From 35b8cbe662e1e31fe4e3bb7ea0aab4b8e157222d Mon Sep 17 00:00:00 2001
From: tommyod <tommy.odland@gmail.com>
Date: Sun, 10 Mar 2019 08:49:27 +0100
Subject: [PATCH] small changes

---
 tutorial/Pandas_tutorial_part1.ipynb | 2689 ++------------------------
 1 file changed, 128 insertions(+), 2561 deletions(-)

diff --git a/tutorial/Pandas_tutorial_part1.ipynb b/tutorial/Pandas_tutorial_part1.ipynb
index e67d220..6059ec3 100644
--- a/tutorial/Pandas_tutorial_part1.ipynb
+++ b/tutorial/Pandas_tutorial_part1.ipynb
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,23 +131,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Today is 2019-03-04 21:37:07.208603\n",
-      "----------------------------------------------------------------\n",
-      "pandas       version 0.24.1\n",
-      "numpy        version 1.15.4\n",
-      "matplotlib   version 3.0.2\n",
-      "KDEpy        version 0.6.11\n",
-      "sklearn      version 0.20.1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import datetime\n",
     "\n",
@@ -197,50 +183,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[01;34m.\u001b[00m\r\n",
-      "├── \u001b[01;34mdata\u001b[00m\r\n",
-      "│   ├── google_trends.csv\r\n",
-      "│   ├── movie_metadata.csv\r\n",
-      "│   ├── wine_data.csv\r\n",
-      "│   └── world_population_history.csv\r\n",
-      "├── Pandas_tutorial_part1.ipynb\r\n",
-      "├── Pandas_tutorial_part1.py\r\n",
-      "├── Pandas_tutorial_part2.ipynb\r\n",
-      "├── Pandas_tutorial_part2.py\r\n",
-      "├── \u001b[01;35mpandas_vs_excel_vs_sas.png\u001b[00m\r\n",
-      "└── Tutorial.py\r\n",
-      "\r\n",
-      "1 directory, 10 files\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!tree . -L 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "color,director_name,num_critic_for_reviews,duration,director_facebook_likes,actor_3_facebook_likes,actor_2_name,actor_1_facebook_likes,gross,genres,actor_1_name,movie_title,num_voted_users,cast_total_facebook_likes,actor_3_name,facenumber_in_poster,plot_keywords,movie_imdb_link,num_user_for_reviews,language,country,content_rating,budget,title_year,actor_2_facebook_likes,imdb_score,aspect_ratio,movie_facebook_likes\r",
-      "\r\n",
-      "Color,James Cameron,723,178,0,855,Joel David Moore,1000,760505847,Action|Adventure|Fantasy|Sci-Fi,CCH Pounder,Avatar ,886204,4834,Wes Studi,0,avatar|future|marine|native|paraplegic,http://www.imdb.com/title/tt0499549/?ref_=fn_tt_tt_1,3054,English,USA,PG-13,237000000,2009,936,7.9,1.78,33000\r",
-      "\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!head data/movie_metadata.csv -n 2"
    ]
@@ -262,19 +216,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded data of size (5043, 6) into memory.\n",
-      "CPU times: user 31.3 ms, sys: 3.91 ms, total: 35.2 ms\n",
-      "Wall time: 35.2 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -285,76 +229,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>James Cameron</td>\n",
-       "      <td>760505847.0</td>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Gore Verbinski</td>\n",
-       "      <td>309404152.0</td>\n",
-       "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    director_name        gross                                movie_title  \\\n",
-       "0   James Cameron  760505847.0                                    Avatar    \n",
-       "1  Gore Verbinski  309404152.0  Pirates of the Caribbean: At World's End    \n",
-       "\n",
-       "  country content_rating  imdb_score  \n",
-       "0     USA          PG-13         7.9  \n",
-       "1     USA          PG-13         7.1  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head(2)  # Show the top 2 rows"
    ]
@@ -368,20 +245,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5043, 6)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.shape  # Alternatively, use len(df) for row count"
    ]
@@ -404,66 +270,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "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>name</th>\n",
-       "      <th>age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Max</td>\n",
-       "      <td>31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Mark</td>\n",
-       "      <td>25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Mia</td>\n",
-       "      <td>38</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   name  age\n",
-       "0   Max   31\n",
-       "1  Mark   25\n",
-       "2   Mia   38"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pd.DataFrame({'name':['Max', 'Mark', 'Mia'], 'age':[31, 25, 38]})"
    ]
@@ -477,103 +286,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "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>World ranking</th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Citizenship</th>\n",
-       "      <th>Net worth (USD)</th>\n",
-       "      <th>Sources of wealth</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>21</td>\n",
-       "      <td>Georg Schaeffler</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>26.9 billion</td>\n",
-       "      <td>Schaeffler Group</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>37</td>\n",
-       "      <td>Beate Heister (b. Albrecht) &amp; Karl Albrecht Jr.</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>21.3 billion</td>\n",
-       "      <td>Aldi Süd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>46</td>\n",
-       "      <td>Dieter Schwarz</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>19.4 billion</td>\n",
-       "      <td>Schwarz Gruppe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>49</td>\n",
-       "      <td>Theo Albrecht Jr.</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>19 billion</td>\n",
-       "      <td>Aldi Nord and Trader Joe's</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>Michael Otto</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>18.1 billion</td>\n",
-       "      <td>Otto Group</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   World ranking                                             Name Citizenship  \\\n",
-       "0             21                                 Georg Schaeffler     Germany   \n",
-       "1             37  Beate Heister (b. Albrecht) & Karl Albrecht Jr.     Germany   \n",
-       "2             46                                   Dieter Schwarz     Germany   \n",
-       "3             49                                Theo Albrecht Jr.     Germany   \n",
-       "4             50                                     Michael Otto     Germany   \n",
-       "\n",
-       "  Net worth (USD)           Sources of wealth  \n",
-       "0    26.9 billion            Schaeffler Group  \n",
-       "1    21.3 billion                    Aldi Süd  \n",
-       "2    19.4 billion              Schwarz Gruppe  \n",
-       "3      19 billion  Aldi Nord and Trader Joe's  \n",
-       "4    18.1 billion                  Otto Group  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Read HTML tables into a list of DataFrame objects.\n",
     "url = r'https://en.wikipedia.org/wiki/List_of_Germans_by_net_worth'\n",
@@ -609,18 +324,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2, 4, 6, 9]\n",
-      "Tommy\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Lists are MUTABLE\n",
     "scores = [6, 2, 4, 9, 1]\n",
@@ -642,76 +348,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "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>Director_name</th>\n",
-       "      <th>Gross</th>\n",
-       "      <th>Movie_title</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Content_rating</th>\n",
-       "      <th>Imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>James Cameron</td>\n",
-       "      <td>760505847.0</td>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Gore Verbinski</td>\n",
-       "      <td>309404152.0</td>\n",
-       "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Director_name        Gross                                Movie_title  \\\n",
-       "0   James Cameron  760505847.0                                    Avatar    \n",
-       "1  Gore Verbinski  309404152.0  Pirates of the Caribbean: At World's End    \n",
-       "\n",
-       "  Country Content_rating  Imdb_score  \n",
-       "0     USA          PG-13         7.9  \n",
-       "1     USA          PG-13         7.1  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Alter axes labels\n",
     "df_net_worth = (df_net_worth\n",
@@ -731,102 +370,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "director_name      object\n",
-       "gross             float64\n",
-       "movie_title        object\n",
-       "country            object\n",
-       "content_rating     object\n",
-       "imdb_score        float64\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.dtypes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "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",
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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>world_ranking</th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Citizenship</th>\n",
-       "      <th>net_worth</th>\n",
-       "      <th>wealth_source</th>\n",
-       "      <th>net_worth_num</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>21</td>\n",
-       "      <td>Georg Schaeffler</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>26.9 billion</td>\n",
-       "      <td>Schaeffler Group</td>\n",
-       "      <td>26.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>37</td>\n",
-       "      <td>Beate Heister (b. Albrecht) &amp; Karl Albrecht Jr.</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>21.3 billion</td>\n",
-       "      <td>Aldi Süd</td>\n",
-       "      <td>21.3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   world_ranking                                             Name Citizenship  \\\n",
-       "0             21                                 Georg Schaeffler     Germany   \n",
-       "1             37  Beate Heister (b. Albrecht) & Karl Albrecht Jr.     Germany   \n",
-       "\n",
-       "      net_worth     wealth_source  net_worth_num  \n",
-       "0  26.9 billion  Schaeffler Group           26.9  \n",
-       "1  21.3 billion          Aldi Süd           21.3  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_net_worth['net_worth_num'] = (df_net_worth['net_worth']\n",
     "                             .str.replace(' billion', '')\n",
@@ -850,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -884,76 +439,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>James Cameron</td>\n",
-       "      <td>760505847.0</td>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Gore Verbinski</td>\n",
-       "      <td>309404152.0</td>\n",
-       "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    director_name        gross                                movie_title  \\\n",
-       "0   James Cameron  760505847.0                                    Avatar    \n",
-       "1  Gore Verbinski  309404152.0  Pirates of the Caribbean: At World's End    \n",
-       "\n",
-       "  country content_rating  imdb_score  \n",
-       "0     USA          PG-13         7.9  \n",
-       "1     USA          PG-13         7.1  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Return the first `n` rows.\n",
     "df.head(n=2)  # df.tail(n=2) returns the last rows"
@@ -961,76 +449,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2699</th>\n",
-       "      <td>Mark Piznarski</td>\n",
-       "      <td>10494147.0</td>\n",
-       "      <td>Here on Earth</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>5.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1558</th>\n",
-       "      <td>David Cronenberg</td>\n",
-       "      <td>31493782.0</td>\n",
-       "      <td>A History of Violence</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>R</td>\n",
-       "      <td>7.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         director_name       gross             movie_title country  \\\n",
-       "2699    Mark Piznarski  10494147.0          Here on Earth      USA   \n",
-       "1558  David Cronenberg  31493782.0  A History of Violence      USA   \n",
-       "\n",
-       "     content_rating  imdb_score  \n",
-       "2699          PG-13         5.1  \n",
-       "1558              R         7.5  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.sample(n=2, replace=False, weights=None, random_state=None)"
    ]
@@ -1051,27 +472,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 5043 entries, 0 to 5042\n",
-      "Data columns (total 6 columns):\n",
-      "director_name     4939 non-null object\n",
-      "gross             4159 non-null float64\n",
-      "movie_title       5043 non-null object\n",
-      "country           5038 non-null object\n",
-      "content_rating    4740 non-null object\n",
-      "imdb_score        5043 non-null float64\n",
-      "dtypes: float64(2), object(4)\n",
-      "memory usage: 236.5+ KB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Print a concise summary of a DataFrame\n",
     "df.info(verbose=True, memory_usage=True, null_counts=True)"
@@ -1086,26 +489,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "director_name     104\n",
-       "gross             884\n",
-       "movie_title         0\n",
-       "country             5\n",
-       "content_rating    303\n",
-       "imdb_score          0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Detect missing values -> sum over rows\n",
     "null_values = df.isnull().sum(axis=0)\n",
@@ -1121,20 +507,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "type(null_values)"
    ]
@@ -1156,65 +531,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Missing values</th>\n",
-       "      <td>104</td>\n",
-       "      <td>884</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>303</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                director_name  gross  movie_title  country  content_rating  \\\n",
-       "Missing values            104    884            0        5             303   \n",
-       "\n",
-       "                imdb_score  \n",
-       "Missing values           0  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "null_values.to_frame().T.rename(index={0: 'Missing values'})"
    ]
@@ -1228,65 +547,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Missing values</th>\n",
-       "      <td>104</td>\n",
-       "      <td>884</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>303</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                director_name  gross  movie_title  country  content_rating  \\\n",
-       "Missing values            104    884            0        5             303   \n",
-       "\n",
-       "                imdb_score  \n",
-       "Missing values           0  "
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "(df\n",
     "    .isnull()    # Figure out whether every entry is null (missing), or not\n",
@@ -1309,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1318,220 +581,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>4939</td>\n",
-       "      <td>4159</td>\n",
-       "      <td>5043</td>\n",
-       "      <td>5038</td>\n",
-       "      <td>4740</td>\n",
-       "      <td>5043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>unique</th>\n",
-       "      <td>2398</td>\n",
-       "      <td></td>\n",
-       "      <td>4917</td>\n",
-       "      <td>65</td>\n",
-       "      <td>18</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>top</th>\n",
-       "      <td>Steven Spielberg</td>\n",
-       "      <td></td>\n",
-       "      <td>Pan</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>R</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>freq</th>\n",
-       "      <td>26</td>\n",
-       "      <td></td>\n",
-       "      <td>3</td>\n",
-       "      <td>3807</td>\n",
-       "      <td>2118</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td></td>\n",
-       "      <td>4.84684e+07</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td>6.44214</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td></td>\n",
-       "      <td>6.8453e+07</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td>1.12512</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td></td>\n",
-       "      <td>162</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td>1.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td></td>\n",
-       "      <td>2.55175e+07</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td>6.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td></td>\n",
-       "      <td>7.60506e+08</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td>9.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           director_name        gross movie_title country content_rating  \\\n",
-       "count               4939         4159        5043    5038           4740   \n",
-       "unique              2398                     4917      65             18   \n",
-       "top     Steven Spielberg                     Pan      USA              R   \n",
-       "freq                  26                        3    3807           2118   \n",
-       "mean                      4.84684e+07                                      \n",
-       "std                        6.8453e+07                                      \n",
-       "min                               162                                      \n",
-       "50%                       2.55175e+07                                      \n",
-       "max                       7.60506e+08                                      \n",
-       "\n",
-       "       imdb_score  \n",
-       "count        5043  \n",
-       "unique             \n",
-       "top                \n",
-       "freq               \n",
-       "mean      6.44214  \n",
-       "std       1.12512  \n",
-       "min           1.6  \n",
-       "50%           6.6  \n",
-       "max           9.5  "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.describe(percentiles=[0.5], include='all').fillna('')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5043, 6)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4092, 6)"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.dropna(axis=0, how='any').shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4920, 6)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.drop_duplicates(subset=None).shape  # Use df[df.duplicated()] to see rows"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1547,245 +633,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['PG-13', 'PG', 'G', 'R', 'Unrated', 'Approved', 'NC-17', 'X',\n",
-       "       'Not Rated', 'M', 'GP', 'Passed'], dtype=object)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.content_rating.unique()  # Not the same as: df.content_rating.is_unique"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['PG-13', 'PG', 'G', 'R', 'Unrated', 'Approved', 'NC-17', 'X', 'Not Rated', 'M', 'GP', 'Passed']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(df.content_rating.drop_duplicates().tolist())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "R            1818\n",
-       "PG-13        1352\n",
-       "PG            596\n",
-       "G              95\n",
-       "Not Rated      56\n",
-       "Unrated        34\n",
-       "Approved       18\n",
-       "X               9\n",
-       "NC-17           6\n",
-       "Passed          3\n",
-       "M               2\n",
-       "GP              1\n",
-       "Name: content_rating, dtype: int64"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.content_rating.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "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>movie_title</th>\n",
-       "      <th>gross</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>760505847.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Titanic</td>\n",
-       "      <td>658672302.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Jurassic World</td>\n",
-       "      <td>652177271.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        movie_title        gross\n",
-       "0           Avatar   760505847.0\n",
-       "26         Titanic   658672302.0\n",
-       "29  Jurassic World   652177271.0"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[['movie_title', 'gross']].nlargest(3, 'gross')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4735</th>\n",
-       "      <td>Siddiq Barmak</td>\n",
-       "      <td>1127331.0</td>\n",
-       "      <td>Osama</td>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4000</th>\n",
-       "      <td>Juan José Campanella</td>\n",
-       "      <td>20167424.0</td>\n",
-       "      <td>The Secret in Their Eyes</td>\n",
-       "      <td>Argentina</td>\n",
-       "      <td>R</td>\n",
-       "      <td>8.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4415</th>\n",
-       "      <td>Fabián Bielinsky</td>\n",
-       "      <td>1221261.0</td>\n",
-       "      <td>Nine Queens</td>\n",
-       "      <td>Argentina</td>\n",
-       "      <td>R</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4450</th>\n",
-       "      <td>Lucrecia Martel</td>\n",
-       "      <td>304124.0</td>\n",
-       "      <td>The Holy Girl</td>\n",
-       "      <td>Argentina</td>\n",
-       "      <td>R</td>\n",
-       "      <td>6.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1491</th>\n",
-       "      <td>Hark Tsui</td>\n",
-       "      <td>10076136.0</td>\n",
-       "      <td>Knock Off</td>\n",
-       "      <td>Aruba</td>\n",
-       "      <td>R</td>\n",
-       "      <td>4.8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             director_name       gross                movie_title  \\\n",
-       "4735         Siddiq Barmak   1127331.0                     Osama    \n",
-       "4000  Juan José Campanella  20167424.0  The Secret in Their Eyes    \n",
-       "4415      Fabián Bielinsky   1221261.0               Nine Queens    \n",
-       "4450       Lucrecia Martel    304124.0             The Holy Girl    \n",
-       "1491             Hark Tsui  10076136.0                 Knock Off    \n",
-       "\n",
-       "          country content_rating  imdb_score  \n",
-       "4735  Afghanistan          PG-13         7.4  \n",
-       "4000    Argentina              R         8.2  \n",
-       "4415    Argentina              R         7.9  \n",
-       "4450    Argentina              R         6.7  \n",
-       "1491        Aruba              R         4.8  "
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Sort by country, then by IMDB_score. Put NA values last\n",
     "df.sort_values(by=['country', 'imdb_score'], \n",
@@ -1804,82 +690,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "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>gross</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>gross</th>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.20497</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>imdb_score</th>\n",
-       "      <td>0.20497</td>\n",
-       "      <td>1.00000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              gross  imdb_score\n",
-       "gross       1.00000     0.20497\n",
-       "imdb_score  0.20497     1.00000"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.corr(method='pearson')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "(df.content_rating\n",
     " .value_counts()\n",
@@ -1894,44 +716,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.gross.plot.kde(bw_method=0.1, grid=True, title='IMDB score', lw=3, figsize=(10, 5));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot = pd.plotting.scatter_matrix(df, alpha=0.5, figsize=(10, 5))"
    ]
@@ -1958,75 +754,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Int64Index([   0,    1,    2,    3,    5,    6,    7,    8,    9,   10,\n",
-       "            ...\n",
-       "            5021, 5025, 5026, 5027, 5033, 5034, 5035, 5037, 5041, 5042],\n",
-       "           dtype='int64', length=3990)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.index"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['director_name', 'gross', 'movie_title', 'country', 'content_rating',\n",
-       "       'imdb_score'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['James Cameron', 760505847.0, 'Avatar\\xa0', 'USA', 'PG-13', 7.9],\n",
-       "       ['Gore Verbinski', 309404152.0,\n",
-       "        \"Pirates of the Caribbean: At World's End\\xa0\", 'USA', 'PG-13',\n",
-       "        7.1],\n",
-       "       ['Sam Mendes', 200074175.0, 'Spectre\\xa0', 'UK', 'PG-13', 6.8],\n",
-       "       ...,\n",
-       "       ['Edward Burns', 4584.0, 'Newlyweds\\xa0', 'USA', 'Not Rated', 6.4],\n",
-       "       ['Daniel Hsia', 10443.0, 'Shanghai Calling\\xa0', 'USA', 'PG-13',\n",
-       "        6.3],\n",
-       "       ['Jon Gunn', 85222.0, 'My Date with Drew\\xa0', 'USA', 'PG', 6.6]],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This is very useful when using data with libraries\n",
     "df.to_numpy()"
@@ -2041,21 +789,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([7.60505847e+08, 3.09404152e+08, 2.00074175e+08, ...,\n",
-       "       4.58400000e+03, 1.04430000e+04, 8.52220000e+04])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.gross.dropna().to_numpy()"
    ]
@@ -2069,40 +805,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['director_name', 'gross', 'movie_title', 'country', 'content_rating', 'imdb_score']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(df.columns.tolist())  # Get the columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     James Cameron\n",
-       "1    Gore Verbinski\n",
-       "2        Sam Mendes\n",
-       "Name: director_name, dtype: object"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.director_name.head(3)  # Alternatively, use df['director_name'].head(3)"
    ]
@@ -2116,60 +830,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "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>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 movie_title country\n",
-       "0                                    Avatar      USA\n",
-       "1  Pirates of the Caribbean: At World's End      USA"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df[['movie_title', 'country']].head(2)"
    ]
@@ -2186,78 +849,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\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>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 movie_title country\n",
-       "0                                    Avatar      USA\n",
-       "1  Pirates of the Caribbean: At World's End      USA"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.loc[:, ['movie_title', 'country']].head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.series.Series'>\n",
-      "<class 'pandas.core.frame.DataFrame'>\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "a = df.loc[:, 'gross']  # Returns a Series\n",
     "b = df.loc[:, ['gross']]  # Returns a DataFrame\n",
@@ -2275,70 +878,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "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>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>UK</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>6.8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  country content_rating  imdb_score\n",
-       "0     USA          PG-13         7.9\n",
-       "1     USA          PG-13         7.1\n",
-       "2      UK          PG-13         6.8"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Drop specified labels from rows or columns\n",
     "df.drop(columns=['director_name', 'gross', 'movie_title']).head(3)"
@@ -2346,63 +888,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Gore Verbinski</td>\n",
-       "      <td>309404152.0</td>\n",
-       "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Sam Mendes</td>\n",
-       "      <td>200074175.0</td>\n",
-       "      <td>Spectre</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    director_name        gross                                movie_title\n",
-       "1  Gore Verbinski  309404152.0  Pirates of the Caribbean: At World's End \n",
-       "2      Sam Mendes  200074175.0                                   Spectre "
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Integer-location based indexing\n",
     "df.iloc[1:3, [0, 1, 2]]"
@@ -2417,62 +905,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>James Cameron</td>\n",
-       "      <td>760505847.0</td>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   director_name        gross movie_title country content_rating  imdb_score\n",
-       "0  James Cameron  760505847.0     Avatar      USA          PG-13         7.9"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Return the first `n` rows\n",
     "df.head(n=1)"
@@ -2480,62 +915,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>James Cameron</td>\n",
-       "      <td>760505847.0</td>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   director_name        gross movie_title country content_rating  imdb_score\n",
-       "0  James Cameron  760505847.0     Avatar      USA          PG-13         7.9"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Access a group of rows and columns by label(s) or a boolean array\n",
     "df.loc[[0], :]"
@@ -2543,154 +925,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>James Cameron</td>\n",
-       "      <td>760505847.0</td>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   director_name        gross movie_title country content_rating  imdb_score\n",
-       "0  James Cameron  760505847.0     Avatar      USA          PG-13         7.9"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.loc[[0]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4498</th>\n",
-       "      <td>Sergio Leone</td>\n",
-       "      <td>6100000.0</td>\n",
-       "      <td>The Good, the Bad and the Ugly</td>\n",
-       "      <td>Italy</td>\n",
-       "      <td>Approved</td>\n",
-       "      <td>8.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>270</th>\n",
-       "      <td>Peter Jackson</td>\n",
-       "      <td>313837577.0</td>\n",
-       "      <td>The Lord of the Rings: The Fellowship of the R...</td>\n",
-       "      <td>New Zealand</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>8.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4029</th>\n",
-       "      <td>Fernando Meirelles</td>\n",
-       "      <td>7563397.0</td>\n",
-       "      <td>City of God</td>\n",
-       "      <td>Brazil</td>\n",
-       "      <td>R</td>\n",
-       "      <td>8.7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           director_name        gross  \\\n",
-       "4498        Sergio Leone    6100000.0   \n",
-       "270        Peter Jackson  313837577.0   \n",
-       "4029  Fernando Meirelles    7563397.0   \n",
-       "\n",
-       "                                            movie_title      country  \\\n",
-       "4498                    The Good, the Bad and the Ugly         Italy   \n",
-       "270   The Lord of the Rings: The Fellowship of the R...  New Zealand   \n",
-       "4029                                       City of God        Brazil   \n",
-       "\n",
-       "     content_rating  imdb_score  \n",
-       "4498       Approved         8.9  \n",
-       "270           PG-13         8.8  \n",
-       "4029              R         8.7  "
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Top three movies / TV-series not from the USA\n",
     "df[df.country != 'USA'].nlargest(3, 'imdb_score')"
@@ -2698,92 +944,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>270</th>\n",
-       "      <td>Peter Jackson</td>\n",
-       "      <td>313837577.0</td>\n",
-       "      <td>The Lord of the Rings: The Fellowship of the R...</td>\n",
-       "      <td>New Zealand</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>8.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2323</th>\n",
-       "      <td>Hayao Miyazaki</td>\n",
-       "      <td>2298191.0</td>\n",
-       "      <td>Princess Mononoke</td>\n",
-       "      <td>Japan</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>8.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659</th>\n",
-       "      <td>Asghar Farhadi</td>\n",
-       "      <td>7098492.0</td>\n",
-       "      <td>A Separation</td>\n",
-       "      <td>Iran</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>8.4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       director_name        gross  \\\n",
-       "270    Peter Jackson  313837577.0   \n",
-       "2323  Hayao Miyazaki    2298191.0   \n",
-       "4659  Asghar Farhadi    7098492.0   \n",
-       "\n",
-       "                                            movie_title      country  \\\n",
-       "270   The Lord of the Rings: The Fellowship of the R...  New Zealand   \n",
-       "2323                                 Princess Mononoke         Japan   \n",
-       "4659                                      A Separation          Iran   \n",
-       "\n",
-       "     content_rating  imdb_score  \n",
-       "270           PG-13         8.8  \n",
-       "2323          PG-13         8.4  \n",
-       "4659          PG-13         8.4  "
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Best non-American films, with content rating PG-13\n",
     "mask = (df.country != 'USA') & (df.content_rating == 'PG-13')\n",
@@ -2799,91 +962,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "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",
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-       "\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>director_name</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1196</th>\n",
-       "      <td>James Wan</td>\n",
-       "      <td>The Conjuring 2</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1562</th>\n",
-       "      <td>Martin Scorsese</td>\n",
-       "      <td>Bringing Out the Dead</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2163</th>\n",
-       "      <td>James Wan</td>\n",
-       "      <td>The Conjuring</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2969</th>\n",
-       "      <td>Peter Webber</td>\n",
-       "      <td>Girl with a Pearl Earring</td>\n",
-       "      <td>UK</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3858</th>\n",
-       "      <td>Todd Solondz</td>\n",
-       "      <td>Life During Wartime</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4298</th>\n",
-       "      <td>Lance Mungia</td>\n",
-       "      <td>Six-String Samurai</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        director_name                 movie_title country\n",
-       "1196        James Wan            The Conjuring 2      USA\n",
-       "1562  Martin Scorsese      Bringing Out the Dead      USA\n",
-       "2163        James Wan              The Conjuring      USA\n",
-       "2969     Peter Webber  Girl with a Pearl Earring       UK\n",
-       "3858     Todd Solondz        Life During Wartime      USA\n",
-       "4298     Lance Mungia         Six-String Samurai      USA"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Above average movies, with the title containing 'ring'\n",
     "row_mask = ((df.imdb_score > df.imdb_score.mean()) & \n",
@@ -2893,97 +974,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>director_name</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>James Cameron</td>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Gore Verbinski</td>\n",
-       "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Sam Mendes</td>\n",
-       "      <td>Spectre</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>6.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Christopher Nolan</td>\n",
-       "      <td>The Dark Knight Rises</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>8.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Andrew Stanton</td>\n",
-       "      <td>John Carter</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>6.6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       director_name                                movie_title  \\\n",
-       "0      James Cameron                                    Avatar    \n",
-       "1     Gore Verbinski  Pirates of the Caribbean: At World's End    \n",
-       "2         Sam Mendes                                   Spectre    \n",
-       "3  Christopher Nolan                     The Dark Knight Rises    \n",
-       "5     Andrew Stanton                               John Carter    \n",
-       "\n",
-       "  content_rating  imdb_score  \n",
-       "0          PG-13         7.9  \n",
-       "1          PG-13         7.1  \n",
-       "2          PG-13         6.8  \n",
-       "3          PG-13         8.5  \n",
-       "5          PG-13         6.6  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Columns containing and underscore\n",
     "cols = [c for c in df.columns if '_' in c]\n",
@@ -2992,60 +985,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "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>gross</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>760505847.0</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>309404152.0</td>\n",
-       "      <td>7.1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         gross  imdb_score\n",
-       "0  760505847.0         7.9\n",
-       "1  309404152.0         7.1"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Numerical columns\n",
     "numeric_cols = df.dtypes[df.dtypes == np.float].index.tolist()\n",
@@ -3061,79 +1003,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "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>director_name</th>\n",
-       "      <th>gross</th>\n",
-       "      <th>movie_title</th>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th>imdb_score</th>\n",
-       "      <th>log_gross</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>James Cameron</td>\n",
-       "      <td>760505847.0</td>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.9</td>\n",
-       "      <td>8.881103</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Gore Verbinski</td>\n",
-       "      <td>309404152.0</td>\n",
-       "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>7.1</td>\n",
-       "      <td>8.490526</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    director_name        gross                                movie_title  \\\n",
-       "0   James Cameron  760505847.0                                    Avatar    \n",
-       "1  Gore Verbinski  309404152.0  Pirates of the Caribbean: At World's End    \n",
-       "\n",
-       "  country content_rating  imdb_score  log_gross  \n",
-       "0     USA          PG-13         7.9   8.881103  \n",
-       "1     USA          PG-13         7.1   8.490526  "
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "temp = df.copy()  # Copy the DataFrame\n",
     "\n",
@@ -3145,105 +1017,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "temp.plot.scatter(x='imdb_score', y='log_gross', alpha=0.2, s=15, figsize=(10, 5));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "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></th>\n",
-       "      <th>log_gross</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>country</th>\n",
-       "      <th>content_rating</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Afghanistan</th>\n",
-       "      <th>PG-13</th>\n",
-       "      <td>6.05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Argentina</th>\n",
-       "      <th>R</th>\n",
-       "      <td>6.29</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Aruba</th>\n",
-       "      <th>R</th>\n",
-       "      <td>7.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Australia</th>\n",
-       "      <th>G</th>\n",
-       "      <td>7.54</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>PG-13</th>\n",
-       "      <td>7.32</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            log_gross\n",
-       "country     content_rating           \n",
-       "Afghanistan PG-13                6.05\n",
-       "Argentina   R                    6.29\n",
-       "Aruba       R                    7.00\n",
-       "Australia   G                    7.54\n",
-       "            PG-13                7.32"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Assign new columns to a DataFrame, returning a new object\n",
     "# (a copy) with the new columns added to the original ones.\n",
@@ -3291,22 +1076,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    760505847.0\n",
-       "1    309404152.0\n",
-       "Name: gross, dtype: float64"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Map values of Series using input correspondence (a dict, Series, or function).\n",
     "df.gross.map(float).head(2)"
@@ -3314,62 +1086,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "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>content_rating</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>NaN</th>\n",
-       "      <td>2042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>inappropriate for children under 13</th>\n",
-       "      <td>1352</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>may not be suitable for children</th>\n",
-       "      <td>596</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                     content_rating\n",
-       "NaN                                            2042\n",
-       "inappropriate for children under 13            1352\n",
-       "may not be suitable for children                596"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Dictionaries are also maps, but brittle since no keys maps to NaN\n",
     "(df.content_rating\n",
@@ -3390,22 +1109,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    8.881103\n",
-       "1    8.490526\n",
-       "Name: gross, dtype: float64"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Invoke function on values of Series. Can be ufunc (a NumPy function\n",
     "# that applies to the entire Series) or a Python function that only works\n",
@@ -3415,20 +1121,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "48392390.917042606"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Aggregate using one or more operations over the specified axis.\n",
     "df.gross.aggregate(np.mean, axis=0)"
@@ -3445,164 +1140,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "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>gross</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>20.449494</td>\n",
-       "      <td>2.066863</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>19.550159</td>\n",
-       "      <td>1.960095</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       gross  imdb_score\n",
-       "0  20.449494    2.066863\n",
-       "1  19.550159    1.960095"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.loc[:, ['gross', 'imdb_score']].apply(np.log).head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "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>gross</th>\n",
-       "      <th>imdb_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>760505847</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>309404152</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       gross  imdb_score\n",
-       "0  760505847           7\n",
-       "1  309404152           7"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.loc[:, ['gross', 'imdb_score']].applymap(int).head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "gross         4.839239e+07\n",
-       "imdb_score    6.463283e+00\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.loc[:, ['gross', 'imdb_score']].mean().head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "gross         760505685.0\n",
-       "imdb_score            7.7\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Or specify your own aggregation function\n",
     "def spread(array):\n",