{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 43,
   "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": 44,
   "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>country</th>\n",
       "      <th>date</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>spend_usd</th>\n",
       "      <th>user_bucket</th>\n",
       "      <th>user_id</th>\n",
       "      <th>vertical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IT</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>12624549</td>\n",
       "      <td>1593533581388680355</td>\n",
       "      <td>3.799427</td>\n",
       "      <td>12</td>\n",
       "      <td>3454648139452205402</td>\n",
       "      <td>GAMES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BR</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>12624548</td>\n",
       "      <td>5746748877810015202</td>\n",
       "      <td>2.546213</td>\n",
       "      <td>9</td>\n",
       "      <td>9303514494910504425</td>\n",
       "      <td>GAMES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NZ</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>12624548</td>\n",
       "      <td>17361185188659735484</td>\n",
       "      <td>13.449449</td>\n",
       "      <td>2</td>\n",
       "      <td>13625318472882626688</td>\n",
       "      <td>GAMES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>12624549</td>\n",
       "      <td>13365437971635537789</td>\n",
       "      <td>19.990000</td>\n",
       "      <td>5</td>\n",
       "      <td>11936268987816495850</td>\n",
       "      <td>GAMES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>12624549</td>\n",
       "      <td>13876597780924571273</td>\n",
       "      <td>19.990000</td>\n",
       "      <td>5</td>\n",
       "      <td>11936268987816495850</td>\n",
       "      <td>GAMES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country        date  experiment_id              order_id  spend_usd  \\\n",
       "0      IT  2017-04-01       12624549   1593533581388680355   3.799427   \n",
       "1      BR  2017-04-01       12624548   5746748877810015202   2.546213   \n",
       "2      NZ  2017-04-07       12624548  17361185188659735484  13.449449   \n",
       "3      US  2017-04-02       12624549  13365437971635537789  19.990000   \n",
       "4      US  2017-04-03       12624549  13876597780924571273  19.990000   \n",
       "\n",
       "   user_bucket               user_id vertical  \n",
       "0           12   3454648139452205402    GAMES  \n",
       "1            9   9303514494910504425    GAMES  \n",
       "2            2  13625318472882626688    GAMES  \n",
       "3            5  11936268987816495850    GAMES  \n",
       "4            5  11936268987816495850    GAMES  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(my_data.date.tolist()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def GetSeries(my_data, my_id):\n",
    "    table = my_data[[\"date\", \"spend_usd\"]][my_data.experiment_id == my_id]\n",
    "    series = table.groupby('date')['spend_usd'].sum()\n",
    "    return(series)\n",
    "\n",
    "my_series = GetSeries(my_data, 12624549)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2017-04-01    16047.543276\n",
       "2017-04-02    10098.424397\n",
       "2017-04-03     8810.436441\n",
       "2017-04-04     7717.685555\n",
       "2017-04-05     8376.836991\n",
       "2017-04-06     9793.380668\n",
       "2017-04-07    10152.279732\n",
       "Name: spend_usd, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for float(): 2017-04-07",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-1ae69a6cdaf2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# error message: matplotlib cannot take string as x axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_series\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"red\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"experiment\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;31m#plt.gcf().autofmt_xdate()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mingchang/anaconda/lib/python2.7/site-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1817\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1818\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1819\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1820\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1821\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mingchang/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1382\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1383\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1384\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mingchang/anaconda/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1701\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1702\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1703\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1704\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1705\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_line%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mingchang/anaconda/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1723\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1724\u001b[0m         \"\"\"\n\u001b[0;32m-> 1725\u001b[0;31m         \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\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   1726\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\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[1;32m   1727\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mingchang/anaconda/lib/python2.7/site-packages/matplotlib/lines.pyc\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    936\u001b[0m         \"\"\"\n\u001b[1;32m    937\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecache\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    939\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mingchang/anaconda/lib/python2.7/site-packages/matplotlib/lines.pyc\u001b[0m in \u001b[0;36mrecache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m    623\u001b[0m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxconv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxconv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mingchang/anaconda/lib/python2.7/site-packages/numpy/core/numeric.pyc\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \"\"\"\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for float(): 2017-04-07"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11983cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# error message: matplotlib cannot take string as x axis\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(my_series, color=\"red\", label=\"experiment\")\n",
    "#plt.gcf().autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# how to fix this?\n",
    "# change str format to pandas datetime\n",
    "my_data.date = pd.to_datetime(my_data.date, format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.tslib.Timestamp"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(my_data.date[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# then plot\n",
    "my_series = GetSeries(my_data, 12624549)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-04-01', '2017-04-02', '2017-04-03', '2017-04-04',\n",
       "               '2017-04-05', '2017-04-06', '2017-04-07'],\n",
       "              dtype='datetime64[ns]', name=u'date', freq=None)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CRhY44ghYaqlYjVhERKQMqbCRBZZcEo47LvaP+vTTtKMREREpmAobqev448E9dv4WEREp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\nREQkM1TYiIiISGb8P+pdaUw9d4a4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118924190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(my_series, color=\"red\", label=\"experiment\")\n",
    "plt.gcf().autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# if we don't care specific dates (normally the case)\n",
    "# reset index so it's integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_series = GetSeries(my_data, 12624549)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-04-01', '2017-04-02', '2017-04-03', '2017-04-04',\n",
       "               '2017-04-05', '2017-04-06', '2017-04-07'],\n",
       "              dtype='datetime64[ns]', name=u'date', freq=None)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = my_series.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16047.543275999929,\n",
       " 10098.424396999968,\n",
       " 8810.436440999958,\n",
       " 7717.6855549999636,\n",
       " 8376.8369909999619,\n",
       " 9793.3806679999489,\n",
       " 10152.279731999937]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "my_series = my_series.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>date</th>\n",
       "      <th>spend_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>16047.543276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>10098.424397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>8810.436441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>7717.685555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>8376.836991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>9793.380668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>10152.279732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date     spend_usd\n",
       "0 2017-04-01  16047.543276\n",
       "1 2017-04-02  10098.424397\n",
       "2 2017-04-03   8810.436441\n",
       "3 2017-04-04   7717.685555\n",
       "4 2017-04-05   8376.836991\n",
       "5 2017-04-06   9793.380668\n",
       "6 2017-04-07  10152.279732"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11568a450>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WwYqISO5S2MgApaXw3HOwYEHUlYiIiKSewkYGOOkkaNkSxoyJuhIREZHUU9jIAA0bwjnn\nwMSJ8PXXUVcjIiKSWgobGeL88z1o3Hdf1JWIiIiklsJGhthtNxg40CeKhhB1NSIiIqmjsJFBSkvh\n7bfhpZeirkRERCR1FDYyyCGHQNeuWgYrIiK5RWEjg5hBSQk8/jgsXhx1NSIiIqmhsJFhTj8dtt8e\nxo2LuhIREZHUUNjIMDvt5IHj9tthzZqoqxEREdl2ChsZqKQEli2DRx+NuhIREZFtp7CRgbp3h4MP\n1kRRERHJDQobGaq0FF55BebMiboSERGRbaOwkaEGDoR27WD06KgrERER2TYKGxmqXj3fwnzSJFi5\nMupqREREak5hI4Odey6sXw933RV1JSIiIjWnsJHBWrXy4+fHjIENG6KuRkREpGYUNjJcaSl88AFM\nnRp1JSIiIjWjsJHh+vSBggItgxURkeylsJHhzLx3Y+pUWLgw6mpERESSp7CRBQYPhmbNYOzYqCsR\nERFJnsJGFth+ezj7bLjzTvj226irERERSY7CRpYYPhwqK33fDRERkWyisJEl9tgDjj7aJ4qGEHU1\nIiIiW09hI4uUlsJ//uNnpoiIiGQLhY0scthh0LGjlsGKiEh2UdjIInXqQEkJPPIIfPZZ1NWIiIhs\nHYWNLDN0KNSvD+PHR12JiIjI1lHYyDJNm8Kpp8K4cbB2bdTViIiIbFnSYcPM+pvZZDNbbGYbzGxg\n3L16ZvZXM3vDzL6OtbnbzNomPEcDMxttZivM7Csze8TMWiW0aWZm95tZpZmtMrM7zKxxQpv2ZjbF\nzL4xs6Vmdp2Z5XyAKimBJUvgiSeirkRERGTLavKDuTEwFxgBJC7CbAT0Aq4E9gGOB7oATya0uwk4\nGhgEDAB2AR5NaDMJ6AYUxdoOAMZV3YyFimeAekBf4AxgKHBVDV5TVtl7b+jfXxNFRUQkO9RL9gEh\nhKnAVAAzs4R7q4Ej4q+ZWSkwy8zahRA+NbOdgLOAwSGEF2NtzgTmmdl+IYTZZtYt9jyFIYQ5sTYX\nAFPM7DchhKWx+12Bg0MIK4A3zewy4FozuyKEsC7Z15ZNSkvh5JPhzTdhr72irkZERGTzamPIoSne\nA/Jl7OtCPORMr2oQQlgALAL2j13qC6yqChoxz8eep09cmzdjQaPKNKAJ0CPFryHjHH88tG0Lo0dH\nXYmIiMhPS2vYMLMGwLXApBDC17HLbYA1sV6QeMti96raLI+/GUJYD6xMaLOsmucgrk3O2m47GDYM\n7r0Xvvxyy+1FRESikvQwytYys3rAw3hvxIh0fZ+aGDlyJE2aNNnkWnFxMcXFxRFVVDPnnQd//jPc\nfTdceGHU1YiISK4oKyujrKxsk2uVlZU1fr60hI24oNEeOCSuVwNgKVDfzHZK6N1oHbtX1SZxdUpd\noHlCm30TvnXruHubNWrUKAoKCrby1WSutm1h0CAfSrngAt/0S0REZFtV9wt4RUUFhYWFNXq+lP94\nigsaHYCiEMKqhCblwDp8lUnVY7oAuwGvxi69CjQ1s33iHlcEGDArrs1eZrZzXJvDgUrgndS8msxX\nWgrvvQfPPx91JSIiItWryT4bjc2sp5n1il3qEPu6fSxoPAoUAKcC25lZ69jHdvDfFSsTgBvN7CAz\nKwTuBGaGEGbH2szHJ3uON7N9zawf8HegLLYSBeBZPFTca2Z7m9kRwNXArSGEvNnuql8/XwqrZbAi\nIpKpatKz0RuYg/dQBOAGoALfW2NX4FigHb4XxxLgs9if+8c9x0jgaeAR4IXY/UEJ3+cUYD6+CuVp\n4CVgWNXNEMIG4BhgPfAKcA8wEbi8Bq8pa5l578bTT8NHH0VdjYiIyI9ZCIn7cuUuMysAysvLy3Ni\nzkaVb76BXXf11Sl//WvU1YiISC6Km7NRGEKoSOaxmlKYAxo3hrPOggkT4Lvvoq5GRERkUwobOWLE\nCPjiC3jwwagrERER2ZTCRo7o2BGOPNIniubRyJiIiGQBhY0cUloK5eUwe3bUlYiIiGyksJFDjjwS\n9thDy2BFRCSzKGzkkLp1fe7GQw/B8uVbbi8iIlIbFDZyzFln+bbld9wRdSUiIiJOYSPHNG8Op5wC\nY8fCunVRVyMiIqKwkZNKSuDTT+Gpp6KuRERERGEjJxUUwP77a6KoiIhkBoWNHFVaCjNmwLx5UVci\nIiL5TmEjRw0aBK1awejRUVciIiL5TmEjRzVoAOedB3ffDatXR12NiIjkM4WNHDZsmB/Mdu+9UVci\nIiL5TGEjh7VrB7/6lQ+l6LwUERGJisJGjist9Umi//xn1JWIiEi+UtjIcQceCD16aBmsiIhER2Ej\nx5n5Jl9PPgmLFkVdjYiI5COFjTxw6qnQuDGMGxd1JSIiko8UNvLAjjvC0KEwfjz88EPU1YiISL5R\n2MgTI0bA55/Dww9HXYmIiOQbhY080bUrHHqoJoqKiEjtU9jII6WlMGsWvP561JWIiEg+UdjII8cc\nA7vtpvNSRESkdils5JG6dWH4cCgrgy++iLoaERHJFwobeebss33r8gkToq5ERETyhcJGnmnZEgYP\nhjFjYP36qKsREZF8oLCRh0pL4eOP4Zlnoq5ERETygcJGHtp3X//QMlgREakNCht5qrQUnn0W3n03\n6kpERCTXKWzkqV//Gnbe2eduiIiIpJPCRp5q2BDOOQcmToSvv466GhERyWUKG3ns/PPhq6/g/vuj\nrkRERHKZwkYe+9nP4NhjfaJoCFFXIyIiuUphI8+VlsJbb8HLL0ddiYiI5CqFjTxXVARdumgZrIiI\npI/CRp4zg5ISePxxWLw46mpERCQXKWwIp58ODRrA7bdHXYmIiOQihQ2hSRMPHOPGwZo1UVcjIiK5\nRmFDAB9KWbYMHnss6kpERCTXKGwIAD16wEEHaaKoiIiknsKG/FdpKcycCXPnRl2JiIjkEoUN+a/j\njoNdd4XRo6OuREREconChvxXvXq+hfn998OqVVFXIyIiuUJhQzZx7rmwbh3cdVfUlYiISK5Q2JBN\ntG4NJ53kR89v2BB1NSIikgsUNuRHSkvh/fdh2rSoKxERkVygsCE/0rcv7LOPlsGKiEhqKGzIj5h5\n78Y//uE9HCIiIttCYUOqNXgwNG0KY8dGXYmIiGQ7hQ2pVqNGcPbZMGECfPtt1NWIiEg2SzpsmFl/\nM5tsZovNbIOZDaymzVVmtsTMvjWz58ysY8L9BmY22sxWmNlXZvaImbVKaNPMzO43s0ozW2Vmd5hZ\n44Q27c1sipl9Y2ZLzew6M1OASpHhw6GyEsrKoq5ERERqS2UlvP46PPAAXH21H9S5//5QVFTz56xX\ng8c0BuYCE4AfHdtlZr8HSoHTgY+APwPTzKxbCKHqTNGbgKOAQcBqYDTwKNA/7qkmAa2BIqA+MBEY\nB5wa+z51gGeAJUBfYBfgXmANcGkNXpck6NABfvlLnyg6dCjUrRt1RSIikgqrV8PChfDeexs/qr7+\n/PON7Vq2hI4doXNnXzhQ06F1CyHUuFgz2wD8KoQwOe7aEuBvIYRRsa93ApYBZ4QQHop9/TkwOITw\neKxNF2Ae0DeEMNvMugFvA4UhhDmxNkcAU4B2IYSlZnYUMBloG0JYEWszDLgWaBlCWFdNvQVAeXl5\nOQUFBTV+3flkxgxPs927w1VXwfHHQx31HYmIZLyvvto0UMR/vnz5xnYtWkCnTv7RseOmnzdturFd\nRUUFhYWF4D+bK5KppSY9G5tlZnsAbYDpVddCCKvNbBawP/AQ0Dv2fePbLDCzRbE2s/GeilVVQSPm\neSAAfYAnY23erAoaMdOAsUAP4D+pfG356pBD4LXX4E9/ghNP9GR79dXe42EWdXUiIvnt6683hojE\nnoplyza2a958Y4A47LBNA0WzZumvM6VhAw8aAe/JiLcsdg98aGRNCGH1T7RpAyyPvxlCWG9mKxPa\nVPd9qu4pbKRInz6+wddLL8Gll8Ixx/heHH/+s4cRhQ4RkfT55hsPEtUNe3z22cZ2TZtuDBFFRZv2\nVDRvHl39kPqwkRVGjhxJkyZNNrlWXFxMcXFxRBVlhwED4MUX4bnn4LLL4NBD4aCDvKfjgAOirk5E\nJHt9+63va1TdHIolSza2a9JkY6A4+OBNhz1atEhdPWVlZZQlrA6orKys8fOlOmwsBQzvvYjvdWgN\nzIlrU9/Mdkro3Wgdu1fVJnF1Sl2geUKbfRO+f+u4e5s1atQozdmoITM4/HDvhnv6aQ8d/fvDkUd6\n6OjdO+oKRUQy03ffbQwUib0UixdvbLfTTht7Jfr333TIY+eda6c3ubpfwOPmbCQtpWEjhPChmS3F\nV5C8Af+dINoHX3ECUA6si7WJnyC6G/BqrM2rQFMz2ydu3kYRHmRmxbX5g5ntHDdv43CgEngnla9L\nfswMjj0Wjj4aHn3U53Tsuy8cd5xPJN1776grFBGpfd9/74GiuiGPTz7Z2G6HHTaGiH79Nh3yaNky\n94ankw4bsb0uOuI/+AE6mFlPYGUI4RN8WeulZrYQX/p6NfApPqmzasLoBOBGM1sFfAXcAswMIcyO\ntZlvZtOA8WY2HF/6+negLIRQ1WvxLB4q7o0tt20b+163hhDWJvu6pGbq1PFTYk84ASZNgiuugJ49\n4eST/fOuXaOuUEQktX74AT74oPpVHp98AlWLPBs33hgo9t9/0yGPVq1yL1D8lJr0bPQG/olPBA3A\nDbHrdwNnhRCuM7NG+J4YTYGXgaPi9tgAGAmsBx4BGgBTgZKE73MKcCu+CmVDrO2FVTdDCBvM7Bh8\n9ckrwDf4XhyX1+A1yTaqWxdOO823OZ840YdUevTwa3/6k+/ZISKSLdas2RgoEnspFi3aGCgaNdoY\nIE45ZePnnTpB69b5FSh+yjbts5FttM9G7fnhBxg/Hv7f/4MVK+Css3wlS/v2UVcmIuLWrIEPP6x+\nyOPjj2HDBm+3/fab9krED3m0bZs/gSJj9tkQqdKggZ8ce9ZZMGYMXHut93icfz5ccgm0abPFpxAR\nSbm1a2HcOLjlFu+5WL/erzdsuDFAnHTSpuFil13yJ1Cki8KGpFWjRvCb38CwYXDzzXD99d7jccEF\n8LvfpXaplojI5oQAzzzj/x4tWABDhvjn8YFCuyOnj95aqRU77ujDKB9+CP/3f97bsccecPnlfuiP\niEi6vPUWHHGEb0jYti1UVMC998J55/leFe3aKWikm95eqVXNmvnk0Q8+8N6O667z0HHNNb7trohI\nqixf7kO3PXv6LzpPPAHTp0OvXlFXln8UNiQSLVvC3/7m69GHDPEejg4dYNQo3/hGRKSmfvjB/33p\n1AkefNCHb99+2/cB0tyLaChsSKR22QX+/nefAX7ccfDb3/rErDFjfKa4iMjWCsE3Geze3Sein366\n/9syciTUrx91dflNYUMyws9+5hNH58/3w91KS6FzZ7jzTli3LurqRCTTlZf7WU0nnuibCb75pv8i\ns/POUVcmoLAhGaZjR5+49dZbvv352Wf7bymTJm1coiYiUmXJEhg61P+9WLECpk6FKVOgW7eoK5N4\nChuSkbp3h4cfhjlz/LeUIUN8ktdjj23cuU9E8te33/o5TJ06ebgYPRr+8x9fdSKZR2FDMlqvXjB5\nMrz2ms/vGDQICgt9vbxCh0j+2bAB7rsPunSBP/8ZRozweRnDh0M97RyVsRQ2JCv06QPPPgsvvOCn\nJR59tJ+UOGNG1JWJSG155RU/0Oy002C//WDePF910rRp1JXJlihsSFY58EB48UWYNs3ncBQV+aY8\nM2dGXZmIpMtHH/lJ0v36+YTxF17wVSd77hl1ZbK1FDYk65jB4Yf70MrkybBqFRxwABx1FLz+etTV\niUiqrF7tS1i7doWXX4a77oJ//9t/6ZDsorAhWcsMjj3Wtx5+6CH/7WfffeH4433Zm4hkp/XrfSl8\np05w001+jtK77/qqE20rnp30n02yXp06fkrjW2/BPffAG2/4ypXiYj9wSUSyx/TpUFDg55YcdpiH\njKuu8rlakr0UNiRn1K3rE8fmz/cjpP/1L19CO3Son8UiIpnr3Xd9F+FDD4XGjX2Y9L77oH37qCuT\nVFDYkJyz3XZw7rm+HO6mm3yTny5d/ECmTz+NujoRibdqlW8n3qOH75PxwAM+4btPn6grk1RS2JCc\n1bAhXHAN5MYBAAAS0ElEQVSB92pccw088ojvUHrRRbB0adTVieS3tWt9O/GOHeGOO3yoZN48X3Wi\nw9Jyj8KG5LxGjeA3v/Ejpi+9FCZO9CVzF18MX3wRdXUi+SUE3/Fz773hwgvhhBO8F/KSS2D77aOu\nTtJFYUPyxo47etj48EPvtr31VthjD7jiCqisjLo6kdz31lu+nfgxx0Dbtr6SbPx4aNMm6sok3RQ2\nJO80a+bbHH/4oc94/+tfPXRccw18/XXU1YnknuXLfc5Uz57+9+6JJ3zVSa9eUVcmtUVhQ/JWy5Zw\n/fXw/vt+0Nvll0OHDjBqFHz3XdTViWS/H36A667z/TIefND/vr39tq860byM/KKwIXlvl118otp7\n7/k/gr/9rU9aGzsW1qyJujqR7BOCT8ju1g3+8Ac4/XRYuNCHL+vXj7o6iYLChkjMz37m48fz58Mh\nh0BJCXTu7Fskr1sXdXUi2aG83LcTP+kkDxtvvulhvkWLqCuTKClsiCTo2BHuvdcns+27L5x1lm8O\nNmmSb6MsIj+2eDGccQb07g0rV/phiVOmeOAQUdgQ2Yzu3eHhh33GfJcuPq+jZ0947DHvJhYR+PZb\nuPJK7wV85hkffpw71w9LFKmisCGyBfvsA089Ba++6sv1Bg3y396eeUahQ/LXhg2+nXjnzvCXv/iw\n48KFvuqkXr2oq5NMo7AhspX69oXnnoMXXvCNwo4+Gvr1gxkzoq5MpHbNnOl/H047zf985x1fddKk\nSdSVSaZS2BBJ0oEHwksv+Zj0unVQVOQTSmfOjLoykfT66CPfTvyAA3z+0osv+qqTPfeMujLJdAob\nIjVg5mPSs2bBk0/6tucHHAC//KXPxhfJJatX+3biXbvCyy/7lv///jcMGBB1ZZItFDZEtoEZDBwI\nc+b4pkUffujzOY4/3q+JZLP16305eKdOcPPN8Lvf+VHwZ5wBdfTTQ5Kg/11EUqBOHfj1r3257D33\nwBtvQEEBHHQQPP64lsxK9pk+3f8fPu88OOwwWLDAT2bdYYeoK5NspLAhkkJ16/qkuQULfNnsunV+\nqmWnTr4Nug58k0z37rveW3fooR4sZs3yVSft20ddmWQzhQ2RNKhXD048Ef71Lx/b7tfPu6DbtfNj\ntRcujLpCkU2tXAkXXQQ9enjP3AMP+P+/++0XdWWSCxQ2RNKsd2/fkfTjj/0f80mTfG+C446Df/5T\ne3VItNauhVtu8d63CRN8qGT+fF91osPSJFUUNkRqyS67wNVXw6JFPunugw98yWyvXn7+yvffR12h\n5JMQ4OmnYa+9PASfcIIfRnjJJdCwYdTVSa5R2BCpZdtvD2ef7V3Vzz8Pu+3m56/stpsfc790adQV\nSq57801fun3ssbDrrr5yavx4aNMm6sokVylsiETEzDcEe+opn5R38slwww0eOk4/3c9kEUml5ct9\nO/FevXxY78knPfD27Bl1ZZLrFDZEMkCnTn4M96efwrXX+g6lhYW+adJjj2nprGybH37w7cQ7dfL9\nYG64wZdpDxyoeRlSOxQ2RDJI06bwv//rq1UefdSvDRrkx97feKOWzkpyQvDtxLt1gz/8wTfjWrjQ\n52jUrx91dZJPFDZEMlC9ej5h76WX4PXXoX9/uPhiXzr7P//jE/lEfsrrr/s5PiedBN27e0/GLbdA\nixZRVyb5SGFDJMMVFvqupB9/DCNHQlkZdOnik/umT9fSWdnU4sXeg7Hvvr53xrRpvuqka9eoK5N8\nprAhkiXatvU9ED75xPdDWLTId3nce2//+rvvoq5QovTtt3Dllb6Hyz/+AWPHwty5vupEJGoKGyJZ\npmFDOPNM/0EyYwZ06ADnnuurWC67DJYsibpCqU0bNvimcZ07w1/+AiUlPsx2/vk+HCeSCRQ2RLKU\nGRx8sC9ffPddOOUUuOkm2H13P59FR93nrlWr/IC/khIfUjv9dOjbF+bN81UnTZpEXaHIphQ2RHJA\nx45+BPinn8Jf/+pnWvTu7RNLH3nED4ST7PXddz4/55JL/KySnXf2CcTPPut7tbz8sv937tAh6kpF\nqqewIZJDmjTxSaQLF/pvvnXr+mqEPfeE66+HL7+MukLZGuvX+wF+11zj83KaNfM/77zTg+X48fDR\nRz5cctttcMABUVcs8tM0oieSg+rWhV/9yj/mzPFejz/+Ea64AoYO9eWznTtHXaVUCQEWLPDei+nT\n/YC+L7/0I94PPNA3eisqgp//XJtwSXZS2BDJcfvsAxMn+g+s227zVQqjR8PRR/vmTkVF+gEWhSVL\nPFg8/7z/uXgxbLedz70YOdL/u+y3n18TyXYKGyJ5ok0b79m4+GJ44AGfTHrYYdCjB1x4IZx6qh8S\nJ+lRWQkvvLAxXMyb59d79oTBgz1c9O/vvRkiuUZzNkTyTMOGPpQyZ47/8OvUCYYNg/btfahl8eKo\nK8wNP/zgwyGXXuq9Fc2b+7DWlCk+x+KBB/xgtLlzfT7NUUcpaEjuSnnYMLM6Zna1mX1gZt+a2UIz\nu7SadleZ2ZJYm+fMrGPC/QZmNtrMVpjZV2b2iJm1SmjTzMzuN7NKM1tlZneYWeNUvyaRXGTm8wEe\nf9wnGp52mh8Gt/vuMGSIT1CUrbd+vS83vu4630irWTM45BAYN87f09tug/ffhw8+gNtv91N+W7aM\numqR2pGOno2LgWHACKAr8Dvgd2ZWWtXAzH4PlALnAfsB3wDTzCz+aKCbgKOBQcAAYBfg0YTvNQno\nBhTF2g4AxqX+JYnktj33hFGjfOns9dfDa6/5fIF+/eDhh7V0tjohbFwNcuKJ0KqVLze+8krfTOvq\nq73XYtky78U491wtTZX8ZSHFByuY2VPA0hDCuXHXHgG+DSGcHvt6CfC3EMKo2Nc7AcuAM0IID8W+\n/hwYHEJ4PNamCzAP6BtCmG1m3YC3gcIQwpxYmyOAKUC7EMLSamorAMrLy8spKChI6esWySXr1/t5\nGjff7EMB7dvDBRfAOef4b+z5aulS37W1at7FokW+8qdvX59zceih0KePTlSV3FRRUUFhYSH4z92K\nZB6bjp6NV4AiM+sEYGY9gX7AM7Gv9wDaANOrHhBCWA3MAvaPXeqNT16Nb7MAWBTXpi+wqipoxDwP\nBKBPyl+VSB6pWxeOO85/sM6d6z9EL73UT50dMQLmz4+6wtqxerWHrosugr328vNphgzxE1VPOMHv\nrVrlm6hdeaVP8FTQEPmxdKxGuRbYCZhvZuvxQPPHEMIDsftt8ECwLOFxy2L3AFoDa2IhZHNt2gDL\n42+GENab2cq4NiKyjXr29M2krrnG5x+MGePLZ486yn8IH3ZY7iydXbPGh5Cqei5mzfJent1288D1\nhz/4PIzWraOuVCS7pCNsnAycAgwG3gF6ATeb2ZIQwr1p+H5JGzlyJE0SDg8oLi6muLg4oopEMl/r\n1vCnP8Hvfw8PPuhLZ484Arp337h0tlGjqKtMzoYN8MYbG8PFSy/56anNm3uoGD3ah0f23DN3ApXI\n1igrK6OsrGyTa5WVlTV+vnTM2VgEXBNCGBt37Y/AkBBC99gwyvtArxDCG3FtXgDmhBBGmtnB+JBI\ns/jeDTP7CBgVQrjZzM4Erg8htIi7Xxf4HjgxhPBkNbVpzoZIioTgZ3LcdBM88YTP5Rg2zIdZ2rWL\nurrN++CDjeFixgxYscL3F+nf33svioqgVy+oo40BRDaRaXM2GgHrE65tqPpeIYQPgaX4ChLgvxNE\n++DzPQDKgXUJbboAuwGvxi69CjQ1s33ivk8RYPj8DxFJIzMYMAAee8zPYjnjDO8J2GMPKC72IYhM\nsHz5xtUge+zhvRQjRvjkzvPP9wmwq1bBtGnw299CQYGChkiqpWMY5SngUjP7FF8tUgCMBO6Ia3NT\nrM1C4CPgauBT4EnwCaNmNgG40cxWAV8BtwAzQwizY23mm9k0YLyZDQfqA38HyqpbiSIi6dOhA9x4\no0+SnDjRV7H07esfF13kkylra9vtr7/24ZCqrcDfiPWfdu8OAwd6z8WBB+oYdpHalI6wUYqHh9FA\nK2AJMDZ2DYAQwnVm1gjfE6Mp8DJwVAhhTdzzjMR7SB4BGgBTgZKE73UKcCs+5LIh1vbC1L8kEdka\nO+7oS2RHjIBnnvEhlsGDfViltNR7F5o3T+33XLvWe1GqwsVrr/m+IO3a+bDIb3/rAaNt29R+XxHZ\neimfs5HJNGdDpPa98Yb3dNx/vw9PnHGGnzrbrVvNnm/DBnjrrY3h4qWXvDejWTM4+OCN8y46ddKk\nTpFU2pY5GzqITUTSau+9YcKETZfO3nabr2S56CLf2ntLcyQ++mhjuJgxw+dhNGzoZ4xceqmHi332\n8f1BRCTzKGyISK1o1Qouu8yXzj70kG+PftRR0LWrL5097TRoHDvZaMUKn7hZtWrk/fc9kPTu7buY\nFhXBL37hgUNEMp/ChojUqvr1fU+OIUNg5kyf11FS4htmDRzowy5z5/rS2q5dPZAUFcFBB0HTplFX\nLyI1obAhIpEw82GQAw7wYZJbb4WpU33p6UUXecDYddeoqxSRVFDYEJHI7b67nzZ7/fVRVyIi6aCt\na0RERCStFDZEREQkrRQ2REREJK0UNkRERCStFDZEREQkrRQ2REREJK0UNkRERCStFDZEREQkrRQ2\nREREJK0UNkRERCStFDZEREQkrRQ2REREJK0UNkRERCStFDZEREQkrRQ2ZIvKysqiLiEr6X1Lnt6z\nmtH7ljy9Z7VLYUO2SH8pa0bvW/L0ntWM3rfk6T2rXQobIiIiklYKGyIiIpJWChsiIiKSVvWiLqCW\nNQSYN29e1HVklcrKSioqKqIuI+vofUue3rOa0fuWPL1nyYv72dkw2cdaCCG11WQwMzsFuD/qOkRE\nRLLYkBDCpGQekG9howVwBPAR8H201YiIiGSVhsDuwLQQwhfJPDCvwoaIiIjUPk0QFRERkbRS2BAR\nEZG0UtgQERGRtFLYEBERkbRS2BAREZG0ypuwYWYlZvahmX1nZq+Z2b5R15TpzKy/mU02s8VmtsHM\nBkZdUyYzs0vMbLaZrTazZWb2uJl1jrquTGdm55vZf8ysMvbxipkdGXVd2cTMLo79Hb0x6loymZld\nHnuf4j/eibquTGdmu5jZvWa2wsy+jf19LUjmOfIibJjZycANwOXAPsB/gGlmtnOkhWW+xsBcYASg\nNdJb1h/4O9AHOBTYDnjWzLaPtKrM9wnwe6AAKARmAE+aWbdIq8oSsV+czsP/XZMtewtoDbSJfRwQ\nbTmZzcyaAjOBH/B9qroB/wesSup58mGfDTN7DZgVQrgw9rXh/8DdEkK4LtLisoSZbQB+FUKYHHUt\n2SIWZpcDA0II/4q6nmxiZl8Avwkh3BV1LZnMzHYAyoHhwGXAnBDC/0ZbVeYys8uB40IISf1Wns/M\n7Fpg/xDCgdvyPDnfs2Fm2+G/LU2vuhY8YT0P7B9VXZIXmuI9QiujLiRbmFkdMxsMNAJejbqeLDAa\neCqEMCPqQrJIp9jQ8Ptmdp+ZtY+6oAx3LPC6mT0UGx6uMLNzkn2SnA8bwM5AXWBZwvVleBeaSMrF\nes9uAv4VQtCY8BaY2c/N7Cu8q3YMcHwIYX7EZWW0WCjrBVwSdS1Z5DVgKD4ccD6wB/CSmTWOsqgM\n1wHvOVsAHA6MBW4xs9OSeZJ8O/VVpLaMAboD/aIuJEvMB3oCTYATgXvMbIACR/XMrB0eZg8NIayN\nup5sEUKYFvflW2Y2G/gY+DWgIbvq1QFmhxAui339HzP7OR7W7k3mSXLdCmA9PiEoXmtgae2XI7nO\nzG4FfgkcFEL4LOp6skEIYV0I4YMQwpwQwh/xyY4XRl1XBisEWgIVZrbWzNYCBwIXmtmaWM+abEEI\noRJ4F+gYdS0Z7DNgXsK1ecBuyTxJzoeNWOovB4qqrsX+IhYBr0RVl+SmWNA4Djg4hLAo6nqyWB2g\nQdRFZLDngb3wYZSesY/XgfuAniEfZv6nQGyCbUf8B6pUbybQJeFaF7xHaKvlyzDKjcBEMysHZgMj\n8QloE6MsKtPFxjE7AlW/JXUws57AyhDCJ9FVlpnMbAxQDAwEvjGzqt60yhDC99FVltnM7C/AP4BF\nwI7AEPy39MOjrCuThRC+ATaZC2Rm3wBfhBASfwuVGDP7G/AU/oNyV+BKYC1QFmVdGW4UMNPMLgEe\nwpf2nwOcm8yT5EXYCCE8FFuGeBU+fDIXOCKE8Hm0lWW83sA/8RUVAd+rBOBu4Kyoispg5+Pv0wsJ\n188E7qn1arJHK/z/qbZAJfAGcLhWWCRNvRlb1g6YBLQAPgf+BfQNIXwRaVUZLITwupkdD1yLL6/+\nELgwhPBAMs+TF/tsiIiISHRyfs6GiIiIREthQ0RERNJKYUNERETSSmFDRERE0kphQ0RERNJKYUNE\nRETSSmFDRERE0kphQ0RERNJKYUNERETSSmFDRERE0kphQ0RERNLq/wNbbycBfcekNAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11568a890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "#ax.plot(my_series.spend_usd, color=\"red\", label=\"experiment\")\n",
    "ax.plot(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}