Some Tips for Python

NumPy Subarrays as No-copy Views vs Copy Views

print(x2)
# Output:
# [[12  5  2  4]
#  [ 7  6  8  8]
#  [ 1  6  7  7]]

x2_sub = x2[:2, :2]
print(x2_sub)
# Output:
# [[12  5]
#  [ 7  6]]
x2_sub[0, 0] = 99
print(x2_sub)
# Output:
# [[99  5]
#  [ 7  6]]

print(x2)
#Output:
# [[99  5  2  4]
#  [ 7  6  8  8]
#  [ 1  6  7  7]]

x2_sub_copy = x2[:2, :2].copy()
print(x2_sub_copy)
# Output:
# [[99  5]
#  [ 7  6]]
# If we now modify this subarray, the original array is not touched:
x2_sub_copy[0, 0] = 42
print(x2_sub_copy)
# Output:
# [[42  5]
#  [ 7  6]]
print(x2)
# Output: 
# [[99  5  2  4]
#  [ 7  6  8  8]
#  [ 1  6  7  7]]
  

This default behavior is actually quite useful: it means that when we work with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer.

Jake VanderPlas, Python Data Science Handbook