Appending data to an existing array is a natural thing to want to do for anyone with python experience. However, if you find yourself regularly appending to large arrays, you'll quickly discover that NumPy doesn't easily or efficiently do this the way a python list will. You'll find that every "append" action requires re-allocation of the array memory and short-term doubling of memory requirements. So, the more general solution to the problem is to try to allocate arrays to be as large as the final output of your algorithm. Then perform all your operations on sub-sets (slices) of that array. Array creation and destruction should ideally be minimized.

That said, It's often unavoidable and the functions that do this are:

for 2-D arrays:

• np.hstack
• np.vstack
• np.column_stack
• np.row_stack

for 3-D arrays (the above plus):

• np.dstack

for N-D arrays:

• np.concatenate

Appending data to an existing array is a natural thing to want to do for anyone with python experience. However, if you find yourself regularly appending to large arrays, you'll quickly discover that NumPy doesn't easily or efficiently do this the way a python list will. You'll find that every "append" action requires re-allocation of the array memory and short-term doubling of memory requirements. So, the more general solution to the problem is to try to allocate arrays to be as large as the final output of your algorithm. Then perform all your operations on sub-sets (slices) of that array. Array creation and destruction should ideally be minimized.

That said, It's often unavoidable and the functions that do this are:

for 2-D arrays:

• np.hstack
• np.vstack
• np.column_stack
• np.row_stack

for 3-D arrays (the above plus):

• np.dstack

for N-D arrays:

• np.concatenate

Appending data to an existing array is a natural thing to want to do for anyone with python experience. However, if you find yourself regularly appending to large arrays, you'll quickly discover that NumPy doesn't easily or efficiently do this the way a python list will. You'll find that every "append" action require re-allocation of the array in memory and short-term doubling of memory requirements. So, the more general solution to the problem is to try to allocate arrays as large as the final output of the operation will ultimately be. Then perform all your operations on sub-sets (slices) of that array. Array creation and destruction should ideally be minimized.

That said, It's often unavoidable and the functions that do this are:

for 2-D arrays:

• np.hstack
• np.vstack
• np.column_stack
• np.row_stack

for 3-D arrays (the above plus):

• np.dstack

for N-D arrays:

• np.concatenate

Appending data to an existing array is a natural thing to want to do for anyone with python experience. However, if you find yourself regularly appending to large arrays, you'll quickly discover that NumPy doesn't easily or efficiently do this the way a python list will. You'll find that every "append" action requires re-allocation of the array memory and short-term doubling of memory requirements. So, the more general solution to the problem is to try to allocate arrays to be as large as the final output of your algorithm. Then perform all your operations on sub-sets (slices) of that array. Array creation and destruction should ideally be minimized.

That said, It's often unavoidable and the functions that do this are:

for 2-D arrays:

• np.hstack
• np.vstack
• np.column_stack
• np.row_stack

for 3-D arrays (the above plus):

• np.dstack

for N-D arrays:

• np.concatenate