numpy.matlib.zeros() function | Python Last Updated : 22 Apr, 2020 Comments Improve Suggest changes Like Article Like Report numpy.matlib.zeros() function return matrix of given shape and type, filled with zeros. Syntax : numpy.matlib.zeros(shape, dtype = None, order = 'C') Parameters : shape : [sequence of ints] Shape of the empty matrix. dtype : [data-type, optional] The desired data-type for the matrix, default is float. order : [{āCā, āFā}, optional] Whether to store the result in C- or Fortran-contiguous order, default is āCā. Return : [matrix] Zero matrix of given shape, dtype, and order. Code #1 : Python3 # Python program explaining # numpy.matlib.zeros() function # importing numpy as geek # and importing matlib module import numpy as geek import numpy.matlib gfg = geek.matlib.zeros((3, 3)) print (gfg) Output : [[ 0. 0. 0.] [ 0. 0. 0.] [ 0. 0. 0.]] Code #2 : Python3 # Python program explaining # numpy.matlib.zeros() function # importing numpy as geek # and importing matlib module import numpy as geek import numpy.matlib gfg = geek.matlib.zeros(4) print (gfg) Output : [[ 0. 0. 0. 0.]] Comment More infoAdvertise with us Next Article numpy.matlib.zeros() function | Python sanjoy_62 Follow Improve Article Tags : Python Python-numpy Python numpy-arrayManipulation Python numpy-matlib Practice Tags : python Similar Reads numpy matrix operations | zeros() function numpy.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.matlib.empty() function | Python numpy.matlib.empty() function return 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 empty matrix. dtype : [data-type, optional] Desired output data-type. order : 1 min read numpy.ma.mask_rows() function | Python In this numpy.ma.mask_rows() function, mask rows of a 2D array that contain masked values. This function is a shortcut to mask_rowcols with axis equal to 0. Syntax : numpy.ma.mask_rows(arr, axis = None) Parameters : arr : [array_like, MaskedArray] The array to mask. The result is a MaskedArray. axis 2 min read numpy.roots() function - Python numpy.roots() function return the roots of a polynomial with coefficients given in p. The values in the rank-1 array p are coefficients of a polynomial. If the length of p is n+1 then the polynomial is described by: p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n] Syntax : numpy.roots(p) Parame 1 min read numpy.zeros_like() in Python This numpy method returns an array of given shape and type as given array, with zeros. Syntax: numpy.zeros_like(array, dtype = None, order = 'K', subok = True) Parameters : array : array_like input subok : [optional, boolean]If true, then newly created array will be sub-class of array; otherwise, a 2 min read Numpy size() function | Python numpy.size() function in Python is used to count the number of elements in a NumPy array. You can use it to get the total count of all elements, or to count elements along a specific axis, such as rows or columns in a multidimensional array. This makes it useful when quickly trying to understand the 2 min read numpy.i0() function | Python numpy.i0() function is the modified Bessel function of the first kind, order 0. it's usually denoted by I0. Syntax : numpy.i0(x) Parameters : x : [array_like, dtype float or complex] Argument of the Bessel function. Return : [ndarray, shape = x.shape, dtype = x.dtype] The modified Bessel function ev 1 min read numpy.zeros() in Python numpy.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.pad() function in Python numpy.pad() function is used to pad the Numpy arrays. Sometimes there is a need to perform padding in Numpy arrays, then numPy.pad() function is used. The function returns the padded array of rank equal to the given array and the shape will increase according to pad_width. Syntax: numpy.pad(array, p 2 min read numpy matrix operations | eye() function numpy.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 Like