Adventures in Machine Learning

Track and Improve Your Code’s Performance with Python Progress Bars

Creating a Progress Bar in Python: How to Monitor Your Code’s Performance

As a beginner or experienced programmer, you might have encountered situations where you need to execute codes that take a significant amount of processing time. When running such codes, monitoring their progress can be quite challenging.

In such situations, progress bars come in handy to track your code’s execution and offer you valuable insights into its performance. In this article, we’ll explore how to create progress bars in Python using the tqdm package and how to use this package in list comprehension.

Let’s dive right in!

Installing the tqdm package

Before we start exploring how to create progress bars, we need first to install the tqdm package. The tqdm package is an excellent package that provides progress bars within loops.

The package is incredibly user-friendly and can be installed by running the following command:

“`python

!pip install tqdm

“`

Writing Python code without progress bar

Let’s start with a simple code example to illustrate the challenge of monitoring code execution without a progress bar. In this example, we have a list of numbers from 1 to 10, and we want to print each number with a one-second delay.

Here is how we can do that:

“`python

import time

numbers = list(range(1, 11))

for num in numbers:

print(num)

time.sleep(1)

“`

When you run this code, it will print each number with a one-second delay. However, it’s hard to know how long it will take to finish the list processing.

In large data processing, this can become a frustrating experience.

Adding progress bar to Python code

Now let’s integrate the tqdm package to create a progress bar, which can be a game-changer in situations like the previous one. You can use tqdm by merely wrapping the loop with it.

Here’s how we’ll modify our previous example:

“`python

from tqdm import tqdm

import time

numbers = list(range(1, 11))

for num in tqdm(numbers):

print(num)

time.sleep(1)

“`

As simple as that, we’ve added a progress bar that tells us how long it will take to complete the list processing. One of the significant advantages of using the tqdm package is that it provides us with a time estimate of the progress.

Its that easy!

Adding a GUI for tracking progress

If you’re working on a GUI-based application, you probably want to use a progress bar to monitor the progress of your application. The tqdm package can help you easily integrate progress bars to update your GUI.

Here’s an example:

“`python

from tkinter import *

from tkinter.ttk import Progressbar

from tqdm import tqdm

import time

root = Tk()

root.geometry(“400×200”)

pb = Progressbar(root, length=200, mode=’determinate’)

pb.pack(pady=30)

numbers = list(range(1, 11))

for num in tqdm(numbers):

pb[“value”] = num * 10 # Updating the progress bar’s value

pb.update() # Force update on the UI element

time.sleep(1)

root.mainloop()

“`

In this example, we’re using the Tkinter module to create a simple GUI application that includes a progress bar. As you can see, we are updating the progress bar’s value and then forcing an update on the UI element following each step.

This produces a real-time GUI progress bar, informing users of the code’s progress.

Using the tqdm package for list comprehension

Finally, let’s look at how you can use the tqdm package with list comprehensions. Comprehension is a Python feature that allows programmers to create complex lists using a more concise syntax.

In some circumstances, we might have to deal with a massive list comprehension that could take a while to run, but we can use the tqdm package to monitor its progress. Here’s an example:

“`python

from tqdm import tqdm

my_list = [i for i in tqdm(range(1000000))]

“`

In this example, we’re using tqdm alongside a list comprehension. The result is the creation of a new list that consists of the first one million integers.

The tqdm package will monitor the progress and give us a sense of how long it will take to finish the task.

Conclusion

In this tutorial, we have explored how to use the tqdm package to create progress bars in Python. We have covered how to create a progress bar in plain Python code, how to add a GUI for tracking progress, and also how to use tqdm in list comprehension.

With the examples, you can now monitor your codes’ performance, efficiency, and processing time, and also keep users informed of the progress of your GUI-based applications. Python’s versatility is a major reason why it is a popular programming language, and with tools like tqdm, it becomes an even more flexible and sophisticated language.

In this article, we discussed how to create progress bars in Python using the tqdm package. Progress bars enable tracking the performance and processing time of codes, especially in large data processing.

We showed how to add a progress bar to plain Python code and integrate GUI applications. Additionally, we explored how to use tqdm with list comprehension.

Overall, using the tqdm package can significantly increase the efficiency of your code, helping you save a lot of time and effort in your programming tasks.

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