Week5_DataFrame_Part2_Ex.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "# Without this line, plots won't show unless plt.show() is used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load the data\n",
    "- Use the function defined in last week's notebook to load the MovieLens-small dataset.\n",
    "- Use `pd.read_csv()` to load `ratings.csv`\n",
    "- Show basic information of the dataset:\n",
    "    - Shape\n",
    "    - Feature names\n",
    "    - Feature data types\n",
    "    - First 5 rows\n",
    "    - Statistics of numerical features (Use .describe())\n",
    "    - Frequencies of categorical features (Use .value_counts())\n",
    "    - Any missing values? (Use pd.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "import zipfile\n",
    "\n",
    "def get_movielens(file_path, file_name, delete_zip_file=False):\n",
    "    url = \"http://files.grouplens.org/datasets/movielens/\" + file_name\n",
    "    # Check if the file_path exists; if not, create this path.\n",
    "    if not os.path.exists(file_path):\n",
    "        os.mkdir(file_path)\n",
    "    if not os.path.exists(file_path + file_name):\n",
    "        urllib.request.urlretrieve(url, file_path + file_name)\n",
    "        print(\"File\", file_name, \"downloaded.\")\n",
    "    with zipfile.ZipFile(file_path + file_name, \"r\") as f:\n",
    "        f.extractall(file_path)\n",
    "        print(\"Files extracted:\")\n",
    "        print(f.printdir())\n",
    "    if delete_zip_file:\n",
    "        os.remove(file_path + file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files extracted:\n",
      "File Name                                             Modified             Size\n",
      "ml-latest-small/                               2018-09-26 15:50:12            0\n",
      "ml-latest-small/links.csv                      2018-09-26 15:50:10       197979\n",
      "ml-latest-small/tags.csv                       2018-09-26 15:49:40       118660\n",
      "ml-latest-small/ratings.csv                    2018-09-26 15:49:38      2483723\n",
      "ml-latest-small/README.txt                     2018-09-26 15:50:12         8342\n",
      "ml-latest-small/movies.csv                     2018-09-26 15:49:56       494431\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "file_path = \"Data/\"\n",
    "zip_file = \"ml-latest-small.zip\"\n",
    "get_movielens(file_path, zip_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df = pd.read_csv(\"Data/ml-latest-small/ratings.csv\", delimiter=\",\")\n",
    "ratings_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (100836, 4)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of the data frame\n",
    "print(\"Shape:\", ratings_df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'rating', 'timestamp'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the feature names\n",
    "ratings_df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      " userId         int64\n",
      "movieId        int64\n",
      "rating       float64\n",
      "timestamp      int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Print the data types of features\n",
    "print(\"type\\n\", ratings_df.dtypes)\n",
    "\n",
    "# Change the data type of userId to float\n",
    "# ratings_df[\"userId\"] = ratings_df[\"userId\"].astype(float)\n",
    "# print(\"type\\n\", ratings_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>DT</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "      <td>2000-07-30 14:45:03</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "      <td>2000-07-30 14:20:47</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "      <td>2000-07-30 14:37:04</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "      <td>2000-07-30 15:03:35</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "      <td>2000-07-30 14:48:51</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp                  DT  Year\n",
       "0       1        1     4.0  964982703 2000-07-30 14:45:03  2000\n",
       "1       1        3     4.0  964981247 2000-07-30 14:20:47  2000\n",
       "2       1        6     4.0  964982224 2000-07-30 14:37:04  2000\n",
       "3       1       47     5.0  964983815 2000-07-30 15:03:35  2000\n",
       "4       1       50     5.0  964982931 2000-07-30 14:48:51  2000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a \"Year\" column that represents the year of the rating (Convert datastamp to datetime)\n",
    "from datetime import datetime\n",
    "ratings_df['DT'] = ratings_df['timestamp'].apply(datetime.fromtimestamp)\n",
    "def datetime_to_year(x):\n",
    "    return x.year\n",
    "ratings_df['Year'] = ratings_df['DT'].apply(datetime_to_year)\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>100836.000000</td>\n",
       "      <td>100836.000000</td>\n",
       "      <td>100836.000000</td>\n",
       "      <td>1.008360e+05</td>\n",
       "      <td>100836.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>326.127564</td>\n",
       "      <td>19435.295718</td>\n",
       "      <td>3.501557</td>\n",
       "      <td>1.205946e+09</td>\n",
       "      <td>2007.722619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>182.618491</td>\n",
       "      <td>35530.987199</td>\n",
       "      <td>1.042529</td>\n",
       "      <td>2.162610e+08</td>\n",
       "      <td>6.890284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.281246e+08</td>\n",
       "      <td>1996.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.019124e+09</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>2991.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.186087e+09</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>8122.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.435994e+09</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>193609.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.537799e+09</td>\n",
       "      <td>2018.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              userId        movieId         rating     timestamp  \\\n",
       "count  100836.000000  100836.000000  100836.000000  1.008360e+05   \n",
       "mean      326.127564   19435.295718       3.501557  1.205946e+09   \n",
       "std       182.618491   35530.987199       1.042529  2.162610e+08   \n",
       "min         1.000000       1.000000       0.500000  8.281246e+08   \n",
       "25%       177.000000    1199.000000       3.000000  1.019124e+09   \n",
       "50%       325.000000    2991.000000       3.500000  1.186087e+09   \n",
       "75%       477.000000    8122.000000       4.000000  1.435994e+09   \n",
       "max       610.000000  193609.000000       5.000000  1.537799e+09   \n",
       "\n",
       "                Year  \n",
       "count  100836.000000  \n",
       "mean     2007.722619  \n",
       "std         6.890284  \n",
       "min      1996.000000  \n",
       "25%      2002.000000  \n",
       "50%      2007.000000  \n",
       "75%      2015.000000  \n",
       "max      2018.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .describe() to show the statistics of numerical features\n",
    "\n",
    "ratings_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0    13211\n",
      "4.5     8551\n",
      "4.0    26818\n",
      "3.5    13136\n",
      "3.0    20047\n",
      "2.5     5550\n",
      "2.0     7551\n",
      "1.5     1791\n",
      "1.0     2811\n",
      "0.5     1370\n",
      "Name: rating, dtype: int64\n",
      "2018     6413\n",
      "2017     8203\n",
      "2016     6703\n",
      "2015     6616\n",
      "2014     1439\n",
      "2013     1664\n",
      "2012     4656\n",
      "2011     1690\n",
      "2010     2301\n",
      "2009     4134\n",
      "2008     4375\n",
      "2007     7111\n",
      "2006     4062\n",
      "2005     5813\n",
      "2004     3279\n",
      "2003     4014\n",
      "2002     3478\n",
      "2001     3922\n",
      "2000    10061\n",
      "1999     2439\n",
      "1998      507\n",
      "1997     1916\n",
      "1996     6040\n",
      "Name: Year, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Use .value_counts() to show the frequencies of ratings and years\n",
    "\n",
    "# ratings_df[\"rating\"].value_counts() # The frequencies will follow a decreasing order\n",
    "\n",
    "# Show frequencies by the decreasing order of ratings\n",
    "freqs = ratings_df[\"rating\"].value_counts()\n",
    "freqs = freqs.sort_index(ascending=False)\n",
    "print(freqs)\n",
    "\n",
    "freqs = ratings_df['Year'].value_counts()\n",
    "freqs = freqs.sort_index(ascending=False)\n",
    "print(freqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId       0\n",
       "movieId      0\n",
       "rating       0\n",
       "timestamp    0\n",
       "DT           0\n",
       "Year         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use pd.isnull() to find the number of missing values for each column\n",
    "np.sum(ratings_df.isnull(), axis=0)"
   ]
  },
  {
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>DT</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "      <td>2000-07-30 14:45:03</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "      <td>2000-07-30 14:20:47</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "      <td>2000-07-30 14:37:04</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "      <td>2000-07-30 15:03:35</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "      <td>2000-07-30 14:48:51</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp                  DT  Year\n",
       "0       1        1     4.0  964982703 2000-07-30 14:45:03  2000\n",
       "1       1        3     4.0  964981247 2000-07-30 14:20:47  2000\n",
       "2       1        6     4.0  964982224 2000-07-30 14:37:04  2000\n",
       "3       1       47     5.0  964983815 2000-07-30 15:03:35  2000\n",
       "4       1       50     5.0  964982931 2000-07-30 14:48:51  2000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Analyzing the rating data\n",
    "\n",
    "We will continue the data analysis from last week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. The distribution of the ratings\n",
    "- Show the frequency of each rating (0.5 - 5.0)\n",
    "- Show the rating frequencies in 2018 (First create a sub DataFrame containing only 2018 ratings)\n",
    "- Show the rating frequencies in 2003\n",
    "- Compare ratings distributions in 2003 and 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall rating frequencies:\n",
      " 4.0    26818\n",
      "3.0    20047\n",
      "5.0    13211\n",
      "3.5    13136\n",
      "4.5     8551\n",
      "2.0     7551\n",
      "2.5     5550\n",
      "1.0     2811\n",
      "1.5     1791\n",
      "0.5     1370\n",
      "Name: rating, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f811e60e2b0>]"
      ]
     },
     "execution_count": 13,
     "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 value_counts() to find the rating frequencies\n",
    "rating_frequencies = ratings_df['rating'].value_counts()\n",
    "print(\"Overall rating frequencies:\\n\", rating_frequencies)\n",
    "# print(\"Overall rating frequencies:\\n\", rating_frequencies.sort_index())\n",
    "plt.plot(rating_frequencies.index,\n",
    "         rating_frequencies.values,\n",
    "         'b.')\n",
    "# plt.bar(rating_frequencies.index,\n",
    "#         rating_frequencies.values,\n",
    "#         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "100831    False\n",
       "100832    False\n",
       "100833    False\n",
       "100834    False\n",
       "100835    False\n",
       "Name: Year, Length: 100836, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ratings_df['Year'] == 2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the sub data frame: (6413, 6)\n",
      "5.0     763\n",
      "4.5     770\n",
      "4.0    1345\n",
      "3.5     971\n",
      "3.0     857\n",
      "2.5     556\n",
      "2.0     521\n",
      "1.5     268\n",
      "1.0     180\n",
      "0.5     182\n",
      "Name: rating, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Find the slice that correspond to 2018 ratings\n",
    "index_2018 = (ratings_df['Year'] == 2018)\n",
    "# print(index_2018)\n",
    "\n",
    "# Extract all 2018 ratings\n",
    "ratings_2018 = ratings_df[index_2018]\n",
    "ratings_2018.head()\n",
    "print(\"Shape of the sub data frame:\", ratings_2018.shape)\n",
    "rating_freq_2018 = ratings_2018['rating'].value_counts()\n",
    "print(rating_freq_2018.sort_index(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Relative Frequency')"
      ]
     },
     "execution_count": 16,
     "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": [
    "# What are the rating frequencies in 2003?\n",
    "rating_freq_2003 = ratings_df[ratings_df['Year'] == 2003]['rating'].value_counts()\n",
    "\n",
    "# Plot frequency comparison\n",
    "plt.plot(rating_freq_2003 / np.sum(rating_freq_2003), 'b.', label=\"2003\")\n",
    "plt.plot(rating_freq_2018 / np.sum(rating_freq_2018), 'g^', label=\"2018\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Rating\")\n",
    "plt.ylabel(\"Relative Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** The ratings distribution in 2003 is quite similar to the distribution in 2018. Most ratings are between 3.0 and 4.0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. How consistant are the average ratings over time?\n",
    "- Find a list of years during which ratings are given.\n",
    "- For each year, extract the ratings given in that year, and then calculate the average rating\n",
    "- Plot the average rating against the year number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009\n",
      " 2010 2011 2012 2013 2014 2015 2016 2017 2018]\n"
     ]
    }
   ],
   "source": [
    "# Get the list of all years in the dataset\n",
    "# set(ratings_df['Year'])\n",
    "years = np.sort(ratings_df['Year'].unique())\n",
    "print(years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.5359271523178806, 3.7275574112734864, 3.4358974358974357, 3.772447724477245, 3.39250571513766, 3.5122386537480876, 3.606958021851639, 3.4993771798704536, 3.506099420555047, 3.4315327713745054, 3.488060068931561, 3.3088876388693573, 3.6589714285714288, 3.3886066763425253, 3.4634941329856583, 3.7840236686390534, 3.6413230240549828, 3.8774038461538463, 3.5107713690062545, 3.513830108827086, 3.686334477099806, 3.3487748384737293, 3.3891314517386557]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the average rating for each year\n",
    "all_avg_ratings = []\n",
    "for year in years:\n",
    "    index_year = (ratings_df['Year'] == year)\n",
    "    ratings_year = ratings_df[index_year]\n",
    "    avg_rating = np.mean(ratings_year['rating'])\n",
    "    all_avg_ratings.append(avg_rating)\n",
    "print(all_avg_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 5)"
      ]
     },
     "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": [
    "# Plot\n",
    "plt.plot(years, all_avg_ratings, 'bs--')\n",
    "plt.ylim([0, 5]) # set the upper and lower limits to the y-axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** No significant rating inflations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. How fast does the movie library grow?\n",
    "- For each year, extract all ratings given **in that year or before**.\n",
    "- Find the number of unique movies in the subset.\n",
    "- Plot the number of movies against the year number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[607, 859, 970, 1497, 2578, 3005, 3370, 3894, 4395, 4867, 5187, 5712, 5972, 6269, 6469, 6654, 6973, 7122, 7337, 7789, 8283, 8832, 9724]\n"
     ]
    }
   ],
   "source": [
    "all_num_unique_movies = []\n",
    "for year in years:\n",
    "    index_ratings_year = (ratings_df['Year'] <= year)\n",
    "    ratings_year = ratings_df[index_ratings_year] \n",
    "    num_unique_movies_year = len(np.unique(ratings_year['movieId'])) # OR\n",
    "#     num_unique_movies_year = len(set(ratings_year['movieId']))\n",
    "    all_num_unique_movies.append(num_unique_movies_year)\n",
    "print(all_num_unique_movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f811dcd16d8>]"
      ]
     },
     "execution_count": 21,
     "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 number of movies vs. year number\n",
    "plt.plot(years, all_num_unique_movies, 'g^-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Discussion:** How does the number of movies vary?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. How fast does the user base grow?\n",
    "- For each year, extract all ratings given in that year or before.\n",
    "- Find the number of unique users in the subset.\n",
    "- Plot the number of users against the year number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[97, 124, 133, 159, 207, 227, 241, 261, 272, 296, 319, 341, 363, 384, 399, 421, 446, 459, 467, 514, 546, 581, 610]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f811dca3d68>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAH8RJREFUeJzt3Xu81FW9//HXR8VLdgEFvICIJYe8o+0MIzUj7tjmlJppcQmlkx7D2zF+ZqlZaXlU5FgaqIT3C+mRFC/8uGReMDeGhKGy8UAQJJRI6t5x2XzOH2vNYZg9s/ewmdkz85338/GYx3xnfb8ze83X7YfvXt/P+ixzd0REJLl2KXUHRESkuBToRUQSToFeRCThFOhFRBJOgV5EJOEU6EVEEk6BXkQk4RToRUQSToFeRCThdit1BwA6d+7sPXv2LHU3REQqyoIFC/7m7l1aO64sAn3Pnj2pq6srdTdERCqKma3I5zgN3YiIJJwCvYhIwinQi4gknAK9iEjCKdCLiJTK3LnQs2d4LiIFehGRUpg7F4YPhxUrwnMRg70CvYhIe0sF+YaG8LqhoajBXoFeRKQ9ZQb5lCIG+7wCvZl1NLPpZva6mS0xsxPMbB8zm2VmS+Nzp3ismdkkM6s3s0VmdlzBey0iUqnGjGke5FMaGsL+Asv3iv5m4Cl3/yRwDLAEmADMdvdewOz4GmAI0Cs+xgG3FrTHIiKV7LvfBbPs+z70IZg6teA/stVAb2YfBU4C7gBw903u/i5QC0yLh00DRsTtWuAuD+YDHc3sgIL3XESk0kybBhddBF27wp57br/vQx+Cxx+HU04p+I/N54r+48A6YKqZ/cHMbjezvYH93H0NQHzuGo/vBqxMe/+q2CYiUp22bAkBfvRo+OxnYfFimDkzBHcoapCH/AL9bsBxwK3ufizwAduGabLJ9jeJNzvIbJyZ1ZlZ3bp16/LqrIhIxfn732HQIJg4EcaPh6efhs6dQ1B//HE4+OCiBnnIr3rlKmCVu78UX08nBPq3zewAd18Th2bWph1/UNr7uwOrMz/U3ScDkwFqamqa/UMgIlLxFi2C2lpYsyaMvY8evf3+U06B5cuL3o1Wr+jd/a/ASjPrHZv6A38CZgCjYtso4LG4PQMYGbNv+gIbUkM8IiJV4+GH4YQTYNMmePbZ5kG+HeWbdXMBcK+ZLQL6AD8BrgMGmNlSYEB8DTATeAuoB6YA5xW0xyIi5Sa9lEFTE3zve3DGGXDMMVBXB8cfX9Lu5bXwiLsvBGqy7Oqf5VgHzt/JfomIVIb0CVDDhsHRR8NLL8G558J//RfssUepe6iZsSIibZY5y7WxMQT58ePhl78siyAPCvQiIm2Tq5QBwJQpMG9eu3cpFwV6EZG2KEEpg7ZSoBcRaYupU2H33bPvK1Ipg7ZSoBcRaYulS0Pq5C4ZYbTIs1zbQoFeRGRH3XILfOtbMHRou5YyaCsFehGRHXHjjXDBBWHG6yOPhPIG7VTKoK3yyqMXERHg2mvh8svh9NPh3nuhQ4fQ3k6lDNpKV/QiIq1xh6uvDkH+rLPgvvu2BfkKoCt6EZGWuMMVV8BPfhLq1dx+O+y6a6l7tUMU6EVEcnGH//gPuOGGUNLgttuaZ9lUgMrrsYhIe3APpQxuuAHOP79igzwo0IuIbJOqQjl7Nnz726Eo2cUXh+cKDfKgoRsRkSC9ds2gQaHc8IQJYWw+12LeFaJy/4kSESmUzAJlTU0hq2bAgIoP8qBALyLVLlcVys2b4dRTw/4Kp0AvItWtgqpQtpUCvYhUt5/9LPfwTJlVoWwrBXoRqV6LF8Mll8CeezZfDapMC5S1hQK9iFSn3/4WTjwx3Hh98UV48smyr0LZVgr0IlJ9Hn4YBg6E/fcPQf6YY0JQL/MqlG2lPHoRqS6TJsGFF8JnPwszZsA++2zbV+ZVKNtKV/QiUh22boXLLgtlDUaMgFmztg/yCaYrehFJvk2bQprkfffBeeeFq/oKq0C5M3RFLyLJk6pZM3cu/OMfYcm/++4L5QxuuaWqgjzoil5EkiZ9puuwYXDggbBiBfzqVzBqVKl7VxIK9CKSHJnlDBobYdky+OlPqzbIg4ZuRCQpctWsgbAMYAJq1rSVAr2IJEMV1KxpKwV6EUmGqVO3zWzNlJCaNW2VV6A3s+Vm9kczW2hmdbFtHzObZWZL43On2G5mNsnM6s1skZkdV8wvICIChMlOI0Y0b09YOYO22JEr+lPcvY+718TXE4DZ7t4LmB1fAwwBesXHOODWQnVWRCSnJ54IKZTDhiW2Zk1b7czQTS0wLW5PA0aktd/lwXygo5kdsBM/R0SkZcuXwze+AX36wPTpia1Z01b5plc68IyZOfBLd58M7OfuawDcfY2ZdY3HdgNWpr13VWxbU6A+i4hss3EjnHFGKHEwfXooOZzQmjVtlW+g7+fuq2Mwn2Vmr7dwbLYK/t7sILNxhKEdevTokWc3REQyXHIJvPwyPPoofOITpe5NWcpr6MbdV8fntcCjwPHA26khmfi8Nh6+Cjgo7e3dgdVZPnOyu9e4e02XLl3a/g1EpHo98AD8/Och2Ge7EStAHoHezPY2s4+ktoGBwGJgBpCaajYKeCxuzwBGxuybvsCG1BCPiEjBLFkC55wD/frBtdeWujdlLZ+hm/2ARy2sqbgbcJ+7P2VmLwMPmdlY4M/A6fH4mcBQoB5oAKp3loKIFMcHH8Bpp4WsmgcfhA4dSt2jstZqoHf3t4BjsrT/Heifpd2B8wvSOxGRTO7wb/8WruifeQa6dSt1j8qeipqJSGWZMgXuuSfUr/niF0vdm4qgEggiUjkWLIALLoBBg+CKK0rdm4qhQC8ilWH9ejj9dOjaNVzR76LwlS+dKREpX6mVoubMgdGjYeVKeOgh6Ny51D2rKBqjF5HylF5ffvBg2LwZJk6EE04odc8qjq7oRaT8ZC4isnlzWOf1qKNK268KpUAvIsWXvlh3PsdmWymqqQlOPbWqV4pqKwV6ESmuVOBesSI8ZwvU7vDWW3DvvVBbq5WiCkxj9CJSPJlX5w0N4fX06bD33vDii+Exfz68/XY4Zs89Q0bN1q3NP6/KV4pqKwV6ESmOXEMwDQ0wdOi214ceCgMHhpusJ5wARx4Jv/td8/dqEZE2U6AXkeIYPTr3EAxAly7w2mvhOdMpp4Sgngr2CvI7RWP0IrJjWrqx6h5qw3/nO7BhQ+7PSBUja6lEeSrYa6WonaYrehHJX/pwzPDh2wLw8uVhtuo998Abb8Aee8CXvgRHHx1KCLd1CEYrRRWEAr2I5CfbjdXBg+Ff/gUWLw5tJ58Ml14aSgh37Bja+vXTEEyJaehGRFqX68bqpk1hnH3s2HDlPW9eWAwkFeRBQzBlwEL5+NKqqanxurq6UndDRHLp2TPkwedy8MEaYikBM1vg7jWtHacrehFp3be+lXufctvLngK9iOT2wQdhNafLL4dDDgmTmdJpzL0iKNCLSHYvvQR9+sDkyeEG65IlMHNmCO6gIF9BFOhFZHubN8OVV4ZsmU2bQi34668PKZO6sVqRlF4pItu88QZ8/etQVwcjR8KkSfCxj21/jHLbK46u6EWqVfoMV3f4xS/g2GNDFcmHH4Zp05oHealIuqIXqUbpefHDhsERR4Sr+EGD4M474cADS91DKSBd0YtUm8zJT42NIciPHw9PPqkgn0AK9CLVJNcMV4ApU8LMVkkcBXqRatJS6WCt3pRYCvQi1WLhwrCqUy6a4ZpYCvQiSffee3DRRfCpT8Hf/gYTJmyb9JSiyU+JpkAvklTuIU3yk5+Em2+Gc88NefLXXhuCuma4Vg0FepEkqq+HIUPgjDOga9ewAPdtt0GnTmG/ZrhWlbwDvZntamZ/MLPH4+tDzOwlM1tqZg+a2e6xfY/4uj7u71mcrosIsP3Ep3/+E374w7DA9gsvhCv5l1+Gz3ym+ftSM1wV5BNvR67oxwNL0l7/FLjJ3XsB64GxsX0ssN7dDwVuiseJSDGk0iVXrAhX8IceGurUjBgBr78e1m7dTfMiq11egd7MugPDgNvjawO+AEyPh0wDRsTt2viauL9/PF5ECikzJ37jRli9Gn72M3jgAU18kv+T7xX9ROAyYGt8vS/wrrtvia9XAd3idjdgJUDcvyEevx0zG2dmdWZWt27dujZ2X6RKzZkTruAzc+Ld4aqrwj8CIlGrgd7MhgNr3X1BenOWQz2Pfdsa3Ce7e42713Tp0iWvzopUvQ8+CPXhBw8OV/DZaOKTZMjnir4f8CUzWw48QBiymQh0NLPU4F93YHXcXgUcBBD3fwx4p4B9Fkmm9Juqmd58Ey68ELp1C8v69egBu++e/XM08UkytBro3f3/uXt3d+8JnAnMcfezgbnAafGwUcBjcXtGfE3cP8fLYQVykXKWflN1+PDwuqkJHnsMBg6E3r1DGeGhQ+G552DpUnjqKU18krzszO347wIPmNmPgD8Ad8T2O4C7zayecCV/5s51USThMm+qNjSEcsGdOsHatdC9O1xzTZjwtN9+296XyoVPvVdBXnKwcrjYrqmp8bq6ulJ3Q6T9tVRNcpdd4Ac/gO99r+UUyblzw5j81KkK8lXGzBa4e02rxynQi5RQz55huCaXgw/Wsn2SU76BXiUQREply5YwsSkX3VSVAlGgFymF3/42VJO8+Wbo0wf23HP7/RpvlwJSoBdpTytXwplnwuc/D+++C9OnwyuvwMyZqiYpRaNAL1Jo2fLhGxtD5kzv3iFl8qqrYMkS+MpXwEzVJKWoVO1IpJDSs2iGD4ff/AY2bICLLw43VU8/Ha6/PgT0TKlqkiIFpkAvUijZ8uEHDICtW0PZ4DlzdKUuJaGhG5FCyJUPv3UrdOgAN92kIC8lo0AvUghjxmSf9ASweTOcc0779kckjQK9SCFcfXWYyZqN8uGlxBToRXZGYyNccUWoQ7PXXs0rSipVUsqAAr1IW82cCUccAT/+cciNX7Zs+4qSCvJSJhToRXbUypXw5S/DsGFhRuu8eXDXXaGypPLhpQwp0IvkkjnxafNm+M//hMMOC1fu114LCxfCySdv/75UPryCvJQJ5dGLZJM58em668ISfosXw6mnwqRJ4R8BkQqgQC+SKdvEp+98B7p2hf/+b6itLW3/RHaQhm5E0rW0EMh778FHP9r+fRLZSQr0IulamvjU2Bj2i1QYBXqRlPfeg09/Ovd+TXySCqVAL+Ie6sIfdlh4Hj48TH5Kp5x4qWAK9FLdli2DoUND+eDOneHFF0Np4See0MQnSQwFeqkOmTnxGzeGhUCOPBKefx4mToS6OujbN+zXxCdJEKVXSvJl5sT/8IchJ/7NN+GMM+DGG6Fbt+bv00IgkhAK9JJs2XLiL70UDjwQnn4aBg4sbf9E2oGGbiS5WsqJf/fdsCCISBVQoJfkaiknvqFBOfFSNRToJZk2boSTTsq9XznxUkUU6CV5nnsO+vSBu+8Oi3MrJ16qnAK9JMeGDXDeeXDiiaFcwVNPwTPPKCdeql6rgd7M9jSz35vZq2b2mpldHdsPMbOXzGypmT1oZrvH9j3i6/q4v2dxv4IIMGNGWO3pl7+Eiy4K5YQHDQr7lBMvVS6fK/qNwBfc/RigDzDYzPoCPwVucvdewHpgbDx+LLDe3Q8FborHiRRG5sSnv/415MLX1sK++8L8+SEv/sMf3v59WgxEqlirgd6D9+PLDvHhwBeA6bF9GjAibtfG18T9/c3MCtZjqV6pdMkVK8LzZZeF+jQzZoR1W+vqWi5KJlKl8hqjN7NdzWwhsBaYBSwD3nX3LfGQVUBqamE3YCVA3L8B2LeQnZYqlG3i0/XXQ48e8OqrcPnlyosXySGvQO/uTe7eB+gOHA8clu2w+Jzt6t0zG8xsnJnVmVndunXr8u2vVKOWJj7V18Pq1e3fJ5EKskNZN+7+LjAP6At0NLNUCYXuQOr/tlXAQQBx/8eAd7J81mR3r3H3mi5durSt91IdNPFJZKfkk3XTxcw6xu29gC8CS4C5wGnxsFHAY3F7RnxN3D/H3Ztd0YvkpalpW/ZMNpr4JNKqfK7oDwDmmtki4GVglrs/DnwXuNjM6glj8HfE4+8A9o3tFwMTCt9tqQqvvw79+oVKkyeeqIlPIm3UavVKd18EHJul/S3CeH1m+z+B0wvSO6lOTU0hRfL734e994b774evfhXmzds2Vq8gL5I3zYyV8vLGG+Hq/bLLYMgQeO01OPNMMNPEJ5E2UqCX0sic+NTUBDfcEGrUvP463HsvPPII7L//9u/TxCeRHaaFR6T9Za74dOutoXTBCy+EGa633dY8wItImynQS/vKNvFp1KhQsuDuu+Hss8MwjYgUjIZupP20NPGpqSms26ogL1JwCvTSPrZsga99LffEp8ZGTXwSKRIFetk5mTdV073/frihOmpUGHN/++3cn6OJTyJFozF6abvMm6qPPw69e8NvfhMqSs6eHZb069gRhg0LN1r32ivkxKdf2SsnXqSoFOilbbLdVP3iF2Hr1vD6kEPg298Owb1fv+0rSz7+uCY+ibQjBXrZcbluqm7dGgL6bbeF8fZcN1ZTE5/GjAnDNQryIkVl5VBvrKamxuvq6krdDclXt24tlwY++OAwqUlEisrMFrh7TWvH6Was5O+dd8J6rLqpKlJRFOildRs3hiJjn/gETJoUhlwefjgE9XQabxcpSwr0EmRLk3QPAf3ww+GSS+D44+EPf4ApU+C000JQTwV7BXmRsqVAL80X3Z47F+bPh899Ds44IwTxp56Cp5+Go4/e9j5VkxSpCMq6qXbZ0iQHDAglCfbfP1y9jx4Nu+X4VUlVkxSRsqVAX81ypUk2NYU0yTvugKFDS9M3ESkYDd1Us9Gjc9ee2bwZzjuvXbsjIsWhK/pqtHw53Hln7iAPSpMUSRBd0SdNriJjGzfCQw/BwIHw8Y/Dj34ENTVw1VVKkxRJOF3RJ0m2ImNdu4ax9rvugr//HXr0gCuvDLnwPXqE9510kmrPiCSYAn1StFRkrEMHGDECzjkH+veHXXfd/r2qPSOSaAr0SdBakbEHH4R//deWP0NpkiKJpTH6JGgte+aii9q1OyJSXhToK90zzzQfikmn7BmRqqdAX6kWL4YhQ2DQoPD6Bz9Q9oyIZKVAX2n++lcYNw6OOSbUo7nhBliyBK6+WkXGRCQrBfpylp4T39AA11wDhx4ahmIuuADq6+Hii2GPPcLxKjImIlko66ZcpWfSDB4MH/lIyIP/8pfhuuugV6/s71P2jIhkUKAvR5npkps2wfr1MHEijB9f2r6JSMVpdejGzA4ys7lmtsTMXjOz8bF9HzObZWZL43On2G5mNsnM6s1skZkdV+wvkSgt5cRffnnz0gYiIq3IZ4x+C3CJux8G9AXON7PDgQnAbHfvBcyOrwGGAL3iYxxwa8F7nWRjxuTOiW9oCPtFRHZAq4He3de4+ytx+z1gCdANqAWmxcOmASPidi1wlwfzgY5mdkDBe55UU6bALjn+sygnXkTaYIeybsysJ3As8BKwn7uvgfCPAdA1HtYNWJn2tlWxLfOzxplZnZnVrVu3bsd7nlRz5oRhmt13375d6ZIi0kZ5B3oz+zDwa+BCd/9HS4dmafNmDe6T3b3G3Wu6dOmSbzeS7cknQ0bNueeGNVqVEy8iBZBXoDezDoQgf6+7PxKb304NycTntbF9FXBQ2tu7A6sL090EW7UKRo6Eo46Cm29WTryIFEw+WTcG3AEscfcb03bNAEbF7VHAY2ntI2P2TV9gQ2qIR3LYsgW+9jVobISHH4a99grtqZx4BXkR2Qn55NH3A74B/NHMFsa2y4HrgIfMbCzwZ+D0uG8mMBSoBxoApYm05sor4bnn4J57oHfvUvdGRBKm1UDv7s+RfdwdoH+W4x04fyf7VT2efhp+8hMYOxbOPrvUvRGRBFKtm1L6y1/g61+HI4+ESZNK3RsRSSgF+lLZsgXOOitMgnrooeYlhkVECkS1bkrl6qvh2Wdh2jQ47LBS90ZEEkxX9KUwaxb8+MehnMHIkaXujYgknAJ9e1u9Otx0PfxwuOWWUvdGRKqAAn17amoKQf6DDzQuLyLtRoG+PaRWihozBubNg1/8IlzRi4i0A92MLbb0+vJ33w0DB8KoUa2/T0SkQHRFX0zZFhH53e+0eIiItCsF+mLJtVJUY2NoV7AXkXaiQF8sWilKRMqEAn0xbN4M/frl3q+VokSkHSnQF9r//A+cdBLcdx8MGbKt5HCKFhERkXamQF9I998PffrAn/4EDzwAM2fCE09opSgRKSkF+kJ4//0w5n7WWXDEEfDqq/DVr4Z9WilKREpMefQ765VX4Mwzob4errgiLCKyW8ZpTa0UJSJSArqi3xGpGa5z58LWrXDDDdC3b8iimTMHrrmmeZAXESkxRaV8pefFDxsWhmjq6mDECLj9dth331L3UEQkK13R5yNz8lNjYwjyF14IjzyiIC8iZU2BvjVz54Yr+GyTnyZPDkXKRETKWHUG+vSx9mxWrgwFyMaODUXIGhuzH6cZriJSAapvjD59GGb48JDy2KtXuDJPPZYtC8d26gSf+Qz8/vdhtmsmzXAVkQpQXYE+c6y9oQH69wf38LpjRzj5ZPj3fw8pkUcdBbvskr1AmSY/iUiFqJ5An6uapDt06AA//zl885uw667N35ua9JR6v4K8iFSQ6hmjHz06dzXJzZvDYt3ZgnyKZriKSIWqjiv6d96BAw+EP/85+/58x9o1w1VEKlDyr+jnz4djj4UFC+CCC5ovyK1hGBFJuOQGene48UY48cQwJPPCCzBpUgjqqiYpIlUkmYF+/fpQmuCSS+DUU0PhsZqasE9j7SJSZVoN9GZ2p5mtNbPFaW37mNksM1sanzvFdjOzSWZWb2aLzOy4YnY+q9//PgzVPPkkTJwIv/51SJtMlxprV5AXkSqQzxX9r4DBGW0TgNnu3guYHV8DDAF6xcc44NbCdDOH9Bmu7iGwf+5zYAbPPQfjx4dtEZEq1mrWjbs/a2Y9M5prgc/H7WnAPOC7sf0ud3dgvpl1NLMD3H1NoTr8fzKrSR53HDz/PNTWhgyaTp0K/iNFRCpRW8fo90sF7/jcNbZ3A1amHbcqthVWtmqSzz8P550Hjz6qIC8ikqbQN2OzjZN41gPNxplZnZnVrVu3Lv+fkGuGK8CvfqVqkiIiGdoa6N82swMA4vPa2L4KOCjtuO7A6mwf4O6T3b3G3Wu6dOmS/08eMyb3DFdVkxQRaaatgX4GMCpujwIeS2sfGbNv+gIbCj4+P3Vq80lPKaomKSLSTD7plfcDLwK9zWyVmY0FrgMGmNlSYEB8DTATeAuoB6YA5xW8x6k8eM1wFRHJSz5ZN1/Lsat/lmMdOH9nO9UqVZMUEclb5c6M1QxXEZG8VHb1SlWTFBFpVeVe0YuISF4U6EVEEk6BXkQk4RToRUQSzkJGZIk7YbYO+AD4W6n7UoY6o/OSSeekOZ2T5qrhnBzs7q2WFiiLQA9gZnXuXlPqfpQbnZfmdE6a0zlpTudkGw3diIgknAK9iEjClVOgn1zqDpQpnZfmdE6a0zlpTuckKpsxehERKY5yuqIXEZEiKGqgN7M7zWytmS1OazvGzF40sz+a2W/M7KOxfXczmxrbXzWzz6e9Z56ZvWFmC+Oja5YfVxHM7CAzm2tmS8zsNTMbH9v3MbNZZrY0PneK7WZmk8ys3swWmdlxaZ81Kh6/1MxG5fqZ5a7A56Qp7fdkRqm+085qwzn5ZPz/aqOZXZrxWYPj/z/1ZjahFN+nEAp8TpbHWLPQzOpK8X3albsX7QGcBBwHLE5rexk4OW5/E7gmbp8PTI3bXYEFwC7x9Tygpph9ba8HcABwXNz+CPAmcDjwM2BCbJ8A/DRuDwWeJCzT2Bd4KbbvQ6j9vw/QKW53KvX3K+U5ifveL/X3KdE56Qp8GvgxcGna5+wKLAM+DuwOvAocXurvV8pzEvctBzqX+ju116OoV/Tu/izwTkZzb+DZuD0L+ErcPhyYHd+3FngXSFwOrLuvcfdX4vZ7wBLCAuq1wLR42DRgRNyuBe7yYD7QMS7fOAiY5e7vuPt6wrkc3I5fpWAKeE4SY0fPibuvdfeXgc0ZH3U8UO/ub7n7JuCB+BkVp4DnpOqUYox+MfCluH0629aYfRWoNbPdzOwQ4FNsv/7s1Phn1vfNLNsi5BXHzHoCxwIvAft5XHYxPqeGp7oBK9Petiq25WqvaDt5TgD2jIvOzzezESRAnuckl2r+PWmJA8+Y2QIzG1esfpaLUtSj/yYwycx+QFhjdlNsvxM4DKgDVgAvAFvivrPd/S9m9hHg18A3gLvatdcFZmYfJnyXC939Hy3825Vth7fQXrEKcE4Aerj7ajP7ODDHzP7o7suK0N12sQPnJOdHZGmrlt+TlvSLvyddgVlm9nocgUikdr+id/fX3X2gu38KuJ8wfoi7b3H3i9y9j7vXAh2BpXHfX+Lze8B9hD9HK5aZdSD8ot7r7o/E5rdTww/xeW1sX8X2f9l0B1a30F6RCnROcPfU81uEezvHFr3zRbKD5ySXav49ySnt92Qt8CgVHlNa0+6BPpUxY2a7AFcAt8XXHzKzveP2AGCLu/8pDuV0ju0dgOGE4Z+KFIed7gCWuPuNabtmAKnMmVHAY2ntI2OmSV9gQ/zz9GlgoJl1ilkGA2NbxSnUOYnnYo/4mZ2BfsCf2uVLFFgbzkkuLwO9zOwQM9sdODN+RsUp1Dkxs73j6AAx5gykgmNKXop5p5dwxb6GcDNkFTAWGE+4W/4mcB3bJm31BN4g3GD5/4SqbAB7EzJwFgGvATcDu5biznWBzsnnCH86LwIWxsdQYF/Czeil8XmfeLwBPyf85fNH0rKPCMNg9fExptTfrdTnBPhsfP1qfB5b6u/Wjudk//j/2D8IiQyrgI/GfUPj/2/LgO+V+ruV+pwQMpBejY/XKvmc5PvQzFgRkYTTzFgRkYRToBcRSTgFehGRhFOgFxFJOAV6EZGEU6AXEUk4BXoRkYRToBcRSbj/BbvTVsUseDleAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_num_unique_users = []\n",
    "for year in years:\n",
    "    index_ratings_year = (ratings_df['Year'] <= year)\n",
    "    ratings_year = ratings_df[index_ratings_year] \n",
    "    num_unique_users_year = len(np.unique(ratings_year['userId'])) # OR\n",
    "#     num_unique_movies_year = len(set(ratings_year['movieId']))\n",
    "    all_num_unique_users.append(num_unique_users_year)\n",
    "print(all_num_unique_users)\n",
    "\n",
    "plt.plot(years, all_num_unique_users, 'rD-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Discussion:** How does the user base grow?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E. Time of ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1996-03-29 13:36:55')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# When was the first rating given?\n",
    "ratings_df['DT'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-09-24 10:27:30')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# When was the last rating given?\n",
    "ratings_df['DT'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 24)"
      ]
     },
     "execution_count": 25,
     "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": [
    "# During what time of day do most users give ratings?\n",
    "# ratings_df['Hour'] = ratings_df['DT'].dt.hour\n",
    "def extract_hour(x):\n",
    "    return x.hour\n",
    "ratings_df['Hour'] = ratings_df['DT'].apply(extract_hour)\n",
    "hour_freq = ratings_df['Hour'].value_counts().sort_index()\n",
    "plt.bar(hour_freq.index, hour_freq.values)\n",
    "plt.xlim(0, 24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** Most ratings are given between 14:00 and 18:00. There are a significant number of ratings given late at night, possibly because the users live in a different time zone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F. Popular Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[215, 110, 52, 7, 49, 102, 54, 8, 16, 132, 70, 19, 8, 18, 13, 82, 67, 20, 88, 15, 89, 36, 16, 28, 76, 13, 9, 11, 38, 1, 38, 177, 1, 128, 3, 67, 1, 4, 104, 2, 15, 1, 8, 46, 32, 15, 203, 68, 4, 204, 1, 28, 2, 3, 1, 1, 8, 37, 1, 34, 6, 80, 1, 5, 31, 9, 1, 5, 20, 55, 5, 7, 13, 8, 5, 15, 1, 3, 17, 2, 14, 8, 1, 7, 15, 7, 16, 10, 1, 8, 9, 10, 84, 1, 10, 1, 2, 14, 23, 5, 3, 99, 23, 1, 26, 1, 4, 237, 104, 46, 3, 1, 1, 5, 1, 2, 2, 2, 16, 4, 2, 12, 7, 1, 1, 5, 6, 31, 1, 1, 16, 86, 1, 12, 51, 3, 15, 7, 1, 201, 44, 1, 137, 12, 6, 3, 11, 62, 14, 57, 103, 17, 66, 14, 144, 1, 3, 54, 10, 35, 6, 53, 62, 10, 20, 13, 8, 1, 2, 36, 17, 2, 2, 112, 48, 4, 8, 1, 5, 6, 12, 35, 18, 10, 45, 2, 28, 4, 1, 1, 1, 22, 30, 9, 1, 21, 115, 3, 4, 2, 3, 6, 2, 24, 49, 6, 14, 1, 7, 1, 16, 104, 40, 52, 22, 3, 7, 24, 133, 14, 12, 17, 70, 45, 22, 44, 17, 4, 2, 5, 1, 1, 1, 29, 21, 17, 18, 6, 3, 43, 109, 1, 7, 35, 14, 10, 5, 251, 42, 21, 1, 33, 68, 17, 7, 2, 5, 4, 31, 28, 7, 6, 22, 38, 1, 2, 13, 14, 40, 5, 3, 2, 1, 1, 92, 8, 14, 3, 101, 133, 2, 1, 307, 1, 4, 81, 1, 5, 32, 3, 9, 16, 24, 20, 1, 1, 1, 9, 3, 16, 38, 140, 81, 317, 20, 7, 2, 10, 2, 1, 7, 3, 34, 10, 108, 6, 2, 12, 50, 5, 2, 1, 77, 19, 98, 5, 26, 33, 5, 161, 36, 6, 6, 30, 110, 57, 17, 2, 64, 6, 42, 329, 103, 9, 1, 8, 17, 34, 1, 172, 4, 12, 157, 74, 2, 58, 21, 21, 1, 25, 11, 40, 171, 7, 6, 178, 11, 16, 21, 21, 1, 6, 4, 1, 2, 5, 1, 5, 10, 17, 2, 6, 2, 50, 1, 2, 12, 1, 8, 2, 1, 84, 1, 15, 26, 6, 16, 8, 13, 2, 20, 59, 12, 5, 10, 3, 2, 7, 12, 18, 6, 2, 42, 55, 8, 101, 63, 7, 9, 1, 7, 76, 42, 81, 1, 6, 12, 3, 9, 8, 1, 7, 1, 3, 5, 101, 37, 4, 190, 5, 6, 3, 4, 10, 60, 1, 22, 5, 1, 40, 6, 7, 70, 25, 1, 17, 1, 4, 238, 19, 8, 2, 53, 3, 4, 1, 11, 12, 17, 14, 12, 41, 1, 1, 43, 1, 2, 144, 6, 15, 2, 7, 40, 13, 66, 61, 1, 2, 4, 4, 18, 24, 18, 9, 4, 21, 69, 3, 7, 4, 34, 1, 220, 3, 40, 1, 24, 16, 18, 14, 15, 3, 12, 12, 106, 18, 124, 14, 41, 8, 1, 21, 2, 6, 1, 5, 93, 61, 65, 2, 65, 6, 1, 8, 35, 23, 1, 2, 3, 2, 6, 1, 4, 16, 4, 1, 5, 4, 1, 2, 36, 116, 115, 183, 224, 164, 189, 279, 77, 146, 60, 135, 11, 1, 1, 1, 10, 4, 181, 14, 33, 6, 6, 8, 1, 45, 3, 1, 5, 1, 6, 35, 11, 1, 1, 4, 2, 2, 33, 1, 4, 11, 1, 3, 2, 29, 162, 1, 4, 1, 65, 3, 5, 14, 49, 7, 12, 2, 3, 8, 11, 9, 2, 36, 2, 53, 9, 1, 1, 1, 9, 1, 4, 2, 9, 3, 4, 11, 2, 5, 1, 7, 1, 1, 14, 7, 2, 1, 21, 58, 11, 8, 10, 1, 15, 3, 4, 35, 27, 1, 31, 78, 1, 7, 10, 5, 1, 4, 3, 121, 1, 7, 123, 20, 27, 11, 29, 48, 3, 22, 97, 1, 2, 1, 2, 2, 19, 41, 6, 2, 19, 4, 2, 1, 1, 1, 102, 2, 202, 7, 8, 47, 54, 42, 64, 1, 82, 1, 1, 1, 1, 2, 2, 12, 23, 19, 11, 46, 1, 8, 35, 3, 1, 2, 12, 71, 12, 9, 8, 5, 1, 10, 1, 1, 1, 1, 6, 9, 21, 37, 6, 3, 17, 33, 30, 15, 5, 3, 12, 4, 2, 7, 40, 7, 44, 1, 6, 43, 192, 9, 1, 1, 23, 2, 1, 3, 2, 3, 1, 1, 1, 9, 4, 27, 5, 1, 1, 2, 6, 1, 3, 1, 5, 13, 17, 3, 1, 2, 2, 29, 47, 12, 7, 36, 60, 84, 7, 7, 14, 57, 27, 50, 59, 100, 44, 35, 13, 30, 26, 10, 92, 45, 9, 27, 69, 109, 2, 24, 3, 18, 1, 20, 9, 8, 23, 12, 3, 6, 2, 6, 8, 2, 6, 7, 3, 7, 3, 4, 6, 6, 7, 14, 12, 58, 18, 18, 1, 3, 1, 1, 1, 11, 28, 34, 1, 10, 5, 7, 2, 5, 1, 1, 2, 1, 2, 13, 1, 2, 5, 11, 1, 11, 13, 3, 12, 3, 6, 8, 5, 10, 1, 9, 9, 8, 9, 12, 2, 20, 5, 5, 6, 2, 37, 27, 42, 13, 6, 25, 116, 28, 71, 35, 15, 24, 40, 20, 5, 64, 145, 24, 2, 2, 1, 11, 42, 5, 1, 4, 33, 2, 21, 5, 3, 1, 1, 2, 7, 15, 1, 36, 36, 29, 3, 13, 4, 4, 13, 9, 1, 1, 119, 28, 11, 71, 89, 12, 6, 6, 35, 4, 25, 1, 42, 131, 63, 21, 47, 37, 42, 15, 10, 122, 4, 19, 83, 15, 20, 3, 2, 4, 4, 1, 2, 1, 1, 1, 1, 27, 1, 2, 13, 24, 6, 62, 3, 39, 4, 11, 8, 2, 5, 13, 136, 1, 5, 1, 1, 10, 56, 3, 2, 1, 3, 1, 1, 2, 2, 17, 34, 11, 21, 3, 6, 12, 27, 1, 45, 7, 10, 23, 5, 22, 11, 6, 6, 5, 133, 11, 211, 142, 200, 59, 126, 72, 7, 57, 45, 1, 120, 58, 107, 18, 196, 13, 24, 126, 146, 51, 11, 15, 11, 83, 84, 129, 102, 28, 19, 76, 6, 16, 40, 2, 58, 22, 8, 40, 64, 26, 3, 18, 9, 1, 131, 10, 41, 16, 33, 20, 86, 79, 17, 35, 45, 14, 59, 25, 16, 6, 23, 27, 109, 91, 15, 34, 2, 43, 43, 143, 45, 30, 9, 29, 171, 30, 33, 11, 39, 48, 57, 12, 69, 8, 50, 15, 53, 19, 14, 41, 9, 34, 66, 11, 7, 140, 20, 27, 10, 11, 17, 20, 23, 28, 9, 13, 56, 17, 62, 18, 4, 85, 5, 1, 2, 1, 47, 23, 3, 3, 3, 2, 4, 15, 2, 2, 4, 1, 1, 44, 6, 3, 1, 1, 1, 29, 3, 2, 11, 25, 14, 36, 8, 27, 16, 1, 22, 2, 2, 4, 7, 3, 91, 24, 47, 18, 3, 6, 1, 5, 6, 5, 47, 68, 45, 42, 30, 62, 42, 43, 60, 25, 11, 68, 19, 36, 4, 3, 24, 3, 91, 17, 6, 9, 86, 4, 85, 58, 58, 46, 5, 24, 5, 1, 3, 1, 2, 31, 2, 70, 39, 23, 2, 16, 1, 2, 8, 2, 12, 2, 1, 2, 1, 4, 1, 3, 8, 1, 1, 2, 20, 3, 16, 7, 1, 1, 2, 24, 3, 2, 19, 5, 7, 7, 4, 1, 24, 3, 6, 1, 12, 1, 12, 2, 8, 2, 17, 3, 52, 5, 1, 11, 1, 26, 1, 30, 11, 11, 3, 74, 5, 15, 2, 2, 1, 2, 1, 3, 5, 27, 59, 1, 2, 4, 4, 1, 1, 1, 8, 1, 29, 1, 15, 1, 100, 13, 1, 1, 1, 3, 116, 3, 2, 13, 10, 7, 1, 67, 2, 1, 2, 1, 1, 4, 31, 59, 3, 17, 5, 19, 3, 7, 2, 42, 1, 2, 6, 31, 5, 4, 40, 10, 2, 2, 77, 1, 165, 3, 5, 2, 82, 1, 22, 31, 20, 2, 25, 26, 4, 6, 16, 6, 3, 43, 4, 3, 2, 3, 4, 13, 3, 4, 3, 2, 58, 3, 90, 5, 1, 3, 26, 11, 20, 97, 2, 14, 13, 2, 1, 2, 2, 77, 3, 7, 1, 3, 1, 1, 5, 17, 50, 52, 56, 2, 5, 32, 51, 7, 1, 4, 3, 2, 3, 2, 86, 1, 1, 1, 1, 8, 1, 1, 1, 9, 34, 2, 16, 1, 5, 21, 3, 1, 12, 39, 40, 1, 80, 1, 10, 1, 25, 7, 125, 6, 4, 1, 3, 18, 20, 7, 45, 1, 1, 19, 7, 2, 2, 2, 8, 16, 5, 141, 8, 5, 24, 1, 7, 2, 31, 2, 16, 25, 140, 45, 4, 13, 4, 2, 48, 4, 4, 106, 4, 9, 11, 4, 1, 1, 1, 31, 48, 12, 16, 15, 5, 4, 3, 1, 4, 10, 3, 5, 3, 4, 1, 7, 2, 3, 14, 2, 1, 12, 1, 5, 1, 56, 23, 3, 5, 96, 5, 3, 23, 2, 1, 1, 6, 2, 13, 3, 25, 1, 3, 31, 8, 1, 1, 6, 11, 3, 3, 1, 6, 1, 1, 1, 3, 2, 2, 1, 29, 1, 13, 11, 17, 3, 4, 1, 7, 1, 4, 15, 12, 7, 3, 1, 8, 3, 4, 5, 9, 7, 1, 7, 1, 1, 8, 10, 6, 43, 2, 33, 24, 46, 12, 2, 3, 8, 1, 1, 16, 1, 16, 15, 2, 1, 1, 3, 2, 45, 42, 2, 32, 32, 3, 9, 12, 92, 33, 5, 18, 47, 2, 105, 8, 1, 1, 10, 1, 2, 6, 1, 1, 3, 3, 2, 3, 8, 1, 2, 4, 1, 11, 24, 3, 34, 7, 6, 11, 7, 22, 39, 64, 14, 12, 13, 22, 15, 26, 96, 23, 11, 6, 21, 8, 42, 113, 10, 12, 7, 7, 5, 16, 7, 7, 1, 1, 4, 6, 3, 27, 5, 3, 2, 2, 2, 1, 1, 1, 12, 4, 5, 47, 6, 4, 53, 2, 4, 75, 47, 46, 41, 15, 57, 60, 1, 2, 19, 21, 87, 88, 21, 8, 9, 4, 5, 38, 48, 25, 37, 8, 45, 3, 7, 3, 9, 188, 1, 1, 8, 9, 3, 6, 1, 3, 1, 7, 3, 6, 4, 2, 1, 24, 1, 8, 4, 3, 23, 20, 68, 1, 1, 4, 30, 15, 14, 31, 13, 4, 21, 2, 2, 5, 2, 17, 5, 3, 1, 46, 5, 53, 6, 55, 67, 26, 19, 1, 44, 4, 41, 18, 10, 13, 5, 6, 6, 39, 4, 22, 10, 3, 6, 51, 2, 3, 3, 1, 50, 5, 2, 24, 27, 18, 10, 8, 3, 16, 108, 15, 14, 15, 1, 4, 7, 11, 15, 38, 21, 15, 3, 1, 2, 8, 4, 7, 20, 22, 11, 14, 21, 10, 21, 34, 18, 10, 13, 49, 33, 16, 1, 9, 3, 28, 6, 3, 11, 4, 8, 2, 1, 7, 32, 43, 4, 8, 1, 2, 4, 58, 5, 7, 5, 8, 1, 91, 1, 10, 3, 2, 1, 1, 4, 4, 9, 6, 2, 25, 1, 5, 2, 1, 2, 1, 40, 65, 7, 6, 1, 15, 2, 7, 7, 4, 4, 5, 4, 6, 1, 2, 1, 3, 1, 1, 1, 3, 36, 27, 9, 6, 7, 4, 28, 2, 22, 12, 39, 11, 2, 1, 5, 20, 4, 2, 1, 1, 2, 8, 2, 6, 9, 5, 1, 4, 5, 2, 57, 8, 4, 2, 2, 42, 5, 1, 43, 10, 2, 1, 6, 2, 1, 5, 45, 38, 5, 80, 1, 1, 45, 2, 10, 14, 1, 33, 16, 59, 5, 1, 1, 6, 2, 1, 3, 17, 12, 17, 2, 6, 7, 16, 9, 61, 8, 2, 88, 13, 3, 7, 129, 2, 3, 2, 4, 13, 28, 23, 4, 18, 1, 20, 2, 1, 8, 11, 9, 8, 7, 2, 7, 25, 67, 7, 92, 5, 6, 1, 26, 12, 1, 10, 3, 2, 1, 25, 14, 2, 24, 1, 24, 9, 9, 9, 21, 21, 5, 22, 12, 10, 7, 5, 4, 29, 3, 16, 10, 9, 7, 22, 8, 33, 25, 56, 92, 1, 17, 4, 2, 8, 19, 30, 12, 21, 54, 43, 9, 26, 24, 29, 17, 18, 11, 3, 16, 1, 6, 2, 38, 29, 14, 30, 50, 2, 1, 35, 23, 5, 6, 27, 6, 11, 1, 1, 6, 3, 3, 8, 8, 2, 15, 3, 16, 2, 1, 3, 13, 43, 9, 8, 8, 13, 3, 5, 1, 14, 3, 18, 15, 7, 57, 24, 3, 8, 13, 3, 12, 3, 26, 1, 1, 2, 1, 29, 3, 45, 5, 7, 1, 2, 3, 23, 6, 5, 7, 30, 94, 1, 6, 18, 2, 6, 3, 1, 10, 3, 2, 1, 10, 10, 3, 2, 4, 2, 9, 3, 3, 18, 24, 56, 18, 9, 12, 15, 3, 3, 5, 3, 47, 1, 40, 67, 3, 1, 1, 2, 2, 6, 6, 7, 8, 2, 6, 3, 3, 1, 9, 1, 8, 4, 3, 12, 2, 19, 8, 5, 278, 54, 1, 6, 4, 1, 1, 2, 6, 39, 36, 3, 12, 1, 2, 2, 4, 1, 1, 4, 10, 12, 2, 25, 56, 10, 22, 1, 43, 15, 2, 1, 1, 1, 8, 2, 4, 26, 87, 5, 6, 1, 88, 11, 3, 10, 2, 2, 1, 140, 2, 2, 1, 3, 3, 4, 61, 49, 22, 16, 9, 2, 1, 1, 11, 1, 3, 1, 5, 1, 1, 63, 2, 1, 4, 1, 12, 13, 1, 8, 8, 5, 56, 12, 2, 2, 6, 4, 3, 3, 121, 19, 24, 15, 27, 10, 3, 75, 4, 48, 6, 3, 1, 1, 53, 76, 53, 32, 15, 7, 1, 103, 22, 1, 9, 64, 3, 53, 12, 1, 120, 47, 10, 13, 21, 5, 29, 39, 42, 1, 9, 1, 13, 7, 7, 12, 5, 2, 10, 16, 5, 2, 2, 12, 1, 2, 1, 1, 1, 9, 39, 9, 6, 2, 7, 1, 8, 2, 1, 1, 1, 8, 1, 39, 179, 48, 7, 1, 2, 1, 1, 5, 38, 4, 10, 3, 1, 1, 1, 1, 13, 3, 26, 2, 2, 4, 6, 28, 4, 2, 87, 19, 10, 15, 6, 26, 91, 11, 1, 12, 8, 13, 10, 55, 23, 9, 13, 17, 1, 6, 5, 1, 4, 1, 7, 3, 3, 3, 16, 1, 1, 59, 11, 1, 26, 11, 2, 7, 1, 1, 4, 1, 9, 1, 23, 18, 9, 3, 7, 2, 1, 4, 1, 1, 14, 204, 7, 9, 8, 1, 19, 1, 1, 5, 12, 2, 1, 8, 30, 25, 3, 10, 1, 7, 2, 7, 6, 19, 4, 5, 1, 1, 2, 1, 5, 8, 45, 3, 1, 1, 1, 1, 1, 1, 1, 3, 2, 14, 4, 2, 4, 7, 7, 39, 1, 14, 42, 88, 10, 109, 8, 11, 5, 10, 5, 11, 2, 2, 2, 1, 3, 1, 3, 8, 7, 1, 5, 6, 9, 16, 3, 32, 1, 7, 53, 36, 33, 14, 39, 12, 31, 3, 1, 6, 1, 218, 5, 2, 1, 1, 14, 4, 41, 1, 1, 4, 6, 18, 1, 1, 6, 2, 4, 3, 1, 1, 1, 1, 1, 70, 25, 97, 3, 21, 20, 23, 1, 24, 1, 14, 4, 99, 48, 1, 1, 2, 11, 25, 31, 6, 1, 1, 4, 1, 1, 3, 14, 4, 17, 10, 17, 10, 9, 1, 7, 1, 1, 2, 6, 13, 4, 6, 59, 36, 11, 7, 1, 24, 39, 9, 6, 1, 2, 13, 5, 5, 2, 3, 1, 3, 79, 11, 8, 3, 1, 1, 48, 8, 15, 8, 11, 6, 3, 1, 7, 10, 74, 7, 21, 9, 27, 1, 4, 3, 4, 2, 2, 6, 1, 62, 39, 19, 1, 24, 12, 16, 6, 4, 1, 4, 2, 9, 1, 3, 24, 7, 22, 36, 7, 2, 22, 22, 7, 23, 36, 1, 2, 2, 1, 15, 97, 5, 4, 4, 4, 1, 2, 1, 1, 8, 1, 3, 3, 5, 1, 7, 3, 2, 1, 2, 4, 4, 1, 3, 17, 111, 26, 2, 8, 5, 9, 15, 19, 3, 15, 52, 2, 1, 4, 2, 4, 29, 5, 1, 18, 39, 75, 49, 6, 16, 4, 3, 6, 5, 1, 1, 3, 28, 1, 11, 4, 9, 1, 4, 4, 9, 14, 7, 8, 8, 12, 8, 3, 8, 42, 2, 13, 2, 3, 13, 5, 5, 2, 4, 4, 1, 1, 1, 4, 3, 6, 2, 2, 15, 12, 15, 36, 14, 9, 9, 5, 27, 61, 27, 45, 18, 32, 15, 10, 31, 12, 11, 22, 26, 9, 6, 18, 11, 11, 11, 27, 12, 17, 29, 8, 43, 12, 1, 4, 1, 1, 2, 1, 32, 5, 3, 1, 1, 1, 16, 7, 21, 3, 39, 42, 2, 1, 1, 4, 13, 7, 4, 5, 3, 2, 1, 13, 22, 10, 1, 3, 3, 3, 5, 19, 1, 2, 1, 10, 1, 7, 5, 1, 1, 9, 3, 1, 1, 21, 15, 1, 10, 11, 22, 35, 38, 43, 3, 7, 2, 2, 2, 1, 1, 1, 4, 8, 31, 7, 11, 1, 1, 2, 6, 3, 7, 3, 33, 11, 15, 2, 1, 5, 2, 1, 6, 1, 3, 70, 35, 1, 2, 6, 1, 3, 6, 1, 105, 41, 3, 62, 6, 2, 27, 5, 7, 3, 1, 12, 4, 3, 2, 1, 1, 17, 4, 2, 25, 13, 14, 14, 2, 1, 9, 4, 2, 2, 43, 1, 29, 11, 18, 2, 2, 1, 1, 1, 1, 1, 13, 25, 2, 8, 4, 18, 6, 5, 60, 2, 1, 2, 26, 13, 7, 18, 3, 75, 13, 10, 53, 1, 6, 1, 4, 8, 44, 4, 5, 3, 11, 22, 9, 5, 15, 18, 2, 36, 6, 8, 7, 1, 9, 4, 6, 7, 1, 10, 5, 15, 61, 5, 5, 2, 1, 6, 25, 59, 21, 3, 5, 2, 1, 14, 3, 5, 2, 5, 8, 6, 13, 52, 5, 3, 33, 26, 5, 3, 20, 3, 12, 7, 8, 1, 1, 2, 2, 4, 2, 1, 4, 2, 170, 2, 2, 1, 1, 4, 76, 2, 1, 1, 15, 1, 19, 6, 2, 1, 2, 5, 5, 3, 1, 1, 1, 4, 25, 1, 8, 14, 10, 41, 12, 1, 1, 77, 43, 1, 3, 4, 2, 7, 1, 5, 17, 20, 18, 1, 1, 26, 18, 1, 2, 2, 17, 1, 8, 1, 3, 13, 2, 4, 1, 2, 1, 2, 1, 1, 1, 15, 1, 1, 62, 5, 2, 1, 8, 1, 4, 10, 2, 1, 33, 5, 19, 7, 9, 15, 1, 11, 4, 5, 1, 1, 8, 2, 2, 3, 21, 34, 13, 1, 13, 38, 40, 24, 8, 8, 10, 10, 1, 3, 1, 3, 1, 1, 1, 1, 61, 1, 2, 2, 1, 1, 5, 5, 2, 5, 8, 4, 3, 17, 5, 4, 3, 1, 17, 1, 1, 2, 4, 28, 11, 7, 3, 19, 29, 2, 2, 10, 13, 3, 78, 16, 68, 46, 9, 44, 8, 2, 7, 1, 35, 1, 16, 5, 2, 6, 9, 1, 3, 6, 2, 6, 2, 4, 1, 3, 1, 3, 1, 43, 11, 11, 50, 4, 1, 6, 1, 1, 16, 2, 133, 1, 1, 2, 3, 25, 6, 1, 4, 4, 1, 1, 1, 35, 3, 7, 12, 3, 9, 3, 1, 14, 3, 16, 32, 4, 11, 29, 39, 23, 2, 2, 11, 1, 2, 2, 6, 15, 3, 1, 3, 5, 6, 4, 9, 1, 4, 1, 2, 1, 13, 2, 1, 1, 5, 2, 19, 2, 45, 5, 4, 1, 1, 42, 26, 16, 5, 1, 7, 1, 9, 2, 34, 3, 1, 6, 3, 1, 19, 1, 1, 8, 83, 2, 1, 7, 2, 1, 7, 2, 3, 4, 20, 53, 2, 1, 1, 6, 41, 15, 9, 1, 2, 1, 1, 6, 1, 6, 4, 2, 4, 1, 1, 2, 1, 4, 2, 1, 12, 1, 1, 8, 1, 34, 5, 3, 91, 96, 8, 1, 7, 1, 4, 6, 3, 1, 8, 1, 1, 2, 3, 1, 2, 37, 27, 26, 2, 16, 4, 1, 1, 72, 15, 24, 14, 13, 2, 9, 21, 6, 20, 6, 33, 3, 4, 20, 9, 2, 73, 5, 110, 12, 12, 14, 6, 3, 27, 3, 12, 7, 20, 13, 2, 8, 93, 4, 1, 33, 47, 37, 14, 54, 28, 13, 5, 100, 23, 64, 3, 94, 12, 5, 2, 1, 13, 70, 3, 11, 8, 1, 16, 13, 14, 4, 3, 1, 1, 9, 1, 8, 2, 16, 2, 7, 1, 10, 8, 4, 2, 6, 4, 4, 7, 22, 3, 14, 1, 1, 1, 2, 2, 2, 1, 2, 2, 3, 2, 1, 19, 46, 5, 2, 1, 8, 4, 2, 1, 6, 20, 9, 5, 22, 10, 14, 7, 1, 1, 2, 1, 1, 3, 1, 12, 1, 4, 2, 4, 3, 26, 1, 3, 3, 8, 8, 1, 1, 2, 1, 27, 4, 2, 14, 1, 3, 2, 39, 11, 1, 1, 2, 12, 2, 8, 1, 1, 6, 7, 4, 23, 1, 2, 2, 10, 4, 1, 1, 4, 1, 10, 6, 1, 1, 1, 1, 1, 3, 1, 2, 4, 2, 3, 1, 1, 2, 1, 6, 1, 6, 3, 6, 6, 1, 1, 13, 6, 1, 2, 1, 3, 5, 42, 4, 40, 2, 7, 159, 10, 4, 5, 28, 6, 5, 23, 1, 1, 17, 51, 1, 5, 1, 1, 65, 21, 10, 6, 1, 1, 2, 13, 10, 3, 4, 1, 67, 2, 5, 5, 2, 2, 45, 2, 5, 2, 3, 9, 1, 1, 3, 1, 3, 1, 11, 2, 4, 2, 2, 2, 1, 41, 3, 2, 5, 1, 170, 4, 55, 58, 43, 1, 2, 3, 2, 2, 3, 31, 10, 1, 1, 1, 11, 16, 3, 10, 5, 1, 2, 1, 4, 11, 1, 27, 34, 3, 3, 8, 2, 16, 1, 1, 2, 5, 1, 8, 32, 1, 7, 19, 40, 1, 45, 56, 11, 5, 3, 1, 1, 14, 1, 7, 4, 8, 5, 2, 2, 11, 9, 26, 4, 1, 2, 3, 1, 1, 1, 10, 2, 2, 1, 2, 1, 5, 1, 7, 1, 15, 3, 25, 7, 4, 16, 2, 2, 1, 4, 1, 2, 3, 4, 9, 2, 2, 2, 1, 4, 2, 1, 1, 8, 3, 4, 2, 1, 3, 1, 2, 3, 31, 64, 2, 1, 20, 7, 1, 6, 1, 1, 1, 1, 1, 1, 3, 2, 1, 12, 5, 2, 5, 13, 1, 2, 3, 4, 1, 5, 1, 3, 8, 2, 1, 3, 3, 3, 2, 4, 11, 3, 24, 1, 3, 4, 1, 15, 6, 16, 1, 1, 4, 1, 1, 1, 3, 1, 8, 5, 1, 1, 1, 2, 2, 4, 9, 3, 1, 1, 2, 1, 1, 2, 2, 5, 4, 3, 4, 2, 4, 3, 2, 5, 20, 6, 1, 3, 9, 1, 14, 1, 3, 6, 2, 2, 1, 45, 3, 1, 2, 2, 3, 5, 2, 2, 1, 1, 1, 4, 8, 2, 5, 2, 1, 1, 1, 22, 2, 2, 3, 1, 4, 2, 1, 1, 10, 1, 8, 1, 6, 13, 21, 2, 1, 18, 2, 20, 3, 4, 2, 3, 4, 2, 1, 2, 2, 1, 3, 36, 18, 4, 34, 11, 49, 3, 1, 1, 12, 7, 4, 1, 7, 1, 1, 2, 3, 3, 1, 1, 1, 2, 3, 1, 1, 1, 13, 9, 13, 1, 7, 6, 2, 2, 4, 1, 3, 4, 2, 1, 1, 4, 2, 27, 31, 4, 1, 1, 1, 1, 2, 1, 3, 2, 1, 7, 2, 1, 1, 1, 45, 8, 55, 4, 1, 3, 1, 4, 4, 18, 1, 1, 1, 9, 3, 34, 5, 3, 22, 4, 1, 7, 2, 7, 2, 4, 1, 6, 2, 65, 1, 6, 2, 12, 3, 1, 3, 1, 1, 1, 2, 2, 4, 1, 4, 6, 3, 51, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 10, 1, 9, 3, 1, 4, 3, 3, 2, 1, 10, 8, 1, 4, 4, 2, 1, 2, 1, 4, 3, 1, 2, 6, 1, 5, 15, 54, 6, 1, 1, 5, 3, 25, 3, 2, 1, 1, 1, 1, 1, 2, 3, 3, 2, 16, 1, 1, 6, 3, 5, 2, 51, 3, 4, 3, 20, 8, 1, 3, 2, 1, 20, 1, 1, 8, 5, 1, 1, 19, 33, 12, 1, 109, 1, 9, 17, 1, 1, 132, 14, 4, 7, 31, 2, 107, 4, 8, 5, 27, 10, 10, 1, 2, 2, 4, 8, 5, 3, 1, 1, 2, 3, 5, 1, 1, 14, 1, 1, 1, 4, 4, 1, 5, 1, 9, 1, 1, 8, 1, 1, 4, 19, 3, 1, 17, 16, 1, 7, 6, 2, 11, 1, 1, 2, 20, 11, 2, 44, 3, 119, 9, 27, 1, 13, 1, 4, 3, 5, 5, 23, 120, 14, 44, 4, 6, 68, 20, 1, 2, 3, 2, 2, 1, 9, 5, 3, 17, 198, 10, 123, 3, 1, 7, 1, 1, 9, 8, 50, 2, 1, 27, 17, 18, 6, 1, 3, 2, 15, 14, 5, 1, 4, 19, 2, 3, 6, 18, 6, 8, 1, 3, 9, 6, 11, 2, 1, 1, 8, 1, 46, 10, 5, 32, 13, 14, 2, 6, 1, 1, 1, 13, 3, 3, 3, 3, 1, 2, 1, 7, 5, 5, 2, 2, 1, 1, 9, 5, 9, 7, 1, 2, 4, 1, 4, 24, 3, 7, 5, 11, 1, 24, 1, 1, 2, 1, 1, 1, 10, 3, 1, 17, 3, 11, 1, 2, 2, 12, 2, 3, 9, 1, 9, 4, 18, 18, 2, 1, 1, 7, 1, 2, 1, 1, 2, 22, 1, 2, 1, 1, 2, 3, 1, 3, 1, 1, 1, 1, 2, 2, 1, 1, 1, 4, 85, 30, 10, 1, 4, 2, 21, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 4, 9, 4, 3, 34, 4, 2, 2, 4, 11, 37, 12, 1, 1, 30, 1, 1, 1, 1, 2, 1, 2, 22, 1, 1, 1, 1, 2, 18, 7, 35, 9, 11, 10, 4, 1, 60, 1, 5, 1, 7, 2, 2, 1, 1, 19, 8, 1, 7, 22, 7, 6, 2, 1, 1, 8, 2, 7, 2, 3, 1, 10, 3, 2, 3, 1, 6, 1, 2, 1, 2, 2, 1, 122, 3, 1, 1, 2, 1, 2, 9, 7, 1, 1, 1, 1, 1, 1, 1, 44, 92, 6, 10, 2, 3, 1, 7, 1, 1, 3, 33, 2, 1, 1, 3, 1, 2, 2, 1, 17, 9, 1, 1, 3, 2, 3, 4, 4, 4, 1, 3, 112, 17, 8, 6, 1, 3, 1, 2, 3, 4, 1, 1, 3, 5, 1, 3, 4, 1, 1, 1, 2, 29, 120, 7, 2, 3, 22, 5, 5, 6, 1, 1, 2, 1, 1, 1, 61, 3, 2, 3, 18, 49, 2, 4, 1, 16, 1, 3, 1, 2, 3, 3, 3, 4, 8, 6, 5, 65, 1, 3, 1, 3, 2, 1, 6, 1, 1, 1, 3, 1, 7, 10, 7, 63, 3, 7, 12, 5, 24, 7, 3, 1, 2, 1, 2, 1, 14, 4, 3, 8, 2, 6, 4, 19, 2, 7, 1, 2, 1, 1, 2, 4, 1, 2, 2, 9, 15, 2, 4, 5, 1, 8, 3, 7, 1, 1, 1, 3, 2, 7, 2, 5, 5, 1, 7, 26, 7, 1, 12, 3, 1, 1, 8, 1, 1, 6, 2, 3, 1, 1, 1, 1, 2, 2, 1, 2, 3, 5, 1, 1, 2, 1, 1, 11, 2, 3, 2, 1, 1, 3, 1, 1, 25, 87, 2, 2, 28, 9, 10, 2, 3, 1, 2, 5, 31, 1, 5, 2, 1, 2, 2, 1, 1, 3, 3, 4, 1, 11, 1, 1, 1, 1, 5, 5, 2, 4, 58, 2, 2, 2, 33, 3, 4, 5, 3, 47, 3, 3, 2, 1, 1, 2, 2, 1, 16, 11, 2, 3, 1, 1, 1, 1, 2, 1, 2, 4, 2, 1, 1, 3, 7, 1, 1, 17, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 7, 8, 1, 1, 1, 2, 1, 1, 4, 1, 1, 3, 1, 2, 1, 2, 4, 1, 1, 1, 1, 1, 1, 1, 19, 9, 17, 1, 1, 3, 1, 16, 16, 6, 1, 5, 1, 1, 1, 1, 7, 5, 1, 4, 41, 11, 1, 102, 1, 1, 3, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 3, 1, 4, 5, 1, 1, 2, 1, 1, 2, 27, 4, 5, 1, 5, 14, 4, 2, 20, 8, 1, 1, 1, 1, 1, 2, 4, 2, 2, 1, 4, 8, 1, 46, 44, 1, 21, 5, 1, 5, 2, 1, 2, 1, 2, 4, 1, 11, 1, 1, 2, 1, 11, 2, 5, 6, 6, 19, 19, 26, 2, 1, 1, 2, 4, 188, 1, 22, 9, 55, 19, 1, 9, 1, 1, 2, 1, 3, 2, 1, 10, 37, 1, 1, 1, 3, 2, 1, 5, 6, 2, 1, 4, 115, 1, 49, 15, 5, 14, 46, 2, 53, 2, 7, 3, 15, 3, 3, 8, 1, 4, 2, 5, 1, 75, 1, 1, 3, 2, 5, 3, 1, 4, 2, 1, 1, 2, 2, 1, 4, 4, 3, 1, 6, 1, 1, 3, 2, 1, 10, 17, 2, 6, 3, 1, 1, 1, 15, 1, 2, 2, 1, 1, 1, 4, 1, 3, 1, 9, 1, 1, 1, 2, 2, 9, 1, 1, 2, 1, 2, 1, 3, 2, 1, 1, 2, 5, 1, 1, 3, 1, 2, 2, 29, 19, 32, 1, 2, 3, 1, 8, 2, 3, 1, 1, 1, 3, 4, 3, 1, 8, 39, 1, 3, 2, 3, 3, 2, 1, 1, 2, 1, 12, 1, 6, 7, 9, 3, 2, 2, 40, 3, 4, 1, 2, 1, 1, 1, 1, 1, 1, 3, 1, 9, 2, 1, 11, 2, 1, 1, 4, 7, 1, 4, 2, 2, 8, 4, 8, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1, 2, 32, 20, 1, 4, 34, 4, 4, 7, 3, 1, 11, 1, 14, 20, 3, 9, 3, 5, 8, 8, 1, 10, 3, 4, 1, 2, 2, 3, 1, 3, 2, 6, 22, 2, 3, 1, 2, 13, 4, 76, 3, 3, 1, 4, 6, 4, 1, 4, 1, 1, 1, 3, 1, 3, 3, 24, 2, 1, 5, 10, 96, 1, 10, 4, 5, 1, 71, 3, 4, 2, 141, 59, 2, 7, 1, 1, 19, 14, 1, 3, 1, 1, 1, 1, 2, 1, 1, 2, 2, 34, 1, 1, 1, 1, 2, 1, 2, 1, 3, 1, 1, 3, 14, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 5, 6, 4, 2, 1, 4, 1, 3, 32, 1, 1, 1, 1, 11, 3, 7, 1, 58, 27, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 20, 2, 1, 1, 2, 1, 33, 11, 6, 32, 45, 149, 1, 32, 5, 1, 1, 2, 17, 3, 13, 1, 12, 1, 2, 1, 3, 3, 1, 1, 4, 1, 17, 18, 3, 3, 1, 2, 1, 2, 1, 1, 2, 16, 1, 2, 1, 2, 1, 2, 3, 1, 3, 16, 1, 1, 6, 1, 3, 24, 14, 1, 1, 1, 3, 1, 1, 2, 1, 11, 1, 1, 2, 8, 3, 9, 2, 9, 17, 6, 18, 3, 5, 4, 1, 3, 1, 4, 5, 2, 2, 4, 3, 7, 2, 1, 4, 1, 7, 7, 1, 3, 1, 1, 4, 16, 4, 1, 11, 5, 6, 1, 5, 5, 1, 6, 2, 1, 9, 13, 1, 1, 5, 2, 1, 5, 4, 1, 1, 18, 2, 1, 1, 4, 1, 1, 5, 1, 1, 8, 34, 14, 4, 74, 1, 5, 1, 1, 1, 2, 7, 1, 2, 5, 8, 2, 4, 2, 4, 1, 1, 1, 3, 1, 1, 12, 33, 14, 1, 2, 2, 7, 17, 9, 1, 1, 2, 1, 22, 8, 1, 3, 5, 2, 2, 1, 3, 11, 4, 27, 4, 1, 3, 5, 1, 11, 1, 5, 18, 1, 2, 3, 2, 1, 1, 40, 4, 1, 5, 5, 2, 1, 1, 1, 2, 1, 4, 6, 1, 3, 5, 1, 1, 1, 21, 3, 2, 4, 1, 2, 13, 2, 3, 4, 1, 2, 66, 7, 12, 7, 2, 52, 9, 4, 20, 131, 3, 17, 7, 7, 1, 1, 2, 3, 1, 5, 20, 2, 9, 1, 10, 4, 3, 1, 2, 2, 8, 2, 1, 1, 2, 3, 1, 2, 7, 79, 1, 39, 1, 1, 1, 59, 1, 13, 2, 2, 7, 36, 2, 3, 1, 9, 25, 3, 5, 2, 26, 10, 3, 3, 1, 1, 2, 18, 1, 3, 1, 1, 1, 2, 1, 3, 6, 5, 1, 1, 4, 21, 2, 1, 1, 1, 5, 1, 1, 7, 4, 1, 2, 13, 2, 7, 2, 1, 2, 4, 6, 1, 17, 1, 8, 5, 3, 3, 1, 7, 3, 3, 3, 5, 2, 1, 1, 30, 1, 3, 2, 4, 5, 7, 2, 1, 1, 4, 8, 3, 7, 2, 1, 1, 1, 1, 5, 3, 11, 2, 4, 1, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 3, 1, 1, 8, 6, 1, 1, 2, 1, 4, 5, 1, 3, 1, 1, 1, 8, 1, 3, 2, 3, 2, 1, 4, 2, 1, 2, 1, 2, 38, 1, 2, 2, 2, 1, 1, 23, 1, 4, 4, 1, 3, 31, 6, 2, 1, 2, 1, 1, 5, 4, 1, 2, 5, 1, 4, 1, 9, 3, 1, 1, 1, 9, 2, 1, 2, 10, 5, 7, 1, 3, 62, 69, 17, 10, 12, 185, 10, 8, 13, 1, 9, 1, 17, 8, 12, 17, 6, 3, 1, 1, 1, 8, 2, 5, 2, 24, 2, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 2, 3, 7, 21, 12, 1, 1, 3, 2, 3, 1, 2, 4, 3, 1, 1, 1, 2, 1, 3, 4, 6, 6, 1, 3, 1, 1, 1, 11, 1, 1, 46, 8, 8, 4, 4, 4, 1, 3, 1, 10, 1, 13, 1, 2, 1, 9, 2, 1, 47, 4, 3, 3, 3, 1, 4, 1, 2, 1, 2, 1, 3, 3, 1, 1, 2, 4, 15, 13, 6, 5, 2, 23, 11, 21, 1, 6, 1, 1, 2, 1, 1, 1, 4, 1, 1, 20, 15, 3, 4, 1, 2, 3, 1, 16, 131, 4, 1, 8, 14, 1, 4, 20, 1, 41, 7, 6, 1, 6, 7, 7, 7, 2, 1, 1, 4, 20, 1, 1, 2, 7, 1, 1, 1, 1, 7, 2, 110, 14, 2, 1, 1, 21, 35, 1, 4, 2, 4, 39, 1, 24, 1, 1, 48, 1, 6, 97, 1, 1, 4, 1, 58, 13, 4, 2, 5, 1, 1, 2, 1, 3, 17, 1, 1, 16, 4, 1, 1, 1, 1, 2, 3, 12, 8, 3, 7, 8, 1, 3, 2, 2, 4, 2, 1, 1, 2, 3, 1, 6, 1, 1, 1, 2, 5, 2, 2, 5, 4, 3, 2, 2, 9, 2, 16, 5, 5, 7, 1, 1, 2, 4, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 3, 5, 1, 6, 1, 2, 1, 4, 1, 2, 2, 2, 6, 2, 4, 1, 4, 2, 21, 1, 2, 1, 2, 5, 16, 1, 5, 2, 1, 1, 17, 8, 4, 3, 1, 3, 6, 1, 16, 1, 1, 2, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 4, 1, 2, 5, 3, 4, 3, 1, 1, 1, 27, 1, 1, 1, 1, 2, 2, 2, 2, 1, 4, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 9, 1, 2, 1, 2, 2, 2, 3, 7, 1, 2, 1, 1, 1, 1, 1, 1, 1, 4, 1, 11, 1, 2, 1, 1, 1, 7, 9, 4, 5, 1, 1, 1, 1, 1, 1, 3, 1, 3, 1, 4, 3, 10, 1, 2, 1, 2, 1, 2, 2, 3, 2, 2, 7, 1, 3, 1, 71, 39, 1, 1, 10, 1, 1, 4, 1, 1, 1, 1, 2, 1, 1, 3, 2, 1, 2, 1, 5, 1, 9, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 6, 5, 1, 2, 7, 1, 2, 3, 26, 25, 7, 4, 1, 1, 3, 1, 1, 3, 2, 1, 1, 1, 2, 1, 6, 1, 17, 1, 2, 4, 1, 1, 1, 1, 1, 1, 5, 6, 3, 1, 5, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 31, 3, 1, 2, 1, 2, 1, 5, 1, 1, 2, 7, 1, 7, 3, 1, 1, 1, 2, 92, 50, 4, 2, 4, 14, 93, 3, 12, 23, 5, 12, 2, 37, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 29, 3, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 7, 1, 50, 1, 1, 2, 54, 1, 1, 1, 1, 2, 2, 3, 2, 6, 1, 1, 1, 4, 1, 2, 9, 1, 1, 17, 1, 2, 3, 39, 47, 2, 6, 1, 38, 1, 2, 1, 2, 3, 1, 1, 1, 3, 6, 1, 11, 2, 1, 1, 2, 1, 1, 4, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 5, 2, 5, 2, 2, 1, 16, 20, 6, 25, 1, 4, 2, 1, 37, 11, 1, 1, 1, 7, 79, 15, 13, 57, 9, 61, 2, 8, 1, 2, 1, 75, 9, 1, 1, 1, 2, 1, 1, 1, 1, 2, 3, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 12, 2, 26, 48, 2, 1, 1, 4, 1, 5, 1, 44, 62, 3, 40, 1, 12, 1, 8, 1, 34, 7, 2, 17, 2, 7, 3, 5, 1, 2, 1, 2, 3, 1, 3, 3, 8, 3, 1, 2, 1, 2, 2, 1, 1, 1, 7, 14, 3, 18, 9, 1, 6, 5, 27, 77, 1, 6, 7, 1, 1, 3, 13, 6, 21, 1, 2, 3, 17, 3, 32, 1, 2, 1, 2, 67, 1, 1, 1, 3, 1, 1, 8, 1, 3, 2, 1, 1, 2, 10, 3, 39, 38, 4, 7, 1, 1, 1, 1, 33, 19, 3, 1, 125, 1, 11, 8, 1, 6, 20, 32, 4, 38, 8, 4, 2, 3, 10, 2, 1, 16, 3, 25, 43, 14, 1, 1, 1, 1, 7, 5, 1, 1, 1, 2, 1, 7, 5, 2, 2, 3, 1, 1, 1, 13, 1, 11, 1, 1, 1, 1, 1, 1, 1, 5, 8, 21, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 4, 1, 1, 1, 3, 1, 4, 1, 2, 1, 1, 3, 1, 3, 1, 21, 9, 6, 3, 4, 11, 4, 3, 2, 28, 2, 9, 2, 4, 4, 2, 1, 5, 2, 1, 1, 3, 1, 2, 1, 1, 9, 2, 1, 1, 1, 2, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 7, 1, 2, 5, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 5, 2, 5, 1, 1, 4, 2, 18, 1, 1, 1, 2, 5, 11, 7, 5, 2, 3, 4, 16, 1, 1, 1, 4, 1, 2, 1, 1, 1, 1, 2, 1, 4, 13, 1, 1, 6, 3, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 7, 1, 1, 6, 1, 3, 1, 1, 1, 1, 2, 2, 15, 1, 53, 1, 1, 1, 1, 1, 1, 4, 2, 6, 31, 7, 2, 13, 1, 6, 3, 1, 1, 1, 2, 3, 1, 43, 1, 1, 2, 1, 1, 1, 11, 2, 1, 1, 1, 1, 1, 2, 1, 6, 3, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 9, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 7, 1, 52, 2, 1, 1, 2, 8, 1, 1, 5, 1, 1, 1, 4, 4, 3, 5, 13, 1, 2, 9, 4, 73, 1, 1, 1, 1, 6, 2, 23, 2, 2, 4, 1, 1, 2, 1, 2, 4, 1, 1, 1, 4, 1, 6, 1, 2, 19, 1, 1, 100, 6, 36, 1, 40, 1, 4, 2, 1, 2, 9, 3, 4, 1, 1, 2, 1, 1, 1, 1, 1, 1, 4, 2, 2, 1, 2, 1, 2, 1, 1, 3, 1, 1, 6, 2, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 34, 31, 3, 3, 5, 3, 3, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 38, 1, 1, 13, 4, 8, 1, 1, 5, 1, 1, 1, 3, 1, 1, 3, 12, 3, 1, 1, 6, 47, 1, 1, 5, 11, 1, 1, 1, 4, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 7, 12, 12, 1, 1, 11, 1, 6, 8, 2, 5, 1, 13, 1, 1, 2, 2, 1, 1, 1, 1, 1, 8, 12, 6, 1, 1, 1, 2, 1, 2, 1, 1, 1, 4, 1, 1, 28, 7, 1, 3, 7, 1, 6, 1, 6, 1, 1, 1, 1, 1, 5, 2, 1, 18, 1, 4, 1, 13, 1, 1, 3, 2, 1, 1, 2, 1, 2, 1, 2, 14, 6, 10, 4, 49, 1, 1, 3, 52, 11, 1, 1, 41, 2, 1, 3, 1, 8, 12, 5, 1, 1, 2, 1, 1, 5, 3, 14, 10, 23, 2, 4, 17, 34, 72, 6, 13, 9, 55, 6, 1, 1, 1, 8, 14, 30, 13, 1, 1, 1, 1, 1, 7, 13, 1, 1, 1, 2, 1, 1, 5, 2, 13, 1, 1, 1, 10, 1, 1, 3, 2, 8, 3, 1, 1, 16, 6, 2, 1, 1, 1, 1, 12, 1, 1, 1, 44, 1, 1, 8, 17, 1, 1, 1, 1, 10, 1, 1, 1, 2, 8, 38, 11, 2, 2, 143, 10, 1, 10, 1, 1, 9, 1, 1, 6, 1, 8, 3, 6, 1, 2, 1, 1, 3, 8, 15, 25, 1, 1, 1, 3, 1, 1, 1, 3, 1, 77, 7, 19, 9, 15, 26, 7, 1, 2, 3, 13, 31, 1, 17, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 21, 4, 1, 3, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 17, 44, 9, 2, 14, 4, 28, 23, 6, 44, 1, 1, 3, 3, 38, 37, 2, 1, 10, 1, 1, 15, 46, 1, 3, 1, 9, 2, 1, 1, 1, 1, 1, 14, 1, 1, 1, 5, 1, 3, 3, 5, 1, 59, 1, 1, 1, 4, 3, 3, 4, 4, 1, 1, 6, 2, 1, 1, 5, 1, 1, 12, 3, 3, 3, 1, 14, 3, 1, 1, 1, 1, 7, 43, 1, 13, 1, 6, 7, 1, 1, 1, 1, 1, 1, 1, 57, 1, 2, 1, 22, 1, 1, 3, 1, 1, 1, 1, 2, 1, 1, 27, 2, 1, 1, 1, 2, 4, 4, 1, 1, 3, 3, 1, 5, 2, 1, 1, 4, 1, 5, 8, 1, 1, 5, 1, 2, 3, 1, 1, 1, 2, 18, 7, 1, 1, 1, 1, 1, 4, 14, 1, 1, 1, 7, 1, 1, 1, 1, 2, 11, 1, 6, 3, 1, 2, 12, 2, 6, 1, 1, 2, 1, 3, 2, 1, 1, 3, 1, 1, 19, 1, 1, 1, 7, 3, 1, 6, 2, 8, 1, 1, 2, 1, 1, 1, 65, 6, 3, 81, 4, 2, 1, 1, 1, 1, 1, 20, 1, 2, 1, 107, 2, 6, 3, 1, 3, 1, 1, 3, 2, 2, 1, 1, 2, 16, 1, 5, 1, 1, 2, 1, 2, 4, 2, 1, 1, 1, 20, 1, 1, 1, 1, 3, 3, 12, 1, 1, 1, 10, 64, 90, 11, 18, 20, 1, 42, 1, 1, 1, 11, 2, 1, 2, 1, 1, 1, 1, 8, 1, 1, 7, 1, 1, 8, 2, 7, 6, 1, 1, 7, 1, 3, 1, 1, 1, 2, 1, 2, 47, 2, 3, 2, 4, 4, 58, 3, 24, 4, 2, 1, 1, 15, 3, 3, 1, 2, 2, 1, 23, 1, 2, 1, 1, 1, 4, 2, 3, 4, 1, 1, 81, 2, 11, 3, 20, 3, 3, 3, 2, 24, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 9, 7, 10, 7, 2, 7, 2, 2, 1, 53, 1, 3, 1, 2, 29, 1, 1, 1, 3, 12, 14, 1, 1, 13, 1, 1, 28, 19, 2, 1, 1, 3, 1, 1, 1, 4, 2, 7, 5, 1, 2, 1, 4, 4, 5, 1, 1, 1, 6, 1, 2, 4, 1, 1, 1, 2, 1, 1, 2, 3, 18, 1, 1, 17, 1, 41, 6, 6, 1, 2, 1, 2, 1, 3, 1, 2, 2, 28, 1, 3, 1, 1, 2, 1, 1, 1, 1, 26, 1, 3, 1, 2, 1, 1, 1, 4, 18, 2, 1, 1, 3, 1, 1, 8, 2, 1, 1, 1, 1, 2, 11, 3, 1, 1, 9, 1, 1, 1, 1, 1, 1, 4, 2, 1, 1, 1, 6, 1, 1, 3, 2, 10, 2, 4, 2, 3, 3, 1, 1, 1, 2, 1, 9, 2, 72, 1, 1, 1, 8, 2, 1, 2, 1, 1, 1, 3, 1, 1, 1, 5, 4, 1, 6, 7, 2, 1, 12, 8, 1, 10, 14, 2, 4, 8, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 61, 50, 1, 6, 1, 1, 1, 23, 1, 1, 1, 2, 1, 10, 30, 1, 1, 1, 1, 14, 3, 2, 1, 1, 3, 1, 1, 1, 1, 2, 1, 1, 38, 1, 1, 1, 1, 1, 1, 2, 1, 5, 1, 1, 1, 14, 2, 10, 17, 3, 2, 1, 3, 1, 80, 1, 1, 1, 3, 1, 2, 2, 7, 2, 1, 1, 4, 1, 1, 2, 1, 1, 6, 6, 1, 2, 1, 3, 1, 1, 3, 2, 1, 3, 5, 1, 25, 2, 6, 1, 1, 1, 1, 23, 1, 4, 15, 2, 1, 1, 1, 11, 1, 2, 1, 2, 1, 1, 1, 1, 2, 17, 1, 1, 21, 1, 1, 6, 10, 17, 1, 1, 4, 4, 1, 1, 27, 3, 11, 5, 2, 1, 1, 1, 5, 7, 14, 1, 1, 5, 4, 6, 4, 2, 1, 1, 3, 11, 1, 1, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 2, 3, 7, 12, 1, 1, 2, 1, 1, 5, 2, 2, 34, 1, 2, 1, 1, 4, 3, 1, 1, 44, 1, 2, 1, 12, 1, 3, 3, 1, 1, 1, 5, 1, 1, 2, 1, 1, 9, 1, 2, 2, 8, 4, 3, 52, 15, 1, 1, 1, 21, 6, 1, 3, 6, 1, 3, 2, 4, 4, 2, 6, 2, 21, 9, 56, 4, 7, 2, 2, 1, 3, 18, 2, 2, 3, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 4, 33, 1, 1, 2, 1, 2, 1, 1, 1, 5, 1, 4, 1, 1, 2, 1, 1, 4, 11, 4, 4, 1, 1, 2, 1, 1, 1, 2, 7, 5, 20, 1, 3, 7, 1, 1, 1, 1, 1, 26, 3, 6, 3, 8, 1, 3, 7, 34, 1, 2, 9, 1, 7, 1, 12, 36, 7, 1, 6, 1, 1, 1, 2, 1, 2, 5, 1, 1, 1, 14, 2, 32, 3, 4, 1, 4, 1, 1, 1, 1, 2, 1, 10, 1, 14, 1, 1, 1, 1, 1, 1, 25, 1, 4, 4, 1, 1, 1, 32, 3, 1, 13, 39, 58, 11, 5, 1, 2, 2, 21, 3, 1, 1, 24, 25, 2, 2, 1, 3, 2, 15, 1, 1, 2, 11, 1, 7, 34, 1, 46, 1, 2, 5, 81, 1, 1, 1, 1, 1, 3, 1, 1, 6, 2, 1, 1, 17, 43, 3, 55, 2, 1, 3, 4, 21, 1, 1, 1, 2, 5, 10, 4, 2, 1, 5, 9, 3, 2, 8, 2, 11, 2, 9, 1, 1, 1, 2, 3, 4, 1, 1, 1, 1, 12, 1, 1, 1, 10, 1, 1, 3, 1, 1, 22, 25, 12, 2, 1, 1, 13, 1, 1, 1, 3, 1, 2, 1, 1, 3, 4, 5, 24, 2, 1, 3, 1, 1, 3, 3, 19, 1, 1, 4, 1, 1, 1, 1, 1, 10, 3, 4, 41, 1, 3, 4, 1, 8, 9, 9, 21, 1, 2, 2, 17, 1, 2, 8, 11, 20, 1, 2, 1, 1, 1, 1, 50, 32, 10, 32, 1, 1, 1, 31, 1, 11, 3, 2, 4, 1, 1, 1, 1, 1, 2, 1, 3, 1, 1, 2, 1, 5, 1, 4, 1, 1, 2, 1, 1, 20, 1, 1, 4, 1, 10, 1, 1, 1, 37, 10, 1, 1, 1, 5, 64, 1, 10, 1, 1, 1, 1, 6, 2, 8, 1, 2, 1, 2, 2, 27, 2, 1, 8, 2, 2, 2, 10, 1, 21, 6, 1, 1, 1, 14, 1, 17, 7, 1, 1, 18, 62, 3, 1, 2, 1, 10, 2, 2, 1, 1, 1, 2, 1, 1, 6, 1, 1, 2, 8, 4, 65, 2, 1, 2, 1, 4, 1, 12, 5, 3, 1, 4, 1, 13, 3, 1, 1, 1, 1, 1, 1, 1, 12, 1, 6, 26, 1, 30, 2, 1, 24, 28, 8, 6, 5, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 5, 11, 20, 1, 69, 3, 8, 3, 2, 11, 9, 1, 1, 3, 1, 1, 19, 1, 1, 11, 1, 2, 1, 2, 3, 3, 3, 8, 1, 2, 1, 47, 2, 41, 1, 6, 1, 27, 7, 6, 27, 5, 54, 27, 11, 13, 20, 27, 22, 22, 14, 16, 1, 1, 1, 1, 1, 1, 6, 1, 1, 10, 2, 2, 1, 1, 9, 3, 2, 2, 4, 1, 1, 1, 1, 2, 4, 1, 3, 14, 1, 18, 1, 4, 1, 41, 7, 1, 1, 2, 2, 1, 1, 1, 1, 2, 6, 1, 1, 1, 4, 2, 1, 1, 1, 5, 3, 1, 1, 8, 1, 4, 3, 1, 1, 1, 12, 1, 1, 1, 18, 1, 1, 14, 3, 1, 3, 1, 18, 6, 5, 2, 3, 5, 7, 10, 7, 1, 1, 1, 1, 1, 2, 1, 1, 17, 13, 1, 8, 4, 1, 3, 3, 5, 1, 11, 1, 2, 1, 4, 1, 10, 3, 7, 4, 1, 3, 6, 1, 2, 1, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 4, 1, 1, 2, 5, 2, 2, 149, 1, 1, 1, 1, 2, 1, 4, 1, 2, 1, 1, 3, 1, 3, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 2, 3, 1, 1, 1, 1, 10, 1, 2, 1, 1, 1, 54, 1, 76, 1, 18, 1, 1, 1, 38, 1, 18, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 4, 1, 1, 1, 3, 5, 2, 3, 22, 1, 1, 1, 2, 2, 1, 2, 5, 42, 1, 2, 1, 2, 1, 5, 6, 2, 1, 1, 1, 2, 5, 1, 3, 1, 2, 1, 3, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 13, 1, 39, 1, 1, 1, 3, 5, 3, 1, 2, 1, 1, 13, 5, 1, 1, 1, 3, 1, 1, 1, 1, 2, 1, 1, 1, 4, 1, 2, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 2, 1, 1, 1, 1, 1, 1, 3, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 3, 1, 5, 6, 3, 2, 1, 1, 1, 1, 1, 4, 3, 1, 11, 3, 1, 2, 1, 2, 2, 1, 2, 1, 3, 1, 2, 1, 1, 2, 94, 1, 1, 2, 2, 2, 1, 1, 1, 2, 4, 1, 1, 3, 3, 1, 1, 1, 1, 42, 1, 2, 2, 9, 11, 1, 6, 3, 1, 2, 1, 2, 8, 1, 2, 17, 1, 1, 1, 5, 2, 3, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 3, 1, 1, 37, 1, 2, 5, 1, 12, 1, 1, 1, 1, 2, 1, 2, 2, 2, 14, 1, 3, 1, 1, 1, 2, 1, 1, 1, 3, 2, 1, 1, 1, 2, 1, 1, 39, 1, 1, 3, 1, 4, 3, 2, 1, 8, 2, 1, 1, 1, 1, 1, 5, 2, 1, 1, 1, 2, 1, 1, 1, 1, 11, 1, 3, 1, 1, 1, 2, 1, 4, 3, 3, 1, 10, 2, 1, 1, 1, 1, 54, 5, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 15, 1, 1, 1, 1, 1, 1, 5, 2, 1, 15, 1, 1, 1, 1, 2, 4, 1, 1, 1, 1, 1, 2, 1, 1, 7, 5, 35, 1, 104, 22, 29, 1, 3, 1, 6, 1, 1, 1, 1, 1, 16, 2, 3, 1, 1, 1, 1, 6, 4, 5, 3, 1, 2, 1, 1, 1, 3, 2, 1, 1, 2, 1, 1, 1, 3, 1, 2, 1, 1, 1, 1, 2, 1, 2, 3, 1, 1, 1, 1, 2, 3, 3, 1, 2, 5, 1, 1, 1, 1, 1, 4, 1, 19, 3, 2, 2, 1, 1, 14, 2, 1, 1, 1, 20, 2, 1, 2, 1, 2, 1, 4, 1, 2, 1, 1, 1, 1, 1, 1, 25, 1, 1, 3, 1, 5, 6, 3, 3, 8, 8, 5, 6, 4, 1, 5, 1, 2, 4, 1, 2, 1, 2, 1, 2, 1, 1, 1, 6, 39, 1, 1, 1, 1, 1, 1, 8, 11, 4, 2, 7, 12, 1, 8, 1, 11, 1, 11, 1, 2, 6, 5, 1, 11, 1, 1, 19, 3, 4, 2, 5, 1, 10, 2, 1, 2, 1, 1, 3, 4, 1, 4, 4, 2, 5, 2, 3, 22, 1, 1, 4, 5, 43, 1, 1, 1, 26, 1, 2, 28, 1, 4, 1, 8, 1, 11, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 7, 4, 1, 1, 16, 5, 2, 1, 1, 3, 4, 1, 6, 1, 31, 3, 2, 1, 1, 4, 9, 1, 22, 1, 1, 1, 2, 1, 1, 33, 2, 7, 1, 1, 1, 2, 1, 1, 5, 17, 1, 1, 4, 9, 10, 3, 2, 1, 1, 3, 4, 1, 1, 39, 3, 3, 4, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 4, 1, 7, 3, 1, 2, 2, 1, 4, 4, 4, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 6, 6, 2, 1, 1, 1, 1, 2, 6, 1, 2, 3, 2, 8, 1, 1, 1, 1, 1, 1, 17, 4, 1, 1, 1, 1, 18, 6, 1, 1, 1, 3, 8, 27, 1, 1, 6, 1, 2, 9, 1, 1, 10, 1, 5, 29, 1, 3, 1, 1, 5, 6, 2, 3, 1, 2, 1, 4, 3, 1, 6, 9, 9, 1, 1, 2, 1, 11, 3, 1, 2, 1, 1, 2, 3, 1, 1, 30, 1, 1, 2, 2, 1, 1, 1, 1, 21, 1, 3, 6, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 4, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 7, 4, 1, 1, 21, 1, 6, 5, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 30, 1, 6, 7, 2, 1, 2, 2, 18, 8, 1, 1, 1, 13, 1, 1, 3, 1, 1, 9, 1, 9, 1, 2, 2, 1, 5, 1, 1, 13, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 9, 1, 71, 5, 5, 4, 8, 35, 20, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 3, 3, 2, 8, 1, 1, 1, 1, 50, 1, 2, 1, 5, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 3, 4, 1, 52, 1, 4, 2, 2, 1, 1, 48, 1, 4, 61, 3, 37, 35, 12, 1, 3, 11, 33, 1, 2, 1, 3, 2, 4, 1, 5, 2, 1, 1, 8, 4, 1, 3, 3, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 10, 20, 1, 3, 1, 32, 3, 1, 5, 1, 18, 2, 2, 11, 2, 3, 3, 1, 2, 1, 2, 1, 3, 1, 6, 1, 14, 1, 10, 1, 22, 7, 1, 1, 6, 3, 6, 1, 6, 6, 2, 20, 10, 1, 1, 1, 7, 1, 1, 2, 1, 1, 5, 1, 1, 1, 3, 3, 1, 20, 8, 1, 1, 6, 1, 2, 2, 11, 2, 1, 2, 1, 2, 6, 1, 2, 1, 6, 1, 5, 1, 1, 1, 3, 1, 6, 1, 1, 1, 1, 1, 2, 3, 2, 1, 40, 1, 2, 1, 45, 11, 1, 2, 1, 35, 1, 3, 1, 7, 1, 1, 1, 1, 1, 1, 1, 28, 1, 15, 1, 2, 1, 4, 7, 46, 1, 9, 7, 1, 19, 1, 1, 1, 1, 1, 3, 1, 1, 2, 11, 1, 1, 1, 1, 3, 6, 7, 8, 3, 10, 19, 1, 1, 1, 3, 34, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 6, 37, 1, 34, 1, 3, 3, 11, 19, 8, 1, 3, 2, 1, 1, 1, 1, 4, 1, 4, 5, 1, 8, 3, 7, 3, 1, 1, 1, 11, 1, 4, 6, 2, 1, 3, 5, 2, 1, 1, 22, 1, 30, 9, 2, 1, 1, 31, 1, 4, 1, 1, 1, 7, 1, 1, 2, 1, 4, 14, 1, 11, 1, 1, 1, 1, 1, 2, 3, 1, 8, 1, 1, 2, 4, 2, 84, 7, 1, 1, 7, 8, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 4, 1, 1, 1, 1, 2, 12, 8, 1, 1, 1, 2, 1, 10, 1]\n"
     ]
    }
   ],
   "source": [
    "# Which movie received the highest number of ratings?\n",
    "\n",
    "# 1. find the set of movie IDs\n",
    "\n",
    "movieIds = list(set(ratings_df[\"movieId\"]))\n",
    "# movieIds = np.unique(ratings_df['movieId'])\n",
    "# movieIds = ratings_df['movieId'].unique()\n",
    "# print(movieIds)\n",
    "\n",
    "\n",
    "# 2. find the number of ratings for each movie (a for loop through all movies is needed)\n",
    "num_ratings = []\n",
    "for movie in movieIds:\n",
    "    index_movie = (ratings_df[\"movieId\"] == movie)\n",
    "    sub_df = ratings_df[index_movie]\n",
    "    num_ratings.append(len(sub_df))\n",
    "print(num_ratings)\n",
    "# 3. find the movie ID that has the maximum number of ratings\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "329\n",
      "(array([341], dtype=int64),)\n",
      "356\n"
     ]
    }
   ],
   "source": [
    "print(np.max(num_ratings)) # 329\n",
    "idx = np.where(np.array(num_ratings) == 329)\n",
    "print(idx)\n",
    "movieId = movieIds[341]\n",
    "print(movieId)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>356</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>Comedy|Drama|Romance|War</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                title                    genres\n",
       "314      356  Forrest Gump (1994)  Comedy|Drama|Romance|War"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the movies.csv file as a second data frame movies_df\n",
    "movies_df = pd.read_csv(\"Data/ml-latest-small/movies.csv\", delimiter=\",\")\n",
    "movies_df.head()\n",
    "\n",
    "# Find the name of the movie with highest number of ratings.\n",
    "index = movies_df['movieId'] == 356\n",
    "movies_df[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[47, 7, 6, 0, 3, 24, 2, 1, 2, 10, 11, 1, 0, 3, 1, 14, 19, 4, 3, 0, 9, 3, 2, 3, 17, 0, 2, 3, 8, 1, 2, 45, 0, 27, 0, 13, 0, 0, 10, 1, 1, 0, 0, 1, 3, 1, 52, 4, 0, 71, 0, 3, 2, 0, 0, 0, 1, 12, 0, 3, 1, 20, 0, 0, 1, 0, 1, 1, 4, 6, 0, 0, 0, 2, 0, 3, 0, 0, 1, 0, 1, 4, 0, 4, 4, 0, 2, 0, 1, 0, 0, 1, 6, 0, 1, 0, 2, 0, 5, 1, 0, 14, 1, 0, 3, 0, 1, 80, 32, 8, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 1, 3, 0, 0, 0, 0, 0, 1, 0, 0, 1, 7, 0, 0, 2, 0, 1, 0, 1, 45, 7, 0, 7, 1, 0, 0, 1, 3, 0, 3, 14, 5, 11, 1, 23, 1, 0, 8, 0, 3, 2, 2, 2, 2, 4, 4, 0, 0, 0, 1, 0, 0, 0, 8, 2, 1, 4, 0, 1, 1, 2, 1, 6, 1, 0, 0, 3, 1, 0, 0, 0, 3, 0, 0, 0, 4, 2, 0, 0, 0, 0, 3, 0, 7, 7, 1, 2, 0, 0, 0, 4, 23, 4, 6, 1, 0, 1, 2, 14, 5, 0, 0, 9, 6, 2, 1, 0, 0, 1, 0, 0, 0, 0, 13, 4, 2, 3, 0, 0, 4, 10, 0, 1, 2, 3, 0, 0, 104, 8, 2, 0, 13, 7, 2, 0, 0, 1, 1, 9, 2, 0, 0, 2, 5, 0, 0, 5, 3, 4, 0, 1, 0, 0, 0, 12, 2, 5, 0, 10, 35, 0, 0, 123, 0, 2, 9, 0, 0, 1, 1, 1, 6, 5, 5, 0, 0, 0, 0, 0, 3, 2, 11, 7, 153, 5, 0, 0, 2, 1, 0, 1, 1, 1, 1, 8, 0, 0, 1, 14, 2, 0, 0, 16, 1, 12, 0, 6, 2, 0, 12, 3, 0, 1, 7, 13, 5, 1, 0, 13, 0, 1, 116, 14, 0, 0, 0, 0, 7, 0, 40, 0, 2, 12, 6, 0, 4, 1, 1, 0, 1, 2, 2, 26, 0, 0, 24, 2, 2, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 6, 0, 0, 0, 0, 1, 0, 0, 1, 0, 3, 1, 0, 0, 0, 4, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 5, 2, 0, 6, 1, 0, 0, 0, 0, 3, 10, 5, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 11, 0, 1, 46, 0, 0, 0, 0, 0, 4, 1, 1, 0, 0, 7, 0, 0, 9, 9, 0, 0, 0, 0, 40, 1, 0, 1, 3, 0, 1, 0, 0, 0, 3, 2, 1, 5, 0, 1, 8, 1, 0, 15, 1, 1, 0, 0, 6, 0, 8, 9, 0, 0, 0, 0, 4, 4, 2, 1, 0, 1, 5, 0, 0, 0, 5, 0, 92, 0, 7, 1, 1, 0, 0, 2, 3, 0, 0, 3, 8, 0, 46, 1, 5, 0, 1, 0, 0, 0, 0, 1, 12, 5, 15, 1, 15, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 6, 16, 35, 64, 35, 17, 92, 10, 31, 7, 10, 3, 0, 0, 0, 0, 0, 57, 1, 4, 1, 0, 2, 0, 3, 0, 0, 1, 1, 1, 5, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 3, 20, 0, 1, 1, 2, 0, 0, 3, 8, 2, 1, 0, 0, 1, 0, 1, 1, 4, 1, 3, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 0, 1, 0, 3, 0, 1, 0, 12, 0, 3, 6, 0, 0, 1, 0, 0, 0, 0, 17, 0, 0, 17, 3, 7, 0, 0, 14, 0, 1, 36, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 24, 1, 26, 0, 1, 3, 5, 5, 5, 0, 4, 0, 0, 0, 0, 0, 1, 1, 1, 8, 1, 5, 0, 1, 4, 0, 0, 0, 1, 13, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 4, 0, 0, 0, 2, 9, 1, 2, 0, 1, 0, 0, 0, 2, 0, 1, 0, 0, 1, 88, 2, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 2, 0, 0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 1, 0, 0, 0, 13, 14, 1, 1, 8, 17, 32, 0, 3, 6, 20, 6, 13, 5, 41, 10, 8, 1, 5, 7, 3, 26, 10, 1, 10, 22, 34, 0, 8, 0, 5, 0, 5, 2, 3, 9, 1, 1, 2, 0, 1, 3, 0, 2, 1, 0, 2, 0, 2, 1, 0, 1, 6, 0, 20, 5, 5, 0, 0, 0, 0, 0, 2, 9, 10, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 2, 0, 3, 3, 1, 3, 16, 1, 11, 3, 0, 5, 2, 0, 1, 21, 29, 0, 0, 0, 0, 6, 4, 0, 0, 2, 2, 0, 4, 0, 1, 0, 0, 0, 0, 2, 0, 6, 8, 3, 0, 1, 2, 1, 0, 1, 0, 0, 27, 3, 2, 19, 23, 1, 0, 0, 9, 0, 4, 1, 1, 45, 17, 0, 1, 4, 4, 3, 4, 28, 0, 1, 9, 5, 10, 2, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 3, 1, 0, 4, 0, 5, 0, 3, 1, 0, 0, 0, 51, 0, 0, 1, 0, 3, 18, 1, 2, 0, 0, 0, 0, 0, 0, 1, 14, 0, 4, 1, 1, 6, 4, 0, 9, 1, 2, 3, 1, 6, 1, 0, 1, 2, 41, 3, 80, 55, 69, 23, 38, 24, 1, 18, 21, 0, 36, 19, 45, 7, 65, 4, 10, 40, 36, 15, 0, 7, 4, 21, 17, 48, 28, 9, 5, 25, 1, 4, 15, 0, 19, 6, 2, 12, 15, 10, 1, 7, 3, 0, 23, 4, 12, 7, 9, 7, 20, 24, 6, 8, 20, 2, 19, 5, 2, 0, 6, 5, 29, 21, 2, 8, 0, 12, 10, 32, 11, 12, 1, 4, 49, 1, 16, 3, 11, 12, 18, 3, 18, 2, 1, 2, 12, 7, 6, 9, 4, 8, 15, 2, 1, 31, 6, 4, 1, 2, 3, 3, 6, 10, 3, 1, 9, 3, 17, 0, 0, 17, 0, 1, 0, 0, 2, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 3, 0, 0, 0, 3, 2, 1, 0, 3, 1, 1, 3, 0, 0, 0, 2, 0, 22, 6, 11, 1, 0, 1, 0, 1, 0, 1, 3, 4, 1, 5, 1, 12, 1, 7, 1, 1, 2, 4, 0, 0, 0, 0, 1, 0, 31, 1, 0, 0, 9, 1, 9, 18, 3, 1, 0, 5, 0, 0, 0, 0, 0, 3, 1, 8, 9, 1, 1, 6, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 2, 0, 0, 5, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 4, 1, 0, 0, 0, 4, 0, 2, 0, 2, 0, 3, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 10, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 11, 1, 0, 0, 0, 0, 16, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 2, 2, 1, 0, 9, 0, 15, 0, 0, 0, 17, 0, 0, 5, 0, 0, 1, 1, 0, 0, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 18, 1, 0, 0, 0, 0, 2, 25, 0, 2, 2, 0, 0, 0, 0, 10, 0, 1, 0, 0, 1, 0, 1, 4, 9, 8, 7, 0, 1, 0, 6, 0, 0, 0, 0, 0, 0, 0, 19, 0, 0, 0, 0, 2, 0, 0, 0, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0, 10, 5, 0, 7, 0, 1, 0, 3, 0, 12, 3, 1, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 2, 1, 0, 34, 0, 0, 5, 0, 1, 0, 2, 0, 7, 4, 17, 3, 0, 1, 1, 0, 2, 0, 0, 32, 2, 3, 1, 0, 0, 0, 0, 3, 7, 0, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 13, 1, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 3, 12, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 4, 4, 0, 0, 9, 0, 0, 3, 8, 0, 0, 0, 6, 0, 15, 1, 0, 0, 4, 0, 0, 1, 0, 0, 1, 1, 0, 0, 3, 0, 0, 1, 0, 1, 8, 0, 10, 0, 3, 3, 1, 8, 6, 12, 2, 4, 3, 5, 1, 3, 12, 2, 0, 2, 1, 1, 10, 22, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 12, 0, 1, 9, 1, 2, 2, 0, 6, 3, 0, 1, 0, 4, 10, 6, 1, 0, 0, 0, 0, 4, 14, 6, 2, 1, 6, 0, 1, 0, 0, 61, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 8, 2, 1, 7, 1, 1, 0, 0, 2, 1, 0, 1, 10, 0, 11, 1, 6, 10, 0, 4, 1, 1, 0, 3, 0, 0, 1, 0, 0, 0, 3, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 1, 1, 0, 3, 2, 2, 0, 0, 0, 2, 11, 2, 2, 3, 1, 0, 0, 0, 0, 3, 2, 1, 0, 0, 0, 1, 2, 2, 1, 1, 3, 1, 3, 1, 1, 3, 3, 0, 0, 6, 1, 0, 0, 1, 0, 9, 2, 0, 0, 0, 1, 0, 0, 2, 9, 5, 1, 0, 0, 0, 0, 4, 0, 0, 0, 1, 0, 4, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 6, 0, 0, 0, 0, 0, 0, 6, 11, 1, 0, 1, 1, 0, 3, 2, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 0, 4, 2, 1, 3, 0, 1, 0, 9, 2, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 6, 1, 0, 0, 0, 2, 1, 0, 6, 0, 0, 0, 1, 0, 0, 1, 14, 11, 1, 4, 0, 0, 2, 1, 0, 2, 0, 9, 2, 6, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 6, 0, 6, 0, 1, 35, 1, 1, 0, 33, 1, 0, 0, 1, 0, 0, 3, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 2, 5, 6, 0, 10, 0, 2, 0, 2, 4, 0, 0, 0, 0, 0, 5, 1, 0, 0, 0, 2, 1, 0, 0, 2, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 2, 0, 0, 2, 3, 0, 2, 4, 13, 19, 0, 4, 0, 0, 1, 0, 3, 0, 0, 1, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 2, 1, 0, 6, 2, 0, 0, 7, 0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 4, 0, 1, 0, 2, 0, 1, 0, 1, 0, 1, 0, 0, 3, 1, 0, 0, 0, 1, 2, 1, 2, 0, 0, 0, 0, 2, 1, 6, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 22, 0, 0, 0, 0, 1, 2, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 12, 2, 1, 1, 1, 0, 0, 0, 0, 1, 0, 2, 19, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 1, 0, 109, 8, 0, 0, 1, 0, 0, 1, 0, 10, 1, 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 11, 0, 3, 1, 2, 0, 0, 0, 0, 0, 1, 0, 0, 1, 7, 0, 0, 0, 21, 1, 0, 1, 0, 1, 0, 11, 0, 0, 0, 0, 0, 0, 6, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 9, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 6, 0, 0, 0, 0, 1, 0, 0, 14, 5, 3, 0, 5, 1, 0, 11, 0, 2, 0, 1, 0, 0, 1, 17, 0, 7, 1, 1, 0, 12, 1, 0, 1, 6, 0, 9, 0, 0, 21, 1, 0, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 10, 36, 4, 1, 0, 0, 0, 0, 1, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 22, 3, 0, 0, 0, 3, 12, 0, 0, 0, 0, 0, 1, 18, 0, 1, 0, 1, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 6, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 3, 0, 1, 0, 0, 1, 0, 0, 0, 65, 1, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 4, 6, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 5, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 1, 2, 4, 0, 3, 5, 11, 2, 18, 1, 3, 0, 4, 2, 1, 0, 0, 0, 0, 0, 1, 0, 3, 2, 0, 1, 0, 1, 1, 1, 5, 0, 0, 6, 6, 3, 0, 5, 0, 3, 1, 0, 0, 0, 81, 0, 0, 0, 0, 1, 0, 8, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 1, 11, 0, 1, 2, 3, 0, 1, 0, 0, 0, 23, 13, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 0, 4, 1, 0, 0, 0, 0, 0, 6, 0, 1, 4, 4, 2, 2, 1, 9, 5, 2, 0, 0, 0, 3, 1, 1, 1, 0, 0, 0, 11, 2, 0, 0, 0, 0, 5, 0, 3, 2, 0, 0, 0, 0, 1, 1, 6, 3, 4, 1, 5, 0, 2, 1, 0, 0, 0, 0, 1, 4, 6, 7, 1, 1, 3, 5, 2, 0, 0, 1, 0, 1, 1, 1, 1, 0, 6, 3, 0, 0, 3, 0, 2, 2, 8, 0, 0, 0, 0, 0, 19, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 2, 0, 4, 2, 2, 1, 1, 0, 2, 8, 0, 0, 1, 0, 0, 1, 0, 0, 0, 3, 12, 4, 0, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 4, 0, 1, 0, 0, 1, 0, 4, 1, 0, 1, 0, 1, 1, 3, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 2, 4, 1, 0, 0, 1, 0, 2, 6, 2, 2, 0, 1, 1, 0, 2, 2, 1, 0, 0, 3, 4, 2, 0, 0, 0, 0, 0, 1, 1, 1, 6, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 1, 4, 1, 0, 1, 0, 3, 1, 0, 1, 1, 0, 0, 1, 2, 0, 0, 0, 0, 1, 0, 6, 0, 0, 0, 2, 0, 2, 1, 0, 0, 2, 0, 0, 0, 0, 1, 0, 3, 2, 2, 7, 6, 5, 0, 2, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 1, 2, 0, 0, 0, 0, 0, 3, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 25, 5, 0, 15, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 7, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 3, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 0, 7, 2, 2, 11, 2, 0, 1, 2, 3, 0, 0, 0, 8, 0, 1, 4, 0, 2, 1, 0, 2, 6, 0, 0, 1, 4, 4, 2, 0, 3, 6, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 10, 0, 0, 0, 1, 2, 1, 12, 1, 1, 1, 0, 0, 3, 0, 0, 1, 0, 2, 1, 1, 13, 0, 0, 1, 3, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 43, 0, 0, 0, 0, 0, 9, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 12, 2, 1, 0, 1, 1, 1, 2, 0, 0, 8, 1, 6, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 2, 4, 13, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 1, 5, 1, 0, 0, 0, 1, 0, 0, 0, 1, 6, 2, 0, 0, 0, 1, 1, 0, 1, 1, 0, 8, 0, 4, 1, 0, 2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 7, 1, 1, 5, 0, 0, 0, 0, 0, 1, 0, 17, 0, 1, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 2, 0, 1, 1, 1, 4, 1, 0, 1, 1, 0, 3, 1, 1, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 4, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 3, 3, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 11, 0, 0, 0, 0, 0, 2, 0, 0, 0, 6, 6, 0, 1, 0, 0, 8, 1, 2, 0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 5, 0, 0, 6, 25, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 7, 0, 0, 0, 1, 1, 0, 1, 0, 0, 4, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 6, 0, 21, 0, 0, 0, 0, 0, 6, 0, 0, 1, 1, 0, 0, 0, 25, 0, 0, 3, 6, 3, 1, 0, 3, 0, 2, 11, 2, 5, 0, 17, 1, 0, 0, 0, 1, 13, 0, 0, 1, 0, 1, 0, 3, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 2, 2, 0, 0, 4, 2, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 1, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 3, 0, 1, 42, 0, 0, 0, 1, 0, 0, 6, 0, 0, 0, 7, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 11, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 2, 0, 1, 0, 0, 30, 0, 11, 10, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 1, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 0, 3, 3, 0, 1, 0, 0, 0, 1, 0, 2, 1, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 0, 2, 0, 2, 0, 8, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 3, 1, 0, 1, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 7, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 2, 4, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 6, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 6, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 10, 0, 0, 0, 2, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 25, 0, 2, 2, 0, 0, 16, 0, 0, 0, 1, 0, 15, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 2, 2, 10, 1, 2, 0, 3, 0, 1, 0, 0, 0, 0, 43, 0, 4, 1, 1, 10, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 62, 1, 21, 0, 0, 0, 0, 0, 2, 0, 6, 0, 0, 4, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 2, 0, 1, 0, 2, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 11, 1, 0, 4, 1, 3, 0, 1, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 0, 0, 1, 7, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 3, 2, 7, 0, 1, 0, 0, 1, 5, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 4, 5, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 2, 0, 0, 0, 1, 1, 13, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 9, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 23, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 4, 0, 0, 0, 6, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 12, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 2, 0, 0, 9, 6, 0, 3, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 1, 49, 0, 1, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 18, 0, 8, 3, 0, 2, 9, 0, 9, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 21, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 2, 8, 0, 0, 1, 1, 0, 3, 0, 1, 0, 22, 2, 0, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 18, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, 3, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 18, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 2, 0, 0, 1, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 5, 1, 0, 0, 5, 0, 1, 1, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 2, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 4, 9, 0, 0, 0, 53, 0, 1, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 33, 0, 0, 0, 0, 0, 0, 4, 0, 3, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 2, 0, 0, 0, 1, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 15, 0, 0, 0, 0, 9, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 4, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 9, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 9, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 10, 0, 0, 0, 0, 1, 12, 0, 1, 1, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 3, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 1, 0, 4, 1, 2, 0, 0, 0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0, 0, 0, 0, 0, 1, 1, 4, 4, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 2, 0, 2, 20, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 2, 0, 5, 0, 0, 0, 0, 13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 5, 0, 0, 1, 0, 0, 0, 2, 1, 0, 0, 11, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 11, 0, 0, 0, 0, 0, 0, 1, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 22, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 4, 0, 0, 10, 1, 5, 0, 6, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 3, 0, 1, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 0, 0, 1, 7, 0, 1, 0, 14, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 41, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 1, 1, 0, 2, 3, 1, 0, 0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 2, 1, 0, 4, 1, 0, 0, 0, 8, 5, 0, 0, 0, 0, 0, 1, 9, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 1, 0, 13, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 7, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 5, 1, 0, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 12, 17, 0, 1, 1, 0, 5, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 13, 0, 1, 1, 0, 0, 11, 1, 4, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 3, 0, 2, 0, 1, 4, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 12, 7, 1, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 11, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 3, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 3, 0, 0, 3, 1, 2, 0, 1, 5, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 10, 1, 2, 0, 0, 1, 4, 0, 0, 0, 2, 4, 0, 0, 0, 1, 1, 1, 0, 0, 0, 3, 0, 0, 4, 0, 3, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 5, 1, 10, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 1, 0, 0, 5, 6, 2, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 10, 5, 4, 1, 0, 0, 0, 4, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 2, 0, 3, 0, 0, 0, 7, 0, 0, 0, 0, 1, 11, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 9, 0, 0, 0, 0, 0, 0, 4, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 0, 2, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 13, 0, 2, 1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 10, 0, 5, 0, 0, 0, 4, 1, 0, 4, 0, 15, 1, 2, 4, 4, 5, 3, 3, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 10, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 49, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 3, 0, 17, 0, 1, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 9, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 11, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 6, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0, 1, 3, 0, 0, 3, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 0, 0, 0, 0, 4, 0, 7, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 13, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 1, 3, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 5, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 5, 2, 1, 0, 0, 2, 1, 0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 0, 0, 1, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 2, 0, 4, 1, 1, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 5, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 12, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 1, 0, 2, 0]\n",
      "153\n"
     ]
    }
   ],
   "source": [
    "# Which movie received the largest amount of 5.0?\n",
    "\n",
    "# 1. extract the 5.0 ratings\n",
    "index = (ratings_df['rating'] == 5.0)\n",
    "ratings_5 = ratings_df[index]\n",
    "ratings_5.head()\n",
    "# ratings_t = ratings_df[ratings_df['rating'] == 5.0]\n",
    "\n",
    "# 2. For each movieId, find the number of 5.0 ratings.\n",
    "movieIds = set(ratings_df[\"movieId\"])\n",
    "\n",
    "all_numRatings = []\n",
    "\n",
    "for Id in movieIds:\n",
    "    index = (ratings_5['movieId'] == Id)\n",
    "    ratings_5_id = ratings_5[index]  # gives the subset of 5.0 ratings to this movie\n",
    "    numRatings = len(ratings_5_id)\n",
    "    all_numRatings.append(numRatings)\n",
    "print(all_numRatings)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "153\n",
      "303\n",
      "318\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>318</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                             title       genres\n",
       "277      318  Shawshank Redemption, The (1994)  Crime|Drama"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the name of the movie\n",
    "# Solution 1:\n",
    "maxRatings = np.max(all_numRatings)\n",
    "print(maxRatings)\n",
    "# idx = np.where(np.array(all_numRatings) == 153)\n",
    "idx = all_numRatings.index(maxRatings)\n",
    "print(idx)\n",
    "movieId = list(movieIds)[idx] # need to convert from set to list\n",
    "print(movieId)\n",
    "\n",
    "index = movies_df['movieId'] == 318\n",
    "movies_df[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "303\n",
      "318\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>318</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                             title       genres\n",
       "277      318  Shawshank Redemption, The (1994)  Crime|Drama"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution 2: use argsort to find the index directly\n",
    "idx = np.argsort(all_numRatings)[-1]\n",
    "print(idx)\n",
    "movieId = list(movieIds)[idx]\n",
    "print(movieId)\n",
    "index = movies_df['movieId'] == movieId\n",
    "movies_df[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Which five movie has the highest average rating?\n",
    "\n",
    "\n",
    "# Find the name of the movies\n",
    "\n",
    "\n",
    "# How many ratings do these movies receive?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Working with multiple data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                    title  \\\n",
       "0        1         Toy Story (1995)   \n",
       "1        2           Jumanji (1995)   \n",
       "2        3  Grumpier Old Men (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df = pd.read_csv(\"Data/ml-latest-small/movies.csv\", delimiter=\",\")\n",
    "movies_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3.9209302325581397\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>avgRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>3.92093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  avgRating  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy    3.92093  \n",
       "1                   Adventure|Children|Fantasy        NaN  \n",
       "2                               Comedy|Romance        NaN  \n",
       "3                         Comedy|Drama|Romance        NaN  \n",
       "4                                       Comedy        NaN  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a \"AvgRating\" column: for each movie, fill in its average rating\n",
    "\n",
    "# Example: Find the average rating for the first movie\n",
    "movieId = movies_df.loc[0, 'movieId']\n",
    "print(movieId)\n",
    "index = (ratings_df['movieId'] == movieId)\n",
    "ratings_movie = ratings_df[index]\n",
    "ratings_movie.head()\n",
    "avgRating = np.mean(ratings_movie['rating'])\n",
    "print(avgRating)\n",
    "movies_df.loc[0, 'avgRating'] = avgRating # = findAvgRating(0)\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>avgRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>3.920930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>3.431818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>3.259615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>2.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>3.071429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  avgRating  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy   3.920930  \n",
       "1                   Adventure|Children|Fantasy   3.431818  \n",
       "2                               Comedy|Romance   3.259615  \n",
       "3                         Comedy|Drama|Romance   2.357143  \n",
       "4                                       Comedy   3.071429  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a function findAvgRating(int) to calculate the average\n",
    "# rating for a particular row index\n",
    "def findAvgRating(idx):\n",
    "    movieId = movies_df.loc[idx, 'movieId']\n",
    "    index = (ratings_df['movieId'] == movieId)\n",
    "    ratings_movie = ratings_df[index]\n",
    "    avgRating = np.mean(ratings_movie['rating'])\n",
    "    return avgRating\n",
    "    \n",
    "# movies_df.loc[1, 'avgRating'] = findAvgRating(1)\n",
    "# movies_df.loc[2, 'avgRating'] = findAvgRating(2)\n",
    "movies_df['avgRating'] = pd.Series(movies_df.index).apply(findAvgRating)\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>avgRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6499</th>\n",
       "      <td>53355</td>\n",
       "      <td>Sun Alley (Sonnenallee) (1999)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4768</th>\n",
       "      <td>7096</td>\n",
       "      <td>Rivers and Tides (2001)</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5878</th>\n",
       "      <td>33138</td>\n",
       "      <td>Palindromes (2004)</td>\n",
       "      <td>Adventure|Comedy|Drama</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>128087</td>\n",
       "      <td>Trinity and Sartana Are Coming (1972)</td>\n",
       "      <td>Comedy|Western</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2880</th>\n",
       "      <td>3851</td>\n",
       "      <td>I'm the One That I Want (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9337</th>\n",
       "      <td>160644</td>\n",
       "      <td>Indignation (2016)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8726</th>\n",
       "      <td>126921</td>\n",
       "      <td>The Fox and the Hound 2 (2006)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>3939</td>\n",
       "      <td>Slumber Party Massacre II (1987)</td>\n",
       "      <td>Horror</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>3940</td>\n",
       "      <td>Slumber Party Massacre III (1990)</td>\n",
       "      <td>Horror</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938</th>\n",
       "      <td>3941</td>\n",
       "      <td>Sorority House Massacre (1986)</td>\n",
       "      <td>Horror</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8717</th>\n",
       "      <td>126088</td>\n",
       "      <td>A Flintstones Christmas Carol (1994)</td>\n",
       "      <td>Animation|Children|Comedy</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2939</th>\n",
       "      <td>3942</td>\n",
       "      <td>Sorority House Massacre II (1990)</td>\n",
       "      <td>Horror</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>3951</td>\n",
       "      <td>Two Family House (2000)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9317</th>\n",
       "      <td>159811</td>\n",
       "      <td>The Bremen Town Musicians (1969)</td>\n",
       "      <td>Animation|Drama|Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8709</th>\n",
       "      <td>124851</td>\n",
       "      <td>Delirium (2014)</td>\n",
       "      <td>Adventure|Romance|Sci-Fi</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8707</th>\n",
       "      <td>124404</td>\n",
       "      <td>Snowflake, the White Gorilla (2011)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7993</th>\n",
       "      <td>96935</td>\n",
       "      <td>My Left Eye Sees Ghosts (Ngo joh aan gin diy g...</td>\n",
       "      <td>Comedy|Fantasy|Romance</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7978</th>\n",
       "      <td>96608</td>\n",
       "      <td>Runaway Brain (1995)</td>\n",
       "      <td>Animation|Comedy|Sci-Fi</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5785</th>\n",
       "      <td>31522</td>\n",
       "      <td>Marriage of Maria Braun, The (Ehe der Maria Br...</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7636</th>\n",
       "      <td>87834</td>\n",
       "      <td>My Life as McDull (Mak dau goo si) (2001)</td>\n",
       "      <td>Animation|Comedy|Drama</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                              title  \\\n",
       "6499    53355                     Sun Alley (Sonnenallee) (1999)   \n",
       "4768     7096                            Rivers and Tides (2001)   \n",
       "5878    33138                                 Palindromes (2004)   \n",
       "8752   128087              Trinity and Sartana Are Coming (1972)   \n",
       "2880     3851                     I'm the One That I Want (2000)   \n",
       "9337   160644                                 Indignation (2016)   \n",
       "8726   126921                     The Fox and the Hound 2 (2006)   \n",
       "2936     3939                   Slumber Party Massacre II (1987)   \n",
       "2937     3940                  Slumber Party Massacre III (1990)   \n",
       "2938     3941                     Sorority House Massacre (1986)   \n",
       "8717   126088               A Flintstones Christmas Carol (1994)   \n",
       "2939     3942                  Sorority House Massacre II (1990)   \n",
       "2947     3951                            Two Family House (2000)   \n",
       "9317   159811                   The Bremen Town Musicians (1969)   \n",
       "8709   124851                                    Delirium (2014)   \n",
       "8707   124404                Snowflake, the White Gorilla (2011)   \n",
       "7993    96935  My Left Eye Sees Ghosts (Ngo joh aan gin diy g...   \n",
       "7978    96608                              Runaway Brain (1995)    \n",
       "5785    31522  Marriage of Maria Braun, The (Ehe der Maria Br...   \n",
       "7636    87834          My Life as McDull (Mak dau goo si) (2001)   \n",
       "\n",
       "                                   genres  avgRating  \n",
       "6499                       Comedy|Romance        5.0  \n",
       "4768                          Documentary        5.0  \n",
       "5878               Adventure|Comedy|Drama        5.0  \n",
       "8752                       Comedy|Western        5.0  \n",
       "2880                               Comedy        5.0  \n",
       "9337                                Drama        5.0  \n",
       "8726  Adventure|Animation|Children|Comedy        5.0  \n",
       "2936                               Horror        5.0  \n",
       "2937                               Horror        5.0  \n",
       "2938                               Horror        5.0  \n",
       "8717            Animation|Children|Comedy        5.0  \n",
       "2939                               Horror        5.0  \n",
       "2947                                Drama        5.0  \n",
       "9317              Animation|Drama|Fantasy        5.0  \n",
       "8709             Adventure|Romance|Sci-Fi        5.0  \n",
       "8707  Adventure|Animation|Children|Comedy        5.0  \n",
       "7993               Comedy|Fantasy|Romance        5.0  \n",
       "7978              Animation|Comedy|Sci-Fi        5.0  \n",
       "5785                                Drama        5.0  \n",
       "7636               Animation|Comedy|Drama        5.0  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the data frame by 'avgRating'\n",
    "movies_df.sort_values(by=['avgRating'], inplace=True, ascending=False)\n",
    "movies_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>avgRating</th>\n",
       "      <th>numRatings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6499</th>\n",
       "      <td>53355</td>\n",
       "      <td>Sun Alley (Sonnenallee) (1999)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4768</th>\n",
       "      <td>7096</td>\n",
       "      <td>Rivers and Tides (2001)</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5878</th>\n",
       "      <td>33138</td>\n",
       "      <td>Palindromes (2004)</td>\n",
       "      <td>Adventure|Comedy|Drama</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>128087</td>\n",
       "      <td>Trinity and Sartana Are Coming (1972)</td>\n",
       "      <td>Comedy|Western</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2880</th>\n",
       "      <td>3851</td>\n",
       "      <td>I'm the One That I Want (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9337</th>\n",
       "      <td>160644</td>\n",
       "      <td>Indignation (2016)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8726</th>\n",
       "      <td>126921</td>\n",
       "      <td>The Fox and the Hound 2 (2006)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>3939</td>\n",
       "      <td>Slumber Party Massacre II (1987)</td>\n",
       "      <td>Horror</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>3940</td>\n",
       "      <td>Slumber Party Massacre III (1990)</td>\n",
       "      <td>Horror</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938</th>\n",
       "      <td>3941</td>\n",
       "      <td>Sorority House Massacre (1986)</td>\n",
       "      <td>Horror</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8717</th>\n",
       "      <td>126088</td>\n",
       "      <td>A Flintstones Christmas Carol (1994)</td>\n",
       "      <td>Animation|Children|Comedy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2939</th>\n",
       "      <td>3942</td>\n",
       "      <td>Sorority House Massacre II (1990)</td>\n",
       "      <td>Horror</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>3951</td>\n",
       "      <td>Two Family House (2000)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9317</th>\n",
       "      <td>159811</td>\n",
       "      <td>The Bremen Town Musicians (1969)</td>\n",
       "      <td>Animation|Drama|Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8709</th>\n",
       "      <td>124851</td>\n",
       "      <td>Delirium (2014)</td>\n",
       "      <td>Adventure|Romance|Sci-Fi</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8707</th>\n",
       "      <td>124404</td>\n",
       "      <td>Snowflake, the White Gorilla (2011)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7993</th>\n",
       "      <td>96935</td>\n",
       "      <td>My Left Eye Sees Ghosts (Ngo joh aan gin diy g...</td>\n",
       "      <td>Comedy|Fantasy|Romance</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7978</th>\n",
       "      <td>96608</td>\n",
       "      <td>Runaway Brain (1995)</td>\n",
       "      <td>Animation|Comedy|Sci-Fi</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5785</th>\n",
       "      <td>31522</td>\n",
       "      <td>Marriage of Maria Braun, The (Ehe der Maria Br...</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7636</th>\n",
       "      <td>87834</td>\n",
       "      <td>My Life as McDull (Mak dau goo si) (2001)</td>\n",
       "      <td>Animation|Comedy|Drama</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9365</th>\n",
       "      <td>162344</td>\n",
       "      <td>Tom Segura: Mostly Stories (2016)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3067</th>\n",
       "      <td>4116</td>\n",
       "      <td>Hollywood Shuffle (1987)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>4135</td>\n",
       "      <td>Monster Squad, The (1987)</td>\n",
       "      <td>Adventure|Comedy|Horror</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6919</th>\n",
       "      <td>64499</td>\n",
       "      <td>Che: Part One (2008)</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9379</th>\n",
       "      <td>163072</td>\n",
       "      <td>Winnie Pooh (1969)</td>\n",
       "      <td>Animation|Children</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                              title  \\\n",
       "6499    53355                     Sun Alley (Sonnenallee) (1999)   \n",
       "4768     7096                            Rivers and Tides (2001)   \n",
       "5878    33138                                 Palindromes (2004)   \n",
       "8752   128087              Trinity and Sartana Are Coming (1972)   \n",
       "2880     3851                     I'm the One That I Want (2000)   \n",
       "9337   160644                                 Indignation (2016)   \n",
       "8726   126921                     The Fox and the Hound 2 (2006)   \n",
       "2936     3939                   Slumber Party Massacre II (1987)   \n",
       "2937     3940                  Slumber Party Massacre III (1990)   \n",
       "2938     3941                     Sorority House Massacre (1986)   \n",
       "8717   126088               A Flintstones Christmas Carol (1994)   \n",
       "2939     3942                  Sorority House Massacre II (1990)   \n",
       "2947     3951                            Two Family House (2000)   \n",
       "9317   159811                   The Bremen Town Musicians (1969)   \n",
       "8709   124851                                    Delirium (2014)   \n",
       "8707   124404                Snowflake, the White Gorilla (2011)   \n",
       "7993    96935  My Left Eye Sees Ghosts (Ngo joh aan gin diy g...   \n",
       "7978    96608                              Runaway Brain (1995)    \n",
       "5785    31522  Marriage of Maria Braun, The (Ehe der Maria Br...   \n",
       "7636    87834          My Life as McDull (Mak dau goo si) (2001)   \n",
       "9365   162344                  Tom Segura: Mostly Stories (2016)   \n",
       "3067     4116                           Hollywood Shuffle (1987)   \n",
       "3081     4135                          Monster Squad, The (1987)   \n",
       "6919    64499                               Che: Part One (2008)   \n",
       "9379   163072                                 Winnie Pooh (1969)   \n",
       "\n",
       "                                   genres  avgRating  numRatings  \n",
       "6499                       Comedy|Romance        5.0           1  \n",
       "4768                          Documentary        5.0           1  \n",
       "5878               Adventure|Comedy|Drama        5.0           1  \n",
       "8752                       Comedy|Western        5.0           1  \n",
       "2880                               Comedy        5.0           1  \n",
       "9337                                Drama        5.0           1  \n",
       "8726  Adventure|Animation|Children|Comedy        5.0           1  \n",
       "2936                               Horror        5.0           1  \n",
       "2937                               Horror        5.0           1  \n",
       "2938                               Horror        5.0           1  \n",
       "8717            Animation|Children|Comedy        5.0           1  \n",
       "2939                               Horror        5.0           1  \n",
       "2947                                Drama        5.0           1  \n",
       "9317              Animation|Drama|Fantasy        5.0           1  \n",
       "8709             Adventure|Romance|Sci-Fi        5.0           1  \n",
       "8707  Adventure|Animation|Children|Comedy        5.0           1  \n",
       "7993               Comedy|Fantasy|Romance        5.0           1  \n",
       "7978              Animation|Comedy|Sci-Fi        5.0           1  \n",
       "5785                                Drama        5.0           1  \n",
       "7636               Animation|Comedy|Drama        5.0           1  \n",
       "9365                               Comedy        5.0           1  \n",
       "3067                               Comedy        5.0           1  \n",
       "3081              Adventure|Comedy|Horror        5.0           1  \n",
       "6919                            Drama|War        5.0           1  \n",
       "9379                   Animation|Children        5.0           1  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a \"NumRatings\" column: for each movie, fill in the number ratings\n",
    "def findNumRatings(movieId):\n",
    "    index = (ratings_df['movieId'] == movieId)\n",
    "    ratings_movie = ratings_df[index]\n",
    "    numRatings = len(ratings_movie)\n",
    "    return numRatings\n",
    "\n",
    "movies_df['numRatings'] = movies_df['movieId'].apply(findNumRatings)\n",
    "movies_df.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>avgRating</th>\n",
       "      <th>numRatings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>318</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.429022</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>858</td>\n",
       "      <td>Godfather, The (1972)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.289062</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2226</th>\n",
       "      <td>2959</td>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "      <td>4.272936</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>1221</td>\n",
       "      <td>Godfather: Part II, The (1974)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.259690</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6315</th>\n",
       "      <td>48516</td>\n",
       "      <td>Departed, The (2006)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "      <td>4.252336</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>1213</td>\n",
       "      <td>Goodfellas (1990)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>912</td>\n",
       "      <td>Casablanca (1942)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "      <td>4.240000</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6710</th>\n",
       "      <td>58559</td>\n",
       "      <td>Dark Knight, The (2008)</td>\n",
       "      <td>Action|Crime|Drama|IMAX</td>\n",
       "      <td>4.238255</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>50</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "      <td>4.237745</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>1197</td>\n",
       "      <td>Princess Bride, The (1987)</td>\n",
       "      <td>Action|Adventure|Comedy|Fantasy|Romance</td>\n",
       "      <td>4.232394</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                             title  \\\n",
       "277       318  Shawshank Redemption, The (1994)   \n",
       "659       858             Godfather, The (1972)   \n",
       "2226     2959                 Fight Club (1999)   \n",
       "922      1221    Godfather: Part II, The (1974)   \n",
       "6315    48516              Departed, The (2006)   \n",
       "914      1213                 Goodfellas (1990)   \n",
       "694       912                 Casablanca (1942)   \n",
       "6710    58559           Dark Knight, The (2008)   \n",
       "46         50        Usual Suspects, The (1995)   \n",
       "899      1197        Princess Bride, The (1987)   \n",
       "\n",
       "                                       genres  avgRating  numRatings  \n",
       "277                               Crime|Drama   4.429022         317  \n",
       "659                               Crime|Drama   4.289062         192  \n",
       "2226              Action|Crime|Drama|Thriller   4.272936         218  \n",
       "922                               Crime|Drama   4.259690         129  \n",
       "6315                     Crime|Drama|Thriller   4.252336         107  \n",
       "914                               Crime|Drama   4.250000         126  \n",
       "694                             Drama|Romance   4.240000         100  \n",
       "6710                  Action|Crime|Drama|IMAX   4.238255         149  \n",
       "46                     Crime|Mystery|Thriller   4.237745         204  \n",
       "899   Action|Adventure|Comedy|Fantasy|Romance   4.232394         142  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract movies with ratings >= 100\n",
    "movies_with_many_ratings = movies_df[movies_df['numRatings'] >= 100]\n",
    "movies_with_many_ratings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use .sort_values() to sort the data frame according to the AvgRating column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ch002\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>avgRating</th>\n",
       "      <th>numRatings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>356</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>Comedy|Drama|Romance|War</td>\n",
       "      <td>4.164134</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>318</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.429022</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "      <td>4.197068</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>593</td>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>Crime|Horror|Thriller</td>\n",
       "      <td>4.161290</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>2571</td>\n",
       "      <td>Matrix, The (1999)</td>\n",
       "      <td>Action|Sci-Fi|Thriller</td>\n",
       "      <td>4.192446</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>260</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>4.231076</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>480</td>\n",
       "      <td>Jurassic Park (1993)</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>110</td>\n",
       "      <td>Braveheart (1995)</td>\n",
       "      <td>Action|Drama|War</td>\n",
       "      <td>4.031646</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>589</td>\n",
       "      <td>Terminator 2: Judgment Day (1991)</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "      <td>3.970982</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>527</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>4.225000</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2226</th>\n",
       "      <td>2959</td>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "      <td>4.272936</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>3.920930</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>1196</td>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back...</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>4.215640</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>50</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "      <td>4.237745</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2145</th>\n",
       "      <td>2858</td>\n",
       "      <td>American Beauty (1999)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "      <td>4.056373</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>47</td>\n",
       "      <td>Seven (a.k.a. Se7en) (1995)</td>\n",
       "      <td>Mystery|Thriller</td>\n",
       "      <td>3.975369</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>780</td>\n",
       "      <td>Independence Day (a.k.a. ID4) (1996)</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "      <td>3.445545</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>150</td>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>Adventure|Drama|IMAX</td>\n",
       "      <td>3.845771</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>1198</td>\n",
       "      <td>Raiders of the Lost Ark (Indiana Jones and the...</td>\n",
       "      <td>Action|Adventure</td>\n",
       "      <td>4.207500</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3638</th>\n",
       "      <td>4993</td>\n",
       "      <td>Lord of the Rings: The Fellowship of the Ring,...</td>\n",
       "      <td>Adventure|Fantasy</td>\n",
       "      <td>4.106061</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                              title  \\\n",
       "314       356                                Forrest Gump (1994)   \n",
       "277       318                   Shawshank Redemption, The (1994)   \n",
       "257       296                                Pulp Fiction (1994)   \n",
       "510       593                   Silence of the Lambs, The (1991)   \n",
       "1939     2571                                 Matrix, The (1999)   \n",
       "224       260          Star Wars: Episode IV - A New Hope (1977)   \n",
       "418       480                               Jurassic Park (1993)   \n",
       "97        110                                  Braveheart (1995)   \n",
       "507       589                  Terminator 2: Judgment Day (1991)   \n",
       "461       527                            Schindler's List (1993)   \n",
       "2226     2959                                  Fight Club (1999)   \n",
       "0           1                                   Toy Story (1995)   \n",
       "898      1196  Star Wars: Episode V - The Empire Strikes Back...   \n",
       "46         50                         Usual Suspects, The (1995)   \n",
       "2145     2858                             American Beauty (1999)   \n",
       "43         47                        Seven (a.k.a. Se7en) (1995)   \n",
       "615       780               Independence Day (a.k.a. ID4) (1996)   \n",
       "123       150                                   Apollo 13 (1995)   \n",
       "900      1198  Raiders of the Lost Ark (Indiana Jones and the...   \n",
       "3638     4993  Lord of the Rings: The Fellowship of the Ring,...   \n",
       "\n",
       "                                           genres  avgRating  numRatings  \n",
       "314                      Comedy|Drama|Romance|War   4.164134         329  \n",
       "277                                   Crime|Drama   4.429022         317  \n",
       "257                   Comedy|Crime|Drama|Thriller   4.197068         307  \n",
       "510                         Crime|Horror|Thriller   4.161290         279  \n",
       "1939                       Action|Sci-Fi|Thriller   4.192446         278  \n",
       "224                       Action|Adventure|Sci-Fi   4.231076         251  \n",
       "418              Action|Adventure|Sci-Fi|Thriller   3.750000         238  \n",
       "97                               Action|Drama|War   4.031646         237  \n",
       "507                                 Action|Sci-Fi   3.970982         224  \n",
       "461                                     Drama|War   4.225000         220  \n",
       "2226                  Action|Crime|Drama|Thriller   4.272936         218  \n",
       "0     Adventure|Animation|Children|Comedy|Fantasy   3.920930         215  \n",
       "898                       Action|Adventure|Sci-Fi   4.215640         211  \n",
       "46                         Crime|Mystery|Thriller   4.237745         204  \n",
       "2145                                Drama|Romance   4.056373         204  \n",
       "43                               Mystery|Thriller   3.975369         203  \n",
       "615              Action|Adventure|Sci-Fi|Thriller   3.445545         202  \n",
       "123                          Adventure|Drama|IMAX   3.845771         201  \n",
       "900                              Action|Adventure   4.207500         200  \n",
       "3638                            Adventure|Fantasy   4.106061         198  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use sort_values() again to find the movies with largest number of ratings\n",
    "movies_with_many_ratings.sort_values(by=['numRatings'],\n",
    "                                     ascending=False,\n",
    "                                     inplace=True)\n",
    "movies_with_many_ratings.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ch002\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\ch002\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>avgRating</th>\n",
       "      <th>numRatings</th>\n",
       "      <th>isComedy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>1197</td>\n",
       "      <td>Princess Bride, The (1987)</td>\n",
       "      <td>Action|Adventure|Comedy|Fantasy|Romance</td>\n",
       "      <td>4.232394</td>\n",
       "      <td>142</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "      <td>4.197068</td>\n",
       "      <td>307</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>4973</td>\n",
       "      <td>Amelie (Fabuleux destin d'Amélie Poulain, Le) ...</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>4.183333</td>\n",
       "      <td>120</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>356</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>Comedy|Drama|Romance|War</td>\n",
       "      <td>4.164134</td>\n",
       "      <td>329</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>1136</td>\n",
       "      <td>Monty Python and the Holy Grail (1975)</td>\n",
       "      <td>Adventure|Comedy|Fantasy</td>\n",
       "      <td>4.161765</td>\n",
       "      <td>136</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>608</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "      <td>4.116022</td>\n",
       "      <td>181</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>778</td>\n",
       "      <td>Trainspotting (1996)</td>\n",
       "      <td>Comedy|Crime|Drama</td>\n",
       "      <td>4.039216</td>\n",
       "      <td>102</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>1270</td>\n",
       "      <td>Back to the Future (1985)</td>\n",
       "      <td>Adventure|Comedy|Sci-Fi</td>\n",
       "      <td>4.038012</td>\n",
       "      <td>171</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4360</th>\n",
       "      <td>6377</td>\n",
       "      <td>Finding Nemo (2003)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy</td>\n",
       "      <td>3.960993</td>\n",
       "      <td>141</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>1265</td>\n",
       "      <td>Groundhog Day (1993)</td>\n",
       "      <td>Comedy|Fantasy|Romance</td>\n",
       "      <td>3.944056</td>\n",
       "      <td>143</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                              title  \\\n",
       "899      1197                         Princess Bride, The (1987)   \n",
       "257       296                                Pulp Fiction (1994)   \n",
       "3622     4973  Amelie (Fabuleux destin d'Amélie Poulain, Le) ...   \n",
       "314       356                                Forrest Gump (1994)   \n",
       "863      1136             Monty Python and the Holy Grail (1975)   \n",
       "520       608                                       Fargo (1996)   \n",
       "613       778                               Trainspotting (1996)   \n",
       "969      1270                          Back to the Future (1985)   \n",
       "4360     6377                                Finding Nemo (2003)   \n",
       "964      1265                               Groundhog Day (1993)   \n",
       "\n",
       "                                       genres  avgRating  numRatings  isComedy  \n",
       "899   Action|Adventure|Comedy|Fantasy|Romance   4.232394         142      True  \n",
       "257               Comedy|Crime|Drama|Thriller   4.197068         307      True  \n",
       "3622                           Comedy|Romance   4.183333         120      True  \n",
       "314                  Comedy|Drama|Romance|War   4.164134         329      True  \n",
       "863                  Adventure|Comedy|Fantasy   4.161765         136      True  \n",
       "520               Comedy|Crime|Drama|Thriller   4.116022         181      True  \n",
       "613                        Comedy|Crime|Drama   4.039216         102      True  \n",
       "969                   Adventure|Comedy|Sci-Fi   4.038012         171      True  \n",
       "4360      Adventure|Animation|Children|Comedy   3.960993         141      True  \n",
       "964                    Comedy|Fantasy|Romance   3.944056         143      True  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the comedy movies\n",
    "\n",
    "def isComedy(genres):\n",
    "    \"\"\"\n",
    "    Return True if comedy is one of its genres\n",
    "    \"\"\"\n",
    "    return (\"Comedy\" in genres)\n",
    "\n",
    "movies_with_many_ratings['isComedy'] = \\\n",
    "    movies_with_many_ratings['genres'].apply(isComedy)\n",
    "movies_with_many_ratings.head()\n",
    "\n",
    "movies_comedy = movies_with_many_ratings[\n",
    "    movies_with_many_ratings['isComedy']\n",
    "]\n",
    "movies_comedy.sort_values(by=['avgRating'], ascending=False, inplace=True)\n",
    "movies_comedy.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ch002\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "C:\\Users\\ch002\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>avgRating</th>\n",
       "      <th>numRatings</th>\n",
       "      <th>isComedy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>318</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.429022</td>\n",
       "      <td>317</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>858</td>\n",
       "      <td>Godfather, The (1972)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.289062</td>\n",
       "      <td>192</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2226</th>\n",
       "      <td>2959</td>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "      <td>4.272936</td>\n",
       "      <td>218</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>1221</td>\n",
       "      <td>Godfather: Part II, The (1974)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.259690</td>\n",
       "      <td>129</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6315</th>\n",
       "      <td>48516</td>\n",
       "      <td>Departed, The (2006)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "      <td>4.252336</td>\n",
       "      <td>107</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>1213</td>\n",
       "      <td>Goodfellas (1990)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>126</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>912</td>\n",
       "      <td>Casablanca (1942)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "      <td>4.240000</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6710</th>\n",
       "      <td>58559</td>\n",
       "      <td>Dark Knight, The (2008)</td>\n",
       "      <td>Action|Crime|Drama|IMAX</td>\n",
       "      <td>4.238255</td>\n",
       "      <td>149</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>527</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>4.225000</td>\n",
       "      <td>220</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>1208</td>\n",
       "      <td>Apocalypse Now (1979)</td>\n",
       "      <td>Action|Drama|War</td>\n",
       "      <td>4.219626</td>\n",
       "      <td>107</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                             title                       genres  \\\n",
       "277       318  Shawshank Redemption, The (1994)                  Crime|Drama   \n",
       "659       858             Godfather, The (1972)                  Crime|Drama   \n",
       "2226     2959                 Fight Club (1999)  Action|Crime|Drama|Thriller   \n",
       "922      1221    Godfather: Part II, The (1974)                  Crime|Drama   \n",
       "6315    48516              Departed, The (2006)         Crime|Drama|Thriller   \n",
       "914      1213                 Goodfellas (1990)                  Crime|Drama   \n",
       "694       912                 Casablanca (1942)                Drama|Romance   \n",
       "6710    58559           Dark Knight, The (2008)      Action|Crime|Drama|IMAX   \n",
       "461       527           Schindler's List (1993)                    Drama|War   \n",
       "909      1208             Apocalypse Now (1979)             Action|Drama|War   \n",
       "\n",
       "      avgRating  numRatings  isComedy  \n",
       "277    4.429022         317      True  \n",
       "659    4.289062         192      True  \n",
       "2226   4.272936         218      True  \n",
       "922    4.259690         129      True  \n",
       "6315   4.252336         107      True  \n",
       "914    4.250000         126      True  \n",
       "694    4.240000         100      True  \n",
       "6710   4.238255         149      True  \n",
       "461    4.225000         220      True  \n",
       "909    4.219626         107      True  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def isComedy(genres):\n",
    "    \"\"\"\n",
    "    Return True if comedy is one of its genres\n",
    "    \"\"\"\n",
    "    return (\"Drama\" in genres)\n",
    "\n",
    "movies_with_many_ratings['isComedy'] = \\\n",
    "    movies_with_many_ratings['genres'].apply(isComedy)\n",
    "movies_with_many_ratings.head()\n",
    "\n",
    "movies_comedy = movies_with_many_ratings[\n",
    "    movies_with_many_ratings['isComedy']\n",
    "]\n",
    "movies_comedy.sort_values(by=['avgRating'], ascending=False, inplace=True)\n",
    "movies_comedy.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Find the 5 top-rated comedies\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load tags.csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For Movie #1: Toy Story, find all tags related to it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a string that contains all the tags for Toy Story, separated by '|'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a \"Tags\" column in the movies dataframe, fill in the string that contains \n",
    "# each movie's tags\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Which movies have a dark hero?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Number of ratings is not calculated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework (Due on Wednesday, Oct 2)\n",
    "\n",
    "Create a new Jupyter notebook, load `ratings.csv` and `movies.csv` as dataframes.\n",
    "\n",
    "1. Create column \"AvgRating50\" in the movies dataframe. For each movie, if there are less than 50 ratings, set its AvgRating50 value to -1.0; otherwise, set the value to the average rating.\n",
    "2. Print the complete list of genres. (Use [`split()` method](https://www.geeksforgeeks.org/python-string-split/)\n",
    "to split a string containing multiple genres)\n",
    "3. Which genre has the largest number of movies?\n",
    "4. For each genre, find the ID of the movie with highest AvgRating50 value.\n",
    "5. For each genre, find the name of the movie with the highest AvgRating50 value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "homeworks = pd.DataFrame({\n",
    "    'Name': ['Alex', 'Alex', 'Alex', 'Brian', 'Brian', 'Brian', 'Cyd', 'Cyd', 'Cyd'],\n",
    "    'Week': [1, 2, 3, 1, 2, 3, 1, 2, 3],\n",
    "    'Grade': [1.2, 2.3, 3.5, 5.7, 7.8,5.3, 6.4, 7.4, 9.2]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.2</td>\n",
       "      <td>Alex</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.3</td>\n",
       "      <td>Alex</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.5</td>\n",
       "      <td>Alex</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.7</td>\n",
       "      <td>Brian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.8</td>\n",
       "      <td>Brian</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.3</td>\n",
       "      <td>Brian</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.4</td>\n",
       "      <td>Cyd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Cyd</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.2</td>\n",
       "      <td>Cyd</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Grade   Name  Week\n",
       "0    1.2   Alex     1\n",
       "1    2.3   Alex     2\n",
       "2    3.5   Alex     3\n",
       "3    5.7  Brian     1\n",
       "4    7.8  Brian     2\n",
       "5    5.3  Brian     3\n",
       "6    6.4    Cyd     1\n",
       "7    7.4    Cyd     2\n",
       "8    9.2    Cyd     3"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homeworks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "7    False\n",
       "8    False\n",
       "Name: Week, dtype: bool"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Question: What is the average score for Week 1?\n",
    "\n",
    "# Extract grades from Week 1\n",
    "index = (homeworks['Week'] == 1)\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.2</td>\n",
       "      <td>Alex</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.7</td>\n",
       "      <td>Brian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.4</td>\n",
       "      <td>Cyd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Grade   Name  Week\n",
       "0    1.2   Alex     1\n",
       "3    5.7  Brian     1\n",
       "6    6.4    Cyd     1"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the boolean indices to extract the grades\n",
    "week1_hw = homeworks[index]\n",
    "week1_hw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.433333333333334"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average grade:\n",
    "np.mean(week1_hw['Grade'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}