How to Convert a List to numpy.void in Python

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Let's say you have the following data represented in two lists — one containing boolean status values and the other containing survival times in days:

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

Ideally, you want to convert these lists into a structured NumPy array that consists of tuples, where each tuple contains a Status (True/False) and Survival_in_days. However, if you try to convert your lists directly into a NumPy array without proper format, you may encounter problems or unexpected outcomes, such as an error message or a poorly formatted array.

The Solution: Creating a Structured NumPy Array

Step 1: Define Your Data

Start by defining your list of tuples. Structure your data as required for the conversion:

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

Step 2: Specify the Data Type

Next, you need to define the data type (dtype) of the structured array you want to create. You can do this using NumPy’s dtype function:

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

Here, ? denotes a boolean type for the Status, and <f8 denotes a float of 64 bits for Survival_in_days.

Step 3: Convert to a NumPy Array

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

Example Output

When executed correctly, you should see output similar to this:

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

Wrapping It Up

Summary

In this guide, we covered the essential steps involved in converting plain Python lists into structured NumPy arrays. Here are the quick takeaways:

Define Your Data: Create a list of tuples that represent your desired structure.

By following these steps, you can easily manipulate lists and achieve your desired data structure in NumPy for further scientific calculations or data analysis.