data_frame 1.2.0

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Example Reliability and Docs Update ✅ #

Improvements

• Verified and fixed all example programs to run without runtime errors
• Numeric-safe aggregations using Series
• Corrected DataFrame operations (element-wise subtraction alignment, cumSum inputs)
• Fixed joins by aligning key columns in examples
• Robust handling for grouped keys (string/int) in month parsing
• Minor formatting and interpolation fixes in examples

Documentation

• Updated README badge and installation version to 1.2.0
• Added notes on running examples and stability improvements

Misc

• No API changes; minor release focused on quality and documentation

Major Release with Comprehensive Examples and Improvements 🚀 #

New Features

• Complete Example Suite: Added 8 comprehensive example files demonstrating all library features
  • main.dart - Complete overview with all major features
  • basic_operations.dart - DataFrame and Series fundamentals
  • statistical_analysis.dart - Statistical tests and analysis
  • data_io_operations.dart - CSV, JSON, and URL data operations
  • mathematical_operations.dart - Mathematical functions and transformations
  • advanced_data_manipulation.dart - Grouping, joining, and advanced operations
  • sample_data_generation.dart - Data generation utilities
  • real_world_analysis.dart - Complete business intelligence workflow

Improvements

• Enhanced Statistical Functions: Fixed confidence interval calculations and chi-square tests
• Better Error Handling: Improved method signatures and parameter validation
• Code Quality: Fixed all linting issues and improved code formatting

Dependencies

• Updated to Latest Versions: All package dependencies updated to latest stable versions
  • collection: ^1.19.1 (from ^1.18.0)
  • meta: ^1.17.0 (from ^1.15.0)
  • intl: ^0.20.2 (from ^0.19.0)
  • test: ^1.26.3 (from ^1.25.6)

Bug Fixes

• Fixed confidenceInterval method signature and parameter handling
• Resolved compatibility issues with updated collection package
• Fixed chiSquareTest method calls and added proper goodness of fit test
• Corrected DataFrame join method parameters
• Fixed mathematical function calls and type conversions

Documentation

• Added comprehensive README with detailed examples
• Created example guide with step-by-step instructions
• Improved inline documentation and code comments

Initial Release 🎉 #

A comprehensive data manipulation and analysis library for Dart, inspired by Python's pandas library.

Package Name: data_frame
Repository: https://github.com/AminMemariani/data-frame

Features

Core Data Structures

• Series<T> - One-dimensional labeled arrays with generic type support
• DataFrame - Two-dimensional labeled data structures with mixed types

Data Manipulation

• Filtering, sorting, grouping operations
• Join operations (inner, left, right)
• Column and row selection
• Null value handling (dropna, fillna)
• Data transformation (map, where, sort)

Statistical Analysis

• Descriptive statistics (mean, std, quartiles, skewness, kurtosis)
• Hypothesis testing (t-tests, ANOVA, chi-square)
• Correlation analysis (Pearson, Spearman)
• Linear regression with R-squared and p-values
• Confidence intervals
• Normality tests (Shapiro-Wilk, Jarque-Bera, Anderson-Darling)

Mathematical Operations

• Element-wise operations (add, subtract, multiply, divide, power)
• Mathematical functions (abs, sqrt, log, exp, sin, cos, tan)
• Rolling statistics (mean, sum, std)
• Cumulative operations (cumsum, cumprod)
• Percentage change and differences
• Data clipping and rounding

Data I/O

• CSV file reading and writing with customizable options
• JSON file operations with multiple orientations (records, columns, index)
• URL data fetching
• Sample data generation (numeric, mixed, time series)
• Database query result conversion

Utility Functions

• Range generation (numeric and date ranges)
• Random data generation (normal and uniform distributions)
• Data concatenation and merging
• Factory methods for common operations

Test Coverage

• 87 comprehensive unit tests
• 100% public API coverage
• Edge case and error condition testing
• 2,656 lines of implementation
• 1,414 lines of test code

Documentation

• Complete API documentation
• Usage examples
• Test coverage report
• Comprehensive README

Known Limitations

• URL fetching requires network access
• Very large datasets (>10M rows) may have performance considerations
• Statistical approximations used for some complex distributions