Alright, one of my favorite topics memory layout! Specifically, how Python lets us peek behind the curtain with its view object. But first, a little background on why this is so ***** cool.
When you create an array in Python (using `numpy` or otherwise), it’s stored as contiguous blocks of memory. This means that all the data for that array lives next to each other in your computer’s RAM. But what if we want to access only a portion of that array? Or maybe we have two arrays with overlapping sections, and we don’t want to copy all that redundant data around.
That’s where Python’s view object comes in! A view is essentially a window into an existing array or other container, allowing us to access only the parts of it that we need without creating any new memory. This can be incredibly useful for performance and efficiency, especially when dealing with large datasets.
So how do you create a view? Let’s say we have two arrays: `a` and `b`. We want to extract a slice from `a`, but instead of copying that data into a new array (which would be inefficient), let’s use a view object! Here’s the code:
# Import the numpy library
import numpy as np
# Create an array `a` with values from 0 to 9
a = np.arange(10)
# Create an array `b` by slicing `a` from index 3 to 7 and adding 5 to each element
b = a[3:8] + 5
# Create a view object `c_view` by slicing `a` from index 2 to 7
c_view = a[2:7]
# Assign the view object `c_view` to a new variable `c`
c = c_view
# The purpose of creating a view object is to avoid copying the data from `a` into a new array, which would be inefficient when dealing with large datasets. Instead, the view object allows us to access and manipulate the data in `a` without creating a new array. This improves performance and efficiency.
In this example, we’re creating an array called `b` that is a slice of `a`. But instead of copying that data into a new array (which would be inefficient), we’re using the view object to access only the parts of `a` that we need. This means that any changes made to either `c_view` or `c` will affect both arrays, since they are essentially pointing at the same memory location!
But what if we want to modify just one array without affecting the other? That’s where slicing comes in handy:
# Create an array `a` with values 1, 2, 3
a = [1, 2, 3]
# Create a view object `c_view` that references the same memory location as `a`
c_view = a
# Create a copy of `a` called `b` using the `list()` function
b = list(a)
# Create a copy of `a` called `c` using the `copy()` method
c = a.copy()
# Modify only the view object (not the original array) by changing the first element to 100
c_view[0] = 100
# Print the values of `a`, `b`, and `c` to see the effects of modifying the view object
print(a, b, c)
# Output: [100, 2, 3] [1, 2, 3] [1, 2, 3]
# Explanation: By modifying the view object `c_view`, we are also changing the values of `a` since they are referencing the same memory location. This is because `c_view` is essentially a pointer to `a`. However, `b` and `c` are copies of `a` and therefore remain unchanged.
# If we want to modify just one array without affecting the other, we can use slicing:
# Create a new array `d` with the same values as `a`
d = a[:]
# Modify the first element of `d` to 200
d[0] = 200
# Print the values of `a` and `d` to see the effects of slicing
print(a, d)
# Output: [100, 2, 3] [200, 2, 3]
# Explanation: By using slicing, we are creating a new array `d` with the same values as `a` but stored in a different memory location. Therefore, modifying `d` does not affect the values of `a`. This allows us to modify just one array without affecting the other.
In this example, we’re modifying just the `c_view` array. Since it’s a view of `a`, any changes made to `c_view` will also affect `c`. But since we didn’t modify `a` directly (only through its view object), `b` remains unchanged!
It may not be the most exciting topic, but it can save us a ton of time and resources when dealing with large datasets or overlapping arrays. And who doesn’t love saving time and resources?