diff --git a/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb b/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb
index baf4bed..12ba838 100644
--- a/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb
+++ b/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ab74fdc9e8ae58388d1fc428599abc86a3d03938d0f51769cef38ab4028d516a"
+  "signature": "sha256:d5895f75b2ac58db150d7b521682366a447ffb2fb0b7db7e551edd40e6d1ab10"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -66,7 +66,8 @@
       "    - [Adding elements to a dictionary](#adding_dict_elements)\n",
       "- [Comprehensions vs. for-loops](#comprehensions)\n",
       "- [Copying files by searching directory trees](#find_copy)\n",
-      "- [Returning column vectors slicing through a numpy array](#row_vectors)"
+      "- [Returning column vectors slicing through a numpy array](#row_vectors)\n",
+      "- [Speed of numpy functions vs Python built-ins and std. lib.](#numpy)"
      ]
     },
     {
@@ -1691,6 +1692,116 @@
      ],
      "prompt_number": 91
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='numpy'></a>\n",
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Speed of numpy functions vs Python built-ins and std. lib."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import timeit\n",
+      "\n",
+      "samples = list(range(1000000))\n",
+      "\n",
+      "%timeit(sum(samples))\n",
+      "%timeit(np.sum(samples))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "100 loops, best of 3: 18.3 ms per loop\n",
+        "10 loops, best of 3: 136 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%timeit(list(range(1000000)))\n",
+      "%timeit(np.arange(1000000))\n",
+      "\n",
+      "# note that in Python range() is implemented as xrange()\n",
+      "# with lazy evaluation (generator)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "10 loops, best of 3: 82.6 ms per loop\n",
+        "100 loops, best of 3: 5.35 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import statistics\n",
+      "\n",
+      "%timeit(statistics.mean(samples))\n",
+      "%timeit(np.mean(samples))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1 loops, best of 3: 1.14 s per loop\n",
+        "10 loops, best of 3: 141 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
     {
      "cell_type": "code",
      "collapsed": false,
diff --git a/benchmarks/timeit_tests.ipynb b/benchmarks/timeit_tests.ipynb
index baf4bed..12ba838 100644
--- a/benchmarks/timeit_tests.ipynb
+++ b/benchmarks/timeit_tests.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ab74fdc9e8ae58388d1fc428599abc86a3d03938d0f51769cef38ab4028d516a"
+  "signature": "sha256:d5895f75b2ac58db150d7b521682366a447ffb2fb0b7db7e551edd40e6d1ab10"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -66,7 +66,8 @@
       "    - [Adding elements to a dictionary](#adding_dict_elements)\n",
       "- [Comprehensions vs. for-loops](#comprehensions)\n",
       "- [Copying files by searching directory trees](#find_copy)\n",
-      "- [Returning column vectors slicing through a numpy array](#row_vectors)"
+      "- [Returning column vectors slicing through a numpy array](#row_vectors)\n",
+      "- [Speed of numpy functions vs Python built-ins and std. lib.](#numpy)"
      ]
     },
     {
@@ -1691,6 +1692,116 @@
      ],
      "prompt_number": 91
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='numpy'></a>\n",
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Speed of numpy functions vs Python built-ins and std. lib."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import timeit\n",
+      "\n",
+      "samples = list(range(1000000))\n",
+      "\n",
+      "%timeit(sum(samples))\n",
+      "%timeit(np.sum(samples))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "100 loops, best of 3: 18.3 ms per loop\n",
+        "10 loops, best of 3: 136 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%timeit(list(range(1000000)))\n",
+      "%timeit(np.arange(1000000))\n",
+      "\n",
+      "# note that in Python range() is implemented as xrange()\n",
+      "# with lazy evaluation (generator)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "10 loops, best of 3: 82.6 ms per loop\n",
+        "100 loops, best of 3: 5.35 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import statistics\n",
+      "\n",
+      "%timeit(statistics.mean(samples))\n",
+      "%timeit(np.mean(samples))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1 loops, best of 3: 1.14 s per loop\n",
+        "10 loops, best of 3: 141 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
     {
      "cell_type": "code",
      "collapsed": false,