In this post, we are going to see the ways in which we can change the dtype of the given numpy array. In order to change the dtype of the given array object, we will use numpy.astype() function. The function takes an argument which is the target data type. The function supports all the generic types and built-in types of data.
Problem #1 : Given a numpy array whose underlying data is of 'int32' type. Change the dtype of the given object to 'float64'.
Solution : We will use numpy.astype() function to change the data type of the underlying data of the given numpy array.
# importing the numpy library as npimport numpy as np # Create a numpy arrayarr = np.array([10, 20, 30, 40, 50]) # Print the arrayprint(arr) |
Output :
Now we will check the dtype of the given array object.
# Print the dtypeprint(arr.dtype) |
Output :
As we can see in the output, the current dtype of the given array object is ‘int32’. Now we will change this to ‘float64’ type.
# change the dtype to 'float64'arr = arr.astype('float64') # Print the array after changing# the data typeprint(arr) # Also print the data typeprint(arr.dtype) |
Output :
Problem #2 : Given a numpy array whose underlying data is of 'int32' type. Change the dtype of the given object to 'complex128'.
Solution : We will use numpy.astype() function to change the data type of the underlying data of the given numpy array.
# importing the numpy library as npimport numpy as np # Create a numpy arrayarr = np.array([10, 20, 30, 40, 50]) # Print the arrayprint(arr) |
Output :
Now we will check the dtype of the given array object.
# Print the dtypeprint(arr.dtype) |
Output :
As we can see in the output, the current dtype of the given array object is ‘int32’. Now we will change this to ‘complex128’ type.
# change the dtype to 'complex128'arr = arr = arr.astype('complex128') # Print the array after changing# the data typeprint(arr) # Also print the data typeprint(arr.dtype) |
Output :
