How do I access the ith column of a NumPy multidimensional array?

Diving deeper into columns

Let's not limit ourselves to accessing just one column at a time! What if we want to pull out multiple columns? Say hello to:

columns = array[:, [i, j]]
# To make it clearer: "Give me all rows, for columns at indices i and j"
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Reversal of roles: Transposing arrays

Ever wished rows could be columns and vice versa? Transposing has you covered:

transposed_array = array.T
# It's like turning your world sideways. Remember, useEffect()'s got nothing on us!
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Transposing creates a new "view" of your array, where rows become columns and vice versa. Rejoice, it’s memory-friendly!

Views, copies and the existential crisis

When you’re accessing a single column, usually you get a view. Wondering if your array's having an existential crisis—are you a view or a copy? Use the .base attribute to see the truth— None means it's a copy!

If you want to force an identity change, use .copy():

column_copy = array[:, i].copy()
# In case you want to go 'Oops, I did it again...', without affecting the original array.
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Turbocharge access with Fortran-style arrays

Performance nerds, this one's for you. Standard NumPy arrays are row-major (C order), but we can go column-major (F style) for a potential speed boost:

f_array = np.asfortranarray(array)
# When array walks into a salon and asks for a Fortran-style blowout!
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Stay tuned for performance gains during column operations!

Dealing with dimensions: How to reshape arrays

Got a pesky multidimensional structure that’s not a 2D matrix? Relax, reshaping will sort it out:

reshaped = array.reshape(-1, array.shape[-1])
# Feeling like an origami master, folding dimensions with style!
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Working with patterns and intervals

Break free from sequence monotony and play with intervals:

columns = array[:, start:end:step]
# "Every step you take, every move you make, I'll be indexing you."
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Regular intervals, irregular intervals — go wild!

Avoid the pitfalls

Watch your step, lest you stumble upon index errors. Keep your i, and other indices, safely within array dimensions. Be mindful that changing views can mutate the original array — use .copy() if you're commitment-averse.

Memory management matters

Large array, single column — sounds like a potential memory hog. Use np.take to prevent it:

preallocated_array = np.empty(array.shape[0], dtype=array.dtype)
np.take(array, indices=i, axis=1, out=preallocated_array)
# The memory equivalent of knowing size of your new apartment before buying furniture.
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