Boost Your Python Productivity with tqdm

tqdm is a fast, extensible, and user-friendly library for adding progress bars to your Python code. It doesn’t just provide a progress bar but comes with a multitude of helpful APIs to enhance your coding experience. Whether you’re processing large datasets or building complex apps, tqdm has you covered.

What is tqdm?

In Arabic, “tqdm” stands for “progress”. It’s widely used in Python for creating progress bars for loops and iterative operations. It integrates beautifully with loops, lists, iterators, and even pandas, allowing you to track progress effortlessly.

Dozens of Useful tqdm API Examples

Basic Progress Bar

Here’s how you can add a basic progress bar to a for-loop:

  from tqdm import tqdm
  import time
  
  for i in tqdm(range(10)):
      time.sleep(0.5)  # Simulate a task

Customizing the Progress Bar

Add a custom description and progress percentage:

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

Working with Iterables and Generators

tqdm seamlessly integrates with Python iterables:

  my_list = [1, 2, 3, 4, 5]
  for item in tqdm(my_list, desc="Iterating"):
      time.sleep(0.3)

Integrating with pandas

Track progress while applying functions to pandas dataframes:

  import pandas as pd
  from tqdm import tqdm
  from tqdm.contrib import pandas

  # Register tqdm with pandas
  tqdm.pandas()

  df = pd.DataFrame({'col1': range(5)})
  df['col2'] = df['col1'].progress_apply(lambda x: x * 2)

Parallel Processing with ThreadPoolExecutor

Integrate with concurrent features:

  from concurrent.futures import ThreadPoolExecutor
  from tqdm import tqdm
  import time

  def task(x):
      time.sleep(0.5)
      return x**2

  data = [1, 2, 3, 4, 5]
  with ThreadPoolExecutor() as executor:
      results = list(tqdm(executor.map(task, data), total=len(data), desc="Parallel Task"))

Nested Progress Bars

Handle nested loops with tqdm:

  for i in tqdm(range(3), desc="Outer Loop"):
      for j in tqdm(range(5), desc="Inner Loop", leave=False):
          time.sleep(0.3)

Total Progress Over Manual Iteration

Manually update progress:

  with tqdm(total=100) as pbar:
      for i in range(10):
          time.sleep(0.2)
          pbar.update(10)

Rich Text Styling

tqdm allows you to stylize progress bars with Unicode and color:

  for i in tqdm(range(10), desc="🚀 Launching Operations", ascii=" █"):
      time.sleep(0.4)

Building an App Example with tqdm

A CLI Progress Tracker App

Let’s build a command-line application that simulates downloading multiple files:

  import time
  from tqdm import tqdm

  def download_file(file_name):
      for _ in tqdm(range(100), desc=f"Downloading {file_name}", unit="blocks"):
          time.sleep(0.02)

  def main():
      files = ["file1.zip", "file2.mp4", "file3.jpg"]
      for file in files:
          download_file(file)

  if __name__ == "__main__":
      main()

Run the application to see tqdm in action for handling multiple tasks in sequence, complete with descriptive labels for each file.

Why Use tqdm in Your Projects?

With its extensive API surface, tqdm makes monitoring long-running tasks simple and visually appealing. It’s customizable, extensible, and integrates seamlessly with Python’s ecosystem.

Conclusion

Start using tqdm in your next Python projects to simplify progress tracking and enhance user experience. Happy coding!