Using tqdm in Python: Installation, Basic Usage, Customization, and Performance Tips

Hello everyone, today we share the amazing Python library tqdm.

tqdm provides simple and powerful progress bars for tracking tasks in Python, useful for loops and long-running processes.

GitHub address: https://github.com/tqdm/tqdm

1. Introduction to tqdm

tqdm (short for "taqaddum", meaning "progress") is a Python library that displays progress bars in the terminal, allowing easy tracking of iteration progress.

1.1 Installation

Before using tqdm , ensure it is installed. If not, install via:

pip install tqdm

2. Basic Usage Examples

2.1 Using tqdm in a for loop

The most common usage is wrapping any iterable with tqdm() :

from tqdm import tqdm
import time

for i in tqdm(range(10)):
    time.sleep(1)  # simulate task execution time

This creates a simple progress bar for 10 tasks.

2.2 Customizing style

tqdm allows customizing length, prefix, suffix, etc.

Example:

from tqdm import tqdm
import time

for i in tqdm(range(10), desc="Processing", ncols=100):
    time.sleep(0.5)

3. Progress Bar Customization

3.1 Setting description

Use the desc parameter to add a description.

for task in tqdm(tasks, desc="Processing"):
    time.sleep(0.5)

3.2 Custom length

Use ncols to set bar length.

for task in tqdm(tasks, ncols=100):
    time.sleep(0.5)

3.3 Changing bar format

Use bar_format to change appearance.

for task in tqdm(tasks, bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt}"):
    time.sleep(0.5)

4. Practical Example: File Download Progress

Demonstrates using tqdm to show download progress:

import requests
from tqdm import tqdm

# File URL
url = "https://example.com/file.zip"
response = requests.get(url, stream=True)

# Get total size
total_size = int(response.headers.get('content-length', 0))
block_size = 1024  # 1 KiB

with open("file.zip", "wb") as file, tqdm(total=total_size, unit='iB', unit_scale=True) as bar:
    for data in response.iter_content(block_size):
        file.write(data)
        bar.update(len(data))

5. Performance Considerations

When handling large data or long-running tasks, tqdm performance may be affected. Tips include adjusting refresh frequency with appropriate tqdm.update calls and using tqdm.write instead of frequent updates.