Скачать или смотреть Understanding the Memory Storage of numpy Arrays

Dive into how `numpy` stores arrays in memory, particularly the relationship between arrays and their views to clarify what happens behind the scenes.
---
This video is based on the question https://stackoverflow.com/q/68285640/ asked by the user 'affan skm' ( https://stackoverflow.com/u/13884293/ ) and on the answer https://stackoverflow.com/a/68289165/ provided by the user 'hpaulj' ( https://stackoverflow.com/u/901925/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: how numpy arrays are stored in memory locations?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding the Memory Storage of numpy Arrays

When working with numpy arrays in Python, one common question that arises is: How are these arrays stored in memory? Understanding how memory storage works allows for more efficient coding and effective memory management when handling large datasets. In this article, we will explore just that, digging into the concept of memory locations of numpy arrays and views, which play a crucial role in how data is accessed and manipulated.

The Basics of numpy Array Storage

numpy is a powerful library in Python that provides substantial support for multidimensional arrays and matrices. Unlike standard Python lists, numpy arrays are more efficient in storage and performance because they are stored in contiguous memory blocks.

Creation of a numpy Array

Let’s start by creating a simple numpy array for demonstration:

[[See Video to Reveal this Text or Code Snippet]]

In the above code, a is a numpy array, and b is its transpose. The transpose does not create a new array but rather creates a view of the original array a — meaning it uses the same data buffer in memory.

Memory Locations Explained

To see how these arrays relate in terms of memory, we can utilize the _array_interface_ attribute of a numpy array. This will provide us with information about the data location and how it is structured.

Checking Memory Locations

When we execute the following code snippet:

[[See Video to Reveal this Text or Code Snippet]]

The output will indicate that both a and b share the same data buffer:

[[See Video to Reveal this Text or Code Snippet]]

This output confirms that a and b both point to the same memory location for their data (74597280), although they have different shapes and strides for data access.

Understanding Views

To clarify, what we have here is a view of the original array. The variable b utilizes the same data buffer but can be manipulated to present the data in a different shape, in this case, transposing it.

Example of Identifying Memory IDs

When we print the IDs of the elements in a and b, we observe:

[[See Video to Reveal this Text or Code Snippet]]

This lets us see how numpy arrays manage their elements differently compared to Python lists. The output may look something like this:

[[See Video to Reveal this Text or Code Snippet]]

You will notice that alternate integers may have the same ID (memory location) for the transposed array b, illustrating how numpy optimizes memory storage through this shared buffer.

Conclusion

The way numpy handles memory locations for arrays and their associated views is pivotal in optimizing performance and efficiency, particularly when manipulating large datasets. By understanding that transposing an array does not create a copy but a view with the same underlying data buffer, you can write more efficient code and manage memory effectively.

Utilizing numpy correctly can lead to significant performance improvements in computational tasks, making it an essential tool for data scientists and engineers.

In summary, keep in mind that:

numpy arrays are stored in continuous memory.

Transposing creates a view, not a new array.

The shared buffer for data reduces memory consumption.

Now that you're familiar with how numpy arrays are stored in memory, you can better leverage this knowledge in your projects. Happy coding!