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Next Article:
How to access different rows of a multidimensional NumPy array?
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Accessing Data Along Multiple Dimensions Arrays in Python Numpy

Last Updated : 01 Jul, 2021
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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 
# 1-dimensional array array1D = np.array([1, 2, 3, 4, 5])  print(array1D)  # to access elements using positive # index print("\nusing positive index :" +str(array1D[0])) print("using positive index :" +str(array1D[4]))  # negative indexing works in opposite # direction 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 
# 2-dimensional array  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 
# 3-dimensional array  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]]


 


Next Article
How to access different rows of a multidimensional NumPy array?

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Article Tags :
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  • Python-numpy
  • Python numpy-Indexing
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    Let us see how to access different rows of a multidimensional array in NumPy. Sometimes we need to access different rows of multidimensional NumPy array-like first row, the last two rows, and even the middle two rows, etc. In NumPy , it is very easy to access any rows of a multidimensional array. Al
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    numpy.tril_indices() function | Python
    numpy.tril_indices() function return the indices for the lower-triangle of an (n, m) array. Syntax : numpy.tril_indices(n, k = 0, m = None) Parameters : n : [int] The row dimension of the arrays for which the returned indices will be valid. k : [int, optional] Diagonal offset. m : [int, optional] Th
    1 min read

    Arithmetic operations on NumPyArray

    NumPy Array Broadcasting
    Broadcasting in NumPy allows us to perform arithmetic operations on arrays of different shapes without reshaping them. It automatically adjusts the smaller array to match the larger array's shape by replicating its values along the necessary dimensions. This makes element-wise operations more effici
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    Estimation of Variable | set 1
    Variability: It is the import dimension that measures the data variation i.e. whether the data is spread out or tightly clustered. Also known as Dispersion When working on data sets in Machine Learning or Data Science, involves many steps - variance measurement, reduction, and distinguishing random
    3 min read
    Python: Operations on Numpy Arrays
    NumPy is a Python package which means 'Numerical Python'. It is the library for logical computing, which contains a powerful n-dimensional array object, gives tools to integrate C, C++ and so on. It is likewise helpful in linear based math, arbitrary number capacity and so on. NumPy exhibits can lik
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    How to use the NumPy sum function?
    NumPy's sum() function is extremely useful for summing all elements of a given array in Python. In this article, we'll be going over how to utilize this function and how to quickly use this to advance your code's functionality. Let's go over how to use these functions and the benefits of using this
    4 min read
    numpy.divide() in Python
    numpy.divide(arr1, arr2, out = None, where = True, casting = 'same_kind', order = 'K', dtype = None) : Array element from first array is divided by elements from second element (all happens element-wise). Both arr1 and arr2 must have same shape and element in arr2 must not be zero; otherwise it will
    3 min read
    numpy.inner() in python
    numpy.inner(arr1, arr2): Computes the inner product of two arrays. Parameters : arr1, arr2 : array to be evaluated. Return: Inner product of the two arrays. Code #1 : Python3 1== # Python Program illustrating # numpy.inner() method import numpy as geek # Scalars product = geek.inner(5, 4) print(
    1 min read
    Absolute Deviation and Absolute Mean Deviation using NumPy | Python
    Absolute value: Absolute value or the modulus of a real number x is the non-negative value of x without regard to its sign. For example absolute value of 7 is 7 and the absolute value of -7 is also 7. Deviation: Deviation is a measure of the difference between the observed value of a variable and so
    3 min read
    Calculate standard deviation of a Matrix in Python
    In this article we will learn how to calculate standard deviation of a Matrix using Python. Standard deviation is used to measure the spread of values within the dataset. It indicates variations or dispersion of values in the dataset and also helps to determine the confidence in a model’s statistica
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    numpy.gcd() in Python
    numpy.gcd() function computes the greatest common divisor (GCD) of two integers element-wise. The GCD of two numbers is the largest positive integer that divides both numbers without leaving a remainder.Pythonimport numpy as np res = np.gcd(36, 60) print(res)Output12 The GCD of 36 and 60 is 12, whic
    2 min read

    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
    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
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