Data Processing

Fill array with value from start to end index using lodash _.fill

Data Processing

Scale and normalize features using various methods for machine learning preprocessing and data standardization

Data Processing

Drop first N items from array using lodash _.drop

Data Processing

Drop last N items from array using lodash _.dropRight

Data Processing

Drop items from array while predicate is true using lodash _.dropWhile

Data Processing

Extract and validate .env style KEY=VALUE configurations with detection of duplicate keys and suspicious spaces/quotes

Data Processing

Remove duplicate columns from CSV data with flexible detection strategies. Perfect for cleaning datasets, removing redundant information, and optimizing data structure. Features: - Detect columns with identical headers - Find columns with identical data content - Support for case-sensitive/insensitive matching - Multiple removal strategies available - Preserve data integrity - Support for large datasets - Fast and efficient processing Common Use Cases: - Clean up merged datasets - Remove redundant data columns - Optimize data for analysis - Prepare data for machine learning - Reduce file size and complexity - Standardize data format

Data Analysis

Comprehensive data distribution analysis with normality tests, outlier detection, and goodness-of-fit assessments

Data Analysis

Detect class imbalance in CSV or JSON datasets, compare resampling strategies, and preview a balanced output dataset

Data Analysis

Profile CSV or JSON datasets for missing values, duplicate rows, format drift, type inference, and numeric outliers

Data Processing

Apply deep default values using lodash _.defaultsDeep

Data Processing

Apply default values to an object using lodash _.defaults