jtablesaw/tablesaw

Features

• Java Data Processing Libraries - Provides a Java-based dataframe library for processing, cleaning, filtering, and transforming large structured datasets.
• Scalar Column Transformations - Applies scalar transformation functions to column data to produce new derived values.
• Column Transformation - Applies map functions to columns to produce new values based on scalar operations.
• Data Import and Export - Provides utilities for loading and saving structured datasets using CSV, JSON, Excel, and RDBMS formats.
• Dataset Import Export - Provides a comprehensive system for loading data from CSV, JSON, and SQL and exporting processed results.
• Exploratory Data Analysis - Supports inspecting dataset structures and sampling rows to understand the layout and quality of imported data.
• Group-By Aggregations - Provides core group-by operations to partition data and compute summary statistics.
• Dataframe Toolkits - Implements a comprehensive toolkit for manipulating, filtering, and aggregating structured data within the JVM.
• Logical Table Subsetting - Extracts subsets of tables or columns using boolean logic and bitmap selections to isolate records.
• ML Data Preparation - Prepares raw datasets for analysis by removing noise, filling gaps, and handling missing values.
• Multi-Source Data Importers - Loads data from diverse sources including CSV files, databases, streams, and HTML tables.
• Pluggable Data Importers - Provides a pluggable interface to load datasets from diverse sources including CSV, JSON, and RDBMS.
• In-Memory Columnar Layouts - Uses a column-oriented memory layout to optimize cache locality and aggregate calculation speed.
• Multi-Format Data Exports - Serializes internal table representations into various flat-file and structured formats for external use.
• Descriptive Statistics Summaries - Computes essential summary metrics such as mean, sum, and maximum for columns and aggregations.
• Statistical Analysis Libraries - Computes descriptive statistics, cross-tabulations, and summary metrics on column-based data.
• Integrations - Integrates with statistical libraries to execute linear regression, clustering, and classification tasks.
• Column Manipulations - Optimizes memory usage by creating new columns via expressions and removing unnecessary data.
• Column Value Aggregations - Computes single summary statistics like mean, median, and standard deviation across a data column.
• Row Iterators - Performs arbitrary operations on table data by iterating through rows or applying consumer functions.
• Cross-Tabulations - Generates contingency tables to analyze frequency observations and percentages across categories.
• External Chart Library Integration - Interfaces with external charting libraries to translate internal data structures into interactive visualizations.
• Bitmap-Based Filtering - Implements high-performance row selection using bitsets to optimize subset extraction.
• Dataset Schema Explorers - Allows inspection of table dimensions, column names, and sample rows to understand dataset layouts.
• Statistical Data Summarization - Generates condensed statistical summaries of results through data structure manipulation operations.
• Row Ordering - Orders table rows based on one or more specified columns in ascending or descending order.
• Library Wrappers - Produces interactive graphical representations of data by wrapping specialized plotting libraries.
• Machine Learning Integrations - Integrates dataframes with statistical libraries to perform linear regression, clustering, and classification.
• Data Visualization Libraries - Offers a charting tool for creating histograms, scatter plots, and interactive graphical representations.
• Data Visualizations - Generates charts, histograms, and scatter plots to visually represent trends and patterns in datasets.
• Data Trend Visualizations - Creates charts and plots, such as histograms and scatter plots, to identify patterns and trends.
• Tabular Data Type Inference - Automatically determines the most appropriate Java data type for columns by sampling raw input files.
• Scientific Computing - Data-frame and column store for summarizing and filtering data.

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