Fastest way to grow a numpy numeric array

np.append() copy all the data in the array every time, but list grow the capacity by a factor (1.125). list is fast, but memory usage is larger than array. You can use array module of the python standard library if you care about the memory.

Here is a discussion about this topic:

How to create a dynamic array

I tried a few different things, with timing.

import numpy as np

1. The method you mention as slow: (32.094 seconds)

   class A:
   
       def __init__(self):
           self.data = np.array([])
   
       def update(self, row):
           self.data = np.append(self.data, row)
   
       def finalize(self):
           return np.reshape(self.data, newshape=(self.data.shape[0]/5, 5))
   

2. Regular ol Python list: (0.308 seconds)

   class B:
   
       def __init__(self):
           self.data = []
   
       def update(self, row):
           for r in row:
               self.data.append(r)
   
       def finalize(self):
           return np.reshape(self.data, newshape=(len(self.data)/5, 5))
   

3. Trying to implement an arraylist in numpy: (0.362 seconds)

   class C:
   
       def __init__(self):
           self.data = np.zeros((100,))
           self.capacity = 100
           self.size = 0
   
       def update(self, row):
           for r in row:
               self.add(r)
   
       def add(self, x):
           if self.size == self.capacity:
               self.capacity *= 4
               newdata = np.zeros((self.capacity,))
               newdata[:self.size] = self.data
               self.data = newdata
   
           self.data[self.size] = x
           self.size += 1
   
       def finalize(self):
           data = self.data[:self.size]
           return np.reshape(data, newshape=(len(data)/5, 5))
   

And this is how I timed it:

x = C()
for i in xrange(100000):
    x.update([i])

So it looks like regular old Python lists are pretty good ;)