Скачать или смотреть How to Convert a Numpy Array of Arrays to Float64 Instead of Object

Learn how to properly handle a Numpy array of varying-length float arrays and ensure the dtype is float64 by using effective padding techniques.
---
This video is based on the question https://stackoverflow.com/q/68969348/ asked by the user 'Gandharv Bakshi' ( https://stackoverflow.com/u/14218396/ ) and on the answer https://stackoverflow.com/a/68969398/ provided by the user 'Mad Physicist' ( https://stackoverflow.com/u/2988730/ ) 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: numpy array of numpy arrays of float numbers becoming an 'object' instead of 'float'

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 Issue: Numpy Array of Arrays as 'Object'

When working with Numpy arrays, one common challenge arises when dealing with lists of arrays containing float numbers of varying lengths. If you find that your Numpy array is showing a dtype of 'object' instead of the desired 'float64', it's likely due to the differing lengths of these arrays.

In this post, we will explore a scenario where a user has appended several float arrays of varying lengths into one larger Numpy array but ended up with an unexpected dtype. We’ll also discuss how to resolve the issue by converting the array back to 'float64', enabling further numerical operations, particularly in contexts like audio classification where precision is key.

The Problem in Depth

Imagine you have multiple arrays representing sound samples:

A = [1.0, 2.0]

B = [3.0]

C = [5.0, 6.0]

When appending these arrays together into a single Numpy array, the resulting dtype might unexpectedly switch to 'object' due to the different lengths of these arrays. This can lead to complications, especially when numerical computations are needed.

Here's a brief view of the code that causes this issue:

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

This is an example of how the varying lengths of B can impact the dtype of the combined array.

The Solution: Properly Padding Arrays

Method 1: Pre-Allocating the Array

One efficient way to handle this issue is to pre-allocate the combined array based on the maximum length of your input arrays. Here’s how you do it:

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

Here, shape defines the dimensions of the new array, and zeros are filled into the positions where the original arrays have no values, ensuring a consistent dtype of float64.

Method 2: Padding Individual Arrays Before Conversion

If for some reason you have already created an array, you can also pad each individual array before converting them to a float array:

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

By concatenating zero arrays to the shorter lengths, you can ensure that each element in your array now has a consistent length, which allows Numpy to infer the correct dtype of float64.

Key Takeaways

Understanding Dtypes: Be aware that differing lengths in arrays lead to 'object' types in Numpy.

Pre-allocation is Key: Specify the shape of your array upfront to maintain dtype consistency.

Padding Options: Use padding strategies either post-creation or during initialization to cater to varying lengths of arrays.

Tackling dtype issues with Numpy can seem tricky at first, but with the right approach, you can efficiently manage arrays of varying lengths without losing the desired float characteristics.

Feel free to leave any comments or questions below about your experiences with similar issues or about handling Numpy arrays!