How to calculate program execution time in python || by Rohit Sharma

Welcome to our Python tutorial series! 🐍 In this video, we're about to reveal a crucial skill for Python developers: measuring and optimizing program execution time.

Understanding how to calculate program execution time is invaluable for identifying bottlenecks, optimizing code, and ensuring your applications run efficiently.

Here's what we cover in this tutorial:

• The importance of measuring program execution time and its relevance in Python development.
• How to use Python's built-in time module to capture the start and end times of your code.
• Step-by-step examples demonstrating different approaches to calculate execution time.
• Best practices for accurate time measurement and interpreting the results.

Whether you're a Python enthusiast eager to improve your code's performance or a developer looking to optimize your applications, this video is a game-changer.

You can calculate the execution time of a program or a specific section of code in Python using the time module. Here's how to do it:

from time import time, sleep

start_time = time()
print("hello")
print("world")
sleep(3)
end_time = time()
print("time taken = ",end_time-start_time)

In this example:
1. Import the time module at the beginning of your script.
2. Use time() to record the current time at the start of the code section you want to measure. Store this value in start_time.
3. Place your program or code that you want to measure for execution time in the middle of the script.
4. After the code section, again use time() to record the current time and store it in end_time.
5. Calculate the execution time by subtracting start_time from end_time. This gives you the time in seconds.
6. Print the execution time to the console.

When you run the script, it will display the execution time of the code section you measured in seconds.
Keep in mind that the accuracy of the timing may vary depending on the system's performance and other factors, so it's best suited for measuring relatively long-running code sections.

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