A new ndarray object can be constructed by any of the following array creation routines or using a low-level ndarray constructor.
It creates an uninitialized array of specified shape and dtype. It uses the following constructor −
numpy.empty(shape, dtype = float, order = 'C')
The constructor takes the following parameters.
Sr.No. | Parameter & Description |
---|---|
1 | Shape Shape of an empty array in int or tuple of int |
2 | Dtype Desired output data type. Optional |
3 | Order 'C' for C-style row-major array, 'F' for FORTRAN style column-major array |
The following code shows an example of an empty array.
import numpy as np x = np.empty([3,2], dtype = int) print x
The output is as follows −
[[22649312 1701344351] [1818321759 1885959276] [16779776 156368896]]
Note − The elements in an array show random values as they are not initialized.
Returns a new array of specified size, filled with zeros.
numpy.zeros(shape, dtype = float, order = 'C')
The constructor takes the following parameters.
Sr.No. | Parameter & Description |
---|---|
1 | Shape Shape of an empty array in int or sequence of int |
2 | Dtype Desired output data type. Optional |
3 | Order 'C' for C-style row-major array, 'F' for FORTRAN style column-major array |
# array of five zeros. Default dtype is float import numpy as np x = np.zeros(5) print x
The output is as follows −
[ 0. 0. 0. 0. 0.]
import numpy as np x = np.zeros((5,), dtype = np.int) print x
Now, the output would be as follows −
[0 0 0 0 0]
# custom type import numpy as np x = np.zeros((2,2), dtype = [('x', 'i4'), ('y', 'i4')]) print x
It should produce the following output −
[[(0,0)(0,0)] [(0,0)(0,0)]]
Returns a new array of specified size and type, filled with ones.
numpy.ones(shape, dtype = None, order = 'C')
The constructor takes the following parameters.
Sr.No. | Parameter & Description |
---|---|
1 | Shape Shape of an empty array in int or tuple of int |
2 | Dtype Desired output data type. Optional |
3 | Order 'C' for C-style row-major array, 'F' for FORTRAN style column-major array |
# array of five ones. Default dtype is float import numpy as np x = np.ones(5) print x
The output is as follows −
[ 1. 1. 1. 1. 1.]
import numpy as np x = np.ones([2,2], dtype = int) print x
Now, the output would be as follows −
[[1 1] [1 1]]