Numpy MaskedArray.reshape() function | Python Last Updated : 03 Oct, 2019 Summarize Suggest changes Share Like Article Like Report numpy.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.reshape(shape, order) Parameters: shape:[ int or tuple of ints] The new shape should be compatible with the original shape. order : [‘C’, ‘F’, ‘A’, ‘K’, optional] By default, ‘C’ index order is used. --> The elements of a are read using this index order. --> ‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. --> ‘F’ means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest. --> ‘A’ means to read the elements in Fortran-like index order if m is Fortran contiguous in memory, C-like order otherwise. --> ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. Return : [ reshaped_array] A new view on the array. Code #1 : Python3 # Python program explaining # numpy.MaskedArray.reshape() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array in_arr = geek.array([1, 2, 3, -1]) print ("Input array : ", in_arr) # Now we are creating a masked array. # by making third entry as invalid. mask_arr = ma.masked_array(in_arr, mask =[1, 0, 1, 0]) print ("Masked array : ", mask_arr) # applying MaskedArray.reshape methods to make # it a 2d masked array out_arr = mask_arr.reshape(2, 2) print ("Output 2D masked array : ", out_arr) Output: Input array : [ 1 2 3 -1] Masked array : [-- 2 -- -1] Output 2D masked array : [[-- 2] [-- -1]] Code #2 : Python3 # Python program explaining # numpy.MaskedArray.reshape() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array in_arr = geek.array([[[ 2e8, 3e-5]], [[ -45.0, 2e5]]]) print ("Input array : ", in_arr) # Now we are creating a masked array. # by making one entry as invalid. mask_arr = ma.masked_array(in_arr, mask =[[[ 1, 0]], [[ 0, 0]]]) print ("3D Masked array : ", mask_arr) # applying MaskedArray.reshape methods to make # it a 2d masked array out_arr = mask_arr.reshape(1, 4) print ("Output 2D masked array : ", out_arr) print() # applying MaskedArray.reshape methods to make # it a 1d masked array out_arr = mask_arr.reshape(4, ) print ("Output 1D masked array : ", out_arr) Output: Input array : [[[ 2.0e+08 3.0e-05]] [[-4.5e+01 2.0e+05]]] 3D Masked array : [[[-- 3e-05]] [[-45.0 200000.0]]] Output 2D masked array : [[-- 3e-05 -45.0 200000.0]] Output 1D masked array : [-- 3e-05 -45.0 200000.0] Advertise with us Next Article Python | Numpy matrix.squeeze() J jana_sayantan Follow Similar Reads NumPy Tutorial - Python Library NumPy (short for Numerical Python ) is one of the most fundamental libraries in Python for scientific computing. 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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. 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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. 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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. 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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. 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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. 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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. 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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 Article Tags : Python Python-numpy Python numpy-arrayManipulation Practice Tags : python Like