Pandas_Cookbook_106.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boolean Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean\n",
    "indexing (also known as boolean selection) can be a confusing term, but for the purposes of\n",
    "pandas, it refers to selecting rows by providing a boolean value (True or False) for each\n",
    "row. These boolean values are usually stored in a Series or NumPy ndarray and are\n",
    "usually created by applying a boolean condition to one or more columns in a DataFrame.\n",
    "Each value of a boolean series evaluates to 0 or 1 so all the\n",
    "Series methods that work with numerical values also work with booleans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>...</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",
       "    <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",
       "      <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",
       "      <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>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>...</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>Pirates of the Caribbean: At World's End</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>...</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>Spectre</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>...</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>The Dark Knight Rises</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>...</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>Star Wars: Episode VII - The Force Awakens</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>...</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",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            color      director_name  \\\n",
       "movie_title                                                            \n",
       "Avatar                                      Color      James Cameron   \n",
       "Pirates of the Caribbean: At World's End    Color     Gore Verbinski   \n",
       "Spectre                                     Color         Sam Mendes   \n",
       "The Dark Knight Rises                       Color  Christopher Nolan   \n",
       "Star Wars: Episode VII - The Force Awakens    NaN        Doug Walker   \n",
       "\n",
       "                                            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_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",
       "                                                  gross  \\\n",
       "movie_title                                               \n",
       "Avatar                                      760505847.0   \n",
       "Pirates of the Caribbean: At World's End    309404152.0   \n",
       "Spectre                                     200074175.0   \n",
       "The Dark Knight Rises                       448130642.0   \n",
       "Star Wars: Episode VII - The Force Awakens          NaN   \n",
       "\n",
       "                                                                     genres  \\\n",
       "movie_title                                                                   \n",
       "Avatar                                      Action|Adventure|Fantasy|Sci-Fi   \n",
       "Pirates of the Caribbean: At World's End           Action|Adventure|Fantasy   \n",
       "Spectre                                           Action|Adventure|Thriller   \n",
       "The Dark Knight Rises                                       Action|Thriller   \n",
       "Star Wars: Episode VII - The Force Awakens                      Documentary   \n",
       "\n",
       "                                                   ...           \\\n",
       "movie_title                                        ...            \n",
       "Avatar                                             ...            \n",
       "Pirates of the Caribbean: At World's End           ...            \n",
       "Spectre                                            ...            \n",
       "The Dark Knight Rises                              ...            \n",
       "Star Wars: Episode VII - The Force Awakens         ...            \n",
       "\n",
       "                                           num_user_for_reviews  language  \\\n",
       "movie_title                                                                 \n",
       "Avatar                                                   3054.0   English   \n",
       "Pirates of the Caribbean: At World's End                 1238.0   English   \n",
       "Spectre                                                   994.0   English   \n",
       "The Dark Knight Rises                                    2701.0   English   \n",
       "Star Wars: Episode VII - The Force Awakens                  NaN       NaN   \n",
       "\n",
       "                                            country content_rating  \\\n",
       "movie_title                                                          \n",
       "Avatar                                          USA          PG-13   \n",
       "Pirates of the Caribbean: At World's End        USA          PG-13   \n",
       "Spectre                                          UK          PG-13   \n",
       "The Dark Knight Rises                           USA          PG-13   \n",
       "Star Wars: Episode VII - The Force Awakens      NaN            NaN   \n",
       "\n",
       "                                                 budget title_year  \\\n",
       "movie_title                                                          \n",
       "Avatar                                      237000000.0     2009.0   \n",
       "Pirates of the Caribbean: At World's End    300000000.0     2007.0   \n",
       "Spectre                                     245000000.0     2015.0   \n",
       "The Dark Knight Rises                       250000000.0     2012.0   \n",
       "Star Wars: Episode VII - The Force Awakens          NaN        NaN   \n",
       "\n",
       "                                           actor_2_facebook_likes  imdb_score  \\\n",
       "movie_title                                                                     \n",
       "Avatar                                                      936.0         7.9   \n",
       "Pirates of the Caribbean: At World's End                   5000.0         7.1   \n",
       "Spectre                                                     393.0         6.8   \n",
       "The Dark Knight Rises                                     23000.0         8.5   \n",
       "Star Wars: Episode VII - The Force Awakens                   12.0         7.1   \n",
       "\n",
       "                                           aspect_ratio movie_facebook_likes  \n",
       "movie_title                                                                   \n",
       "Avatar                                             1.78                33000  \n",
       "Pirates of the Caribbean: At World's End           2.35                    0  \n",
       "Spectre                                            2.35                85000  \n",
       "The Dark Knight Rises                              2.35               164000  \n",
       "Star Wars: Episode VII - The Force Awakens          NaN                    0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "movie = pd.read_csv('https://raw.githubusercontent.com/DatasRev/source-files/master/csv/movie.csv', index_col='movie_title')\n",
    "movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title\n",
       "Avatar                                          True\n",
       "Pirates of the Caribbean: At World's End        True\n",
       "Spectre                                         True\n",
       "The Dark Knight Rises                           True\n",
       "Star Wars: Episode VII - The Force Awakens     False\n",
       "John Carter                                     True\n",
       "Spider-Man 3                                    True\n",
       "Tangled                                        False\n",
       "Avengers: Age of Ultron                         True\n",
       "Harry Potter and the Half-Blood Prince          True\n",
       "Batman v Superman: Dawn of Justice              True\n",
       "Superman Returns                                True\n",
       "Quantum of Solace                              False\n",
       "Pirates of the Caribbean: Dead Man's Chest      True\n",
       "The Lone Ranger                                 True\n",
       "Man of Steel                                    True\n",
       "The Chronicles of Narnia: Prince Caspian        True\n",
       "The Avengers                                    True\n",
       "Pirates of the Caribbean: On Stranger Tides     True\n",
       "Men in Black 3                                 False\n",
       "The Hobbit: The Battle of the Five Armies       True\n",
       "The Amazing Spider-Man                          True\n",
       "Robin Hood                                      True\n",
       "The Hobbit: The Desolation of Smaug             True\n",
       "The Golden Compass                             False\n",
       "King Kong                                       True\n",
       "Titanic                                         True\n",
       "Captain America: Civil War                      True\n",
       "Battleship                                      True\n",
       "Jurassic World                                  True\n",
       "                                               ...  \n",
       "Manito                                         False\n",
       "Rampage                                        False\n",
       "Slacker                                        False\n",
       "Dutch Kills                                    False\n",
       "Dry Spell                                      False\n",
       "Flywheel                                       False\n",
       "Exeter                                         False\n",
       "The Ridges                                      True\n",
       "The Puffy Chair                                False\n",
       "Stories of Our Lives                           False\n",
       "Breaking Upwards                               False\n",
       "All Superheroes Must Die                       False\n",
       "Pink Flamingos                                 False\n",
       "Clean                                          False\n",
       "The Circle                                     False\n",
       "Tin Can Man                                    False\n",
       "The Cure                                       False\n",
       "On the Downlow                                 False\n",
       "Sanctuary; Quite a Conundrum                   False\n",
       "Bang                                           False\n",
       "Primer                                         False\n",
       "Cavite                                         False\n",
       "El Mariachi                                    False\n",
       "The Mongol King                                False\n",
       "Newlyweds                                      False\n",
       "Signed Sealed Delivered                        False\n",
       "The Following                                  False\n",
       "A Plague So Pleasant                           False\n",
       "Shanghai Calling                               False\n",
       "My Date with Drew                              False\n",
       "Name: duration, Length: 4916, dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie['duration'] > 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title\n",
       "Avatar                                         True\n",
       "Pirates of the Caribbean: At World's End       True\n",
       "Spectre                                        True\n",
       "The Dark Knight Rises                          True\n",
       "Star Wars: Episode VII - The Force Awakens    False\n",
       "John Carter                                    True\n",
       "Spider-Man 3                                   True\n",
       "Tangled                                       False\n",
       "Avengers: Age of Ultron                        True\n",
       "Harry Potter and the Half-Blood Prince         True\n",
       "Name: duration, dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_2_hours = movie['duration'] > 120   # Itt még nem filtereli le, csak True és False értékeket kapunk.\n",
    "movie_2_hours.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1039"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can now use this Series to determine the number of movies that are longer than two hours:\n",
    "movie_2_hours.sum()   # megszámolja a fenti oszlopban a True értékeket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2113506916192026"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To find the percentage of movies in the dataset longer than two hours, use the mean method:\n",
    "# Ezt úgy kell érteni hogy a nulla és 1 értékek átlaga adja meg a %-ot.\n",
    "movie_2_hours.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    3877\n",
       "True     1039\n",
       "Name: duration, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_2_hours.value_counts()  # Jegyezzük meg ezeket a számokat , 4 lépéssel később kelleni fognak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.788649\n",
       "True     0.211351\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_2_hours.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, the output from step 4 is misleading. The duration column has a\n",
    "few missing values. If you look back at the DataFrame output from step 1, you\n",
    "will see that the last row is missing a value for duration. The boolean condition\n",
    "in step 2 returns False for this. We need to drop the missing values first, then\n",
    "evaluate the condition and take the mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21199755152009794"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie['duration'].dropna().gt(120).mean()    #120 a 120 percet jelöli   Ez a .gt nagyon érdekes, de csak feltételezni\n",
    "# tudom, hogy a greater than rövidítése."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title\n",
       "Avatar                                         True\n",
       "Pirates of the Caribbean: At World's End       True\n",
       "Spectre                                        True\n",
       "The Dark Knight Rises                          True\n",
       "Star Wars: Episode VII - The Force Awakens    False\n",
       "Name: duration, dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie['duration'].gt(120).head()    #  ugyanaz, mint movie['duration'] > 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      4916\n",
       "unique        2\n",
       "top       False\n",
       "freq       3877\n",
       "Name: duration, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_2_hours.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency. Timestamps also include the first and last items.\n",
    "\n",
    "If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                        1038\n",
       "director_name                1031\n",
       "num_critic_for_reviews       1035\n",
       "duration                     1039\n",
       "director_facebook_likes      1031\n",
       "actor_3_facebook_likes       1039\n",
       "actor_2_name                 1039\n",
       "actor_1_facebook_likes       1039\n",
       "gross                         938\n",
       "genres                       1039\n",
       "actor_1_name                 1039\n",
       "num_voted_users              1039\n",
       "cast_total_facebook_likes    1039\n",
       "actor_3_name                 1039\n",
       "facenumber_in_poster         1038\n",
       "plot_keywords                1028\n",
       "movie_imdb_link              1039\n",
       "num_user_for_reviews         1039\n",
       "language                     1037\n",
       "country                      1039\n",
       "content_rating               1001\n",
       "budget                        985\n",
       "title_year                   1031\n",
       "actor_2_facebook_likes       1039\n",
       "imdb_score                   1039\n",
       "aspect_ratio                 1016\n",
       "movie_facebook_likes         1039\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie[movie['duration'].gt(120)].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to compare two columns from the same DataFrame to produce a boolean\n",
    "Series. For instance, we could determine the percentage of movies that have actor 1 with\n",
    "more Facebook likes than actor 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777687130328371"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors = movie[['actor_1_facebook_likes',\n",
    "'actor_2_facebook_likes']].dropna()\n",
    "(actors['actor_1_facebook_likes'] >\n",
    "actors['actor_2_facebook_likes']).mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constructing multiple boolean conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python, boolean expressions use the built-in logical operators and, or, and not. These\n",
    "keywords do not work with boolean indexing in pandas and are respectively replaced with\n",
    "&, |, and ~. Additionally, each expression must be wrapped in parentheses or an error will\n",
    "be raised.\n",
    "All values in a Series can be compared against a scalar value using the standard comparison\n",
    "operators( <, >, ==, !=, <=, >=)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this recipe, we construct multiple boolean\n",
    "expressions before combining them together to find all the movies that have an\n",
    "imdb_score greater than 8, a content_rating of PG-13, and a title_year either before\n",
    "2000 or after 2009."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "criteria1 = movie.imdb_score > 8\n",
    "criteria2 = movie.content_rating == 'PG-13'\n",
    "criteria3 = ((movie.title_year < 2000) | (movie.title_year > 2009))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The criteria3 variable is created by two independent boolean expressions. Each\n",
    "expression must be enclosed in parentheses to function properly. The pipe character, |, is\n",
    "used to create a logical or condition between each of the values in both Series.\n",
    "All three criteria need to be True to match the requirements of the recipe. They are each\n",
    "combined together with the ampersand character, &, which creates a logical and condition\n",
    "between each Series value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title\n",
       "Avatar                                        False\n",
       "Pirates of the Caribbean: At World's End      False\n",
       "Spectre                                       False\n",
       "The Dark Knight Rises                          True\n",
       "Star Wars: Episode VII - The Force Awakens    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "criteria_final = criteria1 & criteria2 & criteria3\n",
    "criteria_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A consequence of pandas using different syntax for the logical operators is that operator\n",
    "precedence is no longer the same. The comparison operators have a higher precedence than\n",
    "and, or, and not. However, the new operators for pandas (the bitwise operators &, |, and ~)\n",
    "have a higher precedence than the comparison operators, thus the need for parentheses. An\n",
    "example can help clear this up. Take the following expression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 < 10 and 3 > 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at what would happen if the expression in criteria3 was written as\n",
    "follows:\n",
    ">>> movie.title_year < 2000 | movie.title_year > 2009\n",
    "TypeError: cannot compare a dtyped [float64] array with a scalar of type\n",
    "[bool]\n",
    "As the bitwise operators have higher precedence than the comparison operators, 2000 |\n",
    "movie.title_year is evaluated first, which is nonsensical and raises an error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many objects in Python have boolean representation. For instance, all\n",
    "integers except 0 are considered True. All strings except the empty string\n",
    "are True. All non-empty sets, tuples, dictionaries, and lists are True. An\n",
    "empty DataFrame or Series does not evaluate as True or False and instead\n",
    "an error is raised. In general, to retrieve the truthiness of a Python object,\n",
    "pass it to the bool function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering with boolean indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "crit_b1 = movie.imdb_score < 5\n",
    "crit_b2 = movie.content_rating == 'R'\n",
    "crit_b3 = ((movie.title_year >= 2000) &\n",
    "(movie.title_year <= 2010))\n",
    "final_crit_b = crit_b1 & crit_b2 & crit_b3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title\n",
       "Avatar                                        False\n",
       "Pirates of the Caribbean: At World's End      False\n",
       "Spectre                                       False\n",
       "The Dark Knight Rises                          True\n",
       "Star Wars: Episode VII - The Force Awakens    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the two sets of criteria using the pandas or operator. This yields a\n",
    "# boolean Series of all movies that are members of either set:\n",
    "final_crit_a=criteria_final\n",
    "final_crit_all = final_crit_a | final_crit_b\n",
    "final_crit_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have your boolean Series, you simply pass it to the indexing operator\n",
    "to filter the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>...</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",
       "    <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",
       "      <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",
       "      <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>The Dark Knight Rises</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>...</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>The Avengers</th>\n",
       "      <td>Color</td>\n",
       "      <td>Joss Whedon</td>\n",
       "      <td>703.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>26000.0</td>\n",
       "      <td>623279547.0</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>1722.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>220000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>1.85</td>\n",
       "      <td>123000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Captain America: Civil War</th>\n",
       "      <td>Color</td>\n",
       "      <td>Anthony Russo</td>\n",
       "      <td>516.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>Scarlett Johansson</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>407197282.0</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2.35</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guardians of the Galaxy</th>\n",
       "      <td>Color</td>\n",
       "      <td>James Gunn</td>\n",
       "      <td>653.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Vin Diesel</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>333130696.0</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>170000000.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>2.35</td>\n",
       "      <td>96000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interstellar</th>\n",
       "      <td>Color</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>712.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>Anne Hathaway</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>187991439.0</td>\n",
       "      <td>Adventure|Drama|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>2725.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>165000000.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2.35</td>\n",
       "      <td>349000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            color      director_name  num_critic_for_reviews  \\\n",
       "movie_title                                                                    \n",
       "The Dark Knight Rises       Color  Christopher Nolan                   813.0   \n",
       "The Avengers                Color        Joss Whedon                   703.0   \n",
       "Captain America: Civil War  Color      Anthony Russo                   516.0   \n",
       "Guardians of the Galaxy     Color         James Gunn                   653.0   \n",
       "Interstellar                Color  Christopher Nolan                   712.0   \n",
       "\n",
       "                            duration  director_facebook_likes  \\\n",
       "movie_title                                                     \n",
       "The Dark Knight Rises          164.0                  22000.0   \n",
       "The Avengers                   173.0                      0.0   \n",
       "Captain America: Civil War     147.0                     94.0   \n",
       "Guardians of the Galaxy        121.0                    571.0   \n",
       "Interstellar                   169.0                  22000.0   \n",
       "\n",
       "                            actor_3_facebook_likes        actor_2_name  \\\n",
       "movie_title                                                              \n",
       "The Dark Knight Rises                      23000.0      Christian Bale   \n",
       "The Avengers                               19000.0   Robert Downey Jr.   \n",
       "Captain America: Civil War                 11000.0  Scarlett Johansson   \n",
       "Guardians of the Galaxy                     3000.0          Vin Diesel   \n",
       "Interstellar                                6000.0       Anne Hathaway   \n",
       "\n",
       "                            actor_1_facebook_likes        gross  \\\n",
       "movie_title                                                       \n",
       "The Dark Knight Rises                      27000.0  448130642.0   \n",
       "The Avengers                               26000.0  623279547.0   \n",
       "Captain America: Civil War                 21000.0  407197282.0   \n",
       "Guardians of the Galaxy                    14000.0  333130696.0   \n",
       "Interstellar                               11000.0  187991439.0   \n",
       "\n",
       "                                             genres         ...           \\\n",
       "movie_title                                                 ...            \n",
       "The Dark Knight Rises               Action|Thriller         ...            \n",
       "The Avengers                Action|Adventure|Sci-Fi         ...            \n",
       "Captain America: Civil War  Action|Adventure|Sci-Fi         ...            \n",
       "Guardians of the Galaxy     Action|Adventure|Sci-Fi         ...            \n",
       "Interstellar                 Adventure|Drama|Sci-Fi         ...            \n",
       "\n",
       "                           num_user_for_reviews  language  country  \\\n",
       "movie_title                                                          \n",
       "The Dark Knight Rises                    2701.0   English      USA   \n",
       "The Avengers                             1722.0   English      USA   \n",
       "Captain America: Civil War               1022.0   English      USA   \n",
       "Guardians of the Galaxy                  1097.0   English      USA   \n",
       "Interstellar                             2725.0   English      USA   \n",
       "\n",
       "                           content_rating       budget title_year  \\\n",
       "movie_title                                                         \n",
       "The Dark Knight Rises               PG-13  250000000.0     2012.0   \n",
       "The Avengers                        PG-13  220000000.0     2012.0   \n",
       "Captain America: Civil War          PG-13  250000000.0     2016.0   \n",
       "Guardians of the Galaxy             PG-13  170000000.0     2014.0   \n",
       "Interstellar                        PG-13  165000000.0     2014.0   \n",
       "\n",
       "                           actor_2_facebook_likes  imdb_score aspect_ratio  \\\n",
       "movie_title                                                                  \n",
       "The Dark Knight Rises                     23000.0         8.5         2.35   \n",
       "The Avengers                              21000.0         8.1         1.85   \n",
       "Captain America: Civil War                19000.0         8.2         2.35   \n",
       "Guardians of the Galaxy                   14000.0         8.1         2.35   \n",
       "Interstellar                              11000.0         8.6         2.35   \n",
       "\n",
       "                           movie_facebook_likes  \n",
       "movie_title                                      \n",
       "The Dark Knight Rises                    164000  \n",
       "The Avengers                             123000  \n",
       "Captain America: Civil War                72000  \n",
       "Guardians of the Galaxy                   96000  \n",
       "Interstellar                             349000  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie[final_crit_all].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have successfully filtered the data and all the columns of the DataFrame. We\n",
    "can't easily perform a manual check to determine whether the filter worked\n",
    "correctly. Let's filter both rows and columns with the .loc indexer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>title_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>The Dark Knight Rises</th>\n",
       "      <td>8.5</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Avengers</th>\n",
       "      <td>8.1</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Captain America: Civil War</th>\n",
       "      <td>8.2</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guardians of the Galaxy</th>\n",
       "      <td>8.1</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interstellar</th>\n",
       "      <td>8.6</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inception</th>\n",
       "      <td>8.8</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Martian</th>\n",
       "      <td>8.1</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town &amp; Country</th>\n",
       "      <td>4.4</td>\n",
       "      <td>R</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex and the City 2</th>\n",
       "      <td>4.3</td>\n",
       "      <td>R</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rollerball</th>\n",
       "      <td>3.0</td>\n",
       "      <td>R</td>\n",
       "      <td>2002.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            imdb_score content_rating  title_year\n",
       "movie_title                                                      \n",
       "The Dark Knight Rises              8.5          PG-13      2012.0\n",
       "The Avengers                       8.1          PG-13      2012.0\n",
       "Captain America: Civil War         8.2          PG-13      2016.0\n",
       "Guardians of the Galaxy            8.1          PG-13      2014.0\n",
       "Interstellar                       8.6          PG-13      2014.0\n",
       "Inception                          8.8          PG-13      2010.0\n",
       "The Martian                        8.1          PG-13      2015.0\n",
       "Town & Country                     4.4              R      2001.0\n",
       "Sex and the City 2                 4.3              R      2010.0\n",
       "Rollerball                         3.0              R      2002.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['imdb_score', 'content_rating', 'title_year']\n",
    "movie_filtered = movie.loc[final_crit_all, cols]\n",
    "movie_filtered.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To replicate the final_crit_a variable from step 1 with one long line\n",
    "# of code, we can do the following:\n",
    "final_crit_a2 = (movie.imdb_score > 8) & \\\n",
    "(movie.content_rating == 'PG-13') & \\\n",
    "((movie.title_year < 2000) |\n",
    "(movie.title_year > 2009))\n",
    "final_crit_a2.equals(final_crit_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replicating boolean indexing with index selection\n",
    "It is possible to replicate specific cases of boolean selection by taking advantage of the\n",
    "index. Selection through the index is more intuitive and makes for greater readability. könyv 145."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>HBCU</th>\n",
       "      <th>MENONLY</th>\n",
       "      <th>WOMENONLY</th>\n",
       "      <th>RELAFFIL</th>\n",
       "      <th>SATVRMID</th>\n",
       "      <th>SATMTMID</th>\n",
       "      <th>DISTANCEONLY</th>\n",
       "      <th>...</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "      <th>PPTUG_EF</th>\n",
       "      <th>CURROPER</th>\n",
       "      <th>PCTPELL</th>\n",
       "      <th>PCTFLOAN</th>\n",
       "      <th>UG25ABV</th>\n",
       "      <th>MD_EARN_WNE_P10</th>\n",
       "      <th>GRAD_DEBT_MDN_SUPP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3610</th>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>Abilene</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>545.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0454</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.0468</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2595</td>\n",
       "      <td>0.5527</td>\n",
       "      <td>0.0381</td>\n",
       "      <td>40200</td>\n",
       "      <td>25985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3611</th>\n",
       "      <td>Alvin Community College</td>\n",
       "      <td>Alvin</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.7123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1549</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.2841</td>\n",
       "      <td>34500</td>\n",
       "      <td>6750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3612</th>\n",
       "      <td>Amarillo College</td>\n",
       "      <td>Amarillo</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.6922</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3786</td>\n",
       "      <td>0.1573</td>\n",
       "      <td>0.3431</td>\n",
       "      <td>31700</td>\n",
       "      <td>10950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3613</th>\n",
       "      <td>Angelina College</td>\n",
       "      <td>Lufkin</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0264</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5308</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2603</td>\n",
       "      <td>26900</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3614</th>\n",
       "      <td>Angelo State University</td>\n",
       "      <td>San Angelo</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0285</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.1289</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4068</td>\n",
       "      <td>0.5279</td>\n",
       "      <td>0.1407</td>\n",
       "      <td>37700</td>\n",
       "      <td>21319.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            INSTNM        CITY STABBR  HBCU  MENONLY  \\\n",
       "3610  Abilene Christian University     Abilene     TX   0.0      0.0   \n",
       "3611       Alvin Community College       Alvin     TX   0.0      0.0   \n",
       "3612              Amarillo College    Amarillo     TX   0.0      0.0   \n",
       "3613              Angelina College      Lufkin     TX   0.0      0.0   \n",
       "3614       Angelo State University  San Angelo     TX   0.0      0.0   \n",
       "\n",
       "      WOMENONLY  RELAFFIL  SATVRMID  SATMTMID  DISTANCEONLY  \\\n",
       "3610        0.0         1     530.0     545.0           0.0   \n",
       "3611        0.0         0       NaN       NaN           0.0   \n",
       "3612        0.0         0       NaN       NaN           0.0   \n",
       "3613        0.0         0       NaN       NaN           0.0   \n",
       "3614        0.0         0     475.0     490.0           0.0   \n",
       "\n",
       "             ...          UGDS_2MOR  UGDS_NRA  UGDS_UNKN  PPTUG_EF  CURROPER  \\\n",
       "3610         ...             0.0454    0.0423     0.0045    0.0468         1   \n",
       "3611         ...             0.0002    0.0000     0.0143    0.7123         1   \n",
       "3612         ...             0.0000    0.0001     0.0085    0.6922         1   \n",
       "3613         ...             0.0264    0.0005     0.0000    0.5600         1   \n",
       "3614         ...             0.0285    0.0331     0.0011    0.1289         1   \n",
       "\n",
       "      PCTPELL  PCTFLOAN  UG25ABV  MD_EARN_WNE_P10  GRAD_DEBT_MDN_SUPP  \n",
       "3610   0.2595    0.5527   0.0381            40200               25985  \n",
       "3611   0.1549    0.0625   0.2841            34500                6750  \n",
       "3612   0.3786    0.1573   0.3431            31700               10950  \n",
       "3613   0.5308    0.0000   0.2603            26900   PrivacySuppressed  \n",
       "3614   0.4068    0.5279   0.1407            37700             21319.5  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college = pd.read_csv('https://raw.githubusercontent.com/DatasRev/source-files/master/csv/college.csv')\n",
    "college[college['STABBR'] == 'TX'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To replicate this using index selection, we need to move the STABBR column into\n",
    "the index. We can then use label-based selection with the .loc indexer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>TX</th>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>Abilene</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>34500</td>\n",
       "      <td>6750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Amarillo College</td>\n",
       "      <td>Amarillo</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Angelina College</td>\n",
       "      <td>Lufkin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.5308</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2603</td>\n",
       "      <td>26900</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Angelo State University</td>\n",
       "      <td>San Angelo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>37700</td>\n",
       "      <td>21319.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              INSTNM        CITY  HBCU  MENONLY  WOMENONLY  \\\n",
       "STABBR                                                                       \n",
       "TX      Abilene Christian University     Abilene   0.0      0.0        0.0   \n",
       "TX           Alvin Community College       Alvin   0.0      0.0        0.0   \n",
       "TX                  Amarillo College    Amarillo   0.0      0.0        0.0   \n",
       "TX                  Angelina College      Lufkin   0.0      0.0        0.0   \n",
       "TX           Angelo State University  San Angelo   0.0      0.0        0.0   \n",
       "\n",
       "        RELAFFIL  SATVRMID  SATMTMID  DISTANCEONLY    UGDS  \\\n",
       "STABBR                                                       \n",
       "TX             1     530.0     545.0           0.0  3572.0   \n",
       "TX             0       NaN       NaN           0.0  4682.0   \n",
       "TX             0       NaN       NaN           0.0  9346.0   \n",
       "TX             0       NaN       NaN           0.0  3825.0   \n",
       "TX             0     475.0     490.0           0.0  5290.0   \n",
       "\n",
       "               ...          UGDS_2MOR  UGDS_NRA  UGDS_UNKN  PPTUG_EF  \\\n",
       "STABBR         ...                                                     \n",
       "TX             ...             0.0454    0.0423     0.0045    0.0468   \n",
       "TX             ...             0.0002    0.0000     0.0143    0.7123   \n",
       "TX             ...             0.0000    0.0001     0.0085    0.6922   \n",
       "TX             ...             0.0264    0.0005     0.0000    0.5600   \n",
       "TX             ...             0.0285    0.0331     0.0011    0.1289   \n",
       "\n",
       "        CURROPER  PCTPELL  PCTFLOAN  UG25ABV  MD_EARN_WNE_P10  \\\n",
       "STABBR                                                          \n",
       "TX             1   0.2595    0.5527   0.0381            40200   \n",
       "TX             1   0.1549    0.0625   0.2841            34500   \n",
       "TX             1   0.3786    0.1573   0.3431            31700   \n",
       "TX             1   0.5308    0.0000   0.2603            26900   \n",
       "TX             1   0.4068    0.5279   0.1407            37700   \n",
       "\n",
       "        GRAD_DEBT_MDN_SUPP  \n",
       "STABBR                      \n",
       "TX                   25985  \n",
       "TX                    6750  \n",
       "TX                   10950  \n",
       "TX       PrivacySuppressed  \n",
       "TX                 21319.5  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college2 = college.set_index('STABBR')\n",
    "college2.loc['TX'].head()\n",
    "## Ez elég furcsa, korábban az intézmény volt az index, annak nincs sok értelme, hogy az állam legyen az index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean indexing takes three times as long as index selection. As setting the index\n",
    "does not come for free, let's time that operation as well: # könyv 147. oldal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select Texas (TX), California (CA), and New York (NY).\n",
    "With boolean selection, you can use the isin method but with indexing,\n",
    "just pass a list to .loc:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>HBCU</th>\n",
       "      <th>MENONLY</th>\n",
       "      <th>WOMENONLY</th>\n",
       "      <th>RELAFFIL</th>\n",
       "      <th>SATVRMID</th>\n",
       "      <th>SATMTMID</th>\n",
       "      <th>DISTANCEONLY</th>\n",
       "      <th>UGDS</th>\n",
       "      <th>...</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "      <th>PPTUG_EF</th>\n",
       "      <th>CURROPER</th>\n",
       "      <th>PCTPELL</th>\n",
       "      <th>PCTFLOAN</th>\n",
       "      <th>UG25ABV</th>\n",
       "      <th>MD_EARN_WNE_P10</th>\n",
       "      <th>GRAD_DEBT_MDN_SUPP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STABBR</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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>Abilene</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>530.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3572.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0454</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.0468</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2595</td>\n",
       "      <td>0.5527</td>\n",
       "      <td>0.0381</td>\n",
       "      <td>40200</td>\n",
       "      <td>25985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Alvin Community College</td>\n",
       "      <td>Alvin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4682.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.7123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1549</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.2841</td>\n",
       "      <td>34500</td>\n",
       "      <td>6750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Amarillo College</td>\n",
       "      <td>Amarillo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9346.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.6922</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3786</td>\n",
       "      <td>0.1573</td>\n",
       "      <td>0.3431</td>\n",
       "      <td>31700</td>\n",
       "      <td>10950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Angelina College</td>\n",
       "      <td>Lufkin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3825.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0264</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5308</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2603</td>\n",
       "      <td>26900</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Angelo State University</td>\n",
       "      <td>San Angelo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0285</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.1289</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4068</td>\n",
       "      <td>0.5279</td>\n",
       "      <td>0.1407</td>\n",
       "      <td>37700</td>\n",
       "      <td>21319.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Arlington Baptist College</td>\n",
       "      <td>Arlington</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1682</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4978</td>\n",
       "      <td>0.4892</td>\n",
       "      <td>0.2251</td>\n",
       "      <td>34200</td>\n",
       "      <td>22905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Arlington Career Institute</td>\n",
       "      <td>Grand Prairie</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2843</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6186</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.7745</td>\n",
       "      <td>27600</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>The Art Institute of Houston</td>\n",
       "      <td>Houston</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>0.3466</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6183</td>\n",
       "      <td>0.7604</td>\n",
       "      <td>0.3845</td>\n",
       "      <td>32600</td>\n",
       "      <td>30750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Austin College</td>\n",
       "      <td>Sherman</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>600.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0267</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2867</td>\n",
       "      <td>0.7581</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>47800</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Austin Community College District</td>\n",
       "      <td>Austin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32581.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0233</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>0.0532</td>\n",
       "      <td>0.7340</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2393</td>\n",
       "      <td>0.2447</td>\n",
       "      <td>0.3914</td>\n",
       "      <td>34400</td>\n",
       "      <td>8601.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Austin Presbyterian Theological Seminary</td>\n",
       "      <td>Austin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Baldwin Beauty School-South Austin</td>\n",
       "      <td>Austin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0870</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5517</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0789</td>\n",
       "      <td>20700</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Baptist Health System School of Health Profess...</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0240</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3176</td>\n",
       "      <td>0.8491</td>\n",
       "      <td>0.4550</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "      <td>19491.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Baptist Missionary Association Theological Sem...</td>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>0.7407</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6727</td>\n",
       "      <td>0.2545</td>\n",
       "      <td>0.7636</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Baylor College of Medicine</td>\n",
       "      <td>Houston</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163900</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Baylor University</td>\n",
       "      <td>Waco</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>610.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13801.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0451</td>\n",
       "      <td>0.0309</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2135</td>\n",
       "      <td>0.4520</td>\n",
       "      <td>0.0245</td>\n",
       "      <td>48200</td>\n",
       "      <td>25131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Coastal Bend College</td>\n",
       "      <td>Beeville</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3213.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>0.0218</td>\n",
       "      <td>0.5668</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3725</td>\n",
       "      <td>0.1407</td>\n",
       "      <td>0.2027</td>\n",
       "      <td>27300</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Blinn College</td>\n",
       "      <td>Brenham</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17095.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0288</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0137</td>\n",
       "      <td>0.4288</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2906</td>\n",
       "      <td>0.3118</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>36400</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Remington College-Dallas Campus</td>\n",
       "      <td>Garland</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7611</td>\n",
       "      <td>0.6482</td>\n",
       "      <td>0.4314</td>\n",
       "      <td>25900</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Vet Tech Institute of Houston</td>\n",
       "      <td>Houston</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0278</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6212</td>\n",
       "      <td>0.8182</td>\n",
       "      <td>0.2656</td>\n",
       "      <td>28900</td>\n",
       "      <td>13651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Brazosport College</td>\n",
       "      <td>Lake Jackson</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3164.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.7310</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1888</td>\n",
       "      <td>0.0283</td>\n",
       "      <td>0.3385</td>\n",
       "      <td>30100</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Brookhaven College</td>\n",
       "      <td>Farmers Branch</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.8102</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2697</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.4670</td>\n",
       "      <td>37300</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Cedar Valley College</td>\n",
       "      <td>Lancaster</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5962.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.0226</td>\n",
       "      <td>0.7744</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4017</td>\n",
       "      <td>0.4646</td>\n",
       "      <td>0.4456</td>\n",
       "      <td>27700</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Central Texas College</td>\n",
       "      <td>Killeen</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14409.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0235</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0461</td>\n",
       "      <td>0.7565</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3163</td>\n",
       "      <td>0.2946</td>\n",
       "      <td>0.6131</td>\n",
       "      <td>31800</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Charles and Sues School of Hair Design</td>\n",
       "      <td>Bryan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5263</td>\n",
       "      <td>0.5614</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14600</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Cisco College</td>\n",
       "      <td>Cisco</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3480.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>0.0170</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.5420</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3249</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.2972</td>\n",
       "      <td>30200</td>\n",
       "      <td>10750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Clarendon College</td>\n",
       "      <td>Clarendon</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0782</td>\n",
       "      <td>0.5201</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3962</td>\n",
       "      <td>0.2751</td>\n",
       "      <td>0.2117</td>\n",
       "      <td>28900</td>\n",
       "      <td>8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Concordia University-Texas</td>\n",
       "      <td>Austin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>500.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.1279</td>\n",
       "      <td>0.2295</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4076</td>\n",
       "      <td>0.6538</td>\n",
       "      <td>0.3677</td>\n",
       "      <td>39900</td>\n",
       "      <td>25680.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>Conlee's College of Cosmetology</td>\n",
       "      <td>Kerrville</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4143</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4423</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1765</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>North Central Texas College</td>\n",
       "      <td>Gainesville</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9873.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.6484</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2249</td>\n",
       "      <td>0.1826</td>\n",
       "      <td>0.2756</td>\n",
       "      <td>32700</td>\n",
       "      <td>6973</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Stella and Charles Guttman Community College</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0217</td>\n",
       "      <td>0.0347</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0955</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6673</td>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Miami Ad School-New York</td>\n",
       "      <td>Astoria</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2222</td>\n",
       "      <td>0.0707</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0825</td>\n",
       "      <td>0.4845</td>\n",
       "      <td>0.6907</td>\n",
       "      <td>70200</td>\n",
       "      <td>21500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Access Careers</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0804</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>0.1154</td>\n",
       "      <td>0.6958</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.0918</td>\n",
       "      <td>0.8605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Long Island Barber Institute</td>\n",
       "      <td>Hempstead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5536</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.6400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Be'er Yaakov Talmudic Seminary</td>\n",
       "      <td>Spring Valley</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6343</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Aesthetic Science Institute</td>\n",
       "      <td>Latham</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7121</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2538</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.5190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Leon Studio One School of Hair Design &amp; Career...</td>\n",
       "      <td>North Tonawanda</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6061</td>\n",
       "      <td>0.5758</td>\n",
       "      <td>0.5263</td>\n",
       "      <td>17500</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Digital Film Academy</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3947</td>\n",
       "      <td>0.3684</td>\n",
       "      <td>0.5263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Bryant &amp; Stratton College-Online</td>\n",
       "      <td>Orchard Park</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2574.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.3869</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7852</td>\n",
       "      <td>0.8466</td>\n",
       "      <td>0.7911</td>\n",
       "      <td>22400</td>\n",
       "      <td>24200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Sotheby's Institute of Art-NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>American Academy of Personal Training</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.2995</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5787</td>\n",
       "      <td>0.4623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>New York Institute of Beauty</td>\n",
       "      <td>Islandia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.7054</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2596</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.5250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Cactus Academy</td>\n",
       "      <td>Garden City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>0.3740</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3552</td>\n",
       "      <td>0.5246</td>\n",
       "      <td>0.2062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Sharp Edgez Barber Institute</td>\n",
       "      <td>Rochester</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1803</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8103</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.7857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Yeshiva Gedolah Kesser Torah</td>\n",
       "      <td>Monsey</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4286</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Empire Beauty School-Rochester</td>\n",
       "      <td>Rochester</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0945</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>0.2992</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.6916</td>\n",
       "      <td>0.2932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Arrojo Cosmetology School</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1379</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2426</td>\n",
       "      <td>0.3456</td>\n",
       "      <td>0.3647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>DeVry College of New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1301.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.1091</td>\n",
       "      <td>0.3136</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>0.7511</td>\n",
       "      <td>0.6790</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Electrical Training Center</td>\n",
       "      <td>Copiague</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0355</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Grace International Beauty School</td>\n",
       "      <td>Flushing</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1852</td>\n",
       "      <td>0.1852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Alliance Computing Solutions</td>\n",
       "      <td>Flushing</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6389</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Paul Mitchell the School-Schenectady</td>\n",
       "      <td>Schenectady</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>0.2366</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1852</td>\n",
       "      <td>0.1185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Branford Hall Career Institute-Amityville</td>\n",
       "      <td>Amityville</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.3689</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8762</td>\n",
       "      <td>0.8429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Empire Beauty School-Buffalo</td>\n",
       "      <td>Cheektowaga</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0377</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8710</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Berkeley College - Westchester Campus</td>\n",
       "      <td>White Plains</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39000</td>\n",
       "      <td>29252.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Briarcliffe College–Patchogue</td>\n",
       "      <td>Patchogue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38200</td>\n",
       "      <td>28720.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Jamestown Business College -</td>\n",
       "      <td>Salamanca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Pratt Manhattan-A Division of Pratt Institute</td>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40900</td>\n",
       "      <td>26691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Saint Josephs College-Long Island</td>\n",
       "      <td>Patchogue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52000</td>\n",
       "      <td>22143.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>Franklin Career Institute - Brooklyn Campus</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   INSTNM             CITY  \\\n",
       "STABBR                                                                       \n",
       "TX                           Abilene Christian University          Abilene   \n",
       "TX                                Alvin Community College            Alvin   \n",
       "TX                                       Amarillo College         Amarillo   \n",
       "TX                                       Angelina College           Lufkin   \n",
       "TX                                Angelo State University       San Angelo   \n",
       "TX                              Arlington Baptist College        Arlington   \n",
       "TX                             Arlington Career Institute    Grand Prairie   \n",
       "TX                           The Art Institute of Houston          Houston   \n",
       "TX                                         Austin College          Sherman   \n",
       "TX                      Austin Community College District           Austin   \n",
       "TX               Austin Presbyterian Theological Seminary           Austin   \n",
       "TX                     Baldwin Beauty School-South Austin           Austin   \n",
       "TX      Baptist Health System School of Health Profess...      San Antonio   \n",
       "TX      Baptist Missionary Association Theological Sem...     Jacksonville   \n",
       "TX                             Baylor College of Medicine          Houston   \n",
       "TX                                      Baylor University             Waco   \n",
       "TX                                   Coastal Bend College         Beeville   \n",
       "TX                                          Blinn College          Brenham   \n",
       "TX                        Remington College-Dallas Campus          Garland   \n",
       "TX                          Vet Tech Institute of Houston          Houston   \n",
       "TX                                     Brazosport College     Lake Jackson   \n",
       "TX                                     Brookhaven College   Farmers Branch   \n",
       "TX                                   Cedar Valley College        Lancaster   \n",
       "TX                                  Central Texas College          Killeen   \n",
       "TX                 Charles and Sues School of Hair Design            Bryan   \n",
       "TX                                          Cisco College            Cisco   \n",
       "TX                                      Clarendon College        Clarendon   \n",
       "TX                             Concordia University-Texas           Austin   \n",
       "TX                        Conlee's College of Cosmetology        Kerrville   \n",
       "TX                            North Central Texas College      Gainesville   \n",
       "...                                                   ...              ...   \n",
       "NY           Stella and Charles Guttman Community College         New York   \n",
       "NY                               Miami Ad School-New York          Astoria   \n",
       "NY                                         Access Careers         Brooklyn   \n",
       "NY                           Long Island Barber Institute        Hempstead   \n",
       "NY                         Be'er Yaakov Talmudic Seminary    Spring Valley   \n",
       "NY                            Aesthetic Science Institute           Latham   \n",
       "NY      Leon Studio One School of Hair Design & Career...  North Tonawanda   \n",
       "NY                                   Digital Film Academy         New York   \n",
       "NY                       Bryant & Stratton College-Online     Orchard Park   \n",
       "NY                          Sotheby's Institute of Art-NY         New York   \n",
       "NY                  American Academy of Personal Training         New York   \n",
       "NY                           New York Institute of Beauty         Islandia   \n",
       "NY                                         Cactus Academy      Garden City   \n",
       "NY                           Sharp Edgez Barber Institute        Rochester   \n",
       "NY                           Yeshiva Gedolah Kesser Torah           Monsey   \n",
       "NY                         Empire Beauty School-Rochester        Rochester   \n",
       "NY                              Arrojo Cosmetology School         New York   \n",
       "NY                              DeVry College of New York         New York   \n",
       "NY                             Electrical Training Center         Copiague   \n",
       "NY                      Grace International Beauty School         Flushing   \n",
       "NY                           Alliance Computing Solutions         Flushing   \n",
       "NY                   Paul Mitchell the School-Schenectady      Schenectady   \n",
       "NY              Branford Hall Career Institute-Amityville       Amityville   \n",
       "NY                           Empire Beauty School-Buffalo      Cheektowaga   \n",
       "NY                  Berkeley College - Westchester Campus     White Plains   \n",
       "NY                          Briarcliffe College–Patchogue        Patchogue   \n",
       "NY                           Jamestown Business College -        Salamanca   \n",
       "NY          Pratt Manhattan-A Division of Pratt Institute         New York   \n",
       "NY                      Saint Josephs College-Long Island        Patchogue   \n",
       "NY            Franklin Career Institute - Brooklyn Campus         Brooklyn   \n",
       "\n",
       "        HBCU  MENONLY  WOMENONLY  RELAFFIL  SATVRMID  SATMTMID  DISTANCEONLY  \\\n",
       "STABBR                                                                         \n",
       "TX       0.0      0.0        0.0         1     530.0     545.0           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0     475.0     490.0           0.0   \n",
       "TX       0.0      0.0        0.0         1       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         1     600.0     595.0           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         1       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         1       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         1     610.0     620.0           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         1     500.0     515.0           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "TX       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "...      ...      ...        ...       ...       ...       ...           ...   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           1.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       0.0      0.0        0.0         0       NaN       NaN           0.0   \n",
       "NY       NaN      NaN        NaN         1       NaN       NaN           NaN   \n",
       "NY       NaN      NaN        NaN         1       NaN       NaN           NaN   \n",
       "NY       NaN      NaN        NaN         1       NaN       NaN           NaN   \n",
       "NY       NaN      NaN        NaN         1       NaN       NaN           NaN   \n",
       "NY       NaN      NaN        NaN         1       NaN       NaN           NaN   \n",
       "NY       NaN      NaN        NaN         1       NaN       NaN           NaN   \n",
       "\n",
       "           UGDS         ...          UGDS_2MOR  UGDS_NRA  UGDS_UNKN  PPTUG_EF  \\\n",
       "STABBR                  ...                                                     \n",
       "TX       3572.0         ...             0.0454    0.0423     0.0045    0.0468   \n",
       "TX       4682.0         ...             0.0002    0.0000     0.0143    0.7123   \n",
       "TX       9346.0         ...             0.0000    0.0001     0.0085    0.6922   \n",
       "TX       3825.0         ...             0.0264    0.0005     0.0000    0.5600   \n",
       "TX       5290.0         ...             0.0285    0.0331     0.0011    0.1289   \n",
       "TX        214.0         ...             0.0000    0.0047     0.0000    0.1682   \n",
       "TX        204.0         ...             0.0000    0.0000     0.0000    0.2843   \n",
       "TX       1887.0         ...             0.0000    0.0000     0.0419    0.3466   \n",
       "TX       1272.0         ...             0.0031    0.0267     0.0031    0.0016   \n",
       "TX      32581.0         ...             0.0233    0.0335     0.0532    0.7340   \n",
       "TX          NaN         ...                NaN       NaN        NaN       NaN   \n",
       "TX         69.0         ...             0.0000    0.0000     0.0870    0.0000   \n",
       "TX        500.0         ...             0.0240    0.0000     0.0120    0.0000   \n",
       "TX         54.0         ...             0.0000    0.0185     0.0185    0.7407   \n",
       "TX          NaN         ...                NaN       NaN        NaN       NaN   \n",
       "TX      13801.0         ...             0.0451    0.0309     0.0024    0.0162   \n",
       "TX       3213.0         ...             0.0408    0.0324     0.0218    0.5668   \n",
       "TX      17095.0         ...             0.0288    0.0104     0.0137    0.4288   \n",
       "TX        822.0         ...             0.0073    0.0000     0.0000    0.0000   \n",
       "TX        216.0         ...             0.0324    0.0000     0.0278    0.0000   \n",
       "TX       3164.0         ...             0.0234    0.0000     0.0142    0.7310   \n",
       "TX      10160.0         ...             0.0129    0.0043     0.0346    0.8102   \n",
       "TX       5962.0         ...             0.0146    0.0020     0.0226    0.7744   \n",
       "TX      14409.0         ...             0.0235    0.0022     0.0461    0.7565   \n",
       "TX         19.0         ...             0.0000    0.0000     0.0000    0.0000   \n",
       "TX       3480.0         ...             0.0172    0.0170     0.0020    0.5420   \n",
       "TX       1394.0         ...             0.0022    0.0100     0.0782    0.5201   \n",
       "TX       1525.0         ...             0.0000    0.0052     0.1279    0.2295   \n",
       "TX         70.0         ...             0.0000    0.0000     0.0000    0.4143   \n",
       "TX       9873.0         ...             0.0300    0.0126     0.0042    0.6484   \n",
       "...         ...         ...                ...       ...        ...       ...   \n",
       "NY        691.0         ...             0.0217    0.0347     0.0000    0.0955   \n",
       "NY         99.0         ...             0.0000    0.2222     0.0707    0.0000   \n",
       "NY        286.0         ...             0.0804    0.0315     0.1154    0.6958   \n",
       "NY         61.0         ...             0.0000    0.0000     0.0000    0.0000   \n",
       "NY        382.0         ...             0.0000    0.0052     0.0000    0.0000   \n",
       "NY         66.0         ...             0.0000    0.0000     0.0000    0.7121   \n",
       "NY         18.0         ...             0.0000    0.0000     0.0000    0.0000   \n",
       "NY         88.0         ...             0.0000    0.1818     0.0000    0.0000   \n",
       "NY       2574.0         ...             0.0322    0.0000     0.0237    0.3869   \n",
       "NY          NaN         ...                NaN       NaN        NaN       NaN   \n",
       "NY        187.0         ...             0.0000    0.0000     0.0107    0.2995   \n",
       "NY        112.0         ...             0.0000    0.0000     0.0089    0.7054   \n",
       "NY        123.0         ...             0.0000    0.0081     0.0325    0.3740   \n",
       "NY         61.0         ...             0.0000    0.0000     0.0000    0.1803   \n",
       "NY         67.0         ...             0.0000    0.0000     0.0000    0.0000   \n",
       "NY        127.0         ...             0.0945    0.0000     0.0315    0.2992   \n",
       "NY         29.0         ...             0.0000    0.0000     0.0000    0.1379   \n",
       "NY       1301.0         ...             0.0085    0.0300     0.1091    0.3136   \n",
       "NY        141.0         ...             0.0000    0.0000     0.0355    0.0000   \n",
       "NY         12.0         ...             0.0000    0.0000     0.0000    0.0000   \n",
       "NY         36.0         ...             0.0000    0.6389     0.0000    1.0000   \n",
       "NY         93.0         ...             0.0215    0.0000     0.0108    0.2366   \n",
       "NY        328.0         ...             0.0030    0.0000     0.0091    0.3689   \n",
       "NY         53.0         ...             0.0000    0.0000     0.0377    0.0000   \n",
       "NY          NaN         ...                NaN       NaN        NaN       NaN   \n",
       "NY          NaN         ...                NaN       NaN        NaN       NaN   \n",
       "NY          NaN         ...                NaN       NaN        NaN       NaN   \n",
       "NY          NaN         ...                NaN       NaN        NaN       NaN   \n",
       "NY          NaN         ...                NaN       NaN        NaN       NaN   \n",
       "NY          NaN         ...                NaN       NaN        NaN       NaN   \n",
       "\n",
       "        CURROPER  PCTPELL  PCTFLOAN  UG25ABV    MD_EARN_WNE_P10  \\\n",
       "STABBR                                                            \n",
       "TX             1   0.2595    0.5527   0.0381              40200   \n",
       "TX             1   0.1549    0.0625   0.2841              34500   \n",
       "TX             1   0.3786    0.1573   0.3431              31700   \n",
       "TX             1   0.5308    0.0000   0.2603              26900   \n",
       "TX             1   0.4068    0.5279   0.1407              37700   \n",
       "TX             1   0.4978    0.4892   0.2251              34200   \n",
       "TX             1   0.6186    0.7119   0.7745              27600   \n",
       "TX             1   0.6183    0.7604   0.3845              32600   \n",
       "TX             1   0.2867    0.7581   0.0124              47800   \n",
       "TX             1   0.2393    0.2447   0.3914              34400   \n",
       "TX             1      NaN       NaN      NaN                NaN   \n",
       "TX             1   0.5517    0.0000   0.0789              20700   \n",
       "TX             1   0.3176    0.8491   0.4550  PrivacySuppressed   \n",
       "TX             1   0.6727    0.2545   0.7636  PrivacySuppressed   \n",
       "TX             1      NaN       NaN      NaN             163900   \n",
       "TX             1   0.2135    0.4520   0.0245              48200   \n",
       "TX             1   0.3725    0.1407   0.2027              27300   \n",
       "TX             1   0.2906    0.3118   0.1040              36400   \n",
       "TX             1   0.7611    0.6482   0.4314              25900   \n",
       "TX             1   0.6212    0.8182   0.2656              28900   \n",
       "TX             1   0.1888    0.0283   0.3385              30100   \n",
       "TX             1   0.2697    0.0954   0.4670              37300   \n",
       "TX             1   0.4017    0.4646   0.4456              27700   \n",
       "TX             1   0.3163    0.2946   0.6131              31800   \n",
       "TX             1   0.5263    0.5614   0.0000              14600   \n",
       "TX             1   0.3249    0.1847   0.2972              30200   \n",
       "TX             1   0.3962    0.2751   0.2117              28900   \n",
       "TX             1   0.4076    0.6538   0.3677              39900   \n",
       "TX             0   0.4423    0.0000   0.1765  PrivacySuppressed   \n",
       "TX             1   0.2249    0.1826   0.2756              32700   \n",
       "...          ...      ...       ...      ...                ...   \n",
       "NY             1   0.6673    0.0568   0.0162                NaN   \n",
       "NY             1   0.0825    0.4845   0.6907              70200   \n",
       "NY             0   0.1100    0.0918   0.8605                NaN   \n",
       "NY             1   0.5536    0.6875   0.6400                NaN   \n",
       "NY             1   0.6343    0.0000   0.0000                NaN   \n",
       "NY             1   0.2538    0.4000   0.5190                NaN   \n",
       "NY             0   0.6061    0.5758   0.5263              17500   \n",
       "NY             1   0.3947    0.3684   0.5263                NaN   \n",
       "NY             1   0.7852    0.8466   0.7911              22400   \n",
       "NY             1      NaN       NaN      NaN                NaN   \n",
       "NY             1   0.0000    0.5787   0.4623                NaN   \n",
       "NY             1   0.2596    0.3125   0.5250                NaN   \n",
       "NY             1   0.3552    0.5246   0.2062                NaN   \n",
       "NY             1   0.8103    0.6638   0.7857                NaN   \n",
       "NY             1   0.4286    0.0000   0.0000                NaN   \n",
       "NY             1   0.6869    0.6916   0.2932                NaN   \n",
       "NY             1   0.2426    0.3456   0.3647                NaN   \n",
       "NY             1   0.7033    0.7511   0.6790                NaN   \n",
       "NY             1   0.0000    0.0751      NaN                NaN   \n",
       "NY             1   0.1852    0.1852      NaN                NaN   \n",
       "NY             1   0.0000    0.0000      NaN                NaN   \n",
       "NY             1   0.1852    0.1185      NaN                NaN   \n",
       "NY             1   0.8762    0.8429      NaN                NaN   \n",
       "NY             1   0.8710    1.0000      NaN                NaN   \n",
       "NY             1      NaN       NaN      NaN              39000   \n",
       "NY             1      NaN       NaN      NaN              38200   \n",
       "NY             1      NaN       NaN      NaN                NaN   \n",
       "NY             1      NaN       NaN      NaN              40900   \n",
       "NY             1      NaN       NaN      NaN              52000   \n",
       "NY             0      NaN       NaN      NaN              20000   \n",
       "\n",
       "        GRAD_DEBT_MDN_SUPP  \n",
       "STABBR                      \n",
       "TX                   25985  \n",
       "TX                    6750  \n",
       "TX                   10950  \n",
       "TX       PrivacySuppressed  \n",
       "TX                 21319.5  \n",
       "TX                   22905  \n",
       "TX                    9500  \n",
       "TX                   30750  \n",
       "TX                   26000  \n",
       "TX                  8601.5  \n",
       "TX       PrivacySuppressed  \n",
       "TX       PrivacySuppressed  \n",
       "TX                 19491.5  \n",
       "TX                     NaN  \n",
       "TX       PrivacySuppressed  \n",
       "TX                   25131  \n",
       "TX       PrivacySuppressed  \n",
       "TX                   12000  \n",
       "TX                    9500  \n",
       "TX                   13651  \n",
       "TX                    7000  \n",
       "TX       PrivacySuppressed  \n",
       "TX       PrivacySuppressed  \n",
       "TX                    6500  \n",
       "TX                    9500  \n",
       "TX                   10750  \n",
       "TX                    8500  \n",
       "TX                 25680.5  \n",
       "TX       PrivacySuppressed  \n",
       "TX                    6973  \n",
       "...                    ...  \n",
       "NY       PrivacySuppressed  \n",
       "NY                   21500  \n",
       "NY                    9500  \n",
       "NY                    2637  \n",
       "NY       PrivacySuppressed  \n",
       "NY       PrivacySuppressed  \n",
       "NY                    9500  \n",
       "NY                    9500  \n",
       "NY                   24200  \n",
       "NY       PrivacySuppressed  \n",
       "NY       PrivacySuppressed  \n",
       "NY                    5322  \n",
       "NY                    9500  \n",
       "NY                    5433  \n",
       "NY       PrivacySuppressed  \n",
       "NY                    9588  \n",
       "NY       PrivacySuppressed  \n",
       "NY                   40150  \n",
       "NY       PrivacySuppressed  \n",
       "NY       PrivacySuppressed  \n",
       "NY       PrivacySuppressed  \n",
       "NY                    6222  \n",
       "NY                    9800  \n",
       "NY                    9833  \n",
       "NY                 29252.5  \n",
       "NY                 28720.5  \n",
       "NY                   12050  \n",
       "NY                   26691  \n",
       "NY                 22143.5  \n",
       "NY       PrivacySuppressed  \n",
       "\n",
       "[1704 rows x 26 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = ['TX', 'CA', 'NY']\n",
    "college[college['STABBR'].isin(states)]\n",
    "college2.loc[states]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas implements the index differently based on whether the index is unique or sorted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting with unique and sorted indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Index selection performance drastically improves when the index is unique or sorted. The\n",
    "prior recipe used an unsorted index that contained duplicates, which makes for relatively\n",
    "slow selections.   -- Megmondtam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " college2.index.is_monotonic_increasing   # return if the index is monotonic increasing (only equal or increasing) values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the index from college2 and store it as another object:\n",
    "college3 = college2.sort_index()\n",
    "college3.index.is_monotonic_increasing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time the selection of the state of Texas (TX) from all three DataFrames:\n",
    "    -- ezt inkább átugorjuk:\n",
    "    The sorted index performs nearly an order of magnitude faster than boolean\n",
    "selection. Let's now turn towards unique indexes. For this, we use the institution\n",
    "name as the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college_unique = college.set_index('INSTNM')\n",
    "college_unique.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>HBCU</th>\n",
       "      <th>MENONLY</th>\n",
       "      <th>WOMENONLY</th>\n",
       "      <th>RELAFFIL</th>\n",
       "      <th>SATVRMID</th>\n",
       "      <th>SATMTMID</th>\n",
       "      <th>DISTANCEONLY</th>\n",
       "      <th>...</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "      <th>PPTUG_EF</th>\n",
       "      <th>CURROPER</th>\n",
       "      <th>PCTPELL</th>\n",
       "      <th>PCTFLOAN</th>\n",
       "      <th>UG25ABV</th>\n",
       "      <th>MD_EARN_WNE_P10</th>\n",
       "      <th>GRAD_DEBT_MDN_SUPP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4217</th>\n",
       "      <td>Stanford University</td>\n",
       "      <td>Stanford</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>745.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1067</td>\n",
       "      <td>0.0819</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1556</td>\n",
       "      <td>0.1256</td>\n",
       "      <td>0.0401</td>\n",
       "      <td>86000</td>\n",
       "      <td>12782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   INSTNM      CITY STABBR  HBCU  MENONLY  WOMENONLY  \\\n",
       "4217  Stanford University  Stanford     CA   0.0      0.0        0.0   \n",
       "\n",
       "      RELAFFIL  SATVRMID  SATMTMID  DISTANCEONLY         ...          \\\n",
       "4217         0     730.0     745.0           0.0         ...           \n",
       "\n",
       "      UGDS_2MOR  UGDS_NRA  UGDS_UNKN  PPTUG_EF  CURROPER  PCTPELL  PCTFLOAN  \\\n",
       "4217     0.1067    0.0819     0.0031       0.0         1   0.1556    0.1256   \n",
       "\n",
       "      UG25ABV  MD_EARN_WNE_P10  GRAD_DEBT_MDN_SUPP  \n",
       "4217   0.0401            86000               12782  \n",
       "\n",
       "[1 rows x 27 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's select Stanford University with boolean indexing:\n",
    "college[college['INSTNM'] == 'Stanford University']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CITY                  Stanford\n",
       "STABBR                      CA\n",
       "HBCU                         0\n",
       "MENONLY                      0\n",
       "WOMENONLY                    0\n",
       "RELAFFIL                     0\n",
       "SATVRMID                   730\n",
       "SATMTMID                   745\n",
       "DISTANCEONLY                 0\n",
       "UGDS                      7018\n",
       "UGDS_WHITE              0.3752\n",
       "UGDS_BLACK              0.0591\n",
       "UGDS_HISP               0.1607\n",
       "UGDS_ASIAN              0.1979\n",
       "UGDS_AIAN               0.0114\n",
       "UGDS_NHPI               0.0038\n",
       "UGDS_2MOR               0.1067\n",
       "UGDS_NRA                0.0819\n",
       "UGDS_UNKN               0.0031\n",
       "PPTUG_EF                     0\n",
       "CURROPER                     1\n",
       "PCTPELL                 0.1556\n",
       "PCTFLOAN                0.1256\n",
       "UG25ABV                 0.0401\n",
       "MD_EARN_WNE_P10          86000\n",
       "GRAD_DEBT_MDN_SUPP       12782\n",
       "Name: Stanford University, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's select Stanford University with index selection:\n",
    "college_unique.loc['Stanford University']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768 µs ± 21.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit college[college['INSTNM'] == 'Stanford University']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111 µs ± 3.93 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit college_unique.loc['Stanford University']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the index is not sorted and contains duplicates, as with college2, pandas will need\n",
    "to check every single value in the index in order to make the correct selection. When the\n",
    "index is sorted, as with college3, pandas takes advantage of an algorithm called binary\n",
    "search to greatly improve performance.\n",
    "In the second half of the recipe, we use a unique column as the index. Pandas implements\n",
    "unique indexes with a hash table, which makes for even faster selection. Each index location\n",
    "can be looked up in nearly the same time regardless of its length."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean selection gives much more flexibility than index selection as it is possible to\n",
    "condition on any number of columns. In this recipe, we used a single column as the index. It\n",
    "is possible to concatenate multiple columns together to form an index. For instance, in the\n",
    "following code, we set the index equal to the concatenation of the city and state columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>HBCU</th>\n",
       "      <th>MENONLY</th>\n",
       "      <th>WOMENONLY</th>\n",
       "      <th>RELAFFIL</th>\n",
       "      <th>SATVRMID</th>\n",
       "      <th>SATMTMID</th>\n",
       "      <th>DISTANCEONLY</th>\n",
       "      <th>...</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "      <th>PPTUG_EF</th>\n",
       "      <th>CURROPER</th>\n",
       "      <th>PCTPELL</th>\n",
       "      <th>PCTFLOAN</th>\n",
       "      <th>UG25ABV</th>\n",
       "      <th>MD_EARN_WNE_P10</th>\n",
       "      <th>GRAD_DEBT_MDN_SUPP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARTESIA, CA</th>\n",
       "      <td>Angeles Institute</td>\n",
       "      <td>ARTESIA</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0175</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6275</td>\n",
       "      <td>0.8138</td>\n",
       "      <td>0.5429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aberdeen, SD</th>\n",
       "      <td>Presentation College</td>\n",
       "      <td>Aberdeen</td>\n",
       "      <td>SD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>440.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0284</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.0823</td>\n",
       "      <td>0.2865</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4829</td>\n",
       "      <td>0.7560</td>\n",
       "      <td>0.3097</td>\n",
       "      <td>35900</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aberdeen, SD</th>\n",
       "      <td>Northern State University</td>\n",
       "      <td>Aberdeen</td>\n",
       "      <td>SD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0219</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.1872</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2272</td>\n",
       "      <td>0.4303</td>\n",
       "      <td>0.1766</td>\n",
       "      <td>33600</td>\n",
       "      <td>24847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aberdeen, WA</th>\n",
       "      <td>Grays Harbor College</td>\n",
       "      <td>Aberdeen</td>\n",
       "      <td>WA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0937</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>0.1820</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4530</td>\n",
       "      <td>0.1502</td>\n",
       "      <td>0.5087</td>\n",
       "      <td>27000</td>\n",
       "      <td>11490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abilene, TX</th>\n",
       "      <td>Hardin-Simmons University</td>\n",
       "      <td>Abilene</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>508.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0298</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.0685</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3256</td>\n",
       "      <td>0.5547</td>\n",
       "      <td>0.0982</td>\n",
       "      <td>38700</td>\n",
       "      <td>25864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 INSTNM      CITY STABBR  HBCU  MENONLY  \\\n",
       "ARTESIA, CA           Angeles Institute   ARTESIA     CA   0.0      0.0   \n",
       "Aberdeen, SD       Presentation College  Aberdeen     SD   0.0      0.0   \n",
       "Aberdeen, SD  Northern State University  Aberdeen     SD   0.0      0.0   \n",
       "Aberdeen, WA       Grays Harbor College  Aberdeen     WA   0.0      0.0   \n",
       "Abilene, TX   Hardin-Simmons University   Abilene     TX   0.0      0.0   \n",
       "\n",
       "              WOMENONLY  RELAFFIL  SATVRMID  SATMTMID  DISTANCEONLY  \\\n",
       "ARTESIA, CA         0.0         0       NaN       NaN           0.0   \n",
       "Aberdeen, SD        0.0         1     440.0     480.0           0.0   \n",
       "Aberdeen, SD        0.0         0     480.0     475.0           0.0   \n",
       "Aberdeen, WA        0.0         0       NaN       NaN           0.0   \n",
       "Abilene, TX         0.0         1     508.0     515.0           0.0   \n",
       "\n",
       "                     ...          UGDS_2MOR  UGDS_NRA  UGDS_UNKN  PPTUG_EF  \\\n",
       "ARTESIA, CA          ...             0.0175    0.0088     0.0088    0.0000   \n",
       "Aberdeen, SD         ...             0.0284    0.0142     0.0823    0.2865   \n",
       "Aberdeen, SD         ...             0.0219    0.0425     0.0024    0.1872   \n",
       "Aberdeen, WA         ...             0.0937    0.0009     0.0250    0.1820   \n",
       "Abilene, TX          ...             0.0298    0.0159     0.0102    0.0685   \n",
       "\n",
       "              CURROPER  PCTPELL  PCTFLOAN  UG25ABV  MD_EARN_WNE_P10  \\\n",
       "ARTESIA, CA          1   0.6275    0.8138   0.5429              NaN   \n",
       "Aberdeen, SD         1   0.4829    0.7560   0.3097            35900   \n",
       "Aberdeen, SD         1   0.2272    0.4303   0.1766            33600   \n",
       "Aberdeen, WA         1   0.4530    0.1502   0.5087            27000   \n",
       "Abilene, TX          1   0.3256    0.5547   0.0982            38700   \n",
       "\n",
       "              GRAD_DEBT_MDN_SUPP  \n",
       "ARTESIA, CA                16850  \n",
       "Aberdeen, SD               25000  \n",
       "Aberdeen, SD               24847  \n",
       "Aberdeen, WA               11490  \n",
       "Abilene, TX                25864  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college.index = college['CITY'] + ', ' + college['STABBR']\n",
    "college = college.sort_index()\n",
    "college.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>HBCU</th>\n",
       "      <th>MENONLY</th>\n",
       "      <th>WOMENONLY</th>\n",
       "      <th>RELAFFIL</th>\n",
       "      <th>SATVRMID</th>\n",
       "      <th>SATMTMID</th>\n",
       "      <th>DISTANCEONLY</th>\n",
       "      <th>...</th>\n",
       "      <th>UGDS_2MOR</th>\n",
       "      <th>UGDS_NRA</th>\n",
       "      <th>UGDS_UNKN</th>\n",
       "      <th>PPTUG_EF</th>\n",
       "      <th>CURROPER</th>\n",
       "      <th>PCTPELL</th>\n",
       "      <th>PCTFLOAN</th>\n",
       "      <th>UG25ABV</th>\n",
       "      <th>MD_EARN_WNE_P10</th>\n",
       "      <th>GRAD_DEBT_MDN_SUPP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Miami, FL</th>\n",
       "      <td>New Professions Technical Institute</td>\n",
       "      <td>Miami</td>\n",
       "      <td>FL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4464</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8701</td>\n",
       "      <td>0.6780</td>\n",
       "      <td>0.8358</td>\n",
       "      <td>18700</td>\n",
       "      <td>8682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miami, FL</th>\n",
       "      <td>Management Resources College</td>\n",
       "      <td>Miami</td>\n",
       "      <td>FL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4239</td>\n",
       "      <td>0.5458</td>\n",
       "      <td>0.8698</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "      <td>12182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miami, FL</th>\n",
       "      <td>Strayer University-Doral</td>\n",
       "      <td>Miami</td>\n",
       "      <td>FL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49200</td>\n",
       "      <td>36173.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miami, FL</th>\n",
       "      <td>Keiser University- Miami</td>\n",
       "      <td>Miami</td>\n",
       "      <td>FL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29700</td>\n",
       "      <td>26063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miami, FL</th>\n",
       "      <td>George T Baker Aviation Technical College</td>\n",
       "      <td>Miami</td>\n",
       "      <td>FL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5686</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2567</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>38600</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              INSTNM   CITY STABBR  HBCU  \\\n",
       "Miami, FL        New Professions Technical Institute  Miami     FL   0.0   \n",
       "Miami, FL               Management Resources College  Miami     FL   0.0   \n",
       "Miami, FL                   Strayer University-Doral  Miami     FL   NaN   \n",
       "Miami, FL                   Keiser University- Miami  Miami     FL   NaN   \n",
       "Miami, FL  George T Baker Aviation Technical College  Miami     FL   0.0   \n",
       "\n",
       "           MENONLY  WOMENONLY  RELAFFIL  SATVRMID  SATMTMID  DISTANCEONLY  \\\n",
       "Miami, FL      0.0        0.0         0       NaN       NaN           0.0   \n",
       "Miami, FL      0.0        0.0         0       NaN       NaN           0.0   \n",
       "Miami, FL      NaN        NaN         1       NaN       NaN           NaN   \n",
       "Miami, FL      NaN        NaN         1       NaN       NaN           NaN   \n",
       "Miami, FL      0.0        0.0         0       NaN       NaN           0.0   \n",
       "\n",
       "                  ...          UGDS_2MOR  UGDS_NRA  UGDS_UNKN  PPTUG_EF  \\\n",
       "Miami, FL         ...             0.0000       0.0        0.0    0.4464   \n",
       "Miami, FL         ...             0.0000       0.0        0.0    0.0000   \n",
       "Miami, FL         ...                NaN       NaN        NaN       NaN   \n",
       "Miami, FL         ...                NaN       NaN        NaN       NaN   \n",
       "Miami, FL         ...             0.0046       0.0        0.0    0.5686   \n",
       "\n",
       "           CURROPER  PCTPELL  PCTFLOAN  UG25ABV    MD_EARN_WNE_P10  \\\n",
       "Miami, FL         1   0.8701    0.6780   0.8358              18700   \n",
       "Miami, FL         1   0.4239    0.5458   0.8698  PrivacySuppressed   \n",
       "Miami, FL         1      NaN       NaN      NaN              49200   \n",
       "Miami, FL         1      NaN       NaN      NaN              29700   \n",
       "Miami, FL         1   0.2567    0.0000   0.4366              38600   \n",
       "\n",
       "           GRAD_DEBT_MDN_SUPP  \n",
       "Miami, FL                8682  \n",
       "Miami, FL               12182  \n",
       "Miami, FL             36173.5  \n",
       "Miami, FL               26063  \n",
       "Miami, FL   PrivacySuppressed  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''From here, we can select all colleges from a particular city and state combination without\n",
    "boolean indexing. Let's select all colleges from Miami, FL:'''\n",
    "college.loc['Miami, FL'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare the speed of this compound index selection with boolean indexing. There\n",
    "is more than an order of magnitude difference:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Work faster using keyboard shortcuts ⌨️\n",
    "\n",
    "a, b = insert cell above, below\n",
    "x, c, v, z = cut, copy, paste, undo\n",
    "m, y = change cell to markdown, code\n",
    "f = find\n",
    "h = help\n",
    "p = command palette\n",
    "dd = delete cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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