{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Compute\n",
    "\n",
    "We oftentimes want to interact with other libraries and have them handle pandas objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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IkSMjHu/bt6/y8vI0bdo0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zToP7ooXenhIVzamaEpyd4sk25P16g/jDJCq73Fe2uFW6P+MEquXJf8I/1G77DtRdu1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cmoMCOGxbr/5PXqA1ek4fsG64cC6zNKXJ6mucqqW6lXVlUrDWs13elMdmGl29cBIFTGrXmi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\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OFf83adJEV9lWW448vWqep0+fxvz583Ht2jXTlnDG9Q8ODtbN6/z586blMMbC53379onv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\n2hVQ1U7oSkM51Fp5pNJejKU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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "image/png": {
       "height": 1024,
       "width": 1024
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='data/pydata-ecosystem.png',height=1024,width=1024) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We are going to look at some interactions with:\n",
    "\n",
    "- ``scikit-learn``\n",
    "- ``statsmodels``\n",
    "- ``numba`` & ``cython``\n",
    "- ``dask``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.display.max_rows = 8\n",
    "pd.options.display.max_columns = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Scikit-Learn\n",
    "\n",
    "http://scikit-learn.org/stable/documentation.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn's algorithms all deal with numpy arrays. typically:\n",
    "\n",
    "- data munging in pandas\n",
    "- pass numpy array to an Estimator\n",
    "- wrap result in a DataFrame or Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.17'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import california_housing\n",
    "data = california_housing.fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>2.5568</td>\n",
       "      <td>18</td>\n",
       "      <td>6.114035</td>\n",
       "      <td>1.315789</td>\n",
       "      <td>356</td>\n",
       "      <td>3.122807</td>\n",
       "      <td>39.49</td>\n",
       "      <td>-121.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>1.7000</td>\n",
       "      <td>17</td>\n",
       "      <td>5.205543</td>\n",
       "      <td>1.120092</td>\n",
       "      <td>1007</td>\n",
       "      <td>2.325635</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>1.8672</td>\n",
       "      <td>18</td>\n",
       "      <td>5.329513</td>\n",
       "      <td>1.171920</td>\n",
       "      <td>741</td>\n",
       "      <td>2.123209</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>2.3886</td>\n",
       "      <td>16</td>\n",
       "      <td>5.254717</td>\n",
       "      <td>1.162264</td>\n",
       "      <td>1387</td>\n",
       "      <td>2.616981</td>\n",
       "      <td>39.37</td>\n",
       "      <td>-121.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  Longitude\n",
       "0      8.3252        41  6.984127   1.023810         322  2.555556     37.88    -122.23\n",
       "1      8.3014        21  6.238137   0.971880        2401  2.109842     37.86    -122.22\n",
       "2      7.2574        52  8.288136   1.073446         496  2.802260     37.85    -122.24\n",
       "3      5.6431        52  5.817352   1.073059         558  2.547945     37.85    -122.25\n",
       "...       ...       ...       ...        ...         ...       ...       ...        ...\n",
       "20636  2.5568        18  6.114035   1.315789         356  3.122807     39.49    -121.21\n",
       "20637  1.7000        17  5.205543   1.120092        1007  2.325635     39.43    -121.22\n",
       "20638  1.8672        18  5.329513   1.171920         741  2.123209     39.43    -121.32\n",
       "20639  2.3886        16  5.254717   1.162264        1387  2.616981     39.37    -121.24\n",
       "\n",
       "[20640 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4.526\n",
       "1        3.585\n",
       "2        3.521\n",
       "3        3.413\n",
       "         ...  \n",
       "20636    0.771\n",
       "20637    0.923\n",
       "20638    0.847\n",
       "20639    0.894\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pd.Series(data.target)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.grid_search import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 835 ms, sys: 142 ms, total: 977 ms\n",
      "Wall time: 6.91 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "param_grid = dict(\n",
    "    max_features=np.arange(2, 8),\n",
    "    max_depth=[2, 4],\n",
    "    min_samples_split=[5, 10, 15, 20],\n",
    ")\n",
    "rfc = RandomForestRegressor(n_estimators=10)\n",
    "gs = GridSearchCV(rfc, param_grid, cv=5, n_jobs=-1)\n",
    "gs.fit(X.values, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[mean: 0.20146, std: 0.05887, params: {'max_depth': 2, 'min_samples_split': 5, 'max_features': 2},\n",
       " mean: 0.25245, std: 0.07831, params: {'max_depth': 2, 'min_samples_split': 10, 'max_features': 2},\n",
       " mean: 0.31007, std: 0.05206, params: {'max_depth': 2, 'min_samples_split': 15, 'max_features': 2},\n",
       " mean: 0.25166, std: 0.04992, params: {'max_depth': 2, 'min_samples_split': 20, 'max_features': 2},\n",
       " mean: 0.28584, std: 0.06496, params: {'max_depth': 2, 'min_samples_split': 5, 'max_features': 3},\n",
       " mean: 0.33954, std: 0.08037, params: {'max_depth': 2, 'min_samples_split': 10, 'max_features': 3},\n",
       " mean: 0.26840, std: 0.05488, params: {'max_depth': 2, 'min_samples_split': 15, 'max_features': 3},\n",
       " mean: 0.25677, std: 0.08151, params: {'max_depth': 2, 'min_samples_split': 20, 'max_features': 3},\n",
       " mean: 0.36317, std: 0.09080, params: {'max_depth': 2, 'min_samples_split': 5, 'max_features': 4},\n",
       " mean: 0.35095, std: 0.09386, params: {'max_depth': 2, 'min_samples_split': 10, 'max_features': 4}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = gs.grid_scores_\n",
    "scores[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def unpack_grid_scores(scores):\n",
    "    rows = []\n",
    "    params = sorted(scores[0].parameters)\n",
    "    for row in scores:\n",
    "        mean = row.mean_validation_score\n",
    "        std = row.cv_validation_scores.std()\n",
    "        rows.append([mean, std] + [row.parameters[k] for k in params])\n",
    "    return pd.DataFrame(rows, columns=['mean_', 'std_'] + params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_</th>\n",
       "      <th>std_</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>max_features</th>\n",
       "      <th>min_samples_split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.201463</td>\n",
       "      <td>0.058873</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.252446</td>\n",
       "      <td>0.078307</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.310067</td>\n",
       "      <td>0.052059</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.251659</td>\n",
       "      <td>0.049922</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.535333</td>\n",
       "      <td>0.076316</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.536545</td>\n",
       "      <td>0.064577</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.529594</td>\n",
       "      <td>0.075195</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.536525</td>\n",
       "      <td>0.064559</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean_      std_  max_depth  max_features  min_samples_split\n",
       "0   0.201463  0.058873          2             2                  5\n",
       "1   0.252446  0.078307          2             2                 10\n",
       "2   0.310067  0.052059          2             2                 15\n",
       "3   0.251659  0.049922          2             2                 20\n",
       "..       ...       ...        ...           ...                ...\n",
       "44  0.535333  0.076316          4             7                  5\n",
       "45  0.536545  0.064577          4             7                 10\n",
       "46  0.529594  0.075195          4             7                 15\n",
       "47  0.536525  0.064559          4             7                 20\n",
       "\n",
       "[48 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = unpack_grid_scores(gs.grid_scores_)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jreback/miniconda/envs/python3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10cd4c668>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0+rJ8qjYwqUXz8CkYLUZkRKejLLl4xU/WyESWa2arGc+dfBm1usYF68WERUOl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd4ce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(scores\n",
    "       .pipe((sns.factorplot,'data'), x='max_features', y='mean_', hue='max_depth', col='min_samples_split')\n",
    " )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d0813c8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZmjNLc2ZpnmM0xvLDv2Y/7OunKsxbgFuBKzPzJ8Bi4LSIuI6qoBuZ+QiwfUR8D/gmcF5m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\nSSqMxSxJhfn/fjJpXKmf8LwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd3cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = pd.Series(gs.best_estimator_.feature_importances_,index=X.columns)\n",
    "(s.sort_values()\n",
    "  .plot\n",
    "  .barh(figsize=(5,8))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Statsmodels\n",
    "\n",
    "http://statsmodels.sourceforge.net/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.6.1'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels\n",
    "import statsmodels.api as sm\n",
    "statsmodels.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_points</th>\n",
       "      <th>home_team</th>\n",
       "      <th>...</th>\n",
       "      <th>away_strength</th>\n",
       "      <th>home_strength</th>\n",
       "      <th>away_rest</th>\n",
       "      <th>home_rest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-10-28 22:30:00</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>0.256098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>...</td>\n",
       "      <td>0.304878</td>\n",
       "      <td>0.548780</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-10-28 20:00:00</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>...</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>0.670732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-10-29 19:30:00</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>105</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>...</td>\n",
       "      <td>0.463415</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>103</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670732</td>\n",
       "      <td>0.548780</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>112</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>...</td>\n",
       "      <td>0.390244</td>\n",
       "      <td>0.207317</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>2015-04-15 20:00:00</td>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>105</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>...</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>2015-04-15 19:00:00</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>87</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>...</td>\n",
       "      <td>0.402439</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1236 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       date          away_team  away_points             home_team    ...      away_strength home_strength  away_rest  home_rest\n",
       "game_id                                                                              ...                                                       \n",
       "0       2014-10-28 22:30:00    Houston Rockets          108    Los Angeles Lakers    ...           0.682927      0.256098        NaN        NaN\n",
       "1       2014-10-28 20:00:00      Orlando Magic           84  New Orleans Pelicans    ...           0.304878      0.548780        NaN        NaN\n",
       "2       2014-10-28 20:00:00   Dallas Mavericks          100     San Antonio Spurs    ...           0.609756      0.670732        NaN        NaN\n",
       "3       2014-10-29 19:30:00      Brooklyn Nets          105        Boston Celtics    ...           0.463415      0.487805        NaN        NaN\n",
       "...                     ...                ...          ...                   ...    ...                ...           ...        ...        ...\n",
       "1232    2015-04-15 20:00:00  San Antonio Spurs          103  New Orleans Pelicans    ...           0.670732      0.548780          2          1\n",
       "1233    2015-04-15 20:00:00    Detroit Pistons          112       New York Knicks    ...           0.390244      0.207317          1          1\n",
       "1234    2015-04-15 20:00:00         Miami Heat          105    Philadelphia 76ers    ...           0.451220      0.219512          1          1\n",
       "1235    2015-04-15 19:00:00  Charlotte Hornets           87       Toronto Raptors    ...           0.402439      0.597561          1         -1\n",
       "\n",
       "[1236 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# created in 4. Tidy Data\n",
    "df = pd.read_hdf('data/games.hdf','df')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1236 entries, 0 to 1235\n",
      "Data columns (total 10 columns):\n",
      "date             1230 non-null datetime64[ns]\n",
      "away_team        1230 non-null object\n",
      "away_points      1230 non-null float64\n",
      "home_team        1230 non-null object\n",
      "home_points      1230 non-null float64\n",
      "home_win         1236 non-null bool\n",
      "away_strength    1230 non-null float64\n",
      "home_strength    1230 non-null float64\n",
      "away_rest        1217 non-null float64\n",
      "home_rest        1213 non-null float64\n",
      "dtypes: bool(1), datetime64[ns](1), float64(6), object(2)\n",
      "memory usage: 97.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "df['home_win'] = df.home_win.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.564584\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "f ='home_win ~ home_strength + away_strength + home_rest + away_rest'\n",
    "res = (sm\n",
    "         .Logit\n",
    "         .from_formula(f, df)\n",
    "         .fit()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>home_win</td>     <th>  No. Observations:  </th>  <td>  1213</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  1208</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 11 Nov 2015</td> <th>  Pseudo R-squ.:     </th>  <td>0.1727</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>08:33:12</td>     <th>  Log-Likelihood:    </th> <td> -684.84</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -827.83</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.148e-60</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>   -0.3254</td> <td>    0.296</td> <td>   -1.098</td> <td> 0.272</td> <td>   -0.906     0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_strength</th> <td>    5.5558</td> <td>    0.449</td> <td>   12.374</td> <td> 0.000</td> <td>    4.676     6.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_strength</th> <td>   -4.3365</td> <td>    0.442</td> <td>   -9.817</td> <td> 0.000</td> <td>   -5.202    -3.471</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_rest</th>     <td>    0.1215</td> <td>    0.063</td> <td>    1.944</td> <td> 0.052</td> <td>   -0.001     0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_rest</th>     <td>   -0.0411</td> <td>    0.061</td> <td>   -0.669</td> <td> 0.503</td> <td>   -0.161     0.079</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:               home_win   No. Observations:                 1213\n",
       "Model:                          Logit   Df Residuals:                     1208\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Wed, 11 Nov 2015   Pseudo R-squ.:                  0.1727\n",
       "Time:                        08:33:12   Log-Likelihood:                -684.84\n",
       "converged:                       True   LL-Null:                       -827.83\n",
       "                                        LLR p-value:                 1.148e-60\n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept        -0.3254      0.296     -1.098      0.272        -0.906     0.255\n",
       "home_strength     5.5558      0.449     12.374      0.000         4.676     6.436\n",
       "away_strength    -4.3365      0.442     -9.817      0.000        -5.202    -3.471\n",
       "home_rest         0.1215      0.063      1.944      0.052        -0.001     0.244\n",
       "away_rest        -0.0411      0.061     -0.669      0.503        -0.161     0.079\n",
       "=================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "df2 = df.assign(rest_difference=df.home_rest - df.away_rest,\n",
    "                spread=df.home_points - df.away_points)\n",
    "\n",
    "f = 'spread ~ home_strength + away_strength + rest_difference'\n",
    "res = (sm\n",
    "         .OLS\n",
    "         .from_formula(f, df2)\n",
    "         .fit()\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>spread</td>      <th>  R-squared:         </th> <td>   0.236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   124.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 11 Nov 2015</td> <th>  Prob (F-statistic):</th> <td>2.57e-70</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:33:12</td>     <th>  Log-Likelihood:    </th> <td> -4712.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1213</td>      <th>  AIC:               </th> <td>   9432.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1209</td>      <th>  BIC:               </th> <td>   9453.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>   -0.4035</td> <td>    1.546</td> <td>   -0.261</td> <td> 0.794</td> <td>   -3.437     2.630</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>home_strength</th>   <td>   30.8045</td> <td>    2.097</td> <td>   14.687</td> <td> 0.000</td> <td>   26.689    34.919</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>away_strength</th>   <td>  -25.6003</td> <td>    2.105</td> <td>  -12.159</td> <td> 0.000</td> <td>  -29.731   -21.470</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rest_difference</th> <td>    0.5745</td> <td>    0.278</td> <td>    2.064</td> <td> 0.039</td> <td>    0.028     1.121</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.335</td> <th>  Durbin-Watson:     </th> <td>   1.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.009</td> <th>  Jarque-Bera (JB):  </th> <td>  11.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.099</td> <th>  Prob(JB):          </th> <td> 0.00286</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.438</td> <th>  Cond. No.          </th> <td>    10.9</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 spread   R-squared:                       0.236\n",
       "Model:                            OLS   Adj. R-squared:                  0.234\n",
       "Method:                 Least Squares   F-statistic:                     124.6\n",
       "Date:                Wed, 11 Nov 2015   Prob (F-statistic):           2.57e-70\n",
       "Time:                        08:33:12   Log-Likelihood:                -4712.1\n",
       "No. Observations:                1213   AIC:                             9432.\n",
       "Df Residuals:                    1209   BIC:                             9453.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept          -0.4035      1.546     -0.261      0.794        -3.437     2.630\n",
       "home_strength      30.8045      2.097     14.687      0.000        26.689    34.919\n",
       "away_strength     -25.6003      2.105    -12.159      0.000       -29.731   -21.470\n",
       "rest_difference     0.5745      0.278      2.064      0.039         0.028     1.121\n",
       "==============================================================================\n",
       "Omnibus:                        9.335   Durbin-Watson:                   1.997\n",
       "Prob(Omnibus):                  0.009   Jarque-Bera (JB):               11.716\n",
       "Skew:                           0.099   Prob(JB):                      0.00286\n",
       "Kurtosis:                       3.438   Cond. No.                         10.9\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Numba & Cython\n",
    "\n",
    "http://pandas.pydata.org/pandas-docs/stable/enhancingperf.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jreback/miniconda/envs/python3/lib/python3.4/site-packages/ipykernel/__main__.py:7: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    }
   ],
   "source": [
    "from numba import jit\n",
    "import cython\n",
    "%load_ext cython\n",
    "\n",
    "np.random.seed(1234)\n",
    "pd.set_option('max_row',12)\n",
    "s = Series(np.random.randn(1e5))\n",
    "com = 20.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def python(s):\n",
    "    output = Series(index=range(len(s)))\n",
    "\n",
    "    alpha = 1. / (1. + com)\n",
    "    old_weight = 1.0\n",
    "    new_weight = 1.0\n",
    "    weighted_avg = s[0]\n",
    "    output[0] = weighted_avg\n",
    "    \n",
    "    for i in range(1,len(s)):\n",
    "        v = s[i]\n",
    "        old_weight *= (1-alpha)\n",
    "        weighted_avg = ((old_weight * weighted_avg) + \n",
    "                        (new_weight * v)) / (old_weight + new_weight)\n",
    "        old_weight += new_weight\n",
    "        output[i] = weighted_avg\n",
    "        \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "cimport cython\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _cython(double[:] arr, double com, double[:] output):\n",
    "    cdef:\n",
    "        double alpha, old_weight, new_weight, weighted_avg, v\n",
    "        int i\n",
    "    \n",
    "    alpha = 1. / (1. + com)\n",
    "    old_weight = 1.0\n",
    "    new_weight = 1.0\n",
    "    weighted_avg = arr[0]\n",
    "    output[0] = weighted_avg\n",
    "    \n",
    "    for i in range(1,arr.shape[0]):\n",
    "        v = arr[i]\n",
    "        old_weight *= (1-alpha)\n",
    "        weighted_avg = ((old_weight * weighted_avg) + \n",
    "                        (new_weight * v)) / (old_weight + new_weight)\n",
    "        old_weight += new_weight\n",
    "        output[i] = weighted_avg\n",
    "        \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def cython1(s):\n",
    "    output = np.empty(len(s),dtype='float64')\n",
    "    _cython(s.values, com, output)\n",
    "    return Series(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def cython2(s):\n",
    "    return pd.ewma(s,com=com,adjust=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "@jit\n",
    "def _numba(arr, output):\n",
    "    alpha = 1. / (1. + com)\n",
    "    old_weight = 1.0\n",
    "    new_weight = 1.0\n",
    "    weighted_avg = arr[0]\n",
    "    output[0] = weighted_avg\n",
    "    \n",
    "    for i in range(1,arr.shape[0]):\n",
    "        v = arr[i]\n",
    "        old_weight *= (1-alpha)\n",
    "        weighted_avg = ((old_weight * weighted_avg) + \n",
    "                        (new_weight * v)) / (old_weight + new_weight)\n",
    "        old_weight += new_weight\n",
    "        output[i] = weighted_avg\n",
    "    \n",
    "\n",
    "def numba(s):\n",
    " \n",
    "    output = np.empty(len(s),dtype='float64')\n",
    "    _numba(s.values, output)\n",
    "    return Series(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1 = python(s)\n",
    "result2 = cython1(s)\n",
    "result3 = cython2(s)\n",
    "result4 = numba(s)\n",
    "result1.equals(\n",
    "    result2) and result1.equals(\n",
    "    result3) and result1.equals(\n",
    "    result4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 1.11 s per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit python(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 971 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit cython1(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 6.98 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit cython2(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 954 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit numba(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Dask\n",
    "\n",
    "https://dask.readthedocs.org/en/latest/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import dask.dataframe as dd\n",
    "from dask import threaded, multiprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dd.DataFrame<from_pandas-6bba1156f06742bbdaf58b9d400597eb, divisions=(0, 1250000, 2500000, ..., 8750000, 9999999)>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1234)\n",
    "N = int(1e7)\n",
    "df = DataFrame({'key' : np.random.randint(0,1000,size=N), \n",
    "                'value' : np.random.randn(N)})\n",
    "ddf = dd.from_pandas(df, npartitions=8)\n",
    "ddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 258 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit df.groupby('key').value.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 106 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit ddf.groupby('key').value.sum().compute(get=threaded.get)"
   ]
  }
 ],
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