saulpw/visidata

Features

• Dataset Explorers - Provides a terminal-based interface for filtering, sorting, and calculating statistics on large tabular datasets.
• Tabular Data Wrangling - A utility for cleaning and transforming datasets using Python expressions, regular expressions, and column manipulation.
• Terminal Data Visualizations - Renders complex data visualizations, such as scatter plots and histograms, using text-based graphics in the terminal.
• Data Formats and Parsing - Loads hierarchical data formats like JSON and XML by expanding nested objects into columns.
• File Management and Navigation - Provides an interactive terminal interface for browsing, navigating, and managing files and directories.
• Cell Editors - Allows direct modification of individual cell values to clean and update data.
• Programmatic Cell Transformation - Updates values in cells or columns using Python expressions and regex to transform data.
• Column Manipulations - Changes column types and expands nested data to reshape the tabular dataset.
• Conditional Data Filters - Filters rows using Python expressions or value matching to create specific views of the data.
• Data Analysis Tools - Acts as an interactive tool for performing ad-hoc analysis and generating statistical summaries on records.
• Statistical Aggregators - Computes essential summary statistics such as sums, averages, and medians across selected rows.
• Data Visualization - Renders tabular data into visual formats like scatter plots to identify relationships between variables.
• Cross-Source Joins - Enables performing joins across disparate data sources to create a unified view.
• Dataset Reshapers - Supports merging and concatenating different datasets to reorganize data for analysis.
• Data Joins - Implements inner, outer, and full joins to merge multiple datasets based on shared keys.
• Data Sorting Engines - Orders data ascending or descending based on one or more designated key columns.
• Data Visualization - Renders structured data into interactive graphs and charts to identify visual patterns.
• Data Wrangling Platforms - Offers an interactive environment for cleaning and transforming datasets using Python expressions and regular expressions.
• Multivariate Relationship Visualizers - Generates scatter plots to visualize pairwise correlations and joint distributions across multiple numeric variables.
• Conditional Row Filters - Identifies rows based on Python expressions or cell values to target specific subsets for operations.
• Filtered View Generation - Creates new views or copies of data containing only currently selected rows.
• Delimited Data Parsers - Parses delimited text files such as CSV and TSV using configurable delimiters and dialects.
• Exploratory Data Analysis - Provides tools for generating summary statistics, pivot tables, and frequency distributions to identify patterns in datasets.
• Expression-Based Data Querying - Allows users to filter rows and apply conditional formatting using dynamic Python expressions evaluated against cell values.
• Generated Columns - Provides the ability to create new columns based on Python expressions referencing other data in the sheet.
• In-Place Cell Editing - Provides direct modification of individual cell values to update or correct data within a sheet.
• Lazy-Loading Data Adapters - Uses specialized lazy-loading adapters to efficiently parse and flatten diverse file formats into interactive tabular sheets.
• Nested Data Manipulations - Flattens complex hierarchical data like JSON arrays into multiple top-level columns.
• Row Manipulations - Filters, sorts, moves, and removes rows based on specific criteria to isolate data.
• Search and Indexing - Implements fast information discovery across multiple columns using regex and Python expressions.
• Structured Data Inspections - Loading and flattening complex formats like JSON, XML, and PCAP files into a tabular view for easier analysis.
• Structured Data Parsers - Imports hierarchical data from JSON, XML, and HTML by expanding nested lists into tabular columns.
• Pivot Table Aggregators - Rearranges data by grouping key columns and pivoting categorical variables into cross-tabulations.
• Cursor Navigation - Implements keyboard-driven cursor movement for efficient navigation across large tabular datasets.
• Tabular Data Loaders - Imports datasets from various file formats or standard input using specialized loading mechanisms.
• Terminal Graphics - Renders data visualizations and graphs directly in the terminal using Unicode Braille characters.
• Terminal Data Interfaces - Provides a terminal-based interface that formats complex tabular data for high readability.
• Numerical Data Plotting - Renders interactive scatter plots and histograms in the terminal using Unicode Braille characters.
• Expression-Oriented Evaluation - Filters datasets by evaluating Python expressions using column names as variables.
• CSV Descriptive Statistics - Calculates descriptive statistics and creates pivot tables to summarize grouped data.
• Braille Pattern Mapping - Renders high-resolution data visualizations and graphs in the terminal using Unicode Braille characters.
• Data Inspectors - Provides an interactive container for viewing and navigating complex structured data formats.
• File Format Processing - Parses and converts various document and data formats into different file types.
• Data Formats - Exports explored data into various formats such as CSV, JSON, HTML, and geoJSON.
• Terminal File Managers - Includes a terminal-based file manager for navigating directories and performing staged filesystem operations.
• Column Transformation - Creates new columns by splitting cell content using regular expression patterns.
• Regex Text Replacement - Provides the ability to transform column text using regular expression patterns.
• Data Characteristic Analyzers - Generates a detailed statistical profile of a column to analyze data characteristics.
• Frequency Table Analysis - Generates tables showing the count of distinct values for a column to identify distribution.
• Data Format Converters - Transforms data between various supported file formats by saving the dataset in a different format.
• Data Import Utilities - Utilizes the Pandas library to import and map numerous external data formats into the tool.
• Dataframe Engines - Integrates with Pandas dataframe abstractions to load and process complex tabular data.
• External Library Adapters - Uses Pandas adapters to read a wide variety of complex file formats.
• Grouped Filters - Isolates and opens new sheets containing only rows that belong to a specific group.
• Grouped Value Aggregations - Collects multiple values from a group into a single list or joined string for reporting.
• Command Sequence Logging - Records sequences of commands and macros to files, enabling the reuse of complex analysis workflows.
• Interactive Visualization Rendering - Provides interactive graphs that update and render data trends based on user interaction.
• Multi-Dataset Loading - Loads several files simultaneously from the command line to maintain multiple concurrent data views.
• Multi-Dataset Management - Loads several files simultaneously and allows rapid switching between different data sources.
• Comparative Result Views - Displays two separate data sheets side-by-side in a split-screen view for simultaneous analysis.
• Schema Type Assignment - Defines columns as strings, integers, or dates to ensure correct parsing and calculations.
• Regex-Based File Search - Locates and highlights matching text within columns using regular expression patterns.
• Shell-Augmented Data Processing - Augments tabular data by executing shell commands for each row and capturing the output as new columns.
• SQL Database Connectivity - Establishes live connections to SQL databases for browsing and editing tabular data.
• Tabular Data Processors - Provides capabilities for filtering, aggregating, and sorting tabular data processed via shell pipelines.
• Regex Column Splitting - Creates new columns by splitting cell content using regular expressions or capture groups.
• Parameterized Sequences - Supports placeholders in recorded macros that prompt the user for specific values before execution.
• Custom Command Definitions - Allows users to define new application-level commands by associating names with functional strings.
• User-Defined Data Functions - Allows users to define custom Python functions to calculate specific statistics for grouped data.
• Macro Recorders - Provides a macro recorder to capture and replay sequences of keystrokes and commands for automating repetitive data tasks.
• Plugin Architectures - Features a plugin architecture that lets users extend functionality by importing external Python modules for new commands and handlers.
• Task Automation Tools - Provides tools to record sequences of commands and bind them to keystrokes for automating repetitive data analysis tasks.
• Terminal Text Highlighting - Visually highlights matching text or expressions across cells to identify data patterns.
• Binary Metadata Inspection - Provides low-level inspection of raw binary files like PCAP and Shapefiles.
• Staged Operations - Implements a deferred mode to track and apply filesystem changes in a single batch operation.
• Numerical Binning - Groups numeric values into calculated ranges to create histograms and visualize distribution.
• Session Replay - Serializes interaction sequences to a file, allowing users to restore a previous data exploration state by replaying commands.
• Module Functionality Extenders - Uses Python to implement custom loaders, data handlers, and commands that extend the interface.
• Plugin Extenders - Supports augmenting core application functionality and command behavior through a plugin architecture.
• Random Sampling - Selects a random population sample of a specified number of rows from the dataset.
• Column Pinning - Pins specific columns to the left for persistent visibility during horizontal scrolling.
• Column Reordering - Moves columns within a section or pins them as key columns for better visibility.
• Dataset Switchers - Navigates between multiple loaded datasets using a centralized management sheet.
• Session Restoration - Saves and restores the user's session state, including open sheets and command history, to recreate specific data views.
• File Previewers - Ships a split-pane previewer that updates file contents during filesystem navigation.
• Column - Hides or unhides specific columns from the current view to reduce visual clutter.
• Conditional Formatting - Visually highlights cells, rows, or columns by evaluating Python expressions against cell values.
• Hierarchical Navigation Stacks - Maintains a hierarchical navigation stack to track history and jump between different levels of data granularity.
• Tabular Sheet Navigation - Provides a way to search for content and switch between multiple open datasets within the terminal.
• Terminal View Management - Manages multiple active data views and metadata within the terminal to enable rapid switching between datasets.
• Data Manipulation - Spreadsheet multitool for data discovery and arrangement.
• Data Processing - Terminal-based spreadsheet multitool for data exploration.
• Command Line Utilities - Explores tabular data interactively in the terminal.
• Desktop Productivity Tools - Interactive terminal tool for analyzing and converting datasets.

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