How to create a vector in Python using NumPy Last Updated : 28 Oct, 2021 Comments Improve Suggest changes Like Article Like Report 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. Numpy is basically used for creating array of n dimensions. Vector are built from components, which are ordinary numbers. We can think of a vector as a list of numbers, and vector algebra as operations performed on the numbers in the list. In other words vector is the numpy 1-D array. In order to create a vector, we use np.array method. Syntax : np.array(list)Argument : It take 1-D list it can be 1 row and n columns or n rows and 1 columnReturn : It returns vector which is numpy.ndarray Note: We can create vector with other method as well which return 1-D numpy array for example np.arange(10), np.zeros((4, 1)) gives 1-D array, but most appropriate way is using np.array with the 1-D list. Creating a Vector In this example we will create a horizontal vector and a vertical vector Python3 # importing numpy import numpy as np # creating a 1-D list (Horizontal) list1 = [1, 2, 3] # creating a 1-D list (Vertical) list2 = [[10], [20], [30]] # creating a vector1 # vector as row vector1 = np.array(list1) # creating a vector 2 # vector as column vector2 = np.array(list2) # showing horizontal vector print("Horizontal Vector") print(vector1) print("----------------") # showing vertical vector print("Vertical Vector") print(vector2) Output : Horizontal Vector [1 2 3] ---------------- Vertical Vector [[10] [20] [30]] Basic Arithmetic operation: In this example we will see do arithmetic operations which are element-wise between two vectors of equal length to result in a new vector with the same length Python3 # importing numpy import numpy as np # creating a 1-D list (Horizontal) list1 = [5, 6, 9] # creating a 1-D list (Horizontal) list2 = [1, 2, 3] # creating first vector vector1 = np.array(list1) # printing vector1 print("First Vector : " + str(vector1)) # creating second vector vector2 = np.array(list2) # printing vector2 print("Second Vector : " + str(vector2)) # adding both the vector # a + b = (a1 + b1, a2 + b2, a3 + b3) addition = vector1 + vector2 # printing addition vector print("Vector Addition : " + str(addition)) # subtracting both the vector # a - b = (a1 - b1, a2 - b2, a3 - b3) subtraction = vector1 - vector2 # printing addition vector print("Vector Subtraction : " + str(subtraction)) # multiplying both the vector # a * b = (a1 * b1, a2 * b2, a3 * b3) multiplication = vector1 * vector2 # printing multiplication vector print("Vector Multiplication : " + str(multiplication)) # dividing both the vector # a / b = (a1 / b1, a2 / b2, a3 / b3) division = vector1 / vector2 # printing division vector print("Vector Division : " + str(division)) Output : First Vector: [5 6 9] Second Vector: [1 2 3] Vector Addition: [ 6 8 12] Vector Subtraction: [4 4 6] Vector Multiplication: [ 5 12 27] Vector Division: [5 3 3] Vector Dot Product In mathematics, the dot product or scalar product is an algebraic operation that takes two equal-length sequences of numbers and returns a single number. For this we will use dot method. Python3 # importing numpy import numpy as np # creating a 1-D list (Horizontal) list1 = [5, 6, 9] # creating a 1-D list (Horizontal) list2 = [1, 2, 3] # creating first vector vector1 = np.array(list1) # printing vector1 print("First Vector : " + str(vector1)) # creating second vector vector2 = np.array(list2) # printing vector2 print("Second Vector : " + str(vector2)) # getting dot product of both the vectors # a . b = (a1 * b1 + a2 * b2 + a3 * b3) # a . b = (a1b1 + a2b2 + a3b3) dot_product = vector1.dot(vector2) # printing dot product print("Dot Product : " + str(dot_product)) Output: First Vector : [5 6 9] Second Vector : [1 2 3] Dot Product : 44 Vector-Scalar Multiplication Multiplying a vector by a scalar is called scalar multiplication. To perform scalar multiplication, we need to multiply the scalar by each component of the vector. Python3 # importing numpy import numpy as np # creating a 1-D list (Horizontal) list1 = [1, 2, 3] # creating first vector vector = np.array(list1) # printing vector1 print("Vector : " + str(vector)) # scalar value scalar = 2 # printing scalar value print("Scalar : " + str(scalar)) # getting scalar multiplication value # s * v = (s * v1, s * v2, s * v3) scalar_mul = vector * scalar # printing dot product print("Scalar Multiplication : " + str(scalar_mul)) Output Vector : [1 2 3] Scalar : 2 Scalar Multiplication : [2 4 6] Comment More infoAdvertise with us Next Article Python - Numpy fromrecords() method R rakshitarora Follow Improve Article Tags : Python Python-numpy Practice Tags : python Similar Reads NumPy Tutorial - Python Library NumPy (short for Numerical Python ) is one of the most fundamental libraries in Python for scientific computing. It provides support for large, multi-dimensional arrays and matrices along with a collection of mathematical functions to operate on arrays.At its core it introduces the ndarray (n-dimens 3 min read IntroductionNumPy IntroductionNumPy(Numerical Python) is a fundamental library for Python numerical computing. It provides efficient multi-dimensional array objects and various mathematical functions for handling large datasets making it a critical tool for professionals in fields that require heavy computation.Table of ContentK 7 min read Python NumPyNumpy 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 6 min read NumPy Array in PythonNumPy (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 2 min read Basics of NumPy ArraysNumPy stands for Numerical Python and is used for handling large, multi-dimensional arrays and matrices. Unlike Python's built-in lists NumPy arrays provide efficient storage and faster processing for numerical and scientific computations. It offers functions for linear algebra and random number gen 4 min read Numpy - ndarrayndarray is a short form for N-dimensional array which is a important component of NumPy. It’s allows us to store and manipulate large amounts of data efficiently. All elements in an ndarray must be of same type making it a homogeneous array. This structure supports multiple dimensions which makes it 3 min read Data type Object (dtype) in NumPy PythonEvery ndarray has an associated data type (dtype) object. This data type object (dtype) informs us about the layout of the array. This means it gives us information about: Type of the data (integer, float, Python object, etc.)Size of the data (number of bytes)The byte order of the data (little-endia 3 min read Creating NumPy ArrayNumpy - Array CreationNumpy Arrays are grid-like structures similar to lists in Python but optimized for numerical operations. The most straightforward way to create a NumPy array is by converting a regular Python list into an array using the np.array() function.Let's understand this with the help of an example:Pythonimp 5 min read numpy.arange() in Pythonnumpy.arange() function creates an array of evenly spaced values within a given interval. It is similar to Python's built-in range() function but returns a NumPy array instead of a list. Let's understand with a simple example:Pythonimport numpy as np #create an array arr= np.arange(5 , 10) print(arr 2 min read numpy.zeros() in Pythonnumpy.zeros() function creates a new array of specified shapes and types, filled with zeros. It is beneficial when you need a placeholder array to initialize variables or store intermediate results. We can create 1D array using numpy.zeros().Let's understand with the help of an example:Pythonimport 2 min read NumPy - Create array filled with all onesTo create an array filled with all ones, given the shape and type of array, we can use numpy.ones() method of NumPy library in Python.Pythonimport numpy as np array = np.ones(5) print(array) Output:[1. 1. 1. 1. 1.] 2D Array of OnesWe can also create a 2D array (matrix) filled with ones by passing a 2 min read NumPy - linspace() Functionlinspace() function in NumPy returns an array of evenly spaced numbers over a specified range. Unlike the range() function in Python that generates numbers with a specific step size. linspace() allows you to specify the total number of points you want in the array, and NumPy will calculate the spaci 2 min read numpy.eye() in Pythonnumpy.eye() is a function in the NumPy library that creates a 2D array with ones on the diagonal and zeros elsewhere. This function is often used to generate identity matrices with ones along the diagonal and zeros in all other positions.Let's understand with the help of an example:Pythonimport nump 2 min read Creating a one-dimensional NumPy arrayOne-dimensional array contains elements only in one dimension. In other words, the shape of the NumPy array should contain only one value in the tuple. We can create a 1-D array in NumPy using the array() function, which converts a Python list or iterable object. Pythonimport numpy as np # Create a 2 min read How to create an empty and a full NumPy array?Creating arrays is a basic operation in NumPy. Empty array: This array isn’t initialized with any specific values. It’s like a blank page, ready to be filled with data later. However, it will contain random leftover values in memory until you update it.Full array: This is an array where all the elem 2 min read Create a Numpy array filled with all zeros - PythonIn this article, we will learn how to create a Numpy array filled with all zeros, given the shape and type of array. We can use Numpy.zeros() method to do this task. Let's understand with the help of an example:Pythonimport numpy as np # Create a 1D array of zeros with 5 elements array_1d = np.zeros 2 min read How to generate 2-D Gaussian array using NumPy?In this article, let us discuss how to generate a 2-D Gaussian array using NumPy. To create a 2 D Gaussian array using the Numpy python module.Functions used:numpy.meshgrid()- It is used to create a rectangular grid out of two given one-dimensional arrays representing the Cartesian indexing or Matri 2 min read How to create a vector in Python using NumPyNumPy 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. Numpy is basically used for creating array of n dimensions. Vector are built 4 min read Python - Numpy fromrecords() methodnumpy.fromrecords() method is a powerful tool in the NumPy library that allows you to create structured arrays from a sequence of tuples or other array-like objects. Let's understand the help of an example:Pythonimport numpy as np # Define a list of records records = [(1, 'Alice', 25.5), (2, 'Bob', 2 min read NumPy Array ManipulationNumPy Copy and View of ArrayWhile working with NumPy, you may notice that some operations return a copy, while others return a view. A copy creates a new, independent array with its own memory, while a view shares the same memory as the original array. As a result, changes made to a view also affect the original and vice versa 4 min read How to Copy NumPy array into another array?Many times there is a need to copy one array to another. Numpy provides the facility to copy array using different methods. In this Copy NumPy Array into Another ArrayThere are various ways to copies created in NumPy arrays in Python, here we are discussing some generally used methods for copies cre 2 min read Appending values at the end of an NumPy arrayLet us see how to append values at the end of a NumPy array. Adding values at the end of the array is a necessary task especially when the data is not fixed and is prone to change. For this task, we can use numpy.append() and numpy.concatenate(). This function can help us to append a single value as 4 min read How to swap columns of a given NumPy array?Swapping columns of a NumPy array means exchanging the positions of two specified columns across all rows. For example, if you have a 3x3 array with values like [[0, 1, 2], [3, 4, 5], [6, 7, 9]] and you swap column 0 with column 2, the array becomes [[2, 1, 0], [5, 4, 3], [9, 7, 6]]. Let’s explore d 4 min read Insert a new axis within a NumPy arrayThis post deals with the ways to increase the dimension of an array in NumPy. NumPy provides us with two different built-in functions to increase the dimension of an array i.e., 1D array will become 2D array2D array will become 3D array3D array will become 4D array4D array will become 5D arrayAdd a 4 min read numpy.hstack() in Pythonnumpy.hstack() function stacks arrays in sequence horizontally (column-wise). It joins arrays along their second axis for 2D arrays or flattens and joins them for 1D arrays. This is useful for combining arrays side by side. Example:Pythonimport numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 2 min read numpy.vstack() in pythonnumpy.vstack() is a function in NumPy used to stack arrays vertically (row-wise). It takes a sequence of arrays as input and returns a single array by stacking them along the vertical axis (axis 0).Example: Vertical Stacking of 1D Arrays Using numpy.vstack()Pythonimport numpy as geek a = geek.array( 2 min read Joining NumPy ArrayJoining NumPy arrays means combining multiple arrays into one larger array. For example, joining two arrays [1, 2] and [3, 4] results in a combined array [1, 2, 3, 4]. Let’s explore some common ways to join arrays using NumPy.1. Using numpy.concatenate()numpy.concatenate() joins two or more arrays a 3 min read Combining a one and a two-dimensional NumPy ArraySometimes we need to combine 1-D and 2-D arrays and display their elements. Numpy has a function named as numpy.nditer(), which provides this facility. Syntax: numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0) Example 1 2 min read Numpy np.ma.concatenate() method-Pythonnp.ma.concatenate() method joins two or more masked arrays along an existing axis. It works similarly to np.concatenate() but it is specifically designed for masked arrays which may contain missing or invalid data that is "masked" i.e marked to be ignored in computations. Example:Pythonimport numpy 2 min read Numpy dstack() method-Pythonnumpy.dstack() stacks arrays depth-wise along the third axis (axis=2). For 1D arrays, it promotes them to (1, N, 1) before stacking. For 2D arrays, it stacks them along axis=2 to form a 3D array. Example: Pythonimport numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) res = np.dstack((a, b) 2 min read Splitting Arrays in NumPyNumPy arrays are an essential tool for scientific computing. However, at times, it becomes necessary to manipulate and analyze specific parts of the data. This is where array splitting comes into play, allowing you to break down an array into smaller sub-arrays, making the data more manageable. It i 6 min read How to compare two NumPy arrays?Here we will be focusing on the comparison done using NumPy on arrays. Comparing two NumPy arrays determines whether they are equivalent by checking if every element at each corresponding index is the same. Method 1: We generally use the == operator to compare two NumPy arrays to generate a new arr 2 min read Find the union of two NumPy arraysFinding the union of two NumPy arrays means combining both arrays into one while removing any duplicate values. For example, if you have two arrays [10, 20, 30] and [20, 30, 40], the union would be [10, 20, 30, 40]. Let's explore different ways to do this efficiently. Using np.union1d()If you want t 2 min read Find unique rows in a NumPy arrayFinding unique rows means removing duplicate rows from a 2D array so that each row appears only once. For example, given [[1, 2], [3, 4], [1, 2], [5, 6]], the unique rows would be [[1, 2], [3, 4], [5, 6]]. Let’s explore efficient ways to achieve this using NumPy.Using np.unique(axis=0)np.unique(axis 3 min read Numpy np.unique() method-Pythonnumpy.unique() finds the unique elements of an array. It is often used in data analysis to eliminate duplicate values and return only the distinct values in sorted order. Example:Pythonimport numpy as np a = np.array([1, 2, 2, 3, 4, 4, 4]) res = np.unique(a) print(res)Output[1 2 3 4] Explanation: nu 2 min read numpy.trim_zeros() in Pythonnumpy.trim_zeros() removes the leading and trailing zeros from a 1-D array. It is often used to clean up data by trimming unnecessary zeros from the beginning or end of the array. Example:Pythonimport numpy as np a = np.array([0, 0, 3, 4, 0, 5, 0, 0]) res = np.trim_zeros(a) print(res)Output[3 4 0 5] 2 min read Matrix in NumPyMatrix manipulation in PythonIn python matrix can be implemented as 2D list or 2D Array. Forming matrix from latter, gives the additional functionalities for performing various operations in matrix. These operations and array are defines in module "numpy". Operation on Matrix : 1. add() :- This function is used to perform eleme 4 min read numpy matrix operations | empty() functionnumpy.matlib.empty() is another function for doing matrix operations in numpy.It returns a new matrix of given shape and type, without initializing entries. Syntax : numpy.matlib.empty(shape, dtype=None, order='C') Parameters : shape : [int or tuple of int] Shape of the desired output empty matrix. 1 min read numpy matrix operations | zeros() functionnumpy.matlib.zeros() is another function for doing matrix operations in numpy. It returns a matrix of given shape and type, filled with zeros. Syntax : numpy.matlib.zeros(shape, dtype=None, order='C') Parameters : shape : [int, int] Number of rows and columns in the output matrix.If shape has length 2 min read numpy matrix operations | ones() functionnumpy.matlib.ones() is another function for doing matrix operations in numpy. It returns a matrix of given shape and type, filled with ones. Syntax : numpy.matlib.ones(shape, dtype=None, order='C') Parameters : shape : [int, int] Number of rows and columns in the output matrix.If shape has length on 2 min read numpy matrix operations | eye() functionnumpy.matlib.eye() is another function for doing matrix operations in numpy. It returns a matrix with ones on the diagonal and zeros elsewhere. Syntax : numpy.matlib.eye(n, M=None, k=0, dtype='float', order='C') Parameters : n : [int] Number of rows in the output matrix. M : [int, optional] Number o 2 min read numpy matrix operations | identity() functionnumpy.matlib.identity() is another function for doing matrix operations in numpy. It returns a square identity matrix of given input size. Syntax : numpy.matlib.identity(n, dtype=None) Parameters : n : [int] Number of rows and columns in the output matrix. dtype : [optional] Desired output data-type 1 min read Adding and Subtracting Matrices in PythonIn this article, we will discuss how to add and subtract elements of the matrix in Python. Example: Suppose we have two matrices A and B. A = [[1,2],[3,4]] B = [[4,5],[6,7]] then we get A+B = [[5,7],[9,11]] A-B = [[-3,-3],[-3,-3]] Now let us try to implement this using Python 1. Adding elements of 4 min read Matrix Multiplication in NumPyLet us see how to compute matrix multiplication with NumPy. We will be using the numpy.dot() method to find the product of 2 matrices. For example, for two matrices A and B. A = [[1, 2], [2, 3]] B = [[4, 5], [6, 7]] So, A.B = [[1*4 + 2*6, 2*4 + 3*6], [1*5 + 2*7, 2*5 + 3*7] So the computed answer wil 2 min read Numpy ndarray.dot() function | PythonThe numpy.ndarray.dot() function computes the dot product of two arrays. It is widely used in linear algebra, machine learning and deep learning for operations like matrix multiplication and vector projections.Example:Pythonimport numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) result = 2 min read NumPy | Vector MultiplicationNumPy is a Python library used for performing numerical computations. It provides an efficient way to work with vectors and matrices especially when performing vector multiplication operations. It is used in various applications such as data science, machine learning, physics simulations and many mo 4 min read How to calculate dot product of two vectors in Python?dot product or also known as the scalar product is an algebraic operation that takes two equal-length sequences of numbers and returns a single number. Let us given two vectors A and B, and we have to find the dot product of two vectors.Given that, A = a_1i + a_2j + a_3k B = b_1i + b_2j + b_3k Where 3 min read Multiplication of two Matrices in Single line using Numpy in PythonMatrix multiplication is an operation that takes two matrices as input and produces single matrix by multiplying rows of the first matrix to the column of the second matrix.In matrix multiplication make sure that the number of columns of the first matrix should be equal to the number of rows of the 3 min read Python | Numpy np.eigvals() methodWith the help of np.eigvals() method, we can get the eigen values of a matrix by using np.eigvals() method. Syntax : np.eigvals(matrix) Return : Return the eigen values of a matrix. Example #1 : In this example we can see that by using np.eigvals() method, we are able to get the eigen values of a ma 1 min read How to Calculate the determinant of a matrix using NumPy?The determinant of a square matrix is a special number that helps determine whether the matrix is invertible and how it transforms space. It is widely used in linear algebra, geometry and solving equations. NumPy provides built-in functions to easily compute the determinant of a matrix, let's explor 2 min read Python | Numpy matrix.transpose()With the help of Numpy matrix.transpose() method, we can find the transpose of the matrix by using the matrix.transpose()method in Python. Numpy matrix.transpose() Syntax Syntax : matrix.transpose() Parameter: No parameters; transposes the matrix it is called on. Return : Return transposed matrix Wh 3 min read Python | Numpy matrix.var()With the help of Numpy matrix.var() method, we can find the variance of a matrix by using the matrix.var() method. Syntax : matrix.var() Return : Return variance of a matrix Example #1 : In this example we can see that by using matrix.var() method we are able to find the variance of a given matrix. 1 min read Compute the inverse of a matrix using NumPyThe inverse of a matrix is just a reciprocal of the matrix as we do in normal arithmetic for a single number which is used to solve the equations to find the value of unknown variables. The inverse of a matrix is that matrix which when multiplied with the original matrix will give as an identity mat 2 min read Operations on NumPy ArrayNumpy | Binary OperationsBinary operators acts on bits and performs bit by bit operation. Binary operation is simply a rule for combining two values to create a new value. numpy.bitwise_and() : This function is used to Compute the bit-wise AND of two array element-wise. This function computes the bit-wise AND of the underly 8 min read Numpy | Mathematical FunctionNumPy contains a large number of various mathematical operations. NumPy provides standard trigonometric functions, functions for arithmetic operations, handling complex numbers, etc. Trigonometric Functions –NumPy has standard trigonometric functions which return trigonometric ratios for a given ang 9 min read Numpy - String Functions & OperationsNumPy String functions belong to the numpy.char module and are designed to perform element-wise operations on arrays. These functions can help to handle and manipulate string data efficiently.Table of ContentString OperationsString Information String Comparison In this article, we’ll explore the var 5 min read Reshaping NumPy ArrayReshape NumPy ArrayNumPy 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. Numpy is basically used for creating array of n dimensions.Reshaping numpy a 5 min read Python | Numpy matrix.resize()With the help of Numpy matrix.resize() method, we are able to resize the shape of the given matrix. Remember all elements should be covered after resizing the given matrix. Syntax : matrix.resize(shape) Return: new resized matrix Example #1 : In the given example we are able to resize the given matr 1 min read Python | Numpy matrix.reshape()With the help of Numpy matrix.reshape() method, we are able to reshape the shape of the given matrix. Remember all elements should be covered after reshaping the given matrix. Syntax : matrix.reshape(shape) Return: new reshaped matrix Example #1 : In the given example we are able to reshape the give 1 min read NumPy Array ShapeThe shape of an array can be defined as the number of elements in each dimension. Dimension is the number of indices or subscripts, that we require in order to specify an individual element of an array. How can we get the Shape of an Array?In NumPy, we will use an attribute called shape which return 2 min read Change the dimension of a NumPy arrayLet's discuss how to change the dimensions of an array. In NumPy, this can be achieved in many ways. Let's discuss each of them. Method #1: Using Shape() Syntax : array_name.shape()Python3 # importing numpy import numpy as np def main(): # initialising array print('Initialised array') gfg = np.arra 3 min read numpy.ndarray.resize() function - Pythonnumpy.ndarray.resize() function change shape and size of array in-place. Syntax : numpy.ndarray.resize(new_shape, refcheck = True) Parameters : new_shape :[tuple of ints, or n ints] Shape of resized array. refcheck :[bool, optional] If False, reference count will not be checked. Default is True. Ret 1 min read Flatten a Matrix in Python using NumPyLet's discuss how to flatten a Matrix using NumPy in Python. By using ndarray.flatten() function we can flatten a matrix to one dimension in python. Syntax:numpy_array.flatten(order='C') order:'C' means to flatten in row-major.'F' means to flatten in column-major.'A' means to flatten in column-major 1 min read numpy.moveaxis() function | Pythonnumpy.moveaxis() function allows you to rearrange axes of an array. It is used when you need to shift dimensions of an array to different positions without altering the actual data. The syntax for the numpy.moveaxis() function is as follows:numpy.moveaxis(array, source, destination)Parameters:array: 2 min read numpy.swapaxes() function - Pythonnumpy.swapaxes() function allow us to interchange two axes of a multi-dimensional NumPy array. It focuses on swapping only two specified axes while leaving the rest unchanged. It is used to rearrange the structure of an array without altering its actual data. The syntax of numpy.swapaxes() is:numpy. 2 min read Python | Numpy matrix.swapaxes()With the help of matrix.swapaxes() method, we are able to swap the axes a matrix by using the same method. Syntax : matrix.swapaxes() Return : Return matrix having interchanged axes Example #1 : In this example we are able to swap the axes of a matrix by using matrix.swapaxes() method. Python3 1=1 # 1 min read numpy.vsplit() function | Pythonnumpy.vsplit() function split an array into multiple sub-arrays vertically (row-wise). vsplit is equivalent to split with axis=0 (default), the array is always split along the first axis regardless of the array dimension. Syntax : numpy.vsplit(arr, indices_or_sections) Parameters : arr : [ndarray] A 2 min read numpy.hsplit() function | PythonThe numpy.hsplit() function is used to split a NumPy array into multiple sub-arrays horizontally (column-wise). It is equivalent to using the numpy.split() function with axis=1. Regardless of the dimensionality of the input array, numpy.hsplit() always divides the array along its second axis (column 2 min read Numpy MaskedArray.reshape() function | Pythonnumpy.MaskedArray.reshape() function is used to give a new shape to the masked array without changing its data.It returns a masked array containing the same data, but with a new shape. The result is a view on the original array; if this is not possible, a ValueError is raised. Syntax : numpy.ma.resh 3 min read Python | Numpy matrix.squeeze()With the help of matrix.squeeze() method, we are able to squeeze the size of a matrix by using the same method. But remember one thing we use this method on Nx1 size of matrix which gives out as 1xN matrix. Syntax : matrix.squeeze() Return : Return a squeezed matrix Example #1 : In this example we a 1 min read Indexing NumPy ArrayBasic Slicing and Advanced Indexing in NumPyIndexing a NumPy array means accessing the elements of the NumPy array at the given index.There are two types of indexing in NumPy: basic indexing and advanced indexing.Slicing a NumPy array means accessing the subset of the array. It means extracting a range of elements from the data.In this tutori 5 min read numpy.compress() in PythonThe numpy.compress() function returns selected slices of an array along mentioned axis, that satisfies an axis. Syntax: numpy.compress(condition, array, axis = None, out = None) Parameters : condition : [array_like]Condition on the basis of which user extract elements. Applying condition on input_ar 2 min read Accessing Data Along Multiple Dimensions Arrays in Python NumpyNumPy (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 3 min read How to access different rows of a multidimensional NumPy array?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 3 min read numpy.tril_indices() function | Pythonnumpy.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 NumPyArrayNumPy Array BroadcastingBroadcasting 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 5 min read Estimation of Variable | set 1Variability: 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 ArraysNumPy 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 3 min read 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 Pythonnumpy.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 pythonnumpy.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 | PythonAbsolute 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 PythonIn 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 2 min read numpy.gcd() in Pythonnumpy.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 ArrayNumpy | Linear AlgebraThe 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 arrayIn 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 Like