The NumPy library also contains a multidimensional array and matrix data structures. It provides ndarray<\/strong>, a homogeneous n-dimensional array object, with methods to efficiently operate on it.<\/p>\n It is one of the very important libraries used in the field of Data Science<\/a> & Machine Learning.<\/p>\n Here is an interesting search trend for NumPy for the last five years.<\/p>\n Does a question arise that why do we need a NumPy array when we have python lists?<\/strong><\/p>\n The answer is we can perform operations on all the elements of a NumPy array at once, which are not possible with python lists.<\/p>\n For example, we can’t multiply two lists directly we will have to do it element-wise. This is where the role of NumPy comes into play.<\/p>\n Example:<\/strong><\/p>\n Output:<\/strong><\/p>\n Where the same thing can be done easily with NumPy arrays. Here is how NumPy works.<\/p>\n Numpy Array Example:<\/strong><\/p>\n Output:<\/strong><\/p>\n In this section, we will start with the installation of the key concepts involved in numPy. Also, we will go through some of the practical examples of NumPy library.<\/p>\n Before you use NumPy you must install the NumPy library as a prerequisite. It can be installed with If you use If you use Array in NumPy is a table of elements, all of the same type, indexed by a tuple of positive integers. In NumPy, the number of dimensions of the array is called the rank of the array. A tuple of integers giving the size of the array along each dimension is known as the shape of the array. An array class in NumPy is called as ndarray<\/strong>.<\/p>\n Example<\/strong><\/p>\n First of all import the numpy library<\/p>\n Creating an array object using np.array()<\/strong><\/p>\n Printing the array dimension using array.ndim<\/strong><\/p>\n Output:<\/p>\n Printing the shape of the array using array.shape<\/strong><\/p>\n Output:<\/p>\n Printing the size of the array using array.size<\/strong>. The size of the array means nothing but the total number of elements in the given array.<\/p>\n Output:<\/p>\n Arrays in NumPy can be created in multiple ways, with various number of Ranks, defining the size of the Array. Arrays can also be created with the use of various data types such as lists, tuples, etc.<\/p>\n Example<\/strong>:<\/p>\n First of all import the numpy library<\/p>\n Creating a rank 1 array by passing one python list<\/p>\n Output:<\/p>\n Creating a rank 2 array by passing two python lists<\/p>\n Output:<\/p>\n Creating array by passing a python tuple<\/p>\n Output:<\/p>\n Creating a 3×4 array with all zeros using np.zeros( )<\/strong><\/p>\n Output:<\/p>\n Creating a constant value array of complex type using np.full( )<\/strong><\/p>\n Output:<\/p>\n Indexing can be done in NumPy by using an array as an index. In case of slice, a view of the array is returned but in index array a copy of the original array is returned. Note that the first element is indexed by 0 second first by 1 and so on, whereas the last element is indexed by -1 second last by -2 and so on.<\/p>\n Example<\/strong>:<\/p>\n Creating a rank 1 array by passing one python list<\/p>\n Output:<\/p>\n Accessing elements and creating new array by passing indices<\/p>\n Output:<\/p>\n Output:<\/p>\n Basic slicing occurs when an object is a slice object that is of the form [start: stop: step]. Note that stop position is not included in slicing.<\/strong><\/p>\n Output:<\/p>\n Slicing and indexing in a multidimensional array are a little bit tricky compared to slicing and indexing in a one-dimensional array.<\/p>\n Output:<\/p>\n In NumPy, arrays allow various operations that can be performed on a particular array or a combination of Arrays. These operations may include some basic Mathematical operations as well as Unary and Binary operations.<\/p>\n Example<\/strong>:<\/p>\n Creating two different two dimensional arrays<\/p>\n Adding 5 to every element in array1<\/p>\n Output:<\/p>\n Adding 5 to every element in array1<\/p>\n Output:<\/p>\n Multiplying 2 to every element in array2<\/p>\n Output:<\/p>\n Sum of every element in array2<\/p>\n Output:<\/p>\n Adding two arrays<\/p>\n Output:<\/p>\n This module is used to perform vectorized string operations for arrays of dtype numpy.string_ or numpy.unicode_.<\/p>\n numpy.upper( )<\/strong><\/p>\n Returns the uppercased string from the given string. It converts all lowercase characters to uppercase. If no lowercase characters exist, it returns the original string.<\/p>\n Example<\/strong>:<\/p>\n Output<\/strong>:<\/p>\n Similary one can use numpy.lower( )<\/strong> to convert all uppercase characters to lowercase.<\/p>\n numpy.split( )<\/strong><\/p>\n Returns a list of strings after breaking the given string by the specified separator.<\/p>\n Example<\/strong>:<\/p>\n Output<\/strong>:<\/p>\n numpy.title( )<\/strong><\/p>\n It is used to convert the first character in each word to Uppercase and remaining characters to Lowercase in the string and returns a new string.<\/p>\n Example<\/strong>:<\/p>\n Output<\/strong>:<\/p>\n numpy.equal( )<\/strong><\/p>\n This function checks for string1 == string2 elementwise and return a boolean value true or false.<\/p>\n Example<\/strong>:<\/p>\n![\"NumPy](\"https:\/\/storage.ghost.io\/c\/5f\/2f\/5f2f4d20-2abf-4534-8d40-7aa233aedd43\/content\/images\/2025\/03\/numpy-min-1.png\")
<\/figure>\nWhy do we need NumPy?<\/h2>\n
list1 = [2, 4, 6, 7, 8]\nlist2 = [3, 4, 6, 1, 5]\n\nprint(list1*list2)<\/code><\/pre>\nTypeError: can't multiply sequence by non-int of type 'list'<\/code><\/pre>\nimport numpy as np\n\nlist1 = [2, 4, 6, 7, 8]\nlist2 = [3, 4, 6, 1, 5]\n\narr1 = np.array(list1)\narr2 = np.array(list2)\n\nprint(arr1*arr2)<\/code><\/pre>\n[ 6 16 36 7 40]<\/code><\/pre>\nPython Numpy Tutorial<\/h2>\n
Installing NumPy In Python<\/h3>\n
conda<\/code><\/strong>, with pip<\/code><\/strong>, or with a package manager.<\/p>\nconda<\/code><\/strong>, you can install it with:<\/p>\nconda install numpy<\/code><\/pre>\npip<\/code><\/strong>, you can install it with:<\/p>\npip install numpy<\/code><\/pre>\nArrays in NumPy<\/h3>\n
import numpy as np<\/code><\/pre>\narray = np.array([\n [1, 2, 3, 5, 6],\n [2, 1, 5, 6, 7]\n])<\/code><\/pre>\nprint(\"No. of dimensions of the array: \", array.ndim)<\/code><\/pre>\nNo. of dimensions of the array: 2<\/code><\/pre>\nprint(\"Shape of the array: \", array.shape)<\/code><\/pre>\nShape of the array: (2, 5)<\/code><\/pre>\nprint(\"Size of the array: \", array.size)<\/code><\/pre>\nSize of the array: 10<\/code><\/pre>\nCreating a NumPy Array<\/h3>\n
import numpy as np<\/code><\/pre>\nlist = [1, 2, 3, 5, 6]\narray = np.array(list)\nprint(array)<\/code><\/pre>\n[1 2 3 5 6]<\/code><\/pre>\nlist1 = [1, 2, 3, 5, 6]\nlist2 = [3, 1, 4, 5, 1]\narray = np.array(\n [list1, list2]\n)\nprint(array)<\/code><\/pre>\n[[1 2 3 5 6] \n[3 1 4 5 1]]<\/code><\/pre>\ntuple = (1, 2, 3, 5, 6)\narray = np.array(tuple)\nprint(array)<\/code><\/pre>\n[1 2 3 5 6]<\/code><\/pre>\narray = np.zeros((3, 4))\nprint(array)<\/code><\/pre>\n[[0. 0. 0. 0.] \n[0. 0. 0. 0.] \n[0. 0. 0. 0.]]<\/code><\/pre>\narray = np.full((3, 3), 6, dtype='complex')\nprint(array)<\/code><\/pre>\n[[6.+0.j 6.+0.j 6.+0.j] \n[6.+0.j 6.+0.j 6.+0.j] \n[6.+0.j 6.+0.j 6.+0.j]]<\/code><\/pre>\nNumPy Array Indexing<\/h3>\n
list = [1, 2, 3, 5, 6]\narray = np.array(list)\nprint(array)<\/code><\/pre>\n[1 2 3 5 6]<\/code><\/pre>\nnewArray = array[\n np.array([2, 0, 1, 4])\n]\nprint(newArray)<\/code><\/pre>\n[3 1 2 6]<\/code><\/pre>\n\n
newArray = array[\n np.array([2, 0, -1, -4])\n]\nprint(newArray)<\/code><\/pre>\n[3 1 6 2]<\/code><\/pre>\nnewArray = array[1:-2:1]\nprint(newArray)<\/code><\/pre>\n[2 3]<\/code><\/pre>\nBasic slicing and indexing in a multidimensional array<\/h3>\n
import numpy as np\n\narray = np.array([\n [2, 4, 5, 6],\n [3, 1, 6, 9],\n [4, 5, 1, 9],\n [2, 9, 1, 7]\n])\nprint(array)\n\n# Slicing and indexing in 4x4 array\n# Print first two rows and first two columns\nprint(\"\\n\", array[0:2, 0:2])\n\n# Print all rows and last two columns\nprint(\"\\n\", array[:, 2:4])\n\n# Print all column but middle two rows\nprint(\"\\n\", array[1:3, :])<\/code><\/pre>\n[[2 4 5 6]\n[3 1 6 9]\n[4 5 1 9]\n[2 9 1 7]]\n\n[[2 4]\n[3 1]]\n\n[[5 6]\n[6 9]\n[1 9]\n[1 7]]\n\n[[3 1 6 9]\n[4 5 1 9]]<\/code><\/pre>\nNumPy Operations on Array<\/h3>\n
array1 = np.array([\n [2, 4, 5],\n [3, 1, 6]\n])\n\narray2 = np.array([\n [1, 5, 9],\n [10, 32, 78]\n])<\/code><\/pre>\nnewArray = array1 + 5\nprint(newArray)<\/code><\/pre>\n[[ 7 9 10] \n[ 8 6 11]]<\/code><\/pre>\nnewArray = array1 + 5\nprint(newArray)<\/code><\/pre>\n[[ 7 9 10] \n[ 8 6 11]]<\/code><\/pre>\nnewArray = array2 * 2\nprint(newArray)<\/code><\/pre>\n[[ 2 10 18] \n[ 20 64 156]]<\/code><\/pre>\nnewArray = array2.sum()\nprint(newArray)<\/code><\/pre>\n135<\/code><\/pre>\nnewArray = array1 + array2\nprint(newArray)<\/code><\/pre>\n[[ 3 9 14] \n[13 33 84]]<\/code><\/pre>\nString Operations using NumPy<\/h2>\n
import numpy as np\n\nstring = \"devopscube\"\nprint(np.char.upper(string))<\/code><\/pre>\nDEVOPSCUBE<\/code><\/pre>\nimport numpy as np\n\nstring = \"devopscube.com\"\nprint(np.char.split(string, sep='.'))<\/code><\/pre>\n['devopscube', 'com']<\/code><\/pre>\nimport numpy as np\n\nstring = \"devopsCube is An amaZing pOrtal\"\nprint(np.char.title(string))<\/code><\/pre>\nDevopscube Is An Amazing Portal<\/code><\/pre>\nimport numpy as np\n\nstring1 = \"Devopscube\"\nstring2 = \"Devopscube.com\"\nprint(np.char.equal(string1, string2))<\/code><\/pre>\n