Python | Numpy matrix.sum()

Python | Numpy matrix.sum()

Last Updated : 20 May, 2019

With the help of matrix.sum() method, we are able to find the sum of values in a matrix by using the same method.

Syntax : matrix.sum() Return : Return sum of values in a matrix

Example #1 : In this example we are able to find the sum of values in a matrix by using matrix.sum() method.

Python3 1=1

# import the important module in python
import numpy as np
           
# make matrix with numpy
gfg = np.matrix('[4, 1; 12, 3]')
           
# applying matrix.sum() method
geek = gfg.sum()
 
print(geek)

Output:

Example #2 :

Python3 1==

# import the important module in python
import numpy as np
           
# make matrix with numpy
gfg = np.matrix('[4, 1, 9; 12, 3, 1; 4, 5, 6]')
           
# applying matrix.sum() method
geek = gfg.sum(axis = 1)
 
print(geek)

Output:

[[14]
 [16]
 [15]]

Python | Numpy matrix.sum()

J

Jitender_1998

Improve

Article Tags :

Practice Tags :

python

Similar Reads

numpy.sum() in Python

This function returns the sum of array elements over the specified axis.Syntax: numpy.sum(arr, axis, dtype, out): Parameters: arr: Input array. axis: The axis along which we want to calculate the sum value. Otherwise, it will consider arr to be flattened(works on all the axes). axis = 0 means along

Python | Numpy matrix.trace()

With the help of Numpy matrix.trace() method, we can find the sum of all the elements of diagonal of a matrix by using the matrix.trace() method. Syntax : matrix.trace() Return : Return sum of a diagonal elements of a matrix Example #1 : In this example we can see that by using matrix.trace() method

NumPy Array in Python

NumPy (Numerical Python) is a powerful library for numerical computations in Python. It is commonly referred to multidimensional container that holds the same data type. It is the core data structure of the NumPy library and is optimized for numerical and scientific computation in Python. Table of C

Python - Matrix

A matrix is a way to organize numbers in a rectangular grid made up of rows and columns. We can assume it like a table, where:Rows go across (left to right)Columns go down (top to bottom)The size of a matrix is defined by the number of rows (m) and columns (n). If a matrix has 3 rows and 4 columns,

numpy.add() in Python

NumPy, the Python powerhouse for scientific computing, provides an array of tools to efficiently manipulate and analyze data. Among its key functionalities lies numpy.add() a potent function that performs element-wise addition on NumPy arrays. numpy.add() SyntaxSyntax :Â numpy.add(arr1, arr2, /, out=

numpy.all() in Python

The numpy.all() function tests whether all array elements along the mentioned axis evaluate to True. Syntax: numpy.all(array, axis = None, out = None, keepdims = class numpy._globals._NoValue at 0x40ba726c) Parameters :Â array :[array_like]Input array or object whose elements, we need to test. axis

numpy.mean() in Python

numpy.mean(arr, axis = None) : Compute the arithmetic mean (average) of the given data (array elements) along the specified axis. Parameters : arr : [array_like]input array. axis : [int or tuples of int]axis along which we want to calculate the arithmetic mean. Otherwise, it will consider arr to be

Numpy size() function | Python

numpy.size() function in Python is used to count the number of elements in a NumPy array. You can use it to get the total count of all elements, or to count elements along a specific axis, such as rows or columns in a multidimensional array. This makes it useful when quickly trying to understand the

Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.Besides its obvious scientific uses, Numpy can also be used as an efficient m

numpy.cumsum() in Python

numpy.cumsum() function is used to compute the cumulative sum of elements in an array. Cumulative sum refers to a sequence where each element is the sum of all previous elements plus itself. For example, given an array [1, 2, 3, 4, 5], the cumulative sum would be [1, 3, 6, 10, 15]. Let's implement t