Accessing Data Along Multiple Dimensions Arrays in Python Numpy (original) (raw)

Last Updated : 01 Jul, 2021

NumPy (Numerical Python) is a Python library that comprises of multidimensional arrays and numerous functions to perform various mathematical and logical operations on them. NumPy also consists of various functions to perform linear algebra operations and generate random numbers. NumPy is often used along with packages like SciPy and Matplotlib for technical computing.
An n-dimensional (multidimensional) array has a fixed size and contains items of the same type. the contents of the multidimensional array can be accessed and modified by using indexing and slicing the array as desired. For accessing elements of an array we need to first import the library:

import numpy as np

We can use Integer Indexing to access elements of data. We can also perform Slicing to access sub-sequences of data.
Example 1:

Python3

array1D = np.array([ 1 , 2 , 3 , 4 , 5 ])

print (array1D)

print ( "\nusing positive index :" + str (array1D[ 0 ]))

print ( "using positive index :" + str (array1D[ 4 ]))

print ( "\nusing negative index :" + str (array1D[ - 5 ]))

print ( "using negative index :" + str (array1D[ - 1 ]))

Output :

[1 2 3 4 5]

using positive index :1 using positive index :5

using negative index :5 using negative index :1

Example 2:

Python3

array2D = np.array([[ 93 , 95 ],

`` [ 84 , 100 ],

`` [ 99 , 87 ]])

print (array2D)

print ( "shape :" + str (array2D.shape))

print ( "\npositive indexing :" + str (array2D[ 1 , 0 ]))

print ( "negative indexing :" + str (array2D[ - 2 , 0 ]))

print ( "\nslicing using positive indices :" + str (array2D[ 0 : 3 , 1 ]))

print ( "slicing using positive indices :" + str (array2D[:, 1 ]))

print ( "slicing using negative indices :" + str (array2D[:, - 1 ]))

Output :

[[ 93 95] [ 84 100] [ 99 87]] shape :(3, 2)

positive indexing :84 negative indexing :84

slicing using positive indices :[ 95 100 87] slicing using positive indices :[ 95 100 87] slicing using negative indices :[ 95 100 87]

Example 3:

Python3

array3D = np.array([[[ 0 , 1 , 2 ],

`` [ 3 , 4 , 5 ],

`` [ 6 , 7 , 8 ]],

`` [[ 9 , 10 , 11 ],

`` [ 12 , 13 , 14 ],

`` [ 15 , 16 , 17 ]],

`` [[ 18 , 19 , 20 ],

`` [ 21 , 22 , 23 ],

`` [ 24 , 25 , 26 ]]])

print (array3D)

print ( "shape :" + str (array3D.shape))

print ( "\naccessing element :" + str (array3D[ 0 , 1 , 0 ]))

print ( "accessing elements of a row and a column of an array:"

`` + str (array3D[:, 1 , 0 ]))

print ( "accessing sub part of an array :" + str (array3D[ 1 ]))

Output :

[[[ 0 1 2] [ 3 4 5] [ 6 7 8]]

[[ 9 10 11] [12 13 14] [15 16 17]]

[[18 19 20] [21 22 23] [24 25 26]]] shape :(3, 3, 3) accessing element :3 accessing elements of a row and a column of an array:[ 3 12 21] accessing sub part of an array :[[ 9 10 11] [12 13 14] [15 16 17]]

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Arithmetic operations on NumPyArray

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Linear Algebra in NumPy Array

• Numpy | Linear Algebra The Linear Algebra module of NumPy offers various methods to apply linear algebra on any numpy array.One can find: rank, determinant, trace, etc. of an array.eigen values of matricesmatrix and vector products (dot, inner, outer,etc. product), matrix exponentiationsolve linear or tensor equations and 6 min read

• Get the QR factorization of a given NumPy array In this article, we will discuss QR decomposition or QR factorization of a matrix. QR factorization of a matrix is the decomposition of a matrix say 'A' into 'A=QR' where Q is orthogonal and R is an upper-triangular matrix. We factorize the matrix using numpy.linalg.qr() function. Syntax : numpy.lin 2 min read

• How to get the magnitude of a vector in NumPy? The fundamental feature of linear algebra are vectors, these are the objects having both direction and magnitude. In Python, NumPy arrays can be used to depict a vector. There are mainly two ways of getting the magnitude of vector: By defining an explicit function which computes the magnitude of a g 3 min read

• How to compute the eigenvalues and right eigenvectors of a given square array using NumPY? In this article, we will discuss how to compute the eigenvalues and right eigenvectors of a given square array using NumPy library. Example: Suppose we have a matrix as: [[1,2], [2,3]] Eigenvalue we get from this matrix or square array is: [-0.23606798 4.23606798] Eigenvectors of this matrix are: [[ 2 min read