diff --git a/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb b/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb
index 28e49fa..8afaa8c 100644
--- a/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb
+++ b/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9e62469f6250ac6d58e6d3d2c67a5995d1505778b147ae918d71f4ebc1c12fb6"
+  "signature": "sha256:8294645ddf3a6997a4764ca0bb61458953a6e579d858ecbf59e69882af05d0df"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -65,7 +65,8 @@
       "- [Dictionary operations](#dict_ops) \n",
       "    - [Adding elements to a dictionary](#adding_dict_elements)\n",
       "- [Comprehensions vs. for-loops](#comprehensions)\n",
-      "- [Copying files by searching directory trees](#find_copy)"
+      "- [Copying files by searching directory trees](#find_copy)\n",
+      "- [Returning column vectors slicing through a numpy array](#row_vectors)"
      ]
     },
     {
@@ -1409,14 +1410,6 @@
      ],
      "prompt_number": 35
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -1424,6 +1417,241 @@
       "I have to say that I am really positively surprised. The shell's `find` scales even better than expected!"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "<a name='row_vectors'></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Returning column vectors slicing through a numpy array"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Given a numpy matrix, I want to iterate through it and return each column as a 1-column vector.  \n",
+      "E.g., if I want to return the 1st column from matrix A below\n",
+      "\n",
+      "<pre>\n",
+      "A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])\n",
+      ">>> A\n",
+      "array([[1, 2, 3],\n",
+      "       [4, 5, 6],\n",
+      "       [7, 8, 9]])</pre>\n",
+      "\n",
+      "I want my result to be:\n",
+      "<pre>\n",
+      "array([[1],\n",
+      "       [4],\n",
+      "       [7]])</pre>\n",
+      "\n",
+      "with `.shape` = `(3,1)`\n",
+      "\n",
+      "\n",
+      "However, the default behavior of numpy is to return the column as a row vector:\n",
+      "\n",
+      "<pre>\n",
+      ">>> A[:,0]\n",
+      "array([1, 4, 7])\n",
+      ">>> A[:,0].shape\n",
+      "(3,)\n",
+      "</pre>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "def colvec_method1(A):\n",
+      "    for col in A.T:\n",
+      "        colvec = row[:,np.newaxis]\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 83
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def colvec_method2(A):\n",
+      "    for idx in range(A.shape[1]):\n",
+      "        colvec = A[:,idx:idx+1]\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 82
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def colvec_method3(A):\n",
+      "    for idx in range(A.shape[1]):\n",
+      "        colvec = A[:,idx].reshape(A.shape[0],1)\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 81
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def colvec_method4(A):\n",
+      "    for idx in range(A.shape[1]):\n",
+      "        colvec = np.vstack(A[:,idx])\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 79
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def colvec_method5(A):\n",
+      "    for idx in range(A.shape[1]):\n",
+      "        colvec = np.row_stack(A[:,idx])\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 77
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def colvec_method6(A):\n",
+      "    for idx in range(A.shape[1]):\n",
+      "        colvec = np.column_stack((A[:,idx],))\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 74
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def test_method(method, A):\n",
+      "    for i in method(A): \n",
+      "        assert i.shape == (A.shape[0],1), \"{}, {}\".format(i.shape, A.shape[0],1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 69
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "A = np.random.random((300, 3))\n",
+      "\n",
+      "for method in [\n",
+      "            colvec_method1, colvec_method2, \n",
+      "            colvec_method3, colvec_method4, \n",
+      "            colvec_method5, colvec_method6]:\n",
+      "    print('\\nTest:', method.__name__)\n",
+      "    %timeit test_method(colvec_method2, A)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Test: colvec_method1\n",
+        "100000 loops, best of 3: 17.7 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Test: colvec_method2\n",
+        "10000 loops, best of 3: 16.4 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Test: colvec_method3\n",
+        "100000 loops, best of 3: 17.3 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Test: colvec_method4\n",
+        "10000 loops, best of 3: 16.4 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Test: colvec_method5\n",
+        "100000 loops, best of 3: 17.1 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Test: colvec_method6\n",
+        "100000 loops, best of 3: 16.6 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 86
+    },
     {
      "cell_type": "code",
      "collapsed": false,
diff --git a/benchmarks/timeit_tests.ipynb b/benchmarks/timeit_tests.ipynb
index 28e49fa..8afaa8c 100644
--- a/benchmarks/timeit_tests.ipynb
+++ b/benchmarks/timeit_tests.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9e62469f6250ac6d58e6d3d2c67a5995d1505778b147ae918d71f4ebc1c12fb6"
+  "signature": "sha256:8294645ddf3a6997a4764ca0bb61458953a6e579d858ecbf59e69882af05d0df"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -65,7 +65,8 @@
       "- [Dictionary operations](#dict_ops) \n",
       "    - [Adding elements to a dictionary](#adding_dict_elements)\n",
       "- [Comprehensions vs. for-loops](#comprehensions)\n",
-      "- [Copying files by searching directory trees](#find_copy)"
+      "- [Copying files by searching directory trees](#find_copy)\n",
+      "- [Returning column vectors slicing through a numpy array](#row_vectors)"
      ]
     },
     {
@@ -1409,14 +1410,6 @@
      ],
      "prompt_number": 35
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -1424,6 +1417,241 @@
       "I have to say that I am really positively surprised. The shell's `find` scales even better than expected!"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "<a name='row_vectors'></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Returning column vectors slicing through a numpy array"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Given a numpy matrix, I want to iterate through it and return each column as a 1-column vector.  \n",
+      "E.g., if I want to return the 1st column from matrix A below\n",
+      "\n",
+      "<pre>\n",
+      "A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])\n",
+      ">>> A\n",
+      "array([[1, 2, 3],\n",
+      "       [4, 5, 6],\n",
+      "       [7, 8, 9]])</pre>\n",
+      "\n",
+      "I want my result to be:\n",
+      "<pre>\n",
+      "array([[1],\n",
+      "       [4],\n",
+      "       [7]])</pre>\n",
+      "\n",
+      "with `.shape` = `(3,1)`\n",
+      "\n",
+      "\n",
+      "However, the default behavior of numpy is to return the column as a row vector:\n",
+      "\n",
+      "<pre>\n",
+      ">>> A[:,0]\n",
+      "array([1, 4, 7])\n",
+      ">>> A[:,0].shape\n",
+      "(3,)\n",
+      "</pre>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "def colvec_method1(A):\n",
+      "    for col in A.T:\n",
+      "        colvec = row[:,np.newaxis]\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 83
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def colvec_method2(A):\n",
+      "    for idx in range(A.shape[1]):\n",
+      "        colvec = A[:,idx:idx+1]\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 82
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def colvec_method3(A):\n",
+      "    for idx in range(A.shape[1]):\n",
+      "        colvec = A[:,idx].reshape(A.shape[0],1)\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 81
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def colvec_method4(A):\n",
+      "    for idx in range(A.shape[1]):\n",
+      "        colvec = np.vstack(A[:,idx])\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 79
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def colvec_method5(A):\n",
+      "    for idx in range(A.shape[1]):\n",
+      "        colvec = np.row_stack(A[:,idx])\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 77
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def colvec_method6(A):\n",
+      "    for idx in range(A.shape[1]):\n",
+      "        colvec = np.column_stack((A[:,idx],))\n",
+      "        yield colvec"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 74
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def test_method(method, A):\n",
+      "    for i in method(A): \n",
+      "        assert i.shape == (A.shape[0],1), \"{}, {}\".format(i.shape, A.shape[0],1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 69
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "A = np.random.random((300, 3))\n",
+      "\n",
+      "for method in [\n",
+      "            colvec_method1, colvec_method2, \n",
+      "            colvec_method3, colvec_method4, \n",
+      "            colvec_method5, colvec_method6]:\n",
+      "    print('\\nTest:', method.__name__)\n",
+      "    %timeit test_method(colvec_method2, A)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Test: colvec_method1\n",
+        "100000 loops, best of 3: 17.7 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Test: colvec_method2\n",
+        "10000 loops, best of 3: 16.4 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Test: colvec_method3\n",
+        "100000 loops, best of 3: 17.3 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Test: colvec_method4\n",
+        "10000 loops, best of 3: 16.4 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Test: colvec_method5\n",
+        "100000 loops, best of 3: 17.1 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Test: colvec_method6\n",
+        "100000 loops, best of 3: 16.6 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 86
+    },
     {
      "cell_type": "code",
      "collapsed": false,