Week_5a.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week 5a - Data Analytics for Engineers "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Monday 12th of February 2018* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Format Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "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": {
      "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>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": [
    "student_grades.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['student', 'assignment1', 'assignment2', 'homework', 'exam', 'resit',\n",
       "       'avg_grade_high_school', 'hours_spent_studying'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get column names\n",
    "student_grades.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "student\n",
      "assignment1\n",
      "assignment2\n",
      "homework\n",
      "exam\n",
      "resit\n",
      "avg_grade_high_school\n",
      "hours_spent_studying\n"
     ]
    }
   ],
   "source": [
    "# print column names on separate lines\n",
    "for column in student_grades.columns:\n",
    "    print(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "11\n",
      "11\n",
      "8\n",
      "4\n",
      "5\n",
      "21\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "# print length of column names \n",
    "for column in student_grades.columns:\n",
    "    print(len(column))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "student 7\n",
      "assignment1 11\n",
      "assignment2 11\n",
      "homework 8\n",
      "exam 4\n",
      "resit 5\n",
      "avg_grade_high_school 21\n",
      "hours_spent_studying 20\n"
     ]
    }
   ],
   "source": [
    "# print both column and length (default order)\n",
    "for column in student_grades.columns:\n",
    "    print(\"{} {}\".format(column, len(column)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 student\n",
      "11 assignment1\n",
      "11 assignment2\n",
      "8 homework\n",
      "4 exam\n",
      "5 resit\n",
      "21 avg_grade_high_school\n",
      "20 hours_spent_studying\n"
     ]
    }
   ],
   "source": [
    "# print both column and length (custom order)\n",
    "for column in student_grades.columns:\n",
    "    print(\"{1} {0}\".format(column, len(column)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "student                        7\n",
      "assignment1                    11\n",
      "assignment2                    11\n",
      "homework                       8\n",
      "exam                           4\n",
      "resit                          5\n",
      "avg_grade_high_school          21\n",
      "hours_spent_studying           20\n"
     ]
    }
   ],
   "source": [
    "# alignment of text \n",
    "for column in student_grades.columns:\n",
    "    print(\"{:30} {}\".format(column, len(column)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. List Comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a student grades list\n",
    "grades_assignment_1 = list(student_grades.assignment1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6.7,\n",
       " 8.2,\n",
       " 9.0,\n",
       " 5.0,\n",
       " 3.4,\n",
       " 4.5,\n",
       " 7.0,\n",
       " 3.4,\n",
       " 8.4,\n",
       " 5.7,\n",
       " 6.3,\n",
       " 8.4,\n",
       " 7.1,\n",
       " 7.3,\n",
       " 5.9,\n",
       " 6.1,\n",
       " 6.4,\n",
       " 6.1,\n",
       " 8.0,\n",
       " 7.3]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades_assignment_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6.7,\n",
       " 8.2,\n",
       " 9.0,\n",
       " 5.0,\n",
       " 3.4,\n",
       " 4.5,\n",
       " 7.0,\n",
       " 3.4,\n",
       " 8.4,\n",
       " 5.7,\n",
       " 6.3,\n",
       " 8.4,\n",
       " 7.1,\n",
       " 7.3,\n",
       " 5.9,\n",
       " 6.1,\n",
       " 6.4,\n",
       " 6.1,\n",
       " 8.0,\n",
       " 7.3]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# basic principle of list comprehension\n",
    "[counter for counter in grades_assignment_1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7.7,\n",
       " 9.2,\n",
       " 10.0,\n",
       " 6.0,\n",
       " 4.4,\n",
       " 5.5,\n",
       " 8.0,\n",
       " 4.4,\n",
       " 9.4,\n",
       " 6.7,\n",
       " 7.3,\n",
       " 9.4,\n",
       " 8.1,\n",
       " 8.3,\n",
       " 6.9,\n",
       " 7.1,\n",
       " 7.4,\n",
       " 7.1,\n",
       " 9.0,\n",
       " 8.3]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# increase all grades by 1\n",
    "[grade + 1 for grade in grades_assignment_1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True,\n",
       " True,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " False,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine whether the grade is larger than 5\n",
    "[grade > 5 for grade in grades_assignment_1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6.7,\n",
       " 8.2,\n",
       " 9.0,\n",
       " 7.0,\n",
       " 8.4,\n",
       " 5.7,\n",
       " 6.3,\n",
       " 8.4,\n",
       " 7.1,\n",
       " 7.3,\n",
       " 5.9,\n",
       " 6.1,\n",
       " 6.4,\n",
       " 6.1,\n",
       " 8.0,\n",
       " 7.3]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use filters within list comprehensions\n",
    "[grade for grade in grades_assignment_1 if grade > 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Selecting rows / columns using iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
       "    .dataframe thead th {\n",
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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>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student</th>\n",
       "      <th>assignment1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sem</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tess</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  student  assignment1\n",
       "0     Sem          6.7\n",
       "1   Lucas          8.2\n",
       "2    Tess          9.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iloc (inclusive: exclusive)\n",
    "student_grades.iloc[0:3, 0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     7.8\n",
       "1     6.9\n",
       "2     9.2\n",
       "3     3.4\n",
       "4     4.9\n",
       "5     5.6\n",
       "6     5.9\n",
       "7     7.3\n",
       "8     6.1\n",
       "9     4.3\n",
       "10    5.4\n",
       "11    6.0\n",
       "12    8.4\n",
       "13    2.9\n",
       "14    7.1\n",
       "15    7.4\n",
       "16    5.2\n",
       "17    4.0\n",
       "18    7.3\n",
       "19    8.1\n",
       "Name: exam, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting the highest exam grade\n",
    "student_grades['exam']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.1999999999999993"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades['exam'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting the index of the row with the maximum value\n",
    "student_grades['exam'].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tess'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combining that with iloc / loc (to retrieve the student who got the highest exam grade)\n",
    "student_grades.loc[2, \"student\"]\n",
    "# what is the difference between iloc and loc?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tess'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# why is this approach preferred?\n",
    "student_grades.loc[student_grades['exam'].idxmax(), \"student\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Setting the Index"
   ]
  },
  {
   "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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       "    }\n",
       "\n",
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       "        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>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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "  </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",
       "2    Tess          9.0          9.8      10.0   9.2    NaN   \n",
       "\n",
       "   avg_grade_high_school  hours_spent_studying  \n",
       "0                    7.9                    45  \n",
       "2                    8.3                    33  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# say that we want to look into Tess' and Sem's grades: \n",
    "student_grades[(student_grades['student'] == 'Sem') | (student_grades['student'] == 'Tess')]\n",
    "# do you like this approach? why?"
   ]
  },
  {
   "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>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>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",
       "  </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",
       "2    Tess          9.0          9.8      10.0   9.2    NaN   \n",
       "\n",
       "   avg_grade_high_school  hours_spent_studying  \n",
       "0                    7.9                    45  \n",
       "2                    8.3                    33  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternative approach using isin()\n",
    "student_grades[student_grades['student'].isin(['Tess', 'Sem'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>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",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sem</th>\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>Lucas</th>\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>Tess</th>\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>Adam</th>\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>James</th>\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",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>4.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fleur</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoë</th>\n",
       "      <td>3.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luuk</th>\n",
       "      <td>8.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Noor</th>\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",
       "    <tr>\n",
       "      <th>Ilja</th>\n",
       "      <td>6.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dirk</th>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pieter</th>\n",
       "      <td>7.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.6</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dieuwke</th>\n",
       "      <td>7.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>6.3</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sandra</th>\n",
       "      <td>5.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anne</th>\n",
       "      <td>6.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arjan</th>\n",
       "      <td>6.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Davey</th>\n",
       "      <td>6.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pim</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom</th>\n",
       "      <td>7.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         assignment1  assignment2  homework  exam  resit  \\\n",
       "student                                                    \n",
       "Sem              6.7          7.5       9.5   7.8    NaN   \n",
       "Lucas            8.2          8.2      10.0   6.9    NaN   \n",
       "Tess             9.0          9.8      10.0   9.2    NaN   \n",
       "Adam             5.0          4.9      10.0   3.4    5.8   \n",
       "James            3.4          5.3       8.0   4.9    6.2   \n",
       "Max              4.5          4.4      10.0   5.6    NaN   \n",
       "Fleur            7.0          8.6      10.0   5.9    NaN   \n",
       "Zoë              3.4          7.4      10.0   7.3    NaN   \n",
       "Luuk             8.4          5.4      10.0   6.1    NaN   \n",
       "Noor             5.7          6.7       8.0   4.3    5.1   \n",
       "Ilja             6.3          6.6       7.0   5.4    NaN   \n",
       "Dirk             8.4          7.3      10.0   6.0    NaN   \n",
       "Pieter           7.1          5.3      10.0   8.4    NaN   \n",
       "Dieuwke          7.3          4.4       9.0   2.9    4.1   \n",
       "Sandra           5.9          8.3      10.0   7.1    NaN   \n",
       "Anne             6.1          7.0      10.0   7.4    NaN   \n",
       "Arjan            6.4          5.3       9.0   5.2    NaN   \n",
       "Davey            6.1          5.0       8.0   4.0    1.0   \n",
       "Pim              8.0          8.2      10.0   7.3    NaN   \n",
       "Tom              7.3          7.9      10.0   8.1    NaN   \n",
       "\n",
       "         avg_grade_high_school  hours_spent_studying  \n",
       "student                                               \n",
       "Sem                        7.9                    45  \n",
       "Lucas                      6.5                    36  \n",
       "Tess                       8.3                    33  \n",
       "Adam                       5.9                    44  \n",
       "James                      5.5                    33  \n",
       "Max                        6.9                    65  \n",
       "Fleur                      6.1                    18  \n",
       "Zoë                        6.5                    33  \n",
       "Luuk                       7.0                    30  \n",
       "Noor                       5.6                    19  \n",
       "Ilja                       6.8                    29  \n",
       "Dirk                       7.0                    30  \n",
       "Pieter                     8.6                    16  \n",
       "Dieuwke                    6.3                    48  \n",
       "Sandra                     7.2                    32  \n",
       "Anne                       7.7                    25  \n",
       "Arjan                      6.5                    26  \n",
       "Davey                      6.0                    32  \n",
       "Pim                        6.6                    11  \n",
       "Tom                        7.6                    53  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades.set_index('student')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "student_grades2 = student_grades.set_index('student')\n",
    "# why do we assign it to a new variable? (vs inplace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
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       "    }\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>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",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sem</th>\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>Lucas</th>\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>Tess</th>\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>Adam</th>\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>James</th>\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": [
       "         assignment1  assignment2  homework  exam  resit  \\\n",
       "student                                                    \n",
       "Sem              6.7          7.5       9.5   7.8    NaN   \n",
       "Lucas            8.2          8.2      10.0   6.9    NaN   \n",
       "Tess             9.0          9.8      10.0   9.2    NaN   \n",
       "Adam             5.0          4.9      10.0   3.4    5.8   \n",
       "James            3.4          5.3       8.0   4.9    6.2   \n",
       "\n",
       "         avg_grade_high_school  hours_spent_studying  \n",
       "student                                               \n",
       "Sem                        7.9                    45  \n",
       "Lucas                      6.5                    36  \n",
       "Tess                       8.3                    33  \n",
       "Adam                       5.9                    44  \n",
       "James                      5.5                    33  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>assignment1</th>\n",
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       "      <th>homework</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adam</th>\n",
       "      <td>5.0</td>\n",
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       "      <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>Anne</th>\n",
       "      <td>6.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arjan</th>\n",
       "      <td>6.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Davey</th>\n",
       "      <td>6.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dieuwke</th>\n",
       "      <td>7.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>6.3</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         assignment1  assignment2  homework  exam  resit  \\\n",
       "student                                                    \n",
       "Adam             5.0          4.9      10.0   3.4    5.8   \n",
       "Anne             6.1          7.0      10.0   7.4    NaN   \n",
       "Arjan            6.4          5.3       9.0   5.2    NaN   \n",
       "Davey            6.1          5.0       8.0   4.0    1.0   \n",
       "Dieuwke          7.3          4.4       9.0   2.9    4.1   \n",
       "\n",
       "         avg_grade_high_school  hours_spent_studying  \n",
       "student                                               \n",
       "Adam                       5.9                    44  \n",
       "Anne                       7.7                    25  \n",
       "Arjan                      6.5                    26  \n",
       "Davey                      6.0                    32  \n",
       "Dieuwke                    6.3                    48  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorting the index\n",
    "student_grades2.sort_index().head()\n",
    "# should we use inplace=True here? Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "      <th>avg_grade_high_school</th>\n",
       "      <th>hours_spent_studying</th>\n",
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       "    <tr>\n",
       "      <th>student</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sem</th>\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>Tess</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         assignment1  assignment2  homework  exam  resit  \\\n",
       "student                                                    \n",
       "Sem              6.7          7.5       9.5   7.8    NaN   \n",
       "Tess             9.0          9.8      10.0   9.2    NaN   \n",
       "\n",
       "         avg_grade_high_school  hours_spent_studying  \n",
       "student                                               \n",
       "Sem                        7.9                    45  \n",
       "Tess                       8.3                    33  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# say that we want to look into Tess' and Sem's grades: \n",
    "student_grades2.loc[[\"Sem\", \"Tess\"], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Sorting a dataframe & Resetting the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James</td>\n",
       "      <td>3.4</td>\n",
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       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Zoë</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Max</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.9</td>\n",
       "      <td>65</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>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",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sandra</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Davey</td>\n",
       "      <td>6.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Anne</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ilja</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Arjan</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student  assignment1  assignment2  homework  exam  resit  \\\n",
       "4    James          3.4          5.3       8.0   4.9    6.2   \n",
       "7      Zoë          3.4          7.4      10.0   7.3    NaN   \n",
       "5      Max          4.5          4.4      10.0   5.6    NaN   \n",
       "3     Adam          5.0          4.9      10.0   3.4    5.8   \n",
       "9     Noor          5.7          6.7       8.0   4.3    5.1   \n",
       "14  Sandra          5.9          8.3      10.0   7.1    NaN   \n",
       "17   Davey          6.1          5.0       8.0   4.0    1.0   \n",
       "15    Anne          6.1          7.0      10.0   7.4    NaN   \n",
       "10    Ilja          6.3          6.6       7.0   5.4    NaN   \n",
       "16   Arjan          6.4          5.3       9.0   5.2    NaN   \n",
       "\n",
       "    avg_grade_high_school  hours_spent_studying  \n",
       "4                     5.5                    33  \n",
       "7                     6.5                    33  \n",
       "5                     6.9                    65  \n",
       "3                     5.9                    44  \n",
       "9                     5.6                    19  \n",
       "14                    7.2                    32  \n",
       "17                    6.0                    32  \n",
       "15                    7.7                    25  \n",
       "10                    6.8                    29  \n",
       "16                    6.5                    26  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the dataframe on assignment 1 grade (low to high)\n",
    "student_grades_a1_sorted = student_grades.sort_values('assignment1')\n",
    "student_grades_a1_sorted.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>index</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>4</td>\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",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>Zoë</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Max</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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>9</td>\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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>Sandra</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>Davey</td>\n",
       "      <td>6.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>Anne</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>Ilja</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>Arjan</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index student  assignment1  assignment2  homework  exam  resit  \\\n",
       "0      4   James          3.4          5.3       8.0   4.9    6.2   \n",
       "1      7     Zoë          3.4          7.4      10.0   7.3    NaN   \n",
       "2      5     Max          4.5          4.4      10.0   5.6    NaN   \n",
       "3      3    Adam          5.0          4.9      10.0   3.4    5.8   \n",
       "4      9    Noor          5.7          6.7       8.0   4.3    5.1   \n",
       "5     14  Sandra          5.9          8.3      10.0   7.1    NaN   \n",
       "6     17   Davey          6.1          5.0       8.0   4.0    1.0   \n",
       "7     15    Anne          6.1          7.0      10.0   7.4    NaN   \n",
       "8     10    Ilja          6.3          6.6       7.0   5.4    NaN   \n",
       "9     16   Arjan          6.4          5.3       9.0   5.2    NaN   \n",
       "\n",
       "   avg_grade_high_school  hours_spent_studying  \n",
       "0                    5.5                    33  \n",
       "1                    6.5                    33  \n",
       "2                    6.9                    65  \n",
       "3                    5.9                    44  \n",
       "4                    5.6                    19  \n",
       "5                    7.2                    32  \n",
       "6                    6.0                    32  \n",
       "7                    7.7                    25  \n",
       "8                    6.8                    29  \n",
       "9                    6.5                    26  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reset the index \n",
    "student_grades_a1_sorted.reset_index().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>assignment1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zoë</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Max</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adam</td>\n",
       "      <td>5.0</td>\n",
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       "      <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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sandra</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Davey</td>\n",
       "      <td>6.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Anne</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ilja</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arjan</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  student  assignment1  assignment2  homework  exam  resit  \\\n",
       "0   James          3.4          5.3       8.0   4.9    6.2   \n",
       "1     Zoë          3.4          7.4      10.0   7.3    NaN   \n",
       "2     Max          4.5          4.4      10.0   5.6    NaN   \n",
       "3    Adam          5.0          4.9      10.0   3.4    5.8   \n",
       "4    Noor          5.7          6.7       8.0   4.3    5.1   \n",
       "5  Sandra          5.9          8.3      10.0   7.1    NaN   \n",
       "6   Davey          6.1          5.0       8.0   4.0    1.0   \n",
       "7    Anne          6.1          7.0      10.0   7.4    NaN   \n",
       "8    Ilja          6.3          6.6       7.0   5.4    NaN   \n",
       "9   Arjan          6.4          5.3       9.0   5.2    NaN   \n",
       "\n",
       "   avg_grade_high_school  hours_spent_studying  \n",
       "0                    5.5                    33  \n",
       "1                    6.5                    33  \n",
       "2                    6.9                    65  \n",
       "3                    5.9                    44  \n",
       "4                    5.6                    19  \n",
       "5                    7.2                    32  \n",
       "6                    6.0                    32  \n",
       "7                    7.7                    25  \n",
       "8                    6.8                    29  \n",
       "9                    6.5                    26  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the previous index column\n",
    "student_grades_a1_sorted.reset_index(drop=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. cumsum() and diff() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>exam</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.2</td>\n",
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       "      <th>3</th>\n",
       "      <td>3.4</td>\n",
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       "      <th>4</th>\n",
       "      <td>4.9</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   exam\n",
       "0   7.8\n",
       "1   6.9\n",
       "2   9.2\n",
       "3   3.4\n",
       "4   4.9"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades[['exam']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.2</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   exam\n",
       "0   7.8\n",
       "1  14.7\n",
       "2  23.9\n",
       "3  27.3\n",
       "4  32.2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cumsum()\n",
    "student_grades[['exam']].cumsum().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>exam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   exam\n",
       "0   NaN\n",
       "1  -0.9\n",
       "2   2.3\n",
       "3  -5.8\n",
       "4   1.5"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# diff()\n",
    "student_grades[['exam']].diff().head()\n",
    "# why is the first record NaN? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# in what other cases can we expect a NaN while using diff()?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>resit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   resit\n",
       "0    NaN\n",
       "1    NaN\n",
       "2    NaN\n",
       "3    5.8\n",
       "4    6.2"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades[['resit']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>resit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   resit\n",
       "0    NaN\n",
       "1    NaN\n",
       "2    NaN\n",
       "3    NaN\n",
       "4    0.4"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades[['resit']].diff().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Calculating the length of numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# we cannot directly use len() on numeric data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "object of type 'int' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-a597fbb1c99d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# integers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2018\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2018\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()"
     ]
    }
   ],
   "source": [
    "# integers\n",
    "print(type(2018))\n",
    "print(len(2018))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'float'>\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "object of type 'float' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-765e46c8721c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# floats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1415\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1415\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'float' has no len()"
     ]
    }
   ],
   "source": [
    "# floats\n",
    "print(type(3.1415))\n",
    "print(len(3.1415))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "# convert to string-format first\n",
    "print(len(str(2018))) #integer\n",
    "print(len(str(3.1415))) #floats (why do we get 6 here?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Creating timeseries data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-02-12', '2018-02-13', '2018-02-14', '2018-02-15',\n",
       "               '2018-02-16', '2018-02-17', '2018-02-18', '2018-02-19',\n",
       "               '2018-02-20', '2018-02-21',\n",
       "               ...\n",
       "               '2018-12-22', '2018-12-23', '2018-12-24', '2018-12-25',\n",
       "               '2018-12-26', '2018-12-27', '2018-12-28', '2018-12-29',\n",
       "               '2018-12-30', '2018-12-31'],\n",
       "              dtype='datetime64[ns]', length=323, freq='D')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2018-02-12', end='2018-12-31') # today till end of the year "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-02-12 00:00:00', '2018-02-12 01:00:00',\n",
       "               '2018-02-12 02:00:00', '2018-02-12 03:00:00',\n",
       "               '2018-02-12 04:00:00', '2018-02-12 05:00:00',\n",
       "               '2018-02-12 06:00:00', '2018-02-12 07:00:00',\n",
       "               '2018-02-12 08:00:00', '2018-02-12 09:00:00',\n",
       "               '2018-02-12 10:00:00', '2018-02-12 11:00:00',\n",
       "               '2018-02-12 12:00:00', '2018-02-12 13:00:00',\n",
       "               '2018-02-12 14:00:00', '2018-02-12 15:00:00',\n",
       "               '2018-02-12 16:00:00', '2018-02-12 17:00:00',\n",
       "               '2018-02-12 18:00:00', '2018-02-12 19:00:00',\n",
       "               '2018-02-12 20:00:00', '2018-02-12 21:00:00',\n",
       "               '2018-02-12 22:00:00', '2018-02-12 23:00:00',\n",
       "               '2018-02-13 00:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# changing the frequency \n",
    "pd.date_range(start='2018-02-12', end='2018-02-13', freq='H')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Rolling mean/median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dates = pd.date_range(start='2018-02-01', end='2018-02-14')\n",
    "avg_temperature = pd.Series([0, 1, -2, 15, 0, -5, -4, -1, 0, 1, 13, -2, -1, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_df = pd.DataFrame({\"dates\": dates, \"avg_temp\": avg_temperature})\n",
    "temp_df.set_index('dates', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>avg_temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-02</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-03</th>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-04</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            avg_temp\n",
       "dates               \n",
       "2018-02-01         0\n",
       "2018-02-02         1\n",
       "2018-02-03        -2\n",
       "2018-02-04        15\n",
       "2018-02-05         0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I1DcUdN9BKsqKXKuXSV+bapsoLy4Y8qCEwPSQ/vejiR6bCZxC+oaAhnkPoHSMpYc07qry\nNg25+wdsTaBYswCQQjwNrYwdmc3ovOywXm/pIY2b9h1oZ29zx7ACgD89pL0HY8MCQAoZah7ggVh6\nSOOWTd5mAOYPsf8fDqWHtBZAbFgASCE1+1uHvQhcf5Ye0rilLwn8MFoAYOkhY8kCQIro6O5hd1Ob\nKy0ASw9p3FDlbWLm+HwKcoc3K32WpYeMGQsAKWLXB2306vAXgevP0kMat2yqbR5W/79fuaWHjBkL\nACnCPwIo0haApYc0bmho6cTb2DboEtDB2JpAsWMBIEX4l4GO9B6ApYc0bhjuDOBANhQ0diwApAhP\nfSsFuVkU5edEXJalhzSR8ieBH8oicP3500PaezD6LACkCP8icCIScVmWHtJEqtrbxPRxI8Oak+JP\nD2ktgOizAJAidtS3hr0ERH+WHtJEqrq2iflhXP37WXrI2LAAkAK6e3rZ+UFr2EtA9GfpIU0kGls7\n2dnQFlb/v5+lh4wNCwApYHdTO109GvENYD9LD2kisanWmQEcxgggP0sPGRsWAFLAoUTw7nQBFeXn\nUJSfY32wJiz+G8CRdQHZUNBYsACQAmr65gC40wIA64M14av2NjFlbB5jIxiRZukhYyPiACAic0Vk\nQ8BPs4h8t98xp4tIU8AxN0d6XnOIp76F3KwMJhSOcK1MSw9pwlXtjewGMBxKD2ktgOiKOACo6mZV\nXaiqC4HjgFbgySCH/tN/nKr+R6TnNYd46luZPm4kGRmRDwH1s/SQJhzN7V3U1LeyYEpkAQD8K9Na\nAIgmt7uAPgZsU1WPy+WaAXjqW5lW5E7/v5+tCWTC4V8C+qhhLAEdiqWHjD63A8CFwCMh9i0RkbdE\n5K8icpTL501bvb2Kp6HFtRFAfn3T8a0JboZhU234S0D0F5ge0kSHawFARHKATwFPBNm9HpimqkcD\nvwZWDlDOFSJSKSKVdXV1blUvZe070EF7Vy/Tx7vbArD0kCYcVd4mJo0ewfiC3IjLsjWBos/NFsA5\nwHpV3dt/h6o2q+pB5/EqIFtExgcrRFWXq2qFqlYUFxe7WL3U1LcKaJG7LYDMDGGmrQlkhql6mDmA\nB2LpIaPPzQBwESG6f0RkojiL1IjIYue89S6eO20dWgXU3RYAOENBbSKOGaKDHd1s398S8QggP0sP\nGX2uBAARyQfOAFYEbLtKRK5ynn4OqBaRjcCvgAvV0v24oqa+hawMoXSMe0NA/Sw9pBmOt2ubUYUF\nUyK/AexXXpJv96GiaHi52kJQ1RZgXL9tdwc8vgO4w41zmcN5GlqZMjaPrEz35/QFpoc8cpJ7H2qT\nmqpdmAHc36ziAv643ouqurLSrTmczQROcp76FteWgOjPpuOb4aj2NlFSmEvJKBcnJFp6yKiyAJDE\nVBXP/lbXh4D6+dNDWh+sGYrqWvduAPvZRUh0WQBIYh+0dnGgo5tpUWoBWHpIM1Stnd1s3XfQ9QBg\nQ0GjywJAEvMvAhetFgD4+mDt6ssM5p3dB+hVmO/CDOBAlh4yuiwAJLG+OQBRagGArwlu6SHNYPw3\ngN1YAyiQpYeMLgsAScxT34oITC3Ki9o5LD2kGYpqbxPjC3KY6OINYL/y4nxrAUSJBYAk5qlvpXR0\nHrlZmVE7h6WHNENR5W3iqNLRURmqOaukgL3NHZYeMgosACSxmvoWV5PABGPpIc1g2rt6eG/fQRa4\nfAPYz/8etPSQ7rMAkMR2OHkAosnSQ5rBvLvnAD29GlEO4IHYUNDosQCQpJrbu6hv6YzqDWA/Sw9p\nBtI3AzhKLQBLDxk9FgCS1I6+ReCi2wIAXx+s3QMwoVR7mxgzMpvJY6IzGMHSQ0aPBYAk5V8F1O1M\nYMGUF1t6SBNadW0TCyZH5wawn6WHjA4LAEmqpm8OQPRbAJYe0oTS0d3D5j0HOMrFBeCCsfSQ0WEB\nIEl56lsoLswlP9eVBV0HZOkhTShb9hykq0ejNgLIb1aJpYeMBgsAScpT3+p6FrBQ/OkhrQ/W9Ffd\nlwM4usuFWys0OiwAJClPfWtMRgDBofSQ9uEz/VV5mygckcW0KF+MWHrI6HArI1iNiFSJyAYRqQyy\nX0TkVyKyVUTeEpFj3Thvumrv6mFPc3tMRgD5WXpIE8wmbxPzozQDOJClh4wON1sAS1V1oapWBNl3\nDjDb+bkCuMvF86adHQ2+ftDp42PTAgBfH6ylhzSBunp6eWfPAdcXgAvF0kO6L1ZdQOcDD6jPWmCM\niEyK0blTTs1+ZwRQjO4BgK8P1p8e0hiA9/YepLO7l6NcXgI6lFnFBWyra8HSibvHrQCgwHMisk5E\nrgiyfzKwM+D5LmebCYOnbxJY7FoANh3f9Ne3BHSURwD5+dND7m229JBucSsAnKyqC/F19XxTRE4N\ntyARuUJEKkWksq6uzqXqpRZPQwtjRmYzemR2zM5p6SFNf9W1TRTkZsXsQmSWjQRynSsBQFW9zu99\nwJPA4n6HeIGpAc+nONuClbVcVStUtaK4uNiN6qWcWA4B9bP0kKa/Km8T80pHkZER3RvAfuWWHtJ1\nEQcAEckXkUL/Y+BMoLrfYU8BlzqjgU4AmlR1d6TnTle+ZaBj1/3jZ+khjV93Ty/v7G6OWfcPWHrI\naHCjBTABeFlENgKvA0+r6t9E5CoRuco5ZhWwHdgK3AN8w4XzpqXO7l68H7TFdAion6WHNH7b6lpo\n7+qN+gSwQJYe0n0RryOgqtuBY4JsvzvgsQLfjPRcBryNbfQqTItHCyAgPeTUGHdBmcQS6xvAfuXF\n+byydX9Mz5nKbCZwkvEvAheXFoClhzSOKm8TI3MymTG+IKbntfSQ7rIAkGT8eQDicQ/A0kMav021\nTcybNIrMGN0A9rP0kO6yAJBkaupbGJmTyfiCnJif29JDGoCeXmVTbXPUMoANxL8yrd0IdocFgCTj\nXwQu2muvhGLpIc37+w/S2tkTlwAwrcjSQ7rJAkCS8dS3xKX/38/SQ5pqbzMQ/SWgg7H0kO6yAJBE\nenqVnQ1tTItjALD0kKbK20RuVkbfzNxYs/SQ7rEAkER2N7XR2dMb0zWA+rPZmKba28SRk0aRlRmf\nrw9LD+keCwBJ5NAIoDh2AdlIoLTW69wAjvX4/0CWHtI9FgCSSE0ch4D6WXrI9OZpaOVgR3dc+v/9\nLD2keywAJBFPfQs5WRlMGjUibnWw9JDprcrrzwEcvxZAuQ0FdY0FgCTiqW9lWtHImK2+GIqlh0xf\nm7xN5GRmMLukMG51KMjNsvSQLrEAkERq6ltivgx0MJYeMn1VeZs4YlIhOVnx/eqw9JDusACQJFS1\nbxJYvFl6yPSkqlR7m+La/eNn6SHdYQEgSdQd6KCtq4ey8YnRAgDrg003OxvaaG7vZn5p/AOApYd0\nhwWAJOFp8I0AmpYAXUAzxlt6yHRUXRufJaCDsfSQ7rAAkCRq9vuXgY5/F5A/PaS1ANJLlbeJ7Exh\nzsT4zAAOZBMS3eFGSsipIvKCiLwtIptE5DtBjjldRJpEZIPzc3Ok5003nvpWMjOEyWPz4l0V4FAf\nrEkf1d4m5kwoJDcrM95VoaQwl0JLDxmxiDOCAd3A91V1vZMbeJ2IPKuqb/c77p+qep4L50tLnoZW\nJo/JIztO0+/7Ky8u4NVt9fT2atyHpZro898APnPexHhXBfClh5xpawJFLOJvE1XdrarrnccHgHeA\nyZGWaw7nqW+J6xIQ/QWmhzSpz9vYxgetXcyfEv/+f7/y4nxrAUTI1ctJESkDFgGvBdm9RETeEpG/\nishRbp43HdTsb0mI/n8/Sw+ZXvqWgC6N3xIQ/Vl6yMi5FgBEpAD4I/BdVW3ut3s9ME1VjwZ+Dawc\noJwrRKRSRCrr6urcql5Sa2ztpLm9O7FaALYoXFqp9jaRmSEcOSlxAsChNYHsXlS4XAkAIpKN78v/\nYVVd0X+/qjar6kHn8SogW0TGBytLVZeraoWqVhQXF7tRvaSXCIvA9TfW0kOmleraJmaXFDAiO/43\ngP3881HsIiR8bowCEuD3wDuq+osQx0x0jkNEFjvnrY/03OnCU+8fApo4LQCwPth0kUgzgANZesjI\nuTEK6CTgEqBKRDY4224EpgGo6t3A54CrRaQbaAMuVJvDPWT+dc+nJsAksECzSgpYvWlvvKthomxv\ncwf7D3YmVP8/+NJDlo23i5BIRBwAVPVlYMBxgKp6B3BHpOdKVzX1LUwaPSKhmt/gTw+5k4aWTory\nc+JdHRMl/iWgFyTQCCA/a4VGJjEGlZsB+RaBS6yrf7DZmOmi2ttEhpBQN4D9LD1kZCwAJAFPfWtC\nDQH1s5FA6aHa20R5cQEjc9zoMXaXpYeMjAWABHewo5v9BzuYloAtAEsPmR6qa5sSYgG4YPxDQe09\nGB4LAAnu0AigxGsBWHrI1LfvQDt7mzs4KlEDgHVDRsQCQILb0TcHIPFaAODchLMPX8ra5MwATtQW\ngKWHjIwFgASXiJPAAs0qKWDXB22WHjJFVXmbEIF5CTYENNCskgK7DxUmCwDDsPJNLyfd9ndmXP80\nJ932d1a+6Y36OT31LYwvyKEgN/FuwIGlh0xlK9/08psXtqIKZ/33SzF5v4ejvDjf0kOGyQLAEK18\n08sNK6rwNrah+FZHvGFFVdQ/FImSBzgUSw+Zmvzv9/Zu3/DKWL3fw2HpIcNnAWCIbl+9mbZ+3Rxt\nXT3cvnpzVM/rqW9heoLNAA5k6SFTU7ze7+Gw9JDhswAwBKoact372iiuh9/e1cPu5vaEbgFYesjU\nU7O/JS7v93CVWys0bInZsZwgDnZ0s2L9Lh5Y4wl5TOmY6KVo3PVBK6pQNj5xWwBg6SFTQW+v8o8t\nddy/poYXN4dehj2a7/dw+dNDWgtg+CwABLF130EeXFPDH9d7OdjRzYLJo7lo8VSefNNLe9ehKeci\n8L2Pz45aPWr2+0YATUvgLiA4lB6yp1fJtPSQSaWptYsn1u3kwbUePPWtFBfm8t2Pz6ZoZDa3/vXw\nbqC87EyuO2tuHGsbnKWHDJ8FAEdPr/L3d/fxwJoa/vnefnIyM/jE0ZO49MTpLJw6BhHh+BnjuH31\nZmob2xibn01DSxfrdzbyuYqpUalTTQJPAgvkTw9Z29iWcCuWmuDe2d3MA2tq+i5qPlI2lmvPnMtZ\nR00kJ8vXMzwqL6fv/V46Jo/rzprLskWJme11VnEBL2+1BFLDlfYB4IOWTh6r3MmDazx4G9uYOGoE\n1545hws+Mo3iwtzDjl22aPJhH4Cf/u1dfvPiNo6ZMpoLPjLN9brtaGhl1IgsxozMdr1sNwWmh7QA\nkLi6enpZvWkPD7zq4fWaBkZkZ7Bs4WQuOXE6R5V+eKJX//d7IisvyeeP63dxoL2LwhGJ/XlJJGkb\nAKq9Tdz/ag1Pbaylo7uXE2YW8W+fOJIz5k0gK3No98a/f+ZcqrxN/PvKTRwxcRTHTB3jah1rnCGg\nTi6dhBW4KNzSuSVxro3pb9+Bdh55bSd/eN3D3uYOphblcdO5R/L5iimMGZkay3gHpodc6PLnMJW5\nEgBE5Gzgl0Am8DtVva3ffnH2nwu0Aper6no3zj0cnd29/LV6N/e/WsP6HY3kZWfyueOmcOmJZcyd\nWDjs8jIzhF9duIjzfv0yVz20jj9/62TGF+QO/sIh8tS3JOwU/ECWHjLxqCrrdzTywJoaVlXtpqtH\nOW1OMbd+ZjqnzSlJuXs1gekhLQAMXcQBQEQygTuBM4BdwBsi8pSqvh1w2DnAbOfneOAu57frVr7p\n/VC/5Qkzx/GH1zz84fWd7D/YwYzx+dx83jw+e9wURudF1lwcm5/Dby85js/e9Srf+sObPPjVxUNu\nQQykq6cX7wdtfPLo0ojLiraVb3o52N7NI6/v5KUt+xO6rziVBHuvnz1/Ik9tqOWBtTVUe5spzM3i\nSydM55ITpjPTuUpORf70kLYu1fC40QJYDGxV1e0AIvIocD4QGADOBx5w0kCuFZExIjJJVXe7cP4+\n/tmL/pEL3sY2vv/4BnoVEFg6t4TLlpRxyqzxZLh4BTR/8mj+69MLuPaJjfx09WZuPPfIiMusbWyj\nu1cTchnoQP6/eWfP4TNGAQsCURTsvX7tExu5YcVbtHX1MmdCAf+5bD6fXjSZ/ARdRsRN/vSQtibQ\n8LjxzpiQmDyIAAAaOUlEQVQM7Ax4vosPX90HO2Yy4GoACDZ7sUehIDeTp799SlQnVH3uuCm8tauR\n5S9t5+gpozkvwit3/yJwiT4CaKAZoxYAoifY3727V8nKFB75+gmcMLMo4e8duc3SQw5fws0EFpEr\nRKRSRCrr6oY3rCvULMWWjp6YzKb9t0/Mo2L6WH7wf2+xec+BiMra0TcENLFbAKH+5ok4YzRVHGjv\nCjlTt6OrlxPLx6Xdlz/47gNYesjhcSMAeIHAgfBTnG3DPQYAVV2uqhWqWlFcXDysioSapRir2Ys5\nWRn85uJjyc/N4qqH1tHU1hV2WTX1reRlZ35oKGqiifffPJ28t/cA/76ymhN+/HzIY9L5715ebOkh\nh8uNAPAGMFtEZohIDnAh8FS/Y54CLhWfE4Amt/v/Aa47ay552ZmHbYv17MWSUSO46+Jj2dnQ6rv/\n0BveErWe+hamjxuZ8Fdywf7mOZmSkDNGk1G3M3b/i/es5Yz/fonH3tjJWfMn8i9nzI77ez3RWHrI\n4Yv4HoCqdovINcBqfMNA71XVTSJylbP/bmAVviGgW/ENA/1ypOcNxt/nHO/ZixVlRdz8yXnc/KdN\n/PrvW/lOGMtFeOpbmVmc2P3/8OG/eUaGMGHUCM5fmPijlxJZQ0snj76xg4fX7sDb2Ebp6BFcd9Zc\nLvzIVMY5Q42nFeXH/b2eSCw95PC5MjxAVVfh+5IP3HZ3wGMFvunGuQaTKLMXLzlhOht2NvI/z29h\nwZRRfPSICUN+bW+v4mloZekRyTGpKvBv/ofXdnDjk1W8srWek2ePj3PNks9buxq5/1UPf36rls7u\nXpaUj+Pfz5vHx48s+dDw4kR5ryeKvvSQ1gIYstQfHxYnIsKPP72Ad3cf4LuPbuCpa06mbPzQruj3\nNLfT2d2bsHmAB/LZ4ybzP89t4TcvbrUAMEQd3T2sqtrN/a962LCzkZE5mVxQMZVLT5zO7AnDn6CY\nzmbZonDDknCjgFLJiOxMfnvJcWRkCFc9tI7Wzu4hvc6TJENAg8nNyuRrp8zg1W31bNjZGO/qJLTd\nTW38bPVmltz6d7732Eaa27q45ZPzWHvjx/jRsvn25R8GSw85PNYCiLKpRSP51YWLuOx/X+f6P1bx\nywsXDnpj1+MMAU30ZaBD+eLx07nzhW385oWtLL+0It7Viav+s3WvPXMOk8bk8cCaGlZv2kuvKh87\nYgKXLZnOSeXuTlBMR7MC0kNOHD0iKucINgPbra64aJYdjAWAGDh1TjHXnjmX21dv5pipY/jqyTMG\nPL6mvpXsTEnaIX0FuVlctqSMXz3/Hlv2HmBOml7JBput+y+Pb0SBMSOz+dopM/jS8dNtBVUXlQek\nh4xGAAj2f+rWzPdolh2KBYAY+cbp5by1q5Efr3qHo0pHccLMcSGP3dHQwtSikUm9YNeXl5Rxz0vb\nufvFbfzigoXxrk5cBJutq8CYvGzW3vAxRvQbxmkiF5ge8qRZ7t+DCjXz/aaVVRF3eT6xbmfMZ9Vb\nAIgREeFnnz+GZXe+wjV/WM+fv3Uyk0YHv8Kv2d+a0Ingh2Jsfg4XLZ7G/Wtq+N4Zc9LyKjfUbOim\nti778o+SaKWHVFVe2VofcgZ2S0cPK9bviugcLR09QbdHc1a9BYAYKhyRzW8vqWDZna9w1UPrefzK\nE8jNOvyLQFXx1LeweEZRnGrpnq+fOoMH19aw/KXt/GjZ/HhXJ+bGjsymofXDs8GTtWsvGfjTQ7o1\nGexAexcr1nt5YE0N2+payBAINrdz8pg8Xrn+oxGd66Tb/h40wETz/WKjgGJsVkkBP/v8MWzc2cgt\nT739of37D3bS0tmT8GsADcWk0Xl8ZtEUHq/cSd2BjnhXJ6a27jvAwY5u+t/vT/fZurEwqzjyoaBb\n9x3g5j/5lt344VObKMjN4hdfOIaffPboqM3AjsdKBtYCiIOz50/kG6eX96WTvHDxoXSSOxp8I4Bi\nsXhdLFx52kweX7eTe195n389+4h4VycmDrR3ccWD6xiVl801H53FPS+9b7N1Yyjc9JA9vcpz7+zl\ngTU1vLK1npzMDM47ZhKXnlh2WJKZ7MyMqIzUicdKBhYA4sSfTvLmP23iiEmj+t5gNft9cwCScRJY\nMDOLCzh3wSQeWuPhqtPKI07Ak+h6e5XvP74RT30rD3/teE6YOY7Llww86su4a9Yw00M2tHTy2Bs7\neWitLy/4JGfZjQs+MjVohr9ozsCO9exu6wKKE386yZJRuVz90Dr2H/R1kXjqff2MU8amRgAAuPq0\ncg50dPPQWk+8qxJ1d/1jG8+8vZcbzz1ywJFeJnrKA9JDDuStXY18//GNnHDr8/zkb+8yrWgkd3/p\nWP75g6V8c+ksV9O7JiprAcTR2Pwc7v7S4ekkPQ2tlI7JIycrdWLz/MmjOW1OMfe+/D5fOWkGeTmp\nOQLmpS11/OyZzZy/sJSvnFQW7+qkrYHSQ3Z09/DXqj3c92pN37IbX6jw5QVPx/kqkshTpisqKrSy\nsjLe1Yi6Fet38S+Pb2Tp3GJe3rqfrh5lcor1F7+2vZ4Llq/l/33qKC5bUhbv6rhuZ0Mr5/36ZSaN\nHsGKbyxhZI5dW8XLyje9XPvERrp7D32Ojp9ZxB9e28Ejr+9g/8FOZozP59ITp/PZ46Ywahj3CZKB\niKxT1SFNwbd3aQL4zLFTeHL9Ll7YfCgDWqrl1l08o4iK6WNZ/tJ2vnj8NLIzU6eF09bZw5UPrkNV\n+e0lx9mXfxz5Z9N2O2M1fbOvN6BOXvCPHVHCpSeWcbLLecGTVep8CpPctrqWD23zzwJMBSLCN5aW\n421s408bauNdHdeoKjc9WcU7e5r55UWLUmb0VrIKNlO3VyE/N4uXrlvK7y77CKfOKbYvf4cFgASx\nu6k96PZUyq27dG4JR0ws5O5/bAs7U1qieWCNhxVvevnex+ewdG5y5G9IZaHzgnen5Wz0wUQUAETk\ndhF5V0TeEpEnRSTomCsRqRGRKhHZICKp36kfhnTIretrBcxi676DPPP23nhXJ2Jv1DTwo7+8zceP\nLOGapbPiXR1DenyO3BRpC+BZYL6qHg1sAW4Y4NilqrpwqDcn0k0i5DOOhXPnT2T6uJH85sWtSb1m\n+97mdr7x8HqmFo3kFxcstC6FBJEunyO3RBQAVPUZVfVnOVkLTIm8Sulp2aLJ3PqZBUwek4fgW1vk\n1s8sSIkbwIGyMjO48tRy3trVxCtb6+NdnbB0dvfyjYfX09LRzW8vOS7lRpEks3T5HLnFtWGgIvJn\n4DFVfSjIvveBJqAH+K2qLh9KmekyDDTddHT3cMpPXmBWSQF/+PoJ8a7OsP37ymoeXOvhzi8eyyeO\nnhTv6hhzmOEMAx20BSAiz4lIdZCf8wOOuQnoBh4OUczJqroQOAf4poicOsD5rhCRShGprKurC3WY\nSWK5WZl8/ZSZvLqtnjd3fBDv6gzLE5U7eXCthytPnWlf/ibpDRoAVPXjqjo/yM+fAETkcuA84GIN\n0ZxQVa/zex/wJLB4gPMtV9UKVa0oLi4O459kksFFx09jdF42v3lxW7yrMmTV3iZuWlnNkvJx1qds\nUkKko4DOBn4AfEpVW0Mcky8ihf7HwJlAdSTnNcnPnzby2bf3smXvgXhXZ1ANLZ1c+eA6xufn8OuL\nFpGVQhPZTPqK9F18B1AIPOsM8bwbQERKRWSVc8wE4GUR2Qi8Djytqn+L8LwmBXx5SRkjczK5O8Fb\nAT29yrcfeZO6gx3cfclxjEuDRcJMeohozrqqBh38rKq1wLnO4+3AMZGcx6Qmf9rI+15N7LSRt6/e\nzMtb9/PTzx3N0VMGX17YmGRh7VgTV187ZQYZAstf2h7vqgT116rd3P2PbVx8/DS+UDE13tUxxlUW\nAExcTRqdx2ePncJjlTvZdyD4chjx8t7eA1z7xEYWTRvDzZ+cF+/qGOM6CwAm7q48rZzunl7ufbkm\n3lXp09zexZUPriMvJ5O7Lj6O3KzUzGFg0psFABN3M8bnc86CSTy01kNTW1e8q3MorWNDK3d+8Vgm\njh4R7yoZExUWAExCuPq0cg4mSNrIu/6xjWff3stN5x7J8ZbW0aQwCwAmIcyfPJrT5/rSRrZ19gz+\ngih5cfM+fvbMZpYtLOXLltbRpDgLACZhfOP0WdS3dPLYGzvicv4d9a1859ENHDFxFLd+5mhEbIVP\nk9osd51JGP60kff8830uPmF6TNJGrnzTy+2rN1Pb2EZmhpCVAb/90nEpm7jemEDWAjAJ5ZtLZ8Us\nbaQ/f6y3sQ0FunuVXhXWJ9kCdcaEywKASSinzy3myEmjuOvFrVFPG3n76nc/lD+2s6c3ZfIwGzMY\nCwAmoYgIV59ezra6Fp55e09UztHa2c0jr+/A25j6eZiNGYjdAzAJ59z5E/n5uJH85sVtnHXURNdu\nxnrqW3hwjYfHK3fS3N5NVobQHaSVYfljTbqwAGASTlZmBledVs4NK6p4ZWs9J88eH3ZZvb3KP96r\n44FXa3hxSx2ZIpw9fyKXLSljV0MrNz5ZfVg3kOWPNenEAoBJSJ85djL/89wW7nxha1gBoKmtiycq\nd/LQWg819a0UF+by7Y/O5ovHT2PCKN/M3o+UFSEifaOASsfkcd1Zcy1/rEkbFgBMQsrNyuRrJ8/k\nv1a9w5s7PmDRtLFDet27e5p5YI2HJ9d7aevqoWL6WP7lzLmcfdREcrI+fMtr2aLJ9oVv0pYFAJOw\nvnj8NO54YSu/eXEb91waOsd1V08vz769l/tfreG19xvIzcpg2cLJXHLidOZPHh3DGhuTXCIKACJy\nC/B1wJ+9/UZVXRXkuLOBXwKZwO9U9bZIzmvSQ35uFpcvKeOXz7/Hlr0HmDOh8LD9dQc6ePT1HTz8\n2g72NLczZWweN5xzBF+omMrY/Jw41dqY5CEh8rgP7cW+AHBQVX82wDGZwBbgDGAX8AZwkaq+PVj5\nFRUVWllZGXb9TPL7oKWTxT9+jqyMDNq7eigdM4LPV0ylZn8LT1ftpqtHOWX2eC47sYylR5SQmWHL\nN5j0JiLrVDV0kzlALLqAFgNbndSQiMijwPnAoAHAmH9sqaNX6Rup421s53+ee4/cTOHi46dzyYnT\nKS8uiHMtjUlObkwE+5aIvCUi94pIsDt1k4GdAc93OduMGdTtqzfTE2SsflFBLrd86ij78jcmAoMG\nABF5TkSqg/ycD9wFzAQWAruBn0daIRG5QkQqRaSyrq5u8BeYlBZqVu6epsRKH2lMMhq0C0hVPz6U\ngkTkHuAvQXZ5gcBs2lOcbaHOtxxYDr57AEM5t0ldpWPy8AYJAjZb15jIRdQFJCKTAp5+GqgOctgb\nwGwRmSEiOcCFwFORnNekj+vOmkte9uFLM9tsXWPcEelN4J+KyEJAgRrgSgARKcU33PNcVe0WkWuA\n1fiGgd6rqpsiPK9JE/5JWjZb1xj3RTQMNNpsGKgxxgzPcIaB2nLQxhiTpiwAGGNMmrIAYIwxacoC\ngDHGpCkLAMYYk6YSehSQiNQBnjBfPg3Y4WJ1Ao0GmqJUdrTLT9ayo12+1T0+5Sdr2dEuP5Lvr+mq\nWjykI1U1JX+AuiiWvTzKdY9a+clattXd6p5IZceg7lH7/gr8SeUuoMYolv3nKJYd7fKTtexol291\nj0/5yVp2tMuP5vdXn4TuAoqEiFTqECdDGGNMIonV91cqtwCWx7sCxhgTpph8f6VsAFDfqqIRE5Gz\nRWSziGwVkeudbbeLyLtOHoQnRWSMy+X/yCl7g4g846yt5ErZAfu+LyIqIuNdrvstIuJ16r5BRM51\ns+4i8i3nb79JRH7qct0fC6h3jYhscLHshSKy1im7UkQWu1j2MSKyRkSqROTPIjIqzLLvFZF9IlId\nsK1IRJ4Vkfec38FyfkRS/ued/8teEQn7ijdE2a58TkOU7cpnNBS3vr+GciL7CX0jJhPYhi/nQQ6w\nEZgHnAlkOcf8BPiJy+WPCjjm28DdbpXt7JuKb3E+DzDe5brfAlwbpb/7UuA5INc5rsTN8vsd83Pg\nZhfr/gxwjnPMucCLLpb9BnCac8xXgB+F+Xc5FTgWqA7Y9lPgeufx9eG+1wco/0hgLvAiUOFy2W59\nToOVHfFnNBF+UqIFEOKqyI0ri750lqraCTwKnK+qz6hqt3PMWnw5DtwsvzngmHx8q626Uraz77+B\nH4RZ7lDKj1Sosq8GblPVDgBV3edy+QCIiABfAB5xsWwF/Ffmo4FaF8ueA7zkHPMs8NkwykZVXwIa\n+m0+H7jfeXw/sCycskOVr6rvqOrmcMscpGxXPqchynbjMwoEb2EE7Iu4pT6QpA8A4ks6fydwDr6r\noYtEZB6+3ASf4dAHIxxDSWf5FeCvbpcvIv8lIjuBi4Gb3SpbfJncvKq6MbwqD1y+83iwNKHhlj0H\nOEVEXhORf4jIR8Kp+ADl+50C7FXV91ws+7vA7c7/6c+AG1wsexOHAtjnOTwBU6QmqOpu5/EeYIKL\nZcdSJJ/ToFz4jPrdB5wdpPyp+Fox0ZrPlPwBgNBX0a5cWQxERG4CuoGH3S5bVW9S1alO2de4VOxI\n4EYie7MOxvU0oQGygCLgBOA64HHnat1tFxHe1f9Arga+5/yffg/4vYtlfwX4hoisAwqBThfL7qO+\n/o6kGzYYrc+pW5/REC0vcKelPqBUCADRTDofMp2liFwOnAdc7HwwXC0/wMOE16QPVvb7wAxgo4jU\nONvWi8hEl8r3qupeVe1R1V7gHnwB2pWy8f3frlCf14FeIJym8UD/r1n4Wo6PhVHuQGVfBqxwtj2B\ni38XVX1XVc9U1ePwBa5tYZQdyl5xMv85v8PtdosLlz6ngwn3MxqSiy31AaVCAIimoOksReRsfJH5\nU6raGoXyZwcccz7wrktlr1DVElUtU9UyfF+ox6rqHhfrPpQ0oWGVDazEdyMYEZmD70bofhfLB/g4\n8K6q7gqj3IHKrgVOc475KBBO91Kov3kJgIhkAP8G3B1m3YN5Cl/wwvn9JxfLjioXP6fBynbjMxqq\n7Fi01H3ifRc60h/gRGB1wPMbgBsCnr9IZKMLzgW24LuqusnZthVfq2OD8xP2CIAQ5f8R3xfnW/hm\nG052q+x++2sIcxTQAHV/EKhy6v4UMMnFsnOAh5y/zXrgo27W3dl+H3BVhO/JYHU/GViHb+TOa8Bx\nLpb9HWfbFuA2nAmeYZT9CL5uuy58FwdfBcYBz+MLWM8BRRH8XYKV/2nncQewN/Cz7ELZrnxOQ5Tt\nymc04BxlOKOMgAX4Wlo1zk83vvsAEyM5R7CfpJ8J7DTZtwAfw9dEfgP4ojp5h0XkRXzDEi23pDEm\nIYlIGfAXVZ0fZF8NvovYcFq7A0r6LiD1DfPyJ51/B3hcVTeJyKdFZBe+FsLTIrI6nvU0xphgROQR\nYA0wV0R2ichXY3buZG8BGGOMCU/StwCMMcaExwKAMcakqaQNACLSI4cW7trg3EQJdezpIvKX2NXO\nGGMSX1a8KxCBNlVdGO9KGGNMskraFkAwIpLpLAH7hrMWzZUBu0eJyNPOonF3O5NmjDEmbSVzCyBP\nDq3X/r6qfhrfBI0mVf2IiOQCr4jIM84xi/EtFucB/oZvuv//xbrSxhiTKJI5AATrAjoTOFpEPuc8\nHw3Mxrc41uuquh36xt2ejAUAY0waS+YAEIwA31LVwyZ9icjpfHhFPZsAYYxJa6nWD74auFpEssG3\nYJiI5Dv7FjuLaGUAFwAvx6uSxhiTCFKtBfA7fIsqrXfWia/jUAajN4A7gFnAC8CT8aigMcYkClsK\nwhhj0lSqdQEZY4wZIgsAxhiTpiwAGGNMmkqaACAiU0XkBRF5W0Q2ich3nO1FIvKsiLzn/B7rbB/n\nHH9QRO7oV9ZFIlLlzBb+m4iEk1fWGGOSWtLcBHZyzU5S1fUiUogvvd4y4HKgQVVvE5HrgbGq+q/O\n8M9FwHxgvqpe45SThS8/6zxV3S8iPwVaVfWW2P+rjDEmfpKmBaCqu1V1vfP4AL7sX5PxJWS+3zns\nfpxhn6raoqovA+39ihLnJ98ZKjoKX0Awxpi0kpTzAJylnxfhS649QVV3O7v2ABMGeq2qdonI1fgS\nl7fgS3b9zahV1hhjElTStAD8RKQA+CPwXVVtDtynvv6sAfu0nFnCV+MLIKXAW8AN0amtMcYkrqQK\nAM6X9x+Bh1V1hbN5r3N/wH+fYN8gxSwEUNVtTsB4HFgSpSobY0zCSpoA4PTX/x54R1V/EbDrKeAy\n5/FlwJ8GKcoLzBORYuf5GfjuJxhjTFpJplFAJwP/xNd33+tsvhHffYDHgWn41vr/gqo2OK+pwXeT\nNwdoBM5U1bdF5CrgO0CX85rLVbU+dv8aY4yJv6QJAMYYY9yVNF1Axhhj3GUBwBhj0pQFAGOMSVMW\nAIwxJk1ZADDGmDRlAcCYfkTkFhG5doD9y0RkXizrZEw0WAAwZviWARYATNKzeQDGACJyE76Z5PuA\nnfiWG28CrsA3kXArcAm+pUT+4uxrAj7rFHEnUAy0Al9X1XdF5PPAD4EeoElVT43ZP8iYIbAAYNKe\niBwH3Accj2+F3PXA3cD/+meIi8h/AntV9dcich/wF1X9P2ff88BVqvqeiBwP3KqqHxWRKuBsVfWK\nyBhVbYz5P86YASTlctDGuOwU4ElVbQUQkaec7fOdL/4xQAGwuv8LndVplwBP+JarAiDX+f0KcJ+I\nPA6s6P9aY+LNAoAxod0HLFPVjSJyOXB6kGMygEZVXdh/h6pe5bQIPgGsE5HjbM0pk0jsJrAx8BKw\nTETynHSjn3S2FwK7nWXILw44/oCzDycnxftOfz/ic4zzuFxVX1PVm4E6YGps/jnGDI3dAzCGD90E\n3oHvPkAL8AN8X96vAYWqermInATcA3QAn8O3Ou1dwCQgG3hUVf9DRFYAs/GlIH0eXxIj+8CZhGEB\nwBhj0pR1ARljTJqyAGCMMWnKAoAxxqQpCwDGGJOmLAAYY0yasgBgjDFpygKAMcakKQsAxhiTpv4/\nesck29Z18A8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148735c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normal plot (notice the 2 outliers!)\n",
    "temp_df.plot(marker='o', legend=False)\n",
    "plt.title(\"Normal Plot\")\n",
    "plt.ylim(-6, 16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fW8G6LbX84YLj6NY5es8vNuxyrr5/Ec8uq+Ln44/i3OP6ZzokaQc0GJy0CX+b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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c524a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rolling mean\n",
    "temp_df.rolling(window=5).mean().plot(marker='o', label=\"adfsa\")\n",
    "plt.title(\"Rolling Mean (n=5)\")\n",
    "plt.ylim(-6, 16);\n",
    "# why do we start at the 5th of February"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MzKyNTAJA0m4kH/43RsStbedHxJqIWJfenwrsJmlAFm2bmVnnZNELSMAvgBci4l9LLLNf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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117be3278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rolling median\n",
    "temp_df.rolling(window=5).median().plot(marker='o', legend=False)\n",
    "plt.title(\"Rolling Median (n=5)\")\n",
    "plt.ylim(-6, 16);\n",
    "# what could be a reason to use the median rather than the mean? can you see the difference in the figures?\n",
    "# can you explain the value for the 6th of February? (0 degrees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>avg_temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-02</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-03</th>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-04</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            avg_temp\n",
       "dates               \n",
       "2018-02-02         1\n",
       "2018-02-03        -2\n",
       "2018-02-04        15\n",
       "2018-02-05         0\n",
       "2018-02-06        -5"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# investigate last 5 records \n",
    "temp_df.iloc[1:6] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dates\n",
       "2018-02-06    -5\n",
       "2018-02-03    -2\n",
       "2018-02-05     0\n",
       "2018-02-02     1\n",
       "2018-02-04    15\n",
       "Name: avg_temp, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# manually compute the median\n",
    "temp_df.iloc[1:6, 0].sort_values() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternatively, use the median() function\n",
    "temp_df.iloc[1:6, 0].median()"
   ]
  },
  {
   "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": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1. Programming Exercises ORG\n",
    "# 2. Programming Exercises DAS\n",
    "# 3. Theory Exercises DAS\n",
    "# 4. Graded Assignment 2 (module 4/5)\n",
    "# 5. Watch Video Lecture DAS"
   ]
  }
 ],
 "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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}