The numpy.zeros() function in Python is a part of the NumPy library, which is widely used for numerical computing in Python. This function is used to create an array filled entirely with zeros. It is particularly useful when you need to initialize an array with a default value of zero before populating it with other values during computations. Syntax of numpy.zeros() The basic syntax of the numpy.zeros() function is: numpy.zeros(shape, dtype=float, order='C') Parameters: shape: This parameter is either an integer or a tuple of integers specifying the dimensions of the array. For example, 5 creates a one-dimensional array with 5 elements, and (2, 3) creates a two-dimensional array with 2 rows and 3 columns. dtype: This optional parameter specifies the data type of the array elements. The default data type is float, but you can specify integers, complex numbers, or other types. order: This optional parameter specifies the memory layout of the array: either row-major (C-style) or column-major (Fortran-style). The default is 'C'. Examples of Using numpy.zeros() Here are several examples illustrating how to use numpy.zeros(): Creating a One-Dimensional Array import numpy as np # Create a 1D array of length 5 arr = np.zeros(5) print(arr) Output: [0. 0. 0. 0. 0.]

A 'zero' for dtype=str is a blank, not 0 or even '0':

In [1]: a = np.zeros((3,3))
In [2]: a
Out[2]: 
array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])
In [3]: a = np.zeros((3,3),int)
In [4]: a
Out[4]: 
array([[0, 0, 0],
       [0, 0, 0],
       [0, 0, 0]])
In [5]: a = np.zeros((3,3),bool)
In [6]: a
Out[6]: 
array([[False, False, False],
       [False, False, False],
       [False, False, False]], dtype=bool)
In [7]: a = np.zeros((3,3),str)
In [8]: a
Out[8]: 
array([['', '', ''],
       ['', '', ''],
       ['', '', '']],
      dtype='<U1')

Often it is better to specify the string length as well, but you still get blanks:

In [9]: a = np.zeros((3,3),'U4')
In [10]: a
Out[10]: 
array([['', '', ''],
       ['', '', ''],
       ['', '', '']],
      dtype='<U4')

You could convert an integer array to string, and add other string values:

In [11]: a = np.zeros((3,3),int)
In [12]: a[[0,1,2],[2,0,1]] = [1,2,3]
In [13]: a
Out[13]: 
array([[0, 0, 1],
       [2, 0, 0],
       [0, 3, 0]])
In [14]: A = a.astype(str)
In [15]: A
Out[15]: 
array([['0', '0', '1'],
       ['2', '0', '0'],
       ['0', '3', '0']],
      dtype='<U11')
In [16]: A[[0,1,2],[0,1,2]] = 'X'
In [17]: A
Out[17]: 
array([['X', '0', '1'],
       ['2', 'X', '0'],
       ['0', '3', 'X']],
      dtype='<U11')

The original 0s turn into string '0'.

You could try np.zeros(shape).astype(int).

EDIT: Actually, zeros accepts the dtype argument. So even better is np.zeros(shape, dtype=int)