Python numpy zeros array being assigned 1 for every value when only one index is updated

Title: Understanding Numpy Arrays: Modifying a Single Index and the Implications
Introduction:
Firstly, make sure to import the numpy library:
Output:
Now, let's modify a single index of the array:
Output:
However, it's essential to note that the data type of the array is maintained. In the example above, the original array was created with floating-point zeros, so when we assigned 1 to the index 2, it became a floating-point 1. If the array was created with integer zeros, the updated value would also be an integer.
Output:
Conclusion:
Understanding how numpy arrays behave when a single index is updated is crucial for proper data manipulation. Whether you are working with floating-point or integer arrays, numpy provides a flexible and efficient way to handle numerical data in Python.
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