Numpy ones() is also referred to as np.ones or numpy.ones. This function creates a Numpy array whose each element is having a value of 1. Even array() function can be used for creating Numpy array whose each element's value is 1. Then the next question that comes to your mind is why do we need to use ones() function? Just understand the reason behind using ones() function with the help of a simple example:
Suppose, you want to create a Numpy array having 3 rows and 10 columns, and each element's value is one. With array() function, you can do it like this:
import numpy as np np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
As you can see creating an array like this is a cumbersome task and it is error-prone. With ones() function, you can achieve the same task very easily. Let's look at it how:
np.ones(shape=(3, 10), dtype=int)
As you can see how simple is it to create array with ones() function.
Finally, the syntax of numpy ones() function is explained below:
np.ones(shape, dtype=None) OR numpy.ones(shape, dtype=None)
shape parameter is used to specify the dimensions of the Numpy array. Here, you specify the value in the form of tuple or list.
dtype is used to specify the data type of array and this parameter is optional.
Python program to create a one-dimensional array with ones() function.
import numpy as np a = np.ones(7) print(a)
Output of the above program
[1. 1. 1. 1. 1. 1. 1.]
Python program to create numpy ones array with the specified data type
import numpy as np a = np.ones(7, dtype=int) print(a)
Output of the above program
[1 1 1 1 1 1 1]
Python program to create numpy ones array with the specific dimension or shape
import numpy as np a = np.ones(shape=(3, 6), dtype=int) print(a) b = np.ones((3, 6), dtype=int) print(b) c = np.ones([3, 6], dtype=int) print(c)
Output of the above program
[[1 1 1 1 1 1] [1 1 1 1 1 1] [1 1 1 1 1 1]] [[1 1 1 1 1 1] [1 1 1 1 1 1] [1 1 1 1 1 1]] [[1 1 1 1 1 1] [1 1 1 1 1 1] [1 1 1 1 1 1]]
As you can see in the above example, there are basically three different methods of specifying shape to Numpy ones() function.
