data_frame 1.0.0

data_frame: ^1.0.0 copied to clipboard

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