abhi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</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\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"data/u.data\")\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</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",
       "    </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  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(\"data/u.item\",encoding=\"ISO-8859-1\")\n",
    "#df2 = df2[[0,1]]\n",
    "#df2.columns = [\"movieId\",\"title\"]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41481</th>\n",
       "      <td>483</td>\n",
       "      <td>7458</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1215897549</td>\n",
       "      <td>Troy (2004)</td>\n",
       "      <td>Action|Adventure|Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41781</th>\n",
       "      <td>227</td>\n",
       "      <td>40629</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1447210583</td>\n",
       "      <td>Pride &amp; Prejudice (2005)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78011</th>\n",
       "      <td>47</td>\n",
       "      <td>3844</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1496206137</td>\n",
       "      <td>Steel Magnolias (1989)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54395</th>\n",
       "      <td>212</td>\n",
       "      <td>33004</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1490203104</td>\n",
       "      <td>Hitchhiker's Guide to the Galaxy, The (2005)</td>\n",
       "      <td>Adventure|Comedy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58969</th>\n",
       "      <td>580</td>\n",
       "      <td>1339</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1167790573</td>\n",
       "      <td>Dracula (Bram Stoker's Dracula) (1992)</td>\n",
       "      <td>Fantasy|Horror|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100334</th>\n",
       "      <td>599</td>\n",
       "      <td>93208</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1519241761</td>\n",
       "      <td>Mickey's The Prince and the Pauper (1990)</td>\n",
       "      <td>Animation|Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47059</th>\n",
       "      <td>125</td>\n",
       "      <td>56174</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1474295135</td>\n",
       "      <td>I Am Legend (2007)</td>\n",
       "      <td>Action|Horror|Sci-Fi|Thriller|IMAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66098</th>\n",
       "      <td>489</td>\n",
       "      <td>5299</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1332773434</td>\n",
       "      <td>My Big Fat Greek Wedding (2002)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10868</th>\n",
       "      <td>282</td>\n",
       "      <td>2028</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1378490303</td>\n",
       "      <td>Saving Private Ryan (1998)</td>\n",
       "      <td>Action|Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34730</th>\n",
       "      <td>457</td>\n",
       "      <td>2701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>993611057</td>\n",
       "      <td>Wild Wild West (1999)</td>\n",
       "      <td>Action|Comedy|Sci-Fi|Western</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating   timestamp  \\\n",
       "41481      483     7458     2.5  1215897549   \n",
       "41781      227    40629     3.5  1447210583   \n",
       "78011       47     3844     4.0  1496206137   \n",
       "54395      212    33004     4.0  1490203104   \n",
       "58969      580     1339     4.0  1167790573   \n",
       "100334     599    93208     2.0  1519241761   \n",
       "47059      125    56174     2.0  1474295135   \n",
       "66098      489     5299     3.5  1332773434   \n",
       "10868      282     2028     4.5  1378490303   \n",
       "34730      457     2701     1.0   993611057   \n",
       "\n",
       "                                               title  \\\n",
       "41481                                    Troy (2004)   \n",
       "41781                       Pride & Prejudice (2005)   \n",
       "78011                         Steel Magnolias (1989)   \n",
       "54395   Hitchhiker's Guide to the Galaxy, The (2005)   \n",
       "58969         Dracula (Bram Stoker's Dracula) (1992)   \n",
       "100334     Mickey's The Prince and the Pauper (1990)   \n",
       "47059                             I Am Legend (2007)   \n",
       "66098                My Big Fat Greek Wedding (2002)   \n",
       "10868                     Saving Private Ryan (1998)   \n",
       "34730                          Wild Wild West (1999)   \n",
       "\n",
       "                                    genres  \n",
       "41481           Action|Adventure|Drama|War  \n",
       "41781                        Drama|Romance  \n",
       "78011                                Drama  \n",
       "54395              Adventure|Comedy|Sci-Fi  \n",
       "58969      Fantasy|Horror|Romance|Thriller  \n",
       "100334                  Animation|Children  \n",
       "47059   Action|Horror|Sci-Fi|Thriller|IMAX  \n",
       "66098                       Comedy|Romance  \n",
       "10868                     Action|Drama|War  \n",
       "34730         Action|Comedy|Sci-Fi|Western  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.merge(df2, on=\"movieId\")\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>no</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>4.429022</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Godfather, The (1972)</th>\n",
       "      <td>4.289062</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fight Club (1999)</th>\n",
       "      <td>4.272936</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964)</th>\n",
       "      <td>4.268041</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rear Window (1954)</th>\n",
       "      <td>4.261905</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Godfather: Part II, The (1974)</th>\n",
       "      <td>4.259690</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Departed, The (2006)</th>\n",
       "      <td>4.252336</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goodfellas (1990)</th>\n",
       "      <td>4.250000</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casablanca (1942)</th>\n",
       "      <td>4.240000</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dark Knight, The (2008)</th>\n",
       "      <td>4.238255</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      rating   no\n",
       "title                                                            \n",
       "Shawshank Redemption, The (1994)                    4.429022  317\n",
       "Godfather, The (1972)                               4.289062  192\n",
       "Fight Club (1999)                                   4.272936  218\n",
       "Dr. Strangelove or: How I Learned to Stop Worry...  4.268041   97\n",
       "Rear Window (1954)                                  4.261905   84\n",
       "Godfather: Part II, The (1974)                      4.259690  129\n",
       "Departed, The (2006)                                4.252336  107\n",
       "Goodfellas (1990)                                   4.250000  126\n",
       "Casablanca (1942)                                   4.240000  100\n",
       "Dark Knight, The (2008)                             4.238255  149"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_top_rated(df, thr=80, no=10):\n",
    "    df3 = df.groupby(\"title\").rating.mean()\n",
    "    df4 = pd.DataFrame(df3)\n",
    "    df4[\"no\"] = df.groupby(\"title\").rating.count()\n",
    "    df5 = df4[df4[\"no\"]>thr]\n",
    "    return df5.sort_values([\"rating\"], ascending=[0]).head(no)\n",
    "get_top_rated(df, 80, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>no</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>4.267477</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forrest Gump (1994)</th>\n",
       "      <td>4.164134</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulp Fiction (1994)</th>\n",
       "      <td>3.916413</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matrix, The (1999)</th>\n",
       "      <td>3.542553</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silence of the Lambs, The (1991)</th>\n",
       "      <td>3.528875</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n",
       "      <td>3.227964</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Braveheart (1995)</th>\n",
       "      <td>2.904255</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fight Club (1999)</th>\n",
       "      <td>2.831307</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>2.825228</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jurassic Park (1993)</th>\n",
       "      <td>2.712766</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             rating   no\n",
       "title                                                   \n",
       "Shawshank Redemption, The (1994)           4.267477  317\n",
       "Forrest Gump (1994)                        4.164134  329\n",
       "Pulp Fiction (1994)                        3.916413  307\n",
       "Matrix, The (1999)                         3.542553  278\n",
       "Silence of the Lambs, The (1991)           3.528875  279\n",
       "Star Wars: Episode IV - A New Hope (1977)  3.227964  251\n",
       "Braveheart (1995)                          2.904255  237\n",
       "Fight Club (1999)                          2.831307  218\n",
       "Schindler's List (1993)                    2.825228  220\n",
       "Jurassic Park (1993)                       2.712766  238"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_most_reviwed_popu(df, no=10):\n",
    "    global df6\n",
    "    df3 = df.groupby(\"title\").rating.sum()\n",
    "    df6 = pd.DataFrame(df3)\n",
    "    df6[\"no\"] =df.groupby(\"title\").rating.count()\n",
    "    maxno = df6[\"no\"].max()\n",
    "    df6[\"rating\"] = df6[\"rating\"]/maxno\n",
    "    return df6.sort_values([\"rating\"], ascending=[0]).head(no)\n",
    "get_most_reviwed_popu(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek tiwari\\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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>no</th>\n",
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       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>4.892548</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forrest Gump (1994)</th>\n",
       "      <td>4.664134</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulp Fiction (1994)</th>\n",
       "      <td>4.630199</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matrix, The (1999)</th>\n",
       "      <td>4.537431</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silence of the Lambs, The (1991)</th>\n",
       "      <td>4.509315</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n",
       "      <td>4.493994</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fight Club (1999)</th>\n",
       "      <td>4.435550</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>4.393693</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Godfather, The (1972)</th>\n",
       "      <td>4.372649</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usual Suspects, The (1995)</th>\n",
       "      <td>4.357806</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             rating   no\n",
       "title                                                   \n",
       "Shawshank Redemption, The (1994)           4.892548  317\n",
       "Forrest Gump (1994)                        4.664134  329\n",
       "Pulp Fiction (1994)                        4.630199  307\n",
       "Matrix, The (1999)                         4.537431  278\n",
       "Silence of the Lambs, The (1991)           4.509315  279\n",
       "Star Wars: Episode IV - A New Hope (1977)  4.493994  251\n",
       "Fight Club (1999)                          4.435550  218\n",
       "Schindler's List (1993)                    4.393693  220\n",
       "Godfather, The (1972)                      4.372649  192\n",
       "Usual Suspects, The (1995)                 4.357806  204"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_curated(df, thr=80, no=10):\n",
    "    df3 = df.groupby(\"title\").rating.mean()\n",
    "    df7 = pd.DataFrame(df3)\n",
    "    df7[\"no\"] = df.groupby(\"title\").rating.count()\n",
    "    df8 = df7[df7[\"no\"]>thr]\n",
    "    maxno = df8[\"no\"].max()\n",
    "    df8[\"rating\"] += df8[\"no\"]/maxno - 0.5\n",
    "    return df8.sort_values([\"rating\"], ascending=[0]).head(no)\n",
    "get_curated(df, 80, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>title</th>\n",
       "      <th>'71 (2014)</th>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <th>'Salem's Lot (2004)</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>'Tis the Season for Love (2015)</th>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <th>'night Mother (1986)</th>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <th>*batteries not included (1987)</th>\n",
       "      <th>...</th>\n",
       "      <th>Zulu (2013)</th>\n",
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       "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
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       "      <th>xXx: State of the Union (2005)</th>\n",
       "      <th>Â¡Three Amigos! (1986)</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>517</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9719 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
       "userId                                                        \n",
       "214            NaN                                      NaN   \n",
       "517            NaN                                      NaN   \n",
       "566            NaN                                      NaN   \n",
       "19             NaN                                      NaN   \n",
       "41             NaN                                      NaN   \n",
       "\n",
       "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
       "userId                                                \n",
       "214                        NaN                  NaN   \n",
       "517                        NaN                  NaN   \n",
       "566                        NaN                  NaN   \n",
       "19                         NaN                  NaN   \n",
       "41                         NaN                  NaN   \n",
       "\n",
       "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
       "userId                                                               \n",
       "214                           NaN                              NaN   \n",
       "517                           NaN                              NaN   \n",
       "566                           NaN                              NaN   \n",
       "19                            NaN                              NaN   \n",
       "41                            NaN                              NaN   \n",
       "\n",
       "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
       "userId                                                                          \n",
       "214                    NaN                   NaN                          NaN   \n",
       "517                    NaN                   NaN                          NaN   \n",
       "566                    NaN                   NaN                          NaN   \n",
       "19                     2.0                   NaN                          NaN   \n",
       "41                     NaN                   NaN                          3.5   \n",
       "\n",
       "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
       "userId                                  ...                              \n",
       "214                                NaN  ...          NaN           NaN   \n",
       "517                                NaN  ...          NaN           NaN   \n",
       "566                                NaN  ...          NaN           NaN   \n",
       "19                                 NaN  ...          NaN           NaN   \n",
       "41                                 NaN  ...          NaN           NaN   \n",
       "\n",
       "title   [REC]Â² (2009)  [REC]Â³ 3 GÃ©nesis (2012)  \\\n",
       "userId                                              \n",
       "214                NaN                        NaN   \n",
       "517                NaN                        NaN   \n",
       "566                NaN                        NaN   \n",
       "19                 NaN                        NaN   \n",
       "41                 NaN                        NaN   \n",
       "\n",
       "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
       "userId                                                           \n",
       "214                                                   NaN        \n",
       "517                                                   NaN        \n",
       "566                                                   NaN        \n",
       "19                                                    NaN        \n",
       "41                                                    NaN        \n",
       "\n",
       "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
       "userId                                                                \n",
       "214                 NaN         NaN                             NaN   \n",
       "517                 NaN         NaN                             NaN   \n",
       "566                 NaN         NaN                             NaN   \n",
       "19                  NaN         NaN                             NaN   \n",
       "41                  NaN         NaN                             NaN   \n",
       "\n",
       "title   Â¡Three Amigos! (1986)  Ã nous la libertÃ© (Freedom for Us) (1931)  \n",
       "userId                                                                       \n",
       "214                        NaN                                          NaN  \n",
       "517                        NaN                                          NaN  \n",
       "566                        NaN                                          NaN  \n",
       "19                         2.0                                          NaN  \n",
       "41                         NaN                                          NaN  \n",
       "\n",
       "[5 rows x 9719 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_matrix = df.pivot_table(index='userId', columns='title', values='rating')\n",
    "movie_matrix.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(610, 9719)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek tiwari\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2526: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "C:\\Users\\Abhishek tiwari\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2455: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>correlation</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jumanji (1995)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Notting Hill (1999)</th>\n",
       "      <td>0.798372</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Escape from L.A. (1996)</th>\n",
       "      <td>0.764223</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tin Cup (1996)</th>\n",
       "      <td>0.731627</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Million Dollar Baby (2004)</th>\n",
       "      <td>0.716712</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blue Velvet (1986)</th>\n",
       "      <td>0.712383</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Butterfly Effect (2004)</th>\n",
       "      <td>0.701322</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others, The (2001)</th>\n",
       "      <td>0.698576</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donnie Brasco (1997)</th>\n",
       "      <td>0.687060</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Network, The (2010)</th>\n",
       "      <td>0.673058</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             correlation  rating\n",
       "title                                           \n",
       "Jumanji (1995)                  1.000000     110\n",
       "Notting Hill (1999)             0.798372      56\n",
       "Escape from L.A. (1996)         0.764223      44\n",
       "Tin Cup (1996)                  0.731627      43\n",
       "Million Dollar Baby (2004)      0.716712      52\n",
       "Blue Velvet (1986)              0.712383      46\n",
       "The Butterfly Effect (2004)     0.701322      46\n",
       "Others, The (2001)              0.698576      55\n",
       "Donnie Brasco (1997)            0.687060      52\n",
       "Social Network, The (2010)      0.673058      57"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_sim(movie_matrix, movie_name,thr=40, no=10):\n",
    "    SW_user_rating = movie_matrix[movie_name]\n",
    "    similar_to_sw=movie_matrix.corrwith(SW_user_rating)\n",
    "    similar_to_sw.sample(10)\n",
    "    corr_SW = pd.DataFrame(similar_to_sw, columns=['correlation'])\n",
    "    corr_SW.dropna(inplace=True)\n",
    "    dftemp = df.groupby(\"title\").rating.count()\n",
    "    dftemp = pd.DataFrame(dftemp)\n",
    "    corr_SW = corr_SW.merge(dftemp, on=\"title\")\n",
    "    return corr_SW[corr_SW['rating'] > thr].sort_values(by='correlation', ascending=False).head(no)\n",
    "get_sim(movie_matrix, 'Jumanji (1995)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sim_users_reviews(df, movie_matrix, uid):\n",
    "    movie_matrix.head()\n",
    "    SW_user_rating = movie_matrix.loc[uid]\n",
    "    similar_to_sw=movie_matrix.corrwith(SW_user_rating, axis=1)\n",
    "    similar_to_sw.head()\n",
    "    corr_SW = pd.DataFrame(similar_to_sw, columns=['correlation'])\n",
    "    corr_SW.dropna(inplace=True)\n",
    "    corr_SW.head()\n",
    "    \n",
    "    coUsers = df.groupby(\"userId\").rating.count()\n",
    "    coUsers = pd.DataFrame(coUsers)\n",
    "    coUsers.columns = [\"no\"]\n",
    "    coUsers.head()\n",
    "    #dfNew.decribe()\n",
    "    corr_SW = corr_SW.join(coUsers['no'])\n",
    "    corr_SW.head()\n",
    "    corr_SW = corr_SW[corr_SW['no'] > 20].sort_values(by='correlation', ascending=False)\n",
    "    corr_SW = corr_SW[corr_SW['correlation'] > 0.5].sort_values(by='correlation', ascending=False)\n",
    "    corr_SW.head(10)\n",
    "    corr_SW.shape\n",
    "    corr_SW.head()\n",
    "    uids = corr_SW.index\n",
    "    newDf = df[df.userId.isin(uids)]\n",
    "    newDf.head(10)\n",
    "    newDf.shape\n",
    "    return newDf\n",
    "    #dfNew\n",
    "\n",
    "def get_sim_user_top_rated(df, movie_matrix, uid):\n",
    "    newDf = get_sim_users_reviews(df, movie_matrix, uid)\n",
    "    return get_top_rated(newDf, 10, 10) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek tiwari\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2526: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "C:\\Users\\Abhishek tiwari\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2455: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    },
    {
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       "      <th>American Beauty (1999)</th>\n",
       "      <td>4.730769</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>4.687500</td>\n",
       "      <td>24</td>\n",
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       "      <td>4.636364</td>\n",
       "      <td>11</td>\n",
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       "      <th>Matrix, The (1999)</th>\n",
       "      <td>4.608696</td>\n",
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       "      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n",
       "      <td>4.550000</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>Seven (a.k.a. Se7en) (1995)</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fight Club (1999)</th>\n",
       "      <td>4.473684</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars: Episode VI - Return of the Jedi (1983)</th>\n",
       "      <td>4.461538</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)</th>\n",
       "      <td>4.437500</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "                                                      rating  no\n",
       "title                                                           \n",
       "American Beauty (1999)                              4.730769  13\n",
       "Shawshank Redemption, The (1994)                    4.687500  24\n",
       "Interstellar (2014)                                 4.636364  11\n",
       "Matrix, The (1999)                                  4.608696  23\n",
       "Schindler's List (1993)                             4.593750  16\n",
       "Star Wars: Episode IV - A New Hope (1977)           4.550000  20\n",
       "Seven (a.k.a. Se7en) (1995)                         4.500000  17\n",
       "Fight Club (1999)                                   4.473684  19\n",
       "Star Wars: Episode VI - Return of the Jedi (1983)   4.461538  13\n",
       "Raiders of the Lost Ark (Indiana Jones and the ...  4.437500  16"
      ]
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     "execution_count": 11,
     "metadata": {},
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   "source": [
    "get_sim_user_top_rated(df, movie_matrix, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek tiwari\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2526: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "C:\\Users\\Abhishek tiwari\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2455: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n"
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       "      <th>Dark Knight, The (2008)</th>\n",
       "      <td>4.653846</td>\n",
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       "      <th>Godfather, The (1972)</th>\n",
       "      <td>4.625000</td>\n",
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       "      <th>Fight Club (1999)</th>\n",
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       "      <td>4.500000</td>\n",
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       "      <td>4.500000</td>\n",
       "      <td>14</td>\n",
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       "      <th>Memento (2000)</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulp Fiction (1994)</th>\n",
       "      <td>4.470588</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saving Private Ryan (1998)</th>\n",
       "      <td>4.464286</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)</th>\n",
       "      <td>4.458333</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alien (1979)</th>\n",
       "      <td>4.416667</td>\n",
       "      <td>12</td>\n",
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       "                                                      rating  no\n",
       "title                                                           \n",
       "Dark Knight, The (2008)                             4.653846  13\n",
       "Godfather, The (1972)                               4.625000  12\n",
       "Fight Club (1999)                                   4.526316  19\n",
       "Silence of the Lambs, The (1991)                    4.500000  16\n",
       "Schindler's List (1993)                             4.500000  14\n",
       "Memento (2000)                                      4.500000  16\n",
       "Pulp Fiction (1994)                                 4.470588  17\n",
       "Saving Private Ryan (1998)                          4.464286  14\n",
       "Raiders of the Lost Ark (Indiana Jones and the ...  4.458333  12\n",
       "Alien (1979)                                        4.416667  12"
      ]
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    "get_sim_user_top_rated(df, movie_matrix, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek tiwari\\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",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
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       "      <th>Celebration, The (Festen) (1998)</th>\n",
       "      <td>4.458333</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>American History X (1998)</th>\n",
       "      <td>4.217054</td>\n",
       "      <td>129</td>\n",
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       "      <th>Saving Private Ryan (1998)</th>\n",
       "      <td>4.146277</td>\n",
       "      <td>188</td>\n",
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       "      <td>4.125000</td>\n",
       "      <td>16</td>\n",
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       "      <th>Lock, Stock &amp; Two Smoking Barrels (1998)</th>\n",
       "      <td>4.052239</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Violin, The (Violon rouge, Le) (1998)</th>\n",
       "      <td>4.026316</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Run Lola Run (Lola rennt) (1998)</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fear and Loathing in Las Vegas (1998)</th>\n",
       "      <td>3.967391</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zero Effect (1998)</th>\n",
       "      <td>3.966667</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>Croupier (1998)</th>\n",
       "      <td>3.954545</td>\n",
       "      <td>11</td>\n",
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       "</table>\n",
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      ],
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       "                                             rating   no\n",
       "title                                                   \n",
       "Celebration, The (Festen) (1998)           4.458333   12\n",
       "American History X (1998)                  4.217054  129\n",
       "Saving Private Ryan (1998)                 4.146277  188\n",
       "Happiness (1998)                           4.125000   16\n",
       "Lock, Stock & Two Smoking Barrels (1998)   4.052239   67\n",
       "Red Violin, The (Violon rouge, Le) (1998)  4.026316   19\n",
       "Run Lola Run (Lola rennt) (1998)           4.000000   75\n",
       "Fear and Loathing in Las Vegas (1998)      3.967391   46\n",
       "Zero Effect (1998)                         3.966667   15\n",
       "Croupier (1998)                            3.954545   11"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_year_reviews(df, st_y, end_y):\n",
    "    df['valid'] = df.title.apply(lambda x: (x.strip()[-5:-1]).isnumeric())\n",
    "    newDf=df[df['valid']]\n",
    "    newDf['year'] = newDf.title.apply(lambda x:int((x.strip()[-5:-1])))\n",
    "    newDf=newDf[newDf['year']>=st_y]\n",
    "    newDf=newDf[newDf['year']<=end_y]\n",
    "    return newDf\n",
    "newdf2 = get_year_reviews(df, 1998, 1998)\n",
    "##  newdf2[newdf2[\"year\"]==False]\n",
    "##  newdf2[\"year\"].describe()\n",
    "  #  print(type(newdf2[\"year\"][0]))\n",
    "#newdf2.sample(10)\n",
    "get_top_rated(newdf2, 10,10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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