EDApt2-Studio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f470e67-1623-4416-b9c2-8367f7834a26",
   "metadata": {
    "tags": []
   },
   "source": [
    "# EDA part 2 Studio\n",
    "\n",
    "## Further Analysis of IMDB movies\n",
    "\n",
    "In this studio, we're returning to this [tutorial](https://towardsdatascience.com/understand-the-data-with-univariate-and-multivariate-charts-and-plots-in-python-3b9fcd68cd8) as a reference guide for our EDA.\n",
    "You've seen the data set before and you've worked through similar kinds of questions with the heart disease dataset. \n",
    "\n",
    "Blocks have been created for your code and we've asked you to add more in some places.\n",
    "\n",
    "The dataset from [Kaggle](https://www.kaggle.com/PromptCloudHQ/imdb-data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34632281-1e15-44b1-a3e3-b82ddacb5cbd",
   "metadata": {},
   "source": [
    "### 1. Find the relative proportions of movie genres represented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "070ebed9-c948-4821-9a24-67f0e3fe8fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dependencies \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# create your data variabe using .read_csv \n",
    "df = pd.read_csv ('IMDB-Movie-Data.csv')\n",
    "\n",
    "# get reaquainted with the data by viewing the first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e8f7f6c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Rank', 'Title', 'Genre', 'Description', 'Director', 'Actors', 'Year',\n",
       "       'Runtime (Minutes)', 'Rating', 'Votes', 'Revenue (Millions)',\n",
       "       'Metascore'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list the column names\n",
    "df.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfd297f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Action,Adventure,Sci-Fi\n",
       "1      Adventure,Mystery,Sci-Fi\n",
       "2               Horror,Thriller\n",
       "3       Animation,Comedy,Family\n",
       "4      Action,Adventure,Fantasy\n",
       "                 ...           \n",
       "995         Crime,Drama,Mystery\n",
       "996                      Horror\n",
       "997         Drama,Music,Romance\n",
       "998            Adventure,Comedy\n",
       "999       Comedy,Family,Fantasy\n",
       "Name: Genre, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the contents of the 'Genre' column only\n",
    "df[\"Genre\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "219a2ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Action,Adventure,Sci-Fi    50\n",
       "Drama                      48\n",
       "Comedy,Drama,Romance       35\n",
       "Comedy                     32\n",
       "Drama,Romance              31\n",
       "                           ..\n",
       "Action,Comedy,Family        1\n",
       "Action,Crime,Fantasy        1\n",
       "Comedy,Mystery              1\n",
       "Adventure,Comedy,Horror     1\n",
       "Comedy,Family,Fantasy       1\n",
       "Name: Genre, Length: 207, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the .value_counts() method, explore the number of each genre type in the series\n",
    "genre_counts = df[\"Genre\"].value_counts()\n",
    "# assign the result to a variable called genre_counts\n",
    "genre_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4adcdd70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Action,Adventure,Sci-Fi    24.154589\n",
       "Drama                      23.188406\n",
       "Comedy,Drama,Romance       16.908213\n",
       "Comedy                     15.458937\n",
       "Drama,Romance              14.975845\n",
       "                             ...    \n",
       "Action,Comedy,Family        0.483092\n",
       "Action,Crime,Fantasy        0.483092\n",
       "Comedy,Mystery              0.483092\n",
       "Adventure,Comedy,Horror     0.483092\n",
       "Comedy,Family,Fantasy       0.483092\n",
       "Name: Genre, Length: 207, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the proportions of each genre by dividing your variable assigned above by the sum of \n",
    "# all of the unique genre values\n",
    "# assign this to the variable genre_proportions\n",
    "genre_proportions = genre_counts/207*100\n",
    "genre_proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd24f821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the length of results a above is too long to know if there are any null values present\n",
    "# we can run .isna() on the series and collect the sum to check if there are missing values\n",
    "# uncomment the line below to see the results\n",
    "# (data['Genre'].isna().sum())\n",
    "(df[\"Genre\"].isna().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77904b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Action,Adventure,Sci-Fi       24.154589\n",
       "Drama                         23.188406\n",
       "Comedy,Drama,Romance          16.908213\n",
       "Comedy                        15.458937\n",
       "Drama,Romance                 14.975845\n",
       "Animation,Adventure,Comedy    13.043478\n",
       "Action,Adventure,Fantasy      13.043478\n",
       "Comedy,Drama                  13.043478\n",
       "Comedy,Romance                12.560386\n",
       "Crime,Drama,Thriller          11.594203\n",
       "Crime,Drama,Mystery           11.111111\n",
       "Action,Adventure,Drama         8.695652\n",
       "Action,Crime,Drama             8.212560\n",
       "Horror,Thriller                7.729469\n",
       "Drama,Thriller                 7.246377\n",
       "Adventure,Family,Fantasy       6.763285\n",
       "Biography,Drama,History        6.763285\n",
       "Action,Adventure,Comedy        6.763285\n",
       "Biography,Drama                6.763285\n",
       "Action,Comedy,Crime            5.797101\n",
       "Name: Genre, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use .head() on genre_proportions to get a better look at the top 20 \n",
    "# remember: .head() can take a parameter to display more than 5 entries \n",
    "genre_proportions.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd9e484",
   "metadata": {},
   "source": [
    "### 2. Find the minimum, maximum, average, and standard deviation of revenue data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7042dfa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    872.000000\n",
       "mean      82.956376\n",
       "std      103.253540\n",
       "min        0.000000\n",
       "25%       13.270000\n",
       "50%       47.985000\n",
       "75%      113.715000\n",
       "max      936.630000\n",
       "Name: Revenue (Millions), dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use .describe() on the revenue column to see some summary statistics of the revenue data present\n",
    "df[\"Revenue (Millions)\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de436940",
   "metadata": {},
   "source": [
    "Do we have any missing revenue entries?\n",
    "Add a new code cell below this and use ``.isna()`` on the column to check.\n",
    "Ask yourself: does this affect the results of ``.describe()``?\n",
    "Hint: look at the count result above and compare that with the ``Length`` returned with ``.isna()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7aa5c09f-e5ba-4ba6-8f72-9e51643091de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Revenue (Millions)\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d50f3ba9",
   "metadata": {},
   "source": [
    "### 3. Make a plot distribution of the revenue data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd50cc47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Revenue (Millions)', ylabel='Count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use sns.histplot to get a birds eye view of the revenue distribution\n",
    "# Note: the refernce article plots Cholesterol data with ``distplot`` but we recieve \n",
    "# a deprecation warning so we've switched it with ``histplot``. The resulting graph is \n",
    "# virtually the same.\n",
    "sns.histplot(df[\"Revenue (Millions)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8afe2aaf",
   "metadata": {},
   "source": [
    "sns.histplot(df[\"Revenue (Millions)\"].head(20))Look back at your ``.describe()`` results above. Does this plotted distribution match the results of the 25%, 50%, and 75% percentile data?\n",
    "\n",
    "Create another code cell below this and make another histplot but with only the top 20 items in the revenue column. Run ``.describe()`` on the top 20 to again check the percentile distributions with what the graph displays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "02f0f0a5-1977-4fba-a47d-3c2a44f4a4c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Revenue (Millions)', ylabel='Count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df[\"Revenue (Millions)\"].head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c4fa18e3-15b0-42f0-96f8-cc739dc165de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7      True\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17    False\n",
       "18    False\n",
       "19    False\n",
       "Name: Revenue (Millions), dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Revenue (Millions)'].head(20).isna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8595c5",
   "metadata": {},
   "source": [
    "### 4. Find the % of movies with a higher than avergage Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "66b6460d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.723200000000003"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a variable called mean_rating.\n",
    "# assign mean_rating the result of running ``.mean()`` on the rating column.\n",
    "mean_rating = df[\"Rating\"].mean()\n",
    "mean_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7454148e",
   "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>Rank</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Description</th>\n",
       "      <th>Director</th>\n",
       "      <th>Actors</th>\n",
       "      <th>Year</th>\n",
       "      <th>Runtime (Minutes)</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Revenue (Millions)</th>\n",
       "      <th>Metascore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Guardians of the Galaxy</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>A group of intergalactic criminals are forced ...</td>\n",
       "      <td>James Gunn</td>\n",
       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
       "      <td>2014</td>\n",
       "      <td>121</td>\n",
       "      <td>8.1</td>\n",
       "      <td>757074</td>\n",
       "      <td>333.13</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Prometheus</td>\n",
       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
       "      <td>Following clues to the origin of mankind, a te...</td>\n",
       "      <td>Ridley Scott</td>\n",
       "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
       "      <td>2012</td>\n",
       "      <td>124</td>\n",
       "      <td>7.0</td>\n",
       "      <td>485820</td>\n",
       "      <td>126.46</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Split</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>Three girls are kidnapped by a man with a diag...</td>\n",
       "      <td>M. Night Shyamalan</td>\n",
       "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
       "      <td>2016</td>\n",
       "      <td>117</td>\n",
       "      <td>7.3</td>\n",
       "      <td>157606</td>\n",
       "      <td>138.12</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sing</td>\n",
       "      <td>Animation,Comedy,Family</td>\n",
       "      <td>In a city of humanoid animals, a hustling thea...</td>\n",
       "      <td>Christophe Lourdelet</td>\n",
       "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
       "      <td>2016</td>\n",
       "      <td>108</td>\n",
       "      <td>7.2</td>\n",
       "      <td>60545</td>\n",
       "      <td>270.32</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>A jazz pianist falls for an aspiring actress i...</td>\n",
       "      <td>Damien Chazelle</td>\n",
       "      <td>Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....</td>\n",
       "      <td>2016</td>\n",
       "      <td>128</td>\n",
       "      <td>8.3</td>\n",
       "      <td>258682</td>\n",
       "      <td>151.06</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>983</td>\n",
       "      <td>Across the Universe</td>\n",
       "      <td>Drama,Fantasy,Musical</td>\n",
       "      <td>The music of the Beatles and the Vietnam War f...</td>\n",
       "      <td>Julie Taymor</td>\n",
       "      <td>Evan Rachel Wood, Jim Sturgess, Joe Anderson, ...</td>\n",
       "      <td>2007</td>\n",
       "      <td>133</td>\n",
       "      <td>7.4</td>\n",
       "      <td>95172</td>\n",
       "      <td>24.34</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>985</td>\n",
       "      <td>Max</td>\n",
       "      <td>Adventure,Family</td>\n",
       "      <td>A Malinois dog that helped American Marines in...</td>\n",
       "      <td>Boaz Yakin</td>\n",
       "      <td>Thomas Haden Church, Josh Wiggins, Luke Kleint...</td>\n",
       "      <td>2015</td>\n",
       "      <td>111</td>\n",
       "      <td>6.8</td>\n",
       "      <td>21405</td>\n",
       "      <td>42.65</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>989</td>\n",
       "      <td>Martyrs</td>\n",
       "      <td>Horror</td>\n",
       "      <td>A young woman's quest for revenge against the ...</td>\n",
       "      <td>Pascal Laugier</td>\n",
       "      <td>Morjana Alaoui, Mylène Jampanoï, Catherine Bég...</td>\n",
       "      <td>2008</td>\n",
       "      <td>99</td>\n",
       "      <td>7.1</td>\n",
       "      <td>63785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>990</td>\n",
       "      <td>Selma</td>\n",
       "      <td>Biography,Drama,History</td>\n",
       "      <td>A chronicle of Martin Luther King's campaign t...</td>\n",
       "      <td>Ava DuVernay</td>\n",
       "      <td>David Oyelowo, Carmen Ejogo, Tim Roth, Lorrain...</td>\n",
       "      <td>2014</td>\n",
       "      <td>128</td>\n",
       "      <td>7.5</td>\n",
       "      <td>67637</td>\n",
       "      <td>52.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>992</td>\n",
       "      <td>Taare Zameen Par</td>\n",
       "      <td>Drama,Family,Music</td>\n",
       "      <td>An eight-year-old boy is thought to be a lazy ...</td>\n",
       "      <td>Aamir Khan</td>\n",
       "      <td>Darsheel Safary, Aamir Khan, Tanay Chheda, Sac...</td>\n",
       "      <td>2007</td>\n",
       "      <td>165</td>\n",
       "      <td>8.5</td>\n",
       "      <td>102697</td>\n",
       "      <td>1.20</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>513 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank                    Title                     Genre  \\\n",
       "0       1  Guardians of the Galaxy   Action,Adventure,Sci-Fi   \n",
       "1       2               Prometheus  Adventure,Mystery,Sci-Fi   \n",
       "2       3                    Split           Horror,Thriller   \n",
       "3       4                     Sing   Animation,Comedy,Family   \n",
       "6       7               La La Land        Comedy,Drama,Music   \n",
       "..    ...                      ...                       ...   \n",
       "982   983      Across the Universe     Drama,Fantasy,Musical   \n",
       "984   985                      Max          Adventure,Family   \n",
       "988   989                  Martyrs                    Horror   \n",
       "989   990                    Selma   Biography,Drama,History   \n",
       "991   992         Taare Zameen Par        Drama,Family,Music   \n",
       "\n",
       "                                           Description              Director  \\\n",
       "0    A group of intergalactic criminals are forced ...            James Gunn   \n",
       "1    Following clues to the origin of mankind, a te...          Ridley Scott   \n",
       "2    Three girls are kidnapped by a man with a diag...    M. Night Shyamalan   \n",
       "3    In a city of humanoid animals, a hustling thea...  Christophe Lourdelet   \n",
       "6    A jazz pianist falls for an aspiring actress i...       Damien Chazelle   \n",
       "..                                                 ...                   ...   \n",
       "982  The music of the Beatles and the Vietnam War f...          Julie Taymor   \n",
       "984  A Malinois dog that helped American Marines in...            Boaz Yakin   \n",
       "988  A young woman's quest for revenge against the ...        Pascal Laugier   \n",
       "989  A chronicle of Martin Luther King's campaign t...          Ava DuVernay   \n",
       "991  An eight-year-old boy is thought to be a lazy ...            Aamir Khan   \n",
       "\n",
       "                                                Actors  Year  \\\n",
       "0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014   \n",
       "1    Noomi Rapace, Logan Marshall-Green, Michael Fa...  2012   \n",
       "2    James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016   \n",
       "3    Matthew McConaughey,Reese Witherspoon, Seth Ma...  2016   \n",
       "6    Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....  2016   \n",
       "..                                                 ...   ...   \n",
       "982  Evan Rachel Wood, Jim Sturgess, Joe Anderson, ...  2007   \n",
       "984  Thomas Haden Church, Josh Wiggins, Luke Kleint...  2015   \n",
       "988  Morjana Alaoui, Mylène Jampanoï, Catherine Bég...  2008   \n",
       "989  David Oyelowo, Carmen Ejogo, Tim Roth, Lorrain...  2014   \n",
       "991  Darsheel Safary, Aamir Khan, Tanay Chheda, Sac...  2007   \n",
       "\n",
       "     Runtime (Minutes)  Rating   Votes  Revenue (Millions)  Metascore  \n",
       "0                  121     8.1  757074              333.13       76.0  \n",
       "1                  124     7.0  485820              126.46       65.0  \n",
       "2                  117     7.3  157606              138.12       62.0  \n",
       "3                  108     7.2   60545              270.32       59.0  \n",
       "6                  128     8.3  258682              151.06       93.0  \n",
       "..                 ...     ...     ...                 ...        ...  \n",
       "982                133     7.4   95172               24.34       56.0  \n",
       "984                111     6.8   21405               42.65       47.0  \n",
       "988                 99     7.1   63785                 NaN       89.0  \n",
       "989                128     7.5   67637               52.07        NaN  \n",
       "991                165     8.5  102697                1.20       42.0  \n",
       "\n",
       "[513 rows x 12 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the dataset where a movie's rating is higher than mean_rating. \n",
    "# Divide that by the length of the total dataset.\n",
    "\n",
    "df[df['Rating']> mean_rating]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c623861",
   "metadata": {},
   "source": [
    "### 5. Plot the rating data against genre to observe the difference in score in different movie types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fce70293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean        6.723200\n",
       "std         0.945429\n",
       "min         1.900000\n",
       "25%         6.200000\n",
       "50%         6.800000\n",
       "75%         7.400000\n",
       "max         9.000000\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run describe on the rating column\n",
    "df[\"Rating\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "212c395b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Genre', ylabel='Rating'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the rating data against genres\n",
    "# First, lets mirror what our reference article plots\n",
    "\n",
    "sns.boxplot(x = \"Genre\", y = \"Rating\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed28d392",
   "metadata": {},
   "source": [
    "That graph isn't very helpful! \n",
    "Create a new code cell below this and create the same plot, but this time for data used,\n",
    "use only the first 20 entries.\n",
    "Do you find that any easier to read and interpret?\n",
    "Next, create one more cell. Use the same axes and the top 20 items in the data, but plot using ``sns.scatterplot()``. Do you learn anything different between the boxplot and the scatterplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5885bffa-b542-47c8-9c57-4b739a202a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Genre', ylabel='Rating'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"Genre\", y = \"Rating\", data=df.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "280f3c48-e7c5-4c38-8b3a-ff24b3f967a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Genre', ylabel='Rating'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = \"Genre\", y = \"Rating\", data=df.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb688aa",
   "metadata": {},
   "source": [
    "### 6. Make a chart to show the number of films in each rating group each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "50a75081",
   "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>Rank</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Description</th>\n",
       "      <th>Director</th>\n",
       "      <th>Actors</th>\n",
       "      <th>Year</th>\n",
       "      <th>Runtime (Minutes)</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>...</th>\n",
       "      <th>(8, 9]</th>\n",
       "      <th>(6, 7]</th>\n",
       "      <th>(7, 8]</th>\n",
       "      <th>(5, 6]</th>\n",
       "      <th>(4, 5]</th>\n",
       "      <th>(2, 3]</th>\n",
       "      <th>(3, 4]</th>\n",
       "      <th>(1, 2]</th>\n",
       "      <th>rating_group</th>\n",
       "      <th>Rating Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Guardians of the Galaxy</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>A group of intergalactic criminals are forced ...</td>\n",
       "      <td>James Gunn</td>\n",
       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
       "      <td>2014</td>\n",
       "      <td>121</td>\n",
       "      <td>8.1</td>\n",
       "      <td>757074</td>\n",
       "      <td>...</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(2, 3]</td>\n",
       "      <td>(3, 4]</td>\n",
       "      <td>(1, 2]</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(8, 9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Prometheus</td>\n",
       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
       "      <td>Following clues to the origin of mankind, a te...</td>\n",
       "      <td>Ridley Scott</td>\n",
       "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
       "      <td>2012</td>\n",
       "      <td>124</td>\n",
       "      <td>7.0</td>\n",
       "      <td>485820</td>\n",
       "      <td>...</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(2, 3]</td>\n",
       "      <td>(3, 4]</td>\n",
       "      <td>(1, 2]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(6, 7]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Split</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>Three girls are kidnapped by a man with a diag...</td>\n",
       "      <td>M. Night Shyamalan</td>\n",
       "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
       "      <td>2016</td>\n",
       "      <td>117</td>\n",
       "      <td>7.3</td>\n",
       "      <td>157606</td>\n",
       "      <td>...</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(2, 3]</td>\n",
       "      <td>(3, 4]</td>\n",
       "      <td>(1, 2]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(7, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sing</td>\n",
       "      <td>Animation,Comedy,Family</td>\n",
       "      <td>In a city of humanoid animals, a hustling thea...</td>\n",
       "      <td>Christophe Lourdelet</td>\n",
       "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
       "      <td>2016</td>\n",
       "      <td>108</td>\n",
       "      <td>7.2</td>\n",
       "      <td>60545</td>\n",
       "      <td>...</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(2, 3]</td>\n",
       "      <td>(3, 4]</td>\n",
       "      <td>(1, 2]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(7, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Suicide Squad</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>A secret government agency recruits some of th...</td>\n",
       "      <td>David Ayer</td>\n",
       "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
       "      <td>2016</td>\n",
       "      <td>123</td>\n",
       "      <td>6.2</td>\n",
       "      <td>393727</td>\n",
       "      <td>...</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(2, 3]</td>\n",
       "      <td>(3, 4]</td>\n",
       "      <td>(1, 2]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(6, 7]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>Secret in Their Eyes</td>\n",
       "      <td>Crime,Drama,Mystery</td>\n",
       "      <td>A tight-knit team of rising investigators, alo...</td>\n",
       "      <td>Billy Ray</td>\n",
       "      <td>Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...</td>\n",
       "      <td>2015</td>\n",
       "      <td>111</td>\n",
       "      <td>6.2</td>\n",
       "      <td>27585</td>\n",
       "      <td>...</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(2, 3]</td>\n",
       "      <td>(3, 4]</td>\n",
       "      <td>(1, 2]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(6, 7]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>Hostel: Part II</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Three American college students studying abroa...</td>\n",
       "      <td>Eli Roth</td>\n",
       "      <td>Lauren German, Heather Matarazzo, Bijou Philli...</td>\n",
       "      <td>2007</td>\n",
       "      <td>94</td>\n",
       "      <td>5.5</td>\n",
       "      <td>73152</td>\n",
       "      <td>...</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(2, 3]</td>\n",
       "      <td>(3, 4]</td>\n",
       "      <td>(1, 2]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(5, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>Step Up 2: The Streets</td>\n",
       "      <td>Drama,Music,Romance</td>\n",
       "      <td>Romantic sparks occur between two dance studen...</td>\n",
       "      <td>Jon M. Chu</td>\n",
       "      <td>Robert Hoffman, Briana Evigan, Cassie Ventura,...</td>\n",
       "      <td>2008</td>\n",
       "      <td>98</td>\n",
       "      <td>6.2</td>\n",
       "      <td>70699</td>\n",
       "      <td>...</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(2, 3]</td>\n",
       "      <td>(3, 4]</td>\n",
       "      <td>(1, 2]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(6, 7]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>Search Party</td>\n",
       "      <td>Adventure,Comedy</td>\n",
       "      <td>A pair of friends embark on a mission to reuni...</td>\n",
       "      <td>Scot Armstrong</td>\n",
       "      <td>Adam Pally, T.J. Miller, Thomas Middleditch,Sh...</td>\n",
       "      <td>2014</td>\n",
       "      <td>93</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4881</td>\n",
       "      <td>...</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(2, 3]</td>\n",
       "      <td>(3, 4]</td>\n",
       "      <td>(1, 2]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(5, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>Nine Lives</td>\n",
       "      <td>Comedy,Family,Fantasy</td>\n",
       "      <td>A stuffy businessman finds himself trapped ins...</td>\n",
       "      <td>Barry Sonnenfeld</td>\n",
       "      <td>Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...</td>\n",
       "      <td>2016</td>\n",
       "      <td>87</td>\n",
       "      <td>5.3</td>\n",
       "      <td>12435</td>\n",
       "      <td>...</td>\n",
       "      <td>(8, 9]</td>\n",
       "      <td>(6, 7]</td>\n",
       "      <td>(7, 8]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(4, 5]</td>\n",
       "      <td>(2, 3]</td>\n",
       "      <td>(3, 4]</td>\n",
       "      <td>(1, 2]</td>\n",
       "      <td>(5, 6]</td>\n",
       "      <td>(5, 6]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank                    Title                     Genre  \\\n",
       "0       1  Guardians of the Galaxy   Action,Adventure,Sci-Fi   \n",
       "1       2               Prometheus  Adventure,Mystery,Sci-Fi   \n",
       "2       3                    Split           Horror,Thriller   \n",
       "3       4                     Sing   Animation,Comedy,Family   \n",
       "4       5            Suicide Squad  Action,Adventure,Fantasy   \n",
       "..    ...                      ...                       ...   \n",
       "995   996     Secret in Their Eyes       Crime,Drama,Mystery   \n",
       "996   997          Hostel: Part II                    Horror   \n",
       "997   998   Step Up 2: The Streets       Drama,Music,Romance   \n",
       "998   999             Search Party          Adventure,Comedy   \n",
       "999  1000               Nine Lives     Comedy,Family,Fantasy   \n",
       "\n",
       "                                           Description              Director  \\\n",
       "0    A group of intergalactic criminals are forced ...            James Gunn   \n",
       "1    Following clues to the origin of mankind, a te...          Ridley Scott   \n",
       "2    Three girls are kidnapped by a man with a diag...    M. Night Shyamalan   \n",
       "3    In a city of humanoid animals, a hustling thea...  Christophe Lourdelet   \n",
       "4    A secret government agency recruits some of th...            David Ayer   \n",
       "..                                                 ...                   ...   \n",
       "995  A tight-knit team of rising investigators, alo...             Billy Ray   \n",
       "996  Three American college students studying abroa...              Eli Roth   \n",
       "997  Romantic sparks occur between two dance studen...            Jon M. Chu   \n",
       "998  A pair of friends embark on a mission to reuni...        Scot Armstrong   \n",
       "999  A stuffy businessman finds himself trapped ins...      Barry Sonnenfeld   \n",
       "\n",
       "                                                Actors  Year  \\\n",
       "0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014   \n",
       "1    Noomi Rapace, Logan Marshall-Green, Michael Fa...  2012   \n",
       "2    James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016   \n",
       "3    Matthew McConaughey,Reese Witherspoon, Seth Ma...  2016   \n",
       "4    Will Smith, Jared Leto, Margot Robbie, Viola D...  2016   \n",
       "..                                                 ...   ...   \n",
       "995  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...  2015   \n",
       "996  Lauren German, Heather Matarazzo, Bijou Philli...  2007   \n",
       "997  Robert Hoffman, Briana Evigan, Cassie Ventura,...  2008   \n",
       "998  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014   \n",
       "999  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016   \n",
       "\n",
       "     Runtime (Minutes)  Rating   Votes  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]  \\\n",
       "0                  121     8.1  757074  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]   \n",
       "1                  124     7.0  485820  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]   \n",
       "2                  117     7.3  157606  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]   \n",
       "3                  108     7.2   60545  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]   \n",
       "4                  123     6.2  393727  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]   \n",
       "..                 ...     ...     ...  ...     ...     ...     ...     ...   \n",
       "995                111     6.2   27585  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]   \n",
       "996                 94     5.5   73152  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]   \n",
       "997                 98     6.2   70699  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]   \n",
       "998                 93     5.6    4881  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]   \n",
       "999                 87     5.3   12435  ...  (8, 9]  (6, 7]  (7, 8]  (5, 6]   \n",
       "\n",
       "     (4, 5]  (2, 3]  (3, 4]  (1, 2] rating_group Rating Group  \n",
       "0    (4, 5]  (2, 3]  (3, 4]  (1, 2]       (8, 9]       (8, 9]  \n",
       "1    (4, 5]  (2, 3]  (3, 4]  (1, 2]       (6, 7]       (6, 7]  \n",
       "2    (4, 5]  (2, 3]  (3, 4]  (1, 2]       (7, 8]       (7, 8]  \n",
       "3    (4, 5]  (2, 3]  (3, 4]  (1, 2]       (7, 8]       (7, 8]  \n",
       "4    (4, 5]  (2, 3]  (3, 4]  (1, 2]       (6, 7]       (6, 7]  \n",
       "..      ...     ...     ...     ...          ...          ...  \n",
       "995  (4, 5]  (2, 3]  (3, 4]  (1, 2]       (6, 7]       (6, 7]  \n",
       "996  (4, 5]  (2, 3]  (3, 4]  (1, 2]       (5, 6]       (5, 6]  \n",
       "997  (4, 5]  (2, 3]  (3, 4]  (1, 2]       (6, 7]       (6, 7]  \n",
       "998  (4, 5]  (2, 3]  (3, 4]  (1, 2]       (5, 6]       (5, 6]  \n",
       "999  (4, 5]  (2, 3]  (3, 4]  (1, 2]       (5, 6]       (5, 6]  \n",
       "\n",
       "[1000 rows x 22 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column called rating_group\n",
    "# Use to pd.cut() to bin(group) the rating_group column values by integer intervals 1-10\n",
    "rating_group = pd.cut(df.Rating, [1,2,3,4,5,6,7,8,9,10])\n",
    "df['Rating Group'] = rating_group\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "e21526ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a chart to show the number of films in each rating group per year\n",
    "sns.histplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f083278f",
   "metadata": {},
   "source": [
    "Each year, which rating range(s) is the most concentrated? Does this match your results from the box and scatter plots above?"
   ]
  }
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