Transposing a 1D NumPy array

Learning transpose and dimensionality

The transposing of a 1D NumPy array can be somewhat tricky. Calling .T on it might leave you scratching your head, wondering why it returned... well, exactly the same 1D array. The punchline? Transpose is only relevant for dimensions two and up. Yeah, tech humor is niche, but we all have our quirks.

Introducing new dimension: np.newaxis

Enter our hero, the underrated np.newaxis. This powerful tool slices your array, not lethally, but dimensionally. So, np.newaxis is more like a dimension chiropractor, fixing the shape of your arrays:

Converting array into column vector:

column_vector = array_1d[:, np.newaxis]
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Converting array into row vector:

row_vector = array_1d[np.newaxis, :]
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Broadcasting: the invisible magic

Broadcasting in NumPy is an incredibly efficient way to handle arrays that want to feel equal, but aren't. It allows you to perform operations between arrays of different--sometimes wildly different--shapes. Just remember, while broadcasting has its charms, you still have to keep a close eye on the wand (read: your arrays' dimensions).

The deprecated knight: np.matrix

Another less common and less recommended way of transposing a 1D array is by using np.matrix:

matrix = np.matrix(array_1d)
transposed_matrix = matrix.T
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Feel special, np.matrix still works but remember it's like using an old Nokia in an iPhone era—limited and outclassed by the nimbler np.array.

More tools in your arsenal

While .reshape and np.newaxis are your go-to strategies for transposing 1D arrays, let's explore more ways to achieve this. You know, just to show off at coding parties.

Forcing into 2D using np.atleast_2d

To ensure that an array has at least two dimensions, NumPy offers np.atleast_2d():

two_d_array = np.atleast_2d(array_1d)
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Mind you, it doesn't transpose, but really just wants everything in 2D.

Misconception about np.transpose

Some might try to use np.transpose() to transpose a 1D array, but here's the gag—np.transpose treats 1D arrays like .T — it's just not dimensionally transformative.

Checking for your own sanity: .shape

And of course, don't forget the shape attribute of your arrays. Familiarize yourself; it's a quick reality check for all your dimension-bending shenanigans:

print(array_1d.shape)         # (n,)
print(transposed_array.shape) # (n, 1)
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Old school: Double brackets [[]]

Turns out, you can meet the old school cool of NumPy using double brackets [[ ]] to manually convert a 1D list to a 2D array:

two_d_array = np.array([[x] for x in array_1d])
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It achieves the same thing as .reshape(-1, 1) or np.newaxis.