# tqdm — Fast Extensible Progress Bars for Python and CLI

> tqdm wraps any Python iterable to display a smart progress bar with speed, ETA, and throughput metrics. It works in terminals, notebooks, and GUI frameworks with zero overhead.

## Install

Save in your project root:

# tqdm — Fast Extensible Progress Bars for Python and CLI

## Quick Use
```bash
pip install tqdm
python -c "from tqdm import tqdm; [None for _ in tqdm(range(1000000))]"
# CLI: cat big.csv | python -m tqdm --bytes > /dev/null
```

## Introduction
tqdm (Arabic for "progress") adds smart progress meters to any Python loop or CLI pipeline. It wraps iterables with minimal overhead (around 60ns per iteration) and auto-detects terminal width, notebook cells, and GUI environments to render the appropriate bar style.

## What tqdm Does
- Wraps any iterable to display elapsed time, speed, and ETA
- Works in terminals, Jupyter notebooks, and GUI frameworks (tkinter, Qt)
- Provides a CLI mode for piping shell commands through a progress meter
- Supports nested bars, manual updates, and custom formatting
- Integrates with pandas via `tqdm.pandas()` for apply operations

## Architecture Overview
tqdm is a pure-Python library with no required dependencies. It polls the system clock on each iteration and only refreshes the display at a configurable minimum interval (default 0.1s) to keep overhead negligible. The core `tqdm` class is subclassed for different frontends: `tqdm.auto` picks the right backend automatically, `tqdm.notebook` renders HTML widgets, and `tqdm.gui` uses matplotlib.

## Self-Hosting & Configuration
- Install via pip, conda, or system package managers
- Use `tqdm.auto` to let tqdm choose terminal or notebook rendering
- Customize bar format with the `bar_format` parameter
- Set `miniters` and `mininterval` to control refresh frequency
- Use `tqdm.write()` instead of `print()` to avoid display corruption

## Key Features
- Near-zero overhead at roughly 60 nanoseconds per iteration
- Automatic rate and ETA calculation with exponential smoothing
- Thread-safe and multiprocessing-compatible with `tqdm.contrib.concurrent`
- Rich integration ecosystem (pandas, Keras, Discord, Telegram bots)
- Supports `asyncio` iterables via `tqdm.asyncio`

## Comparison with Similar Tools
- **Rich Progress** — part of the Rich library with more visual flair; tqdm is lighter and more widely adopted
- **alive-progress** — animated spinners and bars; tqdm has broader ecosystem integration
- **progressbar2** — similar concept but tqdm is faster and has more active maintenance
- **Click progress_bar** — built into Click CLI framework; tqdm works independently of any framework
- **enlighten** — supports multiple simultaneous bars natively; tqdm achieves this via nesting

## FAQ
**Q: Does tqdm slow down my loop?**
A: The overhead is around 60ns per iteration. For any loop doing real work, it is negligible.

**Q: How do I use tqdm with pandas?**
A: Call `tqdm.pandas()` once, then use `df.progress_apply()` instead of `df.apply()`.

**Q: Can I use tqdm in a Jupyter notebook?**
A: Yes. Use `from tqdm.auto import tqdm` and it renders HTML-based progress widgets automatically.

**Q: How do I show multiple nested progress bars?**
A: Pass `position=0`, `position=1`, etc. and set `leave=True` or `leave=False` to control persistence.

## Sources
- https://github.com/tqdm/tqdm
- https://tqdm.github.io

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Source: https://tokrepo.com/en/workflows/14e1973d-40a1-11f1-9bc6-00163e2b0d79
Author: AI Open Source