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Return an array of zeroes with the same shape as a given array but with a different type in Numpy
To return an array of zeroes with the same shape as a given array but with a different type, use the numpy.zeros_like() method in Python Numpy. The 1st parameter here is the shape and data-type of array-like that define these same attributes of the returned array. The 2nd parameter is the dtype i.e. the data-type we want for the resultant array.
The dtype overrides the data type of the result. The order parameter overrides the memory layout of the result. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. The subok parameter, if True, then the newly created array will use the sub-class type of a, otherwise it will be a base-class array. Defaults to True.
Steps
At first, import the required library −
import numpy as np
Create a new array using the numpy.array() method in Python Numpy −
arr = np.array([[35, 56, 66], [88, 73, 98]])
Display the array −
print("Array...
",arr)Get the type of the array −
print("
Array type...
", arr.dtype)
Get the dimensions of the Array −
print("
Array Dimensions...
", arr.ndim)To return an array of zeroes with the same shape as a given array but with a different type, use the numpy.zeros_like() method in Python Numpy. The 2nd parameter is the dtype i.e. the data-type we want for the resultant array −
newArr = np.zeros_like(arr, dtype = float)
print("
New Array..
", newArr)Get the type of the new array −
print("
New Array type...
", newArr.dtype)
Get the dimensions of the new array −
print("
New Array Dimensions...
", newArr.ndim)Example
import numpy as np
# Create a new array using the numpy.array() method in Python Numpy
arr = np.array([[35, 56, 66], [88, 73, 98]])
# Display the array
print("Array...
",arr)
# Get the type of the array
print("
Array type...
", arr.dtype)
# Get the dimensions of the Array
print("
Array Dimensions...
", arr.ndim)
# To return an array of zeroes with the same shape as a given array but with a different type, use the numpy.zeros_like() method in Python Numpy
# The 1st parameter here is the shape and data-type of array-like that define these same attributes of the returned array.
# The 2nd parameter is the dtype i.e. the data-type we want for the resultant array
newArr = np.zeros_like(arr, dtype = float)
print("
New Array..
", newArr)
# Get the type of the new array
print("
New Array type...
", newArr.dtype)
# Get the dimensions of the new array
print("
New Array Dimensions...
", newArr.ndim)Output
Array... [[35 56 66] [88 73 98]] Array type... int64 Array Dimensions... 2 New Array.. [[0. 0. 0.] [0. 0. 0.]] New Array type... float64 New Array Dimensions... 2
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