Week 2/code/.ipynb_checkpoints/Pre-processing with Pandas and Matplotlib-revised-checkpoint.ipynb

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   "source": [
    "# The below shows some basic munging and pre-processing using both Numpy and Pandas on a data set called video store.\n",
    "In many cases I did the task in Pandas. \n",
    "Also some work in Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "#import packages.\n",
    "%matplotlib inline \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/mpgartland/Documents/Courses/Predictive Models/Pred_Models_git/Week 1'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mpgartland/Documents/Courses/Predictive Models/Pred_Models_git/Week 1\n"
     ]
    }
   ],
   "source": [
    "cd /Users/mpgartland/Documents/Courses/Predictive Models/Pred_Models_git/Week 1/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Importing and printing a Pandas array is like a R Dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cust ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rentals</th>\n",
       "      <th>Avg Per Visit</th>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>M</td>\n",
       "      <td>31000</td>\n",
       "      <td>25</td>\n",
       "      <td>42</td>\n",
       "      <td>3.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>M</td>\n",
       "      <td>24000</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "      <td>4.7</td>\n",
       "      <td>No</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cust ID Gender  Income  Age  Rentals  Avg Per Visit Incidentals   Genre\n",
       "0         1      M   45000   25       32            2.5         Yes  Action\n",
       "1         2      F   54000   33       12            3.4          No   Drama\n",
       "2         3      F   32000   20       42            1.6          No  Comedy\n",
       "3         4      F   59000   70       16            4.2         Yes   Drama\n",
       "4         5      M   37000   35       25            3.2         Yes  Action\n",
       "5         6      M   18000   20       29            1.7          No  Action\n",
       "6         7      F   29000   45       19            3.8          No   Drama\n",
       "7         8      M   74000   25       31            2.4         Yes  Action\n",
       "8         9      M   38000   21       18            2.1          No  Comedy\n",
       "9        10      F   65000   40       21            3.3          No   Drama\n",
       "10       11      F   41000   22       48            2.3         Yes   Drama\n",
       "11       12      F   26000   22       32            2.9         Yes  Action\n",
       "12       13      M   83000   46       14            3.6          No  Comedy\n",
       "13       14      M   45000   36       24            2.7          No   Drama\n",
       "14       15      M   68000   30       36            2.7         Yes  Comedy\n",
       "15       16      M   17000   19       26            2.2         Yes  Action\n",
       "16       17      M   36000   35       28            3.5         Yes   Drama\n",
       "17       18      F    6000   16       39            1.8         Yes  Action\n",
       "18       19      F   24000   25       41            3.1          No  Comedy\n",
       "19       20      M   12000   16       23            2.2         Yes  Action\n",
       "20       21      F   47000   52       11            3.1          No   Drama\n",
       "21       22      M   25000   33       16            2.9         Yes   Drama\n",
       "22       23      F    2000   15       30            2.5          No  Comedy\n",
       "23       24      F   79000   35       22            3.8         Yes   Drama\n",
       "24       25      M    1000   16       25            1.4         Yes  Comedy\n",
       "25       26      F   56000   35       40            2.6         Yes  Action\n",
       "26       27      F   62000   47       32            3.6          No   Drama\n",
       "27       28      M   57000   52       22            4.1          No  Comedy\n",
       "28       29      F   15000   18       37            2.1         Yes  Action\n",
       "29       30      M   41000   25       17            1.4         Yes  Action\n",
       "30       31      F   49000   56       15            3.2          No  Comedy\n",
       "31       32      M   47000   30       21            3.1         Yes   Drama\n",
       "32       33      M   23000   25       28            2.7          No  Action\n",
       "33       34      F   29000   32       19            2.9         Yes  Action\n",
       "34       35      M   74000   29       43            4.6         Yes  Action\n",
       "35       36      F   29000   21       34            2.3          No  Comedy\n",
       "36       37      M   89000   46       12            1.2          No  Comedy\n",
       "37       38      M   41000   38       20            3.3         Yes   Drama\n",
       "38       39      F   68000   35       19            3.9          No  Comedy\n",
       "39       40      M   17000   19       32            1.8          No  Action\n",
       "40       41      F   50000   33       17            1.4          No   Drama\n",
       "41       42      M   32000   25       26            2.2         Yes  Action\n",
       "42       43      F   49000   28       48            3.3         Yes   Drama\n",
       "43       44      M   35000   24       24            1.7          No   Drama\n",
       "44       45      M   56000   38       30            3.5         Yes   Drama\n",
       "45       46      F   57000   43        9            1.1          No   Drama\n",
       "46       47      F   69000   35       22            2.8         Yes   Drama\n",
       "47       48      F   52000   47       14            1.6          No   Drama\n",
       "48       49      M   31000   25       42            3.4         Yes  Action\n",
       "49       50      M   24000   20       33            4.7          No  Action"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import Using Pandas\n",
    "video_pandas=pd.read_csv(\"data/Video_Store.csv\")\n",
    "video_pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Pandas works with this basic structure df.var.function()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cust ID', 'Gender', 'Income', 'Age', 'Rentals', 'Avg Per Visit',\n",
       "       'Incidentals', 'Genre'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#similar to names()\n",
    "video_pandas.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#similar to dim()\n",
    "video_pandas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cust ID            int64\n",
       "Gender            object\n",
       "Income             int64\n",
       "Age                int64\n",
       "Rentals            int64\n",
       "Avg Per Visit    float64\n",
       "Incidentals       object\n",
       "Genre             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show variables by type\n",
    "video_pandas.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n",
      "89000\n",
      "21409.753642216627\n",
      "42300.0\n"
     ]
    }
   ],
   "source": [
    "#Descriptives on Income\n",
    "print(video_pandas.Income.min()),\n",
    "print(video_pandas.Income.max()),\n",
    "print(video_pandas.Income.std()),\n",
    "print(video_pandas.Income.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 70 12.000272105758475 31.56\n"
     ]
    }
   ],
   "source": [
    "#Descriptives on Age using Pandas\n",
    "print(video_pandas.Age.min(),video_pandas.Age.max(),video_pandas.Age.std(),video_pandas.Age.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50.000000\n",
       "mean     26.320000\n",
       "std      10.047723\n",
       "min       9.000000\n",
       "25%      19.000000\n",
       "50%      25.000000\n",
       "75%      32.000000\n",
       "max      48.000000\n",
       "Name: Rentals, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Descriptives on Rentals. Similar to Summary in R\n",
    "video_pandas.Rentals.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cust ID             25.500\n",
       "Income           42300.000\n",
       "Age                 31.560\n",
       "Rentals             26.320\n",
       "Avg Per Visit        2.748\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#descptives via Pandas\n",
    "video_pandas.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre\n",
       "Action    18\n",
       "Comedy    12\n",
       "Drama     20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using pandas, count by category on a categorical variable.\n",
    "video_pandas.groupby('Genre').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Cust ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rentals</th>\n",
       "      <th>Avg Per Visit</th>\n",
       "      <th>Incidentals</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>45000</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>54000</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>3.4</td>\n",
       "      <td>No</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>32000</td>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>1.6</td>\n",
       "      <td>No</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>59000</td>\n",
       "      <td>70</td>\n",
       "      <td>16</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>37000</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>74000</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>M</td>\n",
       "      <td>38000</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>2.1</td>\n",
       "      <td>No</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>F</td>\n",
       "      <td>65000</td>\n",
       "      <td>40</td>\n",
       "      <td>21</td>\n",
       "      <td>3.3</td>\n",
       "      <td>No</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>F</td>\n",
       "      <td>41000</td>\n",
       "      <td>22</td>\n",
       "      <td>48</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>M</td>\n",
       "      <td>83000</td>\n",
       "      <td>46</td>\n",
       "      <td>14</td>\n",
       "      <td>3.6</td>\n",
       "      <td>No</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>M</td>\n",
       "      <td>45000</td>\n",
       "      <td>36</td>\n",
       "      <td>24</td>\n",
       "      <td>2.7</td>\n",
       "      <td>No</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>M</td>\n",
       "      <td>68000</td>\n",
       "      <td>30</td>\n",
       "      <td>36</td>\n",
       "      <td>2.7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>M</td>\n",
       "      <td>36000</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>47000</td>\n",
       "      <td>52</td>\n",
       "      <td>11</td>\n",
       "      <td>3.1</td>\n",
       "      <td>No</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>F</td>\n",
       "      <td>79000</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>F</td>\n",
       "      <td>56000</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>62000</td>\n",
       "      <td>47</td>\n",
       "      <td>32</td>\n",
       "      <td>3.6</td>\n",
       "      <td>No</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>57000</td>\n",
       "      <td>52</td>\n",
       "      <td>22</td>\n",
       "      <td>4.1</td>\n",
       "      <td>No</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>M</td>\n",
       "      <td>41000</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>49000</td>\n",
       "      <td>56</td>\n",
       "      <td>15</td>\n",
       "      <td>3.2</td>\n",
       "      <td>No</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>47000</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>M</td>\n",
       "      <td>74000</td>\n",
       "      <td>29</td>\n",
       "      <td>43</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>89000</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>1.2</td>\n",
       "      <td>No</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>41000</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>F</td>\n",
       "      <td>68000</td>\n",
       "      <td>35</td>\n",
       "      <td>19</td>\n",
       "      <td>3.9</td>\n",
       "      <td>No</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>F</td>\n",
       "      <td>50000</td>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>1.4</td>\n",
       "      <td>No</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>M</td>\n",
       "      <td>32000</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>F</td>\n",
       "      <td>49000</td>\n",
       "      <td>28</td>\n",
       "      <td>48</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>M</td>\n",
       "      <td>35000</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>1.7</td>\n",
       "      <td>No</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>56000</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>F</td>\n",
       "      <td>57000</td>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>No</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>F</td>\n",
       "      <td>69000</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>52000</td>\n",
       "      <td>47</td>\n",
       "      <td>14</td>\n",
       "      <td>1.6</td>\n",
       "      <td>No</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>M</td>\n",
       "      <td>31000</td>\n",
       "      <td>25</td>\n",
       "      <td>42</td>\n",
       "      <td>3.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cust ID Gender  Income  Age  Rentals  Avg Per Visit Incidentals   Genre\n",
       "0         1      M   45000   25       32            2.5         Yes  Action\n",
       "1         2      F   54000   33       12            3.4          No   Drama\n",
       "2         3      F   32000   20       42            1.6          No  Comedy\n",
       "3         4      F   59000   70       16            4.2         Yes   Drama\n",
       "4         5      M   37000   35       25            3.2         Yes  Action\n",
       "7         8      M   74000   25       31            2.4         Yes  Action\n",
       "8         9      M   38000   21       18            2.1          No  Comedy\n",
       "9        10      F   65000   40       21            3.3          No   Drama\n",
       "10       11      F   41000   22       48            2.3         Yes   Drama\n",
       "12       13      M   83000   46       14            3.6          No  Comedy\n",
       "13       14      M   45000   36       24            2.7          No   Drama\n",
       "14       15      M   68000   30       36            2.7         Yes  Comedy\n",
       "16       17      M   36000   35       28            3.5         Yes   Drama\n",
       "20       21      F   47000   52       11            3.1          No   Drama\n",
       "23       24      F   79000   35       22            3.8         Yes   Drama\n",
       "25       26      F   56000   35       40            2.6         Yes  Action\n",
       "26       27      F   62000   47       32            3.6          No   Drama\n",
       "27       28      M   57000   52       22            4.1          No  Comedy\n",
       "29       30      M   41000   25       17            1.4         Yes  Action\n",
       "30       31      F   49000   56       15            3.2          No  Comedy\n",
       "31       32      M   47000   30       21            3.1         Yes   Drama\n",
       "34       35      M   74000   29       43            4.6         Yes  Action\n",
       "36       37      M   89000   46       12            1.2          No  Comedy\n",
       "37       38      M   41000   38       20            3.3         Yes   Drama\n",
       "38       39      F   68000   35       19            3.9          No  Comedy\n",
       "40       41      F   50000   33       17            1.4          No   Drama\n",
       "41       42      M   32000   25       26            2.2         Yes  Action\n",
       "42       43      F   49000   28       48            3.3         Yes   Drama\n",
       "43       44      M   35000   24       24            1.7          No   Drama\n",
       "44       45      M   56000   38       30            3.5         Yes   Drama\n",
       "45       46      F   57000   43        9            1.1          No   Drama\n",
       "46       47      F   69000   35       22            2.8         Yes   Drama\n",
       "47       48      F   52000   47       14            1.6          No   Drama\n",
       "48       49      M   31000   25       42            3.4         Yes  Action"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using Pandas Query function. Custumers with incomes greater than $30K\n",
    "video_sub=video_pandas.query('Income > 30000') \n",
    "video_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "F    16\n",
       "M    18\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using pandas, count by category for a categorical variable.\n",
    "video_sub.groupby('Gender').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cust ID             26.029412\n",
       "Income           53470.588235\n",
       "Age                 35.764706\n",
       "Rentals             25.088235\n",
       "Avg Per Visit        2.835294\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Desciptives of customers with incomes greater than $30K\n",
    "video_sub.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama     18\n",
       "Action     8\n",
       "Comedy     8\n",
       "Name: Genre, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_sub.Genre.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre\n",
       "Action     8\n",
       "Comedy     8\n",
       "Drama     18\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using pandas, count by category for a categorical variable.\n",
    "video_sub.groupby('Genre').size()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#shorten the df\n",
    "vp=video_pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>13</th>\n",
       "      <td>0.369992</td>\n",
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       "      <th>14</th>\n",
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       "      <th>15</th>\n",
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       "      <th>16</th>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.296637</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.546654</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-1.296637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.703295</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.119997</td>\n",
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       "      <th>22</th>\n",
       "      <td>-1.379969</td>\n",
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       "      <th>23</th>\n",
       "      <td>0.286660</td>\n",
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       "      <th>24</th>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.286660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.286637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.703295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1.129974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.546654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.036620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.129997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.546654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.036666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>-0.213328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-0.879980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.203306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.536654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.286660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-1.046643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.119997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>-0.546654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>-0.296660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>-0.629986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.536654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.953312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.286660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1.286637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>-0.546654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>-0.963311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age\n",
       "0  -0.546654\n",
       "1   0.119997\n",
       "2  -0.963311\n",
       "3   3.203261\n",
       "4   0.286660\n",
       "5  -0.963311\n",
       "6   1.119975\n",
       "7  -0.546654\n",
       "8  -0.879980\n",
       "9   0.703317\n",
       "10 -0.796649\n",
       "11 -0.796649\n",
       "12  1.203306\n",
       "13  0.369992\n",
       "14 -0.129997\n",
       "15 -1.046643\n",
       "16  0.286660\n",
       "17 -1.296637\n",
       "18 -0.546654\n",
       "19 -1.296637\n",
       "20  1.703295\n",
       "21  0.119997\n",
       "22 -1.379969\n",
       "23  0.286660\n",
       "24 -1.296637\n",
       "25  0.286660\n",
       "26  1.286637\n",
       "27  1.703295\n",
       "28 -1.129974\n",
       "29 -0.546654\n",
       "30  2.036620\n",
       "31 -0.129997\n",
       "32 -0.546654\n",
       "33  0.036666\n",
       "34 -0.213328\n",
       "35 -0.879980\n",
       "36  1.203306\n",
       "37  0.536654\n",
       "38  0.286660\n",
       "39 -1.046643\n",
       "40  0.119997\n",
       "41 -0.546654\n",
       "42 -0.296660\n",
       "43 -0.629986\n",
       "44  0.536654\n",
       "45  0.953312\n",
       "46  0.286660\n",
       "47  1.286637\n",
       "48 -0.546654\n",
       "49 -0.963311"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Transform to Z-Scores\n",
    "#This is the manual way with formulas. Will show a function later.\n",
    "Age_Z=pd.DataFrame((vp.Age-vp.Age.mean())/vp.Age.std())\n",
    "Income_Z=pd.DataFrame((vp.Income-vp.Income.mean())/vp.Income.std())\n",
    "Rentals_Z=pd.DataFrame((vp.Rentals-vp.Rentals.mean())/vp.Rentals.std())\n",
    "#Showing how you use a variable that has spaces.\n",
    "Avg_Per_Visit_Z=pd.DataFrame((vp['Avg Per Visit']-vp['Avg Per Visit'].mean())/vp['Avg Per Visit'].std())\n",
    "Age_Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.126111</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546480</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.481089</td>\n",
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       "      <th>3</th>\n",
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       "      <td>-0.247551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Income\n",
       "0  0.126111\n",
       "1  0.546480\n",
       "2 -0.481089\n",
       "3  0.780018\n",
       "4 -0.247551"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Income_Z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Action  Comedy  Drama\n",
       "0        1       0      0\n",
       "1        0       0      1\n",
       "2        0       1      0\n",
       "3        0       0      1\n",
       "4        1       0      0\n",
       "5        1       0      0\n",
       "6        0       0      1\n",
       "7        1       0      0\n",
       "8        0       1      0\n",
       "9        0       0      1\n",
       "10       0       0      1\n",
       "11       1       0      0\n",
       "12       0       1      0\n",
       "13       0       0      1\n",
       "14       0       1      0\n",
       "15       1       0      0\n",
       "16       0       0      1\n",
       "17       1       0      0\n",
       "18       0       1      0\n",
       "19       1       0      0\n",
       "20       0       0      1\n",
       "21       0       0      1\n",
       "22       0       1      0\n",
       "23       0       0      1\n",
       "24       0       1      0\n",
       "25       1       0      0\n",
       "26       0       0      1\n",
       "27       0       1      0\n",
       "28       1       0      0\n",
       "29       1       0      0\n",
       "30       0       1      0\n",
       "31       0       0      1\n",
       "32       1       0      0\n",
       "33       1       0      0\n",
       "34       1       0      0\n",
       "35       0       1      0\n",
       "36       0       1      0\n",
       "37       0       0      1\n",
       "38       0       1      0\n",
       "39       1       0      0\n",
       "40       0       0      1\n",
       "41       1       0      0\n",
       "42       0       0      1\n",
       "43       0       0      1\n",
       "44       0       0      1\n",
       "45       0       0      1\n",
       "46       0       0      1\n",
       "47       0       0      1\n",
       "48       1       0      0\n",
       "49       1       0      0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dummies via Pandas\n",
    "Genre_dummy=pd.DataFrame(pd.get_dummies(vp['Genre']))\n",
    "Genre_dummy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#My Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4+e+DzHITwD777MO8efPGs/QJ09nZOWn6ApOrP5OpL2B/qmwy9QXsTw2UNiyiEuEE2I98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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ed0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of Income using 9 bins\n",
    "n, bins, patches = plt.hist(vp.Income, 9, facecolor='green', alpha=0.75)\n",
    "plt.ylim(0, 10)\n",
    "plt.title('histogram of Income using 9 bins')\n",
    "plt.xlabel(\"Income\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S5pKRkRFGRkY2aBsdHe3pNuZ80ACeDuwI/LJl+sbmwD8keUspZfeNPXHZsmVz7uqtY2Nj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\nofmAPihKWMEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ed0be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of Age using 7 bins\n",
    "n, bins, patches = plt.hist(vp.Age, 7, facecolor='blue', alpha=0.75)\n",
    "plt.ylim(0, 15)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title('Histogram of Age using 7 bins')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WaE5WzMzMrNCcrJiZmVmhOVkxMzOzQnOyYmZmZoXmZMXMzMwKzcmKmZmZFZqTFTMzMys0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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11775a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(vp.Income,vp.Age)\n",
    "plt.title('Scatter of Income and Age')\n",
    "plt.xlabel(\"Income\")\n",
    "plt.ylabel(\"Age\")\n",
    "plt.xlim(0, 100000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Genre   Action  Comedy  Drama\n",
      "Gender                       \n",
      "F            5       6     13\n",
      "M           13       6      7\n"
     ]
    }
   ],
   "source": [
    "#Crosstab using Pandas\n",
    "video_cross=pd.crosstab(vp.Gender, vp.Genre,colnames=['Genre'],rownames=['Gender'])\n",
    "print(video_cross)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1177d9ba8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jiY+PZ+TIkXlD5cHWrl3L1KlT6dOnD845pkyZku/1Rx99FDOjb9++YWlP48aN\n6d69O0899RRbtmwp8HrwpYz79OnDp59+ytKlS/PKfvrpJ9LS0gpdd+7ZBGPHjmX9+vWanS8icphU\njx5+tC+lX8Htt23blrS0NK666io6duyY70p7H330ES+//DIjRozgpptuYtiwYTz99NPs3LmTbt26\n8dlnnzFr1iwuvfTSfDP0K+qJJ57grLPOolOnTlx77bW0bduWrVu38sknn7B582a+/PJLwPuy8vzz\nz9OzZ0/Gjh1LfHw8zzzzDK1bt2b58uUF1puYmEivXr146aWXOPLII/PN2BcRkcip0oGfmJhI3BFx\nZP+zclxLPzGx/Nf3vfDCC1m+fDmPPPII8+bNY/r06dSuXZuTTjqJv/zlL4waNQqAZ599lnbt2jFj\nxgxeffVVmjVrxh133MFdd92Vb31FXcu+tOUdO3Zk6dKl3HvvvcycOZPt27fTpEkTTjnlFO6+++68\nes2aNWPJkiXceOONPPzwwzRq1IgxY8bQrFkzRo4cWei+Dh06lDfeeIMBAwZQq1atcr1fIiJSNlU6\n8Fu2bMmqlauqzd3y2rVrV+L18mNiYrjzzju58847i62Xe4pdqMKudNeqVatCy1u3bs1zzz1X7HYA\nTjzxRBYvXlyg/Oqrry60fu3atTEzBg8eXOK6RUQkPKp04IMX+rr9atXy9NNP07ZtW7p27RrtpoiI\n+EaVD3ypOl544QWWL1/OggULmDp1arSbIyLiKwp8OWwGDhxIvXr1GDlyJGPGjIl2c0REfEWBL4dN\n6B30RETk8NF5+CIiIj6gwBcREfEBBb6IiIgPKPBFRER8QIEvIiLiAwp8ERERH1Dgi4iI+IACX0RE\nxAeq/IV3Nm3aVOVvnjNz5sx8N5qJjY2lYcOGdOrUib59+3L11VdTt27dcDVVRER8qEoH/qZNm+jY\noQN7s6N/e9z4uDgyV60qd+ibGRMnTqR169YcPHiQLVu2sGTJEv7whz/w2GOPMW/ePDp16hTmVouI\niF9U6cDPyspib3Y2qUDHKLYjExicnU1WVlaF7tzXq1cvkpKS8p7fcsstLFmyhL59+3LRRReRmZlJ\nbGxsocvu3buX+Pj4cm9bRESqt2pxDL8jkBTFRyS/bHTv3p0JEyawceNGUlNTARg+fDj16tVj3bp1\n9OnTh/r16+fdW/7DDz/kyiuvpFWrVsTFxdGyZUv++Mc/kh0yCpK7ju+++45+/fpRr149WrRowbRp\n0wBYsWJvRnR5AAAUoElEQVQF5513HnXr1qV169akp6fnW37nzp386U9/onPnztSrV4+EhAT69OnD\n8uXLI/huiIhIeVWLwK/uhgwZgnOOt956C/CG/w8dOkTPnj1p1qwZjz76KJdddhkAL730Evv27eO6\n667j8ccfp1evXvztb39j2LBh+dZpZuTk5NC7d29atWrFI488Qps2bbjxxhuZOXMmvXv35rTTTmPS\npEnUr1+fYcOGsXHjxrzl161bx7x587jwwguZPHky48eP5+uvv6Z79+5s2bLl8L05IiJSKlV6SN8v\njj76aBISEli7dm1e2YEDBxgwYAD3339/vrqTJk3KN+w/cuRI2rVrxx133MH3339PixYt8l7Lzs5m\n6NChjB8/HoCUlBSaN2/ONddcwwsvvMDll18OQI8ePTj++OOZOXMmd911FwCdO3dm9erV+bY9ZMgQ\nOnTowLPPPssdd9wR3jdBREQqpEw9fDO7zcw+N7OfzWyrmc01s+MKqXefmf1gZnvN7G0zOzZ8Tfan\nunXrsmfPnnxlo0ePLlAvOOz37t3L9u3bOfPMM8nJyeHLL78sUP+aa67J+39CQgIdOnSgTp06eWEP\ncNxxx9GgQQPWrVuXV1arVq28/+fk5LBjxw7i4+Pp0KEDGRkZ5dtJERGJmLIO6Z8F/A04HegB1ALe\nMrMjciuY2S3ADcAooAvwK7DQzGqHpcU+9csvv1CvXr285zVr1szXW8/13XffMXz4cBo1akTdunVp\n3Lgx3bt3x8zYvXt3vrpxcXE0atQoX1lCQkKh601ISGDnzp15z51zTJ48meOOO47Y2FgSExNp0qQJ\nK1asKLAdERGJvjIN6Tvn+gQ/N7PhwDYgGfgwUDwWmOiceyNQZyiwFbgYmFPB9vrS5s2b2b17N+3b\nt88rK2y2fk5ODj169GDXrl3cdttteb31zZs3M2zYMHJycvLVr1GjRqHbK6rcOZf3/wceeIC77rqL\nkSNHcv/999OwYUNiYmIYO3Zsge2IiEj0VfQYfgPAATsAzKwN0Ax4J7eCc+5nM/sMOBMFfrnMmjUL\nM6Nnz57F1luxYgVr1qzh+eefZ9CgQXnlixYtCnubXnnlFc4991yefvrpfOW7du2icePGYd+eiIhU\nTLln6ZuZAVOAD51z3wSKm+F9AdgaUn1r4DUpo8WLF3P//ffTtm1bBg4cWGzd3J55aA97ypQpeB9X\n+NSoUSNfjx+8MwQ2b94c1u2IiEh4VKSHPw04AfhdmNpSbpnVYPvOOebPn09mZiaHDh1i69atLF68\nmLfffps2bdowb948atcufhrE8ccfT7t27bj55pv5/vvvqV+/Pq+88gq7du0KQwvz69evHxMnTmTE\niBF07dqVFStWMHv2bNq1axf2bYmISMWVK/DN7HGgD3CWc+7HoJe2AAY0JX8vvylQcIp4kHHjxpGQ\nkJCvLCUlhZSUlCKXSUxMJD4ujsGV5NK6iYmJ5V7ezLj77rsBqF27dt619KdOncrw4cOpU6dOgfqh\natasyRtvvMFNN93EQw89RFxcHJdeeinXX389J598cqHbLKothZUFl99+++3s3buXtLQ05syZQ3Jy\nMvPnz+fWW28N+2iCiIhAenp6gYuglWWStIUOy5a4gBf2FwHdnHPrCnn9B+AR59zkwPP6eOE/1Dn3\nUiH1k4Bly5Yty3dZ2WAZGRkkJydTWJ3qcPMcKV7e5493ZcPKIgNvtmpxP7tSdVTWnzMpH7/8fub+\n3ALJzrliz4kuUw/fzKYBKUB/4Fczaxp4abdzLrebPQW408y+BTYAE4HvgdfKsq3SatmypYJWRESk\nBGUd0h+NNylvSUj51cAsAOfcJDOLB57Cm8X/AdDbOXegYk0VERGR8irrefilmtXvnLsHuKcc7RER\nEZEI0M1zREREfECBLyIi4gMKfBERER9Q4IuIiPhARa+lLyJSrUT7yp0SHvocC6pSgZ+ZqY/Qj/S5\ny+Gwf/9+YoDB0W6IhE0M3ucqnioR+ImJicTHxzN4sH4V/So+JoZE3XZXIig2NpYcgHOAI6PcGKm4\nnZDzbuG3EverKhH4LVu2JDMzs1JcQre8MjMzGTx4MKlAx2g3pgpKzMlB11OUw6I90DzajZAK+wF4\nN9qNqFyqROBD9bmEbkd0nW4RETn8NEtfRETEBxT4IiIiPqDAFxER8QEFvoiIiA8o8EVERHxAgS8i\nIuIDCnwREREfUOCLiIj4gAJfRETEBxT4IiIiPqDAFxER8QEFvoiIiA8o8EVERHxAgS8iIuIDCnwR\nEREfUOCLiIj4gAJfRETEBxT4IiIiPqDAFxER8QEFvoiIiA8o8EVERHxAgS8iIuIDCnwREREfUOCL\niIj4gAJfRETEBxT4IiIiPqDAFxER8QEFvoiIiA8o8EVERHxAgS8iIuIDCnwREREfUOCLiIj4QJkD\n38zOMrN5ZrbZzHLMrH/I688FyoMf88PXZBERESmr8vTw6wBfAdcBrog6C4CmQLPAI6VcrRMREZGw\nqFnWBZxz/wL+BWBmVkS1/c65nyrSMBEREQmfSB3D725mW81spZlNM7OGEdqOiIiIlEKZe/ilsAB4\nBVgPtAMeBOab2ZnOuaIOAYiIiEgEhT3wnXNzgp7+x8xWAGuB7sC74d6eiIiIlCwSPfx8nHPrzSwL\nOJZiAn/cuHEkJCTkK0tJSSElRfP9RERE0tPTSU9Pz1e2e/fuUi8f8cA3sxZAI+DH4upNnjyZpKSk\nSDdHRESkSiqsE5yRkUFycnKpli9z4JtZHbzeeu4M/bZmdjKwI/C4G+8Y/pZAvYeB1cDCsm5LRERE\nwqM8PfxT8YbmXeDxaKB8Jt65+Z2BoUAD4Ae8oL/LOXewwq0VERGRcinPefjvUfzpfL3K3xwRERGJ\nBF1LX0RExAcU+CIiIj6gwBcREfEBBb6IiIgPKPBFRER8QIEvIiLiAwp8ERERH1Dgi4iI+IACX0RE\nxAcU+CIiIj6gwBcREfEBBb6IiIgPKPBFRER8QIEvIiLiA2W+Pa6ISLWWFe0GSFjocyxAgS8iAuzf\nvx8M+Ge0WyJhY4HPVQAFvogIALGxseAAJgJtotwaqbj14CZ4n6sACnwRkRB9gKRoN0IqLAOYEO1G\nVCqatCciIuIDCnwREREfUOCLiIj4gAJfRETEBxT4IiIiPqDAFxER8QEFvoiIiA8o8EVERHxAgS8i\nIuIDCnwREREfUOCLiIj4gAJfRETEBxT4IiIiPqDAFxER8QEFvoiIiA8o8EVERHxAgS8iIuIDNaPd\nAL/JjHYDJGz0WYpIVaLAP0z2799PDDA42g2RsIrB+2xFRCo7Bf5hEhsbSw7AOcCRUW6MhMdOyHnX\n+2xFRCo7Bf7h1h5oHu1GSFj8ALwb7UaIiJSOJu2JiIj4gAJfRETEB8oc+GZ2lpnNM7PNZpZjZv0L\nqXOfmf1gZnvN7G0zOzY8zRUREZHyKE8Pvw7wFXAd4EJfNLNbgBuAUUAX4FdgoZnVrkA7RUREpALK\nPGnPOfcv4F8AZmaFVBkLTHTOvRGoMxTYClwMzCl/U0VERKS8wnoM38zaAM2Ad3LLnHM/A58BZ4Zz\nWyIiIlJ64Z601wxvmH9rSPnWwGsiIiISBZXmPPxx48aRkJCQrywlJYWUlJQotUhERKTySE9PJz09\nPV/Z7t27S718uAN/C2BAU/L38psCXxa34OTJk0lKSgpzc0RERKqHwjrBGRkZJCcnl2r5sA7pO+fW\n44X+ebllZlYfOB34OJzbEhERkdIrcw/fzOoAx+L15AHamtnJwA7n3HfAFOBOM/sW2ABMBL4HXgtL\ni0VERKTMyjOkfyreFcRd4PFooHwmMMI5N8nM4oGngAbAB0Bv59yBMLRXREREyqE85+G/RwmHApxz\n9wD3lK9JIiIiEm66lr6IiIgPKPBFRER8QIEvIiLiAwp8ERERH1Dgi4iI+IACX0RExAcU+CIiIj6g\nwBcREfEBBb6IiIgPKPBFRER8QIEvIiLiAwp8ERERH1Dgi4iI+IACX0RExAcU+CIiIj6gwBcREfEB\nBb6IiIgPKPBFRER8QIEvIiLiAwp8ERERH1Dgi4iI+IACX0RExAcU+CIiIj6gwBcREfEBBb6IiIgP\nKPBFRER8QIEvIiLiAwp8ERERH1Dgi4iI+IACX0RExAcU+CIiIj6gwBcREfEBBb6IiIgPKPBFRER8\nQIEvIiLiAwp8ERERH1Dgi4iI+IACX0RExAcU+CIiIj6gwBcREfGBsAe+md1tZjkhj2/CvR0REREp\nvZoRWu/XwHmABZ4fitB2REREpBQiFfiHnHM/RWjdIiIiUkaROobf3sw2m9laM0s1s2MitB0REREp\nhUgE/qfAcKAnMBpoA7xvZnUisC0REREphbAP6TvnFgY9/drMPgc2AlcCz4V7e1VOVrQbIGGjz1JE\nqpBIHcPP45zbbWargWOLqzdu3DgSEhLylaWkpJCSkhLJ5h02+/fv96Yw/jPaLZGwssBnKyISYenp\n6aSnp+cr2717d6mXj3jgm1ldvLCfVVy9yZMnk5SUFOnmRE1sbCw4gIl4Rzmk6lsPboL32YqIRFhh\nneCMjAySk5NLtXzYA9/MHgFexxvGPxq4FzgIpBe3nH/0AarvFxt/yQAmRLsRIiKlEokefgsgDWgE\n/AR8CJzhnNsegW2JiIhIKURi0l71OOguIiJSjeha+iIiIj6gwBcREfEBBb6IiIgPKPBFRER8QIEv\nIiLiAwp8ERERH1Dgi4iI+IACX0RExAcU+CIiIj6gwBcREfEBBb6IiIgPKPBFRER8QIEvIiLiAwp8\nERERH1Dgi4iI+IACX0RExAcU+CIiIj6gwBcREfEBBb6IiIgPKPBFRER8QIEvIiLiAwp8ERERH1Dg\ni4iI+IACX0RExAcU+CIiIj6gwBcREfEBBb6IiIgPKPBFRER8QIEvIiLiAwp8ERERH1Dgi4iI+IAC\nX0RExAcU+CIiIj6gwBcREfEBBb6IiIgPKPBFRER8QIEvIiLiAwp8ERERH1Dgi4iI+IACX0RExAcU\n+CIiIj4QscA3s+vNbL2Z7TOzT83stEhtSyqj9Gg3QESKpN9PP4pI4JvZAOBR4G7gFODfwEIzS4zE\n9qQy0h8UkcpLv59+FKke/jjgKefcLOfcSmA0sBcYEaHtiYiISDHCHvhmVgtIBt7JLXPOOWARcGa4\ntyciIiIli0QPPxGoAWwNKd8KNIvA9kRERKQENaPdACAOIDMzM9rtiKj/7t98oHrvq+d7YHa0GxFh\n64Hq/7PrF/76HdXvZ3URtH9xJdU1b7Q9fAJD+nuBy5xz84LKZwAJzrlLQuoPpPr/5ImIiETSIOdc\nWnEVwt7Dd84dNLNlwHnAPAAzs8DzqYUsshAYBGwAssPdHhERkWosDmiNl6XFCnsPH8DMrgRm4M3O\n/xxv1v7lwPHOuZ/CvkEREREpVkSO4Tvn5gTOub8PaAp8BfRU2IuIiERHRHr4IiIiUrnoWvoiIiI+\noMAXERHxAQW+VIiZtQ2chSEiIpWYjuFLhZjZb8BRzrltgecvAjc550KvtCgih5mZ/aM09Zxzus+J\nDyjwpULMLAdoFhT4e4CTnXProtsyEQn8fm4EvgSKHIkLvSCaVE+V4dK6IiISGU8CKUAb4Dkg1Tm3\nI7pNkmjRMXypKBd4hJaJSJQ5564HjgImARcC35nZHDPrqbk3/qMhfamQwJDhAmB/oOhCYDHwa3A9\n59ylh7lpIhLCzFoBw4GheCO8Jzrnfolqo+Sw0ZC+VNTMkOepUWmFiJRGDt4InOHdxlx8RD18EZFq\nzMxigUuBEcDvgTfwjuf/yzmXE822yeGlHr6ISDVlZtOAq4DvgH8AKc65rOi2SqJFPXwRkWoqMMdm\nE95peUX+sdccG39QD19EpPqahc6akQD18EVERHxA5+GLiIj4gAJfRETEBxT4IiIiPqDAFxER8QEF\nvoiIiA8o8EWkwszsXTN7LNrtEJGiKfBFqgkza2pmfzWzNWa2z8x+NLMPzGy0mR0R7faJSHTpwjsi\n1YCZtQE+BnYAtwJf493BsBMwCvge7xrqlZKZxQDO6cIgIhGjHr5I9fAkcABIds694pxb5Zzb4Jx7\n3Tl3oXPuDQAzSzCzv5vZNjPbbWaLzKxz7krM7G4z+9LMBpvZejPbZWbpZlYnqE68mc0ysz1mttnM\n/hjaGDOrbWZ/MbPvzewXM/vEzLoFvT7MzHaa2YVm9h8gGzgmkm+QiN8p8EWqODNrCJwPPO6cyy6h\n+stAI6AnkARkAIvMrEFQnXbARUAfoC/QDW/UINdfgLOAC4ELgO6BdQV7AjgduBJvlOElYIGZtQuq\nEw+MB64BTgS2lby3IlJeGtIXqfqOxbu/+ergQjP7CYgLPH0cb0j/VKCJc+5goHy8mV0CXA78PXdR\nYJhzbm9gPc8D5wETAj39EcBA59ySwOvD8A4Z5G63JTAcOMY5tyVQ/JiZ9QauBu4MlNUExjjnvq7o\nGyAiJVPgi1Rfp+GN4qUBscDJQD1gh5kF14vD69Xn2pAb9gE/Ak0C/28H1AI+z33RObfTzFYF1T8J\nqAGstvwbqg0E35r1gMJe5PBR4ItUfd/i3RGtQ3Chc24DgJntCxTVBX7AG6LPl/jArqD/Hwx5zVG2\nw391gUN4w/w5Ia/9EvT/fYjIYaPAF6ninHM7zOxt4AYz+5tzrqggzQCaAb855zaVc3Nr8cL8dALD\n+GZ2JHAcsCRQ50u8Hn5T59xH5dyOiISZJu2JVA/X4X2BX2pmV5rZ8WZ2nJkNBo4HDjnnFgGfAq+a\n2flm1srMuprZ/WYWOumuUM65X4FngUfM7BwzOwl4DvgtqM4avMMIs8zsEjNrbWZdzOzWwHF8EYkC\n9fBFqgHn3DozOwW4Hfgz0ALvPPxvgEl4p+0B9AYeAP4BNAa2AO8DW8uwuf8D6gDzgD3Ao0D9kDrD\n8Sbn/QU4Gu/Y/afA62XbMxEJF9N1LkRERKo/DemLiIj4gAJfRETEBxT4IiIiPqDAFxER8QEFvoiI\niA8o8EVERHxAgS8iIuIDCnwREREfUOCLiIj4gAJfRETEBxT4IiIiPqDAFxER8YH/B8SGOybHZNyv\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106c44128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar plot of Genre by Gender\n",
    "video_cross.plot(kind='bar', stacked=True)\n",
    "plt.title('Movie Genre by Gender')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}