Introduction

In the world of Python, there are many ways to achieve the same result. However, the efficiency and performance of these methods can vary significantly. Today, we’re going to study a hotly debated topic among Python enthusiasts – the battle between range() and xrange()in python. We’ll explore their differences, their uses, and ultimately, which one comes out on top.

Python has rapidly become the go-to language in data science and is among the first things recruiters search for in a data scientist’s skill set. Are you looking to learn Python to switch to a data science career?

Understanding range() and xrange()

Before we dive into the comparison, let’s first understand what range() and xrange() are. Both are built-in functions in Python that generate a sequence of numbers. The range() function, available in both Python 2.x and 3.x, returns a list of numbers, while xrange(), only available in Python 2.x, returns an object that generates numbers on the fly, making it more memory efficient.

Using range:

# Example usage of range()

for i in range(10):

    print(i)

# In Python 2.x, you could do:

for i in xrange(10):

    print(i)

Return Type

Example:

# range() creates a list with 1000000 elements

numbers_range = range(1000000)  # Could consume a lot of memory

# xrange() creates a generator that yields values as needed

numbers_xrange = xrange(1000000)  # More memory-efficient

 

# testing the type

print("The return type of range() is : ")

print(type(numbers_range))

 

# testing the type

print("The return type of xrange() is : ")

print(type(numbers_xrange))

Output:

The return type of range() is : 

<type 'list'>

The return type of xrange() is : 

<type 'xrange'>

The Memory Game

One of the key differences between range() and xrange() lies in their memory usage. Since range() generates a list, it consumes more memory, especially when dealing with a large sequence of numbers. On the other hand, xrange() generates numbers on demand, making it a more memory-friendly option. However, it’s worth noting that in Python 3.x, the range() function behaves like xrange(), offering the best of both worlds.

Example:

import sys

# range() creates a list with 1000000 elements

numbers_range = range(1000000)  # Could consume a lot of memory

# xrange() creates a generator that yields values as needed

numbers_xrange = xrange(1000000)  # More memory-efficient

 

# testing the size

# range() takes more memory

print ("The size allotted using range() is : ")

print (sys.getsizeof(numbers_range))

 

# testing the size

# xrange() takes less memory

print ("The size allotted using xrange() is : ")

print (sys.getsizeof(numbers_xrange))

Output:

The size allotted using range() is : 

8000072

The size allotted using xrange() is : 

40

Speed and Performance

When it comes to speed, the results can be surprising. While one might assume that xrange(), with its on-demand generation, would be faster, this isn’t always the case. For smaller ranges, range() can often be quicker due to its pre-generated list. However, for larger ranges, xrange() tends to have the upper hand due to its lower memory usage.

Compatibility and Usage

As mentioned earlier, xrange() is only available in Python 2.x. So, if you’re working with Python 3.x, range() is your only option. However, the revamped range() in Python 3.x offers similar functionality to xrange(), making it a versatile choice for all your number generating needs.

Difference between range() & xrange()

Conclusion

So, who’s the winner in the battle between range() and xrange()? Well, it depends. If you’re working with Python 2.x and dealing with large ranges, xrange() might be your best bet. However, for Python 3.x users or those dealing with smaller ranges, range() is a reliable and efficient choice. Ultimately, understanding the differences and strengths of each function will allow you to make the best choice for your specific needs.

Python has rapidly become the go-to language in data science and is among the first things recruiters search for in a data scientist’s skill set. Are you looking to learn Python to switch to a data science career?

Sakshi Khanna 13 Jan 2024