PyCon un-sad Pandas.ipynb

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   "cell_type": "code",
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   "metadata": {
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from math import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('new_york_hotels.csv', encoding='cp1252')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ean_hotel_id</th>\n",
       "      <th>name</th>\n",
       "      <th>address1</th>\n",
       "      <th>city</th>\n",
       "      <th>state_province</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>high_rate</th>\n",
       "      <th>low_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>269955</td>\n",
       "      <td>Hilton Garden Inn Albany/SUNY Area</td>\n",
       "      <td>1389 Washington Ave</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NY</td>\n",
       "      <td>12206</td>\n",
       "      <td>42.68751</td>\n",
       "      <td>-73.81643</td>\n",
       "      <td>3.0</td>\n",
       "      <td>154.0272</td>\n",
       "      <td>124.0216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>113431</td>\n",
       "      <td>Courtyard by Marriott Albany Thruway</td>\n",
       "      <td>1455 Washington Avenue</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NY</td>\n",
       "      <td>12206</td>\n",
       "      <td>42.68971</td>\n",
       "      <td>-73.82021</td>\n",
       "      <td>3.0</td>\n",
       "      <td>179.0100</td>\n",
       "      <td>134.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108151</td>\n",
       "      <td>Radisson Hotel Albany</td>\n",
       "      <td>205 Wolf Rd</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NY</td>\n",
       "      <td>12205</td>\n",
       "      <td>42.72410</td>\n",
       "      <td>-73.79822</td>\n",
       "      <td>3.0</td>\n",
       "      <td>134.1700</td>\n",
       "      <td>84.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>254756</td>\n",
       "      <td>Hilton Garden Inn Albany Medical Center</td>\n",
       "      <td>62 New Scotland Ave</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NY</td>\n",
       "      <td>12208</td>\n",
       "      <td>42.65157</td>\n",
       "      <td>-73.77638</td>\n",
       "      <td>3.0</td>\n",
       "      <td>308.2807</td>\n",
       "      <td>228.4597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198232</td>\n",
       "      <td>CrestHill Suites SUNY University Albany</td>\n",
       "      <td>1415 Washington Avenue</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NY</td>\n",
       "      <td>12206</td>\n",
       "      <td>42.68873</td>\n",
       "      <td>-73.81854</td>\n",
       "      <td>3.0</td>\n",
       "      <td>169.3900</td>\n",
       "      <td>89.3900</td>\n",
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      "text/plain": [
       "   ean_hotel_id                                     name  \\\n",
       "0        269955       Hilton Garden Inn Albany/SUNY Area   \n",
       "1        113431     Courtyard by Marriott Albany Thruway   \n",
       "2        108151                    Radisson Hotel Albany   \n",
       "3        254756  Hilton Garden Inn Albany Medical Center   \n",
       "4        198232  CrestHill Suites SUNY University Albany   \n",
       "\n",
       "                 address1    city state_province postal_code  latitude  \\\n",
       "0     1389 Washington Ave  Albany             NY       12206  42.68751   \n",
       "1  1455 Washington Avenue  Albany             NY       12206  42.68971   \n",
       "2             205 Wolf Rd  Albany             NY       12205  42.72410   \n",
       "3     62 New Scotland Ave  Albany             NY       12208  42.65157   \n",
       "4  1415 Washington Avenue  Albany             NY       12206  42.68873   \n",
       "\n",
       "   longitude  star_rating  high_rate  low_rate  \n",
       "0  -73.81643          3.0   154.0272  124.0216  \n",
       "1  -73.82021          3.0   179.0100  134.0000  \n",
       "2  -73.79822          3.0   134.1700   84.1600  \n",
       "3  -73.77638          3.0   308.2807  228.4597  \n",
       "4  -73.81854          3.0   169.3900   89.3900  "
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     "execution_count": 53,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Benchmarking example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the normalization function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def normalize(df, pd_series):\n",
    "    pd_series = pd_series.astype(float)\n",
    "\n",
    "    # Find upper and lower bound for outliers\n",
    "    avg = np.mean(pd_series)\n",
    "    sd  = np.std(pd_series)\n",
    "    lower_bound = avg - 2*sd\n",
    "    upper_bound = avg + 2*sd\n",
    "\n",
    "    # Collapse in the outliers\n",
    "    df.loc[pd_series < lower_bound , \"cutoff_rate\" ] = lower_bound\n",
    "    df.loc[pd_series > upper_bound , \"cutoff_rate\" ] = upper_bound\n",
    "\n",
    "    # Finally, take the log\n",
    "    normalized_price = np.log(df[\"cutoff_rate\"].astype(float))\n",
    "    \n",
    "    return normalized_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Timing the normalization function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.84 ms ± 180 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df['high_rate_normalized'] = normalize(df, df['high_rate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profiling the normalization function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\n"
     ]
    }
   ],
   "source": [
    "    %load_ext line_profiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%lprun -f normalize df['high_rate_normalized'] =\\\n",
    "        normalize(df, df['high_rate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Haversine definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def haversine(lat1, lon1, lat2, lon2):\n",
    "    miles_constant = 3959\n",
    "    lat1, lon1, lat2, lon2 = map(np.deg2rad, [lat1, lon1, lat2, lon2])\n",
    "    dlat = lat2 - lat1 \n",
    "    dlon = lon2 - lon1 \n",
    "    a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n",
    "    c = 2 * np.arcsin(np.sqrt(a)) \n",
    "    mi = miles_constant * c\n",
    "    return mi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Iterrows Haversine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "197 ms ± 6.65 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "# Haversine applied on rows via iteration\n",
    "haversine_series = []\n",
    "for index, row in df.iterrows():\n",
    "    haversine_series.append(haversine(40.671, -73.985,\\\n",
    "                                      row['latitude'], row['longitude']))\n",
    "df['distance'] = haversine_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Apply Haversine on rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timing \"apply\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90.6 ms ± 7.55 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df['distance'] =\\\n",
    "df.apply(lambda row: haversine(40.671, -73.985,\\\n",
    "                               row['latitude'], row['longitude']), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profiling \"apply\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Haversine applied on rows\n",
    "%lprun -f haversine \\\n",
    "df.apply(lambda row: haversine(40.671, -73.985,\\\n",
    "                               row['latitude'], row['longitude']), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Vectorized implementation of Haversine applied on Pandas series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Timing vectorized implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.21 ms ± 230 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Vectorized implementation of Haversine applied on Pandas series\n",
    "%timeit df['distance'] = haversine(40.671, -73.985,\\\n",
    "                                   df['latitude'], df['longitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profiling vectorized implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Vectorized implementation profile\n",
    "%lprun -f haversine haversine(40.671, -73.985,\\\n",
    "                              df['latitude'], df['longitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Vectorized implementation of Haversine applied on NumPy arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Timing vectorized implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "370 µs ± 18 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Vectorized implementation of Haversine applied on NumPy arrays\n",
    "%timeit df['distance'] = haversine(40.671, -73.985,\\\n",
    "                         df['latitude'].values, df['longitude'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.66 µs ± 289 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "# Convert pandas arrays to NumPy ndarrays\n",
    "np_lat = df['latitude'].values\n",
    "np_lon = df['longitude'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profiling vectorized implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%lprun -f haversine df['distance'] = haversine(40.671, -73.985,\\\n",
    "                        df['latitude'].values, df['longitude'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Cythonize that loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the cython extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cython extension is already loaded. To reload it, use:\n",
      "  %reload_ext cython\n"
     ]
    }
   ],
   "source": [
    "%load_ext cython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run unaltered Haversine through Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
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       "<div class=\"cython\"><pre class=\"cython line score-0\">&#xA0;<span class=\"\">01</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">02</span>: <span class=\"c\"># Haversine cythonized (no other edits)</span></pre>\n",
       "<pre class=\"cython line score-8\" onclick='toggleDiv(this)'>+<span class=\"\">03</span>: <span class=\"k\">import</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n",
       "<pre class='cython code score-8 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 3, __pyx_L1_error)\n",
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       "static PyObject *__pyx_f_46_cython_magic_4d2d58d72a34088836da9f445cf39e4f_haversine_cy(PyObject *__pyx_v_lat1, PyObject *__pyx_v_lon1, PyObject *__pyx_v_lat2, PyObject *__pyx_v_lon2, CYTHON_UNUSED int __pyx_skip_dispatch) {\n",
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       "<pre class='cython code score-1 '>  <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_int_3959);\n",
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       "</pre><pre class=\"cython line score-84\" onclick='toggleDiv(this)'>+<span class=\"\">06</span>:     <span class=\"n\">lat1</span><span class=\"p\">,</span> <span class=\"n\">lon1</span><span class=\"p\">,</span> <span class=\"n\">lat2</span><span class=\"p\">,</span> <span class=\"n\">lon2</span> <span class=\"o\">=</span> <span class=\"nb\">map</span><span class=\"p\">(</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">deg2rad</span><span class=\"p\">,</span> <span class=\"p\">[</span><span class=\"n\">lat1</span><span class=\"p\">,</span> <span class=\"n\">lon1</span><span class=\"p\">,</span> <span class=\"n\">lat2</span><span class=\"p\">,</span> <span class=\"n\">lon2</span><span class=\"p\">])</span></pre>\n",
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       "<pre class='cython code score-5 '>  __pyx_t_1 = <span class='py_c_api'>PyNumber_Subtract</span>(__pyx_v_lat2, __pyx_v_lat1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
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   "source": [
    "%%cython -a\n",
    "\n",
    "# Haversine cythonized (no other edits)\n",
    "import numpy as np\n",
    "cpdef haversine_cy(lat1, lon1, lat2, lon2):\n",
    "    miles_constant = 3959\n",
    "    lat1, lon1, lat2, lon2 = map(np.deg2rad, [lat1, lon1, lat2, lon2])\n",
    "    dlat = lat2 - lat1 \n",
    "    dlon = lon2 - lon1 \n",
    "    a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n",
    "    c = 2 * np.arcsin(np.sqrt(a)) \n",
    "    mi = miles_constant * c\n",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "#### Time it"
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   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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     "text": [
      "98.3 ms ± 6.42 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df['distance'] =\\\n",
    "       df.apply(lambda row: haversine_cy(40.671, -73.985,\\\n",
    "                row['latitude'], row['longitude']), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Redefine Haversine with data types and C libraries"
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   "execution_count": 70,
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       "static PyObject *__pyx_pw_46_cython_magic_52b523ae42eb5022300cbde7a2455256_1haversine_cy_dtyped(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
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       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;\n",
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       "  __pyx_v_lon2 = __pyx_t_11;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">18</span>:     <span class=\"n\">dlat</span> <span class=\"o\">=</span> <span class=\"n\">lat2</span> <span class=\"o\">-</span> <span class=\"n\">lat1</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_dlat = (__pyx_v_lat2 - __pyx_v_lat1);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">19</span>:     <span class=\"n\">dlon</span> <span class=\"o\">=</span> <span class=\"n\">lon2</span> <span class=\"o\">-</span> <span class=\"n\">lon1</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_dlon = (__pyx_v_lon2 - __pyx_v_lon1);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">20</span>:     <span class=\"n\">a</span> <span class=\"o\">=</span> <span class=\"n\">sin</span><span class=\"p\">(</span><span class=\"n\">dlat</span><span class=\"o\">/</span><span class=\"mf\">2</span><span class=\"p\">)</span><span class=\"o\">**</span><span class=\"mf\">2</span> <span class=\"o\">+</span> <span class=\"n\">cos</span><span class=\"p\">(</span><span class=\"n\">lat1</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">cos</span><span class=\"p\">(</span><span class=\"n\">lat2</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">sin</span><span class=\"p\">(</span><span class=\"n\">dlon</span><span class=\"o\">/</span><span class=\"mf\">2</span><span class=\"p\">)</span><span class=\"o\">**</span><span class=\"mf\">2</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_a = (pow(sin((__pyx_v_dlat / 2.0)), 2.0) + ((cos(__pyx_v_lat1) * cos(__pyx_v_lat2)) * pow(sin((__pyx_v_dlon / 2.0)), 2.0)));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">21</span>:     <span class=\"n\">c</span> <span class=\"o\">=</span> <span class=\"mf\">2</span> <span class=\"o\">*</span> <span class=\"n\">asin</span><span class=\"p\">(</span><span class=\"n\">sqrt</span><span class=\"p\">(</span><span class=\"n\">a</span><span class=\"p\">))</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_c = (2.0 * asin(sqrt(__pyx_v_a)));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">22</span>:     <span class=\"n\">mi</span> <span class=\"o\">=</span> <span class=\"mf\">3959</span> <span class=\"o\">*</span> <span class=\"n\">c</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_mi = (3959.0 * __pyx_v_c);\n",
       "</pre><pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">23</span>:     <span class=\"k\">return</span> <span class=\"n\">mi</span></pre>\n",
       "<pre class='cython code score-6 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
       "  __pyx_t_6 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_mi); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 23, __pyx_L1_error)\n",
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       "  __pyx_t_6 = 0;\n",
       "  goto __pyx_L0;\n",
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     "execution_count": 70,
     "metadata": {},
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    }
   ],
   "source": [
    "%%cython -a\n",
    "# Haversine cythonized\n",
    "from libc.math cimport sin, cos, acos, asin, sqrt\n",
    "\n",
    "cdef deg2rad_cy(float deg):\n",
    "    cdef float rad\n",
    "    rad = 0.01745329252*deg\n",
    "    return rad\n",
    "    \n",
    "cpdef haversine_cy_dtyped(float lat1, float lon1, float lat2, float lon2):\n",
    "    cdef: \n",
    "        float dlon\n",
    "        float dlat\n",
    "        float a\n",
    "        float c\n",
    "        float mi\n",
    "    \n",
    "    lat1, lon1, lat2, lon2 = map(deg2rad_cy, [lat1, lon1, lat2, lon2])\n",
    "    dlat = lat2 - lat1 \n",
    "    dlon = lon2 - lon1 \n",
    "    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
    "    c = 2 * asin(sqrt(a)) \n",
    "    mi = 3959 * c\n",
    "    return mi\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51.1 ms ± 2.74 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df['distance'] =\\\n",
    "df.apply(lambda row: haversine_cy_dtyped(40.671, -73.985,\\\n",
    "                              row['latitude'], row['longitude']), axis=1)"
   ]
  }
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