Chapter 02 Essential DataFrame Operations.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 2: Essential DataFrame Operations\n",
    "\n",
    "## Recipes\n",
    "* [Selecting multiple DataFrame columns](#Selecting-multiple-DataFrame-columns)\n",
    "* [Selecting columns with methods](#Selecting-columns-with-methods)\n",
    "* [Ordering column names sensibly](#Ordering-column-names-sensibly)\n",
    "* [Operating on the entire DataFrame](#Operating-on-the-entire-DataFrame)\n",
    "* [Chaining DataFrame methods together](#Chaining-DataFrame-methods-together)\n",
    "* [Working with operators on a DataFrame](#Working-with-operators-on-a-DataFrame)\n",
    "* [Comparing missing values](#Comparing-missing-values)\n",
    "* [Transposing the direction of a DataFrame operation](#Transposing-the-direction-of-a-DataFrame-operation)\n",
    "* [Determining college campus diversity](#Determining-college-campus-diversity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.options.display.max_columns = 40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting multiple DataFrame columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>director_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCH Pounder</td>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>Wes Studi</td>\n",
       "      <td>James Cameron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Johnny Depp</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>Jack Davenport</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Christoph Waltz</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>Stephanie Sigman</td>\n",
       "      <td>Sam Mendes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>Joseph Gordon-Levitt</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Doug Walker</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      actor_1_name      actor_2_name          actor_3_name      director_name\n",
       "0      CCH Pounder  Joel David Moore             Wes Studi      James Cameron\n",
       "1      Johnny Depp     Orlando Bloom        Jack Davenport     Gore Verbinski\n",
       "2  Christoph Waltz      Rory Kinnear      Stephanie Sigman         Sam Mendes\n",
       "3        Tom Hardy    Christian Bale  Joseph Gordon-Levitt  Christopher Nolan\n",
       "4      Doug Walker        Rob Walker                   NaN        Doug Walker"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie = pd.read_csv('data/movie.csv')\n",
    "movie_actor_director = movie[['actor_1_name', 'actor_2_name', 'actor_3_name', 'director_name']]\n",
    "movie_actor_director.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>director_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>James Cameron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gore Verbinski</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sam Mendes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Christopher Nolan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Doug Walker</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       director_name\n",
       "0      James Cameron\n",
       "1     Gore Verbinski\n",
       "2         Sam Mendes\n",
       "3  Christopher Nolan\n",
       "4        Doug Walker"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "movie[['director_name']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "('actor_1_name', 'actor_2_name', 'actor_3_name', 'director_name')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2441\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2442\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: ('actor_1_name', 'actor_2_name', 'actor_3_name', 'director_name')",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-954222273e42>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmovie\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'actor_1_name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'actor_2_name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'actor_3_name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'director_name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1962\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1964\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1966\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1969\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1970\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1971\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1973\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1643\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1644\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1645\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1646\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3589\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3590\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3591\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3592\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2442\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2444\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2446\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: ('actor_1_name', 'actor_2_name', 'actor_3_name', 'director_name')"
     ]
    }
   ],
   "source": [
    "movie['actor_1_name', 'actor_2_name', 'actor_3_name', 'director_name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There's more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols =['actor_1_name', 'actor_2_name', 'actor_3_name', 'director_name']\n",
    "movie_actor_director = movie[cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting columns with methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    13\n",
       "int64       3\n",
       "object     11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie = pd.read_csv('data/movie.csv', index_col='movie_title')\n",
    "movie.get_dtype_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>num_voted_users</th>\n",
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       "      <th>movie_facebook_likes</th>\n",
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       "    <tr>\n",
       "      <th>Pirates of the Caribbean: At World's End</th>\n",
       "      <td>471220</td>\n",
       "      <td>48350</td>\n",
       "      <td>0</td>\n",
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       "      <th>Spectre</th>\n",
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       "      <td>11700</td>\n",
       "      <td>85000</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            num_voted_users  \\\n",
       "movie_title                                                   \n",
       "Avatar                                               886204   \n",
       "Pirates of the Caribbean: At World's End             471220   \n",
       "Spectre                                              275868   \n",
       "The Dark Knight Rises                               1144337   \n",
       "Star Wars: Episode VII - The Force Awakens                8   \n",
       "\n",
       "                                            cast_total_facebook_likes  \\\n",
       "movie_title                                                             \n",
       "Avatar                                                           4834   \n",
       "Pirates of the Caribbean: At World's End                        48350   \n",
       "Spectre                                                         11700   \n",
       "The Dark Knight Rises                                          106759   \n",
       "Star Wars: Episode VII - The Force Awakens                        143   \n",
       "\n",
       "                                            movie_facebook_likes  \n",
       "movie_title                                                       \n",
       "Avatar                                                     33000  \n",
       "Pirates of the Caribbean: At World's End                       0  \n",
       "Spectre                                                    85000  \n",
       "The Dark Knight Rises                                     164000  \n",
       "Star Wars: Episode VII - The Force Awakens                     0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.select_dtypes(include=['int']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2009.0</td>\n",
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       "      <td>169.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>40000.0</td>\n",
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       "      <td>1238.0</td>\n",
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       "      <td>148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>200074175.0</td>\n",
       "      <td>275868</td>\n",
       "      <td>11700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>994.0</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.35</td>\n",
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       "    <tr>\n",
       "      <th>The Dark Knight Rises</th>\n",
       "      <td>813.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>27000.0</td>\n",
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       "      <td>2701.0</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>23000.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>143</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            num_critic_for_reviews  duration  \\\n",
       "movie_title                                                                    \n",
       "Avatar                                                       723.0     178.0   \n",
       "Pirates of the Caribbean: At World's End                     302.0     169.0   \n",
       "Spectre                                                      602.0     148.0   \n",
       "The Dark Knight Rises                                        813.0     164.0   \n",
       "Star Wars: Episode VII - The Force Awakens                     NaN       NaN   \n",
       "\n",
       "                                            director_facebook_likes  \\\n",
       "movie_title                                                           \n",
       "Avatar                                                          0.0   \n",
       "Pirates of the Caribbean: At World's End                      563.0   \n",
       "Spectre                                                         0.0   \n",
       "The Dark Knight Rises                                       22000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                    131.0   \n",
       "\n",
       "                                            actor_3_facebook_likes  \\\n",
       "movie_title                                                          \n",
       "Avatar                                                       855.0   \n",
       "Pirates of the Caribbean: At World's End                    1000.0   \n",
       "Spectre                                                      161.0   \n",
       "The Dark Knight Rises                                      23000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                     NaN   \n",
       "\n",
       "                                            actor_1_facebook_likes  \\\n",
       "movie_title                                                          \n",
       "Avatar                                                      1000.0   \n",
       "Pirates of the Caribbean: At World's End                   40000.0   \n",
       "Spectre                                                    11000.0   \n",
       "The Dark Knight Rises                                      27000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                   131.0   \n",
       "\n",
       "                                                  gross  num_voted_users  \\\n",
       "movie_title                                                                \n",
       "Avatar                                      760505847.0           886204   \n",
       "Pirates of the Caribbean: At World's End    309404152.0           471220   \n",
       "Spectre                                     200074175.0           275868   \n",
       "The Dark Knight Rises                       448130642.0          1144337   \n",
       "Star Wars: Episode VII - The Force Awakens          NaN                8   \n",
       "\n",
       "                                            cast_total_facebook_likes  \\\n",
       "movie_title                                                             \n",
       "Avatar                                                           4834   \n",
       "Pirates of the Caribbean: At World's End                        48350   \n",
       "Spectre                                                         11700   \n",
       "The Dark Knight Rises                                          106759   \n",
       "Star Wars: Episode VII - The Force Awakens                        143   \n",
       "\n",
       "                                            facenumber_in_poster  \\\n",
       "movie_title                                                        \n",
       "Avatar                                                       0.0   \n",
       "Pirates of the Caribbean: At World's End                     0.0   \n",
       "Spectre                                                      1.0   \n",
       "The Dark Knight Rises                                        0.0   \n",
       "Star Wars: Episode VII - The Force Awakens                   0.0   \n",
       "\n",
       "                                            num_user_for_reviews       budget  \\\n",
       "movie_title                                                                     \n",
       "Avatar                                                    3054.0  237000000.0   \n",
       "Pirates of the Caribbean: At World's End                  1238.0  300000000.0   \n",
       "Spectre                                                    994.0  245000000.0   \n",
       "The Dark Knight Rises                                     2701.0  250000000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                   NaN          NaN   \n",
       "\n",
       "                                            title_year  \\\n",
       "movie_title                                              \n",
       "Avatar                                          2009.0   \n",
       "Pirates of the Caribbean: At World's End        2007.0   \n",
       "Spectre                                         2015.0   \n",
       "The Dark Knight Rises                           2012.0   \n",
       "Star Wars: Episode VII - The Force Awakens         NaN   \n",
       "\n",
       "                                            actor_2_facebook_likes  \\\n",
       "movie_title                                                          \n",
       "Avatar                                                       936.0   \n",
       "Pirates of the Caribbean: At World's End                    5000.0   \n",
       "Spectre                                                      393.0   \n",
       "The Dark Knight Rises                                      23000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                    12.0   \n",
       "\n",
       "                                            imdb_score  aspect_ratio  \\\n",
       "movie_title                                                            \n",
       "Avatar                                             7.9          1.78   \n",
       "Pirates of the Caribbean: At World's End           7.1          2.35   \n",
       "Spectre                                            6.8          2.35   \n",
       "The Dark Knight Rises                              8.5          2.35   \n",
       "Star Wars: Episode VII - The Force Awakens         7.1           NaN   \n",
       "\n",
       "                                            movie_facebook_likes  \n",
       "movie_title                                                       \n",
       "Avatar                                                     33000  \n",
       "Pirates of the Caribbean: At World's End                       0  \n",
       "Spectre                                                    85000  \n",
       "The Dark Knight Rises                                     164000  \n",
       "Star Wars: Episode VII - The Force Awakens                     0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.select_dtypes(include=['number']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>161.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>11700</td>\n",
       "      <td>393.0</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Dark Knight Rises</th>\n",
       "      <td>22000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>106759</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>164000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars: Episode VII - The Force Awakens</th>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>143</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            director_facebook_likes  \\\n",
       "movie_title                                                           \n",
       "Avatar                                                          0.0   \n",
       "Pirates of the Caribbean: At World's End                      563.0   \n",
       "Spectre                                                         0.0   \n",
       "The Dark Knight Rises                                       22000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                    131.0   \n",
       "\n",
       "                                            actor_3_facebook_likes  \\\n",
       "movie_title                                                          \n",
       "Avatar                                                       855.0   \n",
       "Pirates of the Caribbean: At World's End                    1000.0   \n",
       "Spectre                                                      161.0   \n",
       "The Dark Knight Rises                                      23000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                     NaN   \n",
       "\n",
       "                                            actor_1_facebook_likes  \\\n",
       "movie_title                                                          \n",
       "Avatar                                                      1000.0   \n",
       "Pirates of the Caribbean: At World's End                   40000.0   \n",
       "Spectre                                                    11000.0   \n",
       "The Dark Knight Rises                                      27000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                   131.0   \n",
       "\n",
       "                                            cast_total_facebook_likes  \\\n",
       "movie_title                                                             \n",
       "Avatar                                                           4834   \n",
       "Pirates of the Caribbean: At World's End                        48350   \n",
       "Spectre                                                         11700   \n",
       "The Dark Knight Rises                                          106759   \n",
       "Star Wars: Episode VII - The Force Awakens                        143   \n",
       "\n",
       "                                            actor_2_facebook_likes  \\\n",
       "movie_title                                                          \n",
       "Avatar                                                       936.0   \n",
       "Pirates of the Caribbean: At World's End                    5000.0   \n",
       "Spectre                                                      393.0   \n",
       "The Dark Knight Rises                                      23000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                    12.0   \n",
       "\n",
       "                                            movie_facebook_likes  \n",
       "movie_title                                                       \n",
       "Avatar                                                     33000  \n",
       "Pirates of the Caribbean: At World's End                       0  \n",
       "Spectre                                                    85000  \n",
       "The Dark Knight Rises                                     164000  \n",
       "Star Wars: Episode VII - The Force Awakens                     0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.filter(like='facebook').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avatar</th>\n",
       "      <td>855.0</td>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>CCH Pounder</td>\n",
       "      <td>Wes Studi</td>\n",
       "      <td>936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pirates of the Caribbean: At World's End</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>Johnny Depp</td>\n",
       "      <td>Jack Davenport</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spectre</th>\n",
       "      <td>161.0</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>Christoph Waltz</td>\n",
       "      <td>Stephanie Sigman</td>\n",
       "      <td>393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Dark Knight Rises</th>\n",
       "      <td>23000.0</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>Joseph Gordon-Levitt</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars: Episode VII - The Force Awakens</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            actor_3_facebook_likes  \\\n",
       "movie_title                                                          \n",
       "Avatar                                                       855.0   \n",
       "Pirates of the Caribbean: At World's End                    1000.0   \n",
       "Spectre                                                      161.0   \n",
       "The Dark Knight Rises                                      23000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                     NaN   \n",
       "\n",
       "                                                actor_2_name  \\\n",
       "movie_title                                                    \n",
       "Avatar                                      Joel David Moore   \n",
       "Pirates of the Caribbean: At World's End       Orlando Bloom   \n",
       "Spectre                                         Rory Kinnear   \n",
       "The Dark Knight Rises                         Christian Bale   \n",
       "Star Wars: Episode VII - The Force Awakens        Rob Walker   \n",
       "\n",
       "                                            actor_1_facebook_likes  \\\n",
       "movie_title                                                          \n",
       "Avatar                                                      1000.0   \n",
       "Pirates of the Caribbean: At World's End                   40000.0   \n",
       "Spectre                                                    11000.0   \n",
       "The Dark Knight Rises                                      27000.0   \n",
       "Star Wars: Episode VII - The Force Awakens                   131.0   \n",
       "\n",
       "                                               actor_1_name  \\\n",
       "movie_title                                                   \n",
       "Avatar                                          CCH Pounder   \n",
       "Pirates of the Caribbean: At World's End        Johnny Depp   \n",
       "Spectre                                     Christoph Waltz   \n",
       "The Dark Knight Rises                             Tom Hardy   \n",
       "Star Wars: Episode VII - The Force Awakens      Doug Walker   \n",
       "\n",
       "                                                    actor_3_name  \\\n",
       "movie_title                                                        \n",
       "Avatar                                                 Wes Studi   \n",
       "Pirates of the Caribbean: At World's End          Jack Davenport   \n",
       "Spectre                                         Stephanie Sigman   \n",
       "The Dark Knight Rises                       Joseph Gordon-Levitt   \n",
       "Star Wars: Episode VII - The Force Awakens                   NaN   \n",
       "\n",
       "                                            actor_2_facebook_likes  \n",
       "movie_title                                                         \n",
       "Avatar                                                       936.0  \n",
       "Pirates of the Caribbean: At World's End                    5000.0  \n",
       "Spectre                                                      393.0  \n",
       "The Dark Knight Rises                                      23000.0  \n",
       "Star Wars: Episode VII - The Force Awakens                    12.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.filter(regex='\\d').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actor_1_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avatar</th>\n",
       "      <td>CCH Pounder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pirates of the Caribbean: At World's End</th>\n",
       "      <td>Johnny Depp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spectre</th>\n",
       "      <td>Christoph Waltz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Dark Knight Rises</th>\n",
       "      <td>Tom Hardy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars: Episode VII - The Force Awakens</th>\n",
       "      <td>Doug Walker</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               actor_1_name\n",
       "movie_title                                                \n",
       "Avatar                                          CCH Pounder\n",
       "Pirates of the Caribbean: At World's End        Johnny Depp\n",
       "Spectre                                     Christoph Waltz\n",
       "The Dark Knight Rises                             Tom Hardy\n",
       "Star Wars: Episode VII - The Force Awakens      Doug Walker"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.filter(items=['actor_1_name', 'asdf']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ordering column names sensibly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movie = pd.read_csv('data/movie.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>director_name</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>facenumber_in_poster</th>\n",
       "      <th>plot_keywords</th>\n",
       "      <th>movie_imdb_link</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Color</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>723.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>760505847.0</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>CCH Pounder</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>886204</td>\n",
       "      <td>4834</td>\n",
       "      <td>Wes Studi</td>\n",
       "      <td>0.0</td>\n",
       "      <td>avatar|future|marine|native|paraplegic</td>\n",
       "      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.78</td>\n",
       "      <td>33000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Color</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>302.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>309404152.0</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>Johnny Depp</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>471220</td>\n",
       "      <td>48350</td>\n",
       "      <td>Jack Davenport</td>\n",
       "      <td>0.0</td>\n",
       "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
       "      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>300000000.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Color</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>602.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>200074175.0</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>Christoph Waltz</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>275868</td>\n",
       "      <td>11700</td>\n",
       "      <td>Stephanie Sigman</td>\n",
       "      <td>1.0</td>\n",
       "      <td>bomb|espionage|sequel|spy|terrorist</td>\n",
       "      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n",
       "      <td>994.0</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.35</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Color</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>813.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>448130642.0</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>1144337</td>\n",
       "      <td>106759</td>\n",
       "      <td>Joseph Gordon-Levitt</td>\n",
       "      <td>0.0</td>\n",
       "      <td>deception|imprisonment|lawlessness|police offi...</td>\n",
       "      <td>http://www.imdb.com/title/tt1345836/?ref_=fn_t...</td>\n",
       "      <td>2701.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2.35</td>\n",
       "      <td>164000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>8</td>\n",
       "      <td>143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.imdb.com/title/tt5289954/?ref_=fn_t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color      director_name  num_critic_for_reviews  duration  \\\n",
       "0  Color      James Cameron                   723.0     178.0   \n",
       "1  Color     Gore Verbinski                   302.0     169.0   \n",
       "2  Color         Sam Mendes                   602.0     148.0   \n",
       "3  Color  Christopher Nolan                   813.0     164.0   \n",
       "4    NaN        Doug Walker                     NaN       NaN   \n",
       "\n",
       "   director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                      0.0                   855.0  Joel David Moore   \n",
       "1                    563.0                  1000.0     Orlando Bloom   \n",
       "2                      0.0                   161.0      Rory Kinnear   \n",
       "3                  22000.0                 23000.0    Christian Bale   \n",
       "4                    131.0                     NaN        Rob Walker   \n",
       "\n",
       "   actor_1_facebook_likes        gross                           genres  \\\n",
       "0                  1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi   \n",
       "1                 40000.0  309404152.0         Action|Adventure|Fantasy   \n",
       "2                 11000.0  200074175.0        Action|Adventure|Thriller   \n",
       "3                 27000.0  448130642.0                  Action|Thriller   \n",
       "4                   131.0          NaN                      Documentary   \n",
       "\n",
       "      actor_1_name                                 movie_title  \\\n",
       "0      CCH Pounder                                      Avatar   \n",
       "1      Johnny Depp    Pirates of the Caribbean: At World's End   \n",
       "2  Christoph Waltz                                     Spectre   \n",
       "3        Tom Hardy                       The Dark Knight Rises   \n",
       "4      Doug Walker  Star Wars: Episode VII - The Force Awakens   \n",
       "\n",
       "   num_voted_users  cast_total_facebook_likes          actor_3_name  \\\n",
       "0           886204                       4834             Wes Studi   \n",
       "1           471220                      48350        Jack Davenport   \n",
       "2           275868                      11700      Stephanie Sigman   \n",
       "3          1144337                     106759  Joseph Gordon-Levitt   \n",
       "4                8                        143                   NaN   \n",
       "\n",
       "   facenumber_in_poster                                      plot_keywords  \\\n",
       "0                   0.0             avatar|future|marine|native|paraplegic   \n",
       "1                   0.0  goddess|marriage ceremony|marriage proposal|pi...   \n",
       "2                   1.0                bomb|espionage|sequel|spy|terrorist   \n",
       "3                   0.0  deception|imprisonment|lawlessness|police offi...   \n",
       "4                   0.0                                                NaN   \n",
       "\n",
       "                                     movie_imdb_link  num_user_for_reviews  \\\n",
       "0  http://www.imdb.com/title/tt0499549/?ref_=fn_t...                3054.0   \n",
       "1  http://www.imdb.com/title/tt0449088/?ref_=fn_t...                1238.0   \n",
       "2  http://www.imdb.com/title/tt2379713/?ref_=fn_t...                 994.0   \n",
       "3  http://www.imdb.com/title/tt1345836/?ref_=fn_t...                2701.0   \n",
       "4  http://www.imdb.com/title/tt5289954/?ref_=fn_t...                   NaN   \n",
       "\n",
       "  language country content_rating       budget  title_year  \\\n",
       "0  English     USA          PG-13  237000000.0      2009.0   \n",
       "1  English     USA          PG-13  300000000.0      2007.0   \n",
       "2  English      UK          PG-13  245000000.0      2015.0   \n",
       "3  English     USA          PG-13  250000000.0      2012.0   \n",
       "4      NaN     NaN            NaN          NaN         NaN   \n",
       "\n",
       "   actor_2_facebook_likes  imdb_score  aspect_ratio  movie_facebook_likes  \n",
       "0                   936.0         7.9          1.78                 33000  \n",
       "1                  5000.0         7.1          2.35                     0  \n",
       "2                   393.0         6.8          2.35                 85000  \n",
       "3                 23000.0         8.5          2.35                164000  \n",
       "4                    12.0         7.1           NaN                     0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n",
       "       'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n",
       "       'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n",
       "       'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n",
       "       'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n",
       "       'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',\n",
       "       'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',\n",
       "       'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "disc_core = ['movie_title','title_year', 'content_rating','genres']\n",
    "disc_people = ['director_name','actor_1_name', 'actor_2_name','actor_3_name']\n",
    "disc_other = ['color','country','language','plot_keywords','movie_imdb_link']\n",
    "cont_fb = ['director_facebook_likes','actor_1_facebook_likes','actor_2_facebook_likes',\n",
    "           'actor_3_facebook_likes', 'cast_total_facebook_likes', 'movie_facebook_likes']\n",
    "cont_finance = ['budget','gross']\n",
    "cont_num_reviews = ['num_voted_users','num_user_for_reviews', 'num_critic_for_reviews']\n",
    "cont_other = ['imdb_score','duration', 'aspect_ratio', 'facenumber_in_poster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_col_order = disc_core + disc_people + disc_other + \\\n",
    "                    cont_fb + cont_finance + cont_num_reviews + cont_other\n",
    "set(movie.columns) == set(new_col_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genres</th>\n",
       "      <th>director_name</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>color</th>\n",
       "      <th>country</th>\n",
       "      <th>language</th>\n",
       "      <th>plot_keywords</th>\n",
       "      <th>movie_imdb_link</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>duration</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>facenumber_in_poster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>CCH Pounder</td>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>Wes Studi</td>\n",
       "      <td>Color</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>avatar|future|marine|native|paraplegic</td>\n",
       "      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>4834</td>\n",
       "      <td>33000</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>760505847.0</td>\n",
       "      <td>886204</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>178.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>Johnny Depp</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>Jack Davenport</td>\n",
       "      <td>Color</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
       "      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n",
       "      <td>563.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>48350</td>\n",
       "      <td>0</td>\n",
       "      <td>300000000.0</td>\n",
       "      <td>309404152.0</td>\n",
       "      <td>471220</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>Christoph Waltz</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>Stephanie Sigman</td>\n",
       "      <td>Color</td>\n",
       "      <td>UK</td>\n",
       "      <td>English</td>\n",
       "      <td>bomb|espionage|sequel|spy|terrorist</td>\n",
       "      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>11700</td>\n",
       "      <td>85000</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>200074175.0</td>\n",
       "      <td>275868</td>\n",
       "      <td>994.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>Joseph Gordon-Levitt</td>\n",
       "      <td>Color</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>deception|imprisonment|lawlessness|police offi...</td>\n",
       "      <td>http://www.imdb.com/title/tt1345836/?ref_=fn_t...</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>106759</td>\n",
       "      <td>164000</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>448130642.0</td>\n",
       "      <td>1144337</td>\n",
       "      <td>2701.0</td>\n",
       "      <td>813.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.imdb.com/title/tt5289954/?ref_=fn_t...</td>\n",
       "      <td>131.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  movie_title  title_year content_rating  \\\n",
       "0                                      Avatar      2009.0          PG-13   \n",
       "1    Pirates of the Caribbean: At World's End      2007.0          PG-13   \n",
       "2                                     Spectre      2015.0          PG-13   \n",
       "3                       The Dark Knight Rises      2012.0          PG-13   \n",
       "4  Star Wars: Episode VII - The Force Awakens         NaN            NaN   \n",
       "\n",
       "                            genres      director_name     actor_1_name  \\\n",
       "0  Action|Adventure|Fantasy|Sci-Fi      James Cameron      CCH Pounder   \n",
       "1         Action|Adventure|Fantasy     Gore Verbinski      Johnny Depp   \n",
       "2        Action|Adventure|Thriller         Sam Mendes  Christoph Waltz   \n",
       "3                  Action|Thriller  Christopher Nolan        Tom Hardy   \n",
       "4                      Documentary        Doug Walker      Doug Walker   \n",
       "\n",
       "       actor_2_name          actor_3_name  color country language  \\\n",
       "0  Joel David Moore             Wes Studi  Color     USA  English   \n",
       "1     Orlando Bloom        Jack Davenport  Color     USA  English   \n",
       "2      Rory Kinnear      Stephanie Sigman  Color      UK  English   \n",
       "3    Christian Bale  Joseph Gordon-Levitt  Color     USA  English   \n",
       "4        Rob Walker                   NaN    NaN     NaN      NaN   \n",
       "\n",
       "                                       plot_keywords  \\\n",
       "0             avatar|future|marine|native|paraplegic   \n",
       "1  goddess|marriage ceremony|marriage proposal|pi...   \n",
       "2                bomb|espionage|sequel|spy|terrorist   \n",
       "3  deception|imprisonment|lawlessness|police offi...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                     movie_imdb_link  director_facebook_likes  \\\n",
       "0  http://www.imdb.com/title/tt0499549/?ref_=fn_t...                      0.0   \n",
       "1  http://www.imdb.com/title/tt0449088/?ref_=fn_t...                    563.0   \n",
       "2  http://www.imdb.com/title/tt2379713/?ref_=fn_t...                      0.0   \n",
       "3  http://www.imdb.com/title/tt1345836/?ref_=fn_t...                  22000.0   \n",
       "4  http://www.imdb.com/title/tt5289954/?ref_=fn_t...                    131.0   \n",
       "\n",
       "   actor_1_facebook_likes  actor_2_facebook_likes  actor_3_facebook_likes  \\\n",
       "0                  1000.0                   936.0                   855.0   \n",
       "1                 40000.0                  5000.0                  1000.0   \n",
       "2                 11000.0                   393.0                   161.0   \n",
       "3                 27000.0                 23000.0                 23000.0   \n",
       "4                   131.0                    12.0                     NaN   \n",
       "\n",
       "   cast_total_facebook_likes  movie_facebook_likes       budget        gross  \\\n",
       "0                       4834                 33000  237000000.0  760505847.0   \n",
       "1                      48350                     0  300000000.0  309404152.0   \n",
       "2                      11700                 85000  245000000.0  200074175.0   \n",
       "3                     106759                164000  250000000.0  448130642.0   \n",
       "4                        143                     0          NaN          NaN   \n",
       "\n",
       "   num_voted_users  num_user_for_reviews  num_critic_for_reviews  imdb_score  \\\n",
       "0           886204                3054.0                   723.0         7.9   \n",
       "1           471220                1238.0                   302.0         7.1   \n",
       "2           275868                 994.0                   602.0         6.8   \n",
       "3          1144337                2701.0                   813.0         8.5   \n",
       "4                8                   NaN                     NaN         7.1   \n",
       "\n",
       "   duration  aspect_ratio  facenumber_in_poster  \n",
       "0     178.0          1.78                   0.0  \n",
       "1     169.0          2.35                   0.0  \n",
       "2     148.0          2.35                   1.0  \n",
       "3     164.0          2.35                   0.0  \n",
       "4       NaN           NaN                   0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie2 = movie[new_col_order]\n",
    "movie2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Operating on the entire DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4916, 28)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 8\n",
    "movie = pd.read_csv('data/movie.csv')\n",
    "movie.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137648"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4916"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                     4897\n",
       "director_name             4814\n",
       "num_critic_for_reviews    4867\n",
       "duration                  4901\n",
       "                          ... \n",
       "actor_2_facebook_likes    4903\n",
       "imdb_score                4916\n",
       "aspect_ratio              4590\n",
       "movie_facebook_likes      4916\n",
       "Length: 28, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_critic_for_reviews     1.00\n",
       "duration                   7.00\n",
       "director_facebook_likes    0.00\n",
       "actor_3_facebook_likes     0.00\n",
       "                           ... \n",
       "actor_2_facebook_likes     0.00\n",
       "imdb_score                 1.60\n",
       "aspect_ratio               1.18\n",
       "movie_facebook_likes       0.00\n",
       "Length: 16, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>facenumber_in_poster</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4867.000000</td>\n",
       "      <td>4901.000000</td>\n",
       "      <td>4814.000000</td>\n",
       "      <td>4893.000000</td>\n",
       "      <td>4909.000000</td>\n",
       "      <td>4.054000e+03</td>\n",
       "      <td>4.916000e+03</td>\n",
       "      <td>4916.000000</td>\n",
       "      <td>4903.000000</td>\n",
       "      <td>4895.000000</td>\n",
       "      <td>4.432000e+03</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4903.000000</td>\n",
       "      <td>4916.000000</td>\n",
       "      <td>4590.000000</td>\n",
       "      <td>4916.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137.988905</td>\n",
       "      <td>107.090798</td>\n",
       "      <td>691.014541</td>\n",
       "      <td>631.276313</td>\n",
       "      <td>6494.488491</td>\n",
       "      <td>4.764451e+07</td>\n",
       "      <td>8.264492e+04</td>\n",
       "      <td>9579.815907</td>\n",
       "      <td>1.377320</td>\n",
       "      <td>267.668846</td>\n",
       "      <td>3.654749e+07</td>\n",
       "      <td>2002.447609</td>\n",
       "      <td>1621.923516</td>\n",
       "      <td>6.437429</td>\n",
       "      <td>2.222349</td>\n",
       "      <td>7348.294142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.239379</td>\n",
       "      <td>25.286015</td>\n",
       "      <td>2832.954125</td>\n",
       "      <td>1625.874802</td>\n",
       "      <td>15106.986884</td>\n",
       "      <td>6.737255e+07</td>\n",
       "      <td>1.383222e+05</td>\n",
       "      <td>18164.316990</td>\n",
       "      <td>2.023826</td>\n",
       "      <td>372.934839</td>\n",
       "      <td>1.002427e+08</td>\n",
       "      <td>12.453977</td>\n",
       "      <td>4011.299523</td>\n",
       "      <td>1.127802</td>\n",
       "      <td>1.402940</td>\n",
       "      <td>19206.016458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.180000e+02</td>\n",
       "      <td>1916.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>5.019656e+06</td>\n",
       "      <td>8.361750e+03</td>\n",
       "      <td>1394.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>6.000000e+06</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>2.504396e+07</td>\n",
       "      <td>3.313250e+04</td>\n",
       "      <td>3049.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>1.985000e+07</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>191.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>189.750000</td>\n",
       "      <td>633.000000</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>6.110841e+07</td>\n",
       "      <td>9.377275e+04</td>\n",
       "      <td>13616.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>320.500000</td>\n",
       "      <td>4.300000e+07</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>912.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>813.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>640000.000000</td>\n",
       "      <td>7.605058e+08</td>\n",
       "      <td>1.689764e+06</td>\n",
       "      <td>656730.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>5060.000000</td>\n",
       "      <td>4.200000e+09</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>137000.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>349000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_critic_for_reviews     duration  director_facebook_likes  \\\n",
       "count             4867.000000  4901.000000              4814.000000   \n",
       "mean               137.988905   107.090798               691.014541   \n",
       "std                120.239379    25.286015              2832.954125   \n",
       "min                  1.000000     7.000000                 0.000000   \n",
       "25%                 49.000000    93.000000                 7.000000   \n",
       "50%                108.000000   103.000000                48.000000   \n",
       "75%                191.000000   118.000000               189.750000   \n",
       "max                813.000000   511.000000             23000.000000   \n",
       "\n",
       "       actor_3_facebook_likes  actor_1_facebook_likes         gross  \\\n",
       "count             4893.000000             4909.000000  4.054000e+03   \n",
       "mean               631.276313             6494.488491  4.764451e+07   \n",
       "std               1625.874802            15106.986884  6.737255e+07   \n",
       "min                  0.000000                0.000000  1.620000e+02   \n",
       "25%                132.000000              607.000000  5.019656e+06   \n",
       "50%                366.000000              982.000000  2.504396e+07   \n",
       "75%                633.000000            11000.000000  6.110841e+07   \n",
       "max              23000.000000           640000.000000  7.605058e+08   \n",
       "\n",
       "       num_voted_users  cast_total_facebook_likes  facenumber_in_poster  \\\n",
       "count     4.916000e+03                4916.000000           4903.000000   \n",
       "mean      8.264492e+04                9579.815907              1.377320   \n",
       "std       1.383222e+05               18164.316990              2.023826   \n",
       "min       5.000000e+00                   0.000000              0.000000   \n",
       "25%       8.361750e+03                1394.750000              0.000000   \n",
       "50%       3.313250e+04                3049.000000              1.000000   \n",
       "75%       9.377275e+04               13616.750000              2.000000   \n",
       "max       1.689764e+06              656730.000000             43.000000   \n",
       "\n",
       "       num_user_for_reviews        budget   title_year  \\\n",
       "count           4895.000000  4.432000e+03  4810.000000   \n",
       "mean             267.668846  3.654749e+07  2002.447609   \n",
       "std              372.934839  1.002427e+08    12.453977   \n",
       "min                1.000000  2.180000e+02  1916.000000   \n",
       "25%               64.000000  6.000000e+06  1999.000000   \n",
       "50%              153.000000  1.985000e+07  2005.000000   \n",
       "75%              320.500000  4.300000e+07  2011.000000   \n",
       "max             5060.000000  4.200000e+09  2016.000000   \n",
       "\n",
       "       actor_2_facebook_likes   imdb_score  aspect_ratio  movie_facebook_likes  \n",
       "count             4903.000000  4916.000000   4590.000000           4916.000000  \n",
       "mean              1621.923516     6.437429      2.222349           7348.294142  \n",
       "std               4011.299523     1.127802      1.402940          19206.016458  \n",
       "min                  0.000000     1.600000      1.180000              0.000000  \n",
       "25%                277.000000     5.800000      1.850000              0.000000  \n",
       "50%                593.000000     6.600000      2.350000            159.000000  \n",
       "75%                912.000000     7.200000      2.350000           2000.000000  \n",
       "max             137000.000000     9.500000     16.000000         349000.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>facenumber_in_poster</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4867.000000</td>\n",
       "      <td>4901.000000</td>\n",
       "      <td>4814.000000</td>\n",
       "      <td>4893.000000</td>\n",
       "      <td>4909.000000</td>\n",
       "      <td>4.054000e+03</td>\n",
       "      <td>4.916000e+03</td>\n",
       "      <td>4916.000000</td>\n",
       "      <td>4903.000000</td>\n",
       "      <td>4895.000000</td>\n",
       "      <td>4.432000e+03</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4903.000000</td>\n",
       "      <td>4916.000000</td>\n",
       "      <td>4590.000000</td>\n",
       "      <td>4916.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137.988905</td>\n",
       "      <td>107.090798</td>\n",
       "      <td>691.014541</td>\n",
       "      <td>631.276313</td>\n",
       "      <td>6494.488491</td>\n",
       "      <td>4.764451e+07</td>\n",
       "      <td>8.264492e+04</td>\n",
       "      <td>9579.815907</td>\n",
       "      <td>1.377320</td>\n",
       "      <td>267.668846</td>\n",
       "      <td>3.654749e+07</td>\n",
       "      <td>2002.447609</td>\n",
       "      <td>1621.923516</td>\n",
       "      <td>6.437429</td>\n",
       "      <td>2.222349</td>\n",
       "      <td>7348.294142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.239379</td>\n",
       "      <td>25.286015</td>\n",
       "      <td>2832.954125</td>\n",
       "      <td>1625.874802</td>\n",
       "      <td>15106.986884</td>\n",
       "      <td>6.737255e+07</td>\n",
       "      <td>1.383222e+05</td>\n",
       "      <td>18164.316990</td>\n",
       "      <td>2.023826</td>\n",
       "      <td>372.934839</td>\n",
       "      <td>1.002427e+08</td>\n",
       "      <td>12.453977</td>\n",
       "      <td>4011.299523</td>\n",
       "      <td>1.127802</td>\n",
       "      <td>1.402940</td>\n",
       "      <td>19206.016458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.180000e+02</td>\n",
       "      <td>1916.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.080000</td>\n",
       "      <td>8.474800e+03</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.940000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1.330000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>694.000000</td>\n",
       "      <td>7.914069e+06</td>\n",
       "      <td>1.186450e+04</td>\n",
       "      <td>1684.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000e+06</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>2.504396e+07</td>\n",
       "      <td>3.313250e+04</td>\n",
       "      <td>3049.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>1.985000e+07</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>546.680000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>16000.000000</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>44920.000000</td>\n",
       "      <td>3.264128e+08</td>\n",
       "      <td>6.815846e+05</td>\n",
       "      <td>62413.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1999.240000</td>\n",
       "      <td>2.000000e+08</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>93850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>813.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>640000.000000</td>\n",
       "      <td>7.605058e+08</td>\n",
       "      <td>1.689764e+06</td>\n",
       "      <td>656730.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>5060.000000</td>\n",
       "      <td>4.200000e+09</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>137000.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>349000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_critic_for_reviews     duration  director_facebook_likes  \\\n",
       "count             4867.000000  4901.000000              4814.000000   \n",
       "mean               137.988905   107.090798               691.014541   \n",
       "std                120.239379    25.286015              2832.954125   \n",
       "min                  1.000000     7.000000                 0.000000   \n",
       "1%                   2.000000    43.000000                 0.000000   \n",
       "30%                 60.000000    95.000000                11.000000   \n",
       "50%                108.000000   103.000000                48.000000   \n",
       "99%                546.680000   189.000000             16000.000000   \n",
       "max                813.000000   511.000000             23000.000000   \n",
       "\n",
       "       actor_3_facebook_likes  actor_1_facebook_likes         gross  \\\n",
       "count             4893.000000             4909.000000  4.054000e+03   \n",
       "mean               631.276313             6494.488491  4.764451e+07   \n",
       "std               1625.874802            15106.986884  6.737255e+07   \n",
       "min                  0.000000                0.000000  1.620000e+02   \n",
       "1%                   0.000000                6.080000  8.474800e+03   \n",
       "30%                176.000000              694.000000  7.914069e+06   \n",
       "50%                366.000000              982.000000  2.504396e+07   \n",
       "99%              11000.000000            44920.000000  3.264128e+08   \n",
       "max              23000.000000           640000.000000  7.605058e+08   \n",
       "\n",
       "       num_voted_users  cast_total_facebook_likes  facenumber_in_poster  \\\n",
       "count     4.916000e+03                4916.000000           4903.000000   \n",
       "mean      8.264492e+04                9579.815907              1.377320   \n",
       "std       1.383222e+05               18164.316990              2.023826   \n",
       "min       5.000000e+00                   0.000000              0.000000   \n",
       "1%        5.300000e+01                   6.000000              0.000000   \n",
       "30%       1.186450e+04                1684.500000              0.000000   \n",
       "50%       3.313250e+04                3049.000000              1.000000   \n",
       "99%       6.815846e+05               62413.900000              8.000000   \n",
       "max       1.689764e+06              656730.000000             43.000000   \n",
       "\n",
       "       num_user_for_reviews        budget   title_year  \\\n",
       "count           4895.000000  4.432000e+03  4810.000000   \n",
       "mean             267.668846  3.654749e+07  2002.447609   \n",
       "std              372.934839  1.002427e+08    12.453977   \n",
       "min                1.000000  2.180000e+02  1916.000000   \n",
       "1%                 1.940000  6.000000e+04  1951.000000   \n",
       "30%               80.000000  8.000000e+06  2000.000000   \n",
       "50%              153.000000  1.985000e+07  2005.000000   \n",
       "99%             1999.240000  2.000000e+08  2016.000000   \n",
       "max             5060.000000  4.200000e+09  2016.000000   \n",
       "\n",
       "       actor_2_facebook_likes   imdb_score  aspect_ratio  movie_facebook_likes  \n",
       "count             4903.000000  4916.000000   4590.000000           4916.000000  \n",
       "mean              1621.923516     6.437429      2.222349           7348.294142  \n",
       "std               4011.299523     1.127802      1.402940          19206.016458  \n",
       "min                  0.000000     1.600000      1.180000              0.000000  \n",
       "1%                   0.000000     3.100000      1.330000              0.000000  \n",
       "30%                345.000000     6.000000      1.850000              0.000000  \n",
       "50%                593.000000     6.600000      2.350000            159.000000  \n",
       "99%              17000.000000     8.500000      4.000000          93850.000000  \n",
       "max             137000.000000     9.500000     16.000000         349000.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.describe(percentiles=[.01, .3, .99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                      19\n",
       "director_name             102\n",
       "num_critic_for_reviews     49\n",
       "duration                   15\n",
       "                         ... \n",
       "actor_2_facebook_likes     13\n",
       "imdb_score                  0\n",
       "aspect_ratio              326\n",
       "movie_facebook_likes        0\n",
       "Length: 28, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There's more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_critic_for_reviews     NaN\n",
       "duration                   NaN\n",
       "director_facebook_likes    NaN\n",
       "actor_3_facebook_likes     NaN\n",
       "                          ... \n",
       "actor_2_facebook_likes     NaN\n",
       "imdb_score                 1.6\n",
       "aspect_ratio               NaN\n",
       "movie_facebook_likes       0.0\n",
       "Length: 16, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.min(skipna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chaining DataFrame methods together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>director_name</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>facenumber_in_poster</th>\n",
       "      <th>plot_keywords</th>\n",
       "      <th>movie_imdb_link</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color  director_name  num_critic_for_reviews  duration  \\\n",
       "0  False          False                   False     False   \n",
       "1  False          False                   False     False   \n",
       "2  False          False                   False     False   \n",
       "3  False          False                   False     False   \n",
       "4   True          False                    True      True   \n",
       "\n",
       "   director_facebook_likes  actor_3_facebook_likes  actor_2_name  \\\n",
       "0                    False                   False         False   \n",
       "1                    False                   False         False   \n",
       "2                    False                   False         False   \n",
       "3                    False                   False         False   \n",
       "4                    False                    True         False   \n",
       "\n",
       "   actor_1_facebook_likes  gross  genres  actor_1_name  movie_title  \\\n",
       "0                   False  False   False         False        False   \n",
       "1                   False  False   False         False        False   \n",
       "2                   False  False   False         False        False   \n",
       "3                   False  False   False         False        False   \n",
       "4                   False   True   False         False        False   \n",
       "\n",
       "   num_voted_users  cast_total_facebook_likes  actor_3_name  \\\n",
       "0            False                      False         False   \n",
       "1            False                      False         False   \n",
       "2            False                      False         False   \n",
       "3            False                      False         False   \n",
       "4            False                      False          True   \n",
       "\n",
       "   facenumber_in_poster  plot_keywords  movie_imdb_link  num_user_for_reviews  \\\n",
       "0                 False          False            False                 False   \n",
       "1                 False          False            False                 False   \n",
       "2                 False          False            False                 False   \n",
       "3                 False          False            False                 False   \n",
       "4                 False           True            False                  True   \n",
       "\n",
       "   language  country  content_rating  budget  title_year  \\\n",
       "0     False    False           False   False       False   \n",
       "1     False    False           False   False       False   \n",
       "2     False    False           False   False       False   \n",
       "3     False    False           False   False       False   \n",
       "4      True     True            True    True        True   \n",
       "\n",
       "   actor_2_facebook_likes  imdb_score  aspect_ratio  movie_facebook_likes  \n",
       "0                   False       False         False                 False  \n",
       "1                   False       False         False                 False  \n",
       "2                   False       False         False                 False  \n",
       "3                   False       False         False                 False  \n",
       "4                   False       False          True                 False  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie = pd.read_csv('data/movie.csv')\n",
    "movie.isnull().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                       19\n",
       "director_name              102\n",
       "num_critic_for_reviews      49\n",
       "duration                    15\n",
       "director_facebook_likes    102\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.isnull().sum().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2654"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.isnull().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How it works..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bool    28\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.isnull().get_dtype_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There's more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie[['color', 'movie_title', 'color']].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                                                          Color\n",
       "director_name                                          Étienne Faure\n",
       "actor_2_name                                           Zubaida Sahar\n",
       "genres                                                       Western\n",
       "                                         ...                        \n",
       "movie_imdb_link    http://www.imdb.com/title/tt5574490/?ref_=fn_t...\n",
       "language                                                        Zulu\n",
       "country                                                 West Germany\n",
       "content_rating                                                     X\n",
       "Length: 12, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.select_dtypes(['object']).fillna('').max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with operators on a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting ready..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Could not operate 5 with block values must be str, not int",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m   1175\u001b[0m             result = expressions.evaluate(op, str_rep, x, y,\n\u001b[0;32m-> 1176\u001b[0;31m                                           raise_on_error=True, **eval_kwargs)\n\u001b[0m\u001b[1;32m   1177\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(op, op_str, a, b, raise_on_error, use_numexpr, **eval_kwargs)\u001b[0m\n\u001b[1;32m    210\u001b[0m         return _evaluate(op, op_str, a, b, raise_on_error=raise_on_error,\n\u001b[0;32m--> 211\u001b[0;31m                          **eval_kwargs)\n\u001b[0m\u001b[1;32m    212\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_on_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_on_error\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[0;34m(op, op_str, a, b, raise_on_error, truediv, reversed, **eval_kwargs)\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_on_error\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[0;34m(op, op_str, a, b, raise_on_error, **eval_kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: must be str, not int",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, func, other, raise_on_error, try_cast, mgr)\u001b[0m\n\u001b[1;32m   1183\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1184\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(other)\u001b[0m\n\u001b[1;32m   1152\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1153\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m   1201\u001b[0m                     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1202\u001b[0;31m                         \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1203\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: must be str, not int",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-4749f68a2501>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcollege\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data/college.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcollege\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(self, other, axis, level, fill_value)\u001b[0m\n\u001b[1;32m   1239\u001b[0m                 \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1241\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_const\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_op\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1243\u001b[0m     \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_combine_const\u001b[0;34m(self, other, func, raise_on_error)\u001b[0m\n\u001b[1;32m   3541\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_combine_const\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_on_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3542\u001b[0m         new_data = self._data.eval(func=func, other=other,\n\u001b[0;32m-> 3543\u001b[0;31m                                    raise_on_error=raise_on_error)\n\u001b[0m\u001b[1;32m   3544\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   3195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3196\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3197\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'eval'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3199\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m   3089\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3090\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mgr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3091\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3092\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3093\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, func, other, raise_on_error, try_cast, mgr)\u001b[0m\n\u001b[1;32m   1189\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1190\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdetail\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1191\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandle_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1193\u001b[0m         \u001b[0;31m# technically a broadcast error in numpy can 'work' by returning a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mhandle_error\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1172\u001b[0m                 \u001b[0;31m# The 'detail' variable is defined in outer scope.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1173\u001b[0m                 raise TypeError('Could not operate %s with block values %s' %\n\u001b[0;32m-> 1174\u001b[0;31m                                 (repr(other), str(detail)))  # noqa\n\u001b[0m\u001b[1;32m   1175\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m                 \u001b[0;31m# return the values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Could not operate 5 with block values must be str, not int"
     ]
    }
   ],
   "source": [
    "college = pd.read_csv('data/college.csv')\n",
    "college + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "college = pd.read_csv('data/college.csv', index_col='INSTNM')\n",
    "college_ugds_ = college.filter(like='UGDS_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Could not compare ['asdf'] with block values",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-697c8af60bcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcollege\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'asdf'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1302\u001b[0m             \u001b[0;31m# straight boolean comparisions we want to allow all columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1303\u001b[0m             \u001b[0;31m# (regardless of dtype to pass thru) See #4537 for discussion.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1304\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_const\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_on_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1305\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_combine_const\u001b[0;34m(self, other, func, raise_on_error)\u001b[0m\n\u001b[1;32m   3541\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_combine_const\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_on_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3542\u001b[0m         new_data = self._data.eval(func=func, other=other,\n\u001b[0;32m-> 3543\u001b[0;31m                                    raise_on_error=raise_on_error)\n\u001b[0m\u001b[1;32m   3544\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   3195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3196\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3197\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'eval'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3199\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m   3089\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3090\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mgr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3091\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3092\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3093\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, func, other, raise_on_error, try_cast, mgr)\u001b[0m\n\u001b[1;32m   1203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1204\u001b[0m                 raise TypeError('Could not compare [%s] with block values' %\n\u001b[0;32m-> 1205\u001b[0;31m                                 repr(other))\n\u001b[0m\u001b[1;32m   1206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1207\u001b[0m         \u001b[0;31m# transpose if needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Could not compare ['asdf'] with block values"
     ]
    }
   ],
   "source": [
    "college == 'asdf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>0.0333</td>\n",
       "      <td>0.9353</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>0.5922</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.0283</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>0.2990</td>\n",
       "      <td>0.4192</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>0.6988</td>\n",
       "      <td>0.1255</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0376</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>0.0332</td>\n",
       "      <td>0.0350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.9208</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>0.0137</td>\n",
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      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                 0.0333      0.9353     0.0055   \n",
       "University of Alabama at Birmingham      0.5922      0.2600     0.0283   \n",
       "Amridge University                       0.2990      0.4192     0.0069   \n",
       "University of Alabama in Huntsville      0.6988      0.1255     0.0382   \n",
       "Alabama State University                 0.0158      0.9208     0.0121   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                 0.0019     0.0024     0.0019   \n",
       "University of Alabama at Birmingham      0.0518     0.0022     0.0007   \n",
       "Amridge University                       0.0034     0.0000     0.0000   \n",
       "University of Alabama in Huntsville      0.0376     0.0143     0.0002   \n",
       "Alabama State University                 0.0019     0.0010     0.0006   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                0.0000    0.0059     0.0138  \n",
       "University of Alabama at Birmingham     0.0368    0.0179     0.0100  \n",
       "Amridge University                      0.0000    0.0000     0.2715  \n",
       "University of Alabama in Huntsville     0.0172    0.0332     0.0350  \n",
       "Alabama State University                0.0098    0.0243     0.0137  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>UGDS_HISP</th>\n",
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       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>0.03831</td>\n",
       "      <td>0.94031</td>\n",
       "      <td>0.01051</td>\n",
       "      <td>0.00691</td>\n",
       "      <td>0.00741</td>\n",
       "      <td>0.00691</td>\n",
       "      <td>0.00501</td>\n",
       "      <td>0.01091</td>\n",
       "      <td>0.01881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>0.59721</td>\n",
       "      <td>0.26501</td>\n",
       "      <td>0.03331</td>\n",
       "      <td>0.05681</td>\n",
       "      <td>0.00721</td>\n",
       "      <td>0.00571</td>\n",
       "      <td>0.04181</td>\n",
       "      <td>0.02291</td>\n",
       "      <td>0.01501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>0.30401</td>\n",
       "      <td>0.42421</td>\n",
       "      <td>0.01191</td>\n",
       "      <td>0.00841</td>\n",
       "      <td>0.00501</td>\n",
       "      <td>0.00501</td>\n",
       "      <td>0.00501</td>\n",
       "      <td>0.00501</td>\n",
       "      <td>0.27651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>0.70381</td>\n",
       "      <td>0.13051</td>\n",
       "      <td>0.04321</td>\n",
       "      <td>0.04261</td>\n",
       "      <td>0.01931</td>\n",
       "      <td>0.00521</td>\n",
       "      <td>0.02221</td>\n",
       "      <td>0.03821</td>\n",
       "      <td>0.04001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>0.02081</td>\n",
       "      <td>0.92581</td>\n",
       "      <td>0.01711</td>\n",
       "      <td>0.00691</td>\n",
       "      <td>0.00601</td>\n",
       "      <td>0.00561</td>\n",
       "      <td>0.01481</td>\n",
       "      <td>0.02931</td>\n",
       "      <td>0.01871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                0.03831     0.94031    0.01051   \n",
       "University of Alabama at Birmingham     0.59721     0.26501    0.03331   \n",
       "Amridge University                      0.30401     0.42421    0.01191   \n",
       "University of Alabama in Huntsville     0.70381     0.13051    0.04321   \n",
       "Alabama State University                0.02081     0.92581    0.01711   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                0.00691    0.00741    0.00691   \n",
       "University of Alabama at Birmingham     0.05681    0.00721    0.00571   \n",
       "Amridge University                      0.00841    0.00501    0.00501   \n",
       "University of Alabama in Huntsville     0.04261    0.01931    0.00521   \n",
       "Alabama State University                0.00691    0.00601    0.00561   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University               0.00501   0.01091    0.01881  \n",
       "University of Alabama at Birmingham    0.04181   0.02291    0.01501  \n",
       "Amridge University                     0.00501   0.00501    0.27651  \n",
       "University of Alabama in Huntsville    0.02221   0.03821    0.04001  \n",
       "Alabama State University               0.01481   0.02931    0.01871  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.head() + .00501"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>3.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>59.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>30.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>70.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>2.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                    3.0        94.0        1.0   \n",
       "University of Alabama at Birmingham        59.0        26.0        3.0   \n",
       "Amridge University                         30.0        42.0        1.0   \n",
       "University of Alabama in Huntsville        70.0        13.0        4.0   \n",
       "Alabama State University                    2.0        92.0        1.0   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                    0.0        0.0        0.0   \n",
       "University of Alabama at Birmingham         5.0        0.0        0.0   \n",
       "Amridge University                          0.0        0.0        0.0   \n",
       "University of Alabama in Huntsville         4.0        1.0        0.0   \n",
       "Alabama State University                    0.0        0.0        0.0   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                   0.0       1.0        1.0  \n",
       "University of Alabama at Birmingham        4.0       2.0        1.0  \n",
       "Amridge University                         0.0       0.0       27.0  \n",
       "University of Alabama in Huntsville        2.0       3.0        4.0  \n",
       "Alabama State University                   1.0       2.0        1.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(college_ugds_.head() + .00501) // .01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                   0.03        0.94       0.01   \n",
       "University of Alabama at Birmingham        0.59        0.26       0.03   \n",
       "Amridge University                         0.30        0.42       0.01   \n",
       "University of Alabama in Huntsville        0.70        0.13       0.04   \n",
       "Alabama State University                   0.02        0.92       0.01   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                   0.00       0.00        0.0   \n",
       "University of Alabama at Birmingham        0.05       0.00        0.0   \n",
       "Amridge University                         0.00       0.00        0.0   \n",
       "University of Alabama in Huntsville        0.04       0.01        0.0   \n",
       "Alabama State University                   0.00       0.00        0.0   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                  0.00      0.01       0.01  \n",
       "University of Alabama at Birmingham       0.04      0.02       0.01  \n",
       "Amridge University                        0.00      0.00       0.27  \n",
       "University of Alabama in Huntsville       0.02      0.03       0.04  \n",
       "Alabama State University                  0.01      0.02       0.01  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_op_round = (college_ugds_ + .00501) // .01 / 100\n",
    "college_ugds_op_round.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                   0.03        0.94       0.01   \n",
       "University of Alabama at Birmingham        0.59        0.26       0.03   \n",
       "Amridge University                         0.30        0.42       0.01   \n",
       "University of Alabama in Huntsville        0.70        0.13       0.04   \n",
       "Alabama State University                   0.02        0.92       0.01   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                   0.00       0.00        0.0   \n",
       "University of Alabama at Birmingham        0.05       0.00        0.0   \n",
       "Amridge University                         0.00       0.00        0.0   \n",
       "University of Alabama in Huntsville        0.04       0.01        0.0   \n",
       "Alabama State University                   0.00       0.00        0.0   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                  0.00      0.01       0.01  \n",
       "University of Alabama at Birmingham       0.04      0.02       0.01  \n",
       "Amridge University                        0.00      0.00       0.27  \n",
       "University of Alabama in Huntsville       0.02      0.03       0.04  \n",
       "Alabama State University                  0.01      0.02       0.01  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_round = (college_ugds_ + .00001).round(2)\n",
    "college_ugds_round.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.049999999999999996"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".045 + .005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_op_round.equals(college_ugds_round)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There's more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "college_ugds_op_round_methods = college_ugds_.add(.00501).floordiv(.01).div(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "None == None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 > np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan > 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 != np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "college = pd.read_csv('data/college.csv', index_col='INSTNM')\n",
    "college_ugds_ = college.filter(like='UGDS_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                  False       False      False   \n",
       "University of Alabama at Birmingham       False       False      False   \n",
       "Amridge University                        False       False      False   \n",
       "University of Alabama in Huntsville       False       False      False   \n",
       "Alabama State University                  False       False      False   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                   True      False       True   \n",
       "University of Alabama at Birmingham       False      False      False   \n",
       "Amridge University                        False      False      False   \n",
       "University of Alabama in Huntsville       False      False      False   \n",
       "Alabama State University                   True      False      False   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                 False     False      False  \n",
       "University of Alabama at Birmingham      False     False      False  \n",
       "Amridge University                       False     False      False  \n",
       "University of Alabama in Huntsville      False     False      False  \n",
       "Alabama State University                 False     False      False  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.head() == .0019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                   True        True       True   \n",
       "University of Alabama at Birmingham        True        True       True   \n",
       "Amridge University                         True        True       True   \n",
       "University of Alabama in Huntsville        True        True       True   \n",
       "Alabama State University                   True        True       True   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                   True       True       True   \n",
       "University of Alabama at Birmingham        True       True       True   \n",
       "Amridge University                         True       True       True   \n",
       "University of Alabama in Huntsville        True       True       True   \n",
       "Alabama State University                   True       True       True   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                  True      True       True  \n",
       "University of Alabama at Birmingham       True      True       True  \n",
       "Amridge University                        True      True       True  \n",
       "University of Alabama in Huntsville       True      True       True  \n",
       "Alabama State University                  True      True       True  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_self_compare = college_ugds_ == college_ugds_\n",
    "college_self_compare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UGDS_WHITE    False\n",
       "UGDS_BLACK    False\n",
       "UGDS_HISP     False\n",
       "UGDS_ASIAN    False\n",
       "              ...  \n",
       "UGDS_NHPI     False\n",
       "UGDS_2MOR     False\n",
       "UGDS_NRA      False\n",
       "UGDS_UNKN     False\n",
       "Length: 9, dtype: bool"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_self_compare.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UGDS_WHITE    0\n",
       "UGDS_BLACK    0\n",
       "UGDS_HISP     0\n",
       "UGDS_ASIAN    0\n",
       "             ..\n",
       "UGDS_NHPI     0\n",
       "UGDS_2MOR     0\n",
       "UGDS_NRA      0\n",
       "UGDS_UNKN     0\n",
       "Length: 9, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(college_ugds_ == np.nan).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UGDS_WHITE    661\n",
       "UGDS_BLACK    661\n",
       "UGDS_HISP     661\n",
       "UGDS_ASIAN    661\n",
       "             ... \n",
       "UGDS_NHPI     661\n",
       "UGDS_2MOR     661\n",
       "UGDS_NRA      661\n",
       "UGDS_UNKN     661\n",
       "Length: 9, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas.testing import assert_frame_equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "assert_frame_equal(college_ugds_, college_ugds_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There's more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                  False       False      False   \n",
       "University of Alabama at Birmingham       False       False      False   \n",
       "Amridge University                        False       False      False   \n",
       "University of Alabama in Huntsville       False       False      False   \n",
       "Alabama State University                  False       False      False   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                   True      False       True   \n",
       "University of Alabama at Birmingham       False      False      False   \n",
       "Amridge University                        False      False      False   \n",
       "University of Alabama in Huntsville       False      False      False   \n",
       "Alabama State University                   True      False      False   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                 False     False      False  \n",
       "University of Alabama at Birmingham      False     False      False  \n",
       "Amridge University                       False     False      False  \n",
       "University of Alabama in Huntsville      False     False      False  \n",
       "Alabama State University                 False     False      False  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.eq(.0019).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transposing the direction of a DataFrame operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>0.0333</td>\n",
       "      <td>0.9353</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>0.5922</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.0283</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>0.2990</td>\n",
       "      <td>0.4192</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>0.6988</td>\n",
       "      <td>0.1255</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0376</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>0.0332</td>\n",
       "      <td>0.0350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.9208</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>0.0137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                 0.0333      0.9353     0.0055   \n",
       "University of Alabama at Birmingham      0.5922      0.2600     0.0283   \n",
       "Amridge University                       0.2990      0.4192     0.0069   \n",
       "University of Alabama in Huntsville      0.6988      0.1255     0.0382   \n",
       "Alabama State University                 0.0158      0.9208     0.0121   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                 0.0019     0.0024     0.0019   \n",
       "University of Alabama at Birmingham      0.0518     0.0022     0.0007   \n",
       "Amridge University                       0.0034     0.0000     0.0000   \n",
       "University of Alabama in Huntsville      0.0376     0.0143     0.0002   \n",
       "Alabama State University                 0.0019     0.0010     0.0006   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                0.0000    0.0059     0.0138  \n",
       "University of Alabama at Birmingham     0.0368    0.0179     0.0100  \n",
       "Amridge University                      0.0000    0.0000     0.2715  \n",
       "University of Alabama in Huntsville     0.0172    0.0332     0.0350  \n",
       "Alabama State University                0.0098    0.0243     0.0137  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college = pd.read_csv('data/college.csv', index_col='INSTNM')\n",
    "college_ugds_ = college.filter(like='UGDS_')\n",
    "college_ugds_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UGDS_WHITE    6874\n",
       "UGDS_BLACK    6874\n",
       "UGDS_HISP     6874\n",
       "UGDS_ASIAN    6874\n",
       "              ... \n",
       "UGDS_NHPI     6874\n",
       "UGDS_2MOR     6874\n",
       "UGDS_NRA      6874\n",
       "UGDS_UNKN     6874\n",
       "Length: 9, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UGDS_WHITE    6874\n",
       "UGDS_BLACK    6874\n",
       "UGDS_HISP     6874\n",
       "UGDS_ASIAN    6874\n",
       "              ... \n",
       "UGDS_NHPI     6874\n",
       "UGDS_2MOR     6874\n",
       "UGDS_NRA      6874\n",
       "UGDS_UNKN     6874\n",
       "Length: 9, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.count(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UGDS_WHITE    6874\n",
       "UGDS_BLACK    6874\n",
       "UGDS_HISP     6874\n",
       "UGDS_ASIAN    6874\n",
       "              ... \n",
       "UGDS_NHPI     6874\n",
       "UGDS_2MOR     6874\n",
       "UGDS_NRA      6874\n",
       "UGDS_UNKN     6874\n",
       "Length: 9, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.count(axis='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTNM\n",
       "Alabama A & M University               9\n",
       "University of Alabama at Birmingham    9\n",
       "Amridge University                     9\n",
       "University of Alabama in Huntsville    9\n",
       "Alabama State University               9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.count(axis='columns').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTNM\n",
       "Alabama A & M University               1.0000\n",
       "University of Alabama at Birmingham    0.9999\n",
       "Amridge University                     1.0000\n",
       "University of Alabama in Huntsville    1.0000\n",
       "Alabama State University               1.0000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.sum(axis='columns').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UGDS_WHITE    0.55570\n",
       "UGDS_BLACK    0.10005\n",
       "UGDS_HISP     0.07140\n",
       "UGDS_ASIAN    0.01290\n",
       "               ...   \n",
       "UGDS_NHPI     0.00000\n",
       "UGDS_2MOR     0.01750\n",
       "UGDS_NRA      0.00000\n",
       "UGDS_UNKN     0.01430\n",
       "Length: 9, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.median(axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There's more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>0.0333</td>\n",
       "      <td>0.9686</td>\n",
       "      <td>0.9741</td>\n",
       "      <td>0.9760</td>\n",
       "      <td>0.9784</td>\n",
       "      <td>0.9803</td>\n",
       "      <td>0.9803</td>\n",
       "      <td>0.9862</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>0.5922</td>\n",
       "      <td>0.8522</td>\n",
       "      <td>0.8805</td>\n",
       "      <td>0.9323</td>\n",
       "      <td>0.9345</td>\n",
       "      <td>0.9352</td>\n",
       "      <td>0.9720</td>\n",
       "      <td>0.9899</td>\n",
       "      <td>0.9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>0.2990</td>\n",
       "      <td>0.7182</td>\n",
       "      <td>0.7251</td>\n",
       "      <td>0.7285</td>\n",
       "      <td>0.7285</td>\n",
       "      <td>0.7285</td>\n",
       "      <td>0.7285</td>\n",
       "      <td>0.7285</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>0.6988</td>\n",
       "      <td>0.8243</td>\n",
       "      <td>0.8625</td>\n",
       "      <td>0.9001</td>\n",
       "      <td>0.9144</td>\n",
       "      <td>0.9146</td>\n",
       "      <td>0.9318</td>\n",
       "      <td>0.9650</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.9366</td>\n",
       "      <td>0.9487</td>\n",
       "      <td>0.9506</td>\n",
       "      <td>0.9516</td>\n",
       "      <td>0.9522</td>\n",
       "      <td>0.9620</td>\n",
       "      <td>0.9863</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                 0.0333      0.9686     0.9741   \n",
       "University of Alabama at Birmingham      0.5922      0.8522     0.8805   \n",
       "Amridge University                       0.2990      0.7182     0.7251   \n",
       "University of Alabama in Huntsville      0.6988      0.8243     0.8625   \n",
       "Alabama State University                 0.0158      0.9366     0.9487   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                 0.9760     0.9784     0.9803   \n",
       "University of Alabama at Birmingham      0.9323     0.9345     0.9352   \n",
       "Amridge University                       0.7285     0.7285     0.7285   \n",
       "University of Alabama in Huntsville      0.9001     0.9144     0.9146   \n",
       "Alabama State University                 0.9506     0.9516     0.9522   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                0.9803    0.9862     1.0000  \n",
       "University of Alabama at Birmingham     0.9720    0.9899     0.9999  \n",
       "Amridge University                      0.7285    0.7285     1.0000  \n",
       "University of Alabama in Huntsville     0.9318    0.9650     1.0000  \n",
       "Alabama State University                0.9620    0.9863     1.0000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_cumsum = college_ugds_.cumsum(axis=1)\n",
    "college_ugds_cumsum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New Beginning College of Cosmetology</th>\n",
       "      <td>0.8957</td>\n",
       "      <td>0.9305</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia University of Lynchburg</th>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.9921</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turning Point Beauty College</th>\n",
       "      <td>0.1915</td>\n",
       "      <td>0.2341</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>First Coast Barber Academy</th>\n",
       "      <td>0.1667</td>\n",
       "      <td>0.9445</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rasmussen College - Overland Park</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>National Personal Training Institute of Cleveland</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bay Area Medical Academy - San Jose Satellite Location</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Excel Learning Center-San Antonio South</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7535 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    UGDS_WHITE  UGDS_BLACK  \\\n",
       "INSTNM                                                                       \n",
       "New Beginning College of Cosmetology                    0.8957      0.9305   \n",
       "Virginia University of Lynchburg                        0.0120      0.9921   \n",
       "Turning Point Beauty College                            0.1915      0.2341   \n",
       "First Coast Barber Academy                              0.1667      0.9445   \n",
       "...                                                        ...         ...   \n",
       "Rasmussen College - Overland Park                          NaN         NaN   \n",
       "National Personal Training Institute of Cleveland          NaN         NaN   \n",
       "Bay Area Medical Academy - San Jose Satellite L...         NaN         NaN   \n",
       "Excel Learning Center-San Antonio South                    NaN         NaN   \n",
       "\n",
       "                                                    UGDS_HISP  UGDS_ASIAN  \\\n",
       "INSTNM                                                                      \n",
       "New Beginning College of Cosmetology                   1.0001      1.0001   \n",
       "Virginia University of Lynchburg                       1.0001      1.0001   \n",
       "Turning Point Beauty College                           1.0001      1.0001   \n",
       "First Coast Barber Academy                             1.0001      1.0001   \n",
       "...                                                       ...         ...   \n",
       "Rasmussen College - Overland Park                         NaN         NaN   \n",
       "National Personal Training Institute of Cleveland         NaN         NaN   \n",
       "Bay Area Medical Academy - San Jose Satellite L...        NaN         NaN   \n",
       "Excel Learning Center-San Antonio South                   NaN         NaN   \n",
       "\n",
       "                                                    UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                     \n",
       "New Beginning College of Cosmetology                   1.0001     1.0001   \n",
       "Virginia University of Lynchburg                       1.0001     1.0001   \n",
       "Turning Point Beauty College                           1.0001     1.0001   \n",
       "First Coast Barber Academy                             1.0001     1.0001   \n",
       "...                                                       ...        ...   \n",
       "Rasmussen College - Overland Park                         NaN        NaN   \n",
       "National Personal Training Institute of Cleveland         NaN        NaN   \n",
       "Bay Area Medical Academy - San Jose Satellite L...        NaN        NaN   \n",
       "Excel Learning Center-San Antonio South                   NaN        NaN   \n",
       "\n",
       "                                                    UGDS_2MOR  UGDS_NRA  \\\n",
       "INSTNM                                                                    \n",
       "New Beginning College of Cosmetology                   1.0001    1.0001   \n",
       "Virginia University of Lynchburg                       1.0001    1.0001   \n",
       "Turning Point Beauty College                           1.0001    1.0001   \n",
       "First Coast Barber Academy                             1.0001    1.0001   \n",
       "...                                                       ...       ...   \n",
       "Rasmussen College - Overland Park                         NaN       NaN   \n",
       "National Personal Training Institute of Cleveland         NaN       NaN   \n",
       "Bay Area Medical Academy - San Jose Satellite L...        NaN       NaN   \n",
       "Excel Learning Center-San Antonio South                   NaN       NaN   \n",
       "\n",
       "                                                    UGDS_UNKN  \n",
       "INSTNM                                                         \n",
       "New Beginning College of Cosmetology                   1.0001  \n",
       "Virginia University of Lynchburg                       1.0001  \n",
       "Turning Point Beauty College                           1.0001  \n",
       "First Coast Barber Academy                             1.0001  \n",
       "...                                                       ...  \n",
       "Rasmussen College - Overland Park                         NaN  \n",
       "National Personal Training Institute of Cleveland         NaN  \n",
       "Bay Area Medical Academy - San Jose Satellite L...        NaN  \n",
       "Excel Learning Center-San Antonio South                   NaN  \n",
       "\n",
       "[7535 rows x 9 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_cumsum.sort_values('UGDS_HISP', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determining college campus diversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Diversity Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rutgers University--Newark  Newark, NJ</th>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrews University  Berrien Springs, MI</th>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stanford University  Stanford, CA</th>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Houston  Houston, TX</th>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco State University  San Francisco, CA</th>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Illinois--Chicago  Chicago, IL</th>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey Institute of Technology  Newark, NJ</th>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas Woman's University  Denton, TX</th>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Diversity Index\n",
       "School                                                            \n",
       "Rutgers University--Newark  Newark, NJ                        0.76\n",
       "Andrews University  Berrien Springs, MI                       0.74\n",
       "Stanford University  Stanford, CA                             0.74\n",
       "University of Houston  Houston, TX                            0.74\n",
       "...                                                            ...\n",
       "San Francisco State University  San Francisco, CA             0.73\n",
       "University of Illinois--Chicago  Chicago, IL                  0.73\n",
       "New Jersey Institute of Technology  Newark, NJ                0.72\n",
       "Texas Woman's University  Denton, TX                          0.72\n",
       "\n",
       "[10 rows x 1 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/college_diversity.csv', index_col='School')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>0.0333</td>\n",
       "      <td>0.9353</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>0.5922</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.0283</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>0.2990</td>\n",
       "      <td>0.4192</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>0.6988</td>\n",
       "      <td>0.1255</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0376</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>0.0332</td>\n",
       "      <td>0.0350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.9208</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>0.0137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                 0.0333      0.9353     0.0055   \n",
       "University of Alabama at Birmingham      0.5922      0.2600     0.0283   \n",
       "Amridge University                       0.2990      0.4192     0.0069   \n",
       "University of Alabama in Huntsville      0.6988      0.1255     0.0382   \n",
       "Alabama State University                 0.0158      0.9208     0.0121   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                 0.0019     0.0024     0.0019   \n",
       "University of Alabama at Birmingham      0.0518     0.0022     0.0007   \n",
       "Amridge University                       0.0034     0.0000     0.0000   \n",
       "University of Alabama in Huntsville      0.0376     0.0143     0.0002   \n",
       "Alabama State University                 0.0019     0.0010     0.0006   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                0.0000    0.0059     0.0138  \n",
       "University of Alabama at Birmingham     0.0368    0.0179     0.0100  \n",
       "Amridge University                      0.0000    0.0000     0.2715  \n",
       "University of Alabama in Huntsville     0.0172    0.0332     0.0350  \n",
       "Alabama State University                0.0098    0.0243     0.0137  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college = pd.read_csv('data/college.csv', index_col='INSTNM')\n",
    "college_ugds_ = college.filter(like='UGDS_')\n",
    "college_ugds_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTNM\n",
       "Excel Learning Center-San Antonio South         9\n",
       "Philadelphia College of Osteopathic Medicine    9\n",
       "Assemblies of God Theological Seminary          9\n",
       "Episcopal Divinity School                       9\n",
       "Phillips Graduate Institute                     9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.isnull().sum(axis=1).sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "college_ugds_ = college_ugds_.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UGDS_WHITE    0\n",
       "UGDS_BLACK    0\n",
       "UGDS_HISP     0\n",
       "UGDS_ASIAN    0\n",
       "             ..\n",
       "UGDS_NHPI     0\n",
       "UGDS_2MOR     0\n",
       "UGDS_NRA      0\n",
       "UGDS_UNKN     0\n",
       "Length: 9, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama A &amp; M University</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama at Birmingham</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amridge University</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Alabama in Huntsville</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Alabama A & M University                  False        True      False   \n",
       "University of Alabama at Birmingham        True        True      False   \n",
       "Amridge University                         True        True      False   \n",
       "University of Alabama in Huntsville        True       False      False   \n",
       "Alabama State University                  False        True      False   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Alabama A & M University                  False      False      False   \n",
       "University of Alabama at Birmingham       False      False      False   \n",
       "Amridge University                        False      False      False   \n",
       "University of Alabama in Huntsville       False      False      False   \n",
       "Alabama State University                  False      False      False   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Alabama A & M University                 False     False      False  \n",
       "University of Alabama at Birmingham      False     False      False  \n",
       "Amridge University                       False     False       True  \n",
       "University of Alabama in Huntsville      False     False      False  \n",
       "Alabama State University                 False     False      False  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.ge(.15).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTNM\n",
       "Alabama A & M University               1\n",
       "University of Alabama at Birmingham    2\n",
       "Amridge University                     3\n",
       "University of Alabama in Huntsville    1\n",
       "Alabama State University               1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity_metric = college_ugds_.ge(.15).sum(axis='columns')\n",
    "diversity_metric.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3042\n",
       "2    2884\n",
       "3     876\n",
       "4      63\n",
       "0       7\n",
       "5       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity_metric.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTNM\n",
       "Regency Beauty Institute-Austin          5\n",
       "Central Texas Beauty College-Temple      5\n",
       "Sullivan and Cogliano Training Center    4\n",
       "Ambria College of Nursing                4\n",
       "Berkeley College-New York                4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity_metric.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UGDS_WHITE</th>\n",
       "      <th>UGDS_BLACK</th>\n",
       "      <th>UGDS_HISP</th>\n",
       "      <th>UGDS_ASIAN</th>\n",
       "      <th>UGDS_AIAN</th>\n",
       "      <th>UGDS_NHPI</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTNM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Regency Beauty Institute-Austin</th>\n",
       "      <td>0.1867</td>\n",
       "      <td>0.2133</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central Texas Beauty College-Temple</th>\n",
       "      <td>0.1616</td>\n",
       "      <td>0.2323</td>\n",
       "      <td>0.2626</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UGDS_WHITE  UGDS_BLACK  UGDS_HISP  \\\n",
       "INSTNM                                                                   \n",
       "Regency Beauty Institute-Austin          0.1867      0.2133     0.1600   \n",
       "Central Texas Beauty College-Temple      0.1616      0.2323     0.2626   \n",
       "\n",
       "                                     UGDS_ASIAN  UGDS_AIAN  UGDS_NHPI  \\\n",
       "INSTNM                                                                  \n",
       "Regency Beauty Institute-Austin          0.0000        0.0        0.0   \n",
       "Central Texas Beauty College-Temple      0.0202        0.0        0.0   \n",
       "\n",
       "                                     UGDS_2MOR  UGDS_NRA  UGDS_UNKN  \n",
       "INSTNM                                                               \n",
       "Regency Beauty Institute-Austin         0.1733       0.0     0.2667  \n",
       "Central Texas Beauty College-Temple     0.1717       0.0     0.1515  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.loc[['Regency Beauty Institute-Austin', \n",
    "                          'Central Texas Beauty College-Temple']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "us_news_top = ['Rutgers University-Newark', \n",
    "               'Andrews University', \n",
    "               'Stanford University', \n",
    "               'University of Houston',\n",
    "               'University of Nevada-Las Vegas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTNM\n",
       "Rutgers University-Newark         4\n",
       "Andrews University                3\n",
       "Stanford University               3\n",
       "University of Houston             3\n",
       "University of Nevada-Las Vegas    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity_metric.loc[us_news_top]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There's more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTNM\n",
       "Dewey University-Manati                               1.0\n",
       "Yeshiva and Kollel Harbotzas Torah                    1.0\n",
       "Mr Leon's School of Hair Design-Lewiston              1.0\n",
       "Dewey University-Bayamon                              1.0\n",
       "                                                     ... \n",
       "Monteclaro Escuela de Hoteleria y Artes Culinarias    1.0\n",
       "Yeshiva Shaar Hatorah                                 1.0\n",
       "Bais Medrash Elyon                                    1.0\n",
       "Yeshiva of Nitra Rabbinical College                   1.0\n",
       "Length: 10, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.max(axis=1).sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UGDS_WHITE    1.0\n",
       "UGDS_BLACK    0.0\n",
       "UGDS_HISP     0.0\n",
       "UGDS_ASIAN    0.0\n",
       "             ... \n",
       "UGDS_NHPI     0.0\n",
       "UGDS_2MOR     0.0\n",
       "UGDS_NRA      0.0\n",
       "UGDS_UNKN     0.0\n",
       "Name: Talmudical Seminary Oholei Torah, Length: 9, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_ugds_.loc['Talmudical Seminary Oholei Torah']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(college_ugds_ > .01).all(axis=1).any()"
   ]
  }
 ],
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