tqdm notebook with code examples

Introduction

In the world of data science and programming, it is common to have long running processes such as for loops and simulations. These processes can take a long time to complete and it can be difficult to keep track of their progress. To tackle this issue, a python library called tqdm (short for “progress” in Arabic) was created. tqdm provides a simple and intuitive way to track progress of for loops and other iterative tasks in Jupyter notebooks and other environments.

In this article, we will explore the tqdm library and its various features and functions with code examples.

Installing tqdm

tqdm can be installed using the pip package manager by running the following command in the terminal or command prompt:

pip install tqdm

Using tqdm

The simplest way to use tqdm is by wrapping any iterable with the tqdm function. For example, consider the following for loop that calculates the sum of the first 100 numbers:

from tqdm import tqdm

sum = 0
for i in tqdm(range(100)):
    sum += i

print("Sum:", sum)

This will output a progress bar that indicates the progress of the for loop.

Customizing tqdm

In addition to tracking progress, tqdm provides several customization options that allow you to change the appearance and behavior of the progress bar.

For example, you can specify the bar length and the bar character by using the bar_format argument:

from tqdm import tqdm

sum = 0
for i in tqdm(range(100), bar_format="{l_bar}{bar}{r_bar}"):
    sum += i

print("Sum:", sum)

This will change the appearance of the progress bar as follows:

You can also specify the description of the progress bar by using the desc argument:

from tqdm import tqdm

sum = 0
for i in tqdm(range(100), desc="Calculating sum"):
    sum += i

print("Sum:", sum)

This will change the description of the progress bar as follows:

Updating tqdm Manually

In some cases, you may want to manually update the progress bar instead of relying on tqdm to do it automatically. To do this, you can use the update method of the tqdm object.

For example, consider the following code that calculates the sum of the first 100 numbers by updating the progress bar after every 10 iterations:

from tqdm import tqdm

sum = 0
pbar = tqdm(total=100)
for i in range(100):
    sum += i
    if i % 10 == 0:
        pbar.update(10)

pbar.close()
print("Sum:", sum)

This will output a progress bar that is updated after every 10 iterations:

![tqdm manual update](t
Managing Overhead

One of the challenges with using tqdm is that it can add significant overhead to your code, especially if you have a large number of iterations. To minimize this overhead, you can use the disable argument to disable tqdm when you don't need it. For example:

from tqdm import tqdm

sum = 0
for i in tqdm(range(100), disable=(not True)):
    sum += i

print("Sum:", sum)

This code will enable tqdm only when the disable argument is set to False.

In addition to disabling tqdm, you can also use the leave argument to control whether or not the progress bar remains on the screen after the for loop has completed. For example:

from tqdm import tqdm

sum = 0
for i in tqdm(range(100), leave=True):
    sum += i

print("Sum:", sum)

This code will leave the progress bar on the screen after the for loop has completed.

Combining tqdm with Other Libraries

tqdm can also be used in combination with other libraries, such as NumPy and Pandas, to track progress in more complex computations. For example, consider the following code that calculates the dot product of two NumPy arrays:

import numpy as np
from tqdm import tqdm

a = np.random.rand(100, 100)
b = np.random.rand(100, 100)
result = np.zeros((100, 100))

for i in tqdm(range(100)):
    for j in range(100):
        result[i, j] = np.dot(a[i, :], b[:, j])

print("Result:", result)

This code will output a progress bar that indicates the progress of the double for loop.

Conclusion

In this article, we have explored the tqdm library and its various features and functions. We have seen how to use tqdm to track progress in for loops and other iterative tasks, and how to customize the appearance and behavior of the progress bar. We have also seen how to manually update the progress bar and how to minimize tqdm's overhead. Finally, we have seen how to use tqdm in combination with other libraries to track progress in more complex computations.

tqdm is a powerful and flexible library that can greatly improve the user experience when working with long running processes. Whether you are a data scientist, a programmer, or simply someone who wants to keep track of progress in your code, tqdm is a tool that is definitely worth checking out.

Popular questions

1. What is the tqdm library in Python?

Answer: The tqdm library in Python is a library for creating and managing progress bars for long-running tasks, such as for loops and iterative algorithms. It provides an easy-to-use interface for tracking the progress of these tasks, displaying progress in the form of a progress bar in the Jupyter Notebook or the terminal.

2. How can you use tqdm in a for loop?

Answer: To use tqdm in a for loop, simply wrap the range object that you are iterating over with the tqdm function. For example:

from tqdm import tqdm

sum = 0
for i in tqdm(range(100)):
    sum += i

print("Sum:", sum)

This code will output a progress bar that indicates the progress of the for loop.

3. How can you customize the appearance of the tqdm progress bar?

Answer: You can customize the appearance of the tqdm progress bar by using various arguments when creating the tqdm object. For example, you can use the desc argument to provide a description for the progress bar, the total argument to specify the total number of iterations, and the unit argument to specify the units of measurement for the progress bar.

4. Can you manually update the tqdm progress bar?

Answer: Yes, you can manually update the tqdm progress bar by using the update method. For example:

from tqdm import tqdm

sum = 0
pbar = tqdm(total=100)
for i in range(100):
    sum += i
    pbar.update(1)
pbar.close()

print("Sum:", sum)

This code will manually update the progress bar in increments of 1.

5. Can you use tqdm in combination with other libraries, such as NumPy and Pandas?

Answer: Yes, tqdm can be used in combination with other libraries, such as NumPy and Pandas, to track progress in more complex computations. For example, consider the following code that calculates the dot product of two NumPy arrays:

import numpy as np
from tqdm import tqdm

a = np.random.rand(100, 100)
b = np.random.rand(100, 100)
result = np.zeros((100, 100))

for i in tqdm(range(100)):
    for j in range(100):
        result[i, j] = np.dot(a[i, :], b[:, j])

print("Result:", result)

This code will output a progress bar that indicates the progress of the double for loop.

Tag

Progress.