mango-time-series 1.0.2

Library with a collection of useful classes and methods to DRY

A comprehensive Python library for time series analysis, forecasting, and feature engineering built on top of the Mango framework.

Overview

Mango Time Series provides specialized tools for temporal data analysis, including exploratory data analysis, validation techniques, and utility functions for time series processing. It is designed to work seamlessly with the broader Mango ecosystem.

Features

Exploratory Analysis - Comprehensive time series data exploration tools - Statistical analysis and visualization capabilities - Data quality assessment and profiling

Validation - Time series data validation techniques - Cross-validation methods for temporal data - Model validation and performance assessment

Utilities - Data preprocessing and transformation tools - Date and time manipulation functions - Integration with pandas and other data science libraries

Data Management - Efficient handling of large time series datasets - Support for multiple data formats - Memory-optimized processing

Installation

Using uv:

uv add mango-time-series

Using pip:

pip install mango-time-series

Dependencies

• Python >= 3.10

• pandas >= 2.0.0

• numpy >= 1.24.0

• mango[data] >= 1.0.2

Quick Start

from mango_time_series import TimeSeriesAnalyzer
import pandas as pd

# Load time series data
data = pd.read_csv('your_time_series_data.csv')

# Initialize analyzer
analyzer = TimeSeriesAnalyzer(data)

# Perform exploratory analysis
analysis_results = analyzer.explore()

# Generate validation report
validation_report = analyzer.validate()

Documentation

For detailed documentation, visit the Mango Documentation.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For questions, issues, or contributions, please contact:

• Email: [e-mail]

• Create an issue on the repository

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