Exercises-BL-3.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('style-table.css').read() + open('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",
       "      <td>0</td>\n",
       "      <td>Kanzashi</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>24 Hours</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Red Mantis</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Ruby Skye P.I.: The Spam Scam</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>County General</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           title  year\n",
       "0                       Kanzashi  1941\n",
       "1                       24 Hours  1931\n",
       "2                     Red Mantis  2009\n",
       "3  Ruby Skye P.I.: The Spam Scam  2010\n",
       "4                 County General  2005"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = pd.read_csv('data/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",
       "      <td>0</td>\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",
       "      <td>1</td>\n",
       "      <td>Suuri illusioni</td>\n",
       "      <td>1985</td>\n",
       "      <td>Homo $</td>\n",
       "      <td>actor</td>\n",
       "      <td>Guests</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Battle of the Sexes</td>\n",
       "      <td>2017</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bobby Riggs Fan</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Secret in Their Eyes</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>2002 Dodger Fan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>2015</td>\n",
       "      <td>$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('data/cast.csv')\n",
    "cast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "c= cast\n",
    "t = titles"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc840d57c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CdRFNysXZYjzJ5SgiIiJhEBERCYOIiCBhEBERJAwiIoKEQUREkDCIiAgSBhERQcIgIiJIGEREBAmDiIggYRARESQMIiKCXLU0ngfyqWERA8uaQUREJAwiIiJhEBERNBAGktZK2ifp3praFElbJO0uXyeXuiStkdQlaYekM2qWWVHm3y1pRU19lqR7yjJrJPX3EZsREdEmjawZXAss6lNbBWy1PRPYWh4DLAZmlttK4CqowgNYDcwF5gCrewOkzLOyZrm+3ysiItpswDCw/Y/A/j7lJcC6cn8dsLSmvt6VbcAkSScCC4EttvfbPgBsARaVaUfbvtW2gfU1vSIiYpg0e2jpCbYfArD9kKTjS30qsKdmvu5SO1S9u049Rql8CHzE+DTUO5Drbe93E/X6zaWVkjoldfb09DQ5xIiI6KvZMHi4bOKhfN1X6t3A9Jr5pgF7B6hPq1Ovy/bVtmfbnt3R0dHk0CMioq9mw2Aj0HtE0Arghpr68nJU0Tzg0bI5aTOwQNLksuN4AbC5THtM0rxyFNHyml4RETFMBtxnIOlLwJnAcZK6qY4KugK4TtIFwIPAOWX2TcDbgS7gCeB8ANv7JV0GbC/zXWq7d6f0hVRHLB0B3Fhu0aJcgiEiBmPAMLB9bj+T5teZ18BF/fRZC6ytU+8EThtoHDH+JcAiRk7OQI6IiIRBREQkDCIigoRBRESQD7cZcdlpGhGjQdYMIiIiYRAREQmDiIggYRARESQMIiKChEFERJAwiIgIEgYREUHCICIiSBhERAQJg4iIIGEQEREkDCIigoRBREQwisJA0iJJuyR1SVo10uOJiHg+GRVhIGkC8FlgMXAqcK6kU0d2VBERzx+jIgyAOUCX7fttPwVsAJaM8JgiIp43ZHukx4Cks4FFtt9THp8HzLX9gT7zrQRWloevAnY1+C2OA/5piIY7lvu2s/dY69vO3mOtbzt7j7W+7ew9Gvq+zHZHvQmj5WMvVad2UErZvhq4etDNpU7bs5sZ2Hjq287eY61vO3uPtb7t7D3W+raz92jvO1o2E3UD02seTwP2jtBYIiKed0ZLGGwHZko6WdLhwDJg4wiPKSLieWNUbCay/bSkDwCbgQnAWts7h/BbDHrT0jjt287eY61vO3uPtb7t7D3W+raz96juOyp2IEdExMgaLZuJIiJiBCUMIiIiYRAREQmDiIhglBxNFBFxKJKOARYBU6lOSN0LbLb9SIt9T6G69E1t342272ttxO3rLWkhsLRP3xts39RS3/F4NFEb34R2/uC05w1uX9+8xmO0b+k9Zv5QSVoOrAZuBn5SytOAtwGX2F7fZN+PAudSXQutu6bvMmCD7StaGHNbekv6DPBKYH2fvsuB3bY/1PSYx1sYtPFNaOcPTlve4Db2zWs8RvuW3mPqD5WkXVTXKnukT30ycJvtVzbZ9wfAa2z/qk/9cGCn7ZnN9G1nb0k/qPd8JQn4QStjxva4ugE/AA6rUz+c6gdyVPXt7d1PXa2OuV198xqPzb7tfv/a9VoAx9SpH9Ni3+9TXbitb/1lwK4WX+O29AZ2AHPq1OcA97Qy5vG4z+DXwG8AP+5TP7FMG219AZ6UNMf27X3qbwCeHIV98xqP3b7Qvte5XWO+HLhT0s3AnlI7iWoz0WUt9P0wsFXS7j59XwF8oN+lRrb3u4CrJL2EZ9e+pgM/L9OaNh43Ey0C/gdQ901wk9su29W39D4DuAqo9wa/3/Ydo6xvXuP2950FXDnUfUvvdr1/bXktSu/JwEKqfREq/TfbPtBsz9L3BVT/Vdf23W77mVb6DkPvl9b2tf3TlnuOtzCA9r0J7XxzS/8hf4Pb1Tev8ZjvO6b+UJW+J1CzY9r2w0PRt5/vdZTtX7SrfztIOsX295tefjyGAYCkDqqdV08DPxqKN7bspOn9Beo9UuJ2t/FFbPUNLj0O88E7so6z3fQHbZQ/Jtj+ddkpdhrwgO39rYy1zvd5v+0rh7Jn6XsU1c7O+93C4Ynluf+q92dA0luAM6h2ErayJvNa2zuaXb6B/icBP7f9iKQZwGzgPg/BBSIlzaZaI3iaapt+qz+/pwN/RbWPoJsqZKYBj1CtcdzZ2ojrfs8HbZ/UwvKvpbqA3FTgRuCjvWsxkm63PWdoRvqc79nSmMfdPoPy2clrgBlUq753AcdL+jbwIduPNtl3AdVq+26ee3jbK8ofrJtbHXs/bqZ6HoNW/jB9HnihpLuAlbYfqOl7RpN9lwJ/Dfxa0vuAjwGPA6+UdKHt/9Vk3/9Yp/wxSS8CsP2pZvqW3lfafn+5/1vAF4EfUr1/77W9qcnW24EzgQOS/gT4XWAT8BFJv2374ib73iXpR8CXgC/Z/l6TfQ4iaRXwXuCXkv4C+GPg/wCXSLqm2ddZ0m8D/53qj/Ss0nOypF8B59nec6jlD+Fa4L22b+vz/eYBfwO8rsnx1vt5gypsjmqmZ40rgY8D24D3AN+R9E7bPwQOa7appDX9TQImNdsXxmEYAGuBFbZ3SZoDXGR7rqQ/AK4Bzm6y718Cb635YwqApJOpfvlf3eyA2/gG/1dgoe2dqj5adIuk82xvK72btZrqF/AI4LvAG8rr/TLgK0BTYQBcQvVa7qwZ3wSqbdCtmldz/zJgqe07Jb0cuK5832ZMqNlu/XvAv7L9z5KuAO4Emg2DHcB5VIeAbpT0OFUwbOj7M9iE84BTgRcDDwAvt90j6UjgNqDZ0P0MsKD0Ohn4lO03SXob1e/egib7Htk3CABsbytjbtZ/Bv4b1RpMX61eneGomjXDv5B0B3CTqo/0bWVLwvnAR4Bf1pl2bgt9x2UYHGF7F4Dt2yX9Vbn/PyX9UQt9J/LsTrFaP6GFpC/a9QYf3rvab/t6SfcBXy3/Gba0aat3O3BZNe19vX/cu/moSa+h+kN0JNXJRE9IWmH7klbGWsfRvZsWbN8vaUILvX4u6TTb91J9Du2LgH+m+nlp5bVw6flnwJ+Vf2yWAf9b0h7bv9lC72dKYD1Vxvqz8g0fr7aENm2C7Z5y/0GqwyixvaWcg9CsGyV9k+r8hd61i+lU5y+0cgLencDX6+3YlvSeFvqWFjqmd0uE7W9J+rdU/yxNaaHvduBe2/+3zjf8eAt9x2UY/FDSnwNbgX8D3A3VdnNae75rge2SNvDcH8hlVP/1tKJdb/CvJL209w93WUOYD3wD+Bct9EXSC2z/Gnh3TW0C1bHqTbH9IHC2pCVUazGfbmWMfZwiaQfVGscMSZNtHyjh1UqYvw/4gqTvAvuAzrJJ8rVU/3k26zl/lcvhmrdL+gjw5hb6QnWY5hepQncrsE7STcBZQCubozolXVN6LgFuAZD0Yqo1vKbY/qCkxTx7xnTvDu/PtrB5D6p/wvrbx9XqZwp/kmprwbbegu0d5ffvz1voezb9HKZr++QW+o6/HciSJlFtwz6VahPGFbYfU3Vtk1eXTSTN9j4VeCfP/YHc2Or2XElTgCdtP9FKnzp93wr02P5un/okqs1nlzfZ9w1UJ7g82ac+A/gt23/b3Iif0+tIqm2uc223+sePsgmr1kO2n5J0HPBm219tofcEqk0gr+TZNciWrpsj6fdtf7HZ5QfoPRE4h2rt8HpgLtUa6INUf2Afb7LvYcAf8Ozv3lrbz0g6Ajjedt/zGmIUGXdhEBHjS/lH7mKqNYPjS3kfcAPVP3tNhW5N36VAx1D1bXfvQ3zPG20vbnb5cXcJa0mzJX1L0t9Kmi5pi6RHJW2X9PoW+h4l6VJJO0u/HknbJL1rCMbclt7pO3bHPEKvxYoh6nvvEI/5OuAA8Bbbx9o+FngL1VFLXx6Cvmf26Xugxb5t6y3pjH5us4DTWxnwuFszkHQ71dEuk6iOpvmjsvN0PvAJ229ssu8NwNeAvwf+HdX21g3AfwJ+YvtjLYy5Lb3Td+yOOa/Fc/rusv2qwU4bqb7t7C3pGeDb1D8acJ7tI5rpC4zLC9XdVXP/wf6mNdH3u30eby9fXwB8v8Uxt6V3+o7dMee1eE6fm4E/BU6oqZ0AfBT4+9HWt81jvheY2c+0Pa2MedxtJqK6WNYCSecAVnWCVO8JMa2cav+4qpOVkPSvKUchuDqipqXj8drYO33b33us9W1n73b1/T3gWODbkg5I2k91pNIUqjWQ0da3nb0/Tv+b9/+whb7jcs3gdcBmqlPAT6E6WewRqhOZfrOFvq8Fbi+9vgO8stQ7gA+2OOa29E7fsTvmvBYH9T4FeCvVyVy19UWjse8wjHn+kPdt9QmPpRtw/ljqOxbHPNb6jsUxP99eC+CDwC7g61RnTC+pmXbnaOs7Zsfcrh+q0Xijzz6E0d53LI55rPUdi2N+vr0WwD29/wVTXXOsk+o6Y9DafsC29B2rYx53ZyCrOsu07iSqHTijqm87e6dv+3uPtb7t7N3GMU9wueqw7QcknQlcr+pEwlb2RbSr75gc87gLA6ofuoVUx/PWEnDQ5R5GQd929k7f9vcea33b2btdfX8q6XTbdwPY/oWk36G6RMy/HIV9x+SYx2MYfINqNeruvhMk3TIK+7azd/q2v/dY69vO3u3qu5w+Vxa1/TSwXNJfj8K+7ezdtjGPu5POIiJi8MbjeQYRETFICYOIiEgYREREwiBixKi1T1iLGFIJg4gGSLpM0odqHl8u6YOS/kTV5dF3SLqkZvrXJd1RLhO9sqb+i3KZ59uApq6gG9EOCYOIxlwDrABQ9VGZy4CHgZnAHKpryc+S1PupbO+2PYvq4xM/KOnYUj+S6iNO59r+znA+gYhDGY/nGUQMuXK2589UfUDSCcBdwBuoPu7yrjLbUVTh8I9UAfC7pT691H9GdeXcrwzn2CMakTCIaNzngHcBL6U643M+8F9sP+dkn3KJgLcCb7T9RDnh6kVl8pO2W7mUekRbZDNRROO+BiyiWiPYXG7vlnQUgKSpko4HjgEOlCA4BZg3UgOOaFTWDCIaZPspSd8CHin/3d8s6dXArZIAfgH8B+Am4H3lwm27gG0jNeaIRuVyFBENKjuO7wTOsb17pMcTMZSymSiiAZJOBbqArQmCGI+yZhAREVkziIiIhEFERJAwiIgIEgYREUHCICIiSBhERATw/wB/Telec6yJXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.groupby(t.year//10*10).size().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc801b7fc8>"
      ]
     },
     "execution_count": 14,
     "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": [
    "tf = t.groupby(t.year//10*10).size()\n",
    "tf[tf > 1].plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Use groupby() to plot the number of \"Hamlet\" films made each decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc8662ba88>"
      ]
     },
     "execution_count": 17,
     "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[t.title == 'Hamlet'].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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "actor      6692\n",
       "actress    3040\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[(c.n == 1) & (c.year//10 == 195)].groupby(['type']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "actor      6692\n",
       "actress    3040\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[(c.n == 1) & (c.year//10 == 195)].groupby(c.type).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2004    actor\n",
       "2005    actor\n",
       "2006    actor\n",
       "2007    actor\n",
       "2008    actor\n",
       "Name: type, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "type\n",
       "actor    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I am very curious, as before, about how the groupby function actually works here.\n",
    "\n",
    "# I suspect that it will work as long as it has the same index. Let's try it\n",
    "\n",
    "gt1 = c[(c.n == 1) & (c.year//10 == 195)].type.head()\n",
    "gt1\n",
    "\n",
    "c[(c.n == 1) & (c.year//10 == 195)].groupby(gt1).size()"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      actor\n",
       "1    actress\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.Series(['actor', 'actress'])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type     n  \n",
       "actor    1.0    6692\n",
       "         2.0    4640\n",
       "         3.0    5654\n",
       "         4.0    5663\n",
       "         5.0    5674\n",
       "actress  1.0    3040\n",
       "         2.0    4635\n",
       "         3.0    3201\n",
       "         4.0    2881\n",
       "         5.0    2592\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[(c.n <= 5)&(c.year//10 == 195)].groupby([c.type, c.n]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type     n  \n",
       "actor    1.0    6692\n",
       "         2.0    4640\n",
       "         3.0    5654\n",
       "         4.0    5663\n",
       "         5.0    5674\n",
       "actress  1.0    3040\n",
       "         2.0    4635\n",
       "         3.0    3201\n",
       "         4.0    2881\n",
       "         5.0    2592\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[(c.n <= 5)&(c.year//10 == 195)].groupby(['type', 'n']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[(c.n <= 5)&(c.year//10 == 195)].groupby(a).size()"
   ]
  },
  {
   "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": 12,
   "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",
       "      <td>1963</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006</td>\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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[c.title == 'The Pink Panther'].groupby(['year'])[['n']].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                                 year\n",
       "The Muppet Movie                      1979    8\n",
       "An American Werewolf in London        1981    2\n",
       "The Great Muppet Caper                1981    6\n",
       "The Dark Crystal                      1982    2\n",
       "The Muppets Take Manhattan            1984    7\n",
       "Follow That Bird                      1985    3\n",
       "The Muppet Christmas Carol            1992    7\n",
       "Muppet Treasure Island                1996    4\n",
       "Muppets from Space                    1999    4\n",
       "The Adventures of Elmo in Grouchland  1999    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fo = c[c.name == 'Frank Oz'].groupby(['title', 'year']).size()\n",
    "# fo\n",
    "fo[fo > 1].sort_index(level = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# I could have just groupby year first and then title. That way it will be in year ascending order"
   ]
  },
  {
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "character\n",
       "Grover            2\n",
       "Bert              3\n",
       "Fozzie Bear       4\n",
       "Cookie Monster    5\n",
       "Sam the Eagle     5\n",
       "Animal            6\n",
       "Miss Piggy        6\n",
       "Yoda              6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fo1 = c[c.name == 'Frank Oz'].groupby(['character']).size()\n",
    "fo1[fo1 > 1].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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