Python Tutorial: The need for efficient coding I

Want to learn more? Take the full course at https://learn.datacamp.com/courses/writing-efficient-code-with-pandas at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome to this course on optimizing python code with pandas! My name is Leonidas and together, we will explore the most efficient ways to perform a variety of tasks using the pandas library. Even the easiest task in Python will have a faster alternative, which will be very handy in situations where time matters. For the context of this course, we will use a function which captures the current time of the computer in seconds since the 1st of January 1970, as a floating point number. This function is the `time()` function from the time Python package. Each time we are interested in measuring some code's execution time, we will assign the current time before execution, using the time() function, execute the operation we are interested in, and measure the time again right after the code's execution. In the end, we print the result in second, in a compact but meaningful message. Before we apply the notion of time efficiency in any pandas- related problem, we will compare the difference in efficiency between a list comprehension and a for loop in a toy example. We are interested in calculating the square of each number from zero, up to a million. At first we will use a list comprehension to execute this operation, and the repeat the same procedure using a for loop. As you can see, we store the time each operation began, then execute it and in the end store the time it ends on a different variable. The executed time is just the difference between those two variables. Make a guess, which method is faster? I am sure you guessed it right. In the majority of occasions, a list comprehension a faster way to perform a simple operation compared to a for loop. Keep my words 'in the majority' in mind, as One last interesting finding would be to s