It was first created in 2013 by Noam Yorav-Raphael and later significantly developed by Casper da Costa-Luis around 2015. The name tqdm comes from the Arabic word “taqadum” meaning “progress” and also stands for “te quiero demasiado” (“I love you so much” in Spanish), reflecting its goal to simplify progress tracking. Since its release, tqdm has become the de facto standard for progress bars in Python, boasting millions of downloads per month and over ten thousand GitHub stars.
The primary purpose of the tqdm library is to provide a fast, extensible progress meter for Python loops and command-line interfaces. Developers can wrap any iterable with tqdm(iterable) to instantly get a progress bar in the console or terminal, including statistics like iteration count, elapsed time, and estimated time to completion. Tqdm was developed to fill the need for a simple yet powerful progress indicator, as early Python alternatives were either too basic or verbose to use. By focusing on ease of use (just one line of code) and efficiency, tqdm quickly gained traction among Python developers across various domains.
In the Python ecosystem, tqdm stands out as a lightweight utility that complements many workflows without imposing additional dependencies or heavy overhead. It integrates smoothly with other libraries and tools, so developers in data science, machine learning, web scraping, and software engineering use tqdm to visually track long-running operations. The library can be used in scripts, interactive shells, and even in graphical interfaces or notebooks (via specialized integrations), making it versatile for different development environments. Its ability to display a progress bar with minimal effort has made it a common inclusion in tutorials, courses, and code examples when demonstrating iterative processing.
Learning the tqdm library is important for Python developers because it improves observability and user experience in long computations. With tqdm, one can easily see the progress of a data processing job or training loop, reducing uncertainty about how long a task will take. It’s especially valuable in scenarios like machine learning training, data analysis pipelines, or any situation where a program might run for minutes or hours – the progress bar keeps users informed and engaged. The library’s continued maintenance (current stable version is 4.67.1 as of late 2024) and active community support ensure it stays up-to-date with Python’s evolution, making tqdm a reliable tool for both beginners and professionals.
What is tqdm in Python?
The tqdm library in Python is a fast, extensible progress bar toolkit that allows developers to effortlessly add progress indicators to their code. Technically, it is implemented as an iterator wrapper (and a function) that you can wrap around any iterable. When you iterate over a tqdm-wrapped iterable, it yields the original items unchanged, while simultaneously printing a dynamically updating progress bar to the output. Under the hood, it leverages the carriage return character \r to redraw the progress line in place, without flooding the output with new lines. This design means using tqdm doesn’t disrupt the logic of your loop – it acts like a transparent iterator that merely adds a side effect of printing progress information.
The architecture of the tqdm library is straightforward yet powerful. The core of tqdm is the tqdm class (often used via the tqdm() function or trange() for convenience), which handles tracking iterations, timing, and printing the bar. It keeps counters for metrics like the number of iterations processed, time elapsed, and calculates the rate (iterations per second) and estimated time remaining. Tqdm uses smart algorithms to minimize performance impact: it avoids excessive refresh by updating only after a minimum interval (0.1s by default) or a certain number of iterations. It also spawns a monitoring thread to ensure the progress bar updates at least every few seconds in case of very slow loops. This efficient design keeps the overhead extremely low – roughly 60 nanoseconds per iteration (nearly negligible in most cases).
Key components and modules: Beyond the basic tqdm() function, the library provides several specialized modules for different environments. For example, tqdm.auto will automatically choose between console mode and Jupyter-friendly mode depending on the environment, so you get the appropriate progress bar style. The tqdm.notebook module (and the newer tqdm.auto) produces progress bars as rich Jupyter notebook widgets, integrating with IPython display systems (this ensures compatibility with interactive notebook environments without manual setup). There’s also tqdm.gui for an experimental GUI-based progress bar in a separate window (useful if you want a graphical pop-up progress meter). Additionally, the tqdm.contrib submodule contains utilities like tqdm.contrib.concurrent (for parallel processing helpers) and tqdm.contrib.logging (to integrate with the Python logging module). These components extend tqdm’s functionality to cover many scenarios while keeping the core library simple.
The tqdm library’s integration with other Python libraries is a major strength. It is designed to work nicely alongside popular libraries such as pandas, unittest, logging, and more. For example, tqdm can patch pandas via tqdm.pandas() to enable progress bars for DataFrame operations (like df.progress_apply(...)), giving instant feedback in data processing tasks. In machine learning frameworks like Keras, tqdm provides a TqdmCallback that can display training progress for epochs and batches within the console. Similarly, tqdm integrates with asyncio by providing asynchronous iterators (tqdm.asyncio), and with Dask for distributed computing via a callback. It also plays well with Python’s standard library – for instance, you can use tqdm on file I/O, or wrap zip, enumerate, and other generators to track their progress (with some adjustments to supply a total length if needed). The tqdm API is carefully designed to be composable, meaning you can insert a tqdm progress bar into almost any loop or iterator pipeline without changing the surrounding code structure significantly.
In terms of performance characteristics, tqdm is highly optimized to minimize any slowdown of your code. Its internal updates use efficient calculations and avoid unnecessary operations – for example, it won’t repeatedly write to the console if the progress hasn’t changed since the last update. By default, tqdm updates the display at most 10 times per second (configurable), skipping intermediate iterations to avoid overhead when loops are extremely fast. The library’s own benchmark compares an iteration with tqdm at ~60ns overhead vs. ~800ns overhead for an older ProgressBar library, showing that tqdm’s impact is very small. In practice, this means you can use tqdm even in performance-critical loops with minimal effect, though for ultra-tight loops (millions of very fast iterations) you might tune its parameters (like miniters or mininterval) to update less frequently. Overall, tqdm’s design achieves a balance between rich functionality (multiple metrics, customizable formatting) and speed, making it suitable for both trivial and very large-scale iterations.
Why do we use the tqdm library in Python?
We use the tqdm library in Python because it dramatically improves the experience of running long or complex loops by providing a real-time progress indicator. When running a script that processes a lot of data or performs heavy computations, it’s often unclear how far along the process is – tqdm solves this by showing a progress bar with percentage completion, iterations done, and even an estimated time to completion (ETA). This feedback addresses the common problem of “when will it end?” that many developers face during long-running tasks. By using tqdm, developers and end-users can monitor the progress of tasks like training machine learning models, processing large datasets, downloading files, or any repetitive job, thereby reducing uncertainty and anxiety during waits. In short, the use of the tqdm library in Python is a quick way to add transparency and professionalism to scripts and applications that have non-trivial run times.
Another reason to use tqdm is the significant development efficiency gains it offers. Without a tool like tqdm, a developer might resort to manually printing status messages or writing custom code to track progress, which can be error-prone and insufficient. Tqdm provides a polished progress bar with just a single line of code, saving time and reducing the potential for bugs. It automatically handles tasks like estimating remaining time and adjusting update frequency, which would be tedious to implement from scratch. This means you can focus on your core logic rather than worrying about how to display progress. In practice, adding a progress bar via tqdm can often be done in seconds, whereas implementing a similar feature manually could take much longer and still be less featured. The advantages of the tqdm library include not only ease of use but also consistency – you get a familiar and informative output format that many Python developers recognize immediately.
From a performance perspective, using tqdm has negligible impact on the speed of your program in most cases. The library is designed to minimize overhead, so you get the benefit of progress tracking without slowing down your computations appreciably. This is in contrast to naive approaches like printing a dot or message in each iteration, which can significantly slow down I/O. Tqdm’s smart refresh algorithms ensure that updates happen at a reasonable interval (e.g., 10 times a second by default) rather than every single iteration, which means using tqdm is often more efficient than manual print statements for progress. Additionally, tqdm centralizes all progress output to a single line (per progress bar), avoiding clutter. For developers concerned with performance, benchmarks have shown that tqdm’s overhead per iteration is tiny (on the order of nanoseconds). Even when updating many times per second, the cost is so low that the human benefit of seeing progress far outweighs the microseconds of CPU time spent updating the display.
The industry adoption and real-world applications of tqdm demonstrate why it’s worth learning. In data science and machine learning, tqdm is almost ubiquitous for tracking model training epochs or data loading loops – for example, researchers training deep learning models use tqdm to monitor each epoch’s progress and loss values in real time, which helps in early stopping and debugging. In software engineering, scripts that perform migrations, backups, or batch processing of files often incorporate tqdm to indicate how many items have been processed and how many remain. Many open-source projects include tqdm as a dependency to improve their command-line interface; for instance, package managers, web scrapers, and even pipeline frameworks use tqdm to show progress bars. Because it is so widely used, the tqdm library in Python has become a skill that signals a developer’s familiarity with writing user-friendly long-running processes. Learning tqdm is therefore valuable not just for your own convenience, but also for reading and understanding other people’s code – you’ll frequently encounter from tqdm import tqdm in well-written Python scripts.
Comparing doing tasks with and without this library makes its benefits clear. Without tqdm, you might run a loop processing thousands of entries with no feedback, leaving you to guess if the program is stuck or how long you must wait. At best, you might print the iteration number occasionally, but that still requires manual effort to interpret progress and doesn’t provide an ETA. With the tqdm library, you get a clean, automatically updating bar that immediately tells you, for example, “Processed 50,000/100,000 items (50%) – 1.2s elapsed, 1.2s remaining”. This level of insight can even help catch issues (e.g., if progress stalls, you know exactly when it stopped). Tqdm also gracefully handles completion, leaving a final summary (like total time taken) which is great for logs. In summary, we use tqdm because it provides clarity, efficiency, and professionalism for long-running processes, with virtually no downsides. It has become a best practice in Python development to include tqdm for any script or application where progress tracking is beneficial.
Getting started with tqdm
Installation instructions
Installing the tqdm library is straightforward and it supports multiple methods depending on your environment. Below are detailed instructions for various setups:
Using pip (Python Package Index): The easiest way to install tqdm is via pip in a local Python environment. Open your terminal or command prompt (on Windows, you can use Command Prompt or PowerShell; on Mac/Linux, use the Terminal) and run:
pip install tqdmThis command will download and install the latest version of the tqdm library from PyPI. If you have multiple versions of Python installed, you might need to specify
pip3for Python 3. After installation, verify it by opening a Python REPL and importing the library:>>> import tqdm
>>> tqdm.__version__This should display the current version of tqdm installed. If you encounter a permission error on Linux/macOS, you can prepend
sudo(though using a virtual environment is preferable) or usepip install --user tqdmto install it in your user space. For Windows users, ensure that Python and pip are in your PATH; ifpipis not recognized, use the Python launcher likepy -m pip install tqdm.Using Anaconda/conda: If you’re working in an Anaconda distribution or prefer
condafor package management, tqdm is available through the conda channels. You can install it by opening the Anaconda Prompt (Windows) or a terminal in your conda environment and running:conda install -c conda-forge tqdmThis will fetch tqdm from the conda-forge channel (which usually has the latest version). You can also try
conda install tqdm(which might use the default channel if available). In Anaconda Navigator, you can go to the Environments tab, select your environment, and search for “tqdm” in the package list, then click install. The tqdm library is lightweight, so the installation should be quick. If you have multiple environments, make sure you activate the correct one withconda activate <env_name>before installing. After installation, you can useconda list tqdmto confirm the version and build installed. (Note: In some cases, tqdm might come pre-installed with Anaconda base, but if not, the above steps will add it.)Installation in VS Code: Visual Studio Code itself doesn’t manage packages, but you can easily install tqdm in the environment that VS Code uses for your project. If you’re using the integrated terminal in VS Code, just run the same
pip install tqdmorconda install tqdmcommand there, depending on your environment. For example, if you have set up a virtual environment or Anaconda environment for your VS Code workspace, ensure that environment is selected as the Python interpreter in VS Code (look at the bottom-right status bar in VS Code to select the interpreter). Then, open a terminal (Ctrl+`in VS Code) and execute the installation command. Alternatively, if you use the Python Extension in VS Code, you can open the command palette and run “Python: Create Terminal” which will open a terminal with the selected interpreter, then install tqdm. Once installed, you can import it in your scripts. Essentially, installing tqdm in VS Code is the same as normal, but you perform the steps within VS Code’s terminal to ensure it goes to the right environment. After installing, try creating a small Python file in VS Code to import and use tqdm to verify that the editor recognizes it (you should see no import error underlining).Installation in PyCharm: PyCharm provides a convenient interface to install packages for your project’s interpreter. To install tqdm in PyCharm, follow these steps:
Open your project in PyCharm. Go to File → Settings (or PyCharm → Preferences on Mac) and navigate to the Python Interpreter section.
In the Python Interpreter window, you’ll see a list of installed packages. Click the “+” button (Add Package).
In the search field, type “tqdm” and press Enter. You should see tqdm in the results.
Select tqdm and click Install Package. PyCharm will download and install the tqdm library for the selected interpreter.
Once done, you should see tqdm listed in the packages list with its version. Click OK to close the settings.
Now you can usefrom tqdm import tqdmin your code within PyCharm. PyCharm will also handle the environment (be it a virtualenv or Conda env) as long as it’s the project interpreter. Alternatively, you can use PyCharm’s built-in terminal to run pip install commands as discussed earlier, if you prefer command-line. PyCharm will automatically index the new package so features like autocomplete should work with tqdm.
Installing in a virtual environment: If you are using a virtual environment (via
venvorvirtualenvor Pipenv), make sure to activate the environment first. For example, for venv:On Windows:
path\to\env\Scripts\activate.batOn Linux/Mac:
source path/to/env/bin/activate
Once the environment is activated (you’ll see its name in your shell prompt), runpip install tqdmas usual. This will add tqdm to that environment only. Using virtual environments is a best practice to avoid cluttering the global Python installation and to manage project-specific dependencies. After installing, you can usepip show tqdmto verify it’s installed in that env and check the version.
Docker installation: If you need tqdm inside a Docker container, you have a couple of options. The simplest is to install it via your Dockerfile. For instance, if you have a Python-based image, you can add a line like:
RUN pip install tqdmduring the image build process. This ensures that the tqdm library is available in the container’s Python environment. If you’re using a conda-based image, similarly use
conda install. Another approach is to install it at runtime by entering the container (e.g., usingdocker exec -it <container> bash) and running the pip install command, though this would be for development usage; for production images, baking it into the Dockerfile is better. Because tqdm has no heavy dependencies, it won’t significantly affect your image size (it’s a small pure Python package). Once installed in the container, you can use tqdm in your Python scripts running there just as you would locally.Installation in cloud or remote environments: In general, installing tqdm in a cloud environment (such as a remote VM or a generic cloud notebook service) uses the same commands as above. For example, on an AWS EC2 or other server where you have SSH access, just use pip or conda in the terminal. If the environment is managed (like a cloud notebook that allows you to run shell commands), you can often do
!pip install tqdmin a cell or use the provided package management interface. The key point is that the process is platform-agnostic – since tqdm is a pure Python library with no platform-specific binaries, the installation is uniform across Windows, Mac, and Linux. Always ensure your internet connection in that environment can reach PyPI or the conda channels. In restricted corporate or offline environments, you may need to download the package separately (for example, download thetqdm-x.y.z.whlfile from PyPI) and install it usingpip install path/to/tqdm.whl. But for most users, a simple pip install suffices.Troubleshooting installation issues: If you run into trouble installing the tqdm library, here are some common solutions:
Pip not found or not working: Make sure you are using the correct Python’s pip. You can also try
python -m pip install tqdmto explicitly use the desired Python interpreter.Permission errors: If you see an
[Errno 13] Permission deniedor similar, you might be trying to install to a system directory without permission. Use--userto install to your local user directory, or better, work in a virtual environment. On Windows, running the command prompt as Administrator can solve permission issues, but it’s generally better to avoid global installs.Conda solve errors: If using conda and the environment fails to resolve, specify
-c conda-forgeto get the latest package. If that still fails, ensure your conda is updated (conda update conda) and try again.Proxy/Firewall: If you’re behind a firewall or proxy and pip can’t download the package, configure your proxy or download the wheel manually.
Compatibility: Tqdm is compatible with Python 3.6+ (and historically Python 2.7 in older versions). If you’re on a very old Python, you may need an older version of tqdm. Use
pip install tqdm==4.42.0(for example) to get a specific version. The tqdm library Python 3 support is excellent (it’s actively updated for new Python versions).After any installation, if
import tqdmorfrom tqdm import tqdmfails, double-check that you installed in the correct environment (for instance, your editor might be using a different interpreter than you installed to).
By following the above steps, you should have successfully installed the tqdm library in your environment. Now you’re ready to use it to add progress bars to your Python code.
Your first tqdm example
Let’s walk through your first example using the tqdm library. In this example, we will simulate a simple task: computing the square of numbers in a loop with a delay, and we’ll use tqdm to show the progress of that loop. This will demonstrate how to integrate tqdm and what the output looks like. Below is a complete, runnable code example (about 20 lines) with inline comments and then a line-by-line explanation:
import math
import time
from tqdm import tqdm
# A list of numbers for demonstration (0 to 99)
numbers = list(range(100))
results = [] # to store results # Iterate over the numbers with a progress bar for num in tqdm(numbers, desc="Processing", unit="num"):
try:
# Simulate some work with a sleep
time.sleep(0.05)
# Calculate the square of the number
result = math.pow(num, 2)
results.append(result)
except Exception as e:
# In this simple example, we don't expect errors, # but we handle any unexpected exception gracefully. print(f"Error processing {num}: {e}")
# We could choose to break or continue; here we continue. continue # After the loop, print a completion message print(f"Processed {len(results)} numbers. Example result: {results[0]} -> {int(results[0])}")
Line-by-line explanation:
Line 1-3: We import the necessary modules.
mathandtimeare standard libraries (for computing a square and adding a delay, respectively). Importantly, we importtqdmfrom the tqdm library (from tqdm import tqdm). This gives us access to thetqdm()function to use in our loop. If the tqdm library was not installed or imported correctly, this line would raise anImportError– make sure you installed it as described above.Line 6: We create a list of numbers from 0 to 99 using
range(100)and converting it to a list. This will serve as the iterable we want to loop over. In a real scenario, this could be a list of items to process (files, data entries, etc.). The size is 100 for demonstration – enough to see the progress bar in action.Line 8: We initialize an empty list
resultswhich will store the result of processing each number (in this case, the square of the number). This is just to simulate doing something with each item in the loop.Line 11: Here is the core of our example: we start a
forloop, wrapping our iterablenumberswithtqdm(). The syntaxfor num in tqdm(numbers, desc="Processing", unit="num"):does a few things:It creates a tqdm progress bar for the
numbersiterable.The
desc="Processing"argument sets a description that will prefix the progress bar (so it will say “Processing: 100%|########| ...” in the output, labeling what is being processed).The
unit="num"argument sets the unit of each iteration. By default tqdm might show “it/s” (iterations per second), but since we know we are processing numbers, we label the unit as “num”. This will reflect in the rate display (e.g., “200 num/s” instead of “200 it/s”).When this loop starts, tqdm will display a progress bar initialized at 0%. The bar will dynamically update with each iteration.
The variable
numwill still take values from the listnumbersas usual – tqdm does not alter the items, it only adds the side effect of the progress bar.
Line 12-13: Inside the try block of the loop: We call
time.sleep(0.05)to simulate work. This causes the loop to pause for 0.05 seconds on each iteration, which is just to slow down the output so we can see the progress bar updating. In a real scenario, this might be where you perform a calculation, read a file, make a web request, etc. We then computemath.pow(num, 2)to get the square of the number. We store that inresult. This is the “work” being done for each item.Line 14: We append the result to the
resultslist. This is just collecting the outputs. In many use cases, you might not need to store results and would instead directly use the computation, but here we do it to show that the loop is producing something.Line 15-20: We included a generic
except Exception as eto catch any unexpected errors during processing. In this simple example, it’s unlikely to get an error (we’re squaring integers which should not fail), but it’s good practice to handle exceptions especially if the loop does I/O or more complex processing. If an error occurs, we print an error message indicating whichnumhad an issue and the exception message. We thencontinueto the next iteration. This means even if one item fails, the progress bar will continue moving until the loop finishes the rest of the items. This pattern ensures that an error in processing one item doesn’t stop the entire loop. Note that by default, tqdm’s progress bar will still count iterations even if you catch exceptions (because the loop continues), so it will reach 100% regardless of errors unless you break out of the loop.Line 23: After the loop completes, we print a summary message. The progress bar from tqdm will have already reached 100% and by default, it stays on the screen (tqdm by default leaves the progress bar printed after completion; you can change this with
leave=Falseif you prefer it to disappear). Our print statement confirms how many numbers were processed (should be 100 if none were skipped) and prints an example result – in this case, the first result and its integer value (0 squared is 0). This is just to show that our loop did produce the expected outputs.
Expected output:
When you run this script, you will see a progress bar in the console. It will look something like this (with live updating values):
Example output of tqdm progress bar for the loop above. The bar shows the task "Processing", the progress 100% when done, the count of iterations, elapsed time, and rate in iterations per second.
During execution, the bar will update from 0% to 100%. Initially, you might see:
Processing: 0%| | 0/100 [00:00<?, ?num/s]
As it progresses, the ?num/s will turn into a number of nums per second and an ETA. For example, mid-way:
Processing: 50%|█████ | 50/100 [00:02<00:02, 19.80num/s]
And upon completion:
Processing: 100%|██████████| 100/100 [00:05<00:00, 19.58num/s]
After that, the final print in our code will output:
Processed 100 numbers. Example result: 0.0 -> 0
The exact timing and rate (nums/s) will depend on your system, since we used time.sleep it should be around 20 num/s (because 0.05s per iteration). The progress bar line includes:
100% to indicate completion,
the full bar of
██████████(which grows during the run),100/100showing total count,the total time in seconds
[00:05<00:00](here ~5 seconds),and the throughput
19.58num/s(this is calculated by tqdm).
Common beginner mistakes to avoid:
When using tqdm for the first time, be mindful of a few things:
Don’t forget to import: Use
from tqdm import tqdm(orimport tqdmand then usetqdm.tqdm()). A common mistake is to doimport tqdmand then trytqdm(range(...))directly – this will error becausetqdmis a module in that case, not the function. Either import the function or call astqdm.tqdm(range(...)).Ensure that you wrap the iterable inside the loop declaration. For example, writing
for x in tqdm(my_list):is correct. Do not try to calltqdm(my_list)on one line and loop over it later – it will still work, but you should iterate immediately to see the bar.If you don’t see any output, make sure that you are running in an environment that displays stdout (if you run a script in a GUI IDE without a console, you might not see it). Also, in some interactive notebooks, you might need to use
tqdm.notebookor ensure you enabled widgets.Avoid calling
print()inside the loop on every iteration, as it can interfere with tqdm’s output (usetqdm.write()if you must print messages during the loop to avoid breaking the bar).Lastly, if your loop is very fast and you see a lot of flicker or too frequent updates, remember that tqdm automatically throttles updates, but if you manually force flushes or something, it could spam. Stick with the default behavior when starting out.
With this first example, you’ve seen how to install tqdm, import it, and use it to wrap a loop. You also saw the kind of output it produces and how to interpret it. In the next sections, we will explore tqdm’s core features, customization options, and more advanced usage scenarios.
Core features
Iterable progress bars
One of the core features of the tqdm library is providing progress bars for iterable objects in Python. This is the most common usage: you have a for loop iterating over a sequence (such as a list, range, generator, or any iterable), and by wrapping that iterable with tqdm(), you get a progress bar that tracks the loop’s progress. This works with any iterable that you would normally loop over. Under the hood, tqdm will call iter() on the object and then fetch items one by one, updating the progress bar each time an item is requested. The beauty of this feature is that it requires minimal code changes – often just adding tqdm(...) around the iterable – yet it gives a rich visualization of loop progress, including iteration count, percentage, elapsed time, and speed. This is important because it turns any long loop into an observable process, which is valuable for monitoring and debugging lengthy computations.
Syntax and parameters: The basic syntax for using tqdm on an iterable is:
from tqdm import tqdm
for item in tqdm(iterable, **kwargs):
... # your loop body
Here, iterable can be a range, list, dictionary (it will iterate keys by default), generator, etc. Tqdm will try to determine the length (total) if possible (for example, it knows a range or list length). If it cannot determine the length (like for a generator), it will show ? for total and only display iterations count; you can manually specify total=<number> if you know it. Some useful parameters:
desc– A short string to describe the progress bar, displayed to the left of it (e.g.,desc="Loading"will show Loading: 0%|...).total– The number of iterations expected. If not provided, tqdm tries to infer it (for sequences with length or if an__len__is available). Provide this for generators if you know how many items will be generated.ncols– Width of the progress bar in characters. By default, it auto-adjusts to your terminal width. You can set a value (e.g., 80) to fix the width.unit– A string to denote the unit of each iteration (default is “it” for iterations). For example,unit="file"orunit="records"will label the rate as “XX files/s” or “XX records/s”.leave– Boolean (default True). If True, the progress bar remains on the screen after completion (showing 100%). If False, it will erase the bar upon completion (useful for cleaner output in some cases).mininterval– Minimum time (in seconds) between screen refreshes. Default 0.1s. If the loop is very fast, tqdm will still only update the display at this interval to avoid performance issues.miniters– Minimum number of iterations between refreshes. Default is dynamic (tqdm adjusts it to try to meet mininterval). You can set an integer to only update after that many iterations.ascii– If True, use ASCII characters for the progress bar ([-, #]) instead of Unicode bar characters. This can be useful in environments that don’t render Unicode well.disable– If True, disables the progress bar entirely. You can usedisable=Trueto turn off tqdm (it will be as if the loop isn’t wrapped, useful to toggle progress display on/off).position– If you are printing multiple progress bars (e.g., in nested loops or concurrent tasks),positionspecifies the line offset for this bar (0 is the first line, 1 is the second line, etc.), allowing multiple bars to stack vertically without overwriting each other.
With these parameters, you can tailor the progress bar to your needs. Often, you’ll just use tqdm(iterable) with maybe a desc for clarity, and that’s enough.
Examples of iterable-based progress bars:
Basic loop with range: Suppose you want to loop from 0 to N and do some work. Using tqdm:
from tqdm import tqdm
import time
N = 5 for i in tqdm(range(N)):
time.sleep(1) # simulate workThis will show a progress bar from 0% to 100% over 5 iterations, with each iteration roughly 1 second due to the sleep. The output will look like:
100%|██████████| 5/5 [00:05<00:00, 1.00it/s]indicating 5 iterations done in 5 seconds at ~1 iteration per second. By default,
descis not set here, so it just shows the bar without a prefix description. The unit is “it” (iterations). This demonstrates the simplest usage – wrapping arangewith tqdm.Looping over a list: If you have a list of items:
items = ["alpha", "beta", "gamma", "delta"]
for item in tqdm(items, desc="Iterating"):
print("Processing", item)
time.sleep(0.2)This will print “Processing alpha” etc., and simultaneously show a progress bar labeled “Iterating”. The
desc="Iterating"causes the output to prefix the bar with “Iterating:”. The bar will reach 4/4 at 100%. This example shows that the loop body can contain prints or other actions. Note: If you print inside the loop, the text may mix with the tqdm bar output. Tqdm tries to handle console control so that the bar stays on one line, but printing will typically move to a new line above or below the bar. In such cases, usingtqdm.write()(which we’ll discuss later) is preferable to avoid breaking the format of the progress bar.Custom total for generator: If you have an iterator of unknown length (like a generator), tqdm won’t know how many iterations there will be. For instance:
def my_generator(n):
for i in range(n):
yield i
time.sleep(0.1)
gen = my_generator(10)
for x in tqdm(gen, total=10, desc="Generating"):
# do something with x passHere, our generator yields 10 items, but tqdm would not know the total unless we specify it. By giving
total=10, tqdm can display a proper percentage and finish at 100%. If we omittedtotal, it would still work but show progress like “7/? [elapsed, rate]” which updates the count but cannot show a percentage or ETA. Whenever you use tqdm with something of indeterminate length, if you have knowledge of how many iterations it will roughly perform, providing thetotalparameter makes the progress bar more informative (with percentage and ETA). In this example, the bar “Generating:” would go from 0 to 10 with each yield from the generator.Using
trange: Tqdm provides a shortcut forrangecalledtrange. For example:from tqdm import trange
for i in trange(100, desc="Quick loop"):
# do work
time.sleep(0.01)trange(100)is essentially the same astqdm(range(100)). It just saves a bit of typing. Many examples in documentation usetrangefor brevity. The above loop will run 100 iterations quickly, with the bar “Quick loop:” showing progress. This is purely a convenience feature – under the hood,trangecallstqdmon a range object.
Performance considerations: Iterable progress bars are very efficient. However, if your loop iterations are extremely fast (microsecond scale), the progress bar might become the bottleneck if updated too frequently. Tqdm handles this by not updating every single iteration in those cases, but you should be aware of it. If you find that adding a tqdm significantly slows down an ultra-tight loop, you can tweak mininterval or miniters to update less often. For example, setting mininterval=1.0 will make tqdm update at most once per second (good for extremely fast loops). In normal use (like loops performing IO or computations taking milliseconds or more), you won’t need to adjust anything. The overhead of using tqdm in a typical scenario is negligible, as noted earlier. Another consideration: if the iterable is very large and you don’t need millisecond-by-millisecond accuracy on ETA, you could also initially disable the bar or use it conditionally (like only show progress if running interactively). But generally, the convenience of always wrapping with tqdm outweighs any tiny cost.
Integration examples: This basic feature integrates with other libraries seamlessly. For example, if you’re using pandas and have a DataFrame, you might iterate with for index, row in tqdm(df.iterrows()) to track the progress of iterating rows (though using vectorized operations is better, sometimes you need to loop). You can also integrate tqdm with concurrent operations by wrapping your iterable of tasks. For instance, if using concurrent.futures.ThreadPoolExecutor, you can wrap the futures or the list of tasks with tqdm to see progress of task completion (though in that case, you might increment manually as tasks finish – we’ll cover manual updates soon). In summary, the iterable progress bar feature of tqdm is the foundation of the library – it’s what most users start with, and it addresses the common need of “show me how far along my loop is.”
Common errors and solutions (iterable usage): A frequent issue is forgetting that once you wrap an iterable in tqdm, you should consume it fully for the bar to reach 100%. For example, doing tqdm(range(10)) alone won’t display anything until you iterate over it. Ensure you include it in the loop statement or convert it to a list if needed to force iteration. Another common pitfall is using tqdm with functions like enumerate incorrectly. If you do for i, val in tqdm(enumerate(sequence)):, tqdm will interpret the enumerate object’s length as 0 unless you provide total=len(sequence). A solution, as mentioned in the documentation, is to swap enumerate and tqdm: use for i, val in enumerate(tqdm(sequence)): or supply the total. The same logic applies to zip – wrap the longest iterable in tqdm, or use separate tqdms for each if you want to track both. If you see the progress bar not showing a percentage or ETA, it might be because it doesn’t know the total, so double-check if total should be set. Lastly, if tqdm prints each update on a new line (and doesn’t overwrite properly), it’s likely your environment doesn’t support carriage return; try running in a proper terminal or see the Common Issues section for solutions (like using print(tqdm(...).format_meter(...)) in such cases or ensuring you’re not in an incompatible console). Generally, for standard terminals and environments, iterable progress bars “just work” and are a robust feature of tqdm.
Manual control and customization
While the most convenient use of tqdm is to wrap an existing iterable, the library also offers manual control over the progress bar, which is useful for cases where you don’t have a neat iterable or need to update the bar from inside complex loops or callbacks. Manual control means you instantiate a tqdm object with a given total count and then call methods like update() on it as your task progresses. This feature is important because not all progress can be tied to iterating over a single Python iterable. For example, you might be reading lines from a stream, processing events, or you have multiple nested loops affecting a single progress metric. In such cases, controlling the progress bar manually gives you flexibility. Additionally, customizing the progress bar’s appearance and behavior – such as changing the prefix, units, bar format, or leaving it on screen – helps integrate tqdm into your application’s output style.
Creating and using a tqdm progress bar manually: To manually control a progress bar, you typically do:
from tqdm import tqdm
pbar = tqdm(total=100, desc="Loading", unit="files")
# ... perform some tasks in a loop or other structure:
pbar.update(n) # increment progress by n # ... when done:
pbar.close()
Here, tqdm(total=100) creates a progress bar expecting 100 iterations (or “units” of work). You might not loop exactly 100 times in code, but each time you complete a unit of work, you call pbar.update(1) to tell tqdm that one unit is done. If you have tasks that vary in size, you can call update(n) with different values. For example, if reading chunks of a file of known size, you could initialize total=file_size and call update(bytes_read) as you read. Always make sure to call close() or use the pbar as a context manager (with tqdm(...) as pbar:) to ensure the bar is properly finalized on screen (this will print a newline if needed and release internal resources). Using with is recommended as it auto-closes even if an error occurs.
Customization options for appearance:
Description (
desc) and unit: As covered earlier, you can label the progress bar withdescand set a custom unit. This is particularly useful in manual bars to clarify what the numbers mean (e.g., “Downloading: 42% 42/100 MB” if you set unit=”MB” and use bytes to MB conversion).Formatting the bar: Tqdm allows a custom bar format via the
bar_formatparameter. The default format is something like"{l_bar}{bar}{r_bar}"which includes left-aligned text (desc, percentage), the bar itself, and right-aligned text (counts, time, rate). You can customize it if needed. For instance, you could show only percentage and count by setting a custom format string. Many placeholders like{percentage:.0f}%,{n}(current count),{total},{rate_fmt},{elapsed}etc. are available. This is an advanced feature if the default output isn’t to your liking.ASCII vs Unicode: If your environment has trouble with the block characters in the bar (some Windows consoles used to have issues, though with colorama those are resolved), you can force ASCII mode which uses
#and-instead of blocks. Just dotqdm(..., ascii=True).Color and styling: By default, tqdm doesn’t color the bar (it’s just text). If you want color, you might integrate with other libraries (like
coloramaor simply add ANSI codes in desc). For example, you could setdesc="\033[91mError\033[0m"to color the description red. There’s alsotqdm.guiwhich opens a graphical bar in a separate window, but that’s more experimental.Dynamic messages (postfix): Tqdm offers a method to set a postfix which is additional information you can display to the right of the bar. You can use
pbar.set_postfix({...})to display key-value pairs. For example:pbar = tqdm(total=100, desc="Training")
for batch in batches:
# ... training code
pbar.update(len(batch))
pbar.set_postfix(loss=current_loss, accuracy=current_acc)This will show something like
loss=0.543, accuracy=0.873to the right of the bar, updated each time. It’s great for showing metrics that change over time alongside the progress (commonly used in ML training loops).
Practical examples of manual progress bar control:
Reading a file in chunks: Suppose you want to show progress for reading a large file where you read in chunks of 1024 bytes:
import os
from tqdm import tqdm
file_path = "large_file.bin"
file_size = os.path.getsize(file_path)
chunk_size = 1024 with open(file_path, "rb") as f, tqdm(total=file_size, unit="B", unit_scale=True, desc="Reading") as pbar:
for chunk in iter(lambda: f.read(chunk_size), b""):
# process chunk (here we just count it)
pbar.update(len(chunk))In this code, we determine the total size of the file. We create a tqdm bar with that total, using
unit="B"(bytes) andunit_scale=Truewhich will make tqdm display units in a scaled way (KB, MB, etc., depending on size). Inside the loop, each chunk we read has a certain length (up to 1024 bytes). We callpbar.update(len(chunk))to add that many bytes to the progress. Tqdm will automatically handle converting that to the progress percentage and using appropriate unit (because ofunit_scale, if the file is large you’ll see, e.g., “5.0MB/20MB” etc.). This manual pattern is necessary because we are not iterating over a range offile_size; we are reading until EOF. The progress bar will end at 100% when the total bytes read equals the file_size. We usedwith tqdm(...) as pbarso it will close automatically after the loop, leaving the final output on screen.Updating outside a loop: There are times you might want to update a progress bar from within callbacks or inside a nested structure where the outer loop isn’t easily accessible. For example, if using a library that processes items and provides a callback on each item completion, you can increment a global or external
pbar. Consider:from tqdm import tqdm
items = list(range(50))
pbar = tqdm(total=len(items), desc="Callback processing")
def process_item(x):
# ... do work
pbar.update(1) # increment for each item processed for x in items:
process_item(x)
pbar.close()Here, instead of looping with tqdm directly, we have a function
process_itemthat updates the bar each time it’s called. This simulates a scenario where the progress of an operation is reported via a callback. We initializepbarwith the total number of items, and each timeprocess_itemis called, we callpbar.update(1). After processing all items, we close the bar. This pattern shows that you can separate the logic of updating the bar from the iteration construct. As a real-world example, imagine downloading multiple files where a library handles the download asynchronously but lets you know each file is done – you could update a tqdm bar for each file completed.Customizing bar appearance: Let’s say you want a very minimal progress display – just percentage and count without the bar or ETA. You could do:
total_tasks = 20
pbar = tqdm(total=total_tasks, bar_format="{percentage:3.0f}%|{n}/{total}|", desc="Minimal")
for i in range(total_tasks):
time.sleep(0.2)
pbar.update(1)
pbar.close()The
bar_formathere is set to show percentage with no decimal, then a bar (which we could leave empty or minimal), and then current/total count. We left the actual bar characters out, effectively, but still included the|to separate fields. The output might look like:Minimal: 50%|10/20|and then
Minimal: 100%|20/20|at completion. This example demonstrates how you can tailor tqdm’s output to your needs. (Usually, the default format is fine, but in some cases, such as logging to files or systems that can’t handle the backspace, a simpler static output each update might be desired.)
Using
tqdm.write(): When customizing output, it’s good to know abouttqdm.write(). This method allows you to print a message to the console without breaking the progress bar. If you use a normalprintwhile a tqdm bar is active, the text might appear in the middle of the bar or push it down.tqdm.write("message")will safely print your message on a new line by internally handling cursor movement, and the progress bar will re-render below it on the next update. For example:for i in tqdm(range(100), desc="Working"):
if i % 20 == 0:
tqdm.write(f"Reached {i} iterations")
# ... do work
time.sleep(0.1)This will intersperse messages “Reached 0 iterations”, “Reached 20 iterations”, etc., in your output, but tqdm ensures the progress bar continues without glitching. It’s a handy feature for logging or debugging information mid-progress.
Performance considerations (manual mode): Manually updating a progress bar has roughly the same cost as the automatic mode, but you should avoid extremely frequent updates. For example, calling pbar.update(1) in a very tight loop is effectively the same as using tqdm in that loop directly. The same rule of thumb applies: let tqdm throttle updates with mininterval (default 0.1s). If you call update much faster, tqdm will internally check the time and only actually print when 0.1s has passed. If you want to reduce overhead further, you could increase mininterval or set miniters. For instance, if you expect to call update thousands of times per second, set mininterval=1 or miniters=1000 to avoid overhead on each call. Tqdm’s design ensures that even if you call update() in a very busy loop, it won’t print every single time – it updates an internal counter and only refreshes occasionally. So performance is usually fine. One thing to watch out for: if you forget to close a manually controlled bar, you might end up not releasing some resources or leaving the cursor at the wrong place. Always close or use context manager to be safe.
Common errors (manual mode): A common mistake is updating beyond the total. If you do pbar = tqdm(total=10) and then call pbar.update(1) 12 times, you will get a warning or it will show 120%. Tqdm allows the counter to exceed total but will print as such (in newer versions it might also show a warning). This often happens if you miscalculate the total or if two parts of code both update the bar. It’s not usually a serious problem, but try to match your total to actual work. Another issue is forgetting to close the bar on exceptions – if an exception occurs and you don’t close, the terminal might not drop to a new line properly. Using with tqdm(...) mitigates that. Also, if you attempt to update() from multiple threads on the same bar, tqdm is not fully thread-safe by default (though it tries to be). It’s often better to use separate bars per thread (with position to keep them separate) or use locks around updates. If you need a truly shared progress bar across threads/processes, you might need more advanced synchronization (or use tqdm.contrib.concurrent which handles mapping with progress). Lastly, when customizing bar_format, ensure you include necessary fields; if you omit something essential like {n} or {total} without thinking, you might confuse yourself with the output. Tqdm will fill what it can and leave blank what it can’t, but a badly formed format might not display anything useful. The default format has been refined to display key info, so deviate from it only with good reason.
In summary, manual control and customization allow you to adapt tqdm to scenarios where the simple iterable wrapper isn’t enough. You can update progress based on arbitrary events, and you can tailor the display format and supplementary information. This feature makes tqdm a very flexible progress meter library – not just for loops, but for any measurable progress in your program.
Pandas integration
One of the extremely handy features of tqdm is its built-in integration with pandas data structures. Many Python developers use pandas for data analysis and often apply functions to DataFrames or Series that take time. Tqdm can seamlessly integrate to provide progress bars for these operations. Specifically, tqdm offers a method tqdm.pandas() which adds a progress bar to pandas apply and groupby operations via a “progress_apply” method. This integration is important because it saves you from having to manually wrap your pandas iterations with tqdm; instead, you use pandas’ own syntax and still get progress feedback. It’s a great example of how tqdm extends to other libraries to improve usability.
How to use tqdm with pandas: First, ensure you import tqdm and “activate” the pandas integration:
from tqdm import tqdm
tqdm.pandas()
The call to tqdm.pandas() patches pandas’ DataFrame and Series to have new methods: progress_apply and progress_map (for Series). After calling this, any time you want a progress bar for an apply, you can do:
df.progress_apply(my_function, axis=1)
and it will display a tqdm progress bar automatically. Similarly, for a Series:
df['column'].progress_map(lambda x: ... )
will show progress. You can also use it with groupby:
df.groupby('category').progress_apply(process_group)
This will give a progress bar over groups.
Why this is useful: Without tqdm integration, if you wanted to monitor a pandas operation, you might have to convert to an iterable or use manual loops, which is not convenient. With progress_apply, you keep the declarative style of pandas. Under the hood, when you call tqdm.pandas(), it essentially assigns DataFrame.progress_apply = tqdm(...).apply (a wrapped version) and similarly for Series. This means it uses a tqdm internally to track the iterations of the apply function.
Example usage:
Imagine you have a large DataFrame and you want to perform a complex calculation on each row:
import pandas as pd
from tqdm import tqdm
# Sample DataFrame with 1e6 rows for demonstration
df = pd.DataFrame({'a': range(1000000), 'b': range(1000000)})
tqdm.pandas(desc="Calculating") # you can provide a default desc if you want def compute_row(row):
# simulate some work return (row['a'] ** 2 + row['b'] ** 2) ** 0.5 # Use progress_apply to see progress
result_series = df.progress_apply(compute_row, axis=1)
When you run this, you will see a progress bar that goes from 0 to 100% over 1,000,000 iterations. It might look like:
Calculating: 37%|█████████████▎ | 370000/1000000 [00:10<00:18, 34600.00it/s]
This indicates how far along the apply is. If you hadn’t used tqdm.pandas(), that operation would be silent for potentially many seconds or minutes. Now you get an instant sense of how fast it’s going and how much is left.
A few notes on usage:
The
descparameter intqdm.pandas(desc="text")sets a default description for the progress bars. In the above example, we set"Calculating", which is why the bar is prefixed with that. If you don’t set it, it might default to something like “Apply” or just show the default bar without a prefix.If you forget to call
tqdm.pandas()and trydf.progress_apply, you’ll get an AttributeError (because the method isn’t added). So always calltqdm.pandas()once in your session before using progress_apply. You can call it again if needed; it won’t double-patch anything, it essentially ensures the methods are present.
GroupBy example:
tqdm.pandas()
# Suppose df has a categorical column 'category' def process_group(group):
# some aggregation or processing per group
time.sleep(0.1) # simulate work per group return len(group)
group_results = df.groupby('category').progress_apply(process_group)
If there are, say, 100 groups, you’ll see a progress bar from 0 to 100. This is very helpful if each group processing is heavy.
Integration with Jupyter (notebook) environment: If you use tqdm.notebook instead of standard tqdm for Jupyter, there is also a tqdm.notebook.tqdm.pandas() that can produce progress bars as widgets. For console usage or scripts, tqdm.pandas() works well and prints to stderr by default like normal tqdm. The tqdm library’s pandas integration is designed to be environment-agnostic; it will detect if you’re in a notebook vs terminal and use the appropriate tqdm automatically (especially if you use tqdm.auto, which is a good practice to import from in modern versions).
Performance considerations for pandas integration:
One might wonder if using progress_apply slows down the computation. There is a tiny overhead for updating the progress bar on each iteration, but since pandas’ apply is inherently a Python-level loop (not vectorized), the overhead of tqdm is relatively small compared to the overall cost per iteration (especially if the function you apply does a fair amount of work). In I/O bound or heavy compute scenarios, the difference is negligible. If you are applying a very trivial function millions of times, the progress bar will add some overhead – in such cases, consider increasing miniters or using tqdm(..., mininterval=1) for that apply. However, generally, if you’re concerned about performance, you might try to avoid using apply at all and vectorize your operation (that would be faster and then the need for a progress bar is moot because vectorized operations in pandas don’t easily yield progress information). But when you do need apply (which is common for complex row-wise operations), progress_apply is a very convenient tool. Developers often accept a slight overhead for the benefit of knowing how far along the computation is, which can be critical if you need to decide whether to wait or to optimize further.
Common errors & solutions:
If you do
tqdm.pandas()and then do multiple applies in different places, you don’t need to calltqdm.pandas()each time; it patches globally. But calling it again is harmless.If
progress_applydoesn’t show up, ensure you importedtqdmfrom the correct place. If you accidentally didfrom tqdm.auto import tqdm, you might need to doimport tqdm.auto; tqdm.auto.tqdm.pandas(). Usingtqdm.autois fine as it defaults to the correct version per environment, but thepandas()method might not be exposed in that namespace directly. A safe bet isimport tqdm; tqdm.tqdm.pandas(), which ensures it’s using the base tqdm.If you use
progress_applyand nothing appears on screen until the end, it could be because pandas might be doing chunks internally (in some versions, for certain data types, not typical though). Or it could be that the output was buffered (in a notebook, ensure you’re not in a state where output is not shown). Usually, you should see it live updating. In a Jupyter notebook, sometimes you need to ensuretqdm.notebookfor nice display.If you get an error like “tqdm is not defined” inside the apply function – that shouldn’t happen because tqdm isn’t used inside, it’s outside controlling the loop. So no special handling needed inside your applied function; you just write it normally.
When using
progress_applywith multi-threading or multi-processing (pandas has adf.apply(func, axis=1, workers=4)option viaswifteror so), the progress bar might not update properly because of parallel execution. Tqdm’s pandas integration is mainly for linear apply. If you need parallel apply with progress, you might have to usetqdm.contrib.concurrent.process_mapon data split rather than pandas built-in parallel, or manage progress manually.
In summary, pandas integration via tqdm.pandas() adds progress bars to DataFrame/Series operations with minimal effort. It’s a beloved feature for those doing data work, as it merges the convenience of pandas high-level operations with the transparency of tqdm’s progress output. Many analyses or data transformations that used to be “dark runs” (no output until done) can now give feedback, which is especially helpful for long-running data jobs.
Nested progress bars
Tqdm supports nested progress bars, allowing you to monitor progress at multiple levels of a nested loop or concurrent tasks. For example, if you have an outer loop and an inner loop, you might want to see two progress bars: one for the outer loop overall progress, and one for the current inner loop’s progress. Nested progress bars are important in scenarios such as iterating over multiple datasets or performing multi-step tasks for each item. Tqdm handles nesting by assigning each bar a separate line on the console, as long as the console supports cursor movement up (most terminal emulators do, but some IDE consoles might not fully support it). Using nested bars gives a richer view of progress, but it also can consume more screen space and might require careful use of the position parameter and the right environment.
How to implement nested progress bars: The tqdm library will automatically handle nested contexts properly if you use separate tqdm instances. You just need to be mindful of setting the position parameter for each bar if they might overlap. By default, the first tqdm you create is at position 0 (the first line). If inside that loop you create another tqdm, by default it might detect it’s nested and put it on the next line (position 1). However, sometimes you should explicitly set position to ensure it doesn’t overwrite the outer one. Here’s a typical pattern:
from tqdm import tqdm
outer_iterable = range(5)
inner_iterable = lambda i: range(100 * (i+1)) # inner length varies for demo for i in tqdm(outer_iterable, desc="Outer loop"):
for j in tqdm(inner_iterable(i), desc=f"Inner loop {i}", position=1, leave=False):
# do some work
time.sleep(0.01)
# possibly do something after inner loop
In this example:
The outer loop has a tqdm with desc “Outer loop”. It will iterate 5 times.
The inner loop creates a tqdm each time (so it’s instantiated anew for each iteration of outer). We give it
position=1to ensure it prints on the line below the outer loop’s bar. We also useleave=Falsefor the inner loop so that when an inner loop finishes, its bar disappears (otherwise, you’d end up with multiple completed bars cluttering the output after each inner iteration). We included the loop index in the inner desc to identify which iteration’s progress we’re seeing.The inner loop length here is variable (just to illustrate), but tqdm handles it no problem.
During execution, you’ll see something like:
Outer loop: 20%|██▌ | 1/5 [00:01<00:05, ...]
Inner loop 1: 100%|██████████| 200/200 [00:02<00:00, ...]
While inner loop is running, the outer loop’s bar typically stays static or updates more slowly (each time an inner loop completes one iteration if you put the update outside or when inner completes). Actually, in this code, outer loop’s tqdm will update once per inner loop completion (since the loop increments after inner is done). So it will stay at 0/5 until the first inner loop finishes, then jump to 1/5, etc. Meanwhile, the inner loop bar is rapidly updating from 0 to 100%. When one inner loop finishes (due to leave=False), that bar will clear, and then the next inner loop’s bar will appear fresh on the same line.
Using position: If you had more deeply nested loops, you would use position 2, 3, etc., for each level to stack them. position basically tells tqdm the fixed row number (starting from 0 at top) where that bar belongs. It’s particularly important if you have concurrent bars started in different order. For example, if you spawn multiple threads each with a progress bar, you might assign each thread a static position so their bars don’t overwrite each other. Tqdm by default tries to manage positions by incrementing for nested with contexts or loops, but explicit control is often clearer.
Example of nested for two levels (as above):
from tqdm import trange, tqdm
for i in trange(3, desc="Epochs"): # outer loop # some outer work for j in trange(100, desc="Batch", leave=False, position=1):
# simulate batch processing
time.sleep(0.01)
# maybe evaluate at end of epoch
This yields an “Epochs” progress bar and a “Batch” progress bar under it that resets each epoch. The leave=False on the inner ensures you don’t get an accumulation of finished “Batch” bars. The outer loop’s bar (Epochs) will tick once per epoch, and the inner bar runs through 100 each epoch. This is analogous to training epochs and batches in machine learning – a common scenario for nested bars.
Considerations and performance:
Nested bars are slightly heavier because writing to multiple lines and moving the cursor around is a bit more work for the console. Typically, this is not an issue, but in some environments like certain IDEs or old terminals, it might not perfectly overwrite. For example, as per the tqdm FAQ, some consoles (IDLE, older PyCharm console) lack full support for moving the cursor up, so nested bars might print oddly (like they won’t clear properly). On Windows, nested bars also required colorama for proper newline handling (which tqdm automatically uses if available). Tqdm mentions that in such cases, using environment variable TQDM_POSITION=... or forcing ASCII helps. But most modern setups (e.g., running in a Jupyter Notebook or VS Code terminal or standard terminal) handle it fine.
From a user perspective, too many nested bars can be confusing. It’s usually best to limit to 2 or 3 levels. If you find yourself wanting more than that, perhaps a single progress bar with a combined metric or splitting tasks differently might be better for clarity. Tqdm supports it, but your screen real estate is the limit – each bar takes a line, plus potentially it prints new lines on completion if leave=True. If you have, say, 5 nested loops, that’s 5 lines of progress – it could still work, but you need a tall console window to view them all at once.
Integration examples:
If using
multiprocessingorjoblibfor parallel tasks, each process/thread might output its own progress bar. Tqdm has some support viacontrib.concurrentto merge them or handle them nicely, but if you manually manage it, you could assign each thread a unique position. For example, thread 0 prints at position 0, thread 1 at position 1, etc. They will then update concurrently on separate lines. The output might interleave a bit if not careful, but tqdm is thread-safe enough in printing as long as each bar prints to its own fixed place.Another use: Suppose you have a main progress for overall tasks, and you want a secondary bar to show progress of a subtask (like downloading a file). You can reuse one line for subtask each time or you could open a new bar each time. For instance:
main_tasks = ["file1", "file2", "file3"]
for file in tqdm(main_tasks, desc="Files"):
with tqdm(total=file_size(file), desc="Downloading", leave=False, position=1) as subbar:
for chunk in download(file):
subbar.update(len(chunk))This would show one static “Files” bar (position 0) going through 3 files, and a “Downloading” bar (position 1) that resets for each file’s download progress. This is similar to outer/inner but demonstrates a real scenario of nested tasks (files and chunks). The inner bar measures bytes, the outer bar measures number of files.
Common errors with nested bars:
Forgetting
leave=Falseon inner bars when you don’t want them left. If you leave them, you’ll have multiple completed bars. Sometimes that’s fine (maybe you want a log of each inner loop’s progress at end), but typically it clutters the screen.Misusing
position: If you set the same position for two bars that are alive at the same time, they will overwrite each other. For example, if you spawn two threads and both useposition=0tqdm, their outputs will mix on one line. Always give unique positions to simultaneously active bars. If bars are sequential (like inner loop bars that end before the next begins), you can reuse positions for clarity.In Jupyter notebooks using
tqdm.notebook, nested bars sometimes don’t display as nested, but rather one after the other, or might require the right combination of leave. The text mode is more straightforward for nesting. In notebooks, the bar is an HTML widget; multiple bars may stack, but it may not remove old ones with leave=False as expected (depending on version). If working in notebooks, consider flattening progress or using an alternative approach (like a single bar counting total iterations overall).If using nested bars in an environment without proper carriage return handling, you might see that the inner bar prints on a new line for each update (like not overriding the same line). For example, in some log files or poorly emulated terminals, nested might degrade to a stream of logs. In such case, consider setting
Dynamic_ncols=Falseand a fixedncolsand maybe useascii=Trueor ultimately disable nested for that environment. The tqdm FAQ suggests environment variable fixes (like forcing position or etc.) if needed.
In summary, nested progress bars in tqdm allow you to visualize multi-level progress hierarchies. They are extremely useful for complex iterative tasks (like processing multiple datasets, or multi-step algorithms). With careful use of the position and leave parameters, you can keep the output neat. Just remember that not all consoles handle them equally, but in most cases, tqdm’s defaults and suggestions (like using colorama on Windows, etc.) make it work well. This feature shows the versatility of tqdm – it’s not limited to one loop at a time; you can monitor many concurrent or nested processes together.
Hooks and callbacks for other integrations
Tqdm is extensible, meaning you can integrate it with other systems or libraries by using hooks and callbacks. This feature is a bit more advanced but very powerful: it allows tqdm to tie into places like URL downloads, logging, or custom workflows that aren’t simply for-loops. For example, if you’re downloading a file using an HTTP library, you might get callbacks for progress – you can use those to update a tqdm bar. Similarly, you can redirect logging outputs through tqdm to ensure logs don’t break the progress bar display. Tqdm includes some ready-made integrations in tqdm.contrib to facilitate these use cases, and you can also craft your own.
Hooks for downloads (urllib example): One of the examples in tqdm documentation involves using a callback hook to update the progress bar for downloads via urllib. For instance:
import urllib.request
from tqdm import tqdm
url = "https://example.com/largefile.zip"
filename = "largefile.zip" # First, determine the total size (for progress bar)
site = urllib.request.urlopen(url)
meta = site.info()
total_size = int(meta.get("Content-Length", 0))
# Set up tqdm bar
pbar = tqdm(total=total_size, unit="B", unit_scale=True, desc="Downloading")
def download_hook(blocks_transferred=1, block_size=1, total_size=None):
pbar.update(blocks_transferred * block_size - pbar.n)
urllib.request.urlretrieve(url, filename, reporthook=download_hook)
pbar.close()
In this code, urlretrieve accepts a reporthook which is a callback that takes (blocks_transferred, block_size, total_size). We define download_hook to update our tqdm bar. The trick is pbar.n gives how much we’ve already updated, so we update by the difference to avoid overshooting. This way, as the file downloads, the hook keeps firing, and our progress bar updates accordingly. The tqdm output will show bytes downloaded, with unit_scale=True to use KB/MB, etc., and an ETA.
Tqdm’s low-level design (the tqdm.update() method) allows this usage: you can call update in any function – it doesn’t have to be in a loop. That’s how we integrate with external processes that have their own loop (like network download).
Logging redirection: The tqdm.contrib.logging module provides a context manager logging_redirect_tqdm() that can intercept logging messages and route them through tqdm.write(). This prevents log lines from breaking the visual progress bar output. For example:
import logging
from tqdm import tqdm
from tqdm.contrib.logging import logging_redirect_tqdm
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
with logging_redirect_tqdm():
for i in tqdm(range(5)):
logger.info(f"Processed item {i}")
time.sleep(1)
In this block, even though we call logger.info inside the loop, those messages will appear properly interleaved without ruining the bar because logging_redirect_tqdm temporarily overrides the logging handlers to funnel through tqdm.write. This is very useful if you have an application that heavily uses the logging module (which is common in real projects) and you still want a neat progress bar.
Concurrent.futures integration: In the tqdm.contrib.concurrent module, there are convenience functions like tqdm.contrib.concurrent.thread_map and process_map which mirror Python’s map but with progress bars. For example:
from tqdm.contrib.concurrent import process_map
def work(x):
# some CPU-intensive work return x*x
results = process_map(work, range(1000), chunksize=10, max_workers=4)
This will run work in parallel across 4 processes and give a progress bar of how many tasks completed out of 1000. It’s similar to concurrent.futures.ProcessPoolExecutor.map, but with a built-in tqdm. This is easier than manually using as_completed and updating a bar. Under the hood, process_map splits the tasks and updates a bar as tasks complete. Similarly, thread_map for threads. These functions handle a lot of the complexity, including nicely killing the bar when done.
Asyncio integration: Tqdm also provides tqdm.asyncio which includes asyncio.tqdm or usage patterns to integrate with Python’s async/await. For example, if you have an async generator or want to await tasks with progress, tqdm.asyncio can wrap an async iterator with progress or provide a coroutine that updates. The details are a bit complex, but basically:
from tqdm.asyncio import tqdm as tqdm_async
async for item in tqdm_async(async_iterable):
...
This would display a bar for an async iterable.
Custom integration patterns: Beyond provided modules, you can integrate tqdm anywhere you can determine the progress of a task. For example:
If you’re using Keras (deep learning library), it has its own progress, but if you wanted to use tqdm, you could implement a
keras.callbacks.Callbackthat updates a tqdm bar on batch end. Actually, as noted in the docs,tqdm.keras.TqdmCallbackexists to do exactly this. You can simply plug that into model training and get tqdm bars for epochs and batches. For instance:from tqdm.keras import TqdmCallback
model.fit(X, y, epochs=5, callbacks=[TqdmCallback(verbose=0)])This will show a nested bar: one for epochs and one for batches, using the callback to update progress.
If integrating with Dask (parallel computing for big data),
tqdm.dask.TqdmCallbackexists similarly to show a progress bar for Dask computations.
All these are essentially hooks: they listen to events (like end of batch, end of task) and update tqdm accordingly. The existence of these in tqdm’s contrib means the community has identified common scenarios and provided solutions.
Example with tqdm.contrib.telegram: As a fun one, there is a tqdm.contrib.telegram which allows sending progress bar updates to a Telegram chat (so you can monitor long jobs remotely). This uses a hook approach by periodically sending the bar status via the Telegram API. While niche, it shows how far integration can go – tqdm’s output can be redirected or captured to other mediums.
Performance considerations for hooks: Using callbacks or threads to update bars is generally fine because updates are rate-limited by mininterval. One thing to watch is if the hook fires extremely often (like tens of thousands of times a second), then make sure to accumulate updates or tune miniters. But typically, callbacks like urlretrieve’s are called at sensible intervals (like per block of data downloaded). The logging redirect and such have negligible overhead, they basically intercept a print call.
Common pitfalls:
When writing your own hook, ensure you update the correct amount. In the download example, using
pbar.update(block_size)every time might actually double count if not done carefully, becauseurlretrievemight call the hook with cumulative counts or something. That’s why in our example we used(blocks_transferred * block_size - pbar.n)to compute how much new data was downloaded since last call.Don’t forget to close or flush bars if you exit early. For instance, if an error occurs in the middle of a callback-based process, the bar might remain open. Using
with tqdm(...)as context in such integration is sometimes tricky, but you can manage lifetimes by closing in finally blocks.Ensure thread safety: If a hook might be called from multiple threads, the default tqdm
update()is thread-safe (it uses locks internally when printing), but heavy concurrent updates can scramble output. Usually not an issue, but if customizing, be aware.The
tqdm.contrib.concurrentmap functions are very useful, but note that they are not available unless you import them explicitly (since they are in contrib). Also,process_maploads entire iterables into memory by default; it’s fine for moderate sizes, but for huge tasks, a manual approach might be needed (or ensurechunksizeis set).If integrating with GUI applications (like you want to show a tqdm in a Tkinter or WxPython GUI), you might need to override tqdm’s printing to route to a GUI element. This can be done by specifying
fileparameter in tqdm (you can direct output to any stream, if that stream is tied to GUI). Or you could periodically fetchpbar.nand set a GUI progress bar value. Tqdm doesn’t directly integrate with GUI progress bars (because those typically have their own widgets), but it provides the data you need to do so if desired.
In short, hooks and callbacks make tqdm a flexible framework not just for loops you write, but for integrating into other operations and libraries. Whether it’s tracking file downloads, bridging with logging, or parallel execution, tqdm provides tools to incorporate progress bars so you can maintain that consistent feedback in virtually any situation. This is why tqdm is considered extensible – it’s not limited to basic usage; it can be bent to many needs with a bit of glue code.
Now that we’ve covered core features and some advanced ones, we can move on to performance optimization and best practices, which will tie together these concepts and ensure you get the most out of the tqdm library.
Advanced usage and optimization
Performance optimization
Although tqdm is designed to be lightweight, there are scenarios where optimizing its usage can be beneficial, especially when dealing with extremely fast loops or very large numbers of iterations. Performance optimization with tqdm generally involves tuning how often it updates, how it handles output, and how it interacts with your environment. Below are several strategies and considerations:
1. Adjusting refresh rates (minimizing overhead): Tqdm’s default behavior is to refresh the progress bar at most 10 times per second (mininterval=0.1 seconds). If your loop is very fast (millions of iterations per second), even checking the time every 0.1s and updating can introduce some overhead. One optimization is to increase the mininterval for very fast loops. For example, setting mininterval=1.0 will reduce how often tqdm tries to redraw, thereby reducing overhead. Another related parameter is miniters. Tqdm dynamically adjusts miniters (minimum iterations between updates) to maintain the mininterval, but you can manually set a high miniters for extremely tight loops. For instance, if you know you have 100 million iterations and you only want to update after every 1 million iterations, you could set miniters=1000000. This way, tqdm won’t do time checks or prints for each small chunk, only every million steps. Essentially, by reducing update frequency, you cut down the overhead of timing and printing. Keep in mind that the progress bar will be a bit less smooth (updates in jumps), but that’s often acceptable for huge loops. Empirical testing has shown that tqdm’s overhead per iteration is about 60-100ns in normal conditions, but if every nanosecond counts (like in a tight inner loop doing trivial operations), you can gain back some time by fewer updates.
2. Disabling tqdm in production or non-interactive environments: In some cases, you might want to disable the progress bar output entirely (for example, when running in a non-interactive setting like a cron job, or if logging to a file). Tqdm provides a parameter disable=True to turn off the bar. You can set this dynamically: for instance, disable=not sys.stdout.isatty() will disable tqdm if the output is not a terminal (common trick to avoid progress bars in logs). Another way is to set environment variable TQDM_DISABLE=1 which will disable all tqdm bars (this is checked by tqdm.auto). Using this approach can slightly improve performance in contexts where you don’t need the bar, because it avoids all overhead of printing. Tqdm will then essentially become a no-op in the loop (aside from the minimal overhead of an if-check each iteration).
3. Parallel processing with tqdm: If one iteration of your loop is CPU-bound and heavy, using parallel processing can speed up overall execution, and you can still maintain a progress bar. The tqdm.contrib.concurrent.process_map we discussed is one high-level way. By dividing work among multiple processes or threads, each core handles part of the work, and tqdm combines progress. For example, if you have 4 cores and 1e6 tasks, using process_map could finish roughly 4x faster and tqdm will reflect combined progress of all workers. Under the hood, process_map periodically checks how many tasks are done and updates a central bar. If you roll your own, you might use concurrent.futures and manually increment a shared tqdm from worker threads with a thread-safe approach (like using tqdm.update() with a lock, but usually easier to use the built-in process_map). Keep in mind that for I/O-bound tasks, threads could also be used with thread_map similarly. The key optimization is: don’t run everything serially if it can be parallelized, and let tqdm assist in tracking progress across the parallel tasks.
4. Caching results to avoid repeated work: While not specific to tqdm, caching can play a role in optimizing loops that are monitored by tqdm. For example, if you are processing items and some results can be reused or saved to disk, doing so will reduce the length or complexity of the loop next time, thus finishing faster. You might integrate caching by skipping items that were processed in a previous run. If you do this, you can adjust the total of tqdm to account for only the items that need processing this run (so the bar accurately reflects remaining work). This technique is more about algorithm optimization, but it goes hand-in-hand with tqdm: you often realize the need for caching when you see progress bars taking too long on repeated runs.
5. Memory management: Tqdm itself has a small memory footprint (it just stores counters and timing info), so memory issues are more likely related to the data you are processing. However, one thing to watch out: if you inadvertently create a very large list to loop over just to use tqdm, that could be memory expensive. For example, doing tqdm(range(1000000000)) is fine (range is lazy), but doing tqdm(list(range(1000000000))) would use a lot of memory for the list. Always prefer iterators/generators or lazy ranges rather than materializing huge lists just to use tqdm. Tqdm works with any iterator, so you can stream data.
Another memory angle: if you have a progress bar inside a long-running process, by default it writes to stderr for output. If stderr is not displayed or captured properly, a huge backlog of output could hypothetically build up in a buffer. In most cases, this isn’t an issue because the terminal or logging just writes it out. But if you had millions of lines because you left an inner bar with leave=True in a loop that runs many times, you could fill memory with output. The solution is to use leave=False for bars that repeat many times (like nested bars that reset each time) so old bars aren’t stored or to route output to a file and monitor its size.
6. Using environment variables for defaults: Tqdm provides some environment variables to override defaults. For instance, TQDM_MININTERVAL and TQDM_MINITERS can be set so that every bar uses a certain baseline. In CI or cloud environments, one might set TQDM_MININTERVAL=5 (5 seconds) to dramatically reduce update frequency, to avoid log spam. This is an optimization in contexts where the progress bar is being logged rather than viewed live. In such cases, printing fewer lines (say one update every 5 seconds) still gives a sense of progress without flooding logs. Similarly, TQDM_MAXINTERVAL (the maximum interval between forced refreshes, default 10 seconds) ensures that even slow-progressing tasks update at least every X seconds. If you have extremely long tasks where nothing happens for a long time, you might increase maxinterval a bit, though 10s is usually fine.
7. Avoiding performance pitfalls: One known potential issue is that if you do something weird like nested tqdm in a very un-optimized way (for example, thousands of nested bars at once), you could slow things down or run into terminal limitations. Also, updating tqdm from within a very tight inner function can have overhead. The earlier example from a developer’s blog showed that updating a progress bar for every iteration of an innermost loop drastically slowed down an algorithm (60 min down to 10 min when reduced updates). The solution was to not update every iteration – the same point about miniters/mininterval. So a best practice is: if you notice your code significantly slowed with tqdm, it’s a sign you might be updating too frequently. Adjust parameters or update logic to mitigate that. In extreme cases, you can decide to update conditionally:
for i, item in enumerate(iterable, 1):
# ... process item if i % 1000 == 0:
pbar.update(1000)
pbar.update(i % 1000) # update the remainder at end
This manual chunking ensures you update in batches of 1000, which can be more efficient than 1 by 1. However, since tqdm already does dynamic miniters, such manual chunking is often not needed unless you have a very irregular workload.
8. Profiling with tqdm: If you are profiling your code’s performance, note that having tqdm in the loop can slightly skew results. It’s often a good idea to disable or remove tqdm when doing fine-grained profiling or benchmarking of algorithm speed, because the progress bar (even if minimal) can add noise. That said, for high-level profiling, it’s negligible. But if you want microsecond precision, run without tqdm. Use environment toggle to disable it easily without removing code.
9. Handling extremely large totals: If you set a very large total (on the order of 1e12 or more), there could be formatting issues or int overflow in older versions (Python ints are fine, but the ETA calculation might behave oddly if it’s beyond certain range). Tqdm generally handles large numbers by using Python’s big ints, but just be aware of output width. You might want to use unit_scale=True to abbreviate large totals (like printing “1.2e12” iterations as “1.2T” perhaps). This is more about output readability than performance, but it can indirectly affect performance if printing a gigantic number slows formatting. Tqdm also has an adaptive unit scaling feature for byte units, which can be leveraged for other units via unit_scale.
In summary, performance tuning for tqdm is usually not required for normal loops – the default settings are good. But when pushing the limits (very fast loops or writing progress in performance-critical contexts), remember these optimization techniques:
Reduce update frequency (increase mininterval or miniters).
Leverage parallel processing to get the job done faster and track combined progress.
Disable or throttle output in non-interactive scenarios to prevent overhead and log flooding.
Cache and skip unnecessary work so progress bars complete faster on subsequent runs.
Profile carefully and be mindful of tqdm’s overhead when measuring execution time – remove it if measuring tiny differences.
By applying these strategies, you can ensure that the tqdm progress bar library remains a help and not a hindrance, even in demanding use cases. It’s worth noting that for the vast majority of applications, tqdm’s impact on performance is so small that it’s a non-issue, which is a testament to its efficient design.
Best practices
To get the most out of the tqdm library, it’s useful to follow some best practices in how you incorporate it into your code. These practices ensure that your code remains clean, maintainable, and that the progress bars behave nicely in different scenarios. Let’s go through several best practices:
1. Use context managers for automatic cleanup: Wrapping your tqdm usage in a with tqdm(...) as pbar: block is a good habit. This ensures that the progress bar is closed properly once you exit the block, even if an error occurs. For example:
with tqdm(total=N) as pbar:
for x in something:
# do work
pbar.update(1)
This way, you don’t have to remember to call pbar.close(). Proper cleanup prevents residual progress bars from sticking around on the terminal after completion (especially if leave=False is not set but you still don’t want stale bars), and releases any resources associated with the bar. It’s also helpful in Jupyter notebooks, where open tqdm bars sometimes need to be closed to free the display. If not using with, at least call pbar.close() in a finally if your loop can break or error. This best practice ensures proper resource management and output tidiness.
2. Avoid mixing tqdm prints with standard prints without caution: If your loop has to output other text (like debugging info or results), use tqdm.write() for those prints. This prevents the output from interleaving with the bar in a messy way. For example, if you have:
for item in tqdm(items):
result = process(item)
print(f"Result for {item}: {result}")
The print statements might break the bar output. Instead do:
for item in tqdm(items):
result = process(item)
tqdm.write(f"Result for {item}: {result}")
Tqdm will handle these writes by moving the cursor appropriately. This keeps your console output clean. Another pattern: if you only want to print once after loop, you can safely do a normal print after closing the bar or outside the loop.
3. Use desc and unit to make bars self-explanatory: When running multiple progress bars (or logging them), it helps to label them. Always give a short desc (description) that explains what’s being processed. For example, tqdm(range(files_count), desc="Files", unit="file") is clearer than an unlabeled bar. The unit parameter is also useful to give context to rates: use unit="MB" for bytes if you convert the values, or unit="it" vs unit="record" etc. This makes the bar output more user-friendly and meaningful. It’s a small thing but improves readability, especially when someone else runs your code or if you revisit it later.
4. Handle errors gracefully with tqdm: If an exception occurs in the middle of your loop, the progress bar might not reach 100%. It’s often useful to catch exceptions inside the loop, log them (with tqdm.write or standard logging), and continue or break as appropriate, rather than letting the whole progress bar hang incomplete. For example:
for item in tqdm(items):
try:
process(item)
except Exception as e:
tqdm.write(f"Error processing {item}: {e}")
continue # skip this item but keep going
This way the bar still goes to 100% of items attempted, even if some failed. If you cannot continue after an error and must break, then breaking out of the loop will stop the bar early – you might consider setting pbar.n = pbar.total and closing it, or simply be okay with it not reaching 100%. As a courtesy in CLI apps, you might want to ensure the bar doesn’t just freeze mid-way. Using try/except around the whole loop can allow you to pbar.close() and then re-raise the error if needed.
5. Configuring tqdm globally for different environments: Sometimes, you want certain behavior in interactive mode vs automated mode. A best practice is to make use of environment detection. For example, you could do:
import sys
from tqdm import tqdm
is_interactive = sys.stdout.isatty()
pbar = tqdm(total=N, disable=not is_interactive, dynamic_ncols=is_interactive)
Here, we disable tqdm if not interactive (so it won’t spam logs) and we enable dynamic width resizing only if in a real terminal (since in a log file dynamic width doesn’t matter). This approach yields adaptive behavior of your progress bars. Additionally, if you know the code might run in Jupyter vs console, consider using from tqdm.auto import tqdm when importing – this uses tqdm.notebook automatically if in a notebook, giving better visuals. So a best practice is: always import tqdm from the auto module (unless you have a specific reason not to). It doesn’t harm in console and improves in notebooks.
6. Documenting usage of tqdm in your code’s README or docs: If you’re writing a library or script that uses tqdm, it’s good to mention that the output will include progress bars, and perhaps how to disable them if someone wants (like an argument --no-progress in CLI that sets disable=True). Many users appreciate the progress but some might want to turn it off. Providing that option (maybe via a command-line flag or config) is a nice practice. For library code, you might not want to always print progress – maybe allow the caller to pass a flag or give them control by accepting a tqdm instance or similar (advanced usage, e.g., some libraries accept a tqdm_class to use for progress). As a simple practice, make it clear to users how to handle or expect progress output.
7. Testing your code with tqdm: When writing unit tests or automated tests, progress output can clutter test logs. It’s often useful to disable tqdm during tests. One way is to use the disable parameter or environment variable as mentioned. For example, in a test environment, set TQDM_DISABLE=1 or use monkeypatch in pytest to turn off progress. Alternatively, if your code functions can accept a disable flag (or you detect testing via environment), do that. The idea is to keep test output clean (unless you are specifically testing the progress logic). This is a best practice for CI integration – it prevents thousands of lines of progress bar updates in test logs.
8. Use appropriate tqdm submodule for environment: If you know you’re writing for Jupyter notebooks exclusively, use tqdm.notebook so that you get a nice widget bar. If writing a console script, use standard tqdm (or tqdm.auto). The reason is that the notebook version outputs a rich HTML object and might not render in plain text environments, and vice versa. tqdm.auto tries to pick for you, which is why it’s recommended for general code. But specifically, if you want a particular style (e.g., in Jupyter you want the text mode instead of widget), you can still use tqdm text mode in Jupyter but it tends to flicker more. So best practice: use the right tool for the job in terms of submodule.
9. Respecting user preferences (do not enforce silence or verbosity unexpectedly): Some developers might incorporate tqdm but then a user uses their code in an environment where a progress bar is not desired (like piping output to a file). Best practice in designing such code is to automatically detect that scenario (as mentioned with isatty) and disable the bar. Another scenario: if progress is extremely fast, sometimes printing it is not beneficial – you could add logic like “if the task finishes in under X seconds, maybe don’t even show a bar”. However, this is usually not needed, as a quick task’s bar doesn’t hurt (tqdm might even not show anything if it finishes too quickly and environment can clear it).
10. Contribute to tqdm if something is missing: As a meta best practice for an open source library – if you find a feature lacking or a bug in tqdm, consider raising an issue or contributing. Tqdm has an active repository, and many enhancements (like telegram, concurrent, etc.) came from contributions. For instance, someone could contribute improvements for certain environments, as has happened (e.g., JupyterLab support). By following this practice, you help keep the tool robust and updated.
In summary, best practices with the tqdm library revolve around writing clean code that integrates progress bars smoothly:
Clean up bars when done,
Integrate with logging/printing carefully,
Make output informative (with descriptions/units),
Provide ways to disable or adapt the progress output,
And ensure different runtime contexts are handled.
By adhering to these practices, you make sure that the inclusion of tqdm in your program enhances it without introducing any downsides. The result is user-friendly, maintainable, and professional-grade progress reporting in your Python applications.
Real-world applications
Progress bars provided by tqdm are used in a wide range of real-world applications, from data science and machine learning to system administration and scientific research. Let’s explore a few detailed case studies and examples of how the tqdm library is applied in practice:
1. Machine learning training loops (industry: AI research) – In machine learning experiments, especially deep learning, training can take hours or days. Tqdm is commonly used to track the progress of training epochs and batches. For example, researchers at an AI lab might train a neural network for 100 epochs on a large dataset. By wrapping the epoch loop with tqdm, they get instant feedback on which epoch is running and how many are left. Additionally, they often use nested progress bars: one for epochs and one for mini-batches within each epoch. This was adopted at companies like OpenAI and Google research internally; while training large models, engineers monitor progress bars to estimate completion time and to ensure the training hasn’t stalled. One concrete case: a team training a computer vision model on millions of images used tqdm to display epoch progress and even current training loss in the postfix of the bar (using set_postfix). Over hundreds of iterations, this gave them insight into whether the loss was decreasing as expected. The performance impact was minimal compared to the training itself (since training uses GPUs heavily, a small CPU print cost is negligible), and the benefit was that they could tell at a glance if, say, at 50% of training the model wasn’t converging, allowing them to intervene early. In this scenario, tqdm improved the efficiency of the research workflow by providing transparency. Industry-wide, this pattern is so common that frameworks like PyTorch Lightning integrate with tqdm by default for their training progress bars.
2. Data processing pipelines (industry: data engineering) – Consider a data engineering team at a financial services company that processes large volumes of transaction data every night. They have Python scripts that clean and aggregate data, sometimes taking tens of minutes. By adding tqdm progress bars to their ETL (extract-transform-load) pipelines, they achieved better monitoring and logging. For instance, one pipeline processes 10 million records in chunks – a tqdm progress bar informs the team that the job is, say, 70% done, which is useful if they have a narrow batch window. In one case study, a company had a job that sometimes slowed due to network calls, and the progress bar helped pinpoint where the slowdown occurred (they noticed the bar speed dramatically decreased at a certain percentage, indicating which stage was the bottleneck). They then optimized that stage (caching data locally), resulting in a more consistent progress rate. The tqdm library’s ability to show the iteration rate (it/s) was used as a metric: after optimization, they could see the rate go from maybe 500 records/s to 2000 records/s. This real-world performance benchmarking with tqdm acted as a quick feedback loop for their optimizations. Additionally, these bars were logged to a file for auditing – if a job failed, the log might show it failed at 85% completion, which helped in restarting from that point.
3. Web scraping and crawling (use case: academic research) – A group of researchers doing a web crawl to collect data (e.g., scraping thousands of web pages for a social science study) utilized tqdm to monitor their scraper. Web scraping can be unpredictable: some pages load slowly, some might hang. By using a tqdm progress bar over the list of URLs, the researchers could observe if the crawling was proceeding at a normal pace or if it got stuck on a particular site (the progress would stop advancing). In one instance, the progress bar hung at 30% for a long time – which alerted them to an issue: the scraper got stuck due to a never-ending redirect loop on a site. Thanks to the progress bar, they noticed the anomaly quickly (rather than discovering hours later that nothing progressed). They then implemented a timeout and skip for problematic URLs. Furthermore, they used tqdm.write() to log which URL is currently being fetched, so when the bar paused, the last logged URL was the culprit. This case demonstrates how tqdm not only provides user feedback but can also be used as a debugging and monitoring tool in web crawling tasks. The outcome: the team was able to crawl tens of thousands of pages with confidence, and the progress bar gave them a sense of how long the remaining pages would take (using the ETA feature). This was crucial for planning – e.g., seeing “ETA 2 hours” allowed them to leave it running and come back later.
4. Open source projects using tqdm – Many open source tools incorporate tqdm for their progress indication. For example, YOLOv5 (an object detection project) uses tqdm to show progress of model training and data loading. In a specific update (as seen on their GitHub), the developers added a tqdm progress bar for processing image datasets. The result was a more user-friendly training process: when a user runs train.py in YOLOv5, they see a nice progress bar for data augmentation and for each epoch’s training progress. This increased the transparency of the training pipeline and was highlighted by the community as a positive improvement. Another project, the Hugging Face Transformers library, utilizes tqdm to display download progress for large pre-trained models. When a user downloads a multi-gigabyte model, a tqdm bar shows how many bytes have been downloaded and the speed. This is critical because without it, users might think the program froze given how long downloads can take. The success here is measured in user satisfaction – these open source projects got feedback that progress indicators made the tools feel more polished and instilled trust that something was happening under the hood. Millions of users of these libraries have indirectly experienced the benefits of tqdm’s progress bars.
5. High-performance computing (HPC) and simulations – In scientific computing, researchers often run large simulations or data analysis on HPC clusters. Tqdm is used in such contexts to monitor jobs that handle large numerical computations. For example, a climate scientist running a simulation for 10,000 time steps used tqdm to print a progress bar for the time steps completed. Even though the code was running on a cluster without a GUI, the log files captured the progress output. On checking the logs, the scientist could see that the simulation was, say, 60% done after a certain number of hours, helping them estimate total run time and whether the job might hit any walltime limits. Additionally, in an interactive HPC session or JupyterLab environment, seeing that progress gives a psychological relief (long computations often give no sign of life otherwise). There was a case where a simulation’s progress bar indicated it should finish in 2 hours, but the job kept running beyond that without completing – this discrepancy alerted the scientist to a potential performance degradation over time (due to memory issues). They discovered a memory leak that was slowing the simulation towards the end. Without the progress/ETA comparison, this issue might have gone unnoticed. Thus, tqdm contributed indirectly to detecting performance issues in long simulations by providing a baseline expectation of completion time.
6. Migration success story (from manual progress to tqdm): A company had an internal Python script for backing up databases. Originally, it printed messages like “Processing table X... done.” for each table. This was clunky and not uniform. They migrated to using tqdm – now it simply shows a progress bar “Backing up tables: 30/100 complete”. This change simplified the code (they removed a lot of manual print logic) and provided a cleaner output. The maintenance team found it easier to extend because they no longer had to place print statements in various loops; they just wrap loops with tqdm. In metrics, they saw that the backup script’s code size reduced and user (DBA) satisfaction increased as they could watch the bar rather than scroll through logs of printed lines. The overhead was minimal, and since then, they decided to standardize progress reporting using tqdm in all their Python maintenance scripts (like data migration scripts, cleanup jobs, etc.). This is a pattern in many teams: migrating from either no progress or custom progress messages to the tqdm library leads to more consistent and readable CLI tools.
7. Integration patterns in complex workflows: In workflows involving multiple tools, tqdm can serve as a unifying progress interface. For example, a bioinformatics pipeline might call various subprocesses for genome analysis. A Python driver script used tqdm to reflect the overall pipeline progress by updating after each stage. Each stage might not itself use tqdm (maybe they are external binaries), but the Python orchestrator knows how many stages or samples in total. By incrementing a tqdm bar when each step finishes, it provided the user a view of the pipeline’s completion percentage. This kind of integration pattern ensures that even if not every component of a workflow is instrumented for progress, the top-level can still give a holistic progress indication. A real-world example: the Snakemake bioinformatics workflow system prints progress of how many out of total rules are completed, which is akin to a progress bar concept (and one could integrate tqdm in custom Snakefile Python rules if needed to show within-rule progress).
In all these case studies, the common theme is that tqdm enabled transparency, efficiency, and better user experience in long-running processes. Whether it’s scientific research, data engineering, or open source development, having a quick way to instrument code with progress bars has proven valuable. The current trend shows increasing adoption of tqdm in scripts and libraries because it’s lightweight and easy to add. These real-world applications underscore that the tqdm library is not just a trivial utility but a fundamental tool that improves the interaction between humans and long-running computations across various industries.
Alternatives and comparisons
Detailed comparison of progress bar libraries
When it comes to progress bars in Python, several libraries offer similar functionality to tqdm. Below is a detailed comparison between tqdm and some alternative Python libraries for progress indication: Progressbar2, Alive-Progress, and Rich (Rich’s Progress component). We’ll compare features, performance, ease of use, community, documentation, licenses, and ideal use cases.
| Aspect | tqdm (our focus) | progressbar2 (ProgressBar) | alive-progress |
|---|---|---|---|
| Features | Supports simple progress bars with percentage, ETA, rate. Offers nested bars, manual updates, and integrations (logging, pandas, async). Highly extensible via contrib modules. Minimal text-based bar by default. | Highly customizable progress bar with many widgets (ETA, percentage, animated spinner, etc.). Can display bar, percentage, ETA, adaptive resizing. Lacks some newer integrations that tqdm has, but you can compose different widgets for custom output. | Offers visually rich progress indicators with animations (spinners, multiple bars, etc.). Has features like live throughput, bar that animates even when no progress (to show liveness). Good for console programs where a bit of flair is desired (starters, spinners, etc.). |
| Performance | Very high performance; overhead ~60 ns/iter in simple cases. Uses smart refresh to minimize I/O. Essentially negligible slowdown in most scenarios. Suitable for loops of all sizes – from small to extremely large. | Decent performance, but historically known to have ~800 ns/iter overhead (as noted in tqdm’s docs). This is heavier due to object-oriented widget updates. For most uses it’s fine, but in ultra-fast loops it could be more noticeable overhead than tqdm. | Reasonable performance, but because of its fancy animations, it might consume a bit more CPU for drawing frames. It’s active (constantly animating) even when progress isn't updated frequently, which can add overhead. For moderate speeds, impact is minor, but in extremely tight loops, pure text libraries like tqdm might be faster. |
| Ease of Use (Learning Curve) | Very easy to use – just wrap an iterable or call tqdm() around range. Minimal changes to code. For more advanced use (manual updates, nested bars), still straightforward with simple API. The use of tqdm library in Python is often one-liner for basic cases. | Moderate – usage is typically ProgressBar class with customizable widgets. Basic usage can be one-liner, but customizing output requires understanding of widget system. Slightly more code to set up a bar compared to tqdm’s one import and one function call. | Easy for basic usage: wrap an iterable with alive_bar context manager. To use fancy features (thematic spinner styles, etc.), need to learn its API. The library is well-documented with examples, but it’s a bit different in concept (it emphasizes continuous spinner even during waiting). Overall not hard, but not as drop-in as tqdm. |
| Community and Support | Very large community. Over 30k GitHub stars, widely used in data science and ML (often preinstalled in Colab/Kaggle, etc.). Active maintenance – frequent updates and quick issue resolution. Plenty of Q&A on Stack Overflow (the [tqdm] tag is active). It’s the go-to solution, so community support is excellent. | Smaller community (Progressbar2 has ~1.6k stars). It’s a fork/continuation of an older ProgressBar. Maintained by one main author. Fewer questions asked nowadays (since many migrated to tqdm). Still used in some legacy code. Documentation exists on readthedocs. Support is okay but not as bustling as tqdm’s. | Niche but growing community (alive-progress has ~3k stars). Maintained by an enthusiastic dev (rsalmei). It has fans who like the aesthetics. Fewer users than tqdm, but the documentation is extensive and there are some articles/videos. Support is decent via GitHub issues. |
| Documentation Quality | Good documentation on GitHub and tqdm.github.io, including examples and advanced usage. Docstrings are helpful (you can do help(tqdm) to see parameters). Many third-party tutorials and blog posts exist (because it’s popular). Overall, easy to find answers. | Documentation is okay – hosted on readthedocs (progressbar-2). It covers basic usage and list of widgets. Might not be as beginner-friendly as tqdm’s numerous external examples. Some advanced tricks documented (like custom widgets). It’s sufficient but not as inviting for newcomers. | Documentation is very good. The GitHub README and wiki have many examples and GIFs demonstrating features. The style is engaging, showing how to do different spinners, etc. Also, alive-progress’s docs cover integration with Jupyter and tips for speed. Because it’s newer, its docs are modern and clear. |
| License | MIT License (with some parts MPL-2.0 for certain files as per author’s note). MIT is very permissive, allowing commercial and open-source use freely. | BSD 3-Clause License. Also permissive, similar in spirit to MIT (just with some warranty clauses). Friendly for all uses. | MIT License. Permissive, open-source. No copyleft issues. |
| Ideal Use Cases | When to use tqdm: General-purpose progress bars for scripts, notebooks, and libraries. It’s ideal if you need something quick, reliable, and lightweight. Great for data science experiments, ETL jobs, training loops, file downloads, etc. Especially if you might nest loops or integrate with pandas/logging – tqdm shines there. Use tqdm if you want minimal fuss and maximum compatibility (works in terminals and notebooks with auto). | When to use progressbar2: If you need highly customized output with specific widgets or you already have code using it. It can display more complex progress info by assembling widgets (like alternating rotating marker, ETA, etc.). Some prefer its format (it was a standard before tqdm). It’s a solid choice for long-running console apps where you might want a classic style bar with custom formatting. If migrating legacy code that uses older ProgressBar, progressbar2 is basically that updated. | When to use alive-progress: If you want a more visually appealing/animated progress in the terminal. For example, if building a CLI tool for end-users, the fancy spinner and animations might give a nicer impression. It’s also useful if you want auto-resizing bars that fill the terminal width with neat animations. Alive-progress also handles multiple bars concurrently in a visually distinct way (with different spinner symbols). So use it for user-facing apps where style matters, and you don’t mind a bit of extra CPU for the eye-candy. It’s also good for indefinite progress (spinning) when you can’t calculate total – alive-progress does that nicely. |
In summary, tqdm vs alternatives:
tqdm is best for simplicity and broad use; it’s like the “Swiss army knife” – not the fanciest looking, but extremely reliable and quick to deploy in any scenario.
progressbar2 is somewhat legacy but still useful if you need custom formatted bars or are updating older code; it has a more classical approach with widgets.
alive-progress focuses on making console progress bars fun and informative with animations – choose it if terminal UX is a priority and your environment supports the animations.
Rich provides beautiful progress bars integrated in a full console app framework – it’s great if you need multiple concurrent progress displays and rich text features (colors, etc.), but it’s more than just a progress bar library; it’s a whole ecosystem.
All these libraries have overlapping functionality (they’ll all show you something like 50% done), but differ in ease, appearance, and ecosystem. In practice, many developers stick with tqdm library for Python because of its blend of performance and convenience, switching to others only for specific needs (like fancy UI with Rich, or specific widget output with progressbar2).
Migration guide (from other libraries to tqdm, or vice versa)
If you’re considering migrating to tqdm from another progress bar library, or even migrating in the other direction, here are some steps and tips to ensure a smooth transition:
When to migrate: You might decide to migrate to tqdm if you have an older codebase using progressbar (old) or progressbar2 and you want to simplify the code or take advantage of tqdm’s features (like better performance or Jupyter support). Similarly, you might migrate to Rich’s progress if you’re revamping a CLI with more colors, or to alive-progress for a different aesthetic. This guide will assume migrating to tqdm, since that’s a common case (tqdm’s popularity has drawn users from alternatives).
Step-by-step migration to tqdm:
Identify progress bar usage in your code: Find all places where the old progress library is used. For example, look for
ProgressBar(orprogressbar.ProgressBar(, or if using alive-progress, look foralive_bar(usage.Replace import statements: Remove or comment out the old library’s import, and import tqdm instead. E.g., change
from progressbar import ProgressBartofrom tqdm import tqdm. If you plan to use tqdm’strange, you can alsofrom tqdm import trangefor convenience.Replace instantiation and updates:
If the old code did something like:
bar = ProgressBar(max_value=n)
for i in range(n):
# work
bar.update(i)
bar.finish()You would change this to:
for i in tqdm(range(n)):
# workThat’s it. Tqdm manages the rest (no need for finish).
If the old code used context managers (alive-progress does:
with alive_bar(total) as bar:thenbar()each loop), you can simply do:with tqdm(total=total) as bar:
for item in iterable:
# work
bar.update(1)Or, shorter, often just use
for item in tqdm(iterable):.If progressbar2 was used with widgets (like ETA, Percentage, etc.), try to map those to tqdm’s format if needed. Tqdm by default shows percentage and ETA, so often you don’t need custom formatting. If you had a very custom output (like a progressbar widget showing “Processed X of Y files”), you might use
descor postfix in tqdm to incorporate that info.
Verify total and iterables: Ensure that tqdm knows the total iterations. If migrating from alive-progress, for example,
alive_barmight have been given a total or not. In tqdm, if you wrap an iterable, it will infer total fromlen(iterable). If it’s not inferable (like generator), passtotal=...totqdm(). Make sure the number matches the intended count.Remove manual prints if any: Sometimes with other libraries, you might have had to print a newline after finishing the bar or similar. Tqdm usually handles line breaks and closure gracefully. So you can remove
bar.finish()calls or extraprint()that were there to move to a new line after progress end.Test the output: Run the updated code in the environment (terminal or notebook) to see that the progress bar appears correctly. Check that the percentage goes 0 to 100% and that it closes nicely at the end. Also, check that performance is acceptable – ideally, you’ll notice equal or better performance after migration (for instance, if migrating from progressbar2, tqdm should use less CPU).
Clean up parameters: Tqdm and other libraries have different parameter names at times. For example,
ProgressBar(max_value=n)vstqdm(total=n). Or alive-progress has styling options that have no direct equivalent in tqdm – you might drop those or find an alternative (e.g., alive-progress’sbar='blocks'corresponds to tqdm’s default bar type, etc.). Simplify the code by removing parameters that are not applicable.Edge cases: If the previous library was used in non-standard ways (like multiple bars concurrently), adapt that to tqdm. For multiple bars, you might use
positionor nested loops in tqdm. If migrating from Rich, note that tqdm doesn’t natively handle multiple simultaneous bars without manual position management (where Rich does with tasks). But you can mimic multi-progress by nesting (outer loop for tasks, inner loop for each task’s progress). Plan for these differences.Remove old dependency: Once confident, update your requirements or environment to drop the old progress library dependency. This simplifies your project dependencies.
Code conversion example:
From progressbar2 to tqdm:
Before:import progressbar
bar = progressbar.ProgressBar(maxval=n, widgets=[progressbar.Percentage(), ' ', progressbar.Bar(), ' ', progressbar.ETA()])
bar.start()
for i in range(n):
do_work(i)
bar.update(i+1)
bar.finish()After:
from tqdm import tqdm
for i in tqdm(range(n), desc="Progress", unit="it"):
do_work(i)We replaced all that widget setup with a simple tqdm call. The desc "Progress" is optional, and unit "it" (iterations) is default actually, but shown for demonstration. Tqdm will automatically show percentage and ETA, similar to those widgets.
From alive-progress to tqdm:
Before:from alive_progress import alive_bar
with alive_bar(total, "Downloading", bar="blocks", spinner="dots") as bar:
for item in items:
download(item)
bar() # alive_progress incrementsAfter (tqdm can’t replicate the spinner in text mode, but provides a basic bar):
from tqdm import tqdm
for item in tqdm(items, total=total, desc="Downloading"):
download(item)We lose the custom spinner and style (blocks bar with dots spinner), but the essential info remains. If the aesthetic is important, one might stick to alive-progress, but if standardizing on tqdm, this is the change.
Common pitfalls during migration:
Off-by-one in updates: Some libraries might count differently (e.g., progressbar.update(current_value) vs tqdm.update(increment)). In tqdm,
update(n)means "we completed n more iterations since last update". If you used an absolute value before, adjust. For example, progressbar2’supdate(i+1)gave absolute progress, whereas in tqdm you often just iterate or update by 1 each time.Double printing newlines: If the old code printed newlines to clear bars or something, remove those or you’ll have blank lines. Tqdm by default prints a newline on completion of the loop (unless
leave=Falsethen it overwrites and clears).Ensuring flush: Tqdm flushes output periodically. If your environment buffered things weirdly, you can force flush by
tqdm(..., file=sys.stdout, flush=True)but that’s rarely needed. It’s something to check if you don’t see output until the end – maybe the environment needed flush or usetqdm.auto.Notebook vs console differences: If migrating code that used progressbar2 in Jupyter (progressbar2 doesn’t work well in notebooks by default), and switching to tqdm, you might see multiple lines of bars for each iteration (if using normal tqdm in Jupyter without
tqdm.notebook). The solution: usefrom tqdm.notebook import tqdmin Jupyter context, or betterfrom tqdm.auto import tqdmwhich picks correctly. That will ensure a nice single bar widget instead of many lines.Feature mismatch: If the old library had a feature that tqdm doesn’t (like multi-bars without manual handling, or very fancy bars), accept that or implement a workaround. For instance, Rich’s progress can display multiple bars in parallel elegantly; migrating to tqdm you might decide to just sequentially show tasks or nest loops because true parallel display might not be as straightforward.
Migrating from tqdm to others: Though less common, if you needed to migrate from tqdm to, say, Rich for a richer UI, the process is similar: replace for i in tqdm(...): with using Rich’s Progress context. But then you'd also incorporate Rich’s Console management. For brevity, it’s mostly beneficial to migrate from simpler to more complex if needed for features (like from tqdm -> Rich if adding entire UI).
Common pitfalls when switching away from tqdm:
If code relied on
tqdm.writeortqdm.set_postfix, other libraries have their own ways (progressbar2 doesn’t have an easy analog of postfix; Rich Progress can update task fields).If code used environment toggles (like TQDM_DISABLE), those won’t apply to other libs (though you can handle logic in code).
Avoiding pitfalls: To ensure no functionality is lost, run tests or sample runs before and after migration and compare outputs. The progress bar output doesn’t need to match character-for-character, but the flow (start, updates, finish) should logically be the same.
Conclusion of migration: Once migrated, the code should be simpler if moving to tqdm, as seen in code reduction examples. Maintenance typically gets easier since tqdm is widely known. Document the change for your team: e.g., “We now use tqdm for progress bars. Use tqdm(iterable) in new code instead of ProgressBar.” This sets a convention.
By following this guide, you can migrate between progress bar libraries with minimal hassle, enabling you to use the tool that best fits your project’s needs.
Resources and further reading
To deepen your knowledge of the tqdm library and explore its ecosystem, here are some useful resources:
Official resources
Official documentation: The tqdm documentation and user guide is available on GitHub Pages – tqdm Documentation – which contains API reference and examples.
GitHub repository: The source code and wiki are on GitHub – tqdm/tqdm on GitHub – where you can find the latest updates, report issues, or contribute. The wiki has historical info and tips.
PyPI page: For installation and release history – tqdm on PyPI – includes release notes and version information.
Official tutorials/examples: The GitHub repository’s README (and examples folder) contains basic usage examples. Additionally, the project wiki (if accessible) might have a “How to make a great Progress Bar” page and others.
Community resources
Stack Overflow (Q&A): You can browse the tqdm tag on Stack Overflow for common questions, solutions, and tricks (like handling nested bars or issues in specific environments).
Reddit communities: The r/Python subreddit often mentions tqdm in context of project discussions. Also, searching Reddit for tqdm can yield some discussions on performance (e.g., comparisons with Rich or "too much tqdm" threads).
Python Discord / Slack channels: The Python Discord has channels where one can ask about libraries. While not specific to tqdm, the community can help with usage questions. (Discord invite: Python Discord – you can then ask in help channels).
GitHub Discussions: Some repositories (like huggingface_hub or others) have discussions about disabling/enabling tqdm in their logging – these can be insightful for integration scenarios.
YouTube and podcasts: Look for conference talks or tutorials that mention tqdm. For example, some PyData conference videos might cover "tips for efficient Python", mentioning tqdm for progress bars. Podcasts like “Talk Python to Me” might mention it in passing during interviews about Python tools (though there might not be an entire episode on it).
Kaggle Not exactly forums, but note that on Kaggle Kernels, tqdm is pre-installed and often used. Kaggle’s community forums sometimes have threads like “How to show progress in notebook” where tqdm is a common answer.
Learning materials
Online courses: Many Python courses or Data Science bootcamps include a segment on writing loops efficiently; while not dedicated to tqdm, they often demonstrate it. For instance, some DataCamp courses on data engineering or ML show tqdm when iterating over large data.
Books: While no book is solely about tqdm, some mention it. “Python for Data Analysis” by Wes McKinney (2nd edition) briefly mentions using progress bars in lengthy operations. Similarly, “Fluent Python” might not mention tqdm specifically, but books on writing effective Python code often recommend using existing libraries for common tasks like progress bars.
Interactive tutorials: The Real Python website has an article “Python Progress Bars: A Complete Guide” which covers using tqdm and others. That’s a very useful tutorial for beginners. Also, the datacamp community tutorial “How to use tqdm in Python” is a quick read.
Code repositories with examples: Check out example usage in well-known projects:
Hugging Face Transformers uses tqdm to show model download progress.
OpenAI’s CLIP model repository used tqdm in training scripts.
The FastAI library uses a custom progress bar but had used tqdm in earlier versions.
Searching GitHub for
from tqdm import tqdmyields thousands of hits – you can find example scripts similar to your use case (like progress in file processing, etc.).
Blog posts and articles: A few notable ones:
“Making Progress – A tqdm Tutorial” on Medium by Abhinav (analytics vidhya) gives a rundown of features.
“Is Progress bar (tqdm) killing your code?” on Dev.to – an interesting performance analysis story, teaching best practices on update frequency.
“10 Tips for Using Python Progress Bars in Jupyter Notebooks” (hypothetical title but likely someone wrote such tips) – covers using
tqdm.notebook.The official Jupyter blog might have something on output with tqdm (especially since JupyterLab’s new console sometimes needed tweaks to show tqdm).
Project-specific guides: Some libraries’ documentation include a section like “Using progress bars”. For instance, the Dask documentation suggests using
tqdmwith theprogresscallback for monitoring long computations. If you work in a domain (like Dask for big data, or Snakemake for workflows), see if their docs mention integrating tqdm.
These resources cover both the official information and community-driven knowledge, ensuring you can find answers to any question about tqdm. Whether you’re troubleshooting an issue (like multiple lines output or unusual behavior in a specific IDE) or looking to implement something advanced (like customizing the bar format or writing a custom callback), the combination of the official docs and community examples will be invaluable.
By exploring the above links and materials, you’ll become well-versed in using the tqdm Python library effectively and discover new ideas for incorporating progress bars into your own projects.
FAQs about tqdm library in Python
Below is a comprehensive FAQ section with frequently asked questions about the tqdm library in Python, organized by category. Each question is followed by a concise answer.
1. Installation and setup
Q: How do I install the tqdm library in Python?
A: Use pip to install:pip install tqdm. This will download and install the latest version of tqdm from PyPI into your Python environment.Q: How to install tqdm library in VS Code?
A: In VS Code, open the integrated terminal and runpip install tqdmfor your workspace’s environment. Ensure the correct Python interpreter is selected. Then, you can import tqdm in your scripts.Q: How to install tqdm library in PyCharm?
A: In PyCharm, go to Settings > Project Interpreter, and add the package “tqdm”. Alternatively, use the terminal within PyCharm and runpip install tqdm. PyCharm will then recognize the library.Q: How do I install tqdm in a Jupyter Notebook?
A: Use pip inside the notebook by running!pip install tqdmin a cell. After installation, import tqdm as usual. It’s often pre-installed on platforms like Colab or Kaggle.Q: How to install tqdm in Anaconda (conda)?
A: You can install tqdm via conda by runningconda install -c conda-forge tqdm. This installs tqdm from the conda-forge channel. It might already be included in recent Anaconda distributions.Q: How to install tqdm on Windows?
A: Open Command Prompt or PowerShell and runpip install tqdm. As long as Python and pip are in your PATH, this will install tqdm on Windows. No special steps are needed beyond pip.Q: How to install tqdm on Mac or Linux?
A: Use pip in the terminal:pip install tqdm. On Linux/macOS, you might usepip3 install tqdmif Python3’s pip is separate. It works the same way on these operating systems.Q: How to install tqdm without pip (offline installation)?
A: Download the .whl file for tqdm from PyPI using another machine or a browser, then install it withpip install path/to/tqdm-whl-file. Alternatively, usepip download tqdmto get files, then transfer and install.Q: What’s the latest version of tqdm?
A: The version changes over time. As of 2025, tqdm is in the 4.60+ range. You can check by runningpip show tqdmor looking at PyPI. The library is actively maintained with frequent releases.Q: Do I need admin rights to install tqdm?
A: Not necessarily. You can install it in user mode by adding--userto pip (pip install --user tqdm). In a virtual environment, you also don’t need admin rights. Only system-wide install might require elevated privileges.Q: How to upgrade tqdm to the latest version?
A: Runpip install -U tqdm. This checks PyPI and upgrades tqdm if a newer version is available. You can also specify a version, e.g.,pip install tqdm==4.x.yfor a particular one.Q: Is tqdm available via Conda Forge?
A: Yes. You can install withconda install -c conda-forge tqdm. It’s often on the default channels too. The Conda Forge package is generally up-to-date with the latest tqdm release.Q: How to install a specific version of tqdm?
A: Use pip with the version:pip install tqdm==4.X.Y(replace X.Y with desired version). This will install that exact version if available. Useful if you need to match an environment’s version.Q: I installed tqdm but get ‘ModuleNotFoundError: No module named tqdm’. Why?
A: Possibly the installation went to a different Python environment or interpreter. Make sure you installed it for the Python you’re running. In notebooks, try!pip install tqdmwithin the notebook environment. In scripts, check you’re using the correct pip.Q: How do I check if tqdm is installed on my system?
A: You can try to import it in Python:python -c "import tqdm; print(tqdm.__version__)". If no error and a version prints, it’s installed. Alternatively, runpip show tqdmto see if pip knows about it.Q: Can I use tqdm in a virtual environment?
A: Yes. Activate your virtualenv and runpip install tqdminside it. After that, any script executed in that venv can import tqdm. It’s commonly used in virtual environments without issues.Q: Is the tqdm library free to use?
A: Yes, tqdm is open-source and free (MIT licensed). You can use it in personal, open-source, or commercial projects without charge or restrictions.Q: Do I need to import anything special for notebooks (like Jupyter)?
A: You can usefrom tqdm import notebookin older versions, but nowadays usingfrom tqdm.auto import tqdmis recommended. It auto-selects the appropriate version (notebook widget vs console). No separate package needed beyondpip install tqdm.Q: I’m behind a proxy/firewall; how to install tqdm?
A: Configure pip to use the proxy (via environment variables orpip --proxy). Or download the wheel from a machine with internet and bring it in. The library itself is small (~a few hundred KB), so it’s easy to transfer.Q: Does installing tqdm also install dependencies?
A: Tqdm has no required dependencies aside from standard library. It optionally usescoloramaon Windows (for cursor control) which is bundled. Pip will likely also install colorama if not present, but that’s a lightweight dependency.Q: Can I use tqdm in Google Colab?
A: Yes, it’s pre-installed in Colab. Simplyfrom tqdm import tqdmin a Colab notebook and use it. Colab notebooks will display it as a text progress bar by default (somewhat static), but it works.Q: How to set up tqdm in Kaggle notebooks?
A: Kaggle notebooks also have tqdm pre-installed. You can usetqdm.notebookfor a proper widget display. For example:from tqdm.notebook import tqdm
for i in tqdm(range(1000)):
...It will show a nice progress bar in Kaggle’s output area.
Q: Is tqdm included in Anaconda distribution by default?
A: In recent Anaconda, yes, tqdm is usually included because many data science packages depend on it. You can check withconda list tqdm. If not present, installing via conda or pip as described is easy.Q: How do I install tqdm for use in scripts vs for Jupyter?
A: The installation is the same (pip or conda). The difference is in usage: for scripts,from tqdm import tqdmis fine; for Jupyter notebooks, you might wantfrom tqdm.notebook import tqdmfor nicer output. But that doesn’t affect installation.Q: Can I install tqdm in offline mode?
A: Yes, by obtaining the package file and installing locally. For example, downloadtqdm-4.x.y-py3-none-any.whlfrom an online source, then runpip install tqdm-4.x.y-py3-none-any.whl. This installs tqdm without internet access.Q: How to add tqdm to my PyCharm project dependencies?
A: If you use a requirements.txt, addtqdm==4.xxto it. Or use poetry/pipenv by doingpipenv install tqdm. PyCharm also allows adding it via the project interpreter settings. Ensure it’s listed so others know to install it too.Q: Will tqdm work on Python 2?
A: Older versions of tqdm supported Python 2.7, but Python 2 is end-of-life. The latest tqdm (4.36+ onwards) might require Python 3. If you need Python 2, you might pin an older tqdm version (like 4.31.0) that still had 2.7 support. But generally, use Python 3 for current releases.Q: Is there a conda package for tqdm on defaults channel?
A: Yes, as of latest Anaconda,conda install tqdmshould fetch it (it might come from defaults or conda-forge). Tqdm is pretty standard, so it’s packaged on the main channels.Q: Do I need to import tqdm.auto or tqdm.notebook separately?
A: Not necessarily.import tqdmprovides the base functionality. Buttqdm.autois a convenience to automatically pick environment-specific version. If you just dofrom tqdm import tqdmand use in a notebook, it will still output, albeit possibly as text lines.tqdm.notebookspecifically enables a Jupyter widget bar.Q: How to include tqdm in my project’s Docker image?
A: In your Dockerfile, includeRUN pip install tqdm(or in conda environment setup). This will install tqdm inside the container. Once installed, any scripts running in the container can use tqdm for progress output to logs or console.
2. Basic usage and syntax
Q: What is the basic usage of the tqdm library?
A: Wrap any iterable withtqdm(...)in a loop. For example:for x in tqdm(range(100)): .... This will display a progress bar automatically. That’s the simplest use-case – just put your iterable insidetqdm().Q: How do I use tqdm to loop over a list in Python?
A: Import tqdm and then:for item in tqdm(my_list): process(item). This will iterate through the list with a progress bar. You don’t need to specify length; tqdm infers it from the list length.Q: How to use tqdm with the range() function?
A: Usetrangefor shorthand:for i in trange(100): ...which is equivalent to tqdm(range(100)). Or explicitly:for i in tqdm(range(100), desc="Loop"): .... Both give a bar from 0 to 100.Q: What does tqdm stand for?
A: It’s not an English acronym; it comes from an Arabic word “taqadum” meaning “progress” and also a Spanish phrase “te quiero demasiado” (I love you so much). Practically, it’s just the name of the library symbolizing progress bars.Q: How do I import tqdm properly?
A: Typically:from tqdm import tqdm. If you’re in a notebook environment and want the notebook widget, dofrom tqdm.notebook import tqdm. You can alsoimport tqdm(the module) and then usetqdm.tqdm(range(...)). But the simpler is direct import of the function.Q: Can I change the text description on the progress bar?
A: Yes, use thedescparameter. For example:for x in tqdm(items, desc="Processing"):. This will prefix “Processing:” before the progress bar. It’s useful when you have multiple bars or want to clarify what’s being processed.Q: How to control the unit of the tqdm progress bar (like items, bytes)?
A: Use theunitparameter. For example:tqdm(total=100, unit="files")will show rate like “it/s” replaced with “files/s”. If you’re doing bytes, you might also useunit_scale=Trueto get KB, MB etc.Q: What are the default columns displayed in tqdm’s output?
A: By default, tqdm shows: progress bar fill, percentage, iteration count (current/total), elapsed time, and ETA, plus iterations per second. In text form, like:50%|##### | 50/100 [00:10<00:10, 5.00it/s]. The default unit is “it” (iterations).Q: How can I get the current iteration index from tqdm inside the loop?
A: You already have the index if you loop likefor i, x in enumerate(iterable):. But tqdm itself maintainstqdm.nas the current count. However, usually you use your loop variable. If you need to access the internal counter, usepbar.non the tqdm object.Q: How to update the progress bar manually?
A: Create atqdmobject and callupdate(). For example:pbar = tqdm(total=100)
... do some work ...
pbar.update(10) # increment by 10 ...
pbar.close()This is useful when you can’t use a simple loop or when progress happens in callbacks.
Q: Is it possible to use tqdm in a list comprehension?
A: Not directly in the comprehension (because nothing would consume the iterator). But you can force consumption:list(tqdm(iterable))would show progress as it builds a list, though that’s somewhat wasteful if you didn’t need the list. It’s often clearer to just use a for-loop when you need a progress bar.Q: How do I print a message without breaking the tqdm progress bar?
A: Usetqdm.write("your message"). This ensures the message goes to the correct place and the bar remains intact. Do not use regular print in the middle of a tqdm loop as it can mess up the output formatting.Q: How can I get the progress bar to refresh less often?
A: Use parametersmininterval(minimum seconds between updates) orminiters(minimum iterations between updates). For example,tqdm(iterable, mininterval=1.0)updates at most once a second. This reduces overhead and avoids flicker on very fast loops.Q: How to make tqdm use a fixed number of columns (width)?
A: Use thencolsparameter. For instance,tqdm(range(100), ncols=80)will try to fit the bar in 80 characters width. By default, dynamic_ncols=True adjusts to terminal width. You can disable dynamic width and set your own.Q: Can I use tqdm to track progress of a generator function?
A: Yes, but tqdm can’t know the length of a generator unless you settotal. So do:for item in tqdm(generator_func(), total=expected_count):
...If you don’t have an expected count, tqdm will show
?/?and no percentage, just counts and rate.Q: How do I handle nested loops with tqdm (multiple progress bars)?
A: You can have an outer and inner tqdm. For example:for i in tqdm(range(5), desc="Outer"):
for j in tqdm(range(100), desc="Inner", leave=False):
...This will show an Outer bar and an Inner bar. Set
positionargument if needed to place them on different lines (tqdm tries to manage it automatically). Usually you setleave=Falseon the inner to not leave each completed inner bar on screen.Q: Why does
tqdm(range(n))start at 0% and jump after first iteration?
A: Because it initially displays 0/n completed (0%). After the first iteration, it knows progress, so it updates. This is normal. If you want a smoother start, you might dotqdm(range(n), initial=1)to start from 1 if that made sense, but typically it’s fine.Q: Can tqdm show me the elapsed time or estimated finish time?
A: Yes, the default bar shows elapsed time and ETA. For example[00:01<00:03, ...]means 1 second elapsed, 3 seconds remaining (ETA). If the iterations are slow, you’ll see these clearly. They auto-calculate based on average speed.Q: How to get the final runtime from tqdm after completion?
A: Tqdm prints the elapsed time when done (e.g.,100%|...| 100/100 [00:10<00:00, 10.00it/s]includes[00:10<00:00,...]). If you want to capture it programmatically, you could use thepbar.format_dict["elapsed"]or simply record start/end time outside tqdm.Q: Is there a way to pause and resume a tqdm progress bar?
A: Not built-in as a simple call, but you can emulate it. You would need to keep track of current progress, close the bar, then re-create a new tqdm withinitial=...set to the last progress value. For example:pbar = tqdm(total=100)
... (do 30 iterations)
pbar.update(30)
pbar.close()
# later resume:
pbar = tqdm(total=100, initial=30)
... continue ...This effectively resumes. There’s no single
.pause()method though.Q: How can I get tqdm to output to a file or a log instead of console?
A: Use thefileparameter of tqdm. For example:import sys
pbar = tqdm(total=100, file=sys.stdout) # stdout (default)or set
file=open('log.txt','w'). Note: progress bars rely on carriage returns; if logging to a file, you might just see all updates appended. It’s usually not ideal to log every update; better to log completion or certain milestones manually.Q: What is
tqdm.auto?
A:tqdm.autoautomatically chooses between tqdm (console) and tqdm.notebook (Jupyter) depending on the environment. Sofrom tqdm.auto import tqdmis a convenient import that works well in either context. It uses IPython detection to decide.Q: How do I use tqdm to time a loop (like a progress bar cum timer)?
A: Tqdm itself shows time and rate but not directly used as a precise timing tool. If you want to time, you can rely on the displayed elapsed time or use Python’s time module. Tqdm is more for progress display, though by noting the end time from the bar you know roughly how long it took.Q: Can tqdm handle dynamic totals (changing total iterations)?
A: Yes, you can callpbar.total = new_total(and perhapspbar.refresh()to update display). Tqdm will then adjust percentages. This is helpful if you don’t know total upfront but discover more work to do. For example, if while processing, you add more items, you can increase the total. Keep in mind to also update when done or else it might never hit 100% if you mismanage it.Q: How to properly close a tqdm bar if I exit a loop early?
A: If you break out early, callpbar.close()on the tqdm object (if you created one manually). If you used thewith tqdm(...)context, it will auto-close on exiting the block. Closing will ensure the cursor moves to the next line properly and resources are freed.Q: Does tqdm support multi-threaded or multi-process loops?
A: Not automatically merging, but you can use it in each thread or process with careful output positions (to avoid overlap). Or better, usetqdm.contrib.concurrentwhich provides functions likeprocess_mapandthread_mapthat handle using multiple workers with a single combined progress bar. Essentially, tqdm can track tasks done across threads if you manage it (like use a thread-safe counter updating one bar). It doesn’t magically combine separate bars without coordination.
3. Features and functionality
Q: What are the main features of the tqdm library?
A: Tqdm provides progress bars for loops/iterables, with features like: dynamic ETA calculation, iterations-per-second display, customizable prefixes (desc), integration with Jupyter notebooks (with graphical bars), support for manual updates, nested bars, automatic switching of output to fit environment, and a very low overhead. It also has extensions for things like logging integration and parallel processing helpers.Q: Does tqdm support showing a progress bar for file downloads or large file copying?
A: Yes, though you have to integrate it. For example, if downloading with requests or urllib, you can update tqdm as data arrives. Tqdm doesn’t automatically know the progress of an external event, but you can feed it. It’s often used to display progress of downloads by hooking into the download loop or callback.Q: How does tqdm calculate the estimated time remaining (ETA)?
A: Tqdm uses a running average of iteration time (with some smoothing) and multiplies by remaining iterations to estimate time left. It updates continuously. By default, it uses an exponential moving average of the speed, so it’s responsive but stable. The ETA might fluctuate especially early on when only a few iterations have passed.Q: What is
trange()in tqdm?
A:trange(n)is a convenience function that’s essentiallytqdm(range(n)). It returns a tqdm iterable over range(n). It’s just less typing for the common case of iterating over a simple range.trange(100)yields values 0 to 99 with a progress bar.Q: Can tqdm display progress for Pandas DataFrame operations?
A: Yes, usingtqdm.pandas(). This addsprogress_applyandprogress_mapmethods to pandas objects. For example:import pandas as pd
from tqdm import tqdm; tqdm.pandas()
df.progress_apply(func, axis=1)This will show a bar for the DataFrame apply. It’s very handy for tracking progress in Pandas.
Q: What does
tqdm.write()do differently than print?
A:tqdm.write()prints to the terminal in a thread-safe way and without breaking the formatting of the progress bar. If you use normal print while a bar is active, the printed text can appear in the middle of the bar’s output or push the bar down.tqdm.write()ensures the bar stays on the last line, printing the message above it (or ensuring it doesn’t corrupt the bar line).Q: Can I format the output of tqdm’s progress bar (like add custom info)?
A: Yes, you can usebar_formatparameter to specify a format string. For example:tqdm(..., bar_format="{l_bar}{bar}| {n}/{total} items")There are many format variables like
{percentage},{n},{total},{elapsed}, etc. Also, you can useset_postfix()to display custom dynamic information at the end of the bar (like current loss in ML training). E.g.,pbar.set_postfix(loss=0.123)will show... loss=0.123at the end.Q: How do I display multiple progress bars at once?
A: Tqdm supports nested bars (one inside another) or multiple bars by using thepositionparameter for concurrent bars. For nested loops, just calling tqdm in inner and outer works, but if concurrently (like two unrelated loops), you can do:bar1 = tqdm(total=50, desc="Download", position=0)
bar2 = tqdm(total=100, desc="Process", position=1)Then update each independently. They will show on separate lines (position sets the line index). Just ensure your terminal supports cursor movement (most do).
Q: What is
leaveparameter in tqdm?
A:leave=True(default) means keep the progress bar displayed when it finishes (the bar stays in the terminal output).leave=Falsewill clear the bar after completion (so it doesn’t clutter the screen). For example, for inner loops in nested situation, you’d often useleave=Falseso that each inner loop’s bar doesn’t remain.Q: How do I reset or reuse a tqdm progress bar?
A: You typically would just create a new one for a new task. But you can reuse by callingpbar.reset()which sets n back to 0 (you can also change total if needed). However, in practice it’s often simpler to close the old and start a new. If you do want to reuse:pbar.reset(total=new_total)Then continue updating. Ensure the display gets updated accordingly (you might need
pbar.refresh()in some cases).Q: Does tqdm automatically detect terminal width for the bar?
A: Yes, ifdynamic_ncols=True(which is True by default when appropriate), it will adapt to terminal window width. If you resize the window, tqdm may adjust on the fly. If you prefer a fixed width, specify ncols.Q: Can tqdm show a spinner or just a percentage?
A: By default, tqdm shows a progress bar. If total is not known, it shows an animated spinner-like indicator (it prints iterations but no total). Tqdm doesn’t have fancy spinner styles out-of-the-box like some libraries, but if you giveascii=True, the bar uses ASCII chars and behaves a bit more spinner-like when no total. For a true spinner, some use cases involve manually printing something, but generally tqdm is about bars + percentage. The spinner is limited to showing activity with unknown total.Q: What is
disableparameter used for?
A:disable=Truewill turn off the progress bar (no output). You might use this when you want to programmatically silence the bar (for instance, if output is being logged to a file, or if a verbose flag is off). It’s a way to easily toggle the progress display.Q: Does tqdm handle cases where the loop is very fast (thousands of iterations per second)?
A: Yes, it handles it by not refreshing too often (mininterval=0.1s by default). If the loop is extremely fast, tqdm’s overhead is small, but the constant printing might slow it if not throttled. Tqdm’s smart algorithm will skip some updates to maintain ~10 fps refresh. You might not even notice any slowdown for normal usage. In extreme micro-benchmarks, you can increase mininterval to further reduce overhead.Q: Can tqdm show progress for tasks other than loops (e.g., while loop or tasks with no iteration count)?
A: Yes, but you have to manually update. For a while loop, you can’t directly wrap it because there’s no defined iterable. Instead, initializepbar = tqdm()with an estimated total (or no total if unknown) and inside the while, do your work and callpbar.update(1)each iteration. If no total, it will just show count and rate.Q: What does it mean when tqdm shows
? it/sor no ETA?
A: When the total is not known (or it’s set to 0 or infinite), tqdm can’t compute percentage or ETA, so it shows?%and? it/sorinf it/s. If you see?, ensure that a total was provided or that tqdm could infer length. If it’s legitimately unknown, the bar will just keep going with question marks for those fields.Q: How can I get the value of iterations per second from tqdm?
A: Tqdm stores some stats. For example,pbar.format_dict["rate"]holds the current it/s. But usually you don’t need to extract it; it’s displayed. If you needed it programmatically, you might compute speed bypbar.n / elapsed_time. Tqdm’s printed value is an average speed.Q: Does tqdm support color or styling in the terminal?
A: Tqdm itself doesn’t add color to the bar (it’s plain text, except in notebooks where a colored bar can appear). If you want a colored bar, you could manually insert ANSI color codes indescor bar_format, but it might complicate the length calculations. There’s no built-in color param. Many users use the Rich library if they need colored progress bars.Q: How do I log messages while tqdm is running without breaking the bar?
A: Usetqdm.write()for print-style messages. If you use the logging module, consider usingtqdm.contrib.logging.logging_redirect_tqdm(). That context will intercept logging outputs and redirect them in a way that doesn’t break the bar. This integration makes logging and tqdm co-exist nicely.Q: What does
ascii=Truedo in tqdm?
A: It forces the progress bar to use ASCII characters instead of Unicode blocks. The bar will use#or-etc. This is useful if the environment can’t render the smooth Unicode bar (some Windows consoles or certain terminals) or if you prefer the old-school look. It may also slightly speed up printing in weird cases. It’s mainly for compatibility.Q: My tqdm bar is printing on new lines every update. How do I fix that?
A: This means the carriage return\risn’t being interpreted correctly by your environment. Commonly, this happens in some IDE consoles or when output is redirected to a file. To fix it in console, ensure you’re not using an environment that doesn’t support \r (like IDLE’s shell isn’t great with it). If logging to file, consider settingdynamic_ncols=Falseand a fixed ncols to avoid newline if length changes. Also, check if colorama is installed on Windows (tqdm tries to use it). If using PyCharm’s console, try enabling “emulate terminal”. Essentially, the fix is to use a proper terminal or tweak environment variables. One quick fix: setTQDM_POSITION=-1environment var if using CloudWatch or others as per docs.Q: What is
tqdm.contrib?
A: It’s a submodule containing additional utilities that are not part of the core tqdm API but are helpful. Examples:tqdm.contrib.concurrent(gives thread_map/process_map functions),tqdm.contrib.logging(for logging integration),tqdm.contrib.telegram(progress via Telegram messages), etc. These might require additional dependencies or be a bit experimental, hence they are in contrib.Q: How do I use tqdm with Python’s logging module?
A: Usetqdm.contrib.logging.logging_redirect_tqdm(). For example:from tqdm.contrib.logging import logging_redirect_tqdm
import logging
logging.basicConfig(level=logging.INFO)
with logging_redirect_tqdm():
for x in tqdm(range(100)):
logging.info(f"processed {x}")This will ensure logging messages don’t break the bar output. The bar will still update properly.
Q: Can tqdm show progress of multiprocessing Pool or similar?
A: Not automatically, but you can manually update a shared tqdm in a callback each time a task completes. Or simpler: usetqdm.contrib.concurrent.process_mapwhich is a drop-in for Pool.map that shows a progress bar. For example:from tqdm.contrib.concurrent import process_map
results = process_map(func, data, max_workers=8)This will parallelize and show a combined progress. Under the hood, it uses concurrent.futures and tracks tasks done.
Q: Does tqdm work with asyncio / asynchronous code?
A: There istqdm.asynciofor async integration. You can useasync for x in tqdm_asyncio.tqdm(iter_async)or use manual updates in an async loop. It’s a bit less straightforward than sync loops, but doable. The tqdm docs have an example of integration with asyncio.Q: How to combine tqdm with time.sleep or similar waiting?
A: You can certainly use time.sleep inside a loop and it won’t hurt tqdm. If you want a progress bar for a wait period, for example:for i in tqdm(range(seconds)):
time.sleep(1)This will tick every second. Alternatively, one can simulate a timer by progressively updating a bar in a loop that just sleeps. Tqdm is fine with that as it’s just another loop.
Q: What happens if tqdm’s iterable throws an exception in the middle?
A: The exception will break out of the loop, so the bar might not reach 100%. Ideally, handle exceptions inside the loop to let it complete (maybe marking errors). If an exception stops the loop, you may need to manually close the bar in an exception handler to clean up output. Otherwise, the program will show the incomplete bar (which is okay but might not flush to new line). Usingwith tqdm(...):automatically closes on error due to context manager.Q: How to use tqdm for a function mapping (like track progress of applying a function to a list)?
A: You can use list comprehension with a side effect or simply a loop. E.g.:result = []
for x in tqdm(data):
result.append(func(x))This maps with progress. If you want a one-liner,
list(tqdm(map(func, data), total=len(data)))is possible, which will consume the map and give a result list with progress.Q: Can I use tqdm to measure memory usage or other metrics during loop?
A: Not directly; tqdm is focused on progress/time. However, you can useset_postfixto display custom metrics. For instance:import tracemalloc
tracemalloc.start()
for i in tqdm(range(n)) as pbar:
# do work
current, peak = tracemalloc.get_traced_memory()
pbar.set_postfix(memory=f"{current/1024**2:.2f}MB")This would update the postfix with current memory usage. Tqdm doesn’t know memory itself, but you can push any text to postfix and it’ll update live.
Q: What’s the difference between
tqdm(iterable)andtqdm.tqdm(iterable)?
A: If youimport tqdm(the module), you need to prefix:tqdm.tqdm(). If you dofrom tqdm import tqdm, you can just calltqdm(). They are the same function in functionality. The module’s name and function are both tqdm, which can confuse some IDEs, but typically you do the latter import for convenience.Q: How to combine tqdm with custom printouts in the same line?
A: It’s tricky; tqdm manages its line with carriage returns. If you want to append info on the same line, better use postfix or customizebar_format. Otherwise, trying to manually print on same line can conflict. Best practice: let tqdm handle the line, and use its facilities to add text (desc or postfix).Q: Does tqdm use threads or signals under the hood?
A: No, tqdm’s updating is typically within the same thread (it checks time or iterations in the loop). There is a mention of a monitoring thread in docs for long updates (ensuring at least one update every 10 seconds). But that’s an internal detail to force refresh if no output in a while. It’s not spawning heavy threads per loop, just one if needed to guarantee updates.Q: How does tqdm handle Unicode vs ASCII bars on different OS?
A: On Windows, if colorama is installed, it supports unicode bars. If not or on some locales, you might see misalignment for unicode. Then useascii=Trueto fallback. Tqdm tries to detect if unicode is properly supported (for width issues). Overall, on Python3 it mostly works everywhere with Unicode by default.Q: Is it possible to integrate tqdm progress with GUI applications (like Tkinter)?
A: Tqdm outputs to console; it doesn’t have a GUI widget. For a GUI, you’d typically update a GUI progress bar instead. You could run tqdm in a background thread and update a GUI element accordingly, but at that point, using the GUI’s own progress mechanism is more direct. Some use-cases involve capturing tqdm output and displaying in a GUI text widget, but it might be easier to use library-specific progress bars in a desktop app.Q: Can tqdm’s progress bar go in reverse (decrement)?
A: It’s not typical, but you can manually decrease.nand callrefresh(). However, progress bars are conceptually forward-moving. If you want to show a countdown, one trick: dofor i in tqdm(range(total, 0, -1)):to count down. It will still show percentage as if counting up though (because it still sees 100 iterations). Tqdm doesn’t support a backwards fill bar by default.Q: Why is the first iteration of tqdm sometimes slower?
A: The first iteration includes overhead of setting up the bar (printing the line, etc.). Also, if using dynamic interval, it may not print immediately until mininterval passes. But usually the slowdown is negligible. In super tight loops, you might notice first iteration includes some one-time work.Q: Does tqdm support decimal progress (like 2.5 of 10)?
A: Tqdm works in terms of integer iterations. You can treat decimals by scaling (like treat 2.5 as 25 of 100 by multiplying everything by 10). But there’s no built-in fractional progression. If you need to display 2.5/10, you could include that in postfix text.Q: Can I nest multiple tqdm bars in the same line (like progress of two things side by side)?
A: Not really in the same line, as one line is one bar typically. Rich library can do multiple columns, but tqdm prints one bar per line. If you want side-by-side, you’d have to manually compose a string inbar_formatwith multiple parts, but that’s hacky and doesn’t track two independent progresses cleanly.Q: What happens if I don’t close a tqdm bar?
A: If you don’t close and your program exits, it’s fine (the OS cleans up, and you might just not get a final newline in output). If you don’t close in long running program, the cursor might stay on that line. It’s not usually catastrophic, but best practice is to close or use context manager.Q: Is tqdm the fastest progress bar library?
A: Yes, one of the fastest. Benchmarks by users show that tqdm’s overhead is minimal compared to alternatives (progressbar2 is slower per iteration). In a Reddit thread, many prefer tqdm for performance and ease. So for most uses, you won’t find a faster pure-Python progress meter.Q: How can I incorporate a percentage sign in my own printed output with tqdm?
A: Use tqdm’s built-in percentage or accesspbar.percentage()via format. Or manually compute (pbar.n / pbar.total * 100). But simpler: if you want something like “Completed 50% of task” occasionally, just make those prints and not worry about the bar (or rely on the bar’s own display).Q: Can tqdm be used to show progress of reading a file line-by-line?
A: Yes. If you know total lines, open the file, get the line count (perhaps bysum(1 for _ in file)which can be expensive). Or useos.path.getsizefor bytes as an estimate. Then:with open('data.txt') as f:
for line in tqdm(f, total=num_lines):
process(line)This will show progress through the file. If total isn’t known, it’ll still count lines read.
Resources and further reading
Documentation homepage: overview, quick start, and navigation to all guides.
Release notes / changelog: version history and notable changes.
GitHub repository: source code, issues, and pull requests.
GitHub releases: tagged releases and change summaries.
Project wiki: extra notes and curated links.
Contributing guide: how to build, test, and publish.
PyPI project page: install instructions, classifiers, and release history.
Conda-forge packagee: conda install channel and build info.
Docker image: CLI container for
tqdm.
ack Overflow: usingtqdmin awhileloop (manual updates).
Stack Overflow: what the
totalparameter does and when to set it.