Resolving the numpy Data Type Issue with tofile and fromfile

Discover why using `numpy` to save and load arrays can lead to unexpected results regarding data shape and types, and learn the best practices to avoid these pitfalls.
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Understanding the Behavior of Python numpy's tofile and fromfile Methods

When working with data using numpy, you may encounter unexpected behavior when saving and loading arrays. A common issue arises when using the tofile and fromfile methods, especially concerning data types and dimensions. This post explores a specific situation where the returned shape of an array increases unexpectedly after writing and reading it back from a binary file.

The Problem: Unexpected Shape Discrepancy

Let's look at an example in which a user appends values to an empty array and saves it to a binary file. The results before and after reading from the file revealed a shocking difference in shape:

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

In the above code, rx_data shows a shape of (200) before saving but returns (800) when loaded back. Let's dive into why this discrepancy occurs.

Solution: Understanding Data Types and Pre-Allocation Best Practices

Issue 1: Data Type Promotion

The first key factor in this situation is the data type of rx_data. Initially, rx_data is an empty array with int16 type. However, when we append the float64 sine and cosine values, numpy automatically promotes the array type to accommodate the floating-point numbers.

Recommended Approach

Here is how you can handle this properly by pre-allocating the array:

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

Conclusion

By ensuring that you understand data types and avoid inefficient array modifications, you can effectively manage your data with numpy. Preallocating your array not only improves performance but also aligns the expected behavior when saving and loading data, preventing any unforeseen complications.

Follow these practices, and you will have a smoother experience while handling data arrays with numpy.