# Rank 1 array:
vector.shape = (n,)  # Not a row, not a column either, can't .T it

dataframe[column] => (n,)
dataframe[[column]] => (n,1)

vector.flatten # produce a rank 1 array

vector.reshape(-1, 1) # produces a column vector. With -1, numpy calculate itself the length of the column

np.expand_dims(image, 2) # Add a 3rd dimension, from (a,b) to (a,b,1)

# Check size
assert(w.shape == (dim, 1))

# Check type
assert(isinstance(b, float) or isinstance(b, int))

# Warning with reshape
a.reshape(a.shape[0], -1).T  != a.reshape(-1, a.shape[0]) # not sure why, but when it reshapes, it has do to it in some order, which might not be the one we want