tqdm is a practical tool for adding progress bars when handling large amounts of data or long running loops, which can significantly improve user experience and debugging efficiency. 1. Use tqdm(range(n)) in the basic for loop and set the description text through desc to automatically track iteration progress; 2. When processing lists and other data, directly pass the data into the tqdm() wrapping iteration process; 3. It is recommended to use from tqdm.notebook import tqdm in Jupyter Notebook to obtain better dynamic display effect; 4. When implementing multi-threaded/multi-process tasks with concurrent.futures, the result of executor.map() needs to be wrapped with tqdm and explicitly specify the total parameter to ensure the correct display of the progress bar; 5. For non-loop or complex scenarios, you can create a tqdm(total=N) object and manually call the update() method to update the progress, which is suitable for file download or step-by-step tasks; the installation method is pip install tqdm, its performance overhead is extremely low and is widely used in scenarios such as model training and big data processing.
When you are dealing with large amounts of data or long running loops, tqdm
is a very practical Python library that allows you to easily add progress bars. It is simple and easy to use and supports a variety of scenarios (such as for loops, maps, notebooks, etc.).

Here is a basic tqdm
usage example:
? Basic for loop progress bar
from tqdm import tqdm import time for i in tqdm(range(100), desc="Processing"): time.sleep(0.05) # Simulate time-consuming operation
Output effect:

Processing: 100%|██████████████████████████████████████████████████████████████████████████████████
-
desc
parameter sets the description text before the progress bar. -
tqdm
wraps iterable objects to automatically track progress.
? Used when processing lists or data
from tqdm import tqdm data = list(range(200)) result = [] for item in tqdm(data, desc="Processing items"): result.append(item ** 2) time.sleep(0.01)
? Use in Jupyter Notebook (recommended)
If you run it in Jupyter, it is recommended to use the more beautiful progress bar provided by tqdm.notebook
:
from tqdm.notebook import tqdm import time for i in tqdm(range(50), desc="Notebook Progress"): time.sleep(0.1)
?? Note: Using
tqdm.notebook.tqdm
in Jupyter can support dynamic refresh and better UI display.
? Combined with concurrent.futures
multi-threaded/multi-process (with progress bar)
from tqdm import tqdm import concurrent.futures import time def task(n): time.sleep(0.1) return n * n with concurrent.futures.ThreadPoolExecutor() as executor: results = list(tqdm(executor.map(task, range(30)), total=30, desc="Multithreading"))
- The
total
parameter must be specified becausemap
returns an iterator, andtqdm
cannot automatically know the total length.
? Manually control progress (suitable for non-cyclic scenarios)
from tqdm import tqdm import time pbar = tqdm(total=100) for i in range(10): time.sleep(0.1) pbar.update(10) # Manual update progress pbar.close()
Suitable for file downloads, step-by-step execution of complex tasks and other scenarios.
Installation method
If it has not been installed, use pip to install:
pip install tqdm
Basically these common uses. tqdm
hardly increases performance overhead, but it can greatly improve user experience and debugging convenience, especially when training models or processing big data.
The above is the detailed content of python tqdm progress bar example. For more information, please follow other related articles on the PHP Chinese website!

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