NumPy Array Copy vs View

🆚 NumPy — Copy vs View

When working with NumPy arrays, modifying data may or may not affect the original array depending on whether you’re using a copy or a view.

Feature Copy View
Separate memory? ✅ Yes ❌ No
Changes affect original? ❌ No ✅ Yes
Faster? ❌ Slightly slower ✅ Faster
Use case When original data must be preserved Memory-efficient slicing & referencing

 1. Copy (Creates a New Array)

A copy creates a completely independent array.


 

Output:

[10 20 30 40]
[99 20 30 40]

 2. View (Shares Data With Original)

A view does NOT create a new memory; it references the original data.


 

Output:

[10 88 30 40]
[10 88 30 40]

🎯 Checking the Relationship: base Attribute

You can check whether an array owns its data using the .base property.


 Example with 2-D Arrays


 

Result: Both change because vw2 is a view.

 Slicing Creates a View (Not Copy)


 

 To Force a Copy of a Slice


 

🧠 When to Use What?

Use Case Best Option
Working with large datasets View (memory efficient)
Need independent data Copy
Slicing for temporary use View
Prevent accidental modification of original data Copy

📝 Summary

Feature Copy View
Memory usage More Less
Speed Slower Faster
Shares data ❌ No ✅ Yes
Uses .copy()? Yes No (views usually created by slicing)

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