numpy.ndarray.view() in Python Last Updated : 01 Mar, 2024 Summarize Comments Improve Suggest changes Share Like Article Like Report numpy.ndarray.view() helps to get a new view of array with the same data. Syntax: ndarray.view(dtype=None, type=None)Parameters: dtype : Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. type : Python type, optionalReturns : ndarray or matrix. Code #1: Python3 # Python program explaining # numpy.ndarray.view() function import numpy as geek a = geek.arange(10, dtype ='int16') print("a is: \n", a) # using view() method v = a.view('int32') print("\n After using view() with dtype = 'int32' a is : \n", a) v += 1 # addition of 1 to each element of v print("\n After using view() with dtype = 'int32' and adding 1 a is : \n", a) Outputa is: [0 1 2 3 4 5 6 7 8 9] After using view() with dtype = 'int32' a is : [0 1 2 3 4 5 6 7 8 9] After using view() with dtype = 'int32' and adding 1 a is : [1 1 3 3 5 5 7 7 9 9] Code #2: Python3 # Python program explaining # numpy.ndarray.view() function import numpy as geek a = geek.arange(10, dtype ='int16') print("a is:", a) # Using view() method v = a.view('int16') print("\n After using view() with dtype = 'int16' a is :\n", a) v += 1 # addition of 1 to each element of v print("\n After using view() with dtype = 'int16' and adding 1 a is : \n", a) Outputa is: [0 1 2 3 4 5 6 7 8 9] After using view() with dtype = 'int16' a is : [0 1 2 3 4 5 6 7 8 9] After using view() with dtype = 'int16' and adding 1 a is : [ 1 2 3 4 5 6 7 8 9 10] Code #3: Python3 import numpy as geek a = geek.arange(10, dtype ='int16') print("a is: \n", a) v = a.view('int8') print("\n After using view() with dtype = 'int8' a is : \n", a) v += 1 # addition of 1 to each element of v print("\n After using view() with dtype = 'int8' and adding 1 a is : \n", a) Output: a is: [0 1 2 3 4 5 6 7 8 9] After using view() with dtype = 'int8' a is : [0 1 2 3 4 5 6 7 8 9] After using view() with dtype = 'int8' and adding 1 a is : [257 258 259 260 261 262 263 264 265 266] Comment More infoAdvertise with us Next Article Python | Numpy ndarray.item() A ArkadipGhosh Follow Improve Article Tags : Python Python-numpy Python numpy-ndarray Practice Tags : python Similar Reads Python | Numpy ndarray.__iand__() With the help of Numpy ndarray.__iand__() method, we can get the elements that is anded by the value that is provided as a parameter in numpy.ndarray.__iand__() method. Syntax: ndarray.__iand__($self, value, /) Return: self&=value Example #1 : In this example we can see that every element is and 1 min read Python | Numpy ndarray.__ior__() With the help of Numpy ndarray.__ior__() method, we can get the elements that is OR by the value that is provided as a parameter in numpy.ndarray.__ior__() method. Syntax: ndarray.__ior__($self, value, /) Return: self|=value Example #1 : In this example we can see that every element is or by the val 1 min read Python | Numpy ndarray.item() With the help of numpy.ndarray.item() method, we can fetch the data elements that is found at the given index on numpy array. Remember we can give index as one dimensional parameter or can be two dimensional. Parameters: *args : Arguments (variable number and type) -> none: This argument only works 2 min read numpy.ndarray.flat() in Python The numpy.ndarray.flat() function is used as a 1_D iterator over N-dimensional arrays. It is not a subclass of, Pythonâs built-in iterator object, otherwise it a numpy.flatiter instance. Syntax : numpy.ndarray.flat() Parameters : index : [tuple(int)] index of the values to iterate Return :  1-D i 3 min read numpy.ndarray.fill() in Python numpy.ndarray.fill() method is used to fill the numpy array with a scalar value. If we have to initialize a numpy array with an identical value then we use numpy.ndarray.fill(). Suppose we have to create a NumPy array a of length n, each element of which is v. Then we use this function as a.fill(v). 2 min read Numpy ndarray.setfield() function | Python numpy.ndarray.setfield() function Put a value into a specified place in a field defined by a data-type. Place val into aâs field defined by dtype and beginning offset bytes into the field. Syntax : numpy.ndarray.setfield(val, dtype, offset=0) Parameters : val : [object] Value to be placed in field. 1 min read Like