When working with arrays in NumPy, it's crucial to understand the difference between copies and views. Misunderstanding this concept can lead to bugs, memory inefficiencies, or unintended changes in your data.

In this article, you will learn:

• What a copy and a view are in NumPy

• How to create them

• How they behave differently

• Practical examples with code

• Tips and common mistakes to avoid

What Is a Copy in NumPy?

A copy of a NumPy array is a completely independent array that holds its own data in memory.

Changes to the copy do not affect the original array.

✅ Example: Copy

import numpy as np

a = np.array([1, 2, 3])
b = a.copy()

b[0] = 100

print("Original array:", a)  # [1 2 3]
print("Copied array:", b)    # [100 2 3]

As you can see, modifying b does not change a.

What Is a View in NumPy?

A view is a new array object that looks at the same data as the original array.

Changes made to a view will affect the original array (and vice versa), since they share the same memory.

✅ Example: View

a = np.array([1, 2, 3])
b = a.view()

b[0] = 100

print("Original array:", a)  # [100 2 3]
print("Viewed array:", b)    # [100 2 3]

Here, changing b directly changed a—because both reference the same data.

How to Check if Two Arrays Share Memory

You can use np.shares_memory() to verify whether two arrays share the same memory space.

a = np.array([1, 2, 3])
b = a.view()
c = a.copy()

print(np.shares_memory(a, b))  # True
print(np.shares_memory(a, c))  # False

Slicing Usually Returns a View

Slicing a NumPy array usually returns a view, not a copy.

a = np.array([10, 20, 30, 40])
b = a[1:3]  # This is a view

b[0] = 99

print("Original array:", a)  # [10 99 30 40]
print("Sliced view:", b)     # [99 30]

To avoid this, use .copy() explicitly:

b = a[1:3].copy()

Summary Table: Copy vs View

Real-World Example

Let’s say you're normalizing a dataset:

data = np.array([100, 200, 300])

# You want a new normalized version, not affecting the original
normalized = data.copy()
normalized = normalized / np.max(normalized)

print("Original:", data)       # [100 200 300]
print("Normalized:", normalized)  # [0.333 0.666 1.0]

Without .copy(), your original data would be unintentionally modified.

Tips and Best Practices

⚠️ Common Pitfalls

✅ Full Working Example

import numpy as np

# Original array
original = np.array([1, 2, 3, 4, 5])

# Create a view
viewed = original[1:4]
viewed[0] = 99

print("Original after view modification:", original)  # [1 99 3 4 5]

# Create a copy
copied = original[1:4].copy()
copied[0] = 77

print("Original after copy modification:", original)  # [1 99 3 4 5]
print("Copied version:", copied)                      # [77 3 4]

# Check memory sharing
print("View shares memory?", np.shares_memory(original, viewed))  # True
print("Copy shares memory?", np.shares_memory(original, copied))  # False

Conclusion

Understanding the difference between copy and view in NumPy is essential for writing safe and efficient numerical code. Whether you want to save memory with views or isolate data with copies, knowing when and how to use them can save hours of debugging and performance tuning.

What’s Next?

Now that you've mastered copy vs view, explore:

• NumPy Broadcasting

• Advanced Indexing

• Memory Layout with np.flags

• Structured Arrays and Custom dtypes