I know in R there is a rbind function:
list = c(1,2,3)
blah = NULL
blah = rbind(blah,list)
How would I replicate this in python? I know you could possible write:
a = NULL
b= array([1,2,3])
for i in range(100):
a = np.vstack((a,b))
but I am not sure what to write in the a=NULL spot. I am essentially looping and adding rows to a table. What's the most efficient way to do this?
In numpy, things will be more efficient if you first pre-allocate space and then loop to fill that space than if you dynamically create successively larger arrays. If, for instance, the size is 500, you would:
a = np.empty((500, b.shape[0]))
Then, loop and enter values as needed:
for i in range(500):
a[i,:] = ...
Note that if you really just want to repeat b 500 times, you can just do:
In [1]: import numpy as np
In [2]: b = np.array([1,2,3])
In [3]: a = np.empty((500, b.shape[0]))
In [4]: a[:] = b
In [5]: a[0,:] == b
Out[5]: array([ True, True, True], dtype=bool)
To answer your question exactly
a = []
b= np.array([1,2,3])
for i in xrange(100):
a.append(b)
a = np.vstack( tuple(a) )
The tuple function casts an iterable (in this case a list) as a tuple object, and np.vstack takes a tuple of numpy arrays as argument.
