Create a 1D / 2D Numpy Arrays of zeros or ones
We are going to how we can create variou types of numpy arrays.
numpy.zeros( ) :
The numpy module in python makes it able to create a numpy array all initialized with 0’s.
Syntax: numpy.zeros(shape, dtype=, order=)
Parameters :
- shape : The shape of the numpy array.(single Int or a sequence)
- dtype : It is an optional parameter that takes the data type of elements.(default value is float32)
- order : It is also an optional parameter which defines the order in which the array will be stored(‘C’ for column-major which is also the default value and ‘F’ for row-major)
Flattened numpy array filled with all zeros :
Below code is the implementation for that.
import numpy as np #Creating a numpy array containing all 0's zeroArray = np.zeros(5) print("The array contents are : ", zeroArray)
Output : The array contents are : [0. 0. 0. 0. 0.]
Creating a 2D numpy array with 5 rows & 6 columns, filled with 0’s :
To create an array with 5 rows and 6 columns filled with all 0’s, we need to pass 5 and 6 as parameters into the function.
Below code is the implementation for that.
import numpy as np # Creating a 5X6 numpy array containing all 0's zeroArray = np.zeros((5, 6)) print("The array contents are : ", zeroArray)
Output : The array contents are : [[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 created a zero numpy array of 5X6 size for us.
numpy.ones( ) :
Just like the numpy.zeros( )
, numpy.ones( )
is used to initialize the array elements to 1. It has same syntax.
Syntax - numpy.ones(shape, dtype=float, order='C')
Creating a flattened numpy array filled with all Ones :
Below code is the implementation for that.
import numpy as np # Creating a numpy array containing all 1's oneArray = np.ones(5) print("The array contents are : ", oneArray)
Output : The array contents are : [1. 1. 1. 1. 1.]
Creating a 2D numpy array with 3 rows & 4 columns, filled with 1’s :
To create a 2D numpy array with 3 rows and 4 columns filled with 1’s, we have to pass (3,4) into the function.
Below code is the implementation for that.
import numpy as np # Creating a 3X4 numpy array containing all 1's oneArray = np.ones((3, 4)) print("The array contents are : ", oneArray) print("Data Type of elements in Array : ", oneArray.dtype)
Output : The array contents are : [[1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.]] Data Type of elements in Array : float64
Let’s see how we can set the datatype to integer.
import numpy as np # Creating a 3X4 numpy array containing all 1's int64 datatype oneArray = np.ones((3, 4), dtype=np.int64) print("The array contents are : ", oneArray) print("Data Type of elements in Array : ", oneArray.dtype)
Output : The array contents are : [[1 1 1 1] [1 1 1 1] [1 1 1 1]] Data Type of elements in Array : int64