Identity Operators

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What are Identity Operators in Python?

Python has two identity operators:

Operator	Meaning
is	True if both variables point to the same object in memory
is not	True if variables point to different objects in memory

These operators don’t compare values, they compare object identity (location in memory).

Why Identity Operators Matter in Machine Learning

In Machine Learning:

• We often deal with large datasets (numpy, pandas) — copying or referencing matters.

• We deal with model objects — checking if two models are the same instance.

• Helps prevent unexpected bugs when working with shared memory/data.

Step-by-Step: Identity Operators with Examples

Step 1: is and is not basics

x = [1, 2, 3]
y = x
z = [1, 2, 3]

print(x is y)     # True (y points to x's object)
print(x is z)     # False (different object, same value)
print(x == z)     # True (same value)

Note:

• is: checks identity (memory location)

• ==: checks value

Step 2: Checking id() to understand memory address

print(id(x))  # Memory address of x
print(id(y))  # Same as x
print(id(z))  # Different from x and y

Step 3: Practical Example with numpy arrays

import numpy as np

a = np.array([1, 2, 3])
b = a           # b references a
c = a.copy()    # c is a new object

print(a is b)       # True
print(a is c)       # False
print(np.array_equal(a, c))  # True (same values)

copy() creates a new array in memory — which is very important in data preprocessing, so you don’t modify your original dataset unintentionally.

Step 4: Identity check in ML model objects

from sklearn.linear_model import LogisticRegression

model1 = LogisticRegression()
model2 = model1
model3 = LogisticRegression()

print(model1 is model2)  # True
print(model1 is model3)  # False

When comparing models (e.g., during pipeline debugging), use is to check if they’re truly the same instance.

Step 5: Example in a Function (Avoiding Side Effects)

def modify_array(arr):
    if arr is original_array:
        print("You are modifying the original data!")
    else:
        print("You are safe.")
    arr[0] = 999

original_array = np.array([1, 2, 3])
modify_array(original_array)            # Warning: modifying original
modify_array(original_array.copy())     # Safe

In Machine Learning:

• Changing training data accidentally happens often.

• Use .copy() and is to protect your original datasets.

Summary of Identity Operators

Use Case	Operator	Result
Same memory location?	is	True or False
Different memory locations?	is not	True or False
Check values	==	Don't confuse with is

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