Does tqdm work inside Jupyter Notebooks?

Yes, use 'from tqdm.notebook import tqdm' to render aesthetic HTML-based progress bars specifically designed for notebook environments.

What is the performance impact of using tqdm?

tqdm is designed for minimal overhead, typically adding only about 60 nanoseconds per iteration, making it safe for most production loops.

How do I prevent tqdm from breaking when using print()?

Use tqdm.write('message') instead of the standard Python print() function to ensure the progress bar remains at the bottom of the terminal without duplication.

How can I track progress in a Pandas apply function?

Initialize with tqdm.pandas() and then replace your standard .apply() calls with .progress_apply() to see a live status bar for your data operations.

tqdm Progress Monitoring

Name: tqdm Progress Monitoring
Author: tondevrel

bytondevrel

•

Data Science & ML

Implements intelligent, low-overhead progress bars for Python loops, data processing, and machine learning workflows.

tqdm is the industry-standard utility for visualizing the progress of long-running Python tasks with minimal computational overhead. This skill provides Claude with the expertise to correctly implement progress bars across diverse environments—including standard consoles, Jupyter notebooks, and pandas dataframes—while avoiding common pitfalls like terminal output corruption. It covers advanced patterns such as nested loops, manual progress updates for I/O operations, and dynamic statistic tracking for machine learning training, ensuring users always have clear visual feedback on their code's execution state.

Key Features

01Optimized performance with negligible overhead for high-frequency iterations

02Support for nested progress bars to monitor hierarchical tasks and epochs

031 GitHub stars

04Automatic loop tracking with smart ETA and iterations-per-second calculation

05Deep integration with Pandas for tracking progress on data transformations

06Environment-aware rendering for CLI, Jupyter Notebooks, and graphical interfaces

Use Cases

01Monitoring deep learning model training epochs and batch processing

02Visualizing data cleaning and transformation steps in Pandas workflows

03Tracking large file downloads or complex I/O data migrations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add tondevrel/scientific-agent-skills tqdm

For use in Claude.ai and ChatGPT

Download Skill