Скачать или смотреть Understanding Numpy's save Method: Why a 0.33MB Array Becomes 100MB on Disk

Discover why Numpy's `save` function creates a large file for smaller data and learn how to efficiently save your Numpy arrays.
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Why Does Numpy's save Produce a 100MB File for 0.33MB Data?

If you've ever worked with Numpy in Python, you might have encountered a rather puzzling situation: your array takes up only 0.33MB of memory, but when you save it to disk, the output file is a whopping 100MB. So, what's going on here? Let's dive into this issue and unpack the reasons behind such significant size discrepancies, as well as explore ways to save your data more efficiently.

Understanding the Numpy Array

Before we tackle the file size issue, it's important to understand the array in question. In this case, the Numpy array arr has the following properties:

Shape: (14101, 6)

Data Type: dtype('O') (indicating the array is composed of objects)

Memory Size: 338424 bytes (approximately 0.33MB)

While the reported memory size suggests it should occupy little space, the truth lies in the array's data type and composition.

Why the Size Discrepancy?

The Numpy save function serializes the array into disk storage. Here are some key points to understand why the file size increases significantly:

Data Type: The data type dtype('O') suggests that the array contains Python objects rather than directly usable numerical values (like integers or floats). This leads to a higher overhead when saving because Numpy has to store metadata about each object.

Mismatched Dimensions: The significant size may also stem from the nested lists of mismatching dimensions. Even if they contain numerical data, the object type (dtype('O')) forces Numpy to allocate more space to manage this disparity during serialization.

Serialization Overhead: Numpy's saving process involves not only the raw data but also metadata associated with the array's structure and type, increasing the total file size.

How to Efficiently Save Numpy Arrays

So how can we save our Numpy arrays more efficiently and closely match the actual memory size? Here are a few strategic options:

1. Save with allow_pickle

Using the allow_pickle=True option only when necessary permits the serialization of Numpy arrays that are of object type. This can help preserve the array's properties while minimizing size overhead to some extent.

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2. Compress the Data

Compression significantly reduces the file size when saving. Numpy provides a straightforward way to achieve this:

Using np.savez_compressed: This function saves the arrays in a compressed .npz format.

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This approach dramatically cuts down file size, especially for arrays with uniform data. For example, an array containing lots of zeros can compress down to just a few kilobytes, as shown below:

File before compression: 488828 bytes

After compression: 2643 bytes

3. Consider Data Structure

If possible, evaluate and adjust how the data is structured before conversion to Numpy arrays:

Use standard numerical types (like np.float32 or np.int16) instead of object types for homogenous datasets.

Flatten or reshape nested lists to standardize dimensions.

Conclusion

In summary, while Numpy's save method is incredibly useful for persisting complex data structures, understanding how it serializes information can help you avoid excessive file sizes. Using options like allow_pickle judiciously or leveraging compression techniques can dramatically reduce your saved file sizes.

Next time you find your Numpy array expanding beyond its in-memory size, remember these strategies and optimize your data saving practices to achieve more efficient storage!