In this article we will discuss how to create a Numpy array of different shapes and initialized with 0 & 1.

numpy.zeros()

Python’s Numpy module provides a function to create a numpy array of given shape & type and all values in it initialized with 0’s i.e.

numpy.zeros(shape, dtype=float, order='C')

Arguments:

  • shape : Shape of the numpy array. Single int or sequence of int.
  • dtype : (Optional) Data type of elements. Default is float64.
  • order : (Optional) Order in which data is stored in multi-dimension array i.e. in row major(‘F’) or column major (‘C’). Default is ‘C’.

Let’s see some examples,

Create a flattened numpy array filled with all zeros

# create a 1D numpy array with 5 zeros's filled in it
arr = np.zeros(5)

print('Contents of the Numpy Array : ' , arr)

Output:
[0. 0. 0. 0. 0.]

Here, in shape argument we passed 5. So, it returned a flattened numpy array of 5 zeros.

Create a 2D numpy array with 5 rows & 6 columns, filled with 0’s

# create a 2D numpy array with 5 rows & 6 columns, filled with 0's
arr = np.zeros((5, 6))

print('Contents of the Numpy Array : \n', arr)
print('Data Type of elements in  Array : ', arr.dtype)

Output:
Contents of the Numpy Array : 
 [[0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]]
Data Type of elements in  Array :  float64

Here we passed (5,6) as shape argument in numpy.zeros(), therefore it returned a 2D numpy array of 5 rows & 6 column with all zeros.
As default type was float64. Let’s see how to pass the data type int64 i.e.
# create a 2D numpy array with 5 rows & 6 columns filled with 0's and int data type
arr = np.zeros((5, 6) , dtype=np.int64)

print('Contents of the Numpy Array : \n', arr)

Output:
Contents of the Numpy Array : 
 [[0 0 0 0 0 0]
 [0 0 0 0 0 0]
 [0 0 0 0 0 0]
 [0 0 0 0 0 0]
 [0 0 0 0 0 0]]

It will create a 2D numpy array of ints filled with zeros.

numpy.ones()

Python’s Numpy module provides a function to create a numpy array of given shape & type and all values in it initialized with 1’s i.e.

numpy.ones(shape, dtype=float, order='C')

Arguments:

  • shape : Shape of the numpy array. Single int or sequence of int.
  • dtype : (Optional) Data type of elements. Default is float64.
  • order : (Optional) Order in which data is stored in multi-dimension array i.e. in row major(‘F’) or column major (‘C’). Default is ‘C’.

Let’s see some examples,

Create a flattened numpy array filled with all Ones

# create a 1D numpy array with 5 ones filled in it
arr = np.ones(5)

print('Contents of the Numpy Array : ' , arr)

Output:
[1. 1. 1. 1. 1.]

Here, in shape argument we passed 5. So, it returned a flattened numpy array of 5 zeros.

Create a 2D numpy array with 3 rows & 4 columns, filled with 1’s

# create a 2D numpy array with 3 rows & 4 columns, filled with 1's
arr = np.ones((3, 4))

print('Contents of the Numpy Array : \n', arr)
print('Data Type of elements in  Array : ', arr.dtype)

Output:
Contents of the Numpy Array : 
 [[1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]]
Data Type of elements in  Array :  float64

Here we passed (3,4) as shape argument in numpy.ones(), therefore it returned a 2D numpy array of 3 rows & 4 column with all zeros.
As default type was float64. Let’s see how to pass the data type int64 i.e.
# create a 2D numpy array with 3 rows & 4 columns filled with 1's and int data type
arr = np.zeros((3, 4) , dtype=np.int64)

print('Contents of the Numpy Array : \n', arr)

Output:
Contents of the Numpy Array : 
 [[1 1 1 1]
 [1 1 1 1]
 [1 1 1 1]]

It will create a 2D numpy array of ints filled with ones.

Complete example is as follows,

import numpy as np

def main():

   print("*** Create flattened numpy array filled with 0's using numpy.zeros() ***")

   # create a 1D numpy array with 5 zeros's filled in it
   arr = np.zeros(5)

   print('Contents of the Numpy Array : ' , arr)

   # create a 2D numpy array with 5 rows & 6 columns, filled with 0's
   arr = np.zeros((5, 6))

   print('Contents of the Numpy Array : \n', arr)
   print('Data Type of elements in  Array : ', arr.dtype)

   # create a 2D numpy array with 5 rows & 6 columns filled with 0's and int data type
   arr = np.zeros((5, 6) , dtype=np.int64)

   print('Contents of the Numpy Array : \n', arr)

   print("*** Create numpy array filled with 1's using numpy.ones() ***")

   # create a 1D numpy array with 7 one's filled in it
   arr = np.ones(5)

   print('Contents of the Numpy Array : ', arr)

   # create a 2D numpy array with 3 rows & 4 columns, filled with 1's
   arr = np.ones((3, 4))

   print('Contents of the Numpy Array : \n', arr)
   print('Data Type of elements in  Array : ', arr.dtype)

   # create a 2D numpy array with 5 rows & 5 columns, filled with 1's & int data type
   arr = np.ones((3, 4), dtype=np.int64)

   print('Contents of the Numpy Array : \n', arr)


if __name__ == '__main__':
   main()


Output
*** Create flattened numpy array filled with 0's using numpy.zeros() ***
Contents of the Numpy Array :  [0. 0. 0. 0. 0.]
Contents of the Numpy Array : 
 [[0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0.]]
Data Type of elements in  Array :  float64
Contents of the Numpy Array : 
 [[0 0 0 0 0 0]
 [0 0 0 0 0 0]
 [0 0 0 0 0 0]
 [0 0 0 0 0 0]
 [0 0 0 0 0 0]]
*** Create numpy array filled with 1's using numpy.ones() ***
Contents of the Numpy Array :  [1. 1. 1. 1. 1.]
Contents of the Numpy Array : 
 [[1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]]
Data Type of elements in  Array :  float64
Contents of the Numpy Array : 
 [[1 1 1 1]
 [1 1 1 1]
 [1 1 1 1]]

 

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