diff --git a/README.md b/README.md
index 2fc95ab..d0b4ec0 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
 # awesome-pandas
 A collection of resources for [pandas](http://pandas.pydata.org/) 
 ([Python](https://www.python.org/)) and related subjects. 
-**Pull requests are very welcome.**
+**Pull requests are very welcome!**
 
 **Contents:** This is an unofficial collection of resources for learning pandas,
 an open source Python library for data analysis. Here you will find videos,
diff --git a/cheat_sheet/Cheat Sheet.ipynb b/cheat_sheet/Cheat Sheet.ipynb
index 72ecffc..83bc104 100644
--- a/cheat_sheet/Cheat Sheet.ipynb	
+++ b/cheat_sheet/Cheat Sheet.ipynb	
@@ -2,7 +2,9 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "raw_mimetype": "-"
+   },
    "source": [
     "# Pandas Cheat Sheet"
    ]
@@ -81,10 +83,16 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib\n",
-    "\n",
     "%matplotlib inline"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make this Jupyter Notebook reproducible, here are the versions of the libraries we will be using."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -95,8 +103,8 @@
      "output_type": "stream",
      "text": [
       "pandas       version 0.22.0\n",
-      "numpy        version 1.13.3\n",
-      "matplotlib   version 2.1.1\n"
+      "numpy        version 1.14.2\n",
+      "matplotlib   version 2.2.2\n"
      ]
     }
    ],
@@ -105,58 +113,26 @@
     "    print(f'{lib.__name__.ljust(12)} version {lib.__version__}')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option(\"display.max_rows\", 2**6)\n",
-    "pd.set_option(\"display.max_columns\", 2**6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2**6"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Importing data\n",
     "\n",
-    "We'll use"
+    "Using `!` let's us use terminal commands. The `head` command shows the first rows of the file."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "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"
+      "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\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\n"
      ]
     }
    ],
@@ -164,25 +140,43 @@
     "!head data/movie_metadata.csv -n 2"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's a huge file, so we'll only load a couple of columns into a pandas DataFrame.\n",
+    "To familiarize ourselves with with [magic commands](http://ipython.readthedocs.io/en/stable/interactive/magics.html), we'll use `%%time` to time the execution of the cell below."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(5043, 28)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded data of size (5043, 6) into memory.\n",
+      "CPU times: user 44 ms, sys: 525 µs, total: 44.5 ms\n",
+      "Wall time: 42.9 ms\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(r'data/movie_metadata.csv', sep = ',')\n",
-    "df.shape"
+    "%%time\n",
+    "\n",
+    "cols_to_use = ['movie_title', 'director_name', 'country', 'content_rating', 'imdb_score', 'gross']\n",
+    "df = pd.read_csv(r'data/movie_metadata.csv', sep=',', usecols=cols_to_use)\n",
+    "print(f'Loaded data of size {df.shape} into memory.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The df.shape gives the rows and columns of the DataFrame. \n",
+    "This leads us naturally to consider summarizations."
    ]
   },
   {
@@ -192,6 +186,132 @@
     "# Summarizing data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are three methods that are useful to peek at the data, they are df.head, df.tail and df.sample.\n",
+    "Head and tail are $\\mathcal{O}(1)$ operations, while sample is $\\mathcal{O}(n)$, where $n$ is the number of rows.\n",
+    "For small datasets, this makes no difference in practice. We'll use df.sample here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>3097</th>\n",
+       "      <td>Darren Lynn Bousman</td>\n",
+       "      <td>63270259.0</td>\n",
+       "      <td>Saw IV</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>R</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999</th>\n",
+       "      <td>Roman Polanski</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Carnage</td>\n",
+       "      <td>France</td>\n",
+       "      <td>R</td>\n",
+       "      <td>7.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            director_name       gross movie_title country content_rating  \\\n",
+       "3097  Darren Lynn Bousman  63270259.0     Saw IV      USA              R   \n",
+       "1999       Roman Polanski         NaN    Carnage   France              R   \n",
+       "\n",
+       "      imdb_score  \n",
+       "3097         5.9  \n",
+       "1999         7.2  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sample(n=2, replace=False, weights=None, random_state=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We should make sure the data types are correct. To do so, we can use df.dtypes, or df.info() for some more information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "df.info(verbose=True, memory_usage=True, null_counts=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have some null values. Let's count them by chaining df.isnull() and df.sum()."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -200,7 +320,13 @@
     {
      "data": {
       "text/plain": [
-       "(5043, 28)"
+       "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": 7,
@@ -209,7 +335,15 @@
     }
    ],
    "source": [
-    "df.shape"
+    "null_values = df.isnull().sum()\n",
+    "null_values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result of the above is not a DataFrame, but a Series."
    ]
   },
   {
@@ -220,17 +354,7 @@
     {
      "data": {
       "text/plain": [
-       "color                       object\n",
-       "director_name               object\n",
-       "num_critic_for_reviews     float64\n",
-       "duration                   float64\n",
-       "director_facebook_likes    float64\n",
-       "actor_3_facebook_likes     float64\n",
-       "actor_2_name                object\n",
-       "actor_1_facebook_likes     float64\n",
-       "gross                      float64\n",
-       "genres                      object\n",
-       "dtype: object"
+       "pandas.core.series.Series"
       ]
      },
      "execution_count": 8,
@@ -239,7 +363,14 @@
     }
    ],
    "source": [
-    "df.dtypes.head(10)"
+    "type(null_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can make the output prettier by converting null_values to a DataFrame using to_frame(), then transposing using .T, and finally renaming the first index."
    ]
   },
   {
@@ -268,149 +399,22 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</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>color</th>\n",
-       "      <td>Color</td>\n",
-       "      <td>Color</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>director_name</th>\n",
-       "      <td>James Cameron</td>\n",
-       "      <td>Gore Verbinski</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>num_critic_for_reviews</th>\n",
-       "      <td>723</td>\n",
-       "      <td>302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>duration</th>\n",
-       "      <td>178</td>\n",
-       "      <td>169</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>director_facebook_likes</th>\n",
+       "      <th>Missing values</th>\n",
+       "      <td>104</td>\n",
+       "      <td>884</td>\n",
        "      <td>0</td>\n",
-       "      <td>563</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>actor_3_facebook_likes</th>\n",
-       "      <td>855</td>\n",
-       "      <td>1000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>actor_2_name</th>\n",
-       "      <td>Joel David Moore</td>\n",
-       "      <td>Orlando Bloom</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>actor_1_facebook_likes</th>\n",
-       "      <td>1000</td>\n",
-       "      <td>40000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gross</th>\n",
-       "      <td>7.60506e+08</td>\n",
-       "      <td>3.09404e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>genres</th>\n",
-       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
-       "      <td>Action|Adventure|Fantasy</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>actor_1_name</th>\n",
-       "      <td>CCH Pounder</td>\n",
-       "      <td>Johnny Depp</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>movie_title</th>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>num_voted_users</th>\n",
-       "      <td>886204</td>\n",
-       "      <td>471220</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cast_total_facebook_likes</th>\n",
-       "      <td>4834</td>\n",
-       "      <td>48350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>actor_3_name</th>\n",
-       "      <td>Wes Studi</td>\n",
-       "      <td>Jack Davenport</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>facenumber_in_poster</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>plot_keywords</th>\n",
-       "      <td>avatar|future|marine|native|paraplegic</td>\n",
-       "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>movie_imdb_link</th>\n",
-       "      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n",
-       "      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>num_user_for_reviews</th>\n",
-       "      <td>3054</td>\n",
-       "      <td>1238</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>language</th>\n",
-       "      <td>English</td>\n",
-       "      <td>English</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>country</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>USA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>content_rating</th>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>PG-13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>budget</th>\n",
-       "      <td>2.37e+08</td>\n",
-       "      <td>3e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>title_year</th>\n",
-       "      <td>2009</td>\n",
-       "      <td>2007</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>actor_2_facebook_likes</th>\n",
-       "      <td>936</td>\n",
-       "      <td>5000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>imdb_score</th>\n",
-       "      <td>7.9</td>\n",
-       "      <td>7.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>aspect_ratio</th>\n",
-       "      <td>1.78</td>\n",
-       "      <td>2.35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>movie_facebook_likes</th>\n",
-       "      <td>33000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>303</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -418,65 +422,11 @@
        "</div>"
       ],
       "text/plain": [
-       "                                                                           0  \\\n",
-       "color                                                                  Color   \n",
-       "director_name                                                  James Cameron   \n",
-       "num_critic_for_reviews                                                   723   \n",
-       "duration                                                                 178   \n",
-       "director_facebook_likes                                                    0   \n",
-       "actor_3_facebook_likes                                                   855   \n",
-       "actor_2_name                                                Joel David Moore   \n",
-       "actor_1_facebook_likes                                                  1000   \n",
-       "gross                                                            7.60506e+08   \n",
-       "genres                                       Action|Adventure|Fantasy|Sci-Fi   \n",
-       "actor_1_name                                                     CCH Pounder   \n",
-       "movie_title                                                          Avatar    \n",
-       "num_voted_users                                                       886204   \n",
-       "cast_total_facebook_likes                                               4834   \n",
-       "actor_3_name                                                       Wes Studi   \n",
-       "facenumber_in_poster                                                       0   \n",
-       "plot_keywords                         avatar|future|marine|native|paraplegic   \n",
-       "movie_imdb_link            http://www.imdb.com/title/tt0499549/?ref_=fn_t...   \n",
-       "num_user_for_reviews                                                    3054   \n",
-       "language                                                             English   \n",
-       "country                                                                  USA   \n",
-       "content_rating                                                         PG-13   \n",
-       "budget                                                              2.37e+08   \n",
-       "title_year                                                              2009   \n",
-       "actor_2_facebook_likes                                                   936   \n",
-       "imdb_score                                                               7.9   \n",
-       "aspect_ratio                                                            1.78   \n",
-       "movie_facebook_likes                                                   33000   \n",
+       "                director_name  gross  movie_title  country  content_rating  \\\n",
+       "Missing values            104    884            0        5             303   \n",
        "\n",
-       "                                                                           1  \n",
-       "color                                                                  Color  \n",
-       "director_name                                                 Gore Verbinski  \n",
-       "num_critic_for_reviews                                                   302  \n",
-       "duration                                                                 169  \n",
-       "director_facebook_likes                                                  563  \n",
-       "actor_3_facebook_likes                                                  1000  \n",
-       "actor_2_name                                                   Orlando Bloom  \n",
-       "actor_1_facebook_likes                                                 40000  \n",
-       "gross                                                            3.09404e+08  \n",
-       "genres                                              Action|Adventure|Fantasy  \n",
-       "actor_1_name                                                     Johnny Depp  \n",
-       "movie_title                        Pirates of the Caribbean: At World's End   \n",
-       "num_voted_users                                                       471220  \n",
-       "cast_total_facebook_likes                                              48350  \n",
-       "actor_3_name                                                  Jack Davenport  \n",
-       "facenumber_in_poster                                                       0  \n",
-       "plot_keywords              goddess|marriage ceremony|marriage proposal|pi...  \n",
-       "movie_imdb_link            http://www.imdb.com/title/tt0449088/?ref_=fn_t...  \n",
-       "num_user_for_reviews                                                    1238  \n",
-       "language                                                             English  \n",
-       "country                                                                  USA  \n",
-       "content_rating                                                         PG-13  \n",
-       "budget                                                                 3e+08  \n",
-       "title_year                                                              2007  \n",
-       "actor_2_facebook_likes                                                  5000  \n",
-       "imdb_score                                                               7.1  \n",
-       "aspect_ratio                                                            2.35  \n",
-       "movie_facebook_likes                                                       0  "
+       "                imdb_score  \n",
+       "Missing values           0  "
       ]
      },
      "execution_count": 9,
@@ -485,7 +435,14 @@
     }
    ],
    "source": [
-    "df.head(2).T"
+    "null_values.to_frame().T.rename(index={0:'Missing values'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above is called method chaining, and can be written like so:"
    ]
   },
   {
@@ -495,36 +452,53 @@
    "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": [
-       "cast_total_facebook_likes      0\n",
-       "imdb_score                     0\n",
-       "movie_imdb_link                0\n",
-       "num_voted_users                0\n",
-       "movie_title                    0\n",
-       "genres                         0\n",
-       "movie_facebook_likes           0\n",
-       "country                        5\n",
-       "actor_1_facebook_likes         7\n",
-       "actor_1_name                   7\n",
-       "language                      12\n",
-       "actor_2_facebook_likes        13\n",
-       "actor_2_name                  13\n",
-       "facenumber_in_poster          13\n",
-       "duration                      15\n",
-       "color                         19\n",
-       "num_user_for_reviews          21\n",
-       "actor_3_facebook_likes        23\n",
-       "actor_3_name                  23\n",
-       "num_critic_for_reviews        50\n",
-       "director_facebook_likes      104\n",
-       "director_name                104\n",
-       "title_year                   108\n",
-       "plot_keywords                153\n",
-       "content_rating               303\n",
-       "aspect_ratio                 329\n",
-       "budget                       492\n",
-       "gross                        884\n",
-       "dtype: int64"
+       "                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": 10,
@@ -533,13 +507,274 @@
     }
    ],
    "source": [
-    "df.isnull().sum().sort_values()"
+    "(df\n",
+    "    .isnull()    # Figure out whether every entry is null (missing), or not\n",
+    "    .sum(axis=0) # Sum over each column, axis=0 is the default\n",
+    "    .to_frame()  # The result is a Series, convert to DataFrame\n",
+    "    .T           # Transpose (switch rows and columns)\n",
+    "    .rename(index={0:'Missing values'}) # Rename the index and show it\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A tour of summarization would not be completed without df.describe().\n",
+    "Calling df.count(), df.nunique(), df.mean(), df.std(), df.min(), df.quantile(), df.max() is also possible."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "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>King Kong</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               King Kong      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": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe(percentiles=[0.5], include='all').fillna('')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>1.000000</td>\n",
+       "      <td>0.096247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>imdb_score</th>\n",
+       "      <td>0.096247</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               gross  imdb_score\n",
+       "gross       1.000000    0.096247\n",
+       "imdb_score  0.096247    1.000000"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='spearman', min_periods=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = pd.plotting.scatter_matrix(df, alpha=0.5, figsize=(8,5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Honorable mentions are len(df)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# len(df), shape, value_counts, head, tail, max(), min(), mean, dtype, info(), \n",
@@ -555,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -565,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -637,7 +872,7 @@
        "2          PG-13         6.8  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -649,7 +884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -721,7 +956,7 @@
        "102          PG-13         7.8  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -732,9 +967,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/tommy/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: \n",
+      "Passing list-likes to .loc or [] with any missing label will raise\n",
+      "KeyError in the future, you can use .reindex() as an alternative.\n",
+      "\n",
+      "See the documentation here:\n",
+      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "/home/tommy/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:1367: FutureWarning: \n",
+      "Passing list-likes to .loc or [] with any missing label will raise\n",
+      "KeyError in the future, you can use .reindex() as an alternative.\n",
+      "\n",
+      "See the documentation here:\n",
+      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
+      "  return self._getitem_tuple(key)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -773,7 +1028,7 @@
        "      <td>USA</td>\n",
        "      <td>PG-13</td>\n",
        "      <td>7.9</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>760505847.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -783,7 +1038,7 @@
        "      <td>USA</td>\n",
        "      <td>PG-13</td>\n",
        "      <td>7.1</td>\n",
-       "      <td>563.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>309404152.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -796,11 +1051,11 @@
        "1  Pirates of the Caribbean: At World's End   Gore Verbinski     USA   \n",
        "\n",
        "  content_rating  imdb_score  director_facebook_likes        gross  \n",
-       "0          PG-13         7.9                      0.0  760505847.0  \n",
-       "1          PG-13         7.1                    563.0  309404152.0  "
+       "0          PG-13         7.9                      NaN  760505847.0  \n",
+       "1          PG-13         7.1                      NaN  309404152.0  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -812,7 +1067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -877,7 +1132,7 @@
        "1          PG-13         7.1  309404152.0  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -889,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -965,7 +1220,7 @@
        "3466              R         9.2  134821952.0  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -976,7 +1231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -985,7 +1240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1065,7 +1320,7 @@
        "max           10  760,505,847        20"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1076,7 +1331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1135,7 +1390,7 @@
        "gross_log     0.074280  0.616034   1.000000"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1153,7 +1408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1233,7 +1488,7 @@
        "9             PG         7.5  301956980.0  19.525795  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1244,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1324,7 +1579,7 @@
        "4029       Brazil              R         8.7    7563397.0  15.838831  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1336,7 +1591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1346,7 +1601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1450,7 +1705,7 @@
        "1196          USA              R         7.8  102310175.0  18.443520  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1461,7 +1716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1475,7 +1730,7 @@
        "Name: country, dtype: int64"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1486,7 +1741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1500,7 +1755,7 @@
        "Name: country, dtype: int64"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1517,10 +1772,8 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
     "- **Split-apply-combine and pivots**: groupby, dt.month, dt.year, groupby.mean(), agg, stack, unstack, pivot, melt, merge"
    ]
@@ -1534,7 +1787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1600,7 +1853,7 @@
        "Ridley Scott               16"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1611,7 +1864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1677,7 +1930,7 @@
        "Charles Chaplin          8.6"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1688,7 +1941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1792,7 +2045,7 @@
        "4     NaN            NaN         7.1          NaN        NaN  "
       ]
      },
-     "execution_count": 43,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1803,7 +2056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1869,7 +2122,7 @@
        "Aaron Schneider         7.1  16.032162            1"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1882,7 +2135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1948,7 +2201,7 @@
        "Aaron Schneider    0.771125  -0.321155    -0.488133"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1960,7 +2213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -2031,7 +2284,7 @@
        "Clint Eastwood      0.883067   8.041961     8.705881  17.630909"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2047,9 +2300,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/tommy/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:858: FutureWarning: \n",
+      "Passing list-likes to .loc or [] with any missing label will raise\n",
+      "KeyError in the future, you can use .reindex() as an alternative.\n",
+      "\n",
+      "See the documentation here:\n",
+      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
+      "  return self._getitem_lowerdim(tup)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -2071,155 +2337,23 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>content_rating</th>\n",
-       "      <th>Approved</th>\n",
-       "      <th>G</th>\n",
-       "      <th>GP</th>\n",
-       "      <th>M</th>\n",
-       "      <th>NC-17</th>\n",
-       "      <th>Not Rated</th>\n",
-       "      <th>PG</th>\n",
-       "      <th>PG-13</th>\n",
-       "      <th>Passed</th>\n",
-       "      <th>R</th>\n",
-       "      <th>TV-14</th>\n",
-       "      <th>TV-G</th>\n",
-       "      <th>TV-PG</th>\n",
-       "      <th>Unrated</th>\n",
-       "      <th>X</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>title_year</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>2012.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>71.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2013.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "content_rating  Approved    G   GP    M  NC-17  Not Rated    PG  PG-13  \\\n",
-       "title_year                                                               \n",
-       "2012.0               0.0  2.0  0.0  0.0    0.0       18.0  26.0   71.0   \n",
-       "2013.0               0.0  1.0  0.0  0.0    0.0       14.0  26.0   68.0   \n",
-       "2014.0               0.0  3.0  0.0  0.0    0.0        9.0  28.0   72.0   \n",
-       "2015.0               0.0  1.0  0.0  0.0    0.0        5.0  22.0   59.0   \n",
-       "2016.0               0.0  0.0  0.0  0.0    0.0        2.0  11.0   41.0   \n",
-       "\n",
-       "content_rating  Passed     R  TV-14  TV-G  TV-PG  Unrated    X  \n",
-       "title_year                                                      \n",
-       "2012.0             0.0  84.0    0.0   1.0    0.0      0.0  0.0  \n",
-       "2013.0             0.0  93.0    1.0   0.0    0.0      1.0  0.0  \n",
-       "2014.0             0.0  86.0    0.0   0.0    0.0      5.0  0.0  \n",
-       "2015.0             0.0  77.0    1.0   0.0    0.0      1.0  0.0  \n",
-       "2016.0             0.0  31.0    0.0   0.0    0.0      0.0  0.0  "
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: []"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2238,7 +2372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -2262,182 +2396,59 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>color</th>\n",
        "      <th>director_name</th>\n",
-       "      <th>num_critic_for_reviews</th>\n",
-       "      <th>duration</th>\n",
-       "      <th>director_facebook_likes</th>\n",
-       "      <th>actor_3_facebook_likes</th>\n",
-       "      <th>actor_2_name</th>\n",
-       "      <th>actor_1_facebook_likes</th>\n",
        "      <th>gross</th>\n",
-       "      <th>genres</th>\n",
-       "      <th>actor_1_name</th>\n",
        "      <th>movie_title</th>\n",
-       "      <th>num_voted_users</th>\n",
-       "      <th>cast_total_facebook_likes</th>\n",
-       "      <th>actor_3_name</th>\n",
-       "      <th>facenumber_in_poster</th>\n",
-       "      <th>plot_keywords</th>\n",
-       "      <th>movie_imdb_link</th>\n",
-       "      <th>num_user_for_reviews</th>\n",
-       "      <th>language</th>\n",
        "      <th>country</th>\n",
        "      <th>content_rating</th>\n",
-       "      <th>budget</th>\n",
-       "      <th>title_year</th>\n",
-       "      <th>actor_2_facebook_likes</th>\n",
        "      <th>imdb_score</th>\n",
-       "      <th>aspect_ratio</th>\n",
-       "      <th>movie_facebook_likes</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Color</td>\n",
        "      <td>James Cameron</td>\n",
-       "      <td>723.0</td>\n",
-       "      <td>178.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>855.0</td>\n",
-       "      <td>Joel David Moore</td>\n",
-       "      <td>1000.0</td>\n",
        "      <td>760505847.0</td>\n",
-       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
-       "      <td>CCH Pounder</td>\n",
        "      <td>Avatar</td>\n",
-       "      <td>886204</td>\n",
-       "      <td>4834</td>\n",
-       "      <td>Wes Studi</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>avatar|future|marine|native|paraplegic</td>\n",
-       "      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n",
-       "      <td>3054.0</td>\n",
-       "      <td>English</td>\n",
        "      <td>USA</td>\n",
        "      <td>PG-13</td>\n",
-       "      <td>237000000.0</td>\n",
-       "      <td>2009.0</td>\n",
-       "      <td>936.0</td>\n",
        "      <td>7.9</td>\n",
-       "      <td>1.78</td>\n",
-       "      <td>33000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>Color</td>\n",
        "      <td>Gore Verbinski</td>\n",
-       "      <td>302.0</td>\n",
-       "      <td>169.0</td>\n",
-       "      <td>563.0</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>Orlando Bloom</td>\n",
-       "      <td>40000.0</td>\n",
        "      <td>309404152.0</td>\n",
-       "      <td>Action|Adventure|Fantasy</td>\n",
-       "      <td>Johnny Depp</td>\n",
        "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "      <td>471220</td>\n",
-       "      <td>48350</td>\n",
-       "      <td>Jack Davenport</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
-       "      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n",
-       "      <td>1238.0</td>\n",
-       "      <td>English</td>\n",
        "      <td>USA</td>\n",
        "      <td>PG-13</td>\n",
-       "      <td>300000000.0</td>\n",
-       "      <td>2007.0</td>\n",
-       "      <td>5000.0</td>\n",
        "      <td>7.1</td>\n",
-       "      <td>2.35</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>Color</td>\n",
        "      <td>Sam Mendes</td>\n",
-       "      <td>602.0</td>\n",
-       "      <td>148.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>161.0</td>\n",
-       "      <td>Rory Kinnear</td>\n",
-       "      <td>11000.0</td>\n",
        "      <td>200074175.0</td>\n",
-       "      <td>Action|Adventure|Thriller</td>\n",
-       "      <td>Christoph Waltz</td>\n",
        "      <td>Spectre</td>\n",
-       "      <td>275868</td>\n",
-       "      <td>11700</td>\n",
-       "      <td>Stephanie Sigman</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>bomb|espionage|sequel|spy|terrorist</td>\n",
-       "      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n",
-       "      <td>994.0</td>\n",
-       "      <td>English</td>\n",
        "      <td>UK</td>\n",
        "      <td>PG-13</td>\n",
-       "      <td>245000000.0</td>\n",
-       "      <td>2015.0</td>\n",
-       "      <td>393.0</td>\n",
        "      <td>6.8</td>\n",
-       "      <td>2.35</td>\n",
-       "      <td>85000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   color   director_name  num_critic_for_reviews  duration  \\\n",
-       "0  Color   James Cameron                   723.0     178.0   \n",
-       "1  Color  Gore Verbinski                   302.0     169.0   \n",
-       "2  Color      Sam Mendes                   602.0     148.0   \n",
+       "    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",
+       "2      Sam Mendes  200074175.0                                   Spectre    \n",
        "\n",
-       "   director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
-       "0                      0.0                   855.0  Joel David Moore   \n",
-       "1                    563.0                  1000.0     Orlando Bloom   \n",
-       "2                      0.0                   161.0      Rory Kinnear   \n",
-       "\n",
-       "   actor_1_facebook_likes        gross                           genres  \\\n",
-       "0                  1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi   \n",
-       "1                 40000.0  309404152.0         Action|Adventure|Fantasy   \n",
-       "2                 11000.0  200074175.0        Action|Adventure|Thriller   \n",
-       "\n",
-       "      actor_1_name                                movie_title  \\\n",
-       "0      CCH Pounder                                    Avatar    \n",
-       "1      Johnny Depp  Pirates of the Caribbean: At World's End    \n",
-       "2  Christoph Waltz                                   Spectre    \n",
-       "\n",
-       "   num_voted_users  cast_total_facebook_likes      actor_3_name  \\\n",
-       "0           886204                       4834         Wes Studi   \n",
-       "1           471220                      48350    Jack Davenport   \n",
-       "2           275868                      11700  Stephanie Sigman   \n",
-       "\n",
-       "   facenumber_in_poster                                      plot_keywords  \\\n",
-       "0                   0.0             avatar|future|marine|native|paraplegic   \n",
-       "1                   0.0  goddess|marriage ceremony|marriage proposal|pi...   \n",
-       "2                   1.0                bomb|espionage|sequel|spy|terrorist   \n",
-       "\n",
-       "                                     movie_imdb_link  num_user_for_reviews  \\\n",
-       "0  http://www.imdb.com/title/tt0499549/?ref_=fn_t...                3054.0   \n",
-       "1  http://www.imdb.com/title/tt0449088/?ref_=fn_t...                1238.0   \n",
-       "2  http://www.imdb.com/title/tt2379713/?ref_=fn_t...                 994.0   \n",
-       "\n",
-       "  language country content_rating       budget  title_year  \\\n",
-       "0  English     USA          PG-13  237000000.0      2009.0   \n",
-       "1  English     USA          PG-13  300000000.0      2007.0   \n",
-       "2  English      UK          PG-13  245000000.0      2015.0   \n",
-       "\n",
-       "   actor_2_facebook_likes  imdb_score  aspect_ratio  movie_facebook_likes  \n",
-       "0                   936.0         7.9          1.78                 33000  \n",
-       "1                  5000.0         7.1          2.35                     0  \n",
-       "2                   393.0         6.8          2.35                 85000  "
+       "  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": 83,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2448,181 +2459,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 38,
    "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>content_rating</th>\n",
-       "      <th>Approved</th>\n",
-       "      <th>G</th>\n",
-       "      <th>GP</th>\n",
-       "      <th>M</th>\n",
-       "      <th>NC-17</th>\n",
-       "      <th>Not Rated</th>\n",
-       "      <th>PG</th>\n",
-       "      <th>PG-13</th>\n",
-       "      <th>Passed</th>\n",
-       "      <th>R</th>\n",
-       "      <th>TV-14</th>\n",
-       "      <th>TV-G</th>\n",
-       "      <th>TV-PG</th>\n",
-       "      <th>Unrated</th>\n",
-       "      <th>X</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>title_year</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>2012.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>71.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2013.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "content_rating  Approved    G   GP    M  NC-17  Not Rated    PG  PG-13  \\\n",
-       "title_year                                                               \n",
-       "2012.0               0.0  2.0  0.0  0.0    0.0       18.0  26.0   71.0   \n",
-       "2013.0               0.0  1.0  0.0  0.0    0.0       14.0  26.0   68.0   \n",
-       "2014.0               0.0  3.0  0.0  0.0    0.0        9.0  28.0   72.0   \n",
-       "2015.0               0.0  1.0  0.0  0.0    0.0        5.0  22.0   59.0   \n",
-       "2016.0               0.0  0.0  0.0  0.0    0.0        2.0  11.0   41.0   \n",
-       "\n",
-       "content_rating  Passed     R  TV-14  TV-G  TV-PG  Unrated    X  \n",
-       "title_year                                                      \n",
-       "2012.0             0.0  84.0    0.0   1.0    0.0      0.0  0.0  \n",
-       "2013.0             0.0  93.0    1.0   0.0    0.0      1.0  0.0  \n",
-       "2014.0             0.0  86.0    0.0   0.0    0.0      5.0  0.0  \n",
-       "2015.0             0.0  77.0    1.0   0.0    0.0      1.0  0.0  \n",
-       "2016.0             0.0  31.0    0.0   0.0    0.0      0.0  0.0  "
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "KeyError",
+     "evalue": "'title_year'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-38-3c2062a21c2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m      \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'title_year'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m      \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'content_rating'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m      aggfunc=pd.DataFrame.nunique)\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m .tail(5))\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mpivot_table\u001b[0;34m(self, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name)\u001b[0m\n\u001b[1;32m   4466\u001b[0m                            \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4467\u001b[0m                            \u001b[0mmargins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmargins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4468\u001b[0;31m                            margins_name=margins_name)\n\u001b[0m\u001b[1;32m   4469\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4470\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/reshape/pivot.py\u001b[0m in \u001b[0;36mpivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name)\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m     \u001b[0mgrouped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m     \u001b[0magged\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, **kwargs)\u001b[0m\n\u001b[1;32m   5160\u001b[0m         return groupby(self, by=by, axis=axis, level=level, as_index=as_index,\n\u001b[1;32m   5161\u001b[0m                        \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup_keys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5162\u001b[0;31m                        **kwargs)\n\u001b[0m\u001b[1;32m   5163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5164\u001b[0m     def asfreq(self, freq, method=None, how=None, normalize=False,\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(obj, by, **kwds)\u001b[0m\n\u001b[1;32m   1846\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'invalid type: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1847\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1848\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1850\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, **kwargs)\u001b[0m\n\u001b[1;32m    514\u001b[0m                                                     \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m                                                     \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 516\u001b[0;31m                                                     mutated=self.mutated)\n\u001b[0m\u001b[1;32m    517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    518\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36m_get_grouper\u001b[0;34m(obj, key, axis, level, sort, mutated, validate)\u001b[0m\n\u001b[1;32m   2932\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2933\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2934\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2935\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2936\u001b[0m             \u001b[0;31m# Add key to exclusions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'title_year'"
+     ]
     }
    ],
    "source": [
@@ -2680,455 +2535,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/tommy/anaconda3/lib/python3.6/site-packages/pandas/core/groupby.py:4291: FutureWarning: using a dict with renaming is deprecated and will be removed in a future version\n",
-      "  return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)\n"
-     ]
-    },
-    {
-     "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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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>m</th>\n",
-       "      <th>sdf</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>title_year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1916.0</th>\n",
-       "      <td>123.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1920.0</th>\n",
-       "      <td>110.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1925.0</th>\n",
-       "      <td>151.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1927.0</th>\n",
-       "      <td>145.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1929.0</th>\n",
-       "      <td>105.000000</td>\n",
-       "      <td>7.071068</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1930.0</th>\n",
-       "      <td>96.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1932.0</th>\n",
-       "      <td>79.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1933.0</th>\n",
-       "      <td>77.500000</td>\n",
-       "      <td>16.263456</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1934.0</th>\n",
-       "      <td>65.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1935.0</th>\n",
-       "      <td>81.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1936.0</th>\n",
-       "      <td>93.500000</td>\n",
-       "      <td>9.192388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1937.0</th>\n",
-       "      <td>92.000000</td>\n",
-       "      <td>12.727922</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1938.0</th>\n",
-       "      <td>116.000000</td>\n",
-       "      <td>14.142136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1939.0</th>\n",
-       "      <td>149.333333</td>\n",
-       "      <td>67.002488</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1940.0</th>\n",
-       "      <td>108.000000</td>\n",
-       "      <td>21.059440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1941.0</th>\n",
-       "      <td>118.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1942.0</th>\n",
-       "      <td>76.000000</td>\n",
-       "      <td>8.485281</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1943.0</th>\n",
-       "      <td>122.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1944.0</th>\n",
-       "      <td>101.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1945.0</th>\n",
-       "      <td>103.750000</td>\n",
-       "      <td>10.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1946.0</th>\n",
-       "      <td>144.666667</td>\n",
-       "      <td>27.006172</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1947.0</th>\n",
-       "      <td>101.666667</td>\n",
-       "      <td>14.224392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1948.0</th>\n",
-       "      <td>98.666667</td>\n",
-       "      <td>29.143324</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1949.0</th>\n",
-       "      <td>106.000000</td>\n",
-       "      <td>4.242641</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1950.0</th>\n",
-       "      <td>107.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1951.0</th>\n",
-       "      <td>134.666667</td>\n",
-       "      <td>32.593455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1952.0</th>\n",
-       "      <td>106.750000</td>\n",
-       "      <td>31.223656</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1953.0</th>\n",
-       "      <td>106.250000</td>\n",
-       "      <td>24.878036</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1954.0</th>\n",
-       "      <td>140.600000</td>\n",
-       "      <td>36.073536</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1955.0</th>\n",
-       "      <td>112.500000</td>\n",
-       "      <td>19.091883</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1956.0</th>\n",
-       "      <td>103.000000</td>\n",
-       "      <td>13.114877</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1957.0</th>\n",
-       "      <td>128.500000</td>\n",
-       "      <td>45.961941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1985.0</th>\n",
-       "      <td>108.724138</td>\n",
-       "      <td>20.325734</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1986.0</th>\n",
-       "      <td>104.653846</td>\n",
-       "      <td>14.707664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1987.0</th>\n",
-       "      <td>105.500000</td>\n",
-       "      <td>29.359837</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1988.0</th>\n",
-       "      <td>107.000000</td>\n",
-       "      <td>18.638669</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1989.0</th>\n",
-       "      <td>113.121212</td>\n",
-       "      <td>19.582769</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990.0</th>\n",
-       "      <td>114.100000</td>\n",
-       "      <td>32.563307</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1991.0</th>\n",
-       "      <td>113.064516</td>\n",
-       "      <td>26.856328</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1992.0</th>\n",
-       "      <td>116.088235</td>\n",
-       "      <td>24.342714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1993.0</th>\n",
-       "      <td>120.229167</td>\n",
-       "      <td>43.293280</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1994.0</th>\n",
-       "      <td>111.481481</td>\n",
-       "      <td>25.176165</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1995.0</th>\n",
-       "      <td>114.528571</td>\n",
-       "      <td>24.225224</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1996.0</th>\n",
-       "      <td>110.010101</td>\n",
-       "      <td>18.521083</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997.0</th>\n",
-       "      <td>110.254237</td>\n",
-       "      <td>21.591858</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998.0</th>\n",
-       "      <td>109.462687</td>\n",
-       "      <td>20.978204</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999.0</th>\n",
-       "      <td>108.642857</td>\n",
-       "      <td>20.332059</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000.0</th>\n",
-       "      <td>107.535294</td>\n",
-       "      <td>20.673610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2001.0</th>\n",
-       "      <td>106.941489</td>\n",
-       "      <td>23.319033</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2002.0</th>\n",
-       "      <td>104.229665</td>\n",
-       "      <td>18.260268</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2003.0</th>\n",
-       "      <td>106.402367</td>\n",
-       "      <td>22.667451</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2004.0</th>\n",
-       "      <td>107.299065</td>\n",
-       "      <td>20.988579</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2005.0</th>\n",
-       "      <td>107.504545</td>\n",
-       "      <td>22.870790</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2006.0</th>\n",
-       "      <td>107.147059</td>\n",
-       "      <td>20.243023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2007.0</th>\n",
-       "      <td>106.897059</td>\n",
-       "      <td>24.419854</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2008.0</th>\n",
-       "      <td>105.382222</td>\n",
-       "      <td>17.785318</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2009.0</th>\n",
-       "      <td>105.127413</td>\n",
-       "      <td>18.262597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2010.0</th>\n",
-       "      <td>105.434211</td>\n",
-       "      <td>15.323819</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2011.0</th>\n",
-       "      <td>105.035714</td>\n",
-       "      <td>16.797356</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2012.0</th>\n",
-       "      <td>105.900000</td>\n",
-       "      <td>22.001640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2013.0</th>\n",
-       "      <td>108.093220</td>\n",
-       "      <td>22.074145</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014.0</th>\n",
-       "      <td>105.426295</td>\n",
-       "      <td>19.384879</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015.0</th>\n",
-       "      <td>106.022321</td>\n",
-       "      <td>17.848250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016.0</th>\n",
-       "      <td>109.632075</td>\n",
-       "      <td>17.020618</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>91 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     m        sdf\n",
-       "title_year                       \n",
-       "1916.0      123.000000        NaN\n",
-       "1920.0      110.000000        NaN\n",
-       "1925.0      151.000000        NaN\n",
-       "1927.0      145.000000        NaN\n",
-       "1929.0      105.000000   7.071068\n",
-       "1930.0       96.000000        NaN\n",
-       "1932.0       79.000000        NaN\n",
-       "1933.0       77.500000  16.263456\n",
-       "1934.0       65.000000        NaN\n",
-       "1935.0       81.000000        NaN\n",
-       "1936.0       93.500000   9.192388\n",
-       "1937.0       92.000000  12.727922\n",
-       "1938.0      116.000000  14.142136\n",
-       "1939.0      149.333333  67.002488\n",
-       "1940.0      108.000000  21.059440\n",
-       "1941.0      118.000000        NaN\n",
-       "1942.0       76.000000   8.485281\n",
-       "1943.0      122.000000        NaN\n",
-       "1944.0      101.000000        NaN\n",
-       "1945.0      103.750000  10.500000\n",
-       "1946.0      144.666667  27.006172\n",
-       "1947.0      101.666667  14.224392\n",
-       "1948.0       98.666667  29.143324\n",
-       "1949.0      106.000000   4.242641\n",
-       "1950.0      107.000000        NaN\n",
-       "1951.0      134.666667  32.593455\n",
-       "1952.0      106.750000  31.223656\n",
-       "1953.0      106.250000  24.878036\n",
-       "1954.0      140.600000  36.073536\n",
-       "1955.0      112.500000  19.091883\n",
-       "1956.0      103.000000  13.114877\n",
-       "1957.0      128.500000  45.961941\n",
-       "...                ...        ...\n",
-       "1985.0      108.724138  20.325734\n",
-       "1986.0      104.653846  14.707664\n",
-       "1987.0      105.500000  29.359837\n",
-       "1988.0      107.000000  18.638669\n",
-       "1989.0      113.121212  19.582769\n",
-       "1990.0      114.100000  32.563307\n",
-       "1991.0      113.064516  26.856328\n",
-       "1992.0      116.088235  24.342714\n",
-       "1993.0      120.229167  43.293280\n",
-       "1994.0      111.481481  25.176165\n",
-       "1995.0      114.528571  24.225224\n",
-       "1996.0      110.010101  18.521083\n",
-       "1997.0      110.254237  21.591858\n",
-       "1998.0      109.462687  20.978204\n",
-       "1999.0      108.642857  20.332059\n",
-       "2000.0      107.535294  20.673610\n",
-       "2001.0      106.941489  23.319033\n",
-       "2002.0      104.229665  18.260268\n",
-       "2003.0      106.402367  22.667451\n",
-       "2004.0      107.299065  20.988579\n",
-       "2005.0      107.504545  22.870790\n",
-       "2006.0      107.147059  20.243023\n",
-       "2007.0      106.897059  24.419854\n",
-       "2008.0      105.382222  17.785318\n",
-       "2009.0      105.127413  18.262597\n",
-       "2010.0      105.434211  15.323819\n",
-       "2011.0      105.035714  16.797356\n",
-       "2012.0      105.900000  22.001640\n",
-       "2013.0      108.093220  22.074145\n",
-       "2014.0      105.426295  19.384879\n",
-       "2015.0      106.022321  17.848250\n",
-       "2016.0      109.632075  17.020618\n",
-       "\n",
-       "[91 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "to_plot = df.groupby(df.title_year).agg({df.duration.name:{'m':np.mean, 'sdf':np.std}})\n",
     "\n",
@@ -3136,7 +2545,7 @@
     "\n",
     "#to_plot = to_plot.assign(low = lambda df: df.mean - df.std)\n",
     "\n",
-    "to_plot"
+    "to_plot.plot()"
    ]
   },
   {
@@ -3173,6 +2582,78 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://www.kaggle.com/zynicide/wine-reviews"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {