pandas_str_extract_split.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas: str.extract, str.split\n",
    "* use regular expression to clean and transform data\n",
    "* notebook: https://github.com/prasertcbs/pandas/blob/main/pandas_str_extract_split.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "* author:  [Prasert Kanawattanachai](prasert.k@chula.ac.th)\n",
    "* YouTube: https://www.youtube.com/prasertcbs\n",
    "* [Chulalongkorn Business School](https://www.cbs.chula.ac.th/en/)\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"720\"\n",
       "            height=\"405\"\n",
       "            src=\"https://www.youtube.com/embed/7spx-I6Omxk\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x167de8a4a08>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('7spx-I6Omxk', width=720, height=405)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 64
    },
    "colab_type": "code",
    "id": "fdr0pYIf7P-_",
    "outputId": "c919deae-c99b-44b0-8924-4d2355ca0b63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas version:  1.1.3\n"
     ]
    }
   ],
   "source": [
    "print(f'pandas version:  {pd.__version__}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-12-05 11:38:41.401670')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp.now()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>title_year</th>\n",
       "      <th>length</th>\n",
       "      <th>budget_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>1 hour(s) 21 minute(s)</td>\n",
       "      <td>30.0/362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Bug's Life (1998)</td>\n",
       "      <td>1 hour(s) 36 minute(s)</td>\n",
       "      <td>45.0/363.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toy Story 2 (1999)</td>\n",
       "      <td>1 hour(s) 32 minute(s)</td>\n",
       "      <td>90.0/485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monsters, Inc. (2001)</td>\n",
       "      <td>1 hour(s) 30 minute(s)</td>\n",
       "      <td>115.0/528.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finding Nemo (2003)</td>\n",
       "      <td>1 hour(s) 44 minute(s)</td>\n",
       "      <td>94.0/895.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The Incredibles (2004)</td>\n",
       "      <td>1 hour(s) 55 minute(s)</td>\n",
       "      <td>92.0/631.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cars (2006)</td>\n",
       "      <td>1 hour(s) 56 minute(s)</td>\n",
       "      <td>70.0/462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ratatouille (2007)</td>\n",
       "      <td>1 hour(s) 51 minute(s)</td>\n",
       "      <td>150.0/623.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WALL-E (2008)</td>\n",
       "      <td>1 hour(s) 37 minute(s)</td>\n",
       "      <td>180.0/521.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Up (2009)</td>\n",
       "      <td>1 hour(s) 36 minute(s)</td>\n",
       "      <td>175.0/731.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Toy Story 3 (2010)</td>\n",
       "      <td>1 hour(s) 43 minute(s)</td>\n",
       "      <td>200.0/1,063.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cars 2 (2011)</td>\n",
       "      <td>1 hour(s) 53 minute(s)</td>\n",
       "      <td>200.0/559.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Brave (2012)</td>\n",
       "      <td>1 hour(s) 40 minute(s)</td>\n",
       "      <td>185.0/539.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Monsters University (2013)</td>\n",
       "      <td>1 hour(s) 47 minute(s)</td>\n",
       "      <td>200.0/743.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Inside Out (2015)</td>\n",
       "      <td>1 hour(s) 42 minute(s)</td>\n",
       "      <td>175.0/677.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    title_year                  length budget_revenue\n",
       "0             Toy Story (1995)  1 hour(s) 21 minute(s)     30.0/362.0\n",
       "1          A Bug's Life (1998)  1 hour(s) 36 minute(s)     45.0/363.4\n",
       "2           Toy Story 2 (1999)  1 hour(s) 32 minute(s)     90.0/485.0\n",
       "3        Monsters, Inc. (2001)  1 hour(s) 30 minute(s)    115.0/528.8\n",
       "4          Finding Nemo (2003)  1 hour(s) 44 minute(s)     94.0/895.6\n",
       "5       The Incredibles (2004)  1 hour(s) 55 minute(s)     92.0/631.4\n",
       "6                  Cars (2006)  1 hour(s) 56 minute(s)     70.0/462.0\n",
       "7           Ratatouille (2007)  1 hour(s) 51 minute(s)    150.0/623.7\n",
       "8                WALL-E (2008)  1 hour(s) 37 minute(s)    180.0/521.3\n",
       "9                    Up (2009)  1 hour(s) 36 minute(s)    175.0/731.3\n",
       "10          Toy Story 3 (2010)  1 hour(s) 43 minute(s)  200.0/1,063.2\n",
       "11               Cars 2 (2011)  1 hour(s) 53 minute(s)    200.0/559.9\n",
       "12                Brave (2012)  1 hour(s) 40 minute(s)    185.0/539.0\n",
       "13  Monsters University (2013)  1 hour(s) 47 minute(s)    200.0/743.6\n",
       "14           Inside Out (2015)  1 hour(s) 42 minute(s)    175.0/677.1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url='https://github.com/prasertcbs/basic-dataset/raw/master/regexp_extract_split.csv'\n",
    "df=pd.read_csv(url)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## extract movie title and year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Bug's Life</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toy Story 2</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monsters, Inc.</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finding Nemo</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The Incredibles</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cars</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ratatouille</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WALL-E</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Up</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cars 2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Brave</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Monsters University</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Inside Out</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0     1\n",
       "0             Toy Story  1995\n",
       "1          A Bug's Life  1998\n",
       "2           Toy Story 2  1999\n",
       "3        Monsters, Inc.  2001\n",
       "4          Finding Nemo  2003\n",
       "5       The Incredibles  2004\n",
       "6                  Cars  2006\n",
       "7           Ratatouille  2007\n",
       "8                WALL-E  2008\n",
       "9                    Up  2009\n",
       "10          Toy Story 3  2010\n",
       "11               Cars 2  2011\n",
       "12                Brave  2012\n",
       "13  Monsters University  2013\n",
       "14           Inside Out  2015"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.title_year.str.extract('(.{1,})\\s\\((\\d{4})\\)')\n",
    "df.title_year.str.extract('(.+)\\s\\((\\d{4})\\)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>title_year</th>\n",
       "      <th>length</th>\n",
       "      <th>budget_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>1 hour(s) 21 minute(s)</td>\n",
       "      <td>30.0/362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Bug's Life (1998)</td>\n",
       "      <td>1 hour(s) 36 minute(s)</td>\n",
       "      <td>45.0/363.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toy Story 2 (1999)</td>\n",
       "      <td>1 hour(s) 32 minute(s)</td>\n",
       "      <td>90.0/485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monsters, Inc. (2001)</td>\n",
       "      <td>1 hour(s) 30 minute(s)</td>\n",
       "      <td>115.0/528.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finding Nemo (2003)</td>\n",
       "      <td>1 hour(s) 44 minute(s)</td>\n",
       "      <td>94.0/895.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              title_year                  length budget_revenue\n",
       "0       Toy Story (1995)  1 hour(s) 21 minute(s)     30.0/362.0\n",
       "1    A Bug's Life (1998)  1 hour(s) 36 minute(s)     45.0/363.4\n",
       "2     Toy Story 2 (1999)  1 hour(s) 32 minute(s)     90.0/485.0\n",
       "3  Monsters, Inc. (2001)  1 hour(s) 30 minute(s)    115.0/528.8\n",
       "4    Finding Nemo (2003)  1 hour(s) 44 minute(s)     94.0/895.6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Bug's Life</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toy Story 2</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monsters, Inc.</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finding Nemo</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            title  year\n",
       "0       Toy Story  1995\n",
       "1    A Bug's Life  1998\n",
       "2     Toy Story 2  1999\n",
       "3  Monsters, Inc.  2001\n",
       "4    Finding Nemo  2003"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm=df.title_year.str.extract('(.+)\\s\\((\\d{4})\\)')\n",
    "dm.columns=['title', 'year']\n",
    "dm['year']=dm['year'].astype(int)\n",
    "dm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15 entries, 0 to 14\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   title   15 non-null     object\n",
      " 1   year    15 non-null     int32 \n",
      "dtypes: int32(1), object(1)\n",
      "memory usage: 308.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "dm.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### extract movie length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>title_year</th>\n",
       "      <th>length</th>\n",
       "      <th>budget_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>1 hour(s) 21 minute(s)</td>\n",
       "      <td>30.0/362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Bug's Life (1998)</td>\n",
       "      <td>1 hour(s) 36 minute(s)</td>\n",
       "      <td>45.0/363.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toy Story 2 (1999)</td>\n",
       "      <td>1 hour(s) 32 minute(s)</td>\n",
       "      <td>90.0/485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monsters, Inc. (2001)</td>\n",
       "      <td>1 hour(s) 30 minute(s)</td>\n",
       "      <td>115.0/528.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finding Nemo (2003)</td>\n",
       "      <td>1 hour(s) 44 minute(s)</td>\n",
       "      <td>94.0/895.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The Incredibles (2004)</td>\n",
       "      <td>1 hour(s) 55 minute(s)</td>\n",
       "      <td>92.0/631.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cars (2006)</td>\n",
       "      <td>1 hour(s) 56 minute(s)</td>\n",
       "      <td>70.0/462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ratatouille (2007)</td>\n",
       "      <td>1 hour(s) 51 minute(s)</td>\n",
       "      <td>150.0/623.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WALL-E (2008)</td>\n",
       "      <td>1 hour(s) 37 minute(s)</td>\n",
       "      <td>180.0/521.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Up (2009)</td>\n",
       "      <td>1 hour(s) 36 minute(s)</td>\n",
       "      <td>175.0/731.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Toy Story 3 (2010)</td>\n",
       "      <td>1 hour(s) 43 minute(s)</td>\n",
       "      <td>200.0/1,063.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cars 2 (2011)</td>\n",
       "      <td>1 hour(s) 53 minute(s)</td>\n",
       "      <td>200.0/559.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Brave (2012)</td>\n",
       "      <td>1 hour(s) 40 minute(s)</td>\n",
       "      <td>185.0/539.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Monsters University (2013)</td>\n",
       "      <td>1 hour(s) 47 minute(s)</td>\n",
       "      <td>200.0/743.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Inside Out (2015)</td>\n",
       "      <td>1 hour(s) 42 minute(s)</td>\n",
       "      <td>175.0/677.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    title_year                  length budget_revenue\n",
       "0             Toy Story (1995)  1 hour(s) 21 minute(s)     30.0/362.0\n",
       "1          A Bug's Life (1998)  1 hour(s) 36 minute(s)     45.0/363.4\n",
       "2           Toy Story 2 (1999)  1 hour(s) 32 minute(s)     90.0/485.0\n",
       "3        Monsters, Inc. (2001)  1 hour(s) 30 minute(s)    115.0/528.8\n",
       "4          Finding Nemo (2003)  1 hour(s) 44 minute(s)     94.0/895.6\n",
       "5       The Incredibles (2004)  1 hour(s) 55 minute(s)     92.0/631.4\n",
       "6                  Cars (2006)  1 hour(s) 56 minute(s)     70.0/462.0\n",
       "7           Ratatouille (2007)  1 hour(s) 51 minute(s)    150.0/623.7\n",
       "8                WALL-E (2008)  1 hour(s) 37 minute(s)    180.0/521.3\n",
       "9                    Up (2009)  1 hour(s) 36 minute(s)    175.0/731.3\n",
       "10          Toy Story 3 (2010)  1 hour(s) 43 minute(s)  200.0/1,063.2\n",
       "11               Cars 2 (2011)  1 hour(s) 53 minute(s)    200.0/559.9\n",
       "12                Brave (2012)  1 hour(s) 40 minute(s)    185.0/539.0\n",
       "13  Monsters University (2013)  1 hour(s) 47 minute(s)    200.0/743.6\n",
       "14           Inside Out (2015)  1 hour(s) 42 minute(s)    175.0/677.1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1\n",
       "0   1.0  21.0\n",
       "1   1.0  36.0\n",
       "2   1.0  32.0\n",
       "3   1.0  30.0\n",
       "4   1.0  44.0\n",
       "5   1.0  55.0\n",
       "6   1.0  56.0\n",
       "7   1.0  51.0\n",
       "8   1.0  37.0\n",
       "9   1.0  36.0\n",
       "10  1.0  43.0\n",
       "11  1.0  53.0\n",
       "12  1.0  40.0\n",
       "13  1.0  47.0\n",
       "14  1.0  42.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['length'].str.extract('(\\d+)\\shour\\(s\\)\\s(\\d+)\\sminute\\(s\\)').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hour  minute\n",
       "0    1.0    21.0\n",
       "1    1.0    36.0\n",
       "2    1.0    32.0\n",
       "3    1.0    30.0\n",
       "4    1.0    44.0\n",
       "5    1.0    55.0\n",
       "6    1.0    56.0\n",
       "7    1.0    51.0\n",
       "8    1.0    37.0\n",
       "9    1.0    36.0\n",
       "10   1.0    43.0\n",
       "11   1.0    53.0\n",
       "12   1.0    40.0\n",
       "13   1.0    47.0\n",
       "14   1.0    42.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=df['length'].str.extract('(\\d+)\\shour\\(s\\)\\s(\\d+)\\sminute\\(s\\)').astype(float)\n",
    "dt.columns=['hour', 'minute']\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>total_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hour  minute  total_min\n",
       "0    1.0    21.0       81.0\n",
       "1    1.0    36.0       96.0\n",
       "2    1.0    32.0       92.0\n",
       "3    1.0    30.0       90.0\n",
       "4    1.0    44.0      104.0\n",
       "5    1.0    55.0      115.0\n",
       "6    1.0    56.0      116.0\n",
       "7    1.0    51.0      111.0\n",
       "8    1.0    37.0       97.0\n",
       "9    1.0    36.0       96.0\n",
       "10   1.0    43.0      103.0\n",
       "11   1.0    53.0      113.0\n",
       "12   1.0    40.0      100.0\n",
       "13   1.0    47.0      107.0\n",
       "14   1.0    42.0      102.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt['total_min']=dt['hour'] * 60 + dt['minute']\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### extract budget and revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>title_year</th>\n",
       "      <th>length</th>\n",
       "      <th>budget_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>1 hour(s) 21 minute(s)</td>\n",
       "      <td>30.0/362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Bug's Life (1998)</td>\n",
       "      <td>1 hour(s) 36 minute(s)</td>\n",
       "      <td>45.0/363.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toy Story 2 (1999)</td>\n",
       "      <td>1 hour(s) 32 minute(s)</td>\n",
       "      <td>90.0/485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monsters, Inc. (2001)</td>\n",
       "      <td>1 hour(s) 30 minute(s)</td>\n",
       "      <td>115.0/528.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finding Nemo (2003)</td>\n",
       "      <td>1 hour(s) 44 minute(s)</td>\n",
       "      <td>94.0/895.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The Incredibles (2004)</td>\n",
       "      <td>1 hour(s) 55 minute(s)</td>\n",
       "      <td>92.0/631.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cars (2006)</td>\n",
       "      <td>1 hour(s) 56 minute(s)</td>\n",
       "      <td>70.0/462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ratatouille (2007)</td>\n",
       "      <td>1 hour(s) 51 minute(s)</td>\n",
       "      <td>150.0/623.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WALL-E (2008)</td>\n",
       "      <td>1 hour(s) 37 minute(s)</td>\n",
       "      <td>180.0/521.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Up (2009)</td>\n",
       "      <td>1 hour(s) 36 minute(s)</td>\n",
       "      <td>175.0/731.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Toy Story 3 (2010)</td>\n",
       "      <td>1 hour(s) 43 minute(s)</td>\n",
       "      <td>200.0/1,063.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cars 2 (2011)</td>\n",
       "      <td>1 hour(s) 53 minute(s)</td>\n",
       "      <td>200.0/559.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Brave (2012)</td>\n",
       "      <td>1 hour(s) 40 minute(s)</td>\n",
       "      <td>185.0/539.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Monsters University (2013)</td>\n",
       "      <td>1 hour(s) 47 minute(s)</td>\n",
       "      <td>200.0/743.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Inside Out (2015)</td>\n",
       "      <td>1 hour(s) 42 minute(s)</td>\n",
       "      <td>175.0/677.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    title_year                  length budget_revenue\n",
       "0             Toy Story (1995)  1 hour(s) 21 minute(s)     30.0/362.0\n",
       "1          A Bug's Life (1998)  1 hour(s) 36 minute(s)     45.0/363.4\n",
       "2           Toy Story 2 (1999)  1 hour(s) 32 minute(s)     90.0/485.0\n",
       "3        Monsters, Inc. (2001)  1 hour(s) 30 minute(s)    115.0/528.8\n",
       "4          Finding Nemo (2003)  1 hour(s) 44 minute(s)     94.0/895.6\n",
       "5       The Incredibles (2004)  1 hour(s) 55 minute(s)     92.0/631.4\n",
       "6                  Cars (2006)  1 hour(s) 56 minute(s)     70.0/462.0\n",
       "7           Ratatouille (2007)  1 hour(s) 51 minute(s)    150.0/623.7\n",
       "8                WALL-E (2008)  1 hour(s) 37 minute(s)    180.0/521.3\n",
       "9                    Up (2009)  1 hour(s) 36 minute(s)    175.0/731.3\n",
       "10          Toy Story 3 (2010)  1 hour(s) 43 minute(s)  200.0/1,063.2\n",
       "11               Cars 2 (2011)  1 hour(s) 53 minute(s)    200.0/559.9\n",
       "12                Brave (2012)  1 hour(s) 40 minute(s)    185.0/539.0\n",
       "13  Monsters University (2013)  1 hour(s) 47 minute(s)    200.0/743.6\n",
       "14           Inside Out (2015)  1 hour(s) 42 minute(s)    175.0/677.1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "        0       1\n",
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       "7   150.0   623.7\n",
       "8   180.0   521.3\n",
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       "10  200.0  1063.2\n",
       "11  200.0   559.9\n",
       "12  185.0   539.0\n",
       "13  200.0   743.6\n",
       "14  175.0   677.1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.budget_revenue.str.replace(',', '').str.split('/', expand=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "        0       1\n",
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       "6    70.0   462.0\n",
       "7   150.0   623.7\n",
       "8   180.0   521.3\n",
       "9   175.0   731.3\n",
       "10  200.0  1063.2\n",
       "11  200.0   559.9\n",
       "12  185.0   539.0\n",
       "13  200.0   743.6\n",
       "14  175.0   677.1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds=df.budget_revenue.str.replace(',', '').str.split('/', expand=True).astype(float)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "    budget  revenue  profit\n",
       "0     30.0    362.0   332.0\n",
       "1     45.0    363.4   318.4\n",
       "2     90.0    485.0   395.0\n",
       "3    115.0    528.8   413.8\n",
       "4     94.0    895.6   801.6\n",
       "5     92.0    631.4   539.4\n",
       "6     70.0    462.0   392.0\n",
       "7    150.0    623.7   473.7\n",
       "8    180.0    521.3   341.3\n",
       "9    175.0    731.3   556.3\n",
       "10   200.0   1063.2   863.2\n",
       "11   200.0    559.9   359.9\n",
       "12   185.0    539.0   354.0\n",
       "13   200.0    743.6   543.6\n",
       "14   175.0    677.1   502.1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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   ],
   "source": [
    "ds.columns=['budget', 'revenue']\n",
    "ds['profit']=ds.revenue-ds.budget\n",
    "ds"
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  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Toy Story (1995)</td>\n",
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       "      <th>1</th>\n",
       "      <td>A Bug's Life (1998)</td>\n",
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      "text/plain": [
       "              title_year                  length budget_revenue\n",
       "0       Toy Story (1995)  1 hour(s) 21 minute(s)     30.0/362.0\n",
       "1    A Bug's Life (1998)  1 hour(s) 36 minute(s)     45.0/363.4\n",
       "2     Toy Story 2 (1999)  1 hour(s) 32 minute(s)     90.0/485.0\n",
       "3  Monsters, Inc. (2001)  1 hour(s) 30 minute(s)    115.0/528.8\n",
       "4    Finding Nemo (2003)  1 hour(s) 44 minute(s)     94.0/895.6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>115.0</td>\n",
       "      <td>528.8</td>\n",
       "      <td>413.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94.0</td>\n",
       "      <td>895.6</td>\n",
       "      <td>801.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>92.0</td>\n",
       "      <td>631.4</td>\n",
       "      <td>539.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>150.0</td>\n",
       "      <td>623.7</td>\n",
       "      <td>473.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>180.0</td>\n",
       "      <td>521.3</td>\n",
       "      <td>341.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>175.0</td>\n",
       "      <td>731.3</td>\n",
       "      <td>556.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>200.0</td>\n",
       "      <td>1063.2</td>\n",
       "      <td>863.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>200.0</td>\n",
       "      <td>559.9</td>\n",
       "      <td>359.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>185.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>200.0</td>\n",
       "      <td>743.6</td>\n",
       "      <td>543.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>175.0</td>\n",
       "      <td>677.1</td>\n",
       "      <td>502.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    budget  revenue  profit\n",
       "0     30.0    362.0   332.0\n",
       "1     45.0    363.4   318.4\n",
       "2     90.0    485.0   395.0\n",
       "3    115.0    528.8   413.8\n",
       "4     94.0    895.6   801.6\n",
       "5     92.0    631.4   539.4\n",
       "6     70.0    462.0   392.0\n",
       "7    150.0    623.7   473.7\n",
       "8    180.0    521.3   341.3\n",
       "9    175.0    731.3   556.3\n",
       "10   200.0   1063.2   863.2\n",
       "11   200.0    559.9   359.9\n",
       "12   185.0    539.0   354.0\n",
       "13   200.0    743.6   543.6\n",
       "14   175.0    677.1   502.1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtemp=dtemp=df.budget_revenue.str.replace(',', '').str.extract('(.+)/(.+)').astype(float)\n",
    "dtemp.columns=['budget', 'revenue']\n",
    "dtemp['profit']=dtemp.revenue-dtemp.budget\n",
    "dtemp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Bug's Life</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toy Story 2</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monsters, Inc.</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finding Nemo</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            title  year\n",
       "0       Toy Story  1995\n",
       "1    A Bug's Life  1998\n",
       "2     Toy Story 2  1999\n",
       "3  Monsters, Inc.  2001\n",
       "4    Finding Nemo  2003"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>total_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hour  minute  total_min\n",
       "0   1.0    21.0       81.0\n",
       "1   1.0    36.0       96.0\n",
       "2   1.0    32.0       92.0\n",
       "3   1.0    30.0       90.0\n",
       "4   1.0    44.0      104.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45.0</td>\n",
       "      <td>363.4</td>\n",
       "      <td>318.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>115.0</td>\n",
       "      <td>528.8</td>\n",
       "      <td>413.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94.0</td>\n",
       "      <td>895.6</td>\n",
       "      <td>801.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   budget  revenue  profit\n",
       "0    30.0    362.0   332.0\n",
       "1    45.0    363.4   318.4\n",
       "2    90.0    485.0   395.0\n",
       "3   115.0    528.8   413.8\n",
       "4    94.0    895.6   801.6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtemp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>title_year</th>\n",
       "      <th>length</th>\n",
       "      <th>budget_revenue</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>total_min</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>1 hour(s) 21 minute(s)</td>\n",
       "      <td>30.0/362.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Bug's Life (1998)</td>\n",
       "      <td>1 hour(s) 36 minute(s)</td>\n",
       "      <td>45.0/363.4</td>\n",
       "      <td>A Bug's Life</td>\n",
       "      <td>1998</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>363.4</td>\n",
       "      <td>318.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toy Story 2 (1999)</td>\n",
       "      <td>1 hour(s) 32 minute(s)</td>\n",
       "      <td>90.0/485.0</td>\n",
       "      <td>Toy Story 2</td>\n",
       "      <td>1999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monsters, Inc. (2001)</td>\n",
       "      <td>1 hour(s) 30 minute(s)</td>\n",
       "      <td>115.0/528.8</td>\n",
       "      <td>Monsters, Inc.</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>528.8</td>\n",
       "      <td>413.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finding Nemo (2003)</td>\n",
       "      <td>1 hour(s) 44 minute(s)</td>\n",
       "      <td>94.0/895.6</td>\n",
       "      <td>Finding Nemo</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>895.6</td>\n",
       "      <td>801.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The Incredibles (2004)</td>\n",
       "      <td>1 hour(s) 55 minute(s)</td>\n",
       "      <td>92.0/631.4</td>\n",
       "      <td>The Incredibles</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>631.4</td>\n",
       "      <td>539.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cars (2006)</td>\n",
       "      <td>1 hour(s) 56 minute(s)</td>\n",
       "      <td>70.0/462.0</td>\n",
       "      <td>Cars</td>\n",
       "      <td>2006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ratatouille (2007)</td>\n",
       "      <td>1 hour(s) 51 minute(s)</td>\n",
       "      <td>150.0/623.7</td>\n",
       "      <td>Ratatouille</td>\n",
       "      <td>2007</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>623.7</td>\n",
       "      <td>473.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WALL-E (2008)</td>\n",
       "      <td>1 hour(s) 37 minute(s)</td>\n",
       "      <td>180.0/521.3</td>\n",
       "      <td>WALL-E</td>\n",
       "      <td>2008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>521.3</td>\n",
       "      <td>341.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Up (2009)</td>\n",
       "      <td>1 hour(s) 36 minute(s)</td>\n",
       "      <td>175.0/731.3</td>\n",
       "      <td>Up</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>731.3</td>\n",
       "      <td>556.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Toy Story 3 (2010)</td>\n",
       "      <td>1 hour(s) 43 minute(s)</td>\n",
       "      <td>200.0/1,063.2</td>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1063.2</td>\n",
       "      <td>863.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cars 2 (2011)</td>\n",
       "      <td>1 hour(s) 53 minute(s)</td>\n",
       "      <td>200.0/559.9</td>\n",
       "      <td>Cars 2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>559.9</td>\n",
       "      <td>359.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Brave (2012)</td>\n",
       "      <td>1 hour(s) 40 minute(s)</td>\n",
       "      <td>185.0/539.0</td>\n",
       "      <td>Brave</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Monsters University (2013)</td>\n",
       "      <td>1 hour(s) 47 minute(s)</td>\n",
       "      <td>200.0/743.6</td>\n",
       "      <td>Monsters University</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>743.6</td>\n",
       "      <td>543.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Inside Out (2015)</td>\n",
       "      <td>1 hour(s) 42 minute(s)</td>\n",
       "      <td>175.0/677.1</td>\n",
       "      <td>Inside Out</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>677.1</td>\n",
       "      <td>502.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    title_year                  length budget_revenue  \\\n",
       "0             Toy Story (1995)  1 hour(s) 21 minute(s)     30.0/362.0   \n",
       "1          A Bug's Life (1998)  1 hour(s) 36 minute(s)     45.0/363.4   \n",
       "2           Toy Story 2 (1999)  1 hour(s) 32 minute(s)     90.0/485.0   \n",
       "3        Monsters, Inc. (2001)  1 hour(s) 30 minute(s)    115.0/528.8   \n",
       "4          Finding Nemo (2003)  1 hour(s) 44 minute(s)     94.0/895.6   \n",
       "5       The Incredibles (2004)  1 hour(s) 55 minute(s)     92.0/631.4   \n",
       "6                  Cars (2006)  1 hour(s) 56 minute(s)     70.0/462.0   \n",
       "7           Ratatouille (2007)  1 hour(s) 51 minute(s)    150.0/623.7   \n",
       "8                WALL-E (2008)  1 hour(s) 37 minute(s)    180.0/521.3   \n",
       "9                    Up (2009)  1 hour(s) 36 minute(s)    175.0/731.3   \n",
       "10          Toy Story 3 (2010)  1 hour(s) 43 minute(s)  200.0/1,063.2   \n",
       "11               Cars 2 (2011)  1 hour(s) 53 minute(s)    200.0/559.9   \n",
       "12                Brave (2012)  1 hour(s) 40 minute(s)    185.0/539.0   \n",
       "13  Monsters University (2013)  1 hour(s) 47 minute(s)    200.0/743.6   \n",
       "14           Inside Out (2015)  1 hour(s) 42 minute(s)    175.0/677.1   \n",
       "\n",
       "                  title  year  hour  minute  total_min  budget  revenue  \\\n",
       "0             Toy Story  1995   1.0    21.0       81.0    30.0    362.0   \n",
       "1          A Bug's Life  1998   1.0    36.0       96.0    45.0    363.4   \n",
       "2           Toy Story 2  1999   1.0    32.0       92.0    90.0    485.0   \n",
       "3        Monsters, Inc.  2001   1.0    30.0       90.0   115.0    528.8   \n",
       "4          Finding Nemo  2003   1.0    44.0      104.0    94.0    895.6   \n",
       "5       The Incredibles  2004   1.0    55.0      115.0    92.0    631.4   \n",
       "6                  Cars  2006   1.0    56.0      116.0    70.0    462.0   \n",
       "7           Ratatouille  2007   1.0    51.0      111.0   150.0    623.7   \n",
       "8                WALL-E  2008   1.0    37.0       97.0   180.0    521.3   \n",
       "9                    Up  2009   1.0    36.0       96.0   175.0    731.3   \n",
       "10          Toy Story 3  2010   1.0    43.0      103.0   200.0   1063.2   \n",
       "11               Cars 2  2011   1.0    53.0      113.0   200.0    559.9   \n",
       "12                Brave  2012   1.0    40.0      100.0   185.0    539.0   \n",
       "13  Monsters University  2013   1.0    47.0      107.0   200.0    743.6   \n",
       "14           Inside Out  2015   1.0    42.0      102.0   175.0    677.1   \n",
       "\n",
       "    profit  \n",
       "0    332.0  \n",
       "1    318.4  \n",
       "2    395.0  \n",
       "3    413.8  \n",
       "4    801.6  \n",
       "5    539.4  \n",
       "6    392.0  \n",
       "7    473.7  \n",
       "8    341.3  \n",
       "9    556.3  \n",
       "10   863.2  \n",
       "11   359.9  \n",
       "12   354.0  \n",
       "13   543.6  \n",
       "14   502.1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.concat([df, dm, dt, dtemp], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_min    101.533333\n",
       "budget       133.400000\n",
       "revenue      612.486667\n",
       "profit       479.086667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['total_min',\n",
    "    'budget', 'revenue', 'profit']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>total_min</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2006.066667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.533333</td>\n",
       "      <td>101.533333</td>\n",
       "      <td>133.400000</td>\n",
       "      <td>612.486667</td>\n",
       "      <td>479.086667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.933761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.927355</td>\n",
       "      <td>9.927355</td>\n",
       "      <td>59.696614</td>\n",
       "      <td>190.193934</td>\n",
       "      <td>164.986384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>318.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>503.150000</td>\n",
       "      <td>356.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>559.900000</td>\n",
       "      <td>413.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2010.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>704.200000</td>\n",
       "      <td>541.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1063.200000</td>\n",
       "      <td>863.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year  hour     minute   total_min      budget      revenue  \\\n",
       "count    15.000000  15.0  15.000000   15.000000   15.000000    15.000000   \n",
       "mean   2006.066667   1.0  41.533333  101.533333  133.400000   612.486667   \n",
       "std       5.933761   0.0   9.927355    9.927355   59.696614   190.193934   \n",
       "min    1995.000000   1.0  21.000000   81.000000   30.000000   362.000000   \n",
       "25%    2002.000000   1.0  36.000000   96.000000   91.000000   503.150000   \n",
       "50%    2007.000000   1.0  42.000000  102.000000  150.000000   559.900000   \n",
       "75%    2010.500000   1.0  49.000000  109.000000  182.500000   704.200000   \n",
       "max    2015.000000   1.0  56.000000  116.000000  200.000000  1063.200000   \n",
       "\n",
       "           profit  \n",
       "count   15.000000  \n",
       "mean   479.086667  \n",
       "std    164.986384  \n",
       "min    318.400000  \n",
       "25%    356.950000  \n",
       "50%    413.800000  \n",
       "75%    541.500000  \n",
       "max    863.200000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['title_year', 'length', 'budget_revenue', 'title', 'year', 'hour',\n",
       "       'minute', 'total_min', 'budget', 'revenue', 'profit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['title_year', 'length', 'budget_revenue'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('pixar_clean.csv', index=False)"
   ]
  }
 ],
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