first

gistfile1.txt

{
 "metadata": {
  "name": "",
  "signature": "sha256:d4b23e226da34d2551cc53785dec53323456345fb3d351654cdc1cabfa566a91"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Cumulative Population Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This section contains all the data collected. It will serve as the base comparison between each subgroup."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd C:\\tmp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "C:\\tmp\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import datetime\n",
      "import time\n",
      "import numpy as np\n",
      "import matplotlib as mpl\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns\n",
      "import create_list\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Reads the .csv data\n",
      "df = pd.read_csv(\"june_final.csv\", header=None, names=['id', 'name', 'followers', 'current_viewers', 'date_time'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extracts individual pieces to be used later for the new datetime\n",
      "df['years'] = pd.DatetimeIndex(df['date_time']).year\n",
      "df['months'] = pd.DatetimeIndex(df['date_time']).month\n",
      "df['days'] = pd.DatetimeIndex(df['date_time']).day\n",
      "df['hours'] = pd.DatetimeIndex(df['date_time']).hour\n",
      "df['minutes'] = pd.DatetimeIndex(df['date_time']).minute"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1. Check For Consistent Data Distribution"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Checking to see that there is a consistent distribution of the data between every hour. Data was collected every ten minutes starting on May 30, 2015 22:40 PDT until June 30, 2015 22:30 PDT. The decision for ten minute intervals was based on how slowly the viewer numbers changed. Also, data was lost due to server-side failings not allowing for every second updates. Based on the below plot, the difference between each hour is small and should not affect further numbers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.hist(\"hours\", bins=24, range=(0,23))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x083BC410>]], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x2d17610>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2. Difference Between All Data And Non-Tournament Data Streaming Times"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Tournament streams generally lead to higher viewer counts, leading to a higher average. In the below box plots, we can see that around 11:00 PDT there is a large difference in average compared to the non-tournament streams. The differences become much smaller at around 21:00 PDT where it is less likely to see a major tournament."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Code To Remove Tournament Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Remove all the tournament data to compare with previous boxplot\n",
      "tournament_list = ['dreamhackhs', 'viagamehs', 'esl_hearthstone', 'tempo_storm', 'wca_america', 'nvidia', 'nvidiafrance',\n",
      "                   'starladder_hs_ru', 'starladder_hs_eu', 'vulcunhs', 'viagamehs_ru', 'onenationofgamers', 'stormstudio_hs_ru', \n",
      "                   'hkesportstv', 'hearthstonefr', 'readyuptv', 'pvplive']\n",
      "\n",
      "# ~ -> not, isin -> in used for lists\n",
      "no_tourn_df = df[~df['name'].isin(tournament_list)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Box Plot With Tournament Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = sns.boxplot(x='hours', y='current_viewers', data=df, fliersize=0, order=np.arange(0,24))\n",
      "ax.set(ylim=(-100, 4000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[(-100, 4000)]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VTRCR189iXUREpM6kXZlOREQWICUJERGJpSQhIiKxlCRERCRWYpIws1eW2faG6OF3Z71G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\nSYiISCwlCRERifX/AYEDfubOh5BNAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8325a90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Box Plot Without Tournament Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bx = sns.boxplot(x='hours', y='current_viewers', data=no_tourn_df, fliersize=0, order=np.arange(0,24))\n",
      "bx.set(ylim=(-100, 4000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[(-100, 4000)]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mptaWwhdLD0M0VbjIQqQkQe7mr0ziKcD7MhlaCVNHjGSyiZYm7c9kaaXwpP2F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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10536770>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Table Comparing Average Viewers Between Tournament And No Tournament Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This shows the mean of the dataset including tournaments\n",
      "mean_streamer_tourn = df.groupby(by=['hours'])['current_viewers'].agg(np.mean)\n",
      "mean_streamer_tourn.sort(ascending=False, inplace=True)\n",
      "mean_streamer_tourn = pd.DataFrame(mean_streamer_tourn).reset_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Shows the mean of the dataset without tournaments\n",
      "mean_streamer_none_hr = no_tourn_df.groupby(by=['hours'])['current_viewers'].agg(np.mean)\n",
      "mean_streamer_none_hr.sort(ascending=False, inplace=True)\n",
      "mean_streamer_none_hr = pd.DataFrame(mean_streamer_none_hr).reset_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Table showing the mean of the dataset with the tournament channels vs without\n",
      "streamer_mean = pd.merge(mean_streamer_tourn, mean_streamer_none_hr, on='hours')\n",
      "streamer_mean.sort(columns='hours', inplace=True)\n",
      "streamer_mean.columns = ('hours', 'withTournament', 'withoutTournament')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def difference(s):\n",
      "    return s['withTournament'] - s['withoutTournament']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "streamer_mean['difference'] = streamer_mean.apply(difference, axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "streamer_mean.reset_index(drop=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>hours</th>\n",
        "      <th>withTournament</th>\n",
        "      <th>withoutTournament</th>\n",
        "      <th>difference</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0</td>\n",
        "      <td>1195.915636</td>\n",
        "      <td>1155.358417</td>\n",
        "      <td>40.557219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
        "      <td>1128.483657</td>\n",
        "      <td>1070.227273</td>\n",
        "      <td>58.256384</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
        "      <td>979.806627</td>\n",
        "      <td>847.023154</td>\n",
        "      <td>132.783473</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
        "      <td>1143.189767</td>\n",
        "      <td>949.556029</td>\n",
        "      <td>193.633738</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>4</td>\n",
        "      <td>1360.642857</td>\n",
        "      <td>1141.536217</td>\n",
        "      <td>219.106640</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>5</td>\n",
        "      <td>1468.886708</td>\n",
        "      <td>1228.092558</td>\n",
        "      <td>240.794150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>6</td>\n",
        "      <td>1704.873095</td>\n",
        "      <td>1499.790976</td>\n",
        "      <td>205.082119</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>7</td>\n",
        "      <td>1945.114182</td>\n",
        "      <td>1753.298268</td>\n",
        "      <td>191.815914</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>8</td>\n",
        "      <td>2122.034217</td>\n",
        "      <td>1908.880928</td>\n",
        "      <td>213.153289</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>9</td>\n",
        "      <td>2148.573836</td>\n",
        "      <td>1933.758179</td>\n",
        "      <td>214.815658</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>10</td>\n",
        "      <td>2146.965605</td>\n",
        "      <td>1888.814444</td>\n",
        "      <td>258.151161</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>11</td>\n",
        "      <td>2207.114783</td>\n",
        "      <td>1765.159171</td>\n",
        "      <td>441.955611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>12</td>\n",
        "      <td>2267.345432</td>\n",
        "      <td>1698.520000</td>\n",
        "      <td>568.825432</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>13</td>\n",
        "      <td>2250.895422</td>\n",
        "      <td>1818.843523</td>\n",
        "      <td>432.051898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>14</td>\n",
        "      <td>2101.728727</td>\n",
        "      <td>1696.374579</td>\n",
        "      <td>405.354148</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>15</td>\n",
        "      <td>1921.576000</td>\n",
        "      <td>1530.884293</td>\n",
        "      <td>390.691707</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>16</td>\n",
        "      <td>1773.012739</td>\n",
        "      <td>1387.255054</td>\n",
        "      <td>385.757685</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>17</td>\n",
        "      <td>1557.514217</td>\n",
        "      <td>1261.043410</td>\n",
        "      <td>296.470806</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>18</td>\n",
        "      <td>1243.901905</td>\n",
        "      <td>966.188104</td>\n",
        "      <td>277.713801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>19</td>\n",
        "      <td>1142.715636</td>\n",
        "      <td>962.717302</td>\n",
        "      <td>179.998335</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>20</td>\n",
        "      <td>1133.508500</td>\n",
        "      <td>1018.842789</td>\n",
        "      <td>114.665711</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>21</td>\n",
        "      <td>1124.405350</td>\n",
        "      <td>1080.739201</td>\n",
        "      <td>43.666149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>22</td>\n",
        "      <td>1130.197073</td>\n",
        "      <td>1105.253866</td>\n",
        "      <td>24.943207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>23</td>\n",
        "      <td>1156.423210</td>\n",
        "      <td>1122.033943</td>\n",
        "      <td>34.389267</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "    hours  withTournament  withoutTournament  difference\n",
        "0       0     1195.915636        1155.358417   40.557219\n",
        "1       1     1128.483657        1070.227273   58.256384\n",
        "2       2      979.806627         847.023154  132.783473\n",
        "3       3     1143.189767         949.556029  193.633738\n",
        "4       4     1360.642857        1141.536217  219.106640\n",
        "5       5     1468.886708        1228.092558  240.794150\n",
        "6       6     1704.873095        1499.790976  205.082119\n",
        "7       7     1945.114182        1753.298268  191.815914\n",
        "8       8     2122.034217        1908.880928  213.153289\n",
        "9       9     2148.573836        1933.758179  214.815658\n",
        "10     10     2146.965605        1888.814444  258.151161\n",
        "11     11     2207.114783        1765.159171  441.955611\n",
        "12     12     2267.345432        1698.520000  568.825432\n",
        "13     13     2250.895422        1818.843523  432.051898\n",
        "14     14     2101.728727        1696.374579  405.354148\n",
        "15     15     1921.576000        1530.884293  390.691707\n",
        "16     16     1773.012739        1387.255054  385.757685\n",
        "17     17     1557.514217        1261.043410  296.470806\n",
        "18     18     1243.901905         966.188104  277.713801\n",
        "19     19     1142.715636         962.717302  179.998335\n",
        "20     20     1133.508500        1018.842789  114.665711\n",
        "21     21     1124.405350        1080.739201   43.666149\n",
        "22     22     1130.197073        1105.253866   24.943207\n",
        "23     23     1156.423210        1122.033943   34.389267"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3. The Effects Of Followers And Number Of Periods Streaming On Viewer Count"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we want to check to see if there is any correlation on how long a streamer streams or how many followers they have. The reason why both are included is to show that tournaments generally have high viewership, but low followers and streaming periods. From the plots, we can discern that without the tournament data, there are less outliers (as previously mentioned). However, there appears to be no correlation between how many periods a streamer streams and viewer count. There is a slight correlation between followers and viewer count, but that should be expected as people are alerted when their streamer is live."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Code For All Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Mean views of streamers\n",
      "avg_streamer = df.groupby('name')['current_viewers'].agg(np.mean)\n",
      "avg_streamer.sort(ascending=False, inplace=True)\n",
      "new_avg_streamer = pd.DataFrame(avg_streamer).reset_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Number of periods streamer streamed\n",
      "count_streamer = df.groupby('name')['current_viewers'].count()\n",
      "count_streamer.sort(ascending=False, inplace=True)\n",
      "new_count_streamer = pd.DataFrame(count_streamer).reset_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Mean followers of streamers\n",
      "avg_follower = df.groupby('name')['followers'].agg(np.mean)\n",
      "avg_follower = pd.DataFrame(avg_follower).reset_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Combine all data into final combined_streamer\n",
      "combined_streamer = pd.merge(new_avg_streamer, new_count_streamer, on='name')\n",
      "combined_streamer.columns = ('name', 'total_viewers', 'count')\n",
      "combined_streamer.sort(columns='total_viewers', ascending=False, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "combined_streamer_final = pd.merge(combined_streamer, avg_follower, on='name')\n",
      "combined_streamer_final.columns = ('name', 'mean_viewers', 'count', 'mean_followers')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Code For No Tournament Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_streamer_none_view = no_tourn_df.groupby('name')['current_viewers'].agg(np.mean)\n",
      "mean_streamer_none_view = pd.DataFrame(mean_streamer_none_view).reset_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_streamer_none_follow = no_tourn_df.groupby('name')['followers'].agg(np.mean)\n",
      "mean_streamer_none_follow = pd.DataFrame(mean_streamer_none_follow).reset_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count_streamer_none = no_tourn_df.groupby('name')['current_viewers'].count().reset_index()\n",
      "count_streamer_none.columns = ('name', 'count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "no_tourn_merge = pd.merge(mean_streamer_none_view, mean_streamer_none_follow, on='name')\n",
      "no_tourn_merge1 = pd.merge(no_tourn_merge, count_streamer_none, on='name')\n",
      "no_tourn_merge1.columns = ('name', 'mean_viewers', 'mean_followers', 'count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Plots Of Mean Viewers VS Number Of Periods Streaming/Mean Followers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.pairplot(combined_streamer_final, x_vars=['count', 'mean_followers'], y_vars=['mean_viewers'], size=6, kind='reg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<seaborn.axisgrid.PairGrid at 0x108be570>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Pmi2V9Td/d6H+MSkdzaOtAgBs36WrK6QS7iLmIqwVxbFWyskqnR+GyqVpl7he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\noOiqkM91enjYAv+jAWTaxsI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rJO0Gkv5xjgaKORV24SZZP/6fAnYbr3h6zMYqX2660lNb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\naQL/H6k4ND8ktwa2AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108be950>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.pairplot(no_tourn_merge1, x_vars=['count', 'mean_followers'], y_vars=['mean_viewers'], size=6, kind='reg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<seaborn.axisgrid.PairGrid at 0x108948f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7TSlXWzSSFq007XxqXmBVbn2SdGXSLGOy3KCWtDtVJ1fR60ernXL4hTM8dvg4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\nr3V71ogp8otp1OMSQiyP4XnNMN9TTin1BeBZrfWjwc/ntNY7Fnn4qp+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       "text": [
        "<matplotlib.figure.Figure at 0x10c070b0>"
       ]
      }
     ],
     "prompt_number": 24
    }
   ],
   "metadata": {}
  }
 ]
}