Python | Numpy matrix.take()

Last Updated : 29 May, 2019

With the help of Numpy matrix.take() method, we can select the elements from a given matrix by passing the parameter as index value of that element. It will return a matrix having one dimension. Remember it will work for one axis at a time.

Syntax : matrix.take(index, axis)
Return : Return matrix of selected indexes

Example #1 :
In this example we can see that by selecting one index we get only one value in a matrix by using matrix.take() method.

# import the important module in python 
import numpy as np 
              
# make matrix with numpy 
gfg = np.matrix('[4, 1, 12, 3, 4, 6, 7]') 
              
# applying matrix.take() method 
geek = gfg.take(2) 
    
print(geek) 

Output:

[[12]]

Example #2 :

# 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.take() method 
geek = gfg.take(0, 1) 
    
print(geek) 

Output:

[[ 4 12  4]]

J

Jitender_1998

Improve

Python | Numpy matrix.trace()

Similar Reads

numpy.take() in Python

The numpy.take() function returns elements from array along the mentioned axis and indices. Syntax: numpy.take(array, indices, axis = None, out = None, mode ='raise') Parameters : array : array_like, input array indices : index of the values to be fetched axis : [int, optional] axis over which we need to fetch the elements; By Default[axis = None],

Python | Numpy numpy.matrix.any()

With the help of Numpy numpy.matrix.any() method, we are able to compare each and every element of one matrix with another or we can provide the axis on the we want to apply comparison if any of the element matches it return true. Syntax : numpy.matrix.any() Return : Return true if any match found else false Example #1 : In this example we can see

Python | Numpy numpy.matrix.all()

With the help of Numpy numpy.matrix.all() method, we are able to compare each and every element of one matrix with another or we can provide the axis on the we want to apply comparison. Syntax : numpy.matrix.all() Return : Return true if found match else false Example #1 : In this example we can see that with the help of matrix.all() method, we are

Python | Numpy numpy.matrix.A()

With the help of Numpy numpy.matrix.A() method, we can get the same matrix as self. It means through this method we can get the identical matrix. Syntax : numpy.matrix.A() Return : Return self matrix Example #1 : In this example we can see that with the help of matrix.A() method, we are able to get the self matrix. # import the important module in

Python | Numpy numpy.matrix.T()

With the help of Numpy numpy.matrix.T() method, we can make a Transpose of any matrix either having dimension one or more than more. Syntax : numpy.matrix.T() Return : Return transpose of every matrix Example #1 : In this example we can see that with the help of matrix.T() method, we are able to transform any type of matrix. # import the important

Python | Numpy numpy.matrix.H()

With the help of Numpy numpy.matrix.H() method, we can make a conjugate Transpose of any complex matrix either having dimension one or more than more. Syntax : numpy.matrix.H() Return : Return conjugate transpose of every complex matrix Example #1 : In this example we can see that with the help of matrix.H() method, we are able to transform any typ

Take Matrix input from user in Python

Matrix is nothing but a rectangular arrangement of data or numbers. In other words, it is a rectangular array of data or numbers. The horizontal entries in a matrix are called as 'rows' while the vertical entries are called as 'columns'. If a matrix has r number of rows and c number of columns then the order of matrix is given by r x c. Each entrie

Difference between Numpy array and Numpy matrix

While working with Python many times we come across the question that what exactly is the difference between a numpy array and numpy matrix, in this article we are going to read about the same. What is np.array() in PythonThe Numpy array object in Numpy is called ndarray. We can create ndarray using numpy.array() function. It is used to convert a l

Python | Numpy numpy.resize()

With the help of Numpy numpy.resize(), we can resize the size of an array. Array can be of any shape but to resize it we just need the size i.e (2, 2), (2, 3) and many more. During resizing numpy append zeros if values at a particular place is missing. Parameters: new_shape : [tuple of ints, or n ints] Shape of resized array refcheck : [bool, optio

Python | Numpy numpy.transpose()

With the help of Numpy numpy.transpose(), We can perform the simple function of transpose within one line by using numpy.transpose() method of Numpy. It can transpose the 2-D arrays on the other hand it has no effect on 1-D arrays. This method transpose the 2-D numpy array. Parameters: axes : [None, tuple of ints, or n ints] If anyone wants to pass

Article Tags :

Practice Tags :

python