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.
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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
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# create a 1D numpy array with 5 zeros's filled in it arr = np.zeros(5) print('Contents of the Numpy Array : ' , arr) |
Output:
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[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
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# 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:
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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.
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# 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:
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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.
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1 |
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
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# create a 1D numpy array with 5 ones filled in it arr = np.ones(5) print('Contents of the Numpy Array : ' , arr) |
Output:
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[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
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# 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:
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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.
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# 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:
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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,
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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
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*** 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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