ineapy 0.1.3

Python library for accessing Spanish National Statistics Institute (INE) data

INEapy

INEapy is a comprehensive Python library designed to provide seamless access to data from the Spanish National Statistics Institute (INE). The library is structured into two primary modules: ine_wrapper and ine_consultor, each serving distinct purposes to cater to different user needs.

If you want to access to the API's details, you can check it's documentation.

🚀 Quick Start

from ineapy import INEConsultor
import pandas as pd

# Initialize the consultant
consultor = INEConsultor()

# Get the latest CPI data
cpi_data = consultor.get_series_data("IPC277517", nult=12)
df = pd.DataFrame(cpi_data)
print(df.head())

📦 Installation

To install INEapy, simply run the following command:

pip install ineapy

Overview

INEapy offers a robust interface for interacting with the INE's extensive data offerings. Whether you need low-level access to the API or a more abstracted, user-friendly interface, INEapy has you covered.

Key Benefits

• 🔗 Direct API Access: Low-level wrapper for complete control
• 🎯 High-Level Interface: Simplified methods for common tasks
• 🌍 Multi-language Support: Available in Spanish (ES) and English (EN)
• 📊 Pandas Integration: Automatic datetime conversion and DataFrame-ready output
• ⚡ Efficient Pagination: Handle large datasets with built-in pagination
• ✅ Parameter Validation: Comprehensive input validation to prevent errors
• 🛡️ Error Handling: Robust error handling with informative messages

🏗️ Architecture

INEapy
├── INEWrapper (Low-level API access)
│   ├── Direct HTTP requests to INE endpoints
│   ├── Raw JSON responses
│   └── Full parameter control
└── INEConsultor (High-level interface)
    ├── Simplified method names
    ├── Processed data structures
    └── Pandas-ready outputs

📚 Modules

INEWrapper

The ine_wrapper module provides a direct and low-level interface with the INE API, allowing users to perform HTTP requests to various endpoints with precision and control.

Key Features:

• Comprehensive access to all available operations in the INE API
• Rigorous parameter validation to ensure data integrity
• Efficient pagination handling for large datasets
• Multi-language support (ES/EN) for broader accessibility

Initialization:

from ineapy import INEWrapper

# Initialize the wrapper (default in Spanish)
wrapper = INEWrapper()

# Initialize the wrapper in English
wrapper_en = INEWrapper(language="EN")

Core Methods:

• get_operation(cod_operation, det, tip): Retrieve detailed information about a statistical operation.
• get_available_operations(det, tip, geo): Access the list of available operations.
• get_operation_publications(cod_operation, det, tip): Fetch publications associated with a specific operation.
• get_variables(): Access the complete list of variables available in the system.
• get_operation_variables(cod_operation, det, tip): Retrieve variables associated with a specific operation.
• get_variable_values(id_variable, det, tip): Obtain possible values for a given variable.
• get_series(cod_serie, det, tip): Access information about a specific series.
• get_series_data(cod_serie, nult, date, det, tip): Retrieve data from a specific series.
• get_metadata_operation_data(cod_operation, filters, p, det, tip, nult, date): Fetch data with metadata for an operation.
• get_operation_tables(cod_operation, det, tip): Access tables associated with a specific operation.
• get_table_groups(id_table, det, tip): Retrieve groups of a table.
• get_table_group_values(id_table, id_grupo, det, tip): Obtain values of a table group.
• get_table_data(id_table, nult, date, det, tip, filters): Retrieve data from a specific table.

Common Parameters:

• det: Detail level (0, 1, or 2)
• tip: Response type ("", "A", "M", "AM")
  • "": Normal response
  • "A": Friendly response
  • "M": Includes metadata
  • "AM": Friendly with metadata
• nult: Number of latest data points to retrieve
• date: Date filter in the format "yyyymmdd:yyyymmdd"
• filters: List of filters in the format "id_variable:id_value"

Usage Example:

from ineapy import INEWrapper

# Initialize the wrapper
wrapper = INEWrapper()

# List available operations
response = wrapper.get_available_operations(det=0, tip="")
operations = response.json()

# Get information about an operation
response = wrapper.get_operation("IPC", det=0, tip="M")
data = response.json()

# Get data from a series
response = wrapper.get_series_data("IPC277517", nult=12)
series_data = response.json()

INEConsultor

The ine_consultor module offers a high-level abstraction over ine_wrapper, simplifying access to INE data and enhancing data manipulation capabilities.

Key Features:

• Simplified interface for streamlined data access
• Automatic conversion of responses to more manageable formats
• Comprehensive methods for listing and querying operations, variables, and series

Initialization:

from ineapy import INEConsultor

# Initialize the consultant (default in Spanish)
consultor = INEConsultor()

# Initialize the consultant in English
consultor_en = INEConsultor(language="EN")

Core Methods:

• list_operations(filter_geo): List available operations.
• get_operation_info(cod_operation): Retrieve information about a specific operation.
• list_variables(cod_operation): List available variables, either general or specific to an operation.
• list_periodicities(): List available periodicities.
• list_filters_from_variable(id_variable): List values for a specific variable.
• list_filters_from_variable_operation(cod_operation, id_variable): List values for a variable within an operation.
• get_series_info(cod_serie): Retrieve detailed information about a series.
• get_series_data(cod_serie, nult, date): Access data from a specific series.
• get_operation_data(cod_operation, filters, p, date, nult): Retrieve data from an operation with specified filters.
• list_groups_from_table(id_table): List groups within a table.
• list_filters_from_table(id_table): List available filters for a table.
• get_table_data(id_table, nult, date, filters): Access data from a specific table.

Usage Example:

from ineapy import INEConsultor
import pandas as pd

# Initialize the consultant
consultor = INEConsultor()

# List available operations
operations = consultor.list_operations()

# Get information about the IPC operation
operation_info = consultor.get_operation_info("IPC")

# List variables for an operation
variables = consultor.list_variables("IPC")

# Get data from a series with the last 12 values
data = consultor.get_series_data("IPC277517", nult=12)

# Convert to pandas DataFrame
df = pd.DataFrame(data)

📚 Additional Resources

• INE Official API Documentation - Complete API reference and technical specifications
• INE Data Catalog - Browse all available datasets and statistics

Contribution

Contributions to INEapy are highly encouraged. To contribute, please:

1. Fork the repository
2. Create a branch for your feature
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

By following these steps, you can help enhance the functionality and usability of INEapy for the community.