Are you confused about the differences between Python’s builtin sum function and Numpy’s numpy.sum function? If you are, then you’ve come to the right place.
Python’s builtin sum function is great for adding up the elements in a list, tuple or set. It’s simple to use and easy to understand. However, Numpy’s numpy.sum function is a bit more complicated, but it has some very useful features that make it unique.
If you’re looking to take your Python programming skills to the next level, then you need to know the differences between these two functions. Whether you’re working with large data sets or just trying to add up a few numbers, this article will help you understand which function to use and when.
In this article, we’ll show you the various scenarios where you should use Python’s builtin sum function and where Numpy’s numpy.sum function is more appropriate. We’ll also show you some examples of how to use each function correctly.
You’ll learn the advantages and disadvantages of each function, as well as best practices for using them. So, if you’re ready to improve your Python coding skills and understanding of these functions, read on.
By the end of this article, you’ll have a better understanding of Python’s builtin sum function and Numpy’s numpy.sum function, and you’ll be able to apply them to your code with ease.
“Python’S Sum Vs. Numpy’S Numpy.Sum” ~ bbaz
Differences Between Python‘s BuiltIn Sum Function and Numpy’s numpy.sum Function
Python’s builtin sum function and Numpy’s numpy.sum function are two commonly used methods for adding up numerical values in Python. However, there are some key differences between the two that you should be aware of.
Usage and Syntax
The biggest difference between these functions is their usage and syntax. Python’s builtin sum function can be used to add the elements of a list, tuple, or set, while Numpy’s numpy.sum function is designed to work with multidimensional arrays or matrices.
The syntax for using these two functions is also different. For example, to use the builtin sum function, you simply pass the iterable object as an argument:
“`pythonmy_list = [1, 2, 3]print(sum(my_list)) # Output: 6“`
Whereas, to use the numpy.sum function, you first need to import numpy and then pass the array as an argument:
“`pythonimport numpy as npmy_array = np.array([[1, 2, 3], [4, 5, 6]])print(np.sum(my_array)) # Output: 21“`
Performance
Another difference between these two functions is their performance. In general, numpy.sum is faster than Python’s builtin sum function, especially when working with large arrays.
This is because numpy.sum uses highly optimized C code under the hood, whereas Python’s builtin sum function is implemented in pure Python.
When to Use Python’s BuiltIn Sum Function
If you are working with a simple list, tuple, or set, then Python’s builtin sum function would be the best option for you. It’s fast, easy to use, and requires no additional libraries.
For example, if you want to calculate the sum of all the even numbers in a list, you can use the builtin function as shown below:
“`pythonmy_list = [2, 4, 6, 8, 10]even_sum = sum(num for num in my_list if num % 2 == 0)print(even_sum) # Output: 30“`
When to Use Numpy’s numpy.sum Function
If you are working with multidimensional arrays or matrices, then using Numpy’s numpy.sum function would be the best option. It provides additional functionalities such as axis parameter, dtype parameter, and more.
For example, say you have a 2D array and you want to calculate the sum of every row and every column separately. You can use the axis parameter as shown below:
“`pythonimport numpy as npmy_array = np.array([[1, 2, 3], [4, 5, 6]])row_sum = np.sum(my_array, axis=1)col_sum = np.sum(my_array, axis=0)print(row_sum) # Output: [6 15]print(col_sum) # Output: [5 7 9]“`
Advantages and Disadvantages of Each Function
Python’s BuiltIn Sum Function
Advantages:
 Fast and easy to use
 Does not require any additional libraries
Disadvantages:
 Not suitable for multidimensional arrays or matrices
 Slow performance when working with large lists
Numpy’s numpy.sum Function
Advantages:
 Designed to work with multidimensional arrays or matrices
 Faster than Python’s builtin sum function, especially for large arrays
 Provides additional functionalities such as axis parameter, dtype parameter, and more
Disadvantages:
 Requires an additional library (numpy)
 More complicated to use than the builtin function
Conclusion
Both Python’s builtin sum function and Numpy’s numpy.sum function have their own advantages and disadvantages. The choice between the two largely depends on the type of data you are working with and the specific calculations you need to perform.
If you are working with simple lists, tuples, or sets, then the builtin sum function would likely suffice. However, if you are working with multidimensional arrays or matrices, or need additional functionalities, then numpy.sum would be the better option.
By understanding the differences between these two functions, you can improve your Python coding skills and choose the right method for your data manipulation needs.
Python’s BuiltIn Sum Function  Numpy’s numpy.sum Function 

Great for adding up elements in simple lists, tuples, or sets  Designed to work with multidimensional arrays or matrices 
Easy to use and requires no additional libraries  Requires an additional library (numpy) 
Slow performance when working with large lists  Faster than Python’s builtin sum function, especially for large arrays 
Not suitable for complicated mathematical operations  Provides additional functionalities such as axis parameter, dtype parameter, and more 
Thank you for taking the time to read our article on understanding Python’s builtin sum versus NumPy’s numpy.sum function. We hope that the information we’ve provided has given you a better understanding of the differences between these two functions and when to use each one in your code.
As we discussed, the builtin sum function is a great option for small lists or arrays, but it can become slow and memoryintensive when working with large amounts of data. This is where the numpy.sum function really shines – it can handle much larger datasets with ease and is optimized for performance.
Ultimately, the decision of which function to use will depend on the specific needs of your code. If you’re working with smaller datasets or just need a quick solution, the builtin sum function should suffice. But if you’re dealing with larger amounts of data and need faster processing times, the numpy.sum function is definitely the way to go.
When it comes to working with data in Python, you may find yourself needing to perform calculations on arrays or lists. Two popular options for this are Python’s builtin sum function and the numpy.sum function. Here are some common questions people ask about these functions:

What is the difference between Python’s builtin sum function and numpy.sum?
Python’s builtin sum function adds up the elements of a list or iterable. numpy.sum does the same thing, but it can also perform the operation on multidimensional arrays.

Which function should I use?
If you’re working with simple lists or arrays, Python’s builtin sum function will usually suffice. However, if you’re working with multidimensional arrays or need more advanced functionality, numpy.sum is the better choice.

Is one function faster than the other?
In general, numpy.sum tends to be faster than Python’s builtin sum function when working with large arrays or multidimensional arrays. However, for small arrays or simple lists, the difference in speed may not be noticeable.

Can I use numpy.sum on nonnumeric data?
No, numpy.sum is designed to work with numeric data. If you try to use it on nonnumeric data, you’ll get an error.

Are there any other differences between the two functions?
Yes, there are some additional differences. For example, numpy.sum can take an optional axis argument to perform the operation along a specific axis of a multidimensional array. Python’s builtin sum function doesn’t have this capability.