numpy.broadcast_to() function – Python Last Updated : 18 Aug, 2020 Comments Improve Suggest changes Like Article Like Report numpy.broadcast_to() function broadcast an array to a new shape. Syntax : numpy.broadcast_to(array, shape, subok = False) Parameters : array : [array_liket] The array to broadcast. shape : [tuple] The shape of the desired array. subok : [bool, optional] If True, then sub-classes will be passed-through, otherwise by default, the returned array will be forced to be a base-class array. Return : [array] The output array. Code #1 : Python3 # Python program explaining # numpy.broadcast_to() function # importing numpy as geek import numpy as geek arr = geek.array([1, 2, 3, 4]) gfg = geek.broadcast_to(arr, (4, 4)) print(gfg) Output : [[1 2 3 4] [1 2 3 4] [1 2 3 4] [1 2 3 4]] Code #2 : Python3 # Python program explaining # numpy.broadcast_to() function # importing numpy as geek import numpy as geek arr = geek.array([1, 2, 3, 4, 5]) gfg = geek.broadcast_to(arr, (5, 5)) print(gfg) Output : [[1 2 3 4 5] [1 2 3 4 5] [1 2 3 4 5] [1 2 3 4 5] [1 2 3 4 5]] Comment More infoAdvertise with us Next Article numpy.broadcast_to() function – Python sanjoy_62 Follow Improve Article Tags : Python Python-numpy Practice Tags : python Similar Reads numpy.fromstring() function â Python numpy.fromstring() function create a new one-dimensional array initialized from text data in a string. Syntax : numpy.fromstring(string, dtype = float, count = -1, sep = ' ') Parameters : string : [str] A string that contained the data. dtype : [data-type, optional] Data-type of the array. Default d 1 min read numpy.fromiter() function â Python NumPy's fromiter() function is a handy tool for creating a NumPy array from an iterable object. This iterable can be any Python object that provides elements one at a time. The function is especially useful when you need to convert data from a custom data source, like a file or generator, into a Num 2 min read numpy.who function - Python numpy.who() function print the NumPy arrays in the given dictionary. Syntax : numpy.who(vardict = None) Parameters : vardict : [dict, optional] A dictionary possibly containing ndarrays. Return : Returns âNoneâ. If there is no dictionary passed in or vardict is None then returns NumPy arrays in the 1 min read numpy.frombuffer() function â Python numpy.frombuffer() function interpret a buffer as a 1-dimensional array. Syntax : numpy.frombuffer(buffer, dtype = float, count = -1, offset = 0) Parameters : buffer : [buffer_like] An object that exposes the buffer interface. dtype : [data-type, optional] Data-type of the returned array, default da 1 min read How to call a function in Python Python is an object-oriented language and it uses functions to reduce the repetition of the code. In this article, we will get to know what are parts, How to Create processes, and how to call them.In Python, there is a reserved keyword "def" which we use to define a function in Python, and after "de 5 min read numpy.matrix.A() function - Python numpy.matrix.A() function return self as an ndarray object. Syntax : numpy.matrix.A() Parameters : None Return : [ndarray] Return self as an ndarray. Code #1 : Python3 # Python program explaining # numpy.matrix.A() function # importing numpy as geek import numpy as geek mat = geek.matrix(geek.arange 1 min read Python | Numpy.broadcast_arrays() method With the help of Numpy.broadcast_arrays() method, we can get the one broadcasted array with the help of two or more arrays by using Numpy.broadcast_arrays() method. Syntax : Numpy.broadcast_arrays() Return : Return the broadcasted array using numpy. Example #1 : In this example we can see that by us 1 min read How to use Function Decorators in Python ? In Python, a function can be passed as a parameter to another function (a function can also return another function). we can define a function inside another function. In this article, you will learn How to use Function Decorators in Python. Passing Function as ParametersIn Python, you can pass a fu 3 min read numpy.promote_types() function â Python numpy.promote_types() function is a symmetric function which returns the data type with the smallest size and smallest scalar kind to which both type1 and type2 may be safely cast. The returned data type is always in native byte order. Syntax : numpy.promote_types(type1, type2) Parameters : type1 : 1 min read Assign Function to a Variable in Python In Python, functions are first-class objects, meaning they can be assigned to variables, passed as arguments and returned from other functions. Assigning a function to a variable enables function calls using the variable name, enhancing reusability.Example:Python# defining a function def a(): print( 3 min read Like