Exercises-3_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>body {\n",
       "    margin: 0;\n",
       "    font-family: Helvetica;\n",
       "}\n",
       "table.dataframe {\n",
       "    border-collapse: collapse;\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe tr {\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe td, table.dataframe th {\n",
       "    margin: 0;\n",
       "    border: 1px solid white;\n",
       "    padding-left: 0.25em;\n",
       "    padding-right: 0.25em;\n",
       "}\n",
       "table.dataframe th:not(:empty) {\n",
       "    background-color: #fec;\n",
       "    text-align: left;\n",
       "    font-weight: normal;\n",
       "}\n",
       "table.dataframe tr:nth-child(2) th:empty {\n",
       "    border-left: none;\n",
       "    border-right: 1px dashed #888;\n",
       "}\n",
       "table.dataframe td {\n",
       "    border: 2px solid #ccf;\n",
       "    background-color: #f4f4ff;\n",
       "}\n",
       "h3 {\n",
       "    color: white;\n",
       "    background-color: black;\n",
       "    padding: 0.5em;\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "css = open('D:/Data Visualization Project/style-table.css').read() + open('D:/Data Visualization Project/style-notebook.css').read()\n",
    "HTML('<style>{}</style>'.format(css))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Captain of His Soul</td>\n",
       "      <td>1918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Qing chun xing</td>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tangled Web (II)</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Shepherd of the Hills</td>\n",
       "      <td>1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anna mit'n Flimmerfimmel</td>\n",
       "      <td>1919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       title  year\n",
       "0        Captain of His Soul  1918\n",
       "1             Qing chun xing  1975\n",
       "2           Tangled Web (II)  2008\n",
       "3  The Shepherd of the Hills  1919\n",
       "4   Anna mit'n Flimmerfimmel  1919"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = pd.read_csv('D:/Data Visualization Project/titles.csv')\n",
    "titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Closet Monster</td>\n",
       "      <td>2015</td>\n",
       "      <td>Buffy #1</td>\n",
       "      <td>actor</td>\n",
       "      <td>Buffy 4</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Suuri illusioni</td>\n",
       "      <td>1985</td>\n",
       "      <td>Homo $</td>\n",
       "      <td>actor</td>\n",
       "      <td>Guests</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Battle of the Sexes</td>\n",
       "      <td>2017</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bobby Riggs Fan</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Secret in Their Eyes</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>2002 Dodger Fan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>1988 Opera House Patron</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title  year      name   type                character     n\n",
       "0        Closet Monster  2015  Buffy #1  actor                  Buffy 4  31.0\n",
       "1       Suuri illusioni  1985    Homo $  actor                   Guests  22.0\n",
       "2   Battle of the Sexes  2017   $hutter  actor          Bobby Riggs Fan  10.0\n",
       "3  Secret in Their Eyes  2015   $hutter  actor          2002 Dodger Fan   NaN\n",
       "4            Steve Jobs  2015   $hutter  actor  1988 Opera House Patron   NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast = pd.read_csv('D:/Data Visualization Project/cast.csv')\n",
    "cast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Using groupby(), plot the number of films that have been released each decade in the history of cinema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8973a30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = titles\n",
    "t.groupby(t.year // 10 * 10).size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Use groupby() to plot the number of \"Hamlet\" films made each decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x86d04d8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = titles\n",
    "t = t[t.title == 'Hamlet']\n",
    "t.groupby(t.year // 10 * 10).size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### How many leading (n=1) roles were available to actors, and how many to actresses, in each year of the 1950s?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  type   \n",
       "1950  actor      622\n",
       "      actress    287\n",
       "1951  actor      640\n",
       "      actress    283\n",
       "1952  actor      605\n",
       "      actress    300\n",
       "1953  actor      648\n",
       "      actress    305\n",
       "1954  actor      616\n",
       "      actress    311\n",
       "1955  actor      634\n",
       "      actress    281\n",
       "1956  actor      651\n",
       "      actress    304\n",
       "1957  actor      700\n",
       "      actress    303\n",
       "1958  actor      674\n",
       "      actress    292\n",
       "1959  actor      692\n",
       "      actress    319\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.year // 10 == 195]\n",
    "c = c[c.n == 1]\n",
    "c.groupby(['year', 'type']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### In the 1950s decade taken as a whole, how many total roles were available to actors, and how many to actresses, for each \"n\" number 1 through 5?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n    type   \n",
       "1.0  actor      6482\n",
       "     actress    2985\n",
       "2.0  actor      4520\n",
       "     actress    4496\n",
       "3.0  actor      5489\n",
       "     actress    3109\n",
       "4.0  actor      5479\n",
       "     actress    2820\n",
       "5.0  actor      5493\n",
       "     actress    2531\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.year // 10 * 10 == 1950]\n",
    "c = c[c.n < 6]\n",
    "c.groupby(['n', 'type']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Use groupby() to determine how many roles are listed for each of the Pink Panther movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         n\n",
       "year      \n",
       "1963  15.0\n",
       "2006  50.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.title == 'The Pink Panther']\n",
    "c = c.sort_values('n').groupby(['year'])[['n']].max()\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### List, in order by year, each of the films in which Frank Oz has played more than 1 role."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  title                               \n",
       "1979  The Muppet Movie                        8\n",
       "1981  The Great Muppet Caper                  6\n",
       "1982  The Dark Crystal                        2\n",
       "1984  The Muppets Take Manhattan              7\n",
       "1985  Follow That Bird                        3\n",
       "1992  The Muppet Christmas Carol              7\n",
       "1996  Muppet Treasure Island                  4\n",
       "1999  Muppets from Space                      4\n",
       "      The Adventures of Elmo in Grouchland    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cast\n",
    "c = c[c.name == 'Frank Oz']\n",
    "group = c.groupby(['year', 'title']).size()\n",
    "group[group > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### List each of the characters that Frank Oz has portrayed at least twice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c = cast\n",
    "c = c[c.name == 'Frank Oz']\n",
    "group = c.groupby(['character']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
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 },
 "nbformat": 4,
 "nbformat_minor": 1
}