Casual Carpool - Prophet Forecast.ipynb

{
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   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from fbprophet import Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>weekday</th>\n",
       "      <th>line_count</th>\n",
       "      <th>notes</th>\n",
       "      <th>timestamp_arrive</th>\n",
       "      <th>timestamp_depart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-08 07:50:00</td>\n",
       "      <td>2016-06-08 08:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-09 07:26:00</td>\n",
       "      <td>2016-06-09 07:29:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>19</td>\n",
       "      <td>Bus arrived as I got in line</td>\n",
       "      <td>2016-06-09 08:01:00</td>\n",
       "      <td>2016-06-09 08:21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Friday</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-10 07:30:00</td>\n",
       "      <td>2016-06-10 07:38:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>21</td>\n",
       "      <td>Took the bus after 10 minutes</td>\n",
       "      <td>2016-06-10 07:50:00</td>\n",
       "      <td>2016-06-10 08:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     weekday  line_count                           notes    timestamp_arrive  \\\n",
       "0  Wednesday          19                             NaN 2016-06-08 07:50:00   \n",
       "1   Thursday           4                             NaN 2016-06-09 07:26:00   \n",
       "2   Thursday          19    Bus arrived as I got in line 2016-06-09 08:01:00   \n",
       "3     Friday           9                             NaN 2016-06-10 07:30:00   \n",
       "4     Friday          21  Took the bus after 10 minutes  2016-06-10 07:50:00   \n",
       "\n",
       "     timestamp_depart  \n",
       "0 2016-06-08 08:05:00  \n",
       "1 2016-06-09 07:29:00  \n",
       "2 2016-06-09 08:21:00  \n",
       "3 2016-06-10 07:38:00  \n",
       "4 2016-06-10 08:00:00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/data.csv', index_col=0, parse_dates=['timestamp_arrive', 'timestamp_depart'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>weekday</th>\n",
       "      <th>line_count</th>\n",
       "      <th>notes</th>\n",
       "      <th>ds</th>\n",
       "      <th>timestamp_depart</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-08 07:50:00</td>\n",
       "      <td>2016-06-08 08:05:00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-09 07:26:00</td>\n",
       "      <td>2016-06-09 07:29:00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>19</td>\n",
       "      <td>Bus arrived as I got in line</td>\n",
       "      <td>2016-06-09 08:01:00</td>\n",
       "      <td>2016-06-09 08:21:00</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Friday</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-10 07:30:00</td>\n",
       "      <td>2016-06-10 07:38:00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>21</td>\n",
       "      <td>Took the bus after 10 minutes</td>\n",
       "      <td>2016-06-10 07:50:00</td>\n",
       "      <td>2016-06-10 08:00:00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     weekday  line_count                           notes                  ds  \\\n",
       "0  Wednesday          19                             NaN 2016-06-08 07:50:00   \n",
       "1   Thursday           4                             NaN 2016-06-09 07:26:00   \n",
       "2   Thursday          19    Bus arrived as I got in line 2016-06-09 08:01:00   \n",
       "3     Friday           9                             NaN 2016-06-10 07:30:00   \n",
       "4     Friday          21  Took the bus after 10 minutes  2016-06-10 07:50:00   \n",
       "\n",
       "     timestamp_depart     y  \n",
       "0 2016-06-08 08:05:00  15.0  \n",
       "1 2016-06-09 07:29:00   3.0  \n",
       "2 2016-06-09 08:21:00  20.0  \n",
       "3 2016-06-10 07:38:00   8.0  \n",
       "4 2016-06-10 08:00:00  10.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['wait_time'] = df['timestamp_depart'] - df['timestamp_arrive']\n",
    "df['wait_time'] = df['wait_time'].apply(lambda x: x.total_seconds() / 60)\n",
    "# df['y'] = np.log(df['wait_time'])\n",
    "df = df.rename(columns = {'timestamp_arrive': 'ds', 'wait_time': 'y'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-06-08 07:50:00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-09 07:26:00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-09 08:01:00</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-06-10 07:30:00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-06-10 07:50:00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ds     y\n",
       "0 2016-06-08 07:50:00  15.0\n",
       "1 2016-06-09 07:26:00   3.0\n",
       "2 2016-06-09 08:01:00  20.0\n",
       "3 2016-06-10 07:30:00   8.0\n",
       "4 2016-06-10 07:50:00  10.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['weekday', 'line_count', 'notes', 'timestamp_depart'], axis=1)\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>2017-03-20 08:31:00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>2017-03-21 07:21:00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>2017-03-21 08:13:00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>2017-03-22 06:52:00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>2017-03-23 08:06:00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                     ds    y\n",
       "264 2017-03-20 08:31:00  2.0\n",
       "265 2017-03-21 07:21:00  0.0\n",
       "266 2017-03-21 08:13:00  3.0\n",
       "267 2017-03-22 06:52:00  0.0\n",
       "268 2017-03-23 08:06:00  7.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = Prophet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x103fd1240>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>2017-03-25 20:16:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>2017-03-25 20:26:00</td>\n",
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       "      <th>614</th>\n",
       "      <td>2017-03-25 20:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>2017-03-25 20:46:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>2017-03-25 20:56:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                     ds\n",
       "612 2017-03-25 20:16:00\n",
       "613 2017-03-25 20:26:00\n",
       "614 2017-03-25 20:36:00\n",
       "615 2017-03-25 20:46:00\n",
       "616 2017-03-25 20:56:00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future = m.make_future_dataframe(periods=365, freq='10Min')\n",
    "future.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method make_future_dataframe in module fbprophet.forecaster:\n",
      "\n",
      "make_future_dataframe(periods, freq='D', include_history=True) method of fbprophet.forecaster.Prophet instance\n",
      "    Simulate the trend using the extrapolated generative model.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    periods: Int number of periods to forecast forward.\n",
      "    freq: Any valid frequency for pd.date_range, such as 'D' or 'M'.\n",
      "    include_history: Boolean to include the historical dates in the data\n",
      "        frame for predictions.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    pd.Dataframe that extends forward from the end of self.history for the\n",
      "    requested number of periods.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(m.make_future_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forecast = m.predict(future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>t</th>\n",
       "      <th>trend</th>\n",
       "      <th>seasonal_lower</th>\n",
       "      <th>seasonal_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>weekly</th>\n",
       "      <th>weekly_lower</th>\n",
       "      <th>weekly_upper</th>\n",
       "      <th>yearly</th>\n",
       "      <th>yearly_lower</th>\n",
       "      <th>yearly_upper</th>\n",
       "      <th>seasonal</th>\n",
       "      <th>yhat</th>\n",
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       "      <th>612</th>\n",
       "      <td>2017-03-25 20:16:00</td>\n",
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       "      <td>2017-03-25 20:26:00</td>\n",
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       "      <th>614</th>\n",
       "      <td>2017-03-25 20:36:00</td>\n",
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       "      <th>615</th>\n",
       "      <td>2017-03-25 20:46:00</td>\n",
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       "      <th>616</th>\n",
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      "text/plain": [
       "                     ds         t     trend  seasonal_lower  seasonal_upper  \\\n",
       "612 2017-03-25 20:16:00  1.008704  2.399239        4.362663        4.362663   \n",
       "613 2017-03-25 20:26:00  1.008728  2.399018        4.362663        4.362663   \n",
       "614 2017-03-25 20:36:00  1.008753  2.398797        4.362663        4.362663   \n",
       "615 2017-03-25 20:46:00  1.008777  2.398575        4.362663        4.362663   \n",
       "616 2017-03-25 20:56:00  1.008801  2.398354        4.362663        4.362663   \n",
       "\n",
       "     trend_lower  trend_upper  yhat_lower  yhat_upper    weekly  weekly_lower  \\\n",
       "612     2.399239     2.399239    1.861583   11.954018  1.667428      1.667428   \n",
       "613     2.399018     2.399018    1.844718   11.614784  1.667428      1.667428   \n",
       "614     2.398797     2.398797    1.574431   11.840361  1.667428      1.667428   \n",
       "615     2.398575     2.398575    1.813176   12.130512  1.667428      1.667428   \n",
       "616     2.398354     2.398354    1.578558   11.789712  1.667428      1.667428   \n",
       "\n",
       "     weekly_upper    yearly  yearly_lower  yearly_upper  seasonal      yhat  \n",
       "612      1.667428  2.695235      2.695235      2.695235  4.362663  6.761902  \n",
       "613      1.667428  2.695235      2.695235      2.695235  4.362663  6.761681  \n",
       "614      1.667428  2.695235      2.695235      2.695235  4.362663  6.761460  \n",
       "615      1.667428  2.695235      2.695235      2.695235  4.362663  6.761238  \n",
       "616      1.667428  2.695235      2.695235      2.695235  4.362663  6.761017  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ULC6eexP3Hju+a/u/E2omhYjP8W4mgZA5Asa6U6quFyUSiW7vQk3vvfcekskkJiYmkEql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J9duQp/a5/A5x58DVdvqsRVzZVypcYjQ3j7KgZ1mgCcU+kDKNrPQ8c5M74vAOCR1lG8s0Ejbx9e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\nYYBMQ8WcZCIiIiKiOAySiYiIiIjiMEgmIiIiIorDIJmIiIiIKA6DZCIiI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zhx4gT2798P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\nojgMkomIiIiI4jBIJiIiIiKK8//VHs3MBhmYNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157a8588>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HAfkZfe7zbcdB0Al0SBIwMRjC5YSGd5azfVedxPF8ZHQHEUnIn2Ds4DM8n9bh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J9duQp/a5/A5x58DVdvqsRVzZVypcYjQ3j7KgZ1mgCcU+kDKNrPQ8c5M74vAOCR1lG8s0Ejbx9e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\nYYBMQ8WcZCIiIiKiOAySiYiIiIjiMEgmIiIiIorDIJmIiIiIKA6DZCIiI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157a8588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.plot(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'the label [Sunday] is not in the [index]'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/Users/victormora/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1394\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1395\u001b[0;31m                     \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1396\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/victormora/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1389\u001b[0m                 raise KeyError(\"the label [%s] is not in the [%s]\" %\n\u001b[0;32m-> 1390\u001b[0;31m                                (key, self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'the label [Sunday] is not in the [index]'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-34f56bf61ffc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_components\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mforecast\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/victormora/anaconda/lib/python3.5/site-packages/fbprophet/forecaster.py\u001b[0m in \u001b[0;36mplot_components\u001b[0;34m(self, fcst, uncertainty)\u001b[0m\n\u001b[1;32m    905\u001b[0m         for ax, plot in zip(axes,\n\u001b[1;32m    906\u001b[0m                             [getattr(self, plot) for plot, _ in components]):\n\u001b[0;32m--> 907\u001b[0;31m             \u001b[0martists\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfcst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muncertainty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muncertainty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m         \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/victormora/anaconda/lib/python3.5/site-packages/fbprophet/forecaster.py\u001b[0m in \u001b[0;36mplot_weekly\u001b[0;34m(self, fcst, ax, uncertainty)\u001b[0m\n\u001b[1;32m    999\u001b[0m         \u001b[0mdf_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dow'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1000\u001b[0m         \u001b[0mdays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2017-01-01'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperiods\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweekday_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1001\u001b[0;31m         \u001b[0my_weekly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weekly'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1002\u001b[0m         \u001b[0my_weekly_l\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weekly_lower'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0my_weekly_u\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weekly_upper'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/victormora/anaconda/lib/python3.5/site-packages/fbprophet/forecaster.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    999\u001b[0m         \u001b[0mdf_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dow'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1000\u001b[0m         \u001b[0mdays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2017-01-01'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperiods\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweekday_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1001\u001b[0;31m         \u001b[0my_weekly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weekly'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1002\u001b[0m         \u001b[0my_weekly_l\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weekly_lower'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0my_weekly_u\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weekly_upper'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/victormora/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1294\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1295\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1296\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/victormora/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1465\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1466\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1467\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1468\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/victormora/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1401\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1402\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1403\u001b[0;31m                 \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1405\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/victormora/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1388\u001b[0m                                     \"key\")\n\u001b[1;32m   1389\u001b[0m                 raise KeyError(\"the label [%s] is not in the [%s]\" %\n\u001b[0;32m-> 1390\u001b[0;31m                                (key, self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1392\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'the label [Sunday] is not in the [index]'"
     ]
    },
    {
     "data": {
      "image/png": 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AxssRGi9HPJYIVNRdxeGzlcgsKMcPeWVYsiMPMlnr2UcqpIQwPjJHJh1Ympub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\nAQAA1qOwAAAA6/0fkwudbsSOnngAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1185d8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}