np.concatenate() do in NumPy?np.concatenate() joins two or more arrays into one bigger array along a specified axis.
np.concatenate()?The default axis is axis=0, which means arrays are joined row-wise (stacked vertically).
np.concatenate() join arrays of different shapes?No, arrays must have the same shape except in the dimension along the joining axis.
a = [1, 2] and b = [3, 4] using np.concatenate()?Use np.concatenate([a, b]) to get [1, 2, 3, 4].
axis=1 when concatenating 2D arrays?Arrays are joined column-wise (side by side), increasing the number of columns.
np.concatenate([np.array([1,2]), np.array([3,4])])?Arrays are joined end-to-end along axis 0 by default, resulting in a 1D array [1 2 3 4].
np.concatenate()?axis=1 joins arrays column-wise (side by side) in 2D arrays.
Along axis=0, arrays must have the same number of columns. Here, 3 != 4, so no.
np.concatenate()?np.concatenate() requires a list or tuple of arrays to join.
Arrays must match in all dimensions except the concatenation axis; otherwise, an error occurs.
np.concatenate() works and what rules arrays must follow to be joined.np.concatenate() to combine data.