Week_6a.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week 6a - Data Analytics for Engineers "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Monday 19th of March 2018* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. If-else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "# import data\n",
    "student_grades = pd.read_csv(\"http://bit.ly/dae_student_grades_predictors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student</th>\n",
       "      <th>assignment1</th>\n",
       "      <th>assignment2</th>\n",
       "      <th>homework</th>\n",
       "      <th>exam</th>\n",
       "      <th>resit</th>\n",
       "      <th>avg_grade_high_school</th>\n",
       "      <th>hours_spent_studying</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sem</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tess</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.3</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  student  assignment1  assignment2  homework  exam  resit  \\\n",
       "0     Sem          6.7          7.5       9.5   7.8    NaN   \n",
       "1   Lucas          8.2          8.2      10.0   6.9    NaN   \n",
       "2    Tess          9.0          9.8      10.0   9.2    NaN   \n",
       "3    Adam          5.0          4.9      10.0   3.4    5.8   \n",
       "4   James          3.4          5.3       8.0   4.9    6.2   \n",
       "\n",
       "   avg_grade_high_school  hours_spent_studying  \n",
       "0                    7.9                    45  \n",
       "1                    6.5                    36  \n",
       "2                    8.3                    33  \n",
       "3                    5.9                    44  \n",
       "4                    5.5                    33  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identify \"laziness\" among students (Boolean-value: True or False)\n",
    "# define lazyiness as: someone who failed course first time; and studied less than 30 hours  \n",
    "student_grades.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student</th>\n",
       "      <th>assignment1</th>\n",
       "      <th>assignment2</th>\n",
       "      <th>homework</th>\n",
       "      <th>exam</th>\n",
       "      <th>resit</th>\n",
       "      <th>avg_grade_high_school</th>\n",
       "      <th>hours_spent_studying</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Noor</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  student  assignment1  assignment2  homework  exam  resit  \\\n",
       "9    Noor          5.7          6.7       8.0   4.3    5.1   \n",
       "\n",
       "   avg_grade_high_school  hours_spent_studying  \n",
       "9                    5.6                    19  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using list comprehension\n",
    "filter_exam = student_grades.loc[:, 'exam'] < 5\n",
    "filter_hours = student_grades.loc[:, 'hours_spent_studying'] < 30\n",
    "\n",
    "# combine both filters\n",
    "student_grades.loc[filter_exam & filter_hours, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n"
     ]
    }
   ],
   "source": [
    "# using the index in combination with .loc[] (to use two filters with different column names)\n",
    "for index in student_grades.index:\n",
    "    print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# using loc (two times)\n",
    "student_grades.loc[filter_exam & filter_hours, 'laziness'] = True\n",
    "student_grades.loc[~(filter_exam & filter_hours), 'laziness'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# in one line of code (preferred):\n",
    "# structure: [True if <condition> else False]\n",
    "student_grades['laziness'] = [(True if student_grades.loc[index, 'exam'] < 5 & student_grades.loc[index, 'hours_spent_studying'] < 30 else False) for index in student_grades.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student</th>\n",
       "      <th>exam</th>\n",
       "      <th>hours_spent_studying</th>\n",
       "      <th>laziness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adam</td>\n",
       "      <td>3.4</td>\n",
       "      <td>44</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sem</td>\n",
       "      <td>7.8</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dirk</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pim</td>\n",
       "      <td>7.3</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Davey</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Arjan</td>\n",
       "      <td>5.2</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Anne</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sandra</td>\n",
       "      <td>7.1</td>\n",
       "      <td>32</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dieuwke</td>\n",
       "      <td>2.9</td>\n",
       "      <td>48</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pieter</td>\n",
       "      <td>8.4</td>\n",
       "      <td>16</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ilja</td>\n",
       "      <td>5.4</td>\n",
       "      <td>29</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>6.9</td>\n",
       "      <td>36</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Noor</td>\n",
       "      <td>4.3</td>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Luuk</td>\n",
       "      <td>6.1</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Zoë</td>\n",
       "      <td>7.3</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fleur</td>\n",
       "      <td>5.9</td>\n",
       "      <td>18</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Max</td>\n",
       "      <td>5.6</td>\n",
       "      <td>65</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James</td>\n",
       "      <td>4.9</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tess</td>\n",
       "      <td>9.2</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Tom</td>\n",
       "      <td>8.1</td>\n",
       "      <td>53</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student  exam  hours_spent_studying  laziness\n",
       "3      Adam   3.4                    44      True\n",
       "0       Sem   7.8                    45     False\n",
       "11     Dirk   6.0                    30     False\n",
       "18      Pim   7.3                    11     False\n",
       "17    Davey   4.0                    32     False\n",
       "16    Arjan   5.2                    26     False\n",
       "15     Anne   7.4                    25     False\n",
       "14   Sandra   7.1                    32     False\n",
       "13  Dieuwke   2.9                    48     False\n",
       "12   Pieter   8.4                    16     False\n",
       "10     Ilja   5.4                    29     False\n",
       "1     Lucas   6.9                    36     False\n",
       "9      Noor   4.3                    19     False\n",
       "8      Luuk   6.1                    30     False\n",
       "7       Zoë   7.3                    33     False\n",
       "6     Fleur   5.9                    18     False\n",
       "5       Max   5.6                    65     False\n",
       "4     James   4.9                    33     False\n",
       "2      Tess   9.2                    33     False\n",
       "19      Tom   8.1                    53     False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check result (both False and True)\n",
    "student_grades[['student', 'exam', 'hours_spent_studying', 'laziness']].sort_values('laziness', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Calculate Log of Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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bF1xDoYXhgUREaBoL32mM+YfVeVzFGPNbY0ySMSaVponTz4wxPvHX\nqjHmEHBARPo1P3QRsMPCSK5SAIwSkfDmn8uL8IHJ/FMsAW5u/vhmYLGFWVxGRCbRNPw7xRhT44qv\nqYXhmcYCN9L0F3hO89tlVodSrfop8IaIbAGGAX+2OE+7NR8xvQNsBLbS9DvDa6+MFpF5wFdAPxEp\nFJHbgceBiSKSS9MR1eNWZjwbp3ldzwBRwCfNv0Oeb/f30Su9lVJKOUOPMJRSSjlFC0MppZRTtDCU\nUko5RQtDKaWUU7QwlFJKOUULQymllFO0MJRSSjlFC0MppZRT/h9o2WCYsLD9NQAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed0d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot assignment 1 grades (regular)\n",
    "student_grades['assignment1'].plot(kind='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import numpy  and add logarithmic function\n",
    "# note that np.log() assumes the natural logarithm (i.e. ln())\n",
    "import numpy as np\n",
    "student_grades['assignment1_log'] = np.log(student_grades.loc[:, 'assignment1'])"
   ]
  },
  {
   "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>assignment1</th>\n",
       "      <th>assignment1_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.7</td>\n",
       "      <td>1.902108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.2</td>\n",
       "      <td>2.104134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.197225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.609438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.4</td>\n",
       "      <td>1.223775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   assignment1  assignment1_log\n",
       "0          6.7         1.902108\n",
       "1          8.2         2.104134\n",
       "2          9.0         2.197225\n",
       "3          5.0         1.609438\n",
       "4          3.4         1.223775"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check added column\n",
    "student_grades[['assignment1', 'assignment1_log']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H2h3F5y3NS6XhfBdFlc12R1HDoAVCqRFoON/JzpONrNDOabcsnpyKCGwr1eGu/kQLhFIj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\nqtdVga22pYOPr9t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tU0CKRdkWA3OA4svcvwLYAghwDbBzJO/JwnwLgUTXv2+5mM/qz83N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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c797780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot assignment 1 grades (log)\n",
    "student_grades['assignment1_log'].plot(kind='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# side-note: given the distribution for GA-1, does it make sense to compute the natural logarithm here (compare the before and after!)? When should we perform a logarithmic preprocessing transformation? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. SQL Challenges (GA2 - Movies Database)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Determine the movie titles with the biggest loss (worldwide gross - production budget) by genre of which the movie language was not French, German or Spanish and where the loss was at least 10 million dollar.\n",
    "2. What are the top 3 movies released in 2016 which were not profitable but got the highest IMDB-score? \n",
    "3. What is the worldwide gross revenue per weekday for Action movies for which the number of Facebook likes is known (i.e. is different than None)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where can I find these Jupyter Notebooks? \n",
    "https://github.com/RoyKlaasseBos/Data-Analytics-for-Engineers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What am I supposed to do now? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1. Programming Exercises HYP\n",
    "# 2. Theory Exercises HYP\n",
    "# 3. Graded Assignment 2 (module 6)\n",
    "# 4. Watch Video Lecture HYP"
   ]
  }
 ],
 "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": "3.6.0"
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 },
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