Search Results for "csv"

qsv is a fast, command-line CSV data toolkit written in Rust that extends the capabilities of xsv. It’s designed to make working with CSV files at scale easy and efficient, offering over 40 powerful subcommands for tasks like querying, sampling, splitting, deduplicating, and more. qsv is ideal for data engineers, analysts, and developers who need high-performance CSV manipulation on the command line.

NuShell (often shortened to “Nu”) is a modern, cross-platform shell written in Rust that treats all data as structured tables rather than plain text. It supports pipelines on rich typed data, has built-in commands for JSON/CSV/SQL/excel, and offers scripting, autocompletion, scoped variables, and strong error handling—bridging the gap between shell scripting and programming.

...The tool can also pass commands to utilities such as GitHub CLI, Docker, and Kubernetes or interact with supported Electron applications. It ships as a small standalone binary and can return table, JSON, YAML, CSV, or Markdown output.

Supabase Wrappers is a collection of PostgreSQL extensions (written in Rust) that allow Postgres to interact with external systems like APIs, files, and other databases. It uses Foreign Data Wrappers (FDWs) to treat external resources as if they were native SQL tables, enabling seamless integration with external services directly from SQL queries.

...Cache-clearing commands can be set up before each timing run. Statistical outlier detection to detect interference from other programs and caching effects. Export results to various formats: CSV, JSON, Markdown, AsciiDoc. Parameterized benchmarks (e.g. vary the number of threads). Cross-platform. Hyperfine will automatically determine the number of runs to perform for each command. By default, it will perform at least 10 benchmarking runs and measure for at least 3 seconds. For programs that perform a lot of disk I/O, the benchmarking results can be heavily influenced by disk caches and whether they are cold or warm. ...

...Response encoding layer to serialize intermediate Arrow record batch into various formats requested by the client. Its pluggable query core design makes it possible for users to efficiently perform join queries across a diverse set of data sources from simple CSV/Parquet files in Data warehouses to MySQL/Postgres, to SASS-like Google spreadsheets.

Tabularis is a free, open-source desktop database client for PostgreSQL, MySQL/MariaDB, and SQLite. Built with Rust and Tauri v2, it is fast, lightweight, and cross-platform (Windows, macOS, Linux). Features: SQL Notebooks with inline charts and cross-cell variables. AI-powered SQL assistant (OpenAI, Claude, Ollama, and more). Built-in MCP server for AI agent integration. Visual Query Builder with drag-and-drop JOINs. Plugin system to add any database driver. Monaco-based editor with...

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...See How to Analyze Billions of Records per Second on a Single Desktop PC and How to Read 100s of Millions of Records per Second from a Single Disk for an overview of current capabilities. Download the latest binary release, which can be run from the command line on most x64 Linux systems, including Windows Subsystem for Linux. When loading .csv or .csv.gz files with --load, the first line of each file is assumed to be a header containing the names for all columns. The type of each column will be derived automatically, but this might break for columns that contain a mixture of numbers/strings/empty entries. To persist data to disk in LocustDB's internal storage format (which allows fast queries from disk after the initial load), specify the storage location with --db-path.

...Train a machine learning model by running modelfox train with the path to a CSV file and the name of the column you want to predict. The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a .modelfox file. If you want more control, you can provide a config file.

...The next thing you might want to do is get an overview of the kind of data that appears in each column. The stats command will do this for you. The xsv table command takes any CSV data and formats it into aligned columns using elastic tabstops. These commands are instantaneous because they run in time and memory proportional to the size of the slice (which means they will scale to arbitrarily large CSV data).