Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ep135 = pd.read_csv('./episodic/episodes/item_daily-totals_TheEtherReview67CelebratingErikVoorheesWeekwithErikVoorhees_2017-06-24.csv')\n",
    "ep134 = pd.read_csv('./episodic/episodes/item_daily-totals_TBP134MoreMarketingDiscipline_2017-06-24.csv')\n",
    "ep133 = pd.read_csv('./episodic/episodes/item_daily-totals_TBP133ICOWoeswithMyEtherWallet_2017-06-24.csv')\n",
    "ep132 = pd.read_csv('./episodic/episodes/item_daily-totals_TBP132DarkSideofthePrism_2017-06-24.csv')\n",
    "ep131 = pd.read_csv('./episodic/episodes/item_daily-totals_TBP131EndingCorporateDominance_2017-06-24.csv')\n",
    "ep130 = pd.read_csv('./episodic/episodes/item_daily-totals_TBP130ConsensusRecapandSphre_2017-06-24.csv')\n",
    "ep129 = pd.read_csv('./episodic/episodes/item_daily-totals_TBP129TheInherentRisks_2017-06-24.csv')\n",
    "ep128 = pd.read_csv('./episodic/episodes/item_daily-totals_TBP128Family_2017-06-24.csv')\n",
    "ep127 = pd.read_csv('./episodic/episodes/item_daily-totals_TBP127MarketingDiscipline_2017-06-24.csv')\n",
    "ep126 = pd.read_csv('./episodic/episodes/item_daily-totals_Episode9OnRampingwithDee_2017-06-24.csv')\n",
    "ep125 = pd.read_csv('./episodic/episodes/item_daily-totals_Episode10OnRampingwithDee_2017-06-24.csv')\n",
    "ep124 = pd.read_csv('./episodic/episodes/item_daily-totals_BlockChannelEpisode22TacklingtheRealEstateDinosaurwithREX_2017-06-24.csv')\n",
    "ep123 = pd.read_csv('./episodic/episodes/item_daily-totals_BlockChannelEpisode21BuildingUnstoppableOrganizationswithAragon_2017-06-24.csv')\n",
    "eps = [ep135, ep134, ep133, ep132,ep131,ep130, ep129,ep128,ep127,ep126,ep125,ep124,ep123]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3149.5384615384614"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sums = []\n",
    "for ep in eps: \n",
    "    sums.append(ep.total_downloads.sum())\n",
    "print(\"You have an average of sum(sums)/len(eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c847630>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Grps2C0hEpILEpY++I7CcMBj7HvA8cI67L2uzIERE2pk27bpx90/N7HJgHg2n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\nihIK3NoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c851cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ep123.set_index('date').total_downloads.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c2557f0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c7b7358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ep124.set_index('date').total_downloads.plot()"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
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       "      <th>0</th>\n",
       "      <td>2017-05-22</td>\n",
       "      <td>2017</td>\n",
       "      <td>May</td>\n",
       "      <td>22</td>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-23</td>\n",
       "      <td>2017</td>\n",
       "      <td>May</td>\n",
       "      <td>23</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-24</td>\n",
       "      <td>2017</td>\n",
       "      <td>May</td>\n",
       "      <td>24</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-25</td>\n",
       "      <td>2017</td>\n",
       "      <td>May</td>\n",
       "      <td>25</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>2017</td>\n",
       "      <td>May</td>\n",
       "      <td>26</td>\n",
       "      <td>277</td>\n",
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      "text/plain": [
       "         date  year month  day  total_downloads\n",
       "0  2017-05-22  2017   May   22              607\n",
       "1  2017-05-23  2017   May   23              777\n",
       "2  2017-05-24  2017   May   24              424\n",
       "3  2017-05-25  2017   May   25              341\n",
       "4  2017-05-26  2017   May   26              277"
      ]
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     "metadata": {},
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   ],
   "source": [
    "ep123.head()"
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   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>year</th>\n",
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       "      <td>699</td>\n",
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       "      <td>2017-05-28</td>\n",
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       "      <td>28</td>\n",
       "      <td>489</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-29</td>\n",
       "      <td>2017</td>\n",
       "      <td>May</td>\n",
       "      <td>29</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-30</td>\n",
       "      <td>2017</td>\n",
       "      <td>May</td>\n",
       "      <td>30</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>2017</td>\n",
       "      <td>May</td>\n",
       "      <td>31</td>\n",
       "      <td>84</td>\n",
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      "text/plain": [
       "         date  year month  day  total_downloads\n",
       "0  2017-05-27  2017   May   27              699\n",
       "1  2017-05-28  2017   May   28              489\n",
       "2  2017-05-29  2017   May   29              172\n",
       "3  2017-05-30  2017   May   30               97\n",
       "4  2017-05-31  2017   May   31               84"
      ]
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     "execution_count": 14,
     "metadata": {},
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