forecasting_sales-average_only.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# forecasting daily sales by day-specific average sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### import statements (data manipulation and viz packages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import math\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### reading data and creating timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('./sample.csv')\n",
    "data['timestamp'] = pd.to_datetime(data['name-node_ide_dhEmi_$date'])\n",
    "data = data.set_index('timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name-node_complemento_valorTotal</th>\n",
       "      <th>name-node_dets_0_nItem</th>\n",
       "      <th>name-node_dets_0_prod_indTot</th>\n",
       "      <th>name-node_dets_0_prod_qCom</th>\n",
       "      <th>name-node_dets_0_prod_uCom</th>\n",
       "      <th>name-node_dets_0_prod_vProd</th>\n",
       "      <th>name-node_dets_0_prod_vUnCom</th>\n",
       "      <th>name-node_dets_0_prod_xProd</th>\n",
       "      <th>name-node_dets_1_nItem</th>\n",
       "      <th>name-node_dets_1_prod_indTot</th>\n",
       "      <th>...</th>\n",
       "      <th>name-node_total_icmsTot_vcofins</th>\n",
       "      <th>name-node_total_icmsTot_vicms</th>\n",
       "      <th>name-node_total_icmsTot_vicmsDeson</th>\n",
       "      <th>name-node_total_icmsTot_vii</th>\n",
       "      <th>name-node_total_icmsTot_vipi</th>\n",
       "      <th>name-node_total_icmsTot_vnf</th>\n",
       "      <th>name-node_total_icmsTot_vpis</th>\n",
       "      <th>name-node_total_icmsTot_vst</th>\n",
       "      <th>name-node_versaoDocumento</th>\n",
       "      <th>name-node_versaoDocumento_$numberLong</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05 12:01:54</th>\n",
       "      <td>24.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "      <td>UN</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.5</td>\n",
       "      <td>AGUA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05 12:03:52</th>\n",
       "      <td>92.53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.218</td>\n",
       "      <td>KG</td>\n",
       "      <td>83.55</td>\n",
       "      <td>68.6</td>\n",
       "      <td>BUFFET</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name-node_complemento_valorTotal  name-node_dets_0_nItem  \\\n",
       "timestamp                                                                       \n",
       "2016-01-05 12:01:54                             24.90                       1   \n",
       "2016-01-05 12:03:52                             92.53                       1   \n",
       "\n",
       "                     name-node_dets_0_prod_indTot  name-node_dets_0_prod_qCom  \\\n",
       "timestamp                                                                       \n",
       "2016-01-05 12:01:54                             1                       1.000   \n",
       "2016-01-05 12:03:52                             1                       1.218   \n",
       "\n",
       "                    name-node_dets_0_prod_uCom  name-node_dets_0_prod_vProd  \\\n",
       "timestamp                                                                     \n",
       "2016-01-05 12:01:54                         UN                         3.50   \n",
       "2016-01-05 12:03:52                         KG                        83.55   \n",
       "\n",
       "                     name-node_dets_0_prod_vUnCom name-node_dets_0_prod_xProd  \\\n",
       "timestamp                                                                       \n",
       "2016-01-05 12:01:54                           3.5                        AGUA   \n",
       "2016-01-05 12:03:52                          68.6                      BUFFET   \n",
       "\n",
       "                     name-node_dets_1_nItem  name-node_dets_1_prod_indTot  \\\n",
       "timestamp                                                                   \n",
       "2016-01-05 12:01:54                     2.0                           1.0   \n",
       "2016-01-05 12:03:52                     2.0                           1.0   \n",
       "\n",
       "                                     ...                    \\\n",
       "timestamp                            ...                     \n",
       "2016-01-05 12:01:54                  ...                     \n",
       "2016-01-05 12:03:52                  ...                     \n",
       "\n",
       "                     name-node_total_icmsTot_vcofins  \\\n",
       "timestamp                                              \n",
       "2016-01-05 12:01:54                              0.0   \n",
       "2016-01-05 12:03:52                              0.0   \n",
       "\n",
       "                    name-node_total_icmsTot_vicms  \\\n",
       "timestamp                                           \n",
       "2016-01-05 12:01:54                           0.0   \n",
       "2016-01-05 12:03:52                           0.0   \n",
       "\n",
       "                     name-node_total_icmsTot_vicmsDeson  \\\n",
       "timestamp                                                 \n",
       "2016-01-05 12:01:54                                 0.0   \n",
       "2016-01-05 12:03:52                                 0.0   \n",
       "\n",
       "                     name-node_total_icmsTot_vii name-node_total_icmsTot_vipi  \\\n",
       "timestamp                                                                       \n",
       "2016-01-05 12:01:54                          0.0                          0.0   \n",
       "2016-01-05 12:03:52                          0.0                          0.0   \n",
       "\n",
       "                     name-node_total_icmsTot_vnf  \\\n",
       "timestamp                                          \n",
       "2016-01-05 12:01:54                        24.90   \n",
       "2016-01-05 12:03:52                        92.55   \n",
       "\n",
       "                     name-node_total_icmsTot_vpis  \\\n",
       "timestamp                                           \n",
       "2016-01-05 12:01:54                           0.0   \n",
       "2016-01-05 12:03:52                           0.0   \n",
       "\n",
       "                     name-node_total_icmsTot_vst name-node_versaoDocumento  \\\n",
       "timestamp                                                                    \n",
       "2016-01-05 12:01:54                          0.0                       1.0   \n",
       "2016-01-05 12:03:52                          0.0                       1.0   \n",
       "\n",
       "                     name-node_versaoDocumento_$numberLong  \n",
       "timestamp                                                   \n",
       "2016-01-05 12:01:54                                    NaN  \n",
       "2016-01-05 12:03:52                                    NaN  \n",
       "\n",
       "[2 rows x 100 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### aggregating by day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _sum(x):\n",
    "    if len(x) == 0: return 0\n",
    "    else: return sum(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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4/HK48EKXEDSn1VeH3/wm7Vk6b17uNCoHJTyhtGGOPjpNQW3XLneSpqvtStu/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\nU0REREREREKnYlNERERERERC9/+sS0S6ot8FPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a28a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = data['name-node_total_icmsTot_vProd'].groupby(pd.TimeGrouper('D'))\\\n",
    "                                                .transform(np.cumsum)\\\n",
    "                                                .resample('D')\\\n",
    "                                                .apply(_sum)\n",
    "plt.plot(dataset)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### find day-of-week and then average by day-of-week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = dataset.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['timestamp'] = df.index\n",
    "\n",
    "df['weekday'] = df['timestamp'].apply(lambda x: x.weekday())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>name-node_total_icmsTot_vProd</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>361228.32</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>394866.27</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>133111.49</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name-node_total_icmsTot_vProd  timestamp  weekday\n",
       "timestamp                                                    \n",
       "2016-01-05                      361228.32 2016-01-05        1\n",
       "2016-01-06                      394866.27 2016-01-06        2\n",
       "2016-01-07                      133111.49 2016-01-07        3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "look at sum by day-of-week, noticing again **no sales Sunday (i.e. closed)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>name-node_total_icmsTot_vProd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>620146.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1102783.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1422402.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>586374.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>342385.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>306171.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name-node_total_icmsTot_vProd\n",
       "weekday                               \n",
       "0                            620146.54\n",
       "1                           1102783.93\n",
       "2                           1422402.62\n",
       "3                            586374.48\n",
       "4                            342385.91\n",
       "5                            306171.64\n",
       "6                                 0.00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['name-node_total_icmsTot_vProd', 'weekday']].groupby('weekday').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot average by day-of-week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0aJB1NZIkaV0yAEprIEYYPjwFvx9+SGv9jjrK4KeaKcbUBdx++xQGJUlS/jIA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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113949650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[['name-node_total_icmsTot_vProd', 'weekday']].groupby('weekday').mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create a list of averages by day-of-week for the next 7 days and append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "base = df.last_valid_index() + 7\n",
    "date_list = [base - datetime.timedelta(days=x) for x in range(0, 7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_dates = pd.DataFrame(date_list)\n",
    "\n",
    "new_dates = new_dates.set_index(0)\n",
    "\n",
    "new_dates['timestamp'] = new_dates.index\n",
    "\n",
    "new_dates['weekday'] = new_dates['timestamp'].apply(lambda x: x.weekday())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_dates = new_dates.sort_index()\n",
    "\n",
    "daily_avg = df[['name-node_total_icmsTot_vProd', 'weekday']].groupby('weekday').mean()\n",
    "\n",
    "daily_avg['weekday'] = daily_avg.index\n",
    "\n",
    "new_dates = new_dates[['weekday']].join(daily_avg, on='weekday', how='inner', lsuffix='newd', rsuffix='d_avg')\n",
    "\n",
    "new_dates = new_dates[['name-node_total_icmsTot_vProd', 'weekday']]\n",
    "\n",
    "forecasted = df[['name-node_total_icmsTot_vProd', 'weekday']].append(new_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>name-node_total_icmsTot_vProd</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>361228.32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>394866.27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>133111.49</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name-node_total_icmsTot_vProd  weekday\n",
       "2016-01-05                      361228.32        1\n",
       "2016-01-06                      394866.27        2\n",
       "2016-01-07                      133111.49        3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecasted.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = forecasted['name-node_total_icmsTot_vProd'][:-6]\n",
    "b = new_dates['name-node_total_icmsTot_vProd']\n",
    "\n",
    "t = np.linspace(forecasted.index[0].value,forecasted.last_valid_index().value, len(forecasted))\n",
    "t = pd.to_datetime(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-05', '2016-01-06', '2016-01-07', '2016-01-08',\n",
       "               '2016-01-09', '2016-01-10', '2016-01-11', '2016-01-12',\n",
       "               '2016-01-13', '2016-01-14', '2016-01-15', '2016-01-16',\n",
       "               '2016-01-17', '2016-01-18', '2016-01-19', '2016-01-20',\n",
       "               '2016-01-21', '2016-01-22', '2016-01-23', '2016-01-24',\n",
       "               '2016-01-25', '2016-01-26', '2016-01-27', '2016-01-28',\n",
       "               '2016-01-29', '2016-01-30'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot sales with the \"day-of-week-average forecast\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TJ0/m0KFDNG3alKpVq9K7d2/S09OLvHanTp1o1qwZp556KtWrVwdg3LhxhcZQ0P7PP/88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\np2D27NCRVKoYMmYp99EUKVxmcMcdfrjY2rVhY3FOFXmRdJx3nh+W/fb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PmdkB+CFSaxPf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114bccc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t[:-6],a,'r') # plotting t,a separately \n",
    "plt.plot(t[-7:],b,'b') # plotting t,b separately \n",
    "# plt.plot(t,c,'g') # plotting t,c separately \n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}