Demo_1.ipynb

Jupyter Notebook Tricks for Data Science that Enhance your efficiency Demo.ipynb

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      "source": "%load_ext Cython",
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      "cell_type": "code",
      "source": "def fib1(n):\n    a, b = 0, 1\n    while b < n:\n        a, b = b, a + b    ",
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      "outputs": []
    },
    {
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      "cell_type": "code",
      "source": "%time fib1(1000)",
      "execution_count": 57,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Wall time: 0 ns\n"
        }
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      "cell_type": "code",
      "source": "%timeit fib1(1000)",
      "execution_count": 58,
      "outputs": [
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          "text": "1.01 µs ± 33.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
        }
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      "cell_type": "code",
      "source": "def dummy(n):\n    for i in range(int(1e7)):\n        fib1(n)",
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      "outputs": []
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      "cell_type": "code",
      "source": "%prun dummy(50)",
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      "outputs": [
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          "output_type": "stream",
          "text": " "
        }
      ]
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      "cell_type": "code",
      "source": "def fib1(n):\n    a, b = 0, 1\n    while b < n:\n        a, b = b, a + b    ",
      "execution_count": 75,
      "outputs": []
    },
    {
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        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%cython\ndef fib2(n):\n    a, b = 0, 1\n    while b < n:\n        a, b = b, a + b",
      "execution_count": 76,
      "outputs": []
    },
    {
      "metadata": {
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          "start_time": "2018-02-02T17:38:06.645751Z"
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        "trusted": true
      },
      "cell_type": "code",
      "source": "%timeit fib1(50)\n%timeit fib2(50)",
      "execution_count": 77,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "582 ns ± 28 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n285 ns ± 13.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
        }
      ]
    },
    {
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        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%cython\ndef fib3(int n):\n    cdef int b = 1\n    cdef int a = 0\n    cdef int t = 0\n    while b < n:\n        t = a\n        a = b\n        b = t + b",
      "execution_count": 23,
      "outputs": []
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        "trusted": true
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      "cell_type": "code",
      "source": "%timeit fib3(50)",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": "48.4 ns ± 1.33 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n",
          "name": "stdout"
        }
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          "start_time": "2018-02-02T11:33:04.894168Z"
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        "trusted": true
      },
      "cell_type": "code",
      "source": "%timeit fib1(50)\n\n%timeit fib2(50)\n\n%timeit fib3(50)",
      "execution_count": 9,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "627 ns ± 7.46 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n318 ns ± 3.51 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n55.5 ns ± 2.47 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Heading"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## heading"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### heading"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-04T10:09:32.022086Z",
          "start_time": "2018-02-04T10:09:32.001030Z"
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      },
      "cell_type": "code",
      "source": "!cd",
      "execution_count": 1,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "D:\\Sync\\Courses\\fastai\\courses\\ml1\n"
        }
      ]
    }
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