In this article, you will learn how to use the Numpy astype() function in Python.
Numpy astype() Function
The astype() function in NumPy is used to cast a given array to a specified data type. It returns a new array with the specified data type.
The syntax for using the astype() function is as follows:
numpy.astype(arr, dtype, order='K', casting='unsafe', subok=True, copy=True)
Here is an explanation of the parameters:
arr: The input array that you want to cast to a new data type.
dtype: The data type to which you want to cast the input array.
order: Specifies the order in which the array data is stored in memory. It defaults to ‘K’ which means that the memory order is kept the same as the input array.
casting: Specifies the casting rule to be used when casting the array to a new data type. It defaults to ‘unsafe’ which means that no checking is done to ensure that the cast is safe.
subok: Specifies whether to return a subclass of the input array if possible. It defaults to True, which means that a subclass will be returned if possible.
copy: Specifies whether to create a new copy of the input array. It defaults to True, which means that a new copy will be created.
Here is an example usage of the astype() function:
import numpy as np # create an array of integers a = np.array([1, 2, 3, 4, 5]) # cast the array to float data type b = a.astype(float) # print the original and the new arrays print(a) print(b) # Output: # [1 2 3 4 5] # [1. 2. 3. 4. 5.]
In this example, we created an array a containing integers, and then we used the astype() function to cast it to the float data type. The resulting array b contains the same values as a, but with the data type changed to float.