NumPy Basics: Arrays and Vectorized Computation

What is the primary function of the NumPy ndarray object as described in the chapter?

What does the `dtype` attribute of a NumPy ndarray represent?

Consider the following code: `import numpy as np; arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])`. What will be the output of `arr.ndim`?

How does slicing a NumPy array differ from slicing a standard Python list regarding data copying?

Given the code: `import numpy as np; arr = np.arange(10); arr_slice = arr[5:8]; arr_slice[1] = 12345`. What is the value of `arr[6]`?

Which operator should be used to perform a logical AND operation when combining multiple Boolean conditions on NumPy arrays?

What is the purpose of the `seed` argument when creating a random number generator with `np.random.default_rng(seed=12345)`?

What is a universal function (or ufunc) in NumPy?

What does the `numpy.modf` ufunc return when applied to a floating-point array?

What is the primary function of `numpy.where`?

When using a statistical method like `arr.mean(axis=1)` on a 2D NumPy array, what is being computed?

What is the key difference between the `arr.sort()` method and the `numpy.sort(arr)` function?

Which pair of NumPy functions is used for saving and loading a single array in NumPy's uncompressed raw binary format?

For two 2D NumPy arrays, `mat1` and `mat2`, which expression correctly performs matrix multiplication?

In the context of the random walk simulation, what does calling `.argmax()` on a Boolean array like `(np.abs(walk) >= 10).argmax()` compute?

What is the result of casting a float array to an integer dtype using `astype(np.int32)`?

What is the primary characteristic of 'fancy indexing' in NumPy?

If `arr` is a NumPy array, what is the effect of the `.T` attribute?

When comparing the performance of NumPy array operations to pure Python operations on lists for large sequences, what is the typical observation?

What is the output of the following code? `import numpy as np; arr = np.array([3.7, -1.2, -2.6, 0.5, 12.9, 10.1]); print(arr.astype(np.int32))`

In a 2D NumPy array `arr2d`, what does the indexing expression `arr2d[0, 2]` select?

What is the result of selecting data from an array using Boolean indexing, such as `data[names == 'Bob']`, in terms of data copying?

What is the function of `numpy.unique` on a 1D ndarray?

What happens when you pass multiple index arrays to a NumPy array, like `arr[[1, 5, 7, 2], [0, 3, 1, 2]]`?

Which `numpy.linalg` function is used to compute the inverse of a square matrix?

What does the `any()` method check for in a Boolean array?

What is 'vectorization' as the term is used in the context of NumPy arrays?

Given `arr = np.arange(32).reshape((8, 4))`, what is the result of `arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]]`?

What does the `swapaxes` method, for example `arr.swapaxes(0, 1)`, do on an ndarray?

How can you create a 3x6 NumPy array filled with zeros?

What is the primary risk of using the `numpy.empty` function to create an array?

What does the NumPy function `np.arange(15)` produce?

What is the result of the expression `arr.cumsum(axis=1)` on a 2D array `arr = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])`?

If `numeric_strings = np.array(['1.25', '-9.6', '42'], dtype=np.string_)`, which method converts this array to a numeric floating-point form?

According to the chapter, why is it advisable to use `import numpy as np` instead of `from numpy import *`?

In a comparison between arrays of the same size, such as `arr2 > arr`, what is the resulting object?

When simulating many random walks at once, if `nwalks = 5000` and `nsteps = 1000`, what is the shape of the `walks` array created by `steps.cumsum(axis=1)`?

What is the purpose of the `out` argument in some ufuncs, such as `np.add(arr, 1, out=out_array)`?

What would `np.in1d(values, [2, 3, 6])` return if `values` is `np.array([6, 0, 0, 3, 2, 5, 6])`?

When saving multiple arrays to a single file archive, which function should be used?

If `arr = np.array([[1., 2., 3.], [4., 5., 6.]])`, what is the result of `1 / arr`?

In the `numpy.random` module, how does `shuffle` differ from `permutation`?

What is the function `np.meshgrid` typically used for, as shown in the example in Section 4.4?

If `arr` is a NumPy array, what is the key difference between using fancy indexing (`arr[[4, 3, 0]]`) and slicing (`arr[0:4]`) regarding data copying?

What does the `logical_not` ufunc compute?

Consider the code `arr = np.array([False, False, True, False])`. What will `arr.all()` and `arr.any()` return, respectively?

When sorting a multidimensional array in-place along an axis, for example `arr.sort(axis=0)`, what is the behavior?

What is the key advantage of NumPy's array-oriented computing approach, exemplified by its C-based algorithms?

If `arr` is a 5x4 array, what is the shape of the result of `arr.mean(axis=0)`?

What is the purpose of the `svd` function in the `numpy.linalg` module?