How does dtype affect row and column operation speed in Numpy?

I'm trying to understand how to best utilize the C-ordering of numpy arrays to write high performance code. My expectation was that operations which traverse rows should be faster than those which traverse columns. Indeed, this was true for the first example I tried:

X = np.ones((10000,10000),dtype='int64')
print(X.dtype)
print(X.flags)

%timeit np.sum(X,axis=0)

%timeit np.sum(X,axis=1)

This produces output:

int64
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False
10 loops, best of 3: 79.6 ms per loop
10 loops, best of 3: 61.1 ms per loop

Which is what I expected, since summing along the rows should be faster than summing along the columns.

Here is where I get very confused. If I change the dtype to float64, then column operations become almost twice as fast as row operations:

X = np.ones((10000,10000),dtype='float')
print(X.dtype)
print(X.flags)

%timeit np.sum(X,axis=0)

%timeit np.sum(X,axis=1)

Produces output:

float64
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False
10 loops, best of 3: 67.7 ms per loop
10 loops, best of 3: 123 ms per loop

Can someone please clarify why this is happening?

EDIT: It was suggested in the comments that I try again with a smaller matrix, (1000,1000). When I run:

import time
import numpy as np

X = np.ones((1000,1000),dtype='float')
print(X.dtype)
print(X.flags)

%timeit np.sum(X,axis=0)
%timeit np.sum(X,axis=1)

X = np.ones((1000,1000),dtype='int64')
print(X.dtype)
print(X.flags)

%timeit np.sum(X,axis=0)
%timeit np.sum(X,axis=1)

I get output:

float64
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False
1000 loops, best of 3: 598 µs per loop
1000 loops, best of 3: 1.06 ms per loop
int64
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False
1000 loops, best of 3: 788 µs per loop
1000 loops, best of 3: 632 µs per loop

So the effect is persisting.