Data Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>country</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>count</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>s1</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>3%</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>NaN</td>\n",
       "      <td>João Miguel, Bianca Comparato, Michel Gomes, R...</td>\n",
       "      <td>August 14, 2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>4 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Sci-Fi &amp;...</td>\n",
       "      <td>1</td>\n",
       "      <td>In a future where the elite inhabit an island ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>s2</td>\n",
       "      <td>Movie</td>\n",
       "      <td>7:19</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Jorge Michel Grau</td>\n",
       "      <td>Demián Bichir, Héctor Bonilla, Oscar Serrano, ...</td>\n",
       "      <td>December 23, 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>93 min</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>1</td>\n",
       "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>s3</td>\n",
       "      <td>Movie</td>\n",
       "      <td>23:59</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>Gilbert Chan</td>\n",
       "      <td>Tedd Chan, Stella Chung, Henley Hii, Lawrence ...</td>\n",
       "      <td>December 20, 2018</td>\n",
       "      <td>2011</td>\n",
       "      <td>R</td>\n",
       "      <td>78 min</td>\n",
       "      <td>Horror Movies, International Movies</td>\n",
       "      <td>1</td>\n",
       "      <td>When an army recruit is found dead, his fellow...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>s4</td>\n",
       "      <td>Movie</td>\n",
       "      <td>9</td>\n",
       "      <td>United States</td>\n",
       "      <td>Shane Acker</td>\n",
       "      <td>Elijah Wood, John C. Reilly, Jennifer Connelly...</td>\n",
       "      <td>November 16, 2017</td>\n",
       "      <td>2009</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>80 min</td>\n",
       "      <td>Action &amp; Adventure, Independent Movies, Sci-Fi...</td>\n",
       "      <td>1</td>\n",
       "      <td>In a postapocalyptic world, rag-doll robots hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>s5</td>\n",
       "      <td>Movie</td>\n",
       "      <td>21</td>\n",
       "      <td>United States</td>\n",
       "      <td>Robert Luketic</td>\n",
       "      <td>Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...</td>\n",
       "      <td>January 1, 2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>123 min</td>\n",
       "      <td>Dramas</td>\n",
       "      <td>1</td>\n",
       "      <td>A brilliant group of students become card-coun...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  show_id     type  title        country           director  \\\n",
       "0      s1  TV Show     3%         Brazil                NaN   \n",
       "1      s2    Movie   7:19         Mexico  Jorge Michel Grau   \n",
       "2      s3    Movie  23:59      Singapore       Gilbert Chan   \n",
       "3      s4    Movie      9  United States        Shane Acker   \n",
       "4      s5    Movie     21  United States     Robert Luketic   \n",
       "\n",
       "                                                cast         date_added  \\\n",
       "0  João Miguel, Bianca Comparato, Michel Gomes, R...    August 14, 2020   \n",
       "1  Demián Bichir, Héctor Bonilla, Oscar Serrano, ...  December 23, 2016   \n",
       "2  Tedd Chan, Stella Chung, Henley Hii, Lawrence ...  December 20, 2018   \n",
       "3  Elijah Wood, John C. Reilly, Jennifer Connelly...  November 16, 2017   \n",
       "4  Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...    January 1, 2020   \n",
       "\n",
       "   release_year rating   duration  \\\n",
       "0          2020  TV-MA  4 Seasons   \n",
       "1          2016  TV-MA     93 min   \n",
       "2          2011      R     78 min   \n",
       "3          2009  PG-13     80 min   \n",
       "4          2008  PG-13    123 min   \n",
       "\n",
       "                                           listed_in  count  \\\n",
       "0  International TV Shows, TV Dramas, TV Sci-Fi &...      1   \n",
       "1                       Dramas, International Movies      1   \n",
       "2                Horror Movies, International Movies      1   \n",
       "3  Action & Adventure, Independent Movies, Sci-Fi...      1   \n",
       "4                                             Dramas      1   \n",
       "\n",
       "                                         description  \n",
       "0  In a future where the elite inhabit an island ...  \n",
       "1  After a devastating earthquake hits Mexico Cit...  \n",
       "2  When an army recruit is found dead, his fellow...  \n",
       "3  In a postapocalyptic world, rag-doll robots hi...  \n",
       "4  A brilliant group of students become card-coun...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mentions = pd.read_csv('netflix_titles_mod.csv')\n",
    "mentions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 Season     1608\n",
       "2 Seasons     382\n",
       "3 Seasons     184\n",
       "90 min        137\n",
       "93 min        131\n",
       "             ... \n",
       "167 min         1\n",
       "230 min         1\n",
       "203 min         1\n",
       "228 min         1\n",
       "186 min         1\n",
       "Name: duration, Length: 216, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mentions['duration'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>Movie</td>\n",
       "      <td>44 min</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>7785</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>1</td>\n",
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       "      <td>7786</td>\n",
       "      <td>Movie</td>\n",
       "      <td>90 min</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7787</td>\n",
       "      <td>Movie</td>\n",
       "      <td>90 min</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7788 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         type   duration  count\n",
       "0     TV Show  4 Seasons      1\n",
       "1       Movie     93 min      1\n",
       "2       Movie     78 min      1\n",
       "3       Movie     80 min      1\n",
       "4       Movie    123 min      1\n",
       "...       ...        ...    ...\n",
       "7783    Movie    111 min      1\n",
       "7784    Movie     44 min      1\n",
       "7785  TV Show   1 Season      1\n",
       "7786    Movie     90 min      1\n",
       "7787    Movie     90 min      1\n",
       "\n",
       "[7788 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mentions2 = mentions[['type', 'duration', 'count']]\n",
    "mentions2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90 min     137\n",
       "93 min     131\n",
       "94 min     125\n",
       "91 min     125\n",
       "95 min     119\n",
       "          ... \n",
       "186 min      1\n",
       "224 min      1\n",
       "204 min      1\n",
       "194 min      1\n",
       "200 min      1\n",
       "Name: duration, Length: 201, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mentions2[mentions2['type'] == 'Movie']['duration'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
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    {
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       "      <td>Movie</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>7783</td>\n",
       "      <td>Movie</td>\n",
       "      <td>111 min</td>\n",
       "      <td>1</td>\n",
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       "      <td>7784</td>\n",
       "      <td>Movie</td>\n",
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       "      <td>Movie</td>\n",
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       "    </tr>\n",
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       "<p>5378 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "       type duration  count\n",
       "1     Movie   93 min      1\n",
       "2     Movie   78 min      1\n",
       "3     Movie   80 min      1\n",
       "4     Movie  123 min      1\n",
       "6     Movie   95 min      1\n",
       "...     ...      ...    ...\n",
       "7782  Movie   99 min      1\n",
       "7783  Movie  111 min      1\n",
       "7784  Movie   44 min      1\n",
       "7786  Movie   90 min      1\n",
       "7787  Movie   90 min      1\n",
       "\n",
       "[5378 rows x 3 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = mentions2[mentions2['type'] == 'Movie']\n",
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amirahanisadlin/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: 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/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Users/amirahanisadlin/opt/anaconda3/lib/python3.7/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/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
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       "      <td>7784</td>\n",
       "      <td>Movie</td>\n",
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       "      <td>7786</td>\n",
       "      <td>Movie</td>\n",
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       "      <td>Movie</td>\n",
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       "      <td>90</td>\n",
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       "<p>5378 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       type duration  count  durations\n",
       "1     Movie   93 min      1         93\n",
       "2     Movie   78 min      1         78\n",
       "3     Movie   80 min      1         80\n",
       "4     Movie  123 min      1        123\n",
       "6     Movie   95 min      1         95\n",
       "...     ...      ...    ...        ...\n",
       "7782  Movie   99 min      1         99\n",
       "7783  Movie  111 min      1        111\n",
       "7784  Movie   44 min      1         44\n",
       "7786  Movie   90 min      1         90\n",
       "7787  Movie   90 min      1         90\n",
       "\n",
       "[5378 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "def find_number(text):\n",
    "    num = re.findall(r'[0-9]+', text)\n",
    "    return \" \".join(num)\n",
    "\n",
    "movies['durations'] = movies['duration'].apply(lambda x: find_number(x))\n",
    "movies['durations'] = movies['durations'].astype(int)\n",
    "# movies.drop(['duration'], axis=1, inplace=True)\n",
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amirahanisadlin/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: 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/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "movies['duration_bin'] = pd.qcut(movies['durations'], q=10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103.0, 110.0]    583\n",
       "(93.0, 98.0]      582\n",
       "(64.0, 82.0]      558\n",
       "(82.0, 89.0]      553\n",
       "(2.999, 64.0]     542\n",
       "(133.0, 312.0]    533\n",
       "(119.0, 133.0]    525\n",
       "(89.0, 93.0]      511\n",
       "(110.0, 119.0]    498\n",
       "(98.0, 103.0]     493\n",
       "Name: duration_bin, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['duration_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_labels = ['Lowest', 'Low', 'Average', 'High', 'Highest']\n",
    "dfmerge2['prices_bin'] = pd.qcut(dfmerge2['prices'], q=5, labels=bin_labels)\n",
    "dfmerge2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "       type  durations    duration_bin\n",
       "1     Movie         93    (89.0, 93.0]\n",
       "2     Movie         78    (64.0, 82.0]\n",
       "3     Movie         80    (64.0, 82.0]\n",
       "4     Movie        123  (119.0, 133.0]\n",
       "6     Movie         95    (93.0, 98.0]\n",
       "...     ...        ...             ...\n",
       "7782  Movie         99   (98.0, 103.0]\n",
       "7783  Movie        111  (110.0, 119.0]\n",
       "7784  Movie         44   (2.999, 64.0]\n",
       "7786  Movie         90    (89.0, 93.0]\n",
       "7787  Movie         90    (89.0, 93.0]\n",
       "\n",
       "[5378 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amirahanisadlin/opt/anaconda3/lib/python3.7/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: http://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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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5378 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       type duration  count  durations duration_bin\n",
       "1     Movie   93 min      1         93      90 - 95\n",
       "2     Movie   78 min      1         78      60 - 90\n",
       "3     Movie   80 min      1         80      60 - 90\n",
       "4     Movie  123 min      1        123    120 - 150\n",
       "6     Movie   95 min      1         95     95 - 120\n",
       "...     ...      ...    ...        ...          ...\n",
       "7782  Movie   99 min      1         99     95 - 120\n",
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       "7786  Movie   90 min      1         90      90 - 95\n",
       "7787  Movie   90 min      1         90      90 - 95\n",
       "\n",
       "[5378 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 60, 90, 95, 120, 150, 300, 350]\n",
    "labels = ['0 - 60', '60 - 90', '90 - 95', '95 - 120', '120 - 150', '150  - 300', 'above 300']\n",
    "\n",
    "movies['duration_bin'] = pd.cut(movies['durations'], bins, labels=labels, right=False)\n",
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1 Season</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2410 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         type   duration  count\n",
       "0     TV Show  4 Seasons      1\n",
       "5     TV Show   1 Season      1\n",
       "11    TV Show   1 Season      1\n",
       "12    TV Show   1 Season      1\n",
       "16    TV Show   1 Season      1\n",
       "...       ...        ...    ...\n",
       "7767  TV Show   1 Season      1\n",
       "7775  TV Show   1 Season      1\n",
       "7777  TV Show  2 Seasons      1\n",
       "7779  TV Show   1 Season      1\n",
       "7785  TV Show   1 Season      1\n",
       "\n",
       "[2410 rows x 3 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvshows = mentions2[mentions2['type'] == 'TV Show']\n",
    "tvshows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfmerge = pd.merge(dfproduct, price, on='asins', how='inner')\n",
    "dfmerge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amirahanisadlin/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \n"
     ]
    },
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       "      <td>7784</td>\n",
       "      <td>1</td>\n",
       "      <td>44 min</td>\n",
       "      <td>0 - 60</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Movie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7786</td>\n",
       "      <td>1</td>\n",
       "      <td>90 min</td>\n",
       "      <td>90 - 95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Movie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7787</td>\n",
       "      <td>1</td>\n",
       "      <td>90 min</td>\n",
       "      <td>90 - 95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Movie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7788 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      count   duration duration_bin  durations     type\n",
       "0         1  4 Seasons          NaN        NaN  TV Show\n",
       "5         1   1 Season          NaN        NaN  TV Show\n",
       "11        1   1 Season          NaN        NaN  TV Show\n",
       "12        1   1 Season          NaN        NaN  TV Show\n",
       "16        1   1 Season          NaN        NaN  TV Show\n",
       "...     ...        ...          ...        ...      ...\n",
       "7782      1     99 min     95 - 120       99.0    Movie\n",
       "7783      1    111 min     95 - 120      111.0    Movie\n",
       "7784      1     44 min       0 - 60       44.0    Movie\n",
       "7786      1     90 min      90 - 95       90.0    Movie\n",
       "7787      1     90 min      90 - 95       90.0    Movie\n",
       "\n",
       "[7788 rows x 5 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [tvshows, movies]\n",
    "result = pd.concat(frames)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('netflix_durations1.csv', header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_durations.to_csv('netflix_durations.csv', header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>95 - 120</td>\n",
       "      <td>2031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60 - 90</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120 - 150</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90 - 95</td>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0 - 60</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150  - 300</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>above 300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            counts\n",
       "duration          \n",
       "95 - 120      2031\n",
       "60 - 90       1233\n",
       "120 - 150      815\n",
       "90 - 95        636\n",
       "0 - 60         420\n",
       "150  - 300     242\n",
       "above 300        1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_durations = movies['duration_bin'].value_counts().rename_axis('duration').to_frame('counts')\n",
    "# count_durations.sort(ascending=True)\n",
    "count_durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1 Season</td>\n",
       "      <td>1608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3 Seasons</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4 Seasons</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5 Seasons</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6 Seasons</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7 Seasons</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8 Seasons</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9 Seasons</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10 Seasons</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11 Seasons</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12 Seasons</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15 Seasons</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13 Seasons</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16 Seasons</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            counts\n",
       "duration          \n",
       "1 Season      1608\n",
       "2 Seasons      382\n",
       "3 Seasons      184\n",
       "4 Seasons       87\n",
       "5 Seasons       58\n",
       "6 Seasons       30\n",
       "7 Seasons       19\n",
       "8 Seasons       18\n",
       "9 Seasons        8\n",
       "10 Seasons       6\n",
       "11 Seasons       3\n",
       "12 Seasons       2\n",
       "15 Seasons       2\n",
       "13 Seasons       2\n",
       "16 Seasons       1"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_durations2 = tvshows['duration'].value_counts().rename_axis('duration').to_frame('counts')\n",
    "# count_durations.sort(ascending=True)\n",
    "count_durations2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1 Season</td>\n",
       "      <td>1608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3 Seasons</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4 Seasons</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5 Seasons</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6 Seasons</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7 Seasons</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8 Seasons</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <td>9 Seasons</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10 Seasons</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11 Seasons</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12 Seasons</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15 Seasons</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13 Seasons</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16 Seasons</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95 - 120</td>\n",
       "      <td>2031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60 - 90</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120 - 150</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90 - 95</td>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0 - 60</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150  - 300</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>above 300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            counts\n",
       "duration          \n",
       "1 Season      1608\n",
       "2 Seasons      382\n",
       "3 Seasons      184\n",
       "4 Seasons       87\n",
       "5 Seasons       58\n",
       "6 Seasons       30\n",
       "7 Seasons       19\n",
       "8 Seasons       18\n",
       "9 Seasons        8\n",
       "10 Seasons       6\n",
       "11 Seasons       3\n",
       "12 Seasons       2\n",
       "15 Seasons       2\n",
       "13 Seasons       2\n",
       "16 Seasons       1\n",
       "95 - 120      2031\n",
       "60 - 90       1233\n",
       "120 - 150      815\n",
       "90 - 95        636\n",
       "0 - 60         420\n",
       "150  - 300     242\n",
       "above 300        1"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.concat([count_durations2, count_durations])\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv('netflix_durations3.csv', header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "CategoricalIndex: 7 entries, 95 - 120 to above 300\n",
      "Data columns (total 1 columns):\n",
      "counts    7 non-null int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 439.0 bytes\n"
     ]
    }
   ],
   "source": [
    "count_durations.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_durations.to_csv('netflix_durations.csv', header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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