Как найти сумму чисел списка в python

На чтение 4 мин Просмотров 3.8к. Опубликовано

Python — это язык программирования, который предоставляет нам различные встроенные функции и методы для работы со списками. Одной из таких операций является нахождение суммы чисел в списке. Это может быть полезным, когда необходимо произвести анализ числовых данных, например, при подсчете среднего значения или нахождении суммы элементов, удовлетворяющих определенным условиям. В этой статье мы рассмотрим различные методы нахождения суммы чисел в списке Python.

Содержание

  1. Методы для нахождения суммы чисел в списке
  2. Использование цикла for
  3. Использование встроенной функции sum()
  4. Использование рекурсии
  5. Обработка исключений при нахождении суммы чисел в списке

Методы для нахождения суммы чисел в списке

Использование цикла for

Цикл for является одним из наиболее простых и часто используемых способов для нахождения суммы чисел в списке. Просто пройдитесь по каждому элементу списка и добавьте его к накопленной сумме.

Вот пример кода, который демонстрирует использование цикла for для нахождения суммы чисел в списке:

numbers = [1, 2, 3, 4, 5]
total = 0

for num in numbers:
    total += num

print("Сумма чисел в списке: ", total)

В этом примере мы создали список чисел от 1 до 5 и присвоили его переменной numbers. Затем мы создали переменную total и присвоили ей начальное значение 0. Затем мы проходим по каждому элементу списка numbers и добавляем его к переменной total. Наконец, мы выводим сумму чисел на экран.

Важно отметить, что мы должны инициализировать переменную total нулевым значением перед выполнением цикла, чтобы иметь место, куда добавлять числа. Если мы попытаемся добавить число к неинициализированной переменной, возникнет ошибка.

Цикл for также может использоваться для нахождения суммы чисел в многомерном списке. В этом случае нам нужно будет использовать вложенный цикл for, чтобы перебрать каждый элемент списка.

matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
total = 0

for row in matrix:
    for num in row:
        total += num

print("Сумма чисел в многомерном списке: ", total)

В этом примере мы создали многомерный список, содержащий три списка с числами. Затем мы создали переменную total и присвоили ей начальное значение 0. Затем мы используем два вложенных цикла for для перебора каждого элемента списка и добавления его к переменной total. Наконец, мы выводим сумму чисел на экран.

Использование встроенной функции sum()

Использование встроенной функции sum() для нахождения суммы чисел в списке является очень простым и эффективным способом. Функция sum() принимает один аргумент — итерируемый объект, такой как список, и возвращает сумму всех элементов в нем.

Простой пример использования функции sum() для нахождения суммы всех чисел в списке:

my_list = [1, 2, 3, 4, 5]
sum_of_list = sum(my_list)
print(sum_of_list)

Важно отметить, что функция sum() может работать только с итерируемыми объектами, элементы которых могут быть сложены. Если элементы списка не могут быть сложены, будет возбуждено исключение типа TypeError.

Использование рекурсии

Использование рекурсии — это еще один способ нахождения суммы чисел в списке Python. Рекурсия — это процесс вызова функцией самой себя. Для нахождения суммы чисел в списке при помощи рекурсии, необходимо реализовать функцию, которая будет вызывать саму себя до тех пор, пока не достигнет базового случая.

Пример реализации функции для нахождения суммы чисел в списке при помощи рекурсии:

numbers = [1, 2, 3, 4, 5]
result = recursive_sum(numbers)
print(result)

Здесь мы определяем функцию recursive_sum, которая принимает список чисел numbers. Если в списке остается только один элемент, то возвращаем его значение. В противном случае мы возвращаем сумму первого элемента списка и рекурсивного вызова функции для оставшейся части списка.

Хотя использование рекурсии для нахождения суммы чисел в списке может быть удобным и понятным, стоит иметь в виду, что это может привести к переполнению стека вызовов функций при больших списках. Поэтому, в большинстве случаев лучше использовать более эффективные методы, такие как использование встроенной функции sum() или цикла for.

Обработка исключений при нахождении суммы чисел в списке

При работе с данными, особенно с пользовательским вводом, всегда есть вероятность получения ошибочных данных. Для обработки ошибок при нахождении суммы чисел в списке можно использовать конструкцию try-except.

При использовании описанных выше методов для нахождения суммы чисел в списке возможны следующие ошибки:

  1. TypeError — возникает, если элемент списка не является числом.
  2. ValueError — возникает, если в списке есть пустые строки или нечисловые значения.

Для обработки этих ошибок можно использовать конструкцию try-except. Например, чтобы обработать ошибку TypeError, мы можем использовать следующий код:

numbers = [1, 2, 3, '4', 5]

total = 0
for num in numbers:
    try:
        total += num
    except TypeError:
        print(f"Элемент {num} не является числом")
print(f"Сумма чисел в списке: {total}")

В результате выполнения данного кода мы получим следующий вывод:

Элемент 4 не является числом
Сумма чисел в списке: 11

Обработка ошибок позволяет избежать прерывания работы программы при возникновении ошибок и предоставляет возможность корректно обработать их в процессе выполнения программы.

To find the sum of numbers in an iterable in Python, we can

  • Use the built-in sum() function
  • Use a loop
  • Use recursion

In today’s post, we’ll discuss how to use each of the methods above and highlight some of the things to take note when using the built-in sum() function.

For our practice question, we’ll work on a function that accepts multiple inputs (of various data types) and returns the sum of all numerical inputs.

Here are some concepts we’ll cover in today’s post:

  • How to find sum of numbers in an iterable in Python
  • How to use recursion
  • The difference between the sum() and fsum() functions

Using the built-in sum() function

The easiest way to find the sum of numbers in an iterable is to use a built-in function called sum(). This function accepts two arguments – the iterable to sum and an optional value to add to the sum. Its syntax is as follows:

sum(iterable, value_to_add)

If the function is unable to sum the numbers in the iterable, it throws a TypeError exception. Let’s look at some examples:

Finding sum of numbers in a list, set, dictionary and tuple

#Sum numbers in a list
myList = [1, 2, 3.5, 2.1, 6]
print(sum(myList))

#Sum numbers in a set
mySet = {1, 5, 2.7, 8}
print(sum(mySet))

#Sum numbers in a dictionary
myDict = {1:101, 2:102, 3:103, 4:104}
print(sum(myDict))

#Sum numbers in a tuple
myTuple = (1, 2, 6.7, 1.1, 5)
print(sum(myTuple))

If you run the code above, you’ll get the following output:

14.6
16.7
10
15.799999999999999

The sum() function adds the numbers in an iterable and returns the result.

For instance, in the first example, the function adds the numbers in myList (1 + 2 + 3.5 + 2.1 + 6) and returns 14.6 as the result.

In the second example, the function adds the numbers in mySet (1 + 5 + 2.7 + 8) and returns 16.7 as the result.

In the third example, we pass a dictionary to the sum() function. When we do that, the function sums the keys of the dictionary, not the values. Hence, we get 1 + 2 + 3 + 4 = 10 as the result.

Finally, in the fourth example, the function sums the values in myTuple and returns the result. However, notice that the result is not exact? Instead of getting 1 + 2 + 6.7 + 1.1 + 5 = 15.8, we get 15.799999999999999.

This is not due to a bug in our code or in Python. Rather, it has to do with the way floating-point numbers (i.e. numbers with decimal parts) are represented in our computers. We’ve seen another example of this issue previously when we learned to square a number in Python.

If you are interested in finding out more about how floating-point numbers work, you can check out the following website: https://docs.python.org/3/tutorial/floatingpoint.html

If you need to ensure that you always get an exact result when finding the sum of floating-point numbers, you can use another function called fsum(). We’ll discuss this function in a later section.

Passing a second argument to the sum() function

Next, let’s look at an example of how the second argument for the sum() function works.

If we do not pass any value for the second argument, the sum() function simply adds the numbers in the iterable and returns the result. However, if we pass a value for the second argument, the function adds the value of the argument to the sum of numbers in the iterable. Let’s look at an example:

myList = [1, 2, 3.5, 2.1, 6]

#Without second argument
print(sum(myList))

#With second argument
print(sum(myList, 1000))

If you run the code above, you’ll get the following output:

14.6
1014.6

Passing invalid arguments to the sum() function

Last but not least, let’s look at what happens when we pass an invalid argument to the sum() function.

When that happens, the function throws a TypeError exception. Examples of invalid arguments include passing a string to the function, or passing a list that includes strings as elements.

For instance, the examples below will all give us a TypeError exception if we try to run them.

myString = '123456'
print(sum(myString))

myStrList = [1, 2, 3, '4', 5]
print(sum(myStrList))

myList = [1, 2, 3.5, 2.1, 6]
print(sum(myList, '100'))

myList2 = [1, 2, 3, 4, [1, 5]]
print(sum(myList2))

The first three examples fail as the sum() function does not work with strings. If we pass a string to the function, the function throws an exception. The last example fails as myList2 contains a nested list.

Using a loop

Now that we are familiar with the built-in sum() function, let’s proceed to discuss how we can use a loop to find the sum of numbers in an iterable.

The code below shows an example of using a for loop to sum the numbers in a list:

myList = [1, 2, 3.5, 2.1, 6]
sum = 0

for i in myList:
    sum += i

print(sum)

On lines 1 and 2, we declare and initialize two variables – myList and sum.

Next, we use a for loop to iterate through myList. For each element in myList, we add its value to sum (on line 5).

After we finish iterating through myList, we use the print() function (outside the for loop) to print the value of sum.

If you run the code above, you’ll get

14.6

as the output.

Using a for loop gives us more flexibility in terms of what we want to sum. For instance, if we use the built-in sum() function to sum the elements in a dictionary, it returns the sum of the keys in the dictionary. If we want to sum the values instead, we can use a for loop:

myDict = {1:101, 2:102, 3:103, 4:104}
sum = 0

for i in myDict:
    sum += myDict[i]

print(sum)

When we use a for loop to iterate through a dictionary, Python accesses the key of the items in the dictionary, not the value. For instance, in the for loop above, i stores the keys of the items in myDict.

If we want to sum the values of the items, we need to add myDict[i] (instead of i) to sum, as shown in line 5 above.

If you run the code above, you’ll get the following output:

410

The loop sums the values in myDict. Hence, we get 101 + 102 + 103 + 104 = 410 as the output.

Using recursion

Next, let’s discuss the final method to find the sum of numbers in an iterable. This method involves using recursion. We’ve discussed recursion multiple times in previous posts. If you are not familiar with recursion, you should definitely check out some of those posts (e.g. here and here).

Technically speaking, using recursion is kind of unnecessary in this case, as using the built-in sum() function or a simple loop is much more straightforward. However, recursion can be a very powerful technique for solving more complex questions like a Sudoku or permutation problem. Hence, it helps to see more examples of how recursion works, especially with easier questions like the current one.

As mentioned previously, recursion can be used to solve problems where solution to the problem relies on solving smaller instances of the same problem.

When it comes to finding the sum of numbers in an iterable, suppose we have a list with five numbers. We can find the sum of all five numbers by adding the first number to the sum of the remaining four numbers (a smaller instance of the same problem).

This can be easily implemented using recursion.

myList = [1, 2, 3.5]

def sumRec(iterToSum):
    if len(iterToSum) > 1:
        return iterToSum [0] + sumRec(iterToSum [1:])
    else:
        return iterToSum [0]

print(sumRec(myList))

Here, we first declare and initialize a list called myList.

Next, we define a function called sumRec() (from lines 3 to 7) that has one parameter – iterToSum.

Inside the function, we check if the length of iterToSum is greater than 1 (line 4). If it is, we add the first element in iterToSum (i.e. iterToSum [0]) to the result of sumRec(iterToSum[1:]) and return the sum (line 5).

sumRec(iterToSum[1:]) represents a smaller instance of sumRec(iterToSum).

While the latter sums all numbers in iterToSum, the former sums all elements except the first (as the slice [1:] selects all elements starting from index 1 to the end of the list).

We keep calling the sumRec() function recursively until the length of the list we pass to it is no longer greater than 1. When that happens, we have what is known as a base case (lines 6 and 7).

A base case is the case the stops the recursion. When the length of the list to sum is no longer greater than 1, we do not need to use recursion to find the sum of the elements in the list. As there is only one element in the list now, the sum is simply the number itself. For instance, the sum of [12] is simply 12.

Once we reach the base case, the recursion stops and the result is returned to the caller of each recursive call. This gives us the result of the sum of all numbers in the list.

To see how this works, let’s suppose we call the sumRec() function with [1, 2, 3.5] as the argument.

The recursive calls and return values are shown in the diagram below:

recursive function to find sum of numbers in iterable

Line 9 starts by calling sumRec([1, 2, 3.5]), which in turn calls sumRec([2, 3.5]), which calls sumRec([3.5]).

sumRec([3.5]) is the base case. It returns 3.5 to its caller sumRec([2, 3.5]).

sumRec([2, 3.5]) (which is not a base case) in turn returns 2 + sumRec([3.5]) = 2 + 3.5 = 5.5 to its caller sumRec([1, 2, 3.5]).

sumRec([1, 2, 3.5]) (which is also not a base case) returns 1 + sumRec([2, 3.5]) = 1 + 5.5 = 6.5 to its caller line 9.

Therefore, if you run the code above, you’ll get

6.5

as the output.

That’s it. That’s how recursion can be used to help us find the sum of numbers in an iterable. You may need to read through this section more than once to fully appreciate how recursion works.

Working with floating-point numbers

We’ve covered three main ways to find the sum of numbers in an iterable in Python. Now, let’s move on to a topic we skipped previously.

In the previous section, we learned to use the built-in sum() function to sum numbers in an iterable. While this function is very easy to use, we saw that it does not always return an exact value. For instance, when we pass the tuple (1, 2, 6.7, 1.1, 5) to the function, we get 15.799999999999999 as the output.

If it is crucial for us to get an exact answer when finding the sum of numbers in an iterable, we should use another built-in function – fsum().

This function is available in the math module.

Let’s look at an example:

import math

myTuple = (1, 2, 6.7, 1.1, 5)

#Using sum()
print(sum(myTuple))

#Using fsum()
print(math.fsum(myTuple))

If you run the code above, you’ll get the following output:

15.799999999999999
15.8

The fsum() is very similar to the sum() function. The main difference is that the fsum() function gives an exact output while the sum() function does not always do that.

Another difference is the fsum() does not have a second optional argument. Other than that, the two functions are very similar.

That’s it. We are now ready to proceed to the practice question for today.

Practice Question

Today’s practice question requires us to write a function called mySum() that prompts users to enter a list of values, separated by commas. The values entered can be of any data type. For instance, they can be text, symbols, or numbers. The function needs to return the sum of all the numbers entered. This sum should be rounded off to 2 decimal places.

Expected Results

To test your function, you can run the function and enter the following inputs:

1, 2, 3.1, 4, hello, [2, 3], #, $

The function should return 10.1 as the result.

Suggested Solution

The suggested solution is as follows:

Click to see the suggested solution

def mySum():
    userInput = input('Pleae enter the values, separated by commas: ')
    myList = userInput.split(',')

    result = 0

    for i in myList:
        try:
            result += float(i)
        except:
            pass

    return round(result, 2)

sumOfNumbers = mySum()
print(sumOfNumbers)

In the code above, we first define a function called mySum() on line 1.

Inside the function, we use the built-in input() function to prompt users for an input. This function prompts users for an input and returns the input as a string. We store this string in a variable called userInput.

Next, we use a built-in function called split() to split userInput into a list of substrings, using a comma (https://docs.python.org/3/library/stdtypes.html#str.split) as the delimiter. If the input string is '1, 2, 3, 4, 5', we’ll get ['1', ' 2', ' 3', ' 4', ' 5'] as the result, which we store in a variable called myList.

Next, we declare and initialise a variable called result.

We then use a for loop to iterate through myList.

Inside the for loop, we use a try-except statement to try converting i to a float. If that can be done, we add the value of the resulting float to result (line 9).

On the other hand, if i cannot be converted to a float, the float() function throws an exception and the except block is executed. Inside the except block (lines 10 to 11), we use the pass statement to indicate that we do not want to do anything when i cannot be converted to a float.

After we finish iterating through all the elements in myList, we use the round() function to round the sum to 2 decimal places and use the return statement to return the result.

With that, the function is complete.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Summing Values the Pythonic Way With sum()

Python’s built-in function sum() is an efficient and Pythonic way to sum a list of numeric values. Adding several numbers together is a common intermediate step in many computations, so sum() is a pretty handy tool for a Python programmer.

As an additional and interesting use case, you can concatenate lists and tuples using sum(), which can be convenient when you need to flatten a list of lists.

In this tutorial, you’ll learn how to:

  • Sum numeric values by hand using general techniques and tools
  • Use Python’s sum() to add several numeric values efficiently
  • Concatenate lists and tuples with sum()
  • Use sum() to approach common summation problems
  • Use appropriate values for the arguments in sum()
  • Decide between sum() and alternative tools to sum and concatenate objects

This knowledge will help you efficiently approach and solve summation problems in your code using either sum() or other alternative and specialized tools.

Understanding the Summation Problem

Summing numeric values together is a fairly common problem in programming. For example, say you have a list of numbers [1, 2, 3, 4, 5] and want to add them together to compute their total sum. With standard arithmetic, you’ll do something like this:

1 + 2 + 3 + 4 + 5 = 15

As far as math goes, this expression is pretty straightforward. It walks you through a short series of additions until you find the sum of all the numbers.

It’s possible to do this particular calculation by hand, but imagine some other situations where it might not be so possible. If you have a particularly long list of numbers, adding by hand can be inefficient and error-prone. What happens if you don’t even know how many items are in the list? Finally, imagine a scenario where the number of items you need to add changes dynamically or unpredictably.

In situations like these, whether you have a long or short list of numbers, Python can be quite useful to solve summation problems.

If you want to sum the numbers by creating your own solution from scratch, then you can try using a for loop:

>>>

>>> numbers = [1, 2, 3, 4, 5]
>>> total = 0

>>> for number in numbers:
...     total += number
...

>>> total
15

Here, you first create total and initialize it to 0. This variable works as an accumulator in which you store intermediate results until you get the final one. The loop iterates through numbers and updates total by accumulating each successive value using an augmented assignment.

You can also wrap the for loop in a function. This way, you can reuse the code for different lists:

>>>

>>> def sum_numbers(numbers):
...     total = 0
...     for number in numbers:
...         total += number
...     return total
...

>>> sum_numbers([1, 2, 3, 4, 5])
15

>>> sum_numbers([])
0

In sum_numbers(), you take an iterable—specifically, a list of numeric values—as an argument and return the total sum of the values in the input list. If the input list is empty, then the function returns 0. The for loop is the same one that you saw before.

You can also use recursion instead of iteration. Recursion is a functional programming technique where a function is called within its own definition. In other words, a recursive function calls itself in a loop:

>>>

>>> def sum_numbers(numbers):
...     if len(numbers) == 0:
...         return 0
...     return numbers[0] + sum_numbers(numbers[1:])
...

>>> sum_numbers([1, 2, 3, 4, 5])
15

When you define a recursive function, you take the risk of running into an infinite loop. To prevent this, you need to define both a base case that stops the recursion and a recursive case to call the function and start the implicit loop.

In the above example, the base case implies that the sum of a zero-length list is 0. The recursive case implies that the total sum is the first value, numbers[0], plus the sum of the rest of the values, numbers[1:]. Because the recursive case uses a shorter sequence on each iteration, you expect to run into the base case when numbers is a zero-length list. As a final result, you get the sum of all the items in your input list, numbers.

Another option to sum a list of numbers in Python is to use reduce() from functools. To get the sum of a list of numbers, you can pass either operator.add or an appropriate lambda function as the first argument to reduce():

>>>

>>> from functools import reduce
>>> from operator import add

>>> reduce(add, [1, 2, 3, 4, 5])
15

>>> reduce(add, [])
Traceback (most recent call last):
    ...
TypeError: reduce() of empty sequence with no initial value

>>> reduce(lambda x, y: x + y, [1, 2, 3, 4, 5])
15

You can call reduce() with a reduction, or folding, function along with an iterable as arguments. Then reduce() uses the input function to process iterable and returns a single cumulative value.

In the first example, the reduction function is add(), which takes two numbers and adds them together. The final result is the sum of the numbers in the input iterable. As a drawback, reduce() raises a TypeError when you call it with an empty iterable.

In the second example, the reduction function is a lambda function that returns the addition of two numbers.

Since summations like these are commonplace in programming, coding a new function every time you need to sum some numbers is a lot of repetitive work. Additionally, using reduce() isn’t the most readable solution available to you.

Python provides a dedicated built-in function to solve this problem. The function is conveniently called sum(). Since it’s a built-in function, you can use it directly in your code without importing anything.

Getting Started With Python’s sum()

Readability is one of the most important principles behind Python’s philosophy. Visualize what you are asking a loop to do when summing a list of values. You want it to loop over some numbers, accumulate them in an intermediate variable, and return the final sum. However, you can probably imagine a more readable version of summation that doesn’t need a loop. You want Python to take some numbers and sum them together.

Now think about how reduce() does summation. Using reduce() is arguably less readable and less straightforward than even the loop-based solution.

This is why Python 2.3 added sum() as a built-in function to provide a Pythonic solution to the summation problem. Alex Martelli contributed the function, which nowadays is the preferred syntax for summing a list of values:

>>>

>>> sum([1, 2, 3, 4, 5])
15

>>> sum([])
0

Wow! That’s neat, isn’t it? It reads like plain English and clearly communicates the action you’re performing on the input list. Using sum() is way more readable than a for loop or a reduce() call. Unlike reduce(), sum() doesn’t raise a TypeError when you provide an empty iterable. Instead, it understandably returns 0.

You can call sum() with the following two arguments:

  1. iterable is a required argument that can hold any Python iterable. The iterable typically contains numeric values but can also contain lists or tuples.
  2. start is an optional argument that can hold an initial value. This value is then added to the final result. It defaults to 0.

Internally, sum() adds start plus the values in iterable from left to right. The values in the input iterable are normally numbers, but you can also use lists and tuples. The optional argument start can accept a number, list, or tuple, depending on what is passed to iterable. It can’t take a string.

In the following two sections, you’ll learn the basics of using sum() in your code.

The Required Argument: iterable

Accepting any Python iterable as its first argument makes sum() generic, reusable, and polymorphic. Because of this feature, you can use sum() with lists, tuples, sets, range objects, and dictionaries:

>>>

>>> # Use a list
>>> sum([1, 2, 3, 4, 5])
15

>>> # Use a tuple
>>> sum((1, 2, 3, 4, 5))
15

>>> # Use a set
>>> sum({1, 2, 3, 4, 5})
15

>>> # Use a range
>>> sum(range(1, 6))
15

>>> # Use a dictionary
>>> sum({1: "one", 2: "two", 3: "three"})
6
>>> sum({1: "one", 2: "two", 3: "three"}.keys())
6

In all these examples, sum() computes the arithmetic sum of all the values in the input iterable regardless of their types. In the two dictionary examples, both calls to sum() return the sum of the keys of the input dictionary. The first example sums the keys by default and the second example sums the keys because of the .keys() call on the input dictionary.

If your dictionary stores numbers in its values and you would like to sum these values instead of the keys, then you can do this by using .values() just like in the .keys() example.

You can also use sum() with a list comprehension as an argument. Here’s an example that computes the sum of the squares of a range of values:

>>>

>>> sum([x ** 2 for x in range(1, 6)])
55

Python 2.4 added generator expressions to the language. Again, sum() works as expected when you use a generator expression as an argument:

>>>

>>> sum(x ** 2 for x in range(1, 6))
55

This example shows one of the most Pythonic techniques to approach the summation problem. It provides an elegant, readable, and efficient solution in a single line of code.

The Optional Argument: start

The second and optional argument, start, allows you to provide a value to initialize the summation process. This argument is handy when you need to process cumulative values sequentially:

>>>

>>> sum([1, 2, 3, 4, 5], 100)  # Positional argument
115

>>> sum([1, 2, 3, 4, 5], start=100)  # Keyword argument
115

Here, you provide an initial value of 100 to start. The net effect is that sum() adds this value to the cumulative sum of the values in the input iterable. Note that you can provide start as a positional argument or as a keyword argument. The latter option is way more explicit and readable.

If you don’t provide a value to start, then it defaults to 0. A default value of 0 ensures the expected behavior of returning the total sum of the input values.

Summing Numeric Values

The primary purpose of sum() is to provide a Pythonic way to add numeric values together. Up to this point, you’ve seen how to use the function to sum integer numbers. Additionally, you can use sum() with any other numeric Python types, such as float, complex, decimal.Decimal, and fractions.Fraction.

Here are a few examples of using sum() with values of different numeric types:

>>>

>>> from decimal import Decimal
>>> from fractions import Fraction

>>> # Sum floating-point numbers
>>> sum([10.2, 12.5, 11.8])
34.5
>>> sum([10.2, 12.5, 11.8, float("inf")])
inf
>>> sum([10.2, 12.5, 11.8, float("nan")])
nan

>>> # Sum complex numbers
>>> sum([3 + 2j, 5 + 6j])
(8+8j)

>>> # Sum Decimal numbers
>>> sum([Decimal("10.2"), Decimal("12.5"), Decimal("11.8")])
Decimal('34.5')

>>> # Sum Fraction numbers
>>> sum([Fraction(51, 5), Fraction(25, 2), Fraction(59, 5)])
Fraction(69, 2)

Here, you first use sum() with floating-point numbers. It’s worth noting the function’s behavior when you use the special symbols inf and nan in the calls float("inf") and float("nan"). The first symbol represents an infinite value, so sum() returns inf. The second symbol represents NaN (not a number) values. Since you can’t add numbers with non-numbers, you get nan as a result.

The other examples sum iterables of complex, Decimal, and Fraction numbers. In all cases, sum() returns the resulting cumulative sum using the appropriate numeric type.

Concatenating Sequences

Even though sum() is mostly intended to operate on numeric values, you can also use the function to concatenate sequences such as lists and tuples. To do that, you need to provide an appropriate value to start:

>>>

>>> num_lists = [[1, 2, 3], [4, 5, 6]]
>>> sum(num_lists, start=[])
[1, 2, 3, 4, 5, 6]

>>> # Equivalent concatenation
>>> [1, 2, 3] + [4, 5, 6]
[1, 2, 3, 4, 5, 6]

>>> num_tuples = ((1, 2, 3), (4, 5, 6))
>>> sum(num_tuples, start=())
(1, 2, 3, 4, 5, 6)

>>> # Equivalent concatenation
>>> (1, 2, 3) + (4, 5, 6)
(1, 2, 3, 4, 5, 6)

In these examples, you use sum() to concatenate lists and tuples. This is an interesting feature that you can use to flatten a list of lists or a tuple of tuples. The key requirement for these examples to work is to select an appropriate value for start. For example, if you want to concatenate lists, then start needs to hold a list.

In the examples above, sum() is internally performing a concatenation operation, so it works only with those sequence types that support concatenation, with the exception of strings:

>>>

>>> num_strs = ["123", "456"]
>>> sum(num_strs, "0")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: sum() can't sum strings [use ''.join(seq) instead]

When you try to use sum() to concatenate strings, you get a TypeError. As the exception message suggests, you should use str.join() to concatenate strings in Python. You’ll see examples of using this method later on when you get to the section on Using Alternatives to sum().

Practicing With Python’s sum()

So far, you’ve learned the basics of working with sum(). You’ve learned how to use this function to add numeric values together and also to concatenate sequences such as lists and tuples.

In this section, you’ll look at some more examples of when and how to use sum() in your code. With these practical examples, you’ll learn that this built-in function is quite handy when you’re performing computations that require finding the sum of a series of numbers as an intermediate step.

You’ll also learn that sum() can be helpful when you’re working with lists and tuples. A special example you’ll look at is when you need to flatten a list of lists.

Computing Cumulative Sums

The first example you’ll code has to do with how to take advantage of the start argument for summing cumulative lists of numeric values.

Say you’re developing a system to manage the sales of a given product at several different points of sale. Every day, you get a sold units report from each point of sale. You need to systematically compute the cumulative sum to know how many units the whole company sold over the week. To solve this problem, you can use sum():

>>>

>>> cumulative_sales = 0

>>> monday = [50, 27, 42]
>>> cumulative_sales = sum(monday, start=cumulative_sales)
>>> cumulative_sales
119

>>> tuesday = [12, 32, 15]
>>> cumulative_sales = sum(tuesday, start=cumulative_sales)
>>> cumulative_sales
178

>>> wednesday = [20, 24, 42]
>>> cumulative_sales = sum(wednesday, start=cumulative_sales)
>>> cumulative_sales
264
    ...

By using start, you set an initial value to initialize the sum, which allows you to add successive units to the previously computed subtotal. At the end of the week, you’ll have the company’s total count of sold units.

Calculating the Mean of a Sample

Another practical use case of sum() is to use it as an intermediate calculation before doing further calculations. For example, say you need to calculate the arithmetic mean of a sample of numeric values. The arithmetic mean, also known as the average, is the total sum of the values divided by the number of values, or data points, in the sample.

If you have the sample [2, 3, 4, 2, 3, 6, 4, 2] and you want to calculate the arithmetic mean by hand, then you can solve this operation:

(2 + 3 + 4 + 2 + 3 + 6 + 4 + 2) / 8 = 3.25

If you want to speed this up by using Python, you can break it up into two parts. The first part of this computation, where you are adding together the numbers, is a task for sum(). The next part of the operation, where you are dividing by 8, uses the count of numbers in your sample. To calculate your divisor, you can use len():

>>>

>>> data_points = [2, 3, 4, 2, 3, 6, 4, 2]

>>> sum(data_points) / len(data_points)
3.25

Here, the call to sum() computes the total sum of the data points in your sample. Next, you use len() to get the number of data points. Finally, you perform the required division to calculate the sample’s arithmetic mean.

In practice, you may want to turn this code into a function with some additional features, such as a descriptive name and a check for empty samples:

>>>

>>> # Python >= 3.8

>>> def average(data_points):
...     if (num_points := len(data_points)) == 0:
...         raise ValueError("average requires at least one data point")
...     return sum(data_points) / num_points
...

>>> average([2, 3, 4, 2, 3, 6, 4, 2])
3.25

>>> average([])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 3, in average
ValueError: average requires at least one data point

Inside average(), you first check if the input sample has any data points. If not, then you raise a ValueError with a descriptive message. In this example, you use the walrus operator to store the number of data points in the variable num_points so that you won’t need to call len() again. The return statement computes the sample’s arithmetic mean and sends it back to the calling code.

Note that when you call average() with a proper sample, you’ll get the desired mean. If you call average() with an empty sample, then you get a ValueError as expected.

Finding the Dot Product of Two Sequences

Another problem you can solve using sum() is finding the dot product of two equal-length sequences of numeric values. The dot product is the algebraic sum of products of every pair of values in the input sequences. For example, if you have the sequences (1, 2, 3) and (4, 5, 6), then you can calculate their dot product by hand using addition and multiplication:

1 × 4 + 2 × 5 + 3 × 6 = 32

To extract successive pairs of values from the input sequences, you can use zip(). Then you can use a generator expression to multiply each pair of values. Finally, sum() can sum the products:

>>>

>>> x_vector = (1, 2, 3)
>>> y_vector = (4, 5, 6)

>>> sum(x * y for x, y in zip(x_vector, y_vector))
32

With zip(), you generate a list of tuples with the values from each of the input sequences. The generator expression loops over each tuple while multiplying the successive pairs of values previously arranged by zip(). The final step is to add the products together using sum().

The code in the above example works. However, the dot product is defined for sequences of equal length, so what happens if you provide sequences with different lengths? In that case, zip() ignores the extra values from the longest sequence, which leads to an incorrect result.

To deal with this possibility, you can wrap the call to sum() in a custom function and provide a proper check for the length of the input sequences:

>>>

>>> def dot_product(x_vector, y_vector):
...     if len(x_vector) != len(y_vector):
...         raise ValueError("Vectors must have equal sizes")
...     return sum(x * y for x, y in zip(x_vector, y_vector))
...

>>> dot_product((1, 2, 3), (4, 5, 6))
32

>>> dot_product((1, 2, 3, 4), (5, 6, 3))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 3, in dot_product
ValueError: Vectors must have equal sizes

Here, dot_product() takes two sequences as arguments and returns their corresponding dot product. If the input sequences have different lengths, then the function raises a ValueError.

Embedding the functionality in a custom function allows you to reuse the code. It also gives you the opportunity to name the function descriptively so that the user knows what the function does just by reading its name.

Flattening a List of Lists

Flattening a list of lists is a common task in Python. Say you have a list of lists and need to flatten it into a single list containing all the items from the original nested lists. You can use any of several approaches to flattening lists in Python. For example, you can use a for loop, as in the following code:

>>>

>>> def flatten_list(a_list):
...     flat = []
...     for sublist in a_list:
...         flat += sublist
...     return flat
...

>>> matrix = [
...     [1, 2, 3],
...     [4, 5, 6],
...     [7, 8, 9],
... ]

>>> flatten_list(matrix)
[1, 2, 3, 4, 5, 6, 7, 8, 9]

Inside flatten_list(), the loop iterates over all the nested lists contained in a_list. Then it concatenates them in flat using an augmented assignment operation (+=). As the result, you get a flat list with all the items from the original nested lists.

But hold on! You’ve already learned how to use sum() to concatenate sequences in this tutorial. Can you use that feature to flatten a list of lists like you did in the example above? Yes! Here’s how:

>>>

>>> matrix = [
...     [1, 2, 3],
...     [4, 5, 6],
...     [7, 8, 9],
... ]

>>> sum(matrix, [])
[1, 2, 3, 4, 5, 6, 7, 8, 9]

That was quick! A single line of code and matrix is now a flat list. However, using sum() doesn’t seem to be the fastest solution.

An important drawback of any solution that implies concatenation is that behind the scenes, every intermediate step creates a new list. This can be pretty wasteful in terms of memory usage. The list that is eventually returned is just the most recently created list out of all the lists that were created at each round of concatenation. Using a list comprehension instead ensures that you create and return only one list:

>>>

>>> def flatten_list(a_list):
...     return [item for sublist in a_list for item in sublist]
...

>>> matrix = [
...     [1, 2, 3],
...     [4, 5, 6],
...     [7, 8, 9],
... ]

>>> flatten_list(matrix)
[1, 2, 3, 4, 5, 6, 7, 8, 9]

This new version of flatten_list() is more efficient and less wasteful in terms of memory usage. However, the nested comprehensions can be challenging to read and understand.

Using .append() is probably the most readable and Pythonic way to flatten a list of lists:

>>>

>>> def flatten_list(a_list):
...     flat = []
...     for sublist in a_list:
...         for item in sublist:
...             flat.append(item)
...     return flat
...

>>> matrix = [
...     [1, 2, 3],
...     [4, 5, 6],
...     [7, 8, 9],
... ]

>>> flatten_list(matrix)
[1, 2, 3, 4, 5, 6, 7, 8, 9]

In this version of flatten_list(), someone reading your code can see that the function iterates over every sublist in a_list. Inside this first for loop, it iterates over each item in sublist to finally populate the new flat list with .append(). Just like the comprehension from earlier, this solution creates only one list in the process. An advantage of this solution is that it is very readable.

Using Alternatives to sum()

As you’ve already learned, sum() is helpful for working with numeric values in general. However, when it comes to working with floating-point numbers, Python provides an alternative tool. In math, you’ll find a function called fsum() that can help you improve the general precision of your floating-point computations.

You might have a task where you want to concatenate or chain several iterables so that you can work with them as one. For this scenario, you can look to the itertools module’s function chain().

You might also have a task where you want to concatenate a list of strings. You’ve learned in this tutorial that there’s no way to use sum() for concatenating strings. This function just wasn’t built for string concatenation. The most Pythonic alternative is to use str.join().

Summing Floating-Point Numbers: math.fsum()

If your code is constantly summing floating-point numbers with sum(), then you should consider using math.fsum() instead. This function performs floating-point computations more carefully than sum(), which improves the precision of your computation.

According to its documentation, fsum() “avoids loss of precision by tracking multiple intermediate partial sums.” The documentation provides the following example:

>>>

>>> from math import fsum

>>> sum([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])
0.9999999999999999

>>> fsum([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])
1.0

With fsum(), you get a more precise result. However, you should note that fsum() doesn’t solve the representation error in floating-point arithmetic. The following example uncovers this limitation:

>>>

>>> from math import fsum

>>> sum([0.1, 0.2])
0.30000000000000004

>>> fsum([0.1, 0.2])
0.30000000000000004

In these examples, both functions return the same result. This is due to the impossibility of accurately representing both values 0.1 and 0.2 in binary floating-point:

>>>

>>> f"{0.1:.28f}"
'0.1000000000000000055511151231'

>>> f"{0.2:.28f}"
'0.2000000000000000111022302463'

Unlike sum(), however, fsum() can help you reduce floating-point error propagation when you add very large and very small numbers together:

>>>

>>> from math import fsum

>>> sum([1e-16, 1, 1e16])
1e+16
>>> fsum([1e-16, 1, 1e16])
1.0000000000000002e+16

>>> sum([1, 1, 1e100, -1e100] * 10_000)
0.0
>>> fsum([1, 1, 1e100, -1e100] * 10_000)
20000.0

Wow! The second example is pretty surprising and totally defeats sum(). With sum(), you get 0.0 as a result. This is quite far away from the correct result of 20000.0, as you get with fsum().

Concatenating Iterables With itertools.chain()

If you’re looking for a handy tool for concatenating or chaining a series of iterables, then consider using chain() from itertools. This function can take multiple iterables and build an iterator that yields items from the first one, from the second one, and so on until it exhausts all the input iterables:

>>>

>>> from itertools import chain

>>> numbers = chain([1, 2, 3], [4, 5, 6], [7, 8, 9])
>>> numbers
<itertools.chain object at 0x7f0d0f160a30>
>>> next(numbers)
1
>>> next(numbers)
2

>>> list(chain([1, 2, 3], [4, 5, 6], [7, 8, 9]))
[1, 2, 3, 4, 5, 6, 7, 8, 9]

When you call chain(), you get an iterator of the items from the input iterables. In this example, you access successive items from numbers using next(). If you want to work with a list instead, then you can use list() to consume the iterator and return a regular Python list.

chain() is also a good option for flattening a list of lists in Python:

>>>

>>> from itertools import chain

>>> matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

>>> list(chain(*matrix))
[1, 2, 3, 4, 5, 6, 7, 8, 9]

To flatten a list of lists with chain(), you need to use the iterable unpacking operator (*). This operator unpacks all the input iterables so that chain() can work with them and generate the corresponding iterator. The final step is to call list() to build the desired flat list.

Concatenating Strings With str.join()

As you’ve already seen, sum() doesn’t concatenate or join strings. If you need to do so, then the preferred and fastest tool available in Python is str.join(). This method takes a sequence of strings as an argument and returns a new, concatenated string:

>>>

>>> greeting = ["Hello,", "welcome to", "Real Python!"]

>>> " ".join(greeting)
'Hello, welcome to Real Python!'

Using .join() is the most efficient and Pythonic way to concatenate strings. Here, you use a list of strings as an argument and build a single string from the input. Note that .join() uses the string on which you call the method as a separator during the concatenation. In this example, you call .join() on a string that consists of a single space character (" "), so the original strings from greeting are separated by spaces in your final string.

Conclusion

You can now use Python’s built-in function sum() to add multiple numeric values together. This function provides an efficient, readable, and Pythonic way to solve summation problems in your code. If you’re dealing with math computations that require summing numeric values, then sum() can be your lifesaver.

In this tutorial, you learned how to:

  • Sum numeric values using general techniques and tools
  • Add several numeric values efficiently using Python’s sum()
  • Concatenate sequences using sum()
  • Use sum() to approach common summation problems
  • Use appropriate values for the iterable and start arguments in sum()
  • Decide between sum() and alternative tools to sum and concatenate objects

With this knowledge, you’re now able to add multiple numeric values together in a Pythonic, readable, and efficient way.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Summing Values the Pythonic Way With sum()

Списки Python – одна из наиболее часто используемых структур данных. Часто приходится выполнять различные операции со списками. В этой статье обсудим способы нахождения суммы элементов в списке в Python.

@python_job_interview – в нашем канале разобраны все возможные практические задачи Python

Находим сумму элементов в списке с помощью цикла For

Первый способ найти сумму элементов в списке – это выполнить итерацию по списку и добавить каждый элемент с помощью цикла for. Сначала рассчитаем длину списка с помощью метода len(). После этого объявим переменную sumOfElements равной 0. Затем используем функцию range(), чтобы создать последовательность чисел от 0 до (длина list-1). Используя числа в этой последовательности, мы получим доступ к элементам данного списка и добавим их в sumOfElements:

myList = [1, 2, 3, 4, 5, 6, 7, 8, 9]
print("The given list is:")
print(myList)
list_length=len(myList)
sumOfElements=0
for i in range(list_length):
    sumOfElements=sumOfElements+myList[i]

print("Sum of all the elements in the list is:", sumOfElements)

Вывод:

The given list is:
[1, 2, 3, 4, 5, 6, 7, 8, 9]
Sum of all the elements in the list is: 45

Также можно напрямую перебирать список, используя цикл for. Так мы получим прямой доступ к каждому элементу в списке и добавим их в сумму элементов:

myList = [1, 2, 3, 4, 5, 6, 7, 8, 9]
print("The given list is:")
print(myList)
sumOfElements = 0
for element in myList:
    sumOfElements = sumOfElements + element

print("Sum of all the elements in the list is:", sumOfElements)

Вывод:

The given list is:
[1, 2, 3, 4, 5, 6, 7, 8, 9]
Sum of all the elements in the list is: 45

Находим сумму элементов в списке с помощью цикла While

Также можно использовать цикл while, чтобы найти сумму элементов в списке. Для этого сначала рассчитаем длину списка с помощью метода len(). После этого инициализируем переменные с именами count и sumOfElements. Мы инициализируем оба элемента равными 0.

С помощью цикла while мы получим доступ к каждому элементу списка с помощью переменной count и добавим их в sumOfElements. После этого мы увеличим значение count на 1 и продолжим до тех пор, пока количество не станет равным длине списка.

Ваша программа может выглядеть так:

myList = [1, 2, 3, 4, 5, 6, 7, 8, 9]
print("The given list is:")
print(myList)
list_length = len(myList)
sumOfElements = 0
count = 0
while count < list_length:
    sumOfElements = sumOfElements + myList[count]
    count = count + 1

print("Sum of all the elements in the list is:", sumOfElements)

Вывод:

The given list is:
[1, 2, 3, 4, 5, 6, 7, 8, 9]
Sum of all the elements in the list is: 45

Сумма элементов в списке с помощью функции sum()

Также Python предоставляет нам встроенную функцию sum() для вычисления суммы элементов в любом объекте коллекции. Функция sum() принимает повторяющийся объект, такой как список, кортеж или набор, и возвращает сумму элементов в объекте.

Так можно найти сумму элементов списка с помощью функции sum():

myList = [1, 2, 3, 4, 5, 6, 7, 8, 9]
print("The given list is:")
print(myList)
sumOfElements = sum(myList)
print("Sum of all the elements in the list is:", sumOfElements)

Вывод:

The given list is:
[1, 2, 3, 4, 5, 6, 7, 8, 9]
Sum of all the elements in the list is: 45

Заключение

В этой статье мы обсудили различные способы, как найти суммы элементов в списке Python.

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The list is an important container and is used almost in every code of day-day programming as well as web development, more it is used, the more is the requirement to master it and hence knowledge of its operations is necessary. Given a list of lists, the program to suppose to return the sum as the final list. Let’s see some of the methods to sum a list of list and return list.

Method # 1: Using Naive method

Python3

L = [[1, 2, 3],

     [4, 5, 6],

     [7, 8, 9]]

print("Initial List - ", str(L))

res = list()

for j in range(0, len(L[0])):

    tmp = 0

    for i in range(0, len(L)):

        tmp = tmp + L[i][j]

    res.append(tmp)

print("final list - ", str(res))

Output:

Initial List -  [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
final list -  [12, 15, 18]

Time complexity: O(n^2), where n is the length of the outer list.
Auxiliary Space: O(n), the space required to store the result list.

Method #2: Using numpy array 

A numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays.

Python3

import numpy as np

List = np.array([[1, 2, 3],

                 [4, 5, 6],

                 [7, 8, 9]])

print("Initial List - ", str(List))

res = np.sum(List, 0)

print("final list - ", str(res))

Output:

Initial List -  [[1 2 3]
                 [4 5 6]
                 [7 8 9]]
final list -  [12 15 18]

Method #3: Using zip() and list comprehension

Python3

List = [[1, 2, 3],

        [4, 5, 6],

        [7, 8, 9]]

print("Initial List - ", str(List))

res = [sum(i) for i in zip(*List)]

print("final list - ", str(res))

Output:

Initial List -  [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
final list -  [12, 15, 18]

Time Complexity: O(n), where n is the length of List.
Auxiliary Space: O(n), where n is the length of ‘res’ list.

Method #4: Using map() and lambda

One approach that is not present in the article is using the map function and a lambda function to sum the elements of the list of lists.

The map function applies a given function to each element of an iterable (such as a list) and returns a new iterable with the modified elements. In this case, we can use a lambda function that takes a list as an argument and returns the sum of its elements, and apply it to each sublist in the list of lists using map.

Here is an example of how you could use this approach to sum the list of lists:

Python3

lst = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

sum_lst = list(map(lambda *x: sum(x), *lst))

print(sum_lst) 

The time complexity of the code provided is O(n * m), where n is the number of sublists and m is the length of each sublist. This is because the map function needs to iterate over all n sublists and m elements in each sublist. The space complexity is also O(m).

Method #5: Using itertools

Approach:

  1. Import the itertools module: import itertools
  2. Define the sum_lists_itertools function with lst as the parameter:
  3. Use the zip_longest() function from itertools to transpose the list of lists. This function takes the asterisk * operator to unpack the sub-lists and fill any missing values with a fillvalue of 0.
  4. Use a list comprehension to sum the corresponding elements of each sub-list and return the resulting list:
  5. Define the input ‘lst’ with a list of lists.
  6. Call the sum_lists_itertools function with ‘lst’ as an argument and assign the result to result.
  7. Print the result to the console.

Python3

import itertools

def sum_lists_itertools(lst):

    transposed = itertools.zip_longest(*lst, fillvalue=0)

    return [sum(x) for x in transposed]

lst = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

result = sum_lists_itertools(lst)

print(result)

Time Complexity: O(N^2)
Auxiliary Space: O(N)

Method #6: Using built-in function reduce() from the functools module

Python

from functools import reduce

List = [[1, 2, 3],

        [4, 5, 6],

        [7, 8, 9]]

print("Initial List - ", str(List))

res = reduce(lambda x, y: [x[i]+y[i] for i in range(len(x))], List)

print("Final list - ", str(res))

Output

('Initial List - ', '[[1, 2, 3], [4, 5, 6], [7, 8, 9]]')
('Final list - ', '[12, 15, 18]')

Time Complexity: O(N^2)
Auxiliary Space: O(N)

Last Updated :
27 Apr, 2023

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