Create one or more references to an existing ndarray:
- To create one are more references to an existing numpy.ndarray object the assignement operator can be used.
- Once another reference of an existing array object is created any change made to the array through new reference affects the original contents of the array.
- The following example creates three references to an existing numpy.ndarray object.
Example: Creating multiple references to an existing ndarray object
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import numpy as np
# Create an ndarray object reference0 = np.arange(5) print("ndarray object printed through reference0:") print(reference0)
# Make two more references to the same ndarray object reference1 = reference2 = reference0
print("ndarray object printed through reference1:") print(reference1)
print("ndarray object printed through reference2:") print(reference2)
# Change ndarray contents through the reference1 reference1[0] = 10 print("ndarray contents changed through reference1 and printed through reference0:") print(reference0)
# Change the contents of the ndarray through the second reference2 reference2[1] = 20 print("ndarray contents changed through reference2 and printed through reference1:") print(reference1) |
- In the above Python program, the references reference0, reference1 and reference2 all point to the same ndarray object.
- Changes made through any of references reflect in the original ndarray object.
Output:
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ndarray object printed through reference0: [0 1 2 3 4] ndarray object printed through reference1: [0 1 2 3 4] ndarray object printed through reference2: [0 1 2 3 4] ndarray contents changed through reference1 and printed through reference0: [10 1 2 3 4] ndarray contents changed through reference2 and printed through reference1: [10 20 2 3 4] |
Create a deep copy of an ndarray object using : the slicing operator:
- A deep copy of ndarray elements can be created using the slicing operator.
- For this to work there should be an already existing target array with the same shape as the source array.
Example:
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import numpy as np
# Define a class class Container: myVal = 0
def __init__(self, val): self.myVal = val
def __repr__(self): return "%d"%(self.myVal)
def __str__(self): return "%d"%(self.myVal)
# Create container instances c0 = Container(10) c1 = Container(20) c2 = Container(30)
# Create an ndarray of containers array1 = np.ndarray(3, dtype='object')
array1[0] = c0 array1[1] = c1 array1[2] = c2
print("array1 contents:") print(array1)
array2 = np.ndarray(3, dtype='object') array2[:] = array1
array2[0] = 1 array2[1] = 2 array2[2] = 3
print("array2 contents after deep copy and modifications to it:") print(array2)
print("array1 contents after deep copy and modifications to array2:") print(array1)
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Output:
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array1 contents: [10 20 30] array2 contents after deep copy and modifications to it: [1 2 3] array1 contents after deep copy and modifications to array2: [10 20 30] |
Create a deep copy of the numpy.ndarray object using the copy() method:
- The copy method returns a deep copy of the numpy.ndarray object.
- While using the copy() method the memory layout can specified as any of the following using the parameter order:
- ’C’: ‘C’ Style or Row Major – meaning the row elements of the array are stored in subsequent array indices.
- ’F ’: Fortran Style or Column Major – meaning the column elements of the array are stored in subsequent array indices.
- ’A’: If the source array is Fortran style the same style will be applied. Else ‘C’ style will be applied.
- ’K ’: A memory layout that matches close to the memory layout of the source array.
Example:
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import numpy as np
# Define a class class Container: myVal = 0
def __init__(self, val): self.myVal = val
def __repr__(self): return "%d"%(self.myVal)
def __str__(self): return "%d"%(self.myVal)
# Create Container instances c0 = Container(10) c1 = Container(20) c2 = Container(30)
# Create an ndarray of Containers array1 = np.ndarray(3, dtype='object')
array1[0] = c0 array1[1] = c1 array1[2] = c2
print("Array1 contents:") print(array1)
array2 = array1.copy()
print("array1:") print(array2)
print("array2:") print(array1)
array2[0] = 9 array2[1] = 8 array2[2] = 7
print("Array2 contents after modifying its elements:") print(array2)
print("Array1 contents after modifying array2:") print(array1)
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Output:
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Array1 contents: [10 20 30] array1: [10 20 30] array2: [10 20 30] Array2 contents after modifying its elements: [9 8 7] Array1 contents after modifying array2: [10 20 30] |