pandas_07.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         age gender  occupation zip_code\n",
       "user_id                                 \n",
       "1         24      M  technician    85711\n",
       "2         53      F       other    94043\n",
       "3         23      M      writer    32067\n",
       "4         24      M  technician    43537\n",
       "5         33      F       other    15213"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://www.ritchieng.com/pandas-removing-duplicate-rows/\n",
    "import pandas as pd\n",
    "# read a dataset of movie reviewers (modifying the default parameter values for read_table)\n",
    "user_cols = ['user_id', 'age', 'gender', 'occupation', 'zip_code']\n",
    "url = 'http://bit.ly/movieusers'\n",
    "users = pd.read_table(url, sep='|', header=None, names=user_cols, index_col='user_id')\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 4)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "5      False\n",
       "       ...  \n",
       "939    False\n",
       "940     True\n",
       "941    False\n",
       "942    False\n",
       "943    False\n",
       "Name: zip_code, Length: 943, dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use df.cat_name.duplicated()\n",
    "# output True if row above is the same\n",
    "users.zip_code.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type\n",
    "type(users.zip_code.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use .count() since it's a series\n",
    "# there're 148 duplicates\n",
    "users.zip_code.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "5      False\n",
       "       ...  \n",
       "939    False\n",
       "940    False\n",
       "941    False\n",
       "942    False\n",
       "943    False\n",
       "Length: 943, dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it will output True if entire row is duplicated (row above)\n",
    "users.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
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       "      <th>572</th>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>20003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>17</td>\n",
       "      <td>M</td>\n",
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       "      <th>684</th>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
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       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>60630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>20009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender occupation zip_code\n",
       "user_id                                \n",
       "496       21      F    student    55414\n",
       "572       51      M   educator    20003\n",
       "621       17      M    student    60402\n",
       "684       28      M    student    55414\n",
       "733       44      F      other    60630\n",
       "805       27      F      other    20009\n",
       "890       32      M    student    97301"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine duplicated rows\n",
    "users.loc[users.duplicated(), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
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       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>20003</td>\n",
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       "      <th>621</th>\n",
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       "      <th>733</th>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>60630</td>\n",
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       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>20009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97301</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "         age gender occupation zip_code\n",
       "user_id                                \n",
       "496       21      F    student    55414\n",
       "572       51      M   educator    20003\n",
       "621       17      M    student    60402\n",
       "684       28      M    student    55414\n",
       "733       44      F      other    60630\n",
       "805       27      F      other    20009\n",
       "890       32      M    student    97301"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keep='first'\n",
    "# mark duplicates as True except for the first occurence\n",
    "users.loc[users.duplicated(keep='first'), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
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       "      <td>17</td>\n",
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       "      <td>60402</td>\n",
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       "      <th>85</th>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>20003</td>\n",
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       "      <th>198</th>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
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       "      <td>97301</td>\n",
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       "      <th>428</th>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
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       "      <th>437</th>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>20009</td>\n",
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       "      <th>460</th>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>60630</td>\n",
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      ],
      "text/plain": [
       "         age gender occupation zip_code\n",
       "user_id                                \n",
       "67        17      M    student    60402\n",
       "85        51      M   educator    20003\n",
       "198       21      F    student    55414\n",
       "350       32      M    student    97301\n",
       "428       28      M    student    55414\n",
       "437       27      F      other    20009\n",
       "460       44      F      other    60630"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keep='last'\n",
    "# 7 rows that are counted as duplicates, keeping the later one\n",
    "\n",
    "# this is useful for splitting the data\n",
    "users.loc[users.duplicated(keep='last'), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>educator</td>\n",
       "      <td>20003</td>\n",
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       "      <td>60402</td>\n",
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       "      <td>55414</td>\n",
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       "      <td>44</td>\n",
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       "      <td>60630</td>\n",
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       "      <th>805</th>\n",
       "      <td>27</td>\n",
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       "      <td>20009</td>\n",
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       "      <th>890</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender occupation zip_code\n",
       "user_id                                \n",
       "67        17      M    student    60402\n",
       "85        51      M   educator    20003\n",
       "198       21      F    student    55414\n",
       "350       32      M    student    97301\n",
       "428       28      M    student    55414\n",
       "437       27      F      other    20009\n",
       "460       44      F      other    60630\n",
       "496       21      F    student    55414\n",
       "572       51      M   educator    20003\n",
       "621       17      M    student    60402\n",
       "684       28      M    student    55414\n",
       "733       44      F      other    60630\n",
       "805       27      F      other    20009\n",
       "890       32      M    student    97301"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mark all duplicates as True\n",
    "# this combines the two tables above\n",
    "users.loc[users.duplicated(keep=False), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(936, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drops the 7 rows\n",
    "users.drop_duplicates(keep='first').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(936, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drops the last version of the 7 duplicate rows\n",
    "users.drop_duplicates(keep='last').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(929, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drops all 14 rows\n",
    "users.drop_duplicates(keep=False).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only consider \"age\" and \"zip_code\" as the relevant columns\n",
    "users.duplicated(subset=['age', 'zip_code']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "4          8.9              Pulp Fiction              R   Crime       154   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4  [u'John Travolta', u'Uma Thurman', u'Samuel L....  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://www.ritchieng.com/pandas-filtering-converting-series-NaN/\n",
    "import pandas as pd\n",
    "url = 'http://bit.ly/imdbratings'\n",
    "movies = pd.read_csv(url)\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting missing values\n",
    "movies.content_rating.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>8.2</td>\n",
       "      <td>Butch Cassidy and the Sundance Kid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biography</td>\n",
       "      <td>110</td>\n",
       "      <td>[u'Paul Newman', u'Robert Redford', u'Katharin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>7.7</td>\n",
       "      <td>Where Eagles Dare</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Action</td>\n",
       "      <td>158</td>\n",
       "      <td>[u'Richard Burton', u'Clint Eastwood', u'Mary ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>7.4</td>\n",
       "      <td>True Grit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>128</td>\n",
       "      <td>[u'John Wayne', u'Kim Darby', u'Glen Campbell']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     star_rating                               title content_rating  \\\n",
       "187          8.2  Butch Cassidy and the Sundance Kid            NaN   \n",
       "649          7.7                   Where Eagles Dare            NaN   \n",
       "936          7.4                           True Grit            NaN   \n",
       "\n",
       "         genre  duration                                        actors_list  \n",
       "187  Biography       110  [u'Paul Newman', u'Robert Redford', u'Katharin...  \n",
       "649     Action       158  [u'Richard Burton', u'Clint Eastwood', u'Mary ...  \n",
       "936  Adventure       128    [u'John Wayne', u'Kim Darby', u'Glen Campbell']  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.loc[movies.content_rating.isnull(), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R            460\n",
       "PG-13        189\n",
       "PG           123\n",
       "NOT RATED     65\n",
       "APPROVED      47\n",
       "UNRATED       38\n",
       "G             32\n",
       "PASSED         7\n",
       "NC-17          7\n",
       "X              4\n",
       "GP             3\n",
       "NaN            3\n",
       "TV-MA          1\n",
       "Name: content_rating, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting content_rating unique values\n",
    "# you can see there're 65 'NOT RATED' and 3 'NaN'\n",
    "# we want to combine all to make 68 NaN\n",
    "movies.content_rating.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.9</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>96</td>\n",
       "      <td>[u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.9</td>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Western</td>\n",
       "      <td>161</td>\n",
       "      <td>[u'Clint Eastwood', u'Eli Wallach', u'Lee Van ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>8.5</td>\n",
       "      <td>Sunset Blvd.</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>110</td>\n",
       "      <td>[u'William Holden', u'Gloria Swanson', u'Erich...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>8.4</td>\n",
       "      <td>M</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Crime</td>\n",
       "      <td>99</td>\n",
       "      <td>[u'Peter Lorre', u'Ellen Widmann', u'Inge Land...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>8.4</td>\n",
       "      <td>Munna Bhai M.B.B.S.</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>156</td>\n",
       "      <td>[u'Sunil Dutt', u'Sanjay Dutt', u'Arshad Warsi']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>7.7</td>\n",
       "      <td>Lolita</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'James Mason', u'Shelley Winters', u'Sue Lyon']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>7.7</td>\n",
       "      <td>Blow-Up</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>111</td>\n",
       "      <td>[u'David Hemmings', u'Vanessa Redgrave', u'Sar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>7.6</td>\n",
       "      <td>Hunger</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Biography</td>\n",
       "      <td>96</td>\n",
       "      <td>[u'Stuart Graham', u'Laine Megaw', u'Brian Mil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>7.5</td>\n",
       "      <td>The Wind That Shakes the Barley</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>127</td>\n",
       "      <td>[u'Cillian Murphy', u'Padraic Delaney', u'Liam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>7.5</td>\n",
       "      <td>In the Loop</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>106</td>\n",
       "      <td>[u'Tom Hollander', u'Peter Capaldi', u'James G...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     star_rating                            title content_rating      genre  \\\n",
       "5            8.9                     12 Angry Men      NOT RATED      Drama   \n",
       "6            8.9   The Good, the Bad and the Ugly      NOT RATED    Western   \n",
       "41           8.5                     Sunset Blvd.      NOT RATED      Drama   \n",
       "63           8.4                                M      NOT RATED      Crime   \n",
       "66           8.4              Munna Bhai M.B.B.S.      NOT RATED     Comedy   \n",
       "..           ...                              ...            ...        ...   \n",
       "665          7.7                           Lolita      NOT RATED      Drama   \n",
       "673          7.7                          Blow-Up      NOT RATED      Drama   \n",
       "763          7.6                           Hunger      NOT RATED  Biography   \n",
       "827          7.5  The Wind That Shakes the Barley      NOT RATED      Drama   \n",
       "899          7.5                      In the Loop      NOT RATED     Comedy   \n",
       "\n",
       "     duration                                        actors_list  \n",
       "5          96  [u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...  \n",
       "6         161  [u'Clint Eastwood', u'Eli Wallach', u'Lee Van ...  \n",
       "41        110  [u'William Holden', u'Gloria Swanson', u'Erich...  \n",
       "63         99  [u'Peter Lorre', u'Ellen Widmann', u'Inge Land...  \n",
       "66        156   [u'Sunil Dutt', u'Sanjay Dutt', u'Arshad Warsi']  \n",
       "..        ...                                                ...  \n",
       "665       152  [u'James Mason', u'Shelley Winters', u'Sue Lyon']  \n",
       "673       111  [u'David Hemmings', u'Vanessa Redgrave', u'Sar...  \n",
       "763        96  [u'Stuart Graham', u'Laine Megaw', u'Brian Mil...  \n",
       "827       127  [u'Cillian Murphy', u'Padraic Delaney', u'Liam...  \n",
       "899       106  [u'Tom Hollander', u'Peter Capaldi', u'James G...  \n",
       "\n",
       "[65 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examining content_rating's 'NOT RATED'\n",
    "movies.loc[movies.content_rating=='NOT RATED', :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5      NOT RATED\n",
       "6      NOT RATED\n",
       "41     NOT RATED\n",
       "63     NOT RATED\n",
       "66     NOT RATED\n",
       "         ...    \n",
       "665    NOT RATED\n",
       "673    NOT RATED\n",
       "763    NOT RATED\n",
       "827    NOT RATED\n",
       "899    NOT RATED\n",
       "Name: content_rating, Length: 65, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering only 1 column\n",
    "movies.loc[movies.content_rating=='NOT RATED', 'content_rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "type(movies.loc[movies.content_rating=='NOT RATED', 'content_rating'])\n",
    "\n",
    "# there's no error here\n",
    "# however, if you use other methods of slicing, it would output an error\n",
    "\n",
    "# equating this series to np.nan converts all to 'NaN'\n",
    "movies.loc[movies.content_rating=='NOT RATED', 'content_rating'] = np.nan\n",
    "# it has changed from 65 to 68\n",
    "movies.content_rating.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select top_movies\n",
    "top_movies = movies.loc[movies.star_rating >= 9, :]\n",
    "top_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pi/.local/lib/python3.7/site-packages/pandas/core/indexing.py:494: 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",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "# there's a SettingWithCopyWarning here because Pandas is not sure if the DataFrame is a view or copy\n",
    "top_movies.loc[0, 'duration'] = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to get rid of the error, always use .copy()\n",
    "\n",
    "top_movies = movies.loc[movies.star_rating >= 9, :].copy()\n",
    "top_movies.loc[0, 'duration'] = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>150</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       150   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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