Unleash the Power of Uninitialized Arrays with empty()
When working with arrays, sometimes you need to create a new one from scratch without initializing its entries. That’s where the empty() method comes in – a powerful tool in your NumPy arsenal.
What Does empty() Do?
The empty() method creates a new array with a specified shape and type, but without initializing its entries. This means the resultant array contains arbitrary data that happens to be present in the current memory location. This approach can be useful when you need to create an array quickly, without worrying about its initial values.
The Syntax of empty()
The syntax of empty() is straightforward:
numpy.empty(shape, dtype=None, order='C', like=None)
Let’s break down the arguments:
shape: The desired new shape of the array, which can be an integer or a tuple of integers.dtype(optional): The datatype of the returned array. If unspecified, the default is float.order(optional): Specifies the order in which the uninitialized values are filled. The options are ‘C’ for row-wise (default) or ‘F’ for column-wise.like(optional): A reference object to create arrays that are not NumPy arrays.
Examples of empty() in Action
Let’s see how empty() works in practice:
Example 1: Creating an Array with empty()
When you call empty() without specifying the dtype, the default is float:
import numpy as np
arr = np.empty((3, 4))
print(arr)
Example 2: Creating an ndArray with empty()
In this example, we create an ndArray using empty(). Note that the values inside the array are uninitialized and will differ every time you run the code:
import numpy as np
arr = np.empty((3, 4, 2), dtype=int)
print(arr)
Taking Control with the Order Argument
The order argument in empty() specifies the order in which the uninitialized values are filled. You can choose between ‘C’ for row-wise (default) or ‘F’ for column-wise:
import numpy as np
arr = np.empty((3, 4), order='F')
print(arr)
By mastering the empty() method, you’ll be able to create uninitialized arrays with ease, giving you more flexibility and control in your NumPy projects.