Cython can improve the speed with 1d numpy array in a IPython notebook

cython_array_loop.ipynb

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    "%%cython\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "\n",
    "def remove_nan(np.ndarray['double', ndim=1] x_1d):\n",
    "    cdef double y = 0\n",
    "    cdef int i\n",
    "    for i in range(x_1d.shape[0]):\n",
    "        y += x_1d[i]\n",
    "    return y\n",
    "\n",
    "def remove_nan_r0(np.ndarray['double', ndim=1] x_1d):\n",
    "    cdef double y = 0\n",
    "    cdef double x = 0\n",
    "    for x in x_1d:\n",
    "        y += x\n",
    "    return y\n",
    "\n",
    "def cy_remove_nan(x_1d):\n",
    "    y = 0\n",
    "    for x in x_1d:\n",
    "        y += x\n",
    "    return y"
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    "# Python\n",
    "def py_remove_nan(x_1d):\n",
    "    y = 0\n",
    "    for x in x_1d:\n",
    "        y += x\n",
    "    return y"
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      "12.506229871147564\n",
      "12.506229871147564\n",
      "12.5062298711\n",
      "12.5062298711\n",
      "The slowest run took 7.17 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "1000000 loops, best of 3: 1.56 µs per loop\n",
      "10000 loops, best of 3: 69.4 µs per loop\n",
      "10000 loops, best of 3: 129 µs per loop\n",
      "10000 loops, best of 3: 168 µs per loop\n"
     ]
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   "source": [
    "x_1d = np.random.randn( 1000)\n",
    "y = remove_nan(x_1d); print(y)\n",
    "y = remove_nan_r0(x_1d); print(y)\n",
    "y = cy_remove_nan(x_1d); print(y)\n",
    "y = py_remove_nan(x_1d); print(y)\n",
    "\n",
    "%timeit remove_nan(x_1d)\n",
    "%timeit remove_nan_r0(x_1d)\n",
    "%timeit cy_remove_nan(x_1d)\n",
    "%timeit py_remove_nan(x_1d)"
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