



Can you explain the use of axis 1 in working with numerical optimization algorithms implemented with NumPy
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what is axis 1 in numpy Informational
- How does axis 1 behave with arrays that have been created using functions that inherently operate along specific axes (e.g., `np.diff`, `np.cumsum`)
- What are some interactive ways to visualize the effect of axis 1 operations in NumPy
- Can you provide examples of how axis 1 is used with different data types in NumPy
- How does axis 1 behave with arrays that are views of other arrays with different shapes
- What is the output shape when you perform a reduction operation along axis 1
- How does axis 1 behave with arrays containing infinite or NaN values during reductions
- How does axis 1 relate to the concept of data dimensionality reduction while preserving certain information
- How does axis 1 behave with arrays that represent probability distributions or statistical samples
- What are some advanced techniques involving axis 1 in NumPy
- What are some strategies for handling situations where the desired operation needs to be performed along a non-standard axis
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