Best Python tools

Run Python programs and isolate project packages with the official CPython interpreter.

Install Python command-line applications into isolated environments or run them once without a permanent install.

Edit Python modules in a desktop window, run them in an interactive shell, and debug them with built-in tools.

Mutate Python code and rerun its tests to find changes the suite fails to catch.

Turn portfolio return histories into metrics, charts and shareable reports

Rewrite Git history to remove paths, extract directories or apply custom filters.

Find production performance bottlenecks in PHP and Python applications, then inspect the responsible code paths.

Manage Python projects, dependencies, environments, and commands with one fast command-line tool.

Add formatted text, tables, syntax highlighting, and progress displays to Python terminal programs.

Explore portfolio weights with explicit return, risk and constraint choices

Explore Python interactively with completion, object introspection and persistent command history.

Fit portfolio models and evaluate them on later market data

Build and train Python machine-learning models with tensor operations, neural-network layers, and automatic differentiation.

Build interactive Python applications for the terminal with widgets, CSS, and keyboard bindings.

Work with multidimensional arrays and vectorized numerical operations in Python.

Choose Python research and backtesting engines for the task

Python tools for inspecting and manipulating font files.

A Python command-line toolkit for authoring and running declarative chaos experiments against configured systems.

An environment-neutral Python API for font-development objects.

Build composable Python command-line interfaces from decorated functions.

Compile suitable Python numerical functions to machine code when they are first called.

Manage Python project dependencies, virtual environments, and package metadata from `pyproject.toml`.

Construct rate curves and inspect fixed-income price sensitivities

Fit, evaluate, and reuse Python models for common supervised and unsupervised machine-learning tasks.