numpy.zeros() & numpy.ones() | Create a numpy array of zeros or ones
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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