# Python Tips: Understanding Python’s built-in Sum VS. Numpy’s Numpy.Sum Function

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Are you confused about the differences between Python’s built-in sum function and Numpy’s numpy.sum function? If you are, then you’ve come to the right place.

Python’s built-in 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 built-in 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 built-in 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 Built-In Sum Function and Numpy’s numpy.sum Function

Python’s built-in 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 built-in 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 built-in 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 built-in sum function, especially when working with large arrays.

This is because numpy.sum uses highly optimized C code under the hood, whereas Python’s built-in sum function is implemented in pure Python.

## When to Use Python’s Built-In Sum Function

If you are working with a simple list, tuple, or set, then Python’s built-in 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 built-in 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]“`

### Python’s Built-In Sum Function

• Fast and easy to use
• Does not require any additional libraries

• Not suitable for multidimensional arrays or matrices
• Slow performance when working with large lists

### Numpy’s numpy.sum Function

• Designed to work with multidimensional arrays or matrices
• Faster than Python’s built-in sum function, especially for large arrays
• Provides additional functionalities such as axis parameter, dtype parameter, and more

• Requires an additional library (numpy)
• More complicated to use than the built-in function

## Conclusion

Both Python’s built-in 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 built-in 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 Built-In 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 built-in 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 built-in 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 built-in sum function is a great option for small lists or arrays, but it can become slow and memory-intensive 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 built-in 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 built-in sum function and the numpy.sum function. Here are some common questions people ask about these functions:

1. What is the difference between Python’s built-in sum function and numpy.sum?

Python’s built-in 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 multi-dimensional arrays.

2. Which function should I use?

If you’re working with simple lists or arrays, Python’s built-in sum function will usually suffice. However, if you’re working with multi-dimensional arrays or need more advanced functionality, numpy.sum is the better choice.

3. Is one function faster than the other?

In general, numpy.sum tends to be faster than Python’s built-in sum function when working with large arrays or multi-dimensional arrays. However, for small arrays or simple lists, the difference in speed may not be noticeable.

4. Can I use numpy.sum on non-numeric data?

No, numpy.sum is designed to work with numeric data. If you try to use it on non-numeric data, you’ll get an error.

5. 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 multi-dimensional array. Python’s built-in sum function doesn’t have this capability.