{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_location = \"/Users/mingchang/Downloads/\"\n",
    "file_name = \"orders_new.csv\"\n",
    "my_data = pd.read_csv(file_location + file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# what the data looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>date</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>spend_usd</th>\n",
       "      <th>user_id</th>\n",
       "      <th>vertical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>12624549</td>\n",
       "      <td>6720123150182430132</td>\n",
       "      <td>49.99</td>\n",
       "      <td>3659561261588336546</td>\n",
       "      <td>ANDROID_APPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>12624549</td>\n",
       "      <td>17511438899767627798</td>\n",
       "      <td>0.99</td>\n",
       "      <td>3659561261588336546</td>\n",
       "      <td>ANDROID_APPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>12624549</td>\n",
       "      <td>1549342062436664018</td>\n",
       "      <td>39.99</td>\n",
       "      <td>3659561261588336546</td>\n",
       "      <td>ANDROID_APPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>12624549</td>\n",
       "      <td>7771425263197855716</td>\n",
       "      <td>19.99</td>\n",
       "      <td>3659561261588336546</td>\n",
       "      <td>ANDROID_APPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>12624549</td>\n",
       "      <td>17943210556580942992</td>\n",
       "      <td>19.99</td>\n",
       "      <td>3659561261588336546</td>\n",
       "      <td>ANDROID_APPS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country        date  experiment_id              order_id  spend_usd  \\\n",
       "0      US  2017-04-02       12624549   6720123150182430132      49.99   \n",
       "1      US  2017-04-01       12624549  17511438899767627798       0.99   \n",
       "2      US  2017-04-04       12624549   1549342062436664018      39.99   \n",
       "3      US  2017-04-04       12624549   7771425263197855716      19.99   \n",
       "4      US  2017-04-01       12624549  17943210556580942992      19.99   \n",
       "\n",
       "               user_id      vertical  \n",
       "0  3659561261588336546  ANDROID_APPS  \n",
       "1  3659561261588336546  ANDROID_APPS  \n",
       "2  3659561261588336546  ANDROID_APPS  \n",
       "3  3659561261588336546  ANDROID_APPS  \n",
       "4  3659561261588336546  ANDROID_APPS  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12624548, 12624549])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# experiment_id\n",
    "a = my_data.experiment_id\n",
    "np.unique(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# first function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for any given experiment_id, return a list group by date\n",
    "def GetSeries(my_data, my_id):\n",
    "    table = my_data[[\"date\", \"spend_usd\"]][my_data.experiment_id == my_id]\n",
    "    series = my_data.groupby('date')['spend_usd'].sum()\n",
    "    return(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# test if this works\n",
    "red = GetSeries(my_data, 12624548)\n",
    "blue = GetSeries(my_data, 12624549)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for any two series, plot the time series\n",
    "def PlotSeries(index_series, experiment_series, control_series):\n",
    "    df = pd.DataFrame(\n",
    "        {\n",
    "            \"experiment\": experiment_series.tolist(),\n",
    "            \"control\": control_series.tolist()\n",
    "        },\n",
    "        index = index_series.tolist(), # a range of dates\n",
    "        columns = [\"experiment\", \"control\"]\n",
    "    )\n",
    "    df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2017-04-01', '2017-04-02', '2017-04-03', '2017-04-04',\n",
       "       '2017-04-05', '2017-04-06', '2017-04-07'], dtype=object)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test if this works\n",
    "date_series = my_data.date\n",
    "index_series = np.unique(date_series)\n",
    "#index_series\n",
    "\n",
    "PlotSeries(index_series, red, blue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# combine everything together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def PrintDailyTimeSeries(my_data, experiment_ids):\n",
    "    # make sure our x-axis is consistent\n",
    "    date_series = my_data.date\n",
    "    index_series = np.unique(date_series) # unique date\n",
    "    index_series = np.sort(index_series) # sort by date\n",
    "    \n",
    "    # data grouping\n",
    "    my_data = my_data.sort_values(\"date\", ascending=True, inplace=False)\n",
    "    treatment_series = GetSeries(my_data, experiment_ids[0])\n",
    "    control_series = GetSeries(my_data, experiment_ids[1])\n",
    "    \n",
    "    PlotSeries(index_series, treatment_series, control_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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enl7u5/wAvPvlf+j1z+O4teUMHh4emudERESqr6BzfpKccxle93Mo9GyvctSz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af52650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PrintDailyTimeSeries(my_data, [12624548,12624549])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}