In this tutorial, we will look at how to get the sum of values of a numpy array. We will also look at specific use-cases like summing along an axis for higher dimensional arrays.

## How to sum a numpy array?

You can use the numpy `sum()`

function to sum elements of an array. The following is the syntax for a range of different use-cases:

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# arr is a numpy array # sum of all values arr.sum() # sum of each row (for 2D array) arr.sum(axis=1) # sum of each column (for 2D array) arr.sum(axis=0) # sum along a specific axis, n arr.sum(axis=n)

You can also specify the axis to sum the numpy array along with the `axis`

parameter (see the examples below)

Let’s now look at some of the use-cases of using the numpy `sum()`

function.

## Sum of all elements in the array

Use the numpy `sum()`

function without any parameters to get the sum total of all values inside the array.

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Let’s create a numpy array and illustrate its usage.

import numpy as np # create an array arr = np.array([2, 0, 1, 3]) # sum of array values total = arr.sum() print(total)

Output:

6

We get 6 as the output which is the sum of all values in the above array arr: 2+0+1+3

You can use the above syntax to sum values in higher dimensional numpy arrays as well. For example, let’s get the total of all elements in a 2D numpy array –

# create a 2D numpy array arr = np.array([[1, 0, 0], [2, 1, 1]]) # sum of array values total = arr.sum() # display the array and the sum print(arr) print("Sum:", total)

Output:

[[1 0 0] [2 1 1]] Sum: 5

Here, we created a 2D array and then calculated its sum. You can see that we get the sum of all the elements in the above 2D array with the same syntax. This can be extended to higher-dimensional numpy arrays as well.

## Sum of every row in a 2D array

To get the sum of each row in a 2D numpy array, pass `axis=1`

to the `sum()`

function. This argument tells the function of the axis along which the elements are to be summed. Let’s use it to get the sum of each row in the array arr.

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# create a 2D numpy array arr = np.array([[1, 0, 0], [2, 1, 1]]) # sum of each row row_totals = arr.sum(axis=1) # display the array and the sum print(arr) print("Sum of each row:", row_totals)

Output:

[[1 0 0] [2 1 1]] Sum of each row: [1 4]

We get the sum of each row with axis=1. The first row sums to 1 and the second-row sums to 4. The result is returned as a numpy array.

## Sum of every column in a 2D array

To get the sum of each column in a 2D numpy array, pass `axis=0`

to the `sum()`

function. This argument tells the function of the axis along which the elements are to be summed. Let’s use it to get the sum of each column in the array arr.

# create a 2D numpy array arr = np.array([[1, 0, 0], [2, 1, 1]]) # sum of each column col_totals = arr.sum(axis=0) # display the array and the sum print(arr) print("Sum of each column:", col_totals)

Output:

[[1 0 0] [2 1 1]] Sum of each column: [3 1 1]

The resulting array `[3, 1, 1]`

contains the sum of values in each column. That is, in the above example – 1+2, 0+1, and 0+1.

The numpy sum() function also has additional parameters, for example, to specify the data type of the output, etc. For more, refer to its documentation.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.5

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Tutorials on numpy arrays –

- How to sort a Numpy Array?
- Create Pandas DataFrame from a Numpy Array
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- Numpy – Sum of Values in Array
- Numpy – Elementwise sum of two arrays
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- Using the numpy linspace() method
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- Numpy logspace() – Usage and Examples
- Using the numpy arange() method
- Using numpy hstack() to horizontally stack arrays
- Trim zeros from a numpy array in Python
- Get unique values and counts in a numpy array
- Horizontally split numpy array with hsplit()