpyxl') as writer:
            df_info.to_excel(writer, sheet_name='Información General', index=False)
            df_habilidades.to_excel(writer, sheet_name='Habilidades', index=False)
        
        print(f"[DATOS] Archivo Excel guardado con éxito: {ruta_salida}")
        return ruta_salida

Workbook Structure

The Excel file contains two sheets:

Sheet 1: Información General

Contains summary information about the job posting:

Campo	Valor
Término de Búsqueda	Python Developer
Título de la Oferta	Senior Python Backend Developer
URL	https://linkedin.com/jobs/view/…
Fecha de Extracción	2026-03-07 14:23:15
Total de Habilidades	25

Sheet 2: Habilidades

Lists all extracted skills in a numbered table:

Número	Habilidad
1	Python 3.10+
2	FastAPI
3	PostgreSQL
4	Docker
5	AWS
…	…

Features

Multi-Sheet

Organizes data into logical sheets

Pandas DataFrames

Leverages pandas for easy data manipulation

Openpyxl Engine

Uses openpyxl for .xlsx compatibility

No Index

Clean output without pandas index column

Usage Example

Using the Export System

from exportadores.exporter_factory import ExporterFactory

# Prepare data
data = {
    'termino_busqueda': 'Python Developer',
    'titulo_oferta': 'Senior Python Backend Developer',
    'url': 'https://www.linkedin.com/jobs/view/123456789',
    'fecha_extraccion': '2026-03-07 14:23:15',
    'habilidades': [
        'Python 3.10+',
        'FastAPI',
        'PostgreSQL',
        'Docker',
        'AWS'
    ],
    'resumen_ia': 'AI-generated summary...'
}

# Export to JSON
json_exporter = ExporterFactory.obtener_exportador('json')
json_path = json_exporter.exportar(data, 'results/job_analysis.json')
print(f"JSON saved to: {json_path}")

# Export to Excel
excel_exporter = ExporterFactory.obtener_exportador('excel')
excel_path = excel_exporter.exportar(data, 'results/job_analysis.xlsx')
print(f"Excel saved to: {excel_path}")

Data Structure

Both exporters expect a dictionary with the following schema:

termino_busqueda

string

required

The original search term used for the job query

titulo_oferta

string

required

The extracted job title

url

string

required

The LinkedIn job posting URL

fecha_extraccion

string

required

Timestamp when the data was extracted (ISO format recommended)

habilidades

array

required

List of extracted skills and requirements

resumen_ia

string

AI-generated summary (only if analysis was requested)

Error Handling

The factory raises a ValueError if an unsupported format is requested

try:
    exporter = ExporterFactory.obtener_exportador('pdf')
except ValueError as e:
    print(f"Error: {e}")  # Output: Error: Formato pdf no soportado

Handling Export Failures

Both exporters may raise exceptions during file operations:

try:
    exporter = ExporterFactory.obtener_exportador('json')
    path = exporter.exportar(data, '/invalid/path/file.json')
except PermissionError:
    print("Cannot write to directory")
except Exception as e:
    print(f"Export failed: {e}")

Dependencies

JSON Exporter
Excel Exporter

import json
from typing import Dict

No external dependencies - uses Python standard library

import pandas as pd
from typing import Dict

Required packages:

pandas - Data manipulation and Excel writing
openpyxl - Excel file format support

Installation

pip install pandas openpyxl

Extending the System

To add a new export format, follow these steps:

Create Exporter Class

Inherit from DataExporter and implement the exportar method

from .base_exporter import DataExporter
from typing import Dict

class CSVExporter(DataExporter):
    def exportar(self, datos: Dict, ruta_salida: str) -> str:
        # Implementation here
        return ruta_salida

Update Factory

Add the new format to the factory’s conditional logic

@staticmethod
def obtener_exportador(formato: str):
    if formato == 'json':
        return JSONExporter()
    elif formato == 'excel':
        return ExcelExporter()
    elif formato == 'csv':
        return CSVExporter()
    else:
        raise ValueError(f"Formato {formato} no soportado")

Test

Verify the new exporter works correctly

exporter = ExporterFactory.obtener_exportador('csv')
path = exporter.exportar(test_data, 'output.csv')

Best Practices

Use UTF-8 Encoding

Always specify encoding='utf-8' when writing files to support international characters

Validate Data First

Check that all required fields exist before attempting export

Handle Exceptions

Wrap export operations in try-except blocks to gracefully handle failures

Return File Path

Always return the output path for confirmation and further processing

Get Started

Features

Configuration

Overview

Architecture

Base Exporter Interface

Factory Implementation

Export Formats

JSON Export

Features

UTF-8 Encoding

Pretty Printing

ensure_ascii=False

Compact

Example Output

Excel Export

Workbook Structure

Features

Multi-Sheet

Pandas DataFrames

Openpyxl Engine

No Index

Usage Example

Data Structure

Error Handling

Handling Export Failures

Dependencies

Extending the System

Best Practices

Use UTF-8 Encoding

Validate Data First

Handle Exceptions

Return File Path

Build docs developers (and LLMs) love

Get Started

Features

Configuration

​Overview

​Architecture

​Base Exporter Interface

​Factory Implementation

​Export Formats

​JSON Export

​Features

UTF-8 Encoding

Pretty Printing

ensure_ascii=False

Compact

​Example Output

​Excel Export

​Workbook Structure

​Features

Multi-Sheet

Pandas DataFrames

Openpyxl Engine

No Index

​Usage Example

​Data Structure

​Error Handling

​Handling Export Failures

​Dependencies

​Extending the System

​Best Practices

Use UTF-8 Encoding

Validate Data First

Handle Exceptions

Return File Path

Build docs developers (and LLMs) love

Overview

Architecture

Base Exporter Interface

Factory Implementation

Export Formats

JSON Export

Features

Example Output

Excel Export

Workbook Structure

Features

Usage Example

Data Structure

Error Handling

Handling Export Failures

Dependencies

Extending the System

Best Practices