01-exploratory-data-analysis.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Data with Python\n",
    "\n",
    "A significant part of a data scientist's role is to explore, analyze, and visualize data. There's a wide range of tools and programming languages that they can use to do this, and of the most popular approaches is to use Jupyter notebooks (like this one) and Python.\n",
    "\n",
    "Python is a flexible programming language that is used in a wide range of scenarios; from web applications to device programming. It's extremely popular in the data science and machine learning community because of the many packages it supports for data analysis and visualization.\n",
    "\n",
    "In this notebook, we'll explore some of these packages, and apply basic techniques to analyze data. This is not intended to be a comprehensive Python programming exercise; or even a deep dive into data analysis. Rather, it's intended as a crash course in some of the common ways in which data scientists can use Python to work with data.\n",
    "\n",
    "## Exploring data arrays with NumPy\n",
    "\n",
    "Let's start by looking at some simple data.\n",
    "\n",
    "Suppose a college takes a sample of student grades for a data science class.\n",
    "\n",
    "Run the code in the cell below by clicking the **&#9658; Run** button to see the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37, 15, 70, 27, 36, 35, 48, 52, 63, 64]\n"
     ]
    }
   ],
   "source": [
    "data = [50,50,47,97,49,3,53,42,26,74,82,62,37,15,70,27,36,35,48,52,63,64]\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has been loaded into a Python **list** structure, which is a good data type for general data manipulation, but not optimized for numeric analysis. For that, we're going to use the **NumPy** package, which includes specific data types and functions for working with *Num*bers in *Py*thon.\n",
    "\n",
    "Run the cell below to load the data into a NumPy **array**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50 50 47 97 49  3 53 42 26 74 82 62 37 15 70 27 36 35 48 52 63 64]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "grades = np.array(data)\n",
    "print(grades)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just in case you're wondering about the differences between a **list** and a NumPy **array**, let's compare how these data types behave when we use them in an expression that multiplies them by 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'> x 2: [50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37, 15, 70, 27, 36, 35, 48, 52, 63, 64, 50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37, 15, 70, 27, 36, 35, 48, 52, 63, 64]\n",
      "---\n",
      "<class 'numpy.ndarray'> x 2: [100 100  94 194  98   6 106  84  52 148 164 124  74  30 140  54  72  70\n",
      "  96 104 126 128]\n"
     ]
    }
   ],
   "source": [
    "print (type(data),'x 2:', data * 2)\n",
    "print('---')\n",
    "print (type(grades),'x 2:', grades * 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that multiplying a list by 2 creates a new list of twice the length with the original sequence of list elements repeated. Multiplying a NumPy array on the other hand performs an element-wise calculation in which the array behaves like a *vector*, so we end up with an array of the same size in which each element has been multiplied by 2.\n",
    "\n",
    "The key takeaway from this is that NumPy arrays are specifically designed to support mathematical operations on numeric data - which makes them more useful for data analysis than a generic list.\n",
    "\n",
    "You might have spotted that the class type for the numpy array above is a **numpy.ndarray**. The **nd** indicates that this is a structure that can consists of multiple *dimensions* (it can have *n* dimensions). Our specific instance has a single dimension of student grades.\n",
    "\n",
    "Run the cell below to view the **shape** of the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape confirms that this array has only one dimension, which contains 22 elements (there are 22 grades in the original list). You can access the individual elements in the array by their zero-based ordinal position. Let's get the first element (the one in position 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, now you know your way around a NumPy array, it's time to perform some analysis of the grades data.\n",
    "\n",
    "You can apply aggregations across the elements in the array, so let's find the simple average grade (in other words, the *mean* grade value)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.18181818181818"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the mean grade is just around 50 - more or less in the middle of the possible range from 0 to 100.\n",
    "\n",
    "Let's add a second set of data for the same students, this time recording the typical number of hours per week they devoted to studying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.  , 11.5 ,  9.  , 16.  ,  9.25,  1.  , 11.5 ,  9.  ,  8.5 ,\n",
       "        14.5 , 15.5 , 13.75,  9.  ,  8.  , 15.5 ,  8.  ,  9.  ,  6.  ,\n",
       "        10.  , 12.  , 12.5 , 12.  ],\n",
       "       [50.  , 50.  , 47.  , 97.  , 49.  ,  3.  , 53.  , 42.  , 26.  ,\n",
       "        74.  , 82.  , 62.  , 37.  , 15.  , 70.  , 27.  , 36.  , 35.  ,\n",
       "        48.  , 52.  , 63.  , 64.  ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define an array of study hours\n",
    "study_hours = [10.0,11.5,9.0,16.0,9.25,1.0,11.5,9.0,8.5,14.5,15.5,\n",
    "               13.75,9.0,8.0,15.5,8.0,9.0,6.0,10.0,12.0,12.5,12.0]\n",
    "\n",
    "# Create a 2D array (an array of arrays)\n",
    "student_data = np.array([study_hours, grades])\n",
    "\n",
    "# display the array\n",
    "student_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the data consists of a 2-dimensional array - an array of arrays. Let's look at its shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 22)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show shape of 2D array\n",
    "student_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **student_data** array contains two elements, each of which is an array containing 22 elements.\n",
    "\n",
    "To navigate this structure, you need to specify the position of each element in the hierarchy. So to find the first value in the first array (which contains the study hours data), you can use the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first element of the first element\n",
    "student_data[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you have a multidimensional array containing both the student's study time and grade information, which you can use to compare data. For example, how does the mean study time compare to the mean grade?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average study hours: 10.52\n",
      "Average grade: 49.18\n"
     ]
    }
   ],
   "source": [
    "# Get the mean value of each sub-array\n",
    "avg_study = student_data[0].mean()\n",
    "avg_grade = student_data[1].mean()\n",
    "\n",
    "print('Average study hours: {:.2f}\\nAverage grade: {:.2f}'.format(avg_study, avg_grade))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring tabular data with Pandas\n",
    "\n",
    "While NumPy provides a lot of the functionality you need to work with numbers, and specifically arrays of numeric values; when you start to deal with two-dimensional tables of data, the **Pandas** package offers a more convenient structure to work with - the **DataFrame**.\n",
    "\n",
    "Run the following cell to import the Pandas library and create a DataFrame with three columns. The first column is a list of student names, and the second and third columns are the NumPy arrays containing the study time and grade data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>10.00</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>11.50</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>9.00</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>16.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>9.25</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>11.50</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>9.00</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>8.50</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>14.50</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>15.50</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>13.75</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>9.00</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>8.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>15.50</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>8.00</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>9.00</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ismat</td>\n",
       "      <td>6.00</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>10.00</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>12.00</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>12.50</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.00</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade\n",
       "0         Dan       10.00   50.0\n",
       "1       Joann       11.50   50.0\n",
       "2       Pedro        9.00   47.0\n",
       "3       Rosie       16.00   97.0\n",
       "4       Ethan        9.25   49.0\n",
       "5       Vicky        1.00    3.0\n",
       "6    Frederic       11.50   53.0\n",
       "7      Jimmie        9.00   42.0\n",
       "8      Rhonda        8.50   26.0\n",
       "9    Giovanni       14.50   74.0\n",
       "10  Francesca       15.50   82.0\n",
       "11      Rajab       13.75   62.0\n",
       "12    Naiyana        9.00   37.0\n",
       "13       Kian        8.00   15.0\n",
       "14      Jenny       15.50   70.0\n",
       "15     Jakeem        8.00   27.0\n",
       "16     Helena        9.00   36.0\n",
       "17      Ismat        6.00   35.0\n",
       "18      Anila       10.00   48.0\n",
       "19       Skye       12.00   52.0\n",
       "20     Daniel       12.50   63.0\n",
       "21      Aisha       12.00   64.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_students = pd.DataFrame({'Name': ['Dan', 'Joann', 'Pedro', 'Rosie', 'Ethan', 'Vicky', 'Frederic', 'Jimmie', \n",
    "                                     'Rhonda', 'Giovanni', 'Francesca', 'Rajab', 'Naiyana', 'Kian', 'Jenny',\n",
    "                                     'Jakeem','Helena','Ismat','Anila','Skye','Daniel','Aisha'],\n",
    "                            'StudyHours':student_data[0],\n",
    "                            'Grade':student_data[1]})\n",
    "\n",
    "df_students "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in addition to the columns you specified, the DataFrame includes an *index* to unique identify each row. We could have specified the index explicitly, and assigned any kind of appropriate value (for example, an email address); but because we didn't specify an index, one has been created with a unique integer value for each row.\n",
    "\n",
    "### Finding and filtering data in a DataFrame\n",
    "\n",
    "You can use the DataFrame's **loc** method to retrieve data for a specific index value, like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          Vicky\n",
       "StudyHours      1.0\n",
       "Grade           3.0\n",
       "Name: 5, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data for index value 5\n",
    "df_students.loc[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also get the data at a range of index values, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>10.00</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>11.50</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>9.00</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>16.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>9.25</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  StudyHours  Grade\n",
       "0    Dan       10.00   50.0\n",
       "1  Joann       11.50   50.0\n",
       "2  Pedro        9.00   47.0\n",
       "3  Rosie       16.00   97.0\n",
       "4  Ethan        9.25   49.0\n",
       "5  Vicky        1.00    3.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the rows with index values from 0 to 5\n",
    "df_students.loc[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to being able to use the **loc** method to find rows based on the index, you can use the **iloc** method to find rows based on their ordinal position in the DataFrame (regardless of the index):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>10.00</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>11.50</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>9.00</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>16.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>9.25</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  StudyHours  Grade\n",
       "0    Dan       10.00   50.0\n",
       "1  Joann       11.50   50.0\n",
       "2  Pedro        9.00   47.0\n",
       "3  Rosie       16.00   97.0\n",
       "4  Ethan        9.25   49.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get data in the first five rows\n",
    "df_students.iloc[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look carefully at the `iloc[0:5]` results, and compare them to the `loc[0:5]` results you obtained previously. Can you spot the difference?\n",
    "\n",
    "The **loc** method returned rows with index *label* in the list of values from *0* to *5* - which includes *0*, *1*, *2*, *3*, *4*, and *5* (six rows). However, the **iloc** method returns the rows in the *positions* included in the range 0 to 5, and since integer ranges don't include the upper-bound value, this includes positions *0*, *1*, *2*, *3*, and *4* (five rows).\n",
    "\n",
    "**iloc** identifies data values in a DataFrame by *position*, which extends beyond rows to columns. So for example, you can use it to find the values for the columns in positions 1 and 2 in row 0, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StudyHours    10.0\n",
       "Grade         50.0\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.iloc[0,[1,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's return to the **loc** method, and see how it works with columns. Remember that **loc** is used to locate data items based on index values rather than positions. In the absence of an explicit index column, the rows in our dataframe are indexed as integer values, but the columns are identified by name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.loc[0,'Grade']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's another useful trick. You can use the **loc** method to find indexed rows based on a filtering expression that references named columns other than the index, like this:"
   ]
  },
  {
   "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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  StudyHours  Grade\n",
       "21  Aisha        12.0   64.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.loc[df_students['Name']=='Aisha']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, you don't need to explicitly use the **loc** method to do this - you can simply apply a DataFrame filtering expression, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  StudyHours  Grade\n",
       "21  Aisha        12.0   64.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students[df_students['Name']=='Aisha']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for good measure, you can achieve the same results by using the DataFrame's **query** method, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  StudyHours  Grade\n",
       "21  Aisha        12.0   64.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.query('Name==\"Aisha\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three previous examples underline an occassionally confusing truth about working with Pandas. Often, there are multiple ways to achieve the same results. Another example of this is the way you refer to a DataFrame column name. You can specify the column name as a named index value (as in the `df_students['Name']` examples we've seen so far), or you can use the column as a property of the DataFrame, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  StudyHours  Grade\n",
       "21  Aisha        12.0   64.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students[df_students.Name == 'Aisha']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading a DataFrame from a  file\n",
    "\n",
    "We constructed the DataFrame from some existing arrays. However, in many real-world scenarios, data is loaded from sources such as files. Let's replace the student grades DataFrame with the contents of a text file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>10.00</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>11.50</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>9.00</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>16.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>9.25</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  StudyHours  Grade\n",
       "0    Dan       10.00   50.0\n",
       "1  Joann       11.50   50.0\n",
       "2  Pedro        9.00   47.0\n",
       "3  Rosie       16.00   97.0\n",
       "4  Ethan        9.25   49.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students = pd.read_csv('data/grades.csv', delimiter=',', header='infer')\n",
    "df_students.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DataFrame's **read_csv** method is used to load data from text files. As you can see in the example code, you can specify options such as the column delimiter and which row (if any) contains column headers (in this case, the delimiter is a comma and the first row contains the column names - these are the default settings, so the parameters could have been omitted).\n",
    "\n",
    "\n",
    "### Handling missing values\n",
    "\n",
    "One of the most common issues data scientists need to deal with is incomplete or missing data. So how would we know that the DataFrame contains missing values? You can use the **isnull** method to identify which individual values are null, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  StudyHours  Grade\n",
       "0   False       False  False\n",
       "1   False       False  False\n",
       "2   False       False  False\n",
       "3   False       False  False\n",
       "4   False       False  False\n",
       "5   False       False  False\n",
       "6   False       False  False\n",
       "7   False       False  False\n",
       "8   False       False  False\n",
       "9   False       False  False\n",
       "10  False       False  False\n",
       "11  False       False  False\n",
       "12  False       False  False\n",
       "13  False       False  False\n",
       "14  False       False  False\n",
       "15  False       False  False\n",
       "16  False       False  False\n",
       "17  False       False  False\n",
       "18  False       False  False\n",
       "19  False       False  False\n",
       "20  False       False  False\n",
       "21  False       False  False\n",
       "22  False       False   True\n",
       "23  False        True   True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, with a larger DataFrame, it would be inefficient to review all of the rows and columns individually; so we can get the sum of missing values for each column, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          0\n",
       "StudyHours    1\n",
       "Grade         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we know that there's one missing **StudyHours** value, and two missing **Grade** values.\n",
    "\n",
    "To see them in context, we can filter the dataframe to include only rows where any of the columns (axis 1 of the DataFrame) are null."
   ]
  },
  {
   "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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bill</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ted</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  StudyHours  Grade\n",
       "22  Bill         8.0    NaN\n",
       "23   Ted         NaN    NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students[df_students.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the DataFrame is retrieved, the missing numeric values show up as **NaN** (*not a number*).\n",
    "\n",
    "So now that we've found the null values, what can we do about them?\n",
    "\n",
    "One common approach is to *impute* replacement values. For example, if the number of study hours is missing, we could just assume that the student studied for an average amount of time and replace the missing value with the mean study hours. To do this, we can use the **fillna** method, like this:"
   ]
  },
  {
   "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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ismat</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bill</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ted</td>\n",
       "      <td>10.413043</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade\n",
       "0         Dan   10.000000   50.0\n",
       "1       Joann   11.500000   50.0\n",
       "2       Pedro    9.000000   47.0\n",
       "3       Rosie   16.000000   97.0\n",
       "4       Ethan    9.250000   49.0\n",
       "5       Vicky    1.000000    3.0\n",
       "6    Frederic   11.500000   53.0\n",
       "7      Jimmie    9.000000   42.0\n",
       "8      Rhonda    8.500000   26.0\n",
       "9    Giovanni   14.500000   74.0\n",
       "10  Francesca   15.500000   82.0\n",
       "11      Rajab   13.750000   62.0\n",
       "12    Naiyana    9.000000   37.0\n",
       "13       Kian    8.000000   15.0\n",
       "14      Jenny   15.500000   70.0\n",
       "15     Jakeem    8.000000   27.0\n",
       "16     Helena    9.000000   36.0\n",
       "17      Ismat    6.000000   35.0\n",
       "18      Anila   10.000000   48.0\n",
       "19       Skye   12.000000   52.0\n",
       "20     Daniel   12.500000   63.0\n",
       "21      Aisha   12.000000   64.0\n",
       "22       Bill    8.000000    NaN\n",
       "23        Ted   10.413043    NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.StudyHours = df_students.StudyHours.fillna(df_students.StudyHours.mean())\n",
    "df_students"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, it might be important to ensure that you only use data you know to be absolutely correct; so you can drop rows or columns that contains null values by using the **dropna** method. In this case, we'll remove rows (axis 0 of the DataFrame) where any of the columns contain null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>10.00</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>11.50</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>9.00</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>16.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>9.25</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>11.50</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>9.00</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>8.50</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>14.50</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>15.50</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>13.75</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>9.00</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>8.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>15.50</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>8.00</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>9.00</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ismat</td>\n",
       "      <td>6.00</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>10.00</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>12.00</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>12.50</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.00</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade\n",
       "0         Dan       10.00   50.0\n",
       "1       Joann       11.50   50.0\n",
       "2       Pedro        9.00   47.0\n",
       "3       Rosie       16.00   97.0\n",
       "4       Ethan        9.25   49.0\n",
       "5       Vicky        1.00    3.0\n",
       "6    Frederic       11.50   53.0\n",
       "7      Jimmie        9.00   42.0\n",
       "8      Rhonda        8.50   26.0\n",
       "9    Giovanni       14.50   74.0\n",
       "10  Francesca       15.50   82.0\n",
       "11      Rajab       13.75   62.0\n",
       "12    Naiyana        9.00   37.0\n",
       "13       Kian        8.00   15.0\n",
       "14      Jenny       15.50   70.0\n",
       "15     Jakeem        8.00   27.0\n",
       "16     Helena        9.00   36.0\n",
       "17      Ismat        6.00   35.0\n",
       "18      Anila       10.00   48.0\n",
       "19       Skye       12.00   52.0\n",
       "20     Daniel       12.50   63.0\n",
       "21      Aisha       12.00   64.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students = df_students.dropna(axis=0, how='any')\n",
    "df_students"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore data in the DataFrame\n",
    "\n",
    "Now that we've cleaned up the missing values, we're ready to explore the data in the DataFrame. Let's start by comparing the mean study hours and grades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average weekly study hours: 10.52\n",
      "Average grade: 49.18\n"
     ]
    }
   ],
   "source": [
    "# Get the mean study hours using the column name as an index\n",
    "mean_study = df_students['StudyHours'].mean()\n",
    "\n",
    "# Get the mean grade using the column name as a property (just to make the point!)\n",
    "mean_grade = df_students.Grade.mean()\n",
    "\n",
    "# Print the mean study hours and mean grade\n",
    "print('Average weekly study hours: {:.2f}\\nAverage grade: {:.2f}'.format(mean_study, mean_grade))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, let's filter the DataFrame to find only the students who studied for more than the average amount of time."
   ]
  },
  {
   "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",
       "        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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>11.50</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>16.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>11.50</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>14.50</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>15.50</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>13.75</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>15.50</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>12.00</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>12.50</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.00</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade\n",
       "1       Joann       11.50   50.0\n",
       "3       Rosie       16.00   97.0\n",
       "6    Frederic       11.50   53.0\n",
       "9    Giovanni       14.50   74.0\n",
       "10  Francesca       15.50   82.0\n",
       "11      Rajab       13.75   62.0\n",
       "14      Jenny       15.50   70.0\n",
       "19       Skye       12.00   52.0\n",
       "20     Daniel       12.50   63.0\n",
       "21      Aisha       12.00   64.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get students who studied for the mean or more hours\n",
    "df_students[df_students.StudyHours > mean_study]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the filtered result is itself a DataFrame, so you can work with its columns just like any other DataFrame.\n",
    "\n",
    "For example, let's find the average grade for students who undertook more than the average amount of study time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.7"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What was their mean grade?\n",
    "df_students[df_students.StudyHours > mean_study].Grade.mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assume that the passing grade for the course is 60.\n",
    "\n",
    "We can use that information to add a new column to the DataFrame, indicating whether or not each student passed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14897/3095096272.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_students['Pass'] = df_students['Grade'] >= 60\n"
     ]
    },
    {
     "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>10.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>11.50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>9.00</td>\n",
       "      <td>47.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>16.00</td>\n",
       "      <td>97.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>9.25</td>\n",
       "      <td>49.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>11.50</td>\n",
       "      <td>53.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>9.00</td>\n",
       "      <td>42.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>8.50</td>\n",
       "      <td>26.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>14.50</td>\n",
       "      <td>74.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>15.50</td>\n",
       "      <td>82.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>13.75</td>\n",
       "      <td>62.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>9.00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>8.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>15.50</td>\n",
       "      <td>70.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>8.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>9.00</td>\n",
       "      <td>36.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ismat</td>\n",
       "      <td>6.00</td>\n",
       "      <td>35.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>10.00</td>\n",
       "      <td>48.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>12.00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>12.50</td>\n",
       "      <td>63.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade   Pass\n",
       "0         Dan       10.00   50.0  False\n",
       "1       Joann       11.50   50.0  False\n",
       "2       Pedro        9.00   47.0  False\n",
       "3       Rosie       16.00   97.0   True\n",
       "4       Ethan        9.25   49.0  False\n",
       "5       Vicky        1.00    3.0  False\n",
       "6    Frederic       11.50   53.0  False\n",
       "7      Jimmie        9.00   42.0  False\n",
       "8      Rhonda        8.50   26.0  False\n",
       "9    Giovanni       14.50   74.0   True\n",
       "10  Francesca       15.50   82.0   True\n",
       "11      Rajab       13.75   62.0   True\n",
       "12    Naiyana        9.00   37.0  False\n",
       "13       Kian        8.00   15.0  False\n",
       "14      Jenny       15.50   70.0   True\n",
       "15     Jakeem        8.00   27.0  False\n",
       "16     Helena        9.00   36.0  False\n",
       "17      Ismat        6.00   35.0  False\n",
       "18      Anila       10.00   48.0  False\n",
       "19       Skye       12.00   52.0  False\n",
       "20     Daniel       12.50   63.0   True\n",
       "21      Aisha       12.00   64.0   True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this will create a new column called Pass with a boolean value for each row (True if grade >= 60)\n",
    "df_students['Pass'] = df_students['Grade'] >= 60\n",
    "\n",
    "df_students"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrames are designed for tabular data, and you can use them to perform many of the kinds of data analytics operation you can do in a relational database; such as grouping and aggregating tables of data.\n",
    "\n",
    "For example, you can use the **groupby** method to group the student data into groups based on the **Pass** column you added previously, and count the number of names in each group - in other words, you can determine how many students passed and failed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass\n",
      "False    15\n",
      "True      7\n",
      "Name: Name, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_students.groupby(df_students.Pass).Name.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrames are amazingly versatile, and make it easy to manipulate data. Many DataFrame operations return a new copy of the DataFrame; so if you want to modify a DataFrame but keep the existing variable, you need to assign the result of the operation to the existing variable. For example, the following code sorts the student data into descending order of Grade, and assigns the resulting sorted DataFrame to the original **df_students** variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>16.00</td>\n",
       "      <td>97.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>15.50</td>\n",
       "      <td>82.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>14.50</td>\n",
       "      <td>74.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>15.50</td>\n",
       "      <td>70.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>12.50</td>\n",
       "      <td>63.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>13.75</td>\n",
       "      <td>62.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>11.50</td>\n",
       "      <td>53.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>12.00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>11.50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>10.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>9.25</td>\n",
       "      <td>49.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>10.00</td>\n",
       "      <td>48.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>9.00</td>\n",
       "      <td>47.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>9.00</td>\n",
       "      <td>42.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>9.00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>9.00</td>\n",
       "      <td>36.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ismat</td>\n",
       "      <td>6.00</td>\n",
       "      <td>35.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>8.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>8.50</td>\n",
       "      <td>26.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>8.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vicky</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade   Pass\n",
       "3       Rosie       16.00   97.0   True\n",
       "10  Francesca       15.50   82.0   True\n",
       "9    Giovanni       14.50   74.0   True\n",
       "14      Jenny       15.50   70.0   True\n",
       "21      Aisha       12.00   64.0   True\n",
       "20     Daniel       12.50   63.0   True\n",
       "11      Rajab       13.75   62.0   True\n",
       "6    Frederic       11.50   53.0  False\n",
       "19       Skye       12.00   52.0  False\n",
       "1       Joann       11.50   50.0  False\n",
       "0         Dan       10.00   50.0  False\n",
       "4       Ethan        9.25   49.0  False\n",
       "18      Anila       10.00   48.0  False\n",
       "2       Pedro        9.00   47.0  False\n",
       "7      Jimmie        9.00   42.0  False\n",
       "12    Naiyana        9.00   37.0  False\n",
       "16     Helena        9.00   36.0  False\n",
       "17      Ismat        6.00   35.0  False\n",
       "15     Jakeem        8.00   27.0  False\n",
       "8      Rhonda        8.50   26.0  False\n",
       "13       Kian        8.00   15.0  False\n",
       "5       Vicky        1.00    3.0  False"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame with the data sorted by Grade (descending)\n",
    "df_students = df_students.sort_values('Grade', ascending=False)\n",
    "\n",
    "# Show the DataFrame\n",
    "df_students"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing data with Matplotlib\n",
    "\n",
    "DataFrames provide a great way to explore and analyze tabular data, but sometimes a picture is worth a thousand rows and columns. The **Matplotlib** library provides the foundation for plotting data visualizations that can greatly enhance your ability to analyze the data.\n",
    "\n",
    "Let's start with a simple bar chart that shows the grade of each student."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ensure plots are displayed inline in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Create a bar plot of name vs grade\n",
    "plt.bar(x=df_students.Name, height=df_students.Grade)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, that worked; but the chart could use some improvements to make it clearer what we're looking at.\n",
    "\n",
    "Note that you used the **pyplot** class from Matplotlib to plot the chart. This class provides a whole bunch of ways to improve the visual elements of the plot. For example, the following code:\n",
    "\n",
    "- Specifies the color of the bar chart.\n",
    "- Adds a title to the chart (so we know what it represents)\n",
    "- Adds labels to the X and Y (so we know which axis shows which data)\n",
    "- Adds a grid (to make it easier to determine the values for the bars)\n",
    "- Rotates the X markers (so we can read them)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot of name vs grade\n",
    "plt.bar(x=df_students.Name, height=df_students.Grade, color='orange')\n",
    "\n",
    "# Customize the chart\n",
    "plt.title('Student Grades')\n",
    "plt.xlabel('Student')\n",
    "plt.ylabel('Grade')\n",
    "plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7)\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A plot is technically contained with a **Figure**. In the previous examples, the figure was created implicitly for you; but you can create it explicitly. For example, the following code creates a figure with a specific size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Figure\n",
    "fig = plt.figure(figsize=(8,3))\n",
    "\n",
    "# Create a bar plot of name vs grade\n",
    "plt.bar(x=df_students.Name, height=df_students.Grade, color='orange')\n",
    "\n",
    "# Customize the chart\n",
    "plt.title('Student Grades')\n",
    "plt.xlabel('Student')\n",
    "plt.ylabel('Grade')\n",
    "plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7)\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Show the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A figure can contain multiple subplots, each on its own *axis*.\n",
    "\n",
    "For example, the following code creates a figure with two subplots - one is a bar chart showing student grades, and the other is a pie chart comparing the number of passing grades to non-passing grades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14897/2786848323.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax[0].set_xticklabels(df_students.Name, rotation=90)\n",
      "/tmp/ipykernel_14897/2786848323.py:19: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure for 2 subplots (1 row, 2 columns)\n",
    "fig, ax = plt.subplots(1, 2, figsize = (10,4))\n",
    "\n",
    "# Create a bar plot of name vs grade on the first axis\n",
    "ax[0].bar(x=df_students.Name, height=df_students.Grade, color='orange')\n",
    "ax[0].set_title('Grades')\n",
    "ax[0].set_xticklabels(df_students.Name, rotation=90)\n",
    "\n",
    "# Create a pie chart of pass counts on the second axis\n",
    "pass_counts = df_students['Pass'].value_counts()\n",
    "ax[1].pie(pass_counts, labels=pass_counts)\n",
    "ax[1].set_title('Passing Grades')\n",
    "ax[1].legend(pass_counts.keys().tolist())\n",
    "\n",
    "# Add a title to the Figure\n",
    "fig.suptitle('Student Data')\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Until now, you've used methods of the Matplotlib.pyplot object to plot charts. However, Matplotlib is so foundational to graphics in Python that many packages, including Pandas, provide methods that abstract the underlying Matplotlib functions and simplify plotting. For example, the DataFrame provides its own methods for plotting data, as shown in the following example to plot a bar chart of study hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Name'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_students.plot.bar(x='Name', y='StudyHours', color='teal', figsize=(6,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started with statistical analysis\n",
    "\n",
    "Now that you know how to use Python to manipulate and visualize data, you can start analyzing it.\n",
    "\n",
    "A lot of data science is rooted in *statistics*, so we'll explore some basic statistical techniques.\n",
    "\n",
    "> **Note**: This is not intended to teach you statistics - that's much too big a topic for this notebook. It will however introduce you to some statistical concepts and techniques that data scientists use as they explore data in preparation for machine learning modeling.\n",
    "\n",
    "### Descriptive statistics and data distribution\n",
    "\n",
    "When examining a *variable* (for example a sample of student grades), data scientists are particularly interested in its *distribution* (in other words, how are all the different grade values spread across the sample). The starting point for this exploration is often to visualize the data as a histogram, and see how frequently each value for the variable occurs.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14897/4182697007.py:16: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the variable to examine\n",
    "var_data = df_students['Grade']\n",
    "\n",
    "# Create a Figure\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "\n",
    "# Plot a histogram\n",
    "plt.hist(var_data)\n",
    "\n",
    "# Add titles and labels\n",
    "plt.title('Data Distribution')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram for grades is a symmetric shape, where the most frequently occurring grades tend to be in the middle of the range (around 50), with fewer grades at the extreme ends of the scale.\n",
    "\n",
    "#### Measures of central tendency\n",
    "\n",
    "To understand the distribution better, we can examine so-called *measures of central tendency*; which is a fancy way of describing statistics that represent the \"middle\" of the data. The goal of this is to try to find a \"typical\" value. Common ways to define the middle of the data include:\n",
    "\n",
    "- The *mean*: A simple average based on adding together all of the values in the sample set, and then dividing the total by the number of samples.\n",
    "- The *median*: The value in the middle of the range of all of the sample values.\n",
    "- The *mode*: The most commonly occuring value in the sample set<sup>\\*</sup>.\n",
    "\n",
    "Let's calculate these values, along with the minimum and maximum values for comparison, and show them on the histogram.\n",
    "\n",
    "> <sup>\\*</sup>Of course, in some sample sets , there may be a tie for the most common value - in which case the dataset is described as *bimodal* or even *multimodal*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum:3.00\n",
      "Mean:49.18\n",
      "Median:49.50\n",
      "Mode:50.00\n",
      "Maximum:97.00\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14897/2664441753.py:36: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the variable to examine\n",
    "var = df_students['Grade']\n",
    "\n",
    "# Get statistics\n",
    "min_val = var.min()\n",
    "max_val = var.max()\n",
    "mean_val = var.mean()\n",
    "med_val = var.median()\n",
    "mod_val = var.mode()[0]\n",
    "\n",
    "print('Minimum:{:.2f}\\nMean:{:.2f}\\nMedian:{:.2f}\\nMode:{:.2f}\\nMaximum:{:.2f}\\n'.format(min_val,\n",
    "                                                                                        mean_val,\n",
    "                                                                                        med_val,\n",
    "                                                                                        mod_val,\n",
    "                                                                                        max_val))\n",
    "\n",
    "# Create a Figure\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "\n",
    "# Plot a histogram\n",
    "plt.hist(var)\n",
    "\n",
    "# Add lines for the statistics\n",
    "plt.axvline(x=min_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
    "plt.axvline(x=mean_val, color = 'cyan', linestyle='dashed', linewidth = 2)\n",
    "plt.axvline(x=med_val, color = 'red', linestyle='dashed', linewidth = 2)\n",
    "plt.axvline(x=mod_val, color = 'yellow', linestyle='dashed', linewidth = 2)\n",
    "plt.axvline(x=max_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
    "\n",
    "# Add titles and labels\n",
    "plt.title('Data Distribution')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the grade data, the mean, median, and mode all seem to be more or less in the middle of the minimum and maximum, at around 50.\n",
    "\n",
    "Another way to visualize the distribution of a variable is to use a *box* plot (sometimes called a *box-and-whiskers* plot). Let's create one for the grade data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14897/4088679524.py:14: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the variable to examine\n",
    "var = df_students['Grade']\n",
    "\n",
    "# Create a Figure\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "\n",
    "# Plot a histogram\n",
    "plt.boxplot(var)\n",
    "\n",
    "# Add titles and labels\n",
    "plt.title('Data Distribution')\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The box plot shows the distribution of the grade values in a different format to the histogram. The *box* part of the plot shows where the inner two *quartiles* of the data reside - so in this case, half of the grades are between approximately 36 and 63. The *whiskers* extending from the box show the outer two quartiles; so the other half of the grades in this case are between 0 and 36 or 63 and 100. The line in the box indicates the *median* value.\n",
    "\n",
    "It's often useful to combine histograms and box plots, with the box plot's orientation changed to align it with the histogram (in some ways, it can be helpful to think of the histogram as a \"front elevation\" view of the distribution, and the box plot as a \"plan\" view of the distribution from above.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum:3.00\n",
      "Mean:49.18\n",
      "Median:49.50\n",
      "Mode:50.00\n",
      "Maximum:97.00\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14897/1918122812.py:40: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a function that we can re-use\n",
    "def show_distribution(var_data):\n",
    "    from matplotlib import pyplot as plt\n",
    "\n",
    "    # Get statistics\n",
    "    min_val = var_data.min()\n",
    "    max_val = var_data.max()\n",
    "    mean_val = var_data.mean()\n",
    "    med_val = var_data.median()\n",
    "    mod_val = var_data.mode()[0]\n",
    "\n",
    "    print('Minimum:{:.2f}\\nMean:{:.2f}\\nMedian:{:.2f}\\nMode:{:.2f}\\nMaximum:{:.2f}\\n'.format(min_val,\n",
    "                                                                                            mean_val,\n",
    "                                                                                            med_val,\n",
    "                                                                                            mod_val,\n",
    "                                                                                            max_val))\n",
    "\n",
    "    # Create a figure for 2 subplots (2 rows, 1 column)\n",
    "    fig, ax = plt.subplots(2, 1, figsize = (10,4))\n",
    "\n",
    "    # Plot the histogram   \n",
    "    ax[0].hist(var_data)\n",
    "    ax[0].set_ylabel('Frequency')\n",
    "\n",
    "    # Add lines for the mean, median, and mode\n",
    "    ax[0].axvline(x=min_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
    "    ax[0].axvline(x=mean_val, color = 'cyan', linestyle='dashed', linewidth = 2)\n",
    "    ax[0].axvline(x=med_val, color = 'red', linestyle='dashed', linewidth = 2)\n",
    "    ax[0].axvline(x=mod_val, color = 'yellow', linestyle='dashed', linewidth = 2)\n",
    "    ax[0].axvline(x=max_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
    "\n",
    "    # Plot the boxplot   \n",
    "    ax[1].boxplot(var_data, vert=False)\n",
    "    ax[1].set_xlabel('Value')\n",
    "\n",
    "    # Add a title to the Figure\n",
    "    fig.suptitle('Data Distribution')\n",
    "\n",
    "    # Show the figure\n",
    "    fig.show()\n",
    "\n",
    "# Get the variable to examine\n",
    "col = df_students['Grade']\n",
    "# Call the function\n",
    "show_distribution(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the measurements of central tendency are right in the middle of the data distribution, which is symmetric with values becoming progressively lower in both directions from the middle.\n",
    "\n",
    "To explore this distribution in more detail, you need to understand that statistics is fundamentally about taking *samples* of data and using probability functions to extrapolate information about the full *population* of data. For example, the student data consists of 22 samples, and for each sample there is a grade value. You can think of each sample grade as a variable that's been randomly selected from the set of all grades awarded for this course. With enough of these random variables, you can calculate something called a *probability density function*, which estimates the distribution of grades for the full population.\n",
    "\n",
    "The Pandas DataFrame class provides a helpful plot function to show this density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_density(var_data):\n",
    "    from matplotlib import pyplot as plt\n",
    "\n",
    "    fig = plt.figure(figsize=(10,4))\n",
    "\n",
    "    # Plot density\n",
    "    var_data.plot.density()\n",
    "\n",
    "    # Add titles and labels\n",
    "    plt.title('Data Density')\n",
    "\n",
    "    # Show the mean, median, and mode\n",
    "    plt.axvline(x=var_data.mean(), color = 'cyan', linestyle='dashed', linewidth = 2)\n",
    "    plt.axvline(x=var_data.median(), color = 'red', linestyle='dashed', linewidth = 2)\n",
    "    plt.axvline(x=var_data.mode()[0], color = 'yellow', linestyle='dashed', linewidth = 2)\n",
    "\n",
    "    # Show the figure\n",
    "    plt.show()\n",
    "\n",
    "# Get the density of Grade\n",
    "col = df_students['Grade']\n",
    "show_density(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected from the histogram of the sample, the density shows the characteristic 'bell curve\" of what statisticians call a *normal* distribution with the mean and mode at the center and symmetric tails.\n",
    "\n",
    "Now let's take a look at the distribution of the study hours data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum:1.00\n",
      "Mean:10.52\n",
      "Median:10.00\n",
      "Mode:9.00\n",
      "Maximum:16.00\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14897/1918122812.py:40: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the variable to examine\n",
    "col = df_students['StudyHours']\n",
    "# Call the function\n",
    "show_distribution(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the study time data is significantly different from that of the grades.\n",
    "\n",
    "Note that the whiskers of the box plot only extend to around 6.0, indicating that the vast majority of the first quarter of the data is above this value. The minimum is marked with an **o**, indicating that it is statistically an *outlier* - a value that lies significantly outside the range of the rest of the distribution.\n",
    "\n",
    "Outliers can occur for many reasons. Maybe a student meant to record \"10\" hours of study time, but entered \"1\" and missed the \"0\". Or maybe the student was abnormally lazy when it comes to studying! Either way, it's a statistical anomaly that doesn't represent a typical student. Let's see what the distribution looks like without it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum:6.00\n",
      "Mean:10.98\n",
      "Median:10.00\n",
      "Mode:9.00\n",
      "Maximum:16.00\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14897/1918122812.py:40: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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37y9Jqlq1qlasWKGNGzdq165dGjx4sE6fPl3ocz3yyCOaMWOGtm7dqs2bN+u5556Tn5+f6/5evXqpbNmy6tSpk9avX69Dhw5pzZo1GjZs2DWX6gYA3HkoQQAASxg0aJDOnz+vVq1aKSYmRpL0pz/9SfXq1VOrVq3UrFkzRUdHq3PnzoU+z5QpU1SpUiU1adJETz31lF588UUFBwe77g8ODta6desUGxurrl27qkaNGho0aJBycnKYGQIAm3AYvz9xGgAAAADuYMwEAQAAALAVShAAAAAAW6EEAQAAALAVShAAAAAAW6EEAQAAALAVShAAAAAAW6EEAQAAALAVShAAAAAAW6EEAQAAALAVShAAAAAAW6EEAQAAALAVShAAAAAAW/n/Rqq/0F4je2QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the variable to examine\n",
    "col = df_students[df_students.StudyHours>1]['StudyHours']\n",
    "# Call the function\n",
    "show_distribution(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the dataset is small enough to clearly see that the value **1** is an outlier for the **StudyHours** column, so you can exclude it explicitly. In most real-world cases, it's easier to consider outliers as being values that fall below or above percentiles within which most of the data lie. For example, the following code uses the Pandas **quantile** function to exclude observations below the 0.01th percentile (the value above which 99% of the data reside)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum:6.00\n",
      "Mean:10.98\n",
      "Median:10.00\n",
      "Mode:9.00\n",
      "Maximum:16.00\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14897/1918122812.py:40: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q01 = df_students.StudyHours.quantile(0.01)\n",
    "# Get the variable to examine\n",
    "col = df_students[df_students.StudyHours>q01]['StudyHours']\n",
    "# Call the function\n",
    "show_distribution(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tip**: You can also eliminate outliers at the upper end of the distribution by defining a threshold at a high percentile value - for example, you could use the **quantile** function to find the 0.99 percentile below which 99% of the data reside.\n",
    "\n",
    "With the outliers removed, the box plot shows all data within the four quartiles. Note that the distribution is not symmetric like it is for the grade data though - there are some students with very high study times of around 16 hours, but the bulk of the data is between 7 and 13 hours; The few extremely high values pull the mean towards the higher end of the scale.\n",
    "\n",
    "Let's look at the density for this distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the density of StudyHours\n",
    "show_density(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This kind of distribution is called *right skewed*. The mass of the data is on the left side of the distribution, creating a long tail to the right because of the values at the extreme high end; which pull the mean to the right.\n",
    "\n",
    "#### Measures of variance\n",
    "\n",
    "So now we have a good idea where the middle of the grade and study hours data distributions are. However, there's another aspect of the distributions we should examine: how much variability is there in the data?\n",
    "\n",
    "Typical statistics that measure variability in the data include:\n",
    "\n",
    "- **Range**: The difference between the maximum and minimum. There's no built-in function for this, but it's easy to calculate using the **min** and **max** functions.\n",
    "- **Variance**: The average of the squared difference from the mean. You can use the built-in **var** function to find this.\n",
    "- **Standard Deviation**: The square root of the variance. You can use the built-in **std** function to find this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Grade:\n",
      " - Range: 94.00\n",
      " - Variance: 472.54\n",
      " - Std.Dev: 21.74\n",
      "\n",
      "StudyHours:\n",
      " - Range: 15.00\n",
      " - Variance: 12.16\n",
      " - Std.Dev: 3.49\n"
     ]
    }
   ],
   "source": [
    "for col_name in ['Grade','StudyHours']:\n",
    "    col = df_students[col_name]\n",
    "    rng = col.max() - col.min()\n",
    "    var = col.var()\n",
    "    std = col.std()\n",
    "    print('\\n{}:\\n - Range: {:.2f}\\n - Variance: {:.2f}\\n - Std.Dev: {:.2f}'.format(col_name, rng, var, std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of these statistics, the standard deviation is generally the most useful. It provides a measure of variance in the data on the same scale as the data itself (so grade points for the Grade distribution and hours for the StudyHours distribution). The higher the standard deviation, the more variance there is when comparing values in the distribution to the distribution mean - in other words, the data is more spread out.\n",
    "\n",
    "When working with a *normal* distribution, the standard deviation works with the particular characteristics of a normal distribution to provide even greater insight. Run the cell below to see the relationship between standard deviations and the data in the normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C5AZhVNgdLNuVb3Y5InIGCg0idayqlyEjtQGRIYEmV+OZLBYLAzVEIeLxFBpE6tjPHrR1tCcblO78/ixQaBDxWAoNInXIccJR2P1be1ZoSAL+7Go9Qb9W8QRYLezKLWZPfonZ5YjIaSg0iNShzQcKyS+uICI4gK6pDcwup5okYByeExqiQ4Po1rQBoCEKEU+l0CBSh6rmM/Ru0ZCgAM/671YIzHS1nmJg6+O7Q4qI5/GsdzERH1N1CFO/lp63amIHMMzVeoqqyZCLd+ZRaXeYXI2InEyhQaSOlNvsLM90Lh/0tPkMnqpTcgxxEcEcK7exKtsTdpAQkRMpNIjUkdW7j1JaaSc+Mpg2jaPMLscrWK0W9wZYC7ZriELE0yg0iNSRRToK+4JUDVEs2JZrciUicjKFBpE64un7M4QALV2tJxnoGspZv6+A3GPlJlcjIidSaBCpA4VllazdcxTw3NDQAeckyA5mF3KShOhQ2iVFA/DzdvU2iHgShQaROrB0Zx4OA1rEO89UkPMzULtDingkhQaROlA1n8GTjsI+2Tqgkav1NFVHZS/YnovDYZhcjYhUUWgQqQOLqraO9tChCQAbkOtqPU2PZnGEBweQe6ycTTmetP2UiH9TaBCpZQcKythx6BgWC/Rt4bmhwZMFB1rp6zpGXEsvRTyHQoNILasamuicHENMeJDJ1XivQW20pbSIp1FoEKllizx8qaW3qDqH4pesIxwr98RBFBH/o9AgUosMw3Dvz+DJ8xkA0oHFrtYTpcVH0DQuHJvDYIlrjoiImEuhQaQW7Th0jENF5YQEWunWLNbscs4qEujraj2VexWFhihEPIJCg0gtqupl6JkWR2hQgMnVnN1e4HFX66mqtpSer9Ag4hEUGkRqkTfNZzgE/MPVeqq+LRsSaLWwO7+ErNxis8sR8XsKDSK1xGZ3sHSX6yhsLwgN3iAyJJDurmEeLb0UMZ9Cg0gtWbu3gGPlNhqEB9G+SbTZ5fiMqqWX87cqNIiYTaFBpJa4t45u2ZAAq47Cri1VSy+X7MqjwuYwuRoR/6bQIFJLPP0o7JPFA4+4Wk/WPima+MhgSirs/JKdb3Y5In5NoUGkFhSX21i9+wjgPfMZmgJvuVpPZrVa3L0NWkUhYi6FBpFasDwrn0q7QXKDMJrGhZtdTo2UAKtcracb6N6vIdfkSkT8m0KDSC1YtP34LpAWi3fMZ9gCdHe1nq5/a2fvzeacQg4VlplcjYj/UmgQqQXu+QytvWNowtvER4bQKTkGgAXb1dsgYhaFBpGLlHusnC0HigDnygmpGwPTnYFMW0qLmEehQeQiLXYdptQuKZr4yBCTq/FdVZMhf96Ri8NhmFyNiH9SaBC5SMfnM3hXL4MViMJ73gS6NYslMiSQ/OIKNuwvMLscEb/kLe8XIh7pxKOwvWV/hipdgUJX6w2CAqzu4R/tDiliDoUGkYuQnVfCvqOlBAVY6NU8zuxyfJ576aXOoRAxhUKDyEVY6Opl6NY0lvDgQJOrOT+bgA6u1lsMcoWGVbuPUlhWaXI1Iv5HoUHkIpy4P4O3KcMZGLxp14PUuHBaxEdgdxgs3qGllyL1TaFB5ALZHQaLd7pCg/ZnqDdVQxTztTukSL1TaBC5QOv3FVBYZiMqNNC98ZDUvUHuLaUPYxhaeilSnxQaRC7Qz67JeP1aNiQwQP+V6kvvFnEEB1jZd7SUnYeLzS5HxK/onU7kAlUttfTG+QwALYBvXK03CQ8OpGfzWEC7Q4rUN4UGkQtQUmFjZbbrKGzXToXepgFwnav1NlW7Q2rppUj9UmgQuQDLM48fhZ3W0DuOwj7ZAeCvrtbbDGrjDA1Ld+VRVmk3uRoR/6HQIHIBfvbCo7BPth/4o6v1Nm0aR9E4OoSySgcrsvLNLkfEbyg0iFwAHYVtLovFwoDWx1dRiEj9UGgQOU+Hi44fhX2JjsI2zSD3fg0KDSL1RaFB5DxVbejUPimahjoK2zTOoSHYdvAYOQWlZpcj4hcUGkTO00LXfIYBXj400QC4Be9cPQEQGxFM55QGACzU7pAi9UKhQeQ8GIbBIi89CvtkLYAv8L59Gk7kaUMUaWlpvP7662d9TEVFBa1atWLx4sX1U5QJNm3aREpKCsXF2nzL1yg0iJyHXbnF5BSUERxo9fqjsCuAva7WWw1Kdwa3n3fkYnc4t5ResGAB1157LU2aNMFisfD1119f1DU++ugjGjRocJGVHvfuu+/SvHlz+vXrV+327777jt69exMWFkZsbCw33HBDtftXrFjB5ZdfToMGDYiNjWXo0KGsXbv2jNfJz8/nN7/5DW3atCEsLIymTZvy29/+loKCglMe+9FHH9G5c2dCQ0NJSEhgzJgx7vuysrIYOHAgERERDBw4kKysrGrPveaaa/jqq6+q3da+fXv69OnDa6+9VsPvingLhQaR81C11LJHs1hCgwJMrubibABSXa236pLSgKjQQApKK1m79ygAxcXFdOnShbfeesvc4k7DMAzefPNNRo0aVe32r776invuuYdf//rXrF27lkWLFnHnnXe67z927BjDhg2jadOmLFu2jJ9//pmoqCiGDh1KZeXpjwjfv38/+/fv55VXXmHDhg189NFH/PDDD6dc+7XXXuOZZ57h6aefZuPGjcyePZuhQ4e673/iiSdITk5mzZo1JCUl8eSTT7rvmzJlClarlZtvvvmU6//617/mnXfewWazXdD3SjyUISI1dv/HK4xmT80w3pq73exSLtpKwzBwtd7s4U9/MZo9NcN4bdbWU+4DjGnTpp3zNdasWWMMHjzYiIyMNKKiooxu3boZK1asMObOnWsA1T7+/Oc/G4ZhGAcPHjSuueYaIzQ01EhLSzM+/fRTo1mzZsY//vGPM15nxYoVhtVqNQoLC923VVZWGsnJycaHH3541ucBxu7du923rVu3zgCM7dtr/m/x888/N4KDg43KykrDMAwjPz/fCAsLM2bPnn3G57Rr1874/vvvDcMwjP/9739G+/btDcMwjCNHjhitWrWqVtOJysvLjZCQkLO+tngf9TSI1JDN7mDpzjwABrTyzq2jfVFtbCl91113kZKSwooVK1i5ciVPP/00QUFB9OvXj9dff53o6GhycnLIyclx/6Z97733smfPHubOncuXX37J22+/zaFDh856nYULF5Kenk5UVJT7tlWrVrFv3z6sVisZGRkkJSVx1VVXsWHD8T6gNm3a0LBhQyZMmEBFRQWlpaVMmDCBdu3akZaWVuOvs6CggOjoaAIDAwH48ccfcTgc7Nu3j3bt2pGSksJtt93Gnj173M/p0qULs2fPxuFwMGvWLDp37gzA73//e8aMGUNqaupprxUcHEzXrl1ZuHBhjesTz6fQIFJDa/cWUFRuo0F4EO2bRJtdjrgMdE2GXLvnKAUlp++qP5fdu3dzxRVX0LZtW1q3bs2tt95Kly5dCA4OJiYmBovFQmJiIomJiURGRrJt2za+//57PvjgA/r06UP37t2ZMGECpaVnX/qZnZ1NkyZNqt22a9cuAMaNG8ef/vQnZsyYQWxsLIMHDyY/37nbZVRUFPPmzePTTz8lLCyMyMhIfvjhB77//nt3ADiX3Nxcnn/+eR588MFq13Y4HLz00ku8/vrrfPnll+Tn53PllVdSUeGc7fLKK6+wZcsW0tLS2L59O6+88goLFixgzZo1jBgxgttuu40WLVowevRo93OqNGnShOzs7BrVJ95BoUGkhqrmM1zSMp4Aq3duHe2LmjQIo3VCJA7j+E6d5+vxxx/n/vvv54orruDll19m586dZ3385s2bCQwMpHv37u7b2rZte84Jk6WlpYSGhla7zeFwAPDMM89w88030717dyZOnIjFYuGLL75wP2/UqFFccsklLF26lEWLFtGxY0eGDx9+zqACUFhYyPDhw2nfvj3jxo2rdu3KykreeOMNhg4dSp8+fZg8eTLbt29n7ty5ACQnJzNjxgx2797NjBkziI+P55FHHuHdd9/lhRdeICoqiq1bt7J9+3bee++9atcNCwujpKTknPWJ91BoEKkhX1lqWaUrUOZqvd1A99LLsw8PnMm4cePYuHEjw4cP56effqJ9+/ZMmzatNksEID4+niNHjlS7LSkpCXCuOKgSEhJCixYt2L17NwCTJk0iKyuLiRMn0rNnT/r06cOkSZPIzMzkm2++Oes1i4qKGDZsGFFRUUybNo2goKCzXrtRo0bEx8e7r32yl156iSFDhtC9e3fmzZvHzTffTFBQEDfddBPz5s2r9tj8/HwaNdJQni+pWb+WiJ8r3mRj8iN9sFsMgl63QADVP6zn+efaesxFvK41AEJMunaNHnMenTkD0xsx4edMFmzLxTCMCzpELD09nfT0dB577DHuuOMOJk6cyI033khwcDB2e/WTNNu2bYvNZmPlypX07NkTgK1bt3L06NGzXiMjI4N33nmnWo3du3cnJCSErVu30r9/fwAqKyvJysqiWbNmAJSUlGC1Wqt9XVV/ruqpOJ3CwkKGDh1KSEgI06dPP6WX45JLLnHXnpKSAjh/0Ofm5rqvfaLNmzczadIk1qxZA4Ddbnev3qisrDzl+7RhwwZuueWWs35PxMuYPRNTxBssnHHIudTAhz62tjaMQXOdrdm1nPbDYhhGoGEYIYZhhBmGEWkYRoxhGHGGYcQbhtHYMIwmhmGkGoY9zWFkNyg2dsYWGRu/PWCsXr3aWL16tQEYr732mrF69WojOzv7tH+3JSUlxpgxY4y5c+caWVlZxs8//2y0bNnS+MMf/mAYhmEsWrTIAIzZs2cbhw8fNoqLiw3DMIxhw4YZGRkZxtKlS41ffvnF6N+/vxEWFnbW1RO5ublGUFCQsX79+mq3/+53vzOSk5ONmTNnGlu2bDFGjRplJCQkGPn5+YZhGMbmzZuNkJAQ4+GHHzY2bdpkbNiwwbj77ruNmJgYY//+/YZhGMbevXuNNm3aGMuWLTMMwzAKCgqM3r17G506dTJ27Nhh5OTkuD9sNpv72tdff73RoUMHY9GiRcb69euNa665xmjfvr1RUVFRrUaHw2H079/f+Pbbb923Pfzww8bw4cONTZs2GRkZGcbf//53932ZmZmGxWIxsrKyzvj9EO+jngaRGphZeoDHxqzl9m4pPHlFW7Bz/MNB9T+f7raaPOZCn3eBr32sGcwfDMeuBbbXc91n/uX4OAOwuT7OwYqFpoQD8MJP3/PsP25z3/f4448DMHLkSD766KNTnhsQEEBeXh4jRozg4MGDxMfHc9NNN/GXv/wFgH79+jF69Ghuv/128vLy+POf/8y4ceOYOHEi999/P4MGDaJx48a88MILPPvss2ets2HDhtx4443897//5a9//av79vHjxxMYGMg999xDaWkpvXv35qeffiI2NhZw9mx8++23/OUvf6Fv377ulRY//PCDe4ihsrKSrVu3uucQrFq1imXLlgHQqlWranVkZma6V1188sknPPbYYwwfPhyr1cqgQYP44Ycfqg1jALz//vs0btyYa665xn3buHHjuPPOO+nduzfDhg2rtinU5MmTGTJkyGl7LMR7WQzDMMwuQsSTGYbBgL/PZe+RUiaM7MHl7RqbXVKtWAV0B1YC3er74lW7HtRiIJm+ch//WZRNg35BfDCmZ/1+Pedh3bp1XHnllezcuZPIyEizy6kTFRUVtG7dmkmTJrmHQMQ3qKdB5Bwyc4vZe6SUoAALfVroKOxaYXF9WIGgczy2htqlRbNi9xGCc6yUVtgJC/bMHTs7d+7M3/72NzIzM+nUqZPZ5dSJ3bt388c//lGBwQcpNIicwwLXYUg90+KICNF/GU/VKiGSJjGh7C8oY2lmHpe2STC7pDO69957zS6hTrVq1eqUIRHxDVpyKXIOC1z7M1Qt6/MVTYEPXK0vsFgs7r+jBR5y6qWIr1FoEDmLcpudJa6towf5WGiIB+53tb5CoUGkbik0iJzFL1lHKK20kxAVQtvEqHM/wYvkAh+6Wl9xSSvnbp07Dxez94h2IhSpbQoNImcx3/Ub64DWjS5owyBPtht4wNX6ipiwILqmNgBgwTZfikMinkGhQeQsqrq5B7XxraEJX+Y+9VJDFCK1TqFB5AwOFpax5UARFgsM8JHzJvxBVcBbtCOXSntNdpESkZpSaBA5g6qhic7JMcRGBJtcjdRUp+QYGoQHUVRuY82eo2aXI+JTFBpEzsA9NOFjqyaqRAKDXK0vCbBa6O/qGZq/VUMUIrVJoUHkNOwOg4U+uj9DlXRgnqv1NVUbO83ZcmFHZYvI6Sk0iJzGur1HKSitJCo00D0b39c4gHJqdnaUt7m0bQIWC2zOKWT/0VKzyxHxGQoNIqdRNZ+hf6t4AgN887/JGiDU1fqauIhgujV1nhD5k3obRGqNb74bilykua4fNIO11NJrXdbWOUSh0CBSexQaRE5yuKictXsLADz60CM5u8vbOf/uFu3IpbTCbnI1Ir5BoUHkJPO2On8z7ZgcTUJ0qMnVyIVq0ziK5AZhlNscLNqh3SFFaoNCg8hJ5rpCw2XqZfBqFovF3dugVRQitUOhQeQElXYHC11nFlza1rdDQ0dgj6v1VcfnNRzEMAyTqxHxfgoNIidYkZVPUbmNhhHBdElpYHY5dSoYSHG1vqpPi4aEBwdwsLCcjfsLzS5HxOspNIicoGrVxKA2jbBafetUy5PtAm51tb4qNCjAvTvknM0aohC5WAoNIieoWp53mY8PTQAcBb50tb6sal7DT1sOmlyJiPdTaBBx2Z1Xws7DxQRYLQxorf0ZfEXVstm1ews4VFRmcjUi3k2hQcSl6jfRHs1iiQkLMrkaqS0J0aF0TokBjg8/iciFUWgQcfnJdSKiPwxN+JvL2zYGNK9B5GIpNIgAJRU2lu7KA/wnNDQBXnK1vq5qXsPPO3Ipq9TukCIXSqFBBFi0I48Km4PkBmG0Sog0u5x6kQiMdbW+rkOTaBpHh1BSYWdZZr7Z5Yh4LYUGEY7PZ7isbQIWi28vtaxyFJiO76+eAOfukFU9SD9uOmByNSLeS6FB/J7DYTDbNdZ9RfvGJldTf3YB1+Pb+zScaEh7Z5/K7E2HcDi0O6TIhVBoEL+3Zu9RDheVExUSSN8WDc0uR+pI35YNiQgO4EBhGev2FZhdjohXUmgQv/fjJufQxKA2jQgO1H8JXxUaFMBg154NszZqiELkQugdUvxe1Q+QK/1oaMJfDeng/DuetUm7Q4pcCIUG8Wu7Dh9j5+FiggIsPn+q5clCgfau1l8MbpNAoNXCjkPH2Hn4mNnliHgdhQbxa1VDE31aNCQ61L92gWwPbHS1/iImLIi+LZ3zVn5Ub4PIeVNoEL9W1U09REMTfmNIB+cqCs1rEDl/Cg3itw4XlbNq9xHAv5ZaVlkDRLtaf3JlO+ff9eo9RzlUqAOsRM6HQoP4rZ+2HMQwoFNyDEkxYWaXU+8cQJGr9SeJMaF0SW2AYeDen0NEakahQfzWrI0amvBXVX/nMzVEIXJeFBrEL5VU2Ph5Ry4AV3ZQaPA3Q11/54t35lJUVmlyNSLeQ6FB/NKCbbmU2xykxoXRpnGU2eVIPWvZKJIW8RFU2g3muY5EF5FzU2gQvzTLdWjRle0S/eaAqpO1BVa6Wn9jsVjcPUza6Emk5hQaxO+U2+zuNfpXdfKHg6FPLxzo5mr90VDX0su5Ww5RbrObXI2Id1BoEL+zeEceRWU2EqJC6N401uxyTLMbGONq/VHXlAY0jg7hWLmNn7fnml2OiFdQaBC/8936HACu6piI1eqfQxMAucDbrtYfWa0WruqYBBz/NyEiZ6fQIH6lwuZw7wR4Vackk6sRs13t+jfw46aDVNj8bccKkfOn0CB+ZfHOXArLbMRHhtAzLc7scsRk3ZvF0igqhKIyG4t2+Gufi0jNKTSIX/l+vbOXYVjHxgT48dCEOAVYLVzV0Tkh8n8aohA5J4UG8RuVdgczXUstr+6ooYkE4DFX68+qhihmbTpIpV1DFCJno9AgfmPprjyOllTSMCKYXs01NJECvOZq/VnPtDjiI0MoKK1k8c48s8sR8WgKDeI3qrqfh3RIJDBA//SPAUtcrT8LsFoY1tG50dP/1mmIQuRs9M4pfsFmdzDTdUDVcK2aAGAb0M/V+ruqIYqZmw5oiELkLBQaxC8s3ZVPfnEFseFB9G6hoQmprldaHA0jgjlaUsnSXRqiEDkThQbxC9+s2Qc492YI0tCEnCQwwMpQraIQOSe9e4rPK6u088MG56qJG7omm1yNeKqqYauZGw9i0xCFyGkpNIjPm7vlEEXlNprEhNKjmf+eNXGyQCDe1Qr0bu4cosgvrmCRVlGInJZCg/i8b9bsB+Dark38+qyJk3UGDrtacQ5RDO/s7G2oGs4SkeoUGsSnFZRW8tPWQwBc30VDE3J213dtAsDMDQcoq9Rx2SInU2gQnzZzwwEqbA7SG0fSLinK7HI8ykaglasVp25NY0mJDaO4ws6czYfMLkfE4yg0iE/7Zq2zm/n6rslYLBqaOFE5sNPVipPFYuG6Ls7eBg1RiJxKoUF81qHCMve2wFU/CETO5XrXCpt5Ww9TUFJpcjUinkWhQXzW9LX7MQzn8cepceFmlyNeok1iFG0aR1Fhd/DDRu3ZIHIihQbxWVWrJqomt4nU1HVdq4Yo9ptciYhnUWgQn7T1QBHr9xUQaLXorIkzaAX84GqluqrhrCW78jhYWGZyNSKeQ6FBfNKXK/cAcHm7BBpGhphcjWeKBoa6WqkuNS6c7s1iMQz4dq16G0SqKDSIz6m0O5i22vlGf0v3VJOr8Vw5wDhXK6eqGtaartAg4qbQID5n/tbD5B4rJz4ymMFtGpldjsfKAf6CQsOZXN0piQCrhXV7C9h5+JjZ5Yh4BIUG8TlfrtwLOA+n0omWcqHiI0MYlO4MnV+5/k2J+Du9o4pPyS+uYM6WgwDc3D3F5GrE293i+jc0ddU+7A7D5GpEzKfQID7lmzX7qLQbdEyOpl2SpvjJxbm8XQIxYUEcKCxj0Y5cs8sRMZ1Cg/iUqqGJWzUB8pxigbtcrZxeSGCAe0LklxqiEFFoEN+xfm8BG/cXEhxg1bbRNdAc+NTVyplVDVHM3HiAglJtKy3+TaFBfMak5dkADOuYSGxEsMnVeL4yYIerlTPrlBxDeuNIym0OvluntSbi3xQaxCcUlVW6t/y9q3dTk6vxDpuA1q5Wzsxisbh7G6o2DRPxVwoN4hO+XrOfkgo7rRIi6dU8zuxyxMfc0DWZAKuFVbuPas8G8WsKDeL1DMNg0rLdANzRqykWi8XkisTXJESHuvds0IRI8WcKDeL11uw5yuacQoIDrdzcLdnscsRHVQ1RfLVyL5V2h8nViJhDoUG8XlUvwzWdk2gQrgmQUjeuaNeY+MhgDhWVM2fzIbPLETGFQoN4tYLSSr5dpwmQF6IbYLhaObfgQKv7ALRJy3ebXI2IORQaxKt98cseyiodtE2MoltTbVMkdeuOXs7QsHD7Yfbkl5hcjUj9U2gQr2V3GHy0OAuAe/ulaQLkedoK9HW1UjPNGkYwoHU8hgGfrVBvg/gfhQbxWnM2H2TvkVIahAdxfVdNgDxfxcBSVys1d0cv5zDY579oQqT4H4UG8VpVvQy/6tmUsOAAc4sRv3Fl+8bER4ZwuKic2ZsOml2OSL1SaBCvtOVAIYt35hFgtXBP32ZmlyN+JCjAym09nMsvNSFS/I1Cg3ilj129DEM7NCa5QZi5xYjfqRqiWLg9l+w8DfCI/1BoEK9zpLiCaav3AXBvP53ReKHSgP+4Wjk/qXHhDHTtEPnp0myTqxGpPwoN4nX+uyybskoH7ZOi6ZmmZZYXKg6429XK+bu3n3NY7LMVeygut5lcjUj9UGgQr1JWaXdPgHxwYAsts7wIh4G3XK2cv8HpCaQ1DKeozMbUVTqPQvyDQoN4lS9X7iX3WAXJDcIY3jnJ7HK82h7gUVcr589qtTCyXxrgXMnjcBjmFiRSDxQaxGvYHQYfLNwFwP0DmhMUoH++Yq5buqcQGRLIzsPFLNyRa3Y5InVO77riNWZuPEB2XgkNwoO4vWeq2eWIEBUaxK2u5ZcTF2WaXI1I3VNoEK9gGAbvzd8JwIg+zQgPDjS5IhEn5xbmMG/rYXYdPmZ2OSJ1SqFBvMKSXXms3VtASKDVPY4sFycKGOJq5cI1axjB5W0TgOP7h4j4KoUG8Qr/mrMDgNt6pNIwMsTkanxDa2Cmq5WLU7VfyBcr93K0pMLkakTqjkKDeLzlmfks2ZVHUICF0YNbml2Oz7ADha5WLs4lrRrSLimakgo7/1mizZ7Edyk0iMf755xtANzaI1VbRteitUCMq5WLY7FYGD2oBeBcfllaoSgmvkmhQTzaiqx8Fu1w9jI8ol4G8WDDOyWREhtGXnEFX6zU7hfimxQaxKP9c/Z2AG7pnkpKbLjJ1YicWWCAlQcHOnsb3l+wC5vdYXJFIrVPoUE81i9Z+fy8I5dAq3oZxDvc2j2VuIhg9h4p5bv1OWaXI1LrFBrEY/1zTlUvQwqpceplEM8XFhzAr11Lgt+ZtxPD0NbS4lsUGsQjLduVx8Ltzl6GMZe2Mrscn9QJOORqpfaM6JtGRHAAWw4UMWfzIbPLEalVCg3icQzD4OUftgBwe89U9TLUkSCgkauV2hMTHsQIV2/D63O2qbdBfIpCg3icWZsOsnr3UcKCAvjd5dp6qK7sBK5ztVK7HhjQgvDgADbsK1Rvg/gUhQbxKDa7g7+7ehlG9W9OQnSoyRX5rgLgW1crtSsuIti93bl6G8SXKDSIR/lq1V52Hi4mNjyIB12b5Yh4oxN7G2art0F8hEKDeIyySjv/+NG5YmLMpa2IDtVou3ivar0Ns9XbIL5BoUE8xkeLszhQWEZygzDu7tPM7HJELtoDA1oQERzAxv2FzNx4wOxyRC6aQoN4hNxj5bz1k/Mky8euTCc0KMDkinxfMvCqq5W6ERcRzH39nSdg/n3mVu0SKV5PoUE8wquztlJUbqNTcgw3ZejHWH1oDDzuaqXuPDiwBXERwew6XMwXK/eaXY7IRVFoENNt2FfAZyucB/z837XtsVotJlfkH44AX7haqTtRoUH85jLnBmX/+HEbJRU2kysSuXAKDWIqwzB4bsYmDAOu7dKEnmlxZpfkNzKB21yt1K07ezclNS6MQ0XlTFyUZXY5IhdMoUFM9b/1B1iemU9okJWnr2prdjkidSIkMIAnh7QB4N15O8kvrjC5IpELo9AgpimrtPPS/zYD8ODAliQ3CDO5IpG6c23nJrRPiqao3Mbrs7eZXY7IBVFoENO8PW8n+46WkhQTymht5CQ+zmq18Kdr2gHw6dJsthwoNLkikfOn0CCm2HX4GO/Oc5568MzwdoQHB5pckf8JAzJcrdSPfi3juapjIg4Dnvt2kzZ8Eq+j0CD1zjAMnv1mAxV2BwPTGzG8U5LZJfmldsAqVyv1549XtyM40MrinXna8Em8jkKD1Lvpa/ezaEcewYFWnr++AxaLlliK/0iNC+ehgc7huBe+20xZpd3kikRqTqFB6lVBaSXPz3BOfnz00lY0axhhckX+azUQ4mqlfj08uCVJMaHsPVLKe/N3mV2OSI0pNEi9enXWVnKPldMiPoKHNPnRVAZQ4WqlfoUHBzL2aufA0FvzdrDr8DGTKxKpGYUGqTdr9hzlP0uzAXjhho6EBOp8CfFf13ZOYkDreCpsDp6ZtkGTIsUrKDRIvSi32fnDl2sxDLihaxP6tYo3uyQRU1ksFl68oRMhgVaW7Mpj6qp9Zpd0CovFwtdff33Wx+Tl5ZGQkEBWVla91GSGp59+mt/85jdml+ERFBqkXrw1dyfbDh6jYUQw/3dtB7PLEfEITRuG87srWgPwwnebTrtT5F//+ld69uxJVFQUCQkJ3HDDDWzduvWCrzlu3Di6du16wc8/2Ysvvsj1119PWlqa+7Y5c+bQr18/oqKiSExM5KmnnsJmO37mRlZWFhaL5ZSPpUuX1uiaeXl5pKSkYLFYOHr0qPv2efPmnfZ1Dxw4vkrlv//9L6mpqcTGxvL4449Xe92srCzS09MpLKy+h8aTTz7Jxx9/zK5dmn+i0CB1bnNOIW/PdR57/ZfrOxAXEWxyRQLOpZYb0JJLsz0woAVtGkdxpKSSF7/bfMr98+fPZ8yYMSxdupQff/yRyspKhgwZQnFxsQnVVldSUsKECRMYNWqU+7a1a9dy9dVXM2zYMFavXs2UKVOYPn06Tz/99CnPnz17Njk5Oe6P7t271+i6o0aNonPnzme8f+vWrdVeNyEhAYDc3Fzuv/9+XnnlFWbNmsWnn37KjBkz3M975JFHePnll4mOjq72evHx8QwdOpR33nmnRvX5NEOkDlXa7MbwNxYYzZ6aYTz4yQrD4XCYXZKIx/klK99Ie3qG0eypGcb8rYfO+thDhw4ZgDF//vwzPmbu3LlGz549jfDwcCMmJsbo16+fkZWVZUycONHAOffV/TFx4kTDMAxj27ZtxoABA4yQkBCjXbt2xqxZswzAmDZt2hmv88UXXxiNGjWqdtvYsWONHj16VLtt+vTpRmhoqFFYWGgYhmFkZmYagLF69eqzfq2n8/bbbxuDBg0y5syZYwDGkSNHqn3dJ992omXLlhmNGzd2//m2224z/v73vxuGYRiTJk0yrrvuujNe9+OPPzZSUlLOu15fo54GqVPvL9zFhn2FxIQF8fz1HbUngwfJBu53tWKu7s1iGdGnGQBPfbWOgtLKMz62oKAAgLi4058Ia7PZuOGGGxg0aBDr1q1jyZIlPPjgg1gsFm6//XaeeOIJOnTo4P4t/Pbbb8fhcHDTTTcRHBzMsmXLePfdd3nqqafOWffChQtP6R0oLy8nNDS02m1hYWGUlZWxcuXKardfd911JCQk0L9/f6ZPn37O623atInnnnuOTz75BKv1zD++unbtSlJSEldeeSWLFi1y3966dWtKSkpYvXo1+fn5rFixgs6dO3PkyBGeffZZ3nzzzTO+Zq9evdi7d69Pz92oCYUGqTM7Dh3j9dnbAXj2mvYkRIee4xlSn/KACa5WzPfUVW1JaxhOTkEZz3276bSPcTgc/L//9/+45JJL6Nix42kfU1hYSEFBAddccw0tW7akXbt2jBw5kqZNmxIWFkZkZCSBgYEkJiaSmJhIWFgYs2fPZsuWLXzyySd06dKFgQMH8tJLL52z5uzsbJo0aVLttqFDh7J48WImT56M3W5n3759PPfccwDk5OQAEBkZyauvvsoXX3zBd999R//+/bnhhhvOGhzKy8u54447GD9+PE2bNj3tY5KSknj33Xf56quv+Oqrr0hNTWXw4MGsWrUKgNjYWD7++GNGjBhBr169GDFiBEOHDuXJJ5/k0UcfJTMzk4yMDDp27MiXX35Z7bWrvs7sbP+O2drwX+rE9rydXP/xyxymghaJUWSWbeK1JQEEWAIIsDpbq8Xq/jzA6vrzWe6/kOec72taLVb1hogpwoMDeeXWLtz63hK+WrWXYR0TubJ942qPGTNmDBs2bODnn38+4+vExcVx7733MnToUK688kquuOIKbrvtNpKSzrxd++bNm0lNTa0WAPr27XvOmktLS0/pVRgyZAjjx49n9OjR3HPPPYSEhPDss8+ycOFCd+9AfHx8tUmIPXv2ZP/+/YwfP57rrrvutNcaO3Ys7dq14+677z5jPW3atKFNmzbuP/fr14+dO3fyj3/8g//85z8A3Hjjjdx4443ux8yfP59169bxr3/9i1atWjF58mQSExPp1asXAwcOdM+HCAtzntJSUlJyzu+LL1NokDrx+rz5bC75EIJgVQGsmm12RTVnweI5YaYOQ9ae8EaQPpyZO2ays7yw3r7Wk2+zWtThWaVHWhwPDmjBewt2MXbqeno0iyXWNXH40UcfZcaMGSxYsICUlJSzvs7EiRP57W9/yw8//MCUKVP405/+xI8//kifPn1qtd74+HiOHDlyyu2PP/44jz32GDk5OcTGxpKVlcXYsWNp0eLMG7r17t2bH3/88Yz3//TTT6xfv97dA2C49rWIj4/nmWee4S9/+ctpn9erV68zhqzy8nIeeeQR/vOf/7Bjxw5sNhuDBg0CID09nWXLlnHttdcCkJ+fD0CjRo3OWKM/UGiQWrdhXwHTV5URaR1Kr+YNSI0LxWE4sBt27A47dsPu/LPrc7vDfsr9p7vtXM+pyWsaNdj/0MDA5rBhwwa+fCxAYgakD+ePc8bCAXM3k67rgHTe99dRT1ZNntOllUHDDdvZe7Sc+yZn8+7t1/D82OeZNm0a8+bNo3nz5jX6nmZkZJCRkcHYsWPp27cvkyZNok+fPgQHB2O3V/+H3a5dO/bs2UNOTo67R6Imyx8zMjL49NNPT3ufxWJx91xMnjyZ1NRUunXrdsbXWrNmzVl7Q7766itKS0vdf16xYgX33XcfCxcupGXLlhf0ui+88ALDhg2jW7durF69utqy0MrKymrfpw0bNhAUFESHDv69ZFyhQWpVWaWdx6asIcDejLvavsg7d3fzqO5+wzCqhQlPCTMX+5qn3FaD1ygJjSV7/WSaxLUkMDiyzuqqCYfhwGE4qHSceQKg3wmBr/fBjlsvYc/SDXzzzTdERUW59xyIiYlxd5mfKDMzk/fff5/rrruOJk2asHXrVrZv386IESMASEtLIzMzkzVr1pCSkkJUVBRXXHEF6enpjBw5kvHjx1NYWMgzzzxzzhKHDh3K2LFjOXLkCLGxse7bx48fz7Bhw7BarUydOpWXX36Zzz//nIAA5y6wH3/8McHBwWRkZAAwdepU/v3vf/Phhx+6X2PatGmMHTuWLVu2AJwSDHJzcwFn4GnQoAEAr7/+Os2bN6dDhw6UlZXx4Ycf8tNPPzFr1qxTat+0aRNTpkxh9WpnYG7bti1Wq5UJEyaQmJjIli1b6Nmzp/vxCxcuZMCAAaf9nvsThQapVeNnbmX7oWPER4bw0k2dPCowgPO3nwBLAAEEgHaxdup0R52+vGEYnh+yLvY16yBQ5hWXkV9cxoaZztn/gwcPrvZ9nThxIvfee+8p3+/w8HC2bNnCxx9/TF5eHklJSYwZM4aHHnoIgJtvvpmpU6dy6aWXcvToUffrTJs2jVGjRtGrVy/S0tJ44403GDZs2Fn/bjt16kS3bt34/PPP3a8P8P333/Piiy9SXl5Oly5d+Oabb7jqqquqPff5558nOzubwMBA2rZty5QpU7jlllvc9xcUFJz3JlYVFRU88cQT7Nu3j/DwcDp37szs2bO59NJLqz3OMAwefPBBXnvtNSIinIfmhYWF8dFHHzFmzBjKy8t58803SU5Odj/ns88+Y9y4cedVjy+yGIY2PJfasWhHLnd9uAyAf9/bg8vaNj7HM8RMRcBKoDsQZXItciqHw2DkxOUs3J5L28Qovh5zCaFBnpd0v/vuO37/+9+zYcOGsy6D9Gbff/89TzzxBOvWrSMw0L9/1/bNv2GpdwWllTz5xVoA7uzdVIHBC2wHLnW14nmsVguv3taF+MhgthwoOu1ukZ5g+PDhPPjgg+zb53lnZ9SW4uJiJk6c6PeBAdTTILXksSlrmLZ6H2kNw/nutwOICNF/Lk+3Cmcvw0rgzNPTxGzztx1m5L+XA/Du3d0Z1jHR5IrEn6mnQS7a9LX7mbZ6H1YLvHZ7VwUGkVo0KL0RDw50LlX8w5dryc4z/8wJ8V8KDXJRdueV8Mep6wF49NJWdGsae45niMj5enJIGzKaNqCwzMboT1dRWuHLa4HFkyk0yAWrtDv4zWerOVZuo0ezWH57eWuzS5LzEAQku1rxbMGBVt6+qxvxkcFszinkmWnr0ciymEGhQS7YK7O2snbPUaJDA3n9V10JDNA/J2/SCdjrasXzJcWE8a87uhFgtTB19T4+XerfZyCIOfQuLxdkwbbDvDd/FwB/v6UzKbHhJlck4vv6tmzI08PaAvDcjE2szD51C2eRuqTQIOftcFE5j3/uXF55V++mDOt45q1fxXOtB1JcrXiP+wc0Z3inJCrtBo/8dyWHisrMLkn8iEKDnBeHw+CJL9aSe6ycNo2jePaa9maXJBeoEtjnasV7WCwW/nZLZ1olRHKwsJzR/1lJWaUmRkr9UGiQ8/Lhz7tYsO0woUFW/nVnhkfuUCfi6yJDAnn/nu5EhwayavdR/jhVEyOlfig0SI2t2n2Ev//g3Av+/67pQHpjbT4sYpYWjSJ5+67u7omR77rmGInUJYUGqZH84goe/e8qbA6D4Z2SuKNXqtklifi9/q3jGXetc4jw7zO3MGvjAZMrEl+n0CDn5HAY/L8pa9hfUEbz+AhevtnzTq+U89camOtqxXvd0zeNe/o0wzDg/01Zw6b9hWaXJD5MoUHO6c25O9zzGN65uxtRodoOyBdEAYPRCZe+4P+ubc8lrRpSUmHngU9+4XBRudkliY9SaJCz+nl7Lv+YvQ2AF27oRNvEaJMrktqyDxjrasW7BQVYefvO7jSPj2Df0VJGfbyCkgqb2WWJD1JokDM6UFDG7z5bjWHAr3qmckv3FLNLklp0EHjZ1Yr3iwkP4t/39iQuIph1ewt4dNJqbHaH2WWJj1FokNOqtDt4dNIq8ooraJ8UzbjrOphdkoicQ/P4CD4c2YOQQCs/bTnE/03fqKWYUqsUGuS0/v7DFn7JPkJUSCBv39VN+zGIeIluTWP5568ysFhg0rLdvD1vp9kliQ9RaJBTfLt2Px8szARg/K1dSIuPMLkiETkfwzom8mfXbq3jZ27l69WauSK1Q6FBqtm0v5Dff+k8V+KhQS0Y1jHR5IqkrjQERrla8T33XtKcBwY0B+D3X65l8Y5ckysSX2AxNOAlLkeKK7j2zZ/Ze6SUAa3j+ejXvQiwaj8GEW/lcBj85rPVfLcuh6iQQKY81Jf2TbQCSi6cehoEAJvdwaOTV7H3SClN48L51x0ZCgw+rhTY6GrFN1mtFl69tQu90uIoKrcx4t/Lyc4rNrss8WIKDQLAy99vYdGOPMKDA3h/RHcahAebXZLUsc1AR1crvis0KIAPRvagXVI0ucfKuXvCMg4W6jhtuTAKDcLXq/fx4c/OiY+v3tpFGziJ+JiYsCA+vq8nzRqGsye/lBETllNQokPR5fwpNPi59XsLeOqrdQCMubQlV3VKMrkiEakLCVGhfDqqNwlRIWw9WMSvP1quXSPlvCk0+LGcglLu/2QF5TYHl7ZpxONXtjG7JBGpQ6lx4fxnVG+iQwNZtfsoD3+6igqbdo2UmlNo8FPF5TZGffQLBwvLSW8cyT818dHvWIBgVyv+o01iFBN/3YuwoADmbzvM45+vwe7QIjqpGYUGP2R3GPzus9VsyikkPjKYCSN7Eq2TK/1OBlDuasW/dG8Wy7v3dCcowMKMdTk89dU6HAoOUgMKDX7opf9tZvbmQ4QEWnl/RA9S48LNLklE6tmg9Ea88StnD+OXK/fyx2nrFRzknBQa/MynS7OZULVS4rYudGsaa3JFYpbNQDe05NKfXdUpiddu64LVAp+t2MOfdcCVnINCgx/5cdNB/u+bDQA8OSSdazo3MbkiMVMpsBpt7uTvru+azPhbumCxwH+WZvPcjE0KDnJGCg1+YnlmPo9OWoXDgNt6pDDm0lZmlyQiHuLm7im8fFMnACYuyuLl77coOMhpKTT4gS0HCrn/Y+fSyivaJfDSjZ2wWDRnXkSOu71nU164oSMA7y3Yxcs/KDjIqRQafNzeIyWM/PdyCsts9GgWy7/u6EZggP7aReRUd/dpxrhrnUdqvzd/F3/5dpMmR0o1+unhww4XlTNiwnL3XgwfjuxBWHCA2WWJh2gOfO5qRarce0lzXrzR2ePw0eIsxk5dr30cxE1HY/uovGPl3PHBUrYdPEZygzC+fLgvSTFhZpclIl7iq5V7+f2Xa3EYcH3XJrxyaxeC1Evp9/QvwAcdLang7gnL2XbwGI2jQ5j0QG8FBjnFQeA1Vytyspu7pziHM60Wvlmzn0cnraLcZje7LDGZQoOPKSyrZMS/l7M5p5D4yBAmPdCHZg0jzC5LPNA+4AlXK3I6wzsn8e7d3QkOsDJz40Hu+2gFRWU6HdOfKTT4kCPFFdz1wTLW7S0gLiKYSQ/0pmWjSLPLEhEvdkX7xky4twfhwQEs2pHHbe8t5WBhmdlliUkUGnzEocIybn9/Cev3OQPDp6N6k944yuyyRMQHDGjdiCkP9iU+MpjNOYXc9PZidhw6ZnZZYgKFBh+wJ7+EW99bwraDx0iMDuXzh/rQvkm02WWJiA/plBLD1IcvoXl8BPuOlnLLu4tZtivP7LKknik0eLktBwq59d0lZOeV0DQunC9G96VVgnoY5NxigGtdrUhNNG0Yzpej+9IltQFHSyq568NlTF6+2+yyasxisfD111+f9TF5eXkkJCSQlZVVLzWZ4d133+Xaa6+9oOcqNHix+dsOc8s7SzhQWEbrhEi+GN1XJ1ZKjbUEprtakZpqGBnCZw/0YXjnJGwOg7FT1/N/32yg0u6o0fPfeecdOnfuTHR0NNHR0fTt25fvv//+gusZN24cXbt2veDnn+zFF1/k+uuvJy0tzX3bnDlz6NevH1FRUSQmJvLUU09hs9mqPe/zzz+na9euhIeH06xZM8aPH3/W68ybNw+LxXLajxUrVgCwdetWLr30Uho3bkxoaCgtWrTgT3/6E5WVxyej/vjjj6SnpxMdHc0999xDRUWF+76CggLS09PJzs6udu377ruPVatWsXDhwvP+/ig0eKlPl2Zz30crOFZuo0+LOL4c3Y/G0aFmlyVepBI47GpFzkdYcABv3pHBk0PSAfhkSTYjJiwnv7jiHM+ElJQUXn75ZVauXMkvv/zCZZddxvXXX8/GjRvruuxzKikpYcKECYwaNcp929q1a7n66qsZNmwYq1evZsqUKUyfPp2nn37a/Zjvv/+eu+66i9GjR7Nhwwbefvtt/vGPf/Dmm2+e8Vr9+vUjJyen2sf9999P8+bN6dGjBwBBQUGMGDGCWbNmsXXrVl5//XU++OAD/vznPwPgcDi48847GT16NEuWLOGXX37h/fffd1/j6aefZvTo0TRr1qzatYODg7nzzjt54403zv+bZIhXqbDZjb9M32g0e2qG0eypGcbjU9YY5ZV2s8sSL7TSMAxcrciFmrkhx2j/7PdGs6dmGL1fnG0s25V33q8RGxtrfPjhh2e8f+7cuUbPnj2N8PBwIyYmxujXr5+RlZVlTJw40QCqfUycONEwDMPYtm2bMWDAACMkJMRo166dMWvWLAMwpk2bdsbrfPHFF0ajRo2q3TZ27FijR48e1W6bPn26ERoaahQWFhqGYRh33HGHccstt1R7zBtvvGGkpKQYDoejRt+DiooKo1GjRsZzzz131sc99thjRv/+/Q3DMIyDBw8agFFaWmoYhmH84Q9/MB555BHDMAxj0aJFRvfu3Q2bzXba15k/f74RHBxslJSU1Ki+Kupp8CKHCsu464Nl/HtRJuA83vqVWzsTHKi/RhExx5AOiUx95BJaxEdwoLCMOz5Yyltzd9TozAq73c5nn31GcXExffv2Pe1jbDYbN9xwA4MGDWLdunUsWbKEBx98EIvFwu23384TTzxBhw4d3L+t33777TgcDm666SaCg4NZtmwZ7777Lk899dQ561m4cCHdu3evdlt5eTmhodV7ccPCwigrK2PlypVnfczevXtPGRo4k+nTp5OXl8evf/3rMz5mx44d/PDDDwwaNAiARo0akZSUxKxZsygpKWHhwoV07tyZyspKHn74Yd577z0CAk5/dECPHj2w2WwsW7asRvVV0U8bL7FsVx7D//Uzy7PyiQwJ5N27u/HoZa11WqWImK5NYhTf/qY/N2YkY3cYjJ+5lRH/Xk5OQelpH79+/XoiIyMJCQlh9OjRTJs2jfbt25/2sYWFhRQUFHDNNdfQsmVL2rVrx8iRI2natClhYWFERkYSGBhIYmIiiYmJhIWFMXv2bLZs2cInn3xCly5dGDhwIC+99NI5v47s7GyaNGlS7bahQ4eyePFiJk+ejN1uZ9++fTz33HMA5OTkuB8zdepU5syZg8PhYNu2bbz66qvVHnMuEyZMYOjQoaSkpJxyX79+/QgNDaV169YMGDDAfX2LxcLnn3/O888/T4cOHcjIyOC+++7j5Zdf5tJLLyU0NJRLLrmENm3anDJUEh4eTkxMTI1DTRWFBg9XYXPw9x+2cMcHSzlcVE6bxlFMf/QShnVMMrs0ERG3iJBAXrutC+Nv6UxYUAA/78hlyGsL+HzFnlOO2G7Tpg1r1qxh2bJlPPzww4wcOZJNmzad9nXj4uK49957GTp0KNdeey3//Oc/z/mDePPmzaSmplYLAGfqyThRaWnpKT0GQ4YMYfz48YwePZqQkBDS09O5+uqrAbBanT9CH3jgAR599FGuueYagoOD6dOnD7/61a+qPeZs9u7dy8yZM6vNpTjRlClTWLVqFZMmTeK7777jlVdecd/Xv39/VqxYQWZmJm+99RaZmZl88sknvPDCC9xzzz08+OCDLFy4kOeee45169ZVe92wsDBKSkrOWd+JFBo82NYDRdzw1iLenrcThwG3dE9h2ph+tNAujyLigSwWC7f2SOXb3/Qno2kDispt/OGrdYycuIJ9R4/3OgQHB9OqVSu6d+/OX//6V7p06cI///nPM77uxIkTWbJkCf369WPKlCmkp6ezdOnSWq8/Pj6eI0eOnHL7448/ztGjR9m9eze5ublcf/31ALRo0QJwft1/+9vfOHbsGNnZ2Rw4cIBevXpVe8zZTJw4kYYNG3Lddded9v7U1FTat2/PHXfcwcsvv8y4ceOw209/DshDDz3Eq6++isPhYPXq1dx6660kJCQwaNAg5s+fX+2x+fn5NGrU6Jz1nSjwvB4t9aKs0s5bc3fw3vxdVNgdxEUE89KNnRjWMdHs0sSHdAEKAJ1M4kEMAwwHcEJ74ueGAThOas/j/qrPz3X/ya95rvtPeq1WGHx5vZ1ZGw4ydfVe7Dl2XnjvfwzvlMiVfQYSEld9KMLhcFBeXn7Wb01GRgYZGRmMHTuWvn37MmnSJPr06UNwcPApP0DbtWvHnj17yMnJISnJ2Stbk5CRkZHBp59+etr7LBaLu+di8uTJpKam0q1bt2qPCQgIIDk52f2Yvn37nvOHsmEYTJw4kREjRhAUFHTOGh0OB5WVlTgcjlPmK0yYMIG4uDiuu+46d/ipWp5ZWVlZ7fu0c+dOysrKyMjIOOc1T6TQ4GHmbjnE/03fwJ58Zyq/vG0Cf725EwlRfryc0v1mdIY3rTO9gdXlm261N9/zf1Ot1fsv8OsPwEG0N3z9Z7qWr/z9uj/3LQHAVcBVTU+48ShcfUcnLr/rOW4a0IVjx44xadIk5s2bx8yZM0/7OpmZmbz//vtcd911NGnShK1bt7J9+3ZGjBgBQFpaGpmZmaxZs4aUlBSioqK44oorSE9PZ+TIkYwfP57CwkKeeeaZc9Y8dOhQxo4dy5EjR4iNjXXfPn78eIYNG4bVamXq1Km8/PLLfP755+4f2rm5uXz55ZcMHjyYsrIyJk6cyBdffFHtN/vly5czYsQI5syZ4w4WAD/99BOZmZncf//9p9Tz3//+l6CgIDp16kRISAi//PILY8eO5fbbbz8lYBw6dIgXXniBRYsWARAbG0u7du14/fXXGTJkCHPmzKn2PVi4cCEtWrSgZcvz26lFocFD7Fo/nb2r3yO/sIzHIgzCG1jpnBJFUnQolrXm/FCp1d9ULqp+qQvbo1rxaI83efOXR2ldtMPscqRWWMBiAayu1gIWq7M98XN3W4P7T/daNb3f9bmBhaMllew9Ws6efDtP/eYh/lByhOjoGLpndGHmzJlceeWVp/2KwsPD2bJlCx9//DF5eXkkJSUxZswYHnroIQBuvvlmpk6dyqWXXsrRo0eZOHEi9957L9OmTWPUqFH06tWLtLQ03njjDYYNG3bW716nTp3o1q0bn3/+ufv1wbkPw4svvkh5eTldunThm2++4aqrrqr23I8//pgnn3wSwzDo27cv8+bNcw9RgHMPiK1bt1bbmAmcvQP9+vWjbdu2p9QTGBjI3/72N7Zt24ZhGDRr1oxHH32Uxx577JTH/u53v+OJJ56oNo/jo48+YuTIkbzxxhv8/ve/p2fPnu77Jk+ezAMPPHDW78fpWIyTZ6hIvdp6oIg3ftpO3L4PeD75XbPL8WEnv8HV4E21pm+6tfCmesob+JnuP1v95/n1rYpIo3vncaxc/xzdSvbU7ddX06+/6rUu5Ouvdi3P+qFZL1+/F6ykKi638ebcHUz4OZMKm7N3pU+LOH53eTp9WsR5xGqw7777jt///vds2LChRpMYvdHGjRu57LLL2LZtGzExMef1XIUGExiGwbLMfN6bv5O5Ww8D0CFsJ6PTdzEgPYEG4SFnftOoxx8q9fJDs15+qJr/RuSJVgHdgZVAt3M8VqQ2HSws4+25O5i8fA8Vru2nOzSJ5t5+aVzbpQmhQaffW6C+vP7669x8882kpqaaWkddmT17Nna7naFDh573cxUa6tHRkgq+Xr2Pz1bsYcuBIsD58+yqjok8emlrnUwp9UqhQcy2/2gpb8/bwZcr91JW6QwP8ZHB3NQthZu6JdM2Ue+JnkahoY4dK7fx05ZD/LAhh9mbD7m75EICrdzSPYUHBrQgLV7z16X+KTSIpzhSXMFnK/bwyZIscgrK3Le3S4rmhq5NuLJ9Yy019xAKDbXMMAx2HDrG4p15LNh2mIU7ct1BAaBtYhR39GrKDV2TiQk/9/IakbpyGPgcuA04v5XaInWj0u5gzuZDTFu9l5+2HKLSfvzHU4v4CC5rm8CA9EZ0a9qAqFC9f5pBoeEiFZVVsnF/Iev2HmXd3gKWZ+ZzqKj6euPm8REM65jI1R2T6Jgc7RGTfUREPNnRkgpmrMvhhw0HWJaZVy1AWC3Qvkk0PdPi6JragPZJ0TSPjyAwwDcnLnoShYYaKKmwcbCwnJyjpezKLSbT9bHr8DGy80s4+TsYEmilR1os/VrGc0W7xqQ3jlRQEI+TD/wPuBqIM7kWkbMpKqtk4fZc5mw+xPKsPPc+NicKDrSS3jiS1glRNI0Lp2lcOM0aOtuGkSEEWPUeXBt8NjQ4HAYVdgflNgflNjsVNtfnlQ7n7ZV2SivtFJXZKCyrpLDURlFZpfvzvOJyDhaWc7CwjKIy21mvldwgjE7JMXRKiSGjaQO6NY01ffavyLloToN4q5yCUlZkHeGXrHw27Ctgy4EiSipOv60yOHsm4iJCaBTl/IiPDCY2PJjIkECiQp0fkSFBRIYGEhkSQHBAAMGBVkICrQSf8BESaCU4wOrXvwSaGho+XpzFx0uyMAxwGEa11jAMHFV/5vifT7wdV+swwOD4/YYBthocy3o+woMDSIwJpXnDCJrHR9C8UQQt4iNp3TiS+MiQWr2WSH1QaBBf4XAY7DlSwuacQnblFrM7r4Td+SVk55WQU1BKLf84IMBqwWoBq8WC1WIhwGrBYqm6/fT3WS0W9+rvqshhsVjcn2OBkX3TGNkvrXaLrWWm7gh5pKSCXYeL6/w6FgvuhBgcGECIKzGGBgUQFRpIdFgQ0aFBRIcFEhUaRHRoILHhwSTGhNI4OpTG0SGadCMi4qGsVgvNGkbQrOGpK9Fsdgf5xRUcPlbO4SLXx7Fyd+/ysXIbx8psFJXZKCq3UVxuo8Lm7JGucPVUnzifAsDuMHD2a9RuGjlSUlGrr1cXTA0NN3dLoW+LhlitzrRlOSGhnZjMLFiwWnEnODjD41z3W7BU604KtFr8ujtJRMRfBQZYSYgOJSH6ws/vOXG4u8LmwGEY2B2Gu2e86nPHCT3hdsfx3nPn/VAVMlwd5cc/d3X4J8eGXdwXWw9MDQ2pceGkxoWbWYKI34oA+qBTLkXOxWq1EGoN0Fw1fHgipIiIiNQuLWoVERGRGlFoEPFTq3DO4l5ldiEi4jUUGkRERKRGFBpERESkRhQaREREpEYUGkRERKRGtORSxE+VAXuBFODCt70REX+i0CAiIiI1ouEJET+VCdztakVEakKhQcRPHQH+62pFRGpCoUFERERqRKFBREREakShQURERGpEqydERESkRtTTICIiIjWi0CAiIiI1otAgIiIiNaLQICIiIjWi0CAiIiI1otAgIiIiNaLQICIiIjWi0CAiIiI1otAgIiIiNfL/AfOCGd5SovIXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# Get the Grade column\n",
    "col = df_students['Grade']\n",
    "\n",
    "# get the density\n",
    "density = stats.gaussian_kde(col)\n",
    "\n",
    "# Plot the density\n",
    "col.plot.density()\n",
    "\n",
    "# Get the mean and standard deviation\n",
    "s = col.std()\n",
    "m = col.mean()\n",
    "\n",
    "# Annotate 1 stdev\n",
    "x1 = [m-s, m+s]\n",
    "y1 = density(x1)\n",
    "plt.plot(x1,y1, color='magenta')\n",
    "plt.annotate('1 std (68.26%)', (x1[1],y1[1]))\n",
    "\n",
    "# Annotate 2 stdevs\n",
    "x2 = [m-(s*2), m+(s*2)]\n",
    "y2 = density(x2)\n",
    "plt.plot(x2,y2, color='green')\n",
    "plt.annotate('2 std (95.45%)', (x2[1],y2[1]))\n",
    "\n",
    "# Annotate 3 stdevs\n",
    "x3 = [m-(s*3), m+(s*3)]\n",
    "y3 = density(x3)\n",
    "plt.plot(x3,y3, color='orange')\n",
    "plt.annotate('3 std (99.73%)', (x3[1],y3[1]))\n",
    "\n",
    "# Show the location of the mean\n",
    "plt.axvline(col.mean(), color='cyan', linestyle='dashed', linewidth=1)\n",
    "\n",
    "plt.axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The horizontal lines show the percentage of data within 1, 2, and 3 standard deviations of the mean (plus or minus).\n",
    "\n",
    "In any normal distribution:\n",
    "- Approximately 68.26% of values fall within one standard deviation from the mean.\n",
    "- Approximately 95.45% of values fall within two standard deviations from the mean.\n",
    "- Approximately 99.73% of values fall within three standard deviations from the mean.\n",
    "\n",
    "So, since we know that the mean grade is 49.18, the standard deviation is 21.74, and distribution of grades is approximately normal; we can calculate that 68.26% of students should achieve a grade between 27.44 and 70.92.\n",
    "\n",
    "The descriptive statistics we've used to understand the distribution of the student data variables are the basis of statistical analysis; and because they're such an important part of exploring your data, there's a built-in **Describe** method of the DataFrame object that returns the main descriptive statistics for all numeric columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.522727</td>\n",
       "      <td>49.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.487144</td>\n",
       "      <td>21.737912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>36.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>49.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.375000</td>\n",
       "      <td>62.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       StudyHours      Grade\n",
       "count   22.000000  22.000000\n",
       "mean    10.522727  49.181818\n",
       "std      3.487144  21.737912\n",
       "min      1.000000   3.000000\n",
       "25%      9.000000  36.250000\n",
       "50%     10.000000  49.500000\n",
       "75%     12.375000  62.750000\n",
       "max     16.000000  97.000000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_students.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing data\n",
    "\n",
    "Now that you know something about the statistical distribution of the data in your dataset, you're ready to examine your data to identify any apparent relationships between variables.\n",
    "\n",
    "First of all, let's get rid of any rows that contain outliers so that we have a sample that is representative of a typical class of students. We identified that the StudyHours column contains some outliers with extremely low values, so we'll remove those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Name</th>\n",
       "      <th>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>16.00</td>\n",
       "      <td>97.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Francesca</td>\n",
       "      <td>15.50</td>\n",
       "      <td>82.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni</td>\n",
       "      <td>14.50</td>\n",
       "      <td>74.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jenny</td>\n",
       "      <td>15.50</td>\n",
       "      <td>70.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aisha</td>\n",
       "      <td>12.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>12.50</td>\n",
       "      <td>63.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rajab</td>\n",
       "      <td>13.75</td>\n",
       "      <td>62.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Frederic</td>\n",
       "      <td>11.50</td>\n",
       "      <td>53.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Skye</td>\n",
       "      <td>12.00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joann</td>\n",
       "      <td>11.50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dan</td>\n",
       "      <td>10.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>9.25</td>\n",
       "      <td>49.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anila</td>\n",
       "      <td>10.00</td>\n",
       "      <td>48.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>9.00</td>\n",
       "      <td>47.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jimmie</td>\n",
       "      <td>9.00</td>\n",
       "      <td>42.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naiyana</td>\n",
       "      <td>9.00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Helena</td>\n",
       "      <td>9.00</td>\n",
       "      <td>36.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ismat</td>\n",
       "      <td>6.00</td>\n",
       "      <td>35.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jakeem</td>\n",
       "      <td>8.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rhonda</td>\n",
       "      <td>8.50</td>\n",
       "      <td>26.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kian</td>\n",
       "      <td>8.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  StudyHours  Grade   Pass\n",
       "3       Rosie       16.00   97.0   True\n",
       "10  Francesca       15.50   82.0   True\n",
       "9    Giovanni       14.50   74.0   True\n",
       "14      Jenny       15.50   70.0   True\n",
       "21      Aisha       12.00   64.0   True\n",
       "20     Daniel       12.50   63.0   True\n",
       "11      Rajab       13.75   62.0   True\n",
       "6    Frederic       11.50   53.0  False\n",
       "19       Skye       12.00   52.0  False\n",
       "1       Joann       11.50   50.0  False\n",
       "0         Dan       10.00   50.0  False\n",
       "4       Ethan        9.25   49.0  False\n",
       "18      Anila       10.00   48.0  False\n",
       "2       Pedro        9.00   47.0  False\n",
       "7      Jimmie        9.00   42.0  False\n",
       "12    Naiyana        9.00   37.0  False\n",
       "16     Helena        9.00   36.0  False\n",
       "17      Ismat        6.00   35.0  False\n",
       "15     Jakeem        8.00   27.0  False\n",
       "8      Rhonda        8.50   26.0  False\n",
       "13       Kian        8.00   15.0  False"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample = df_students[df_students['StudyHours']>1]\n",
    "df_sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing numeric and categorical variables\n",
    "\n",
    "The data includes two *numeric* variables (**StudyHours** and **Grade**) and two *categorical* variables (**Name** and **Pass**). Let's start by comparing the numeric **StudyHours** column to the categorical **Pass** column to see if there's an apparent relationship between the number of hours studied and a passing grade.\n",
    "\n",
    "To make this comparison, let's create box plots showing the distribution of StudyHours for each possible Pass value (true and false)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'StudyHours'}, xlabel='Pass'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sample.boxplot(column='StudyHours', by='Pass', figsize=(8,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the StudyHours distributions, it's immediately apparent (if not particularly surprising) that students who passed the course tended to study for more hours than students who didn't. So if you wanted to predict whether or not a student is likely to pass the course, the amount of time they spend studying may be a good predictive feature.\n",
    "\n",
    "### Comparing numeric variables\n",
    "\n",
    "Now let's compare two numeric variables. We'll start by creating a bar chart that shows both grade and study hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Name'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot of name vs grade and study hours\n",
    "df_sample.plot(x='Name', y=['Grade','StudyHours'], kind='bar', figsize=(8,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart shows bars for both grade and study hours for each student; but it's not easy to compare because the values are on different scales. Grades are measured in grade points, and range from 3 to 97; while study time is measured in hours and ranges from 1 to 16.\n",
    "\n",
    "A common technique when dealing with numeric data in different scales is to *normalize* the data so that the values retain their proportional distribution, but are measured on the same scale. To accomplish this, we'll use a technique called *MinMax* scaling that distributes the values proportionally on a scale of 0 to 1. You could write the code to apply this transformation; but the **Scikit-Learn** library provides a scaler to do it for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Name'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Get a scaler object\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Create a new dataframe for the scaled values\n",
    "# The copy() method is used to create a copy of the selected columns rather than a view of the original dataframe\n",
    "df_normalized = df_sample[['Name', 'Grade', 'StudyHours']].copy()\n",
    "\n",
    "# Normalize the numeric columns\n",
    "df_normalized[['Grade','StudyHours']] = scaler.fit_transform(df_normalized[['Grade','StudyHours']])\n",
    "\n",
    "# Plot the normalized values\n",
    "df_normalized.plot(x='Name', y=['Grade','StudyHours'], kind='bar', figsize=(8,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the data normalized, it's easier to see an apparent relationship between grade and study time. It's not an exact match, but it definitely seems like students with higher grades tend to have studied more.\n",
    "\n",
    "So there seems to be a correlation between study time and grade; and in fact, there's a statistical *correlation* measurement we can use to quantify the relationship between these columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9117666413789675"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_normalized.Grade.corr(df_normalized.StudyHours)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation statistic is a value between -1 and 1 that indicates the strength of a relationship. Values above 0 indicate a *positive* correlation (high values of one variable tend to coincide with high values of the other), while values below 0 indicate a *negative* correlation (high values of one variable tend to coincide with low values of the other). In this case, the correlation value is close to 1; showing a strongly positive correlation between study time and grade.\n",
    "\n",
    "> **Note**: Data scientists often quote the maxim \"*correlation* is not *causation*\". In other words, as tempting as it might be, you shouldn't interpret the statistical correlation as explaining *why* one of the values is high. In the case of the student data, the statistics demonstrates that students with high grades tend to also have high amounts of study time; but this is not the same as proving that they achieved high grades *because* they studied a lot. The statistic could equally be used as evidence to support the nonsensical conclusion that the students studied a lot *because* their grades were going to be high.\n",
    "\n",
    "Another way to visualise the apparent correlation between two numeric columns is to use a *scatter* plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Study Time vs Grade'}, xlabel='StudyHours', ylabel='Grade'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot\n",
    "df_sample.plot.scatter(title='Study Time vs Grade', x='StudyHours', y='Grade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, it looks like there's a discernible pattern in which the students who studied the most hours are also the students who got the highest grades.\n",
    "\n",
    "We can see this more clearly by adding a *regression* line (or a *line of best fit*) to the plot that shows the general trend in the data. To do this, we'll use a statistical technique called *least squares regression*.\n",
    "\n",
    "> **Warning - Math Ahead!**\n",
    ">\n",
    "> Cast your mind back to when you were learning how to solve linear equations in school, and recall that the *slope-intercept* form of a linear equation looks like this:\n",
    ">\n",
    "> \\begin{equation}y = mx + b\\end{equation}\n",
    ">\n",
    "> In this equation, *y* and *x* are the coordinate variables, *m* is the slope of the line, and *b* is the y-intercept (where the line goes through the Y-axis).\n",
    ">\n",
    "> In the case of our scatter plot for our student data, we already have our values for *x* (*StudyHours*) and *y* (*Grade*), so we just need to calculate the intercept and slope of the straight line that lies closest to those points. Then we can form a linear equation that calculates a new *y* value on that line for each of our *x* (*StudyHours*) values - to avoid confusion, we'll call this new *y* value *f(x)* (because it's the output from a linear equation ***f***unction based on *x*). The difference between the original *y* (*Grade*) value and the *f(x)* value is the *error* between our regression line and the actual *Grade* achieved by the student. Our goal is to calculate the slope and intercept for a line with the lowest overall error.\n",
    ">\n",
    "> Specifically, we define the overall error by taking the error for each point, squaring it, and adding all the squared errors together. The line of best fit is the line that gives us the lowest value for the sum of the squared errors - hence the name *least squares regression*.\n",
    "\n",
    "Fortunately, you don't need to code the regression calculation yourself - the **SciPy** package includes a **stats** class that provides a **linregress** method to do the hard work for you. This returns (among other things) the coefficients you need for the slope equation - slope (*m*) and intercept (*b*) based on a given pair of variable samples you want to compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope: 6.3134\n",
      "y-intercept: -17.9164\n",
      "so...\n",
      " f(x) = 6.3134x + -17.9164\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "#\n",
    "df_regression = df_sample[['Grade', 'StudyHours']].copy()\n",
    "\n",
    "# Get the regression slope and intercept\n",
    "m, b, r, p, se = stats.linregress(df_regression['StudyHours'], df_regression['Grade'])\n",
    "print('slope: {:.4f}\\ny-intercept: {:.4f}'.format(m,b))\n",
    "print('so...\\n f(x) = {:.4f}x + {:.4f}'.format(m,b))\n",
    "\n",
    "# Use the function (mx + b) to calculate f(x) for each x (StudyHours) value\n",
    "df_regression['fx'] = (m * df_regression['StudyHours']) + b\n",
    "\n",
    "# Calculate the error between f(x) and the actual y (Grade) value\n",
    "df_regression['error'] = df_regression['fx'] - df_regression['Grade']\n",
    "\n",
    "# Create a scatter plot of Grade vs StudyHours\n",
    "df_regression.plot.scatter(x='StudyHours', y='Grade')\n",
    "\n",
    "# Plot the regression line\n",
    "plt.plot(df_regression['StudyHours'],df_regression['fx'], color='cyan')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this time, the code plotted two distinct things - the scatter plot of the sample study hours and grades is plotted as before, and then a line of best fit based on the least squares regression coefficients is plotted.\n",
    "\n",
    "The slope and intercept coefficients calculated for the regression line are shown above the plot.\n",
    "\n",
    "The line is based on the ***f*(x)** values calculated for each **StudyHours** value. Run the following cell to see a table that includes the following values:\n",
    "\n",
    "- The **StudyHours** for each student.\n",
    "- The **Grade** achieved by each student.\n",
    "- The ***f(x)*** value calculated using the regression line coefficients.\n",
    "- The *error* between the calculated ***f(x)*** value and the actual **Grade** value.\n",
    "\n",
    "Some of the errors, particularly at the extreme ends, are quite large (up to over 17.5 grade points); but in general, the line is pretty close to the actual grades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>StudyHours</th>\n",
       "      <th>Grade</th>\n",
       "      <th>fx</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.00</td>\n",
       "      <td>97.0</td>\n",
       "      <td>83.098400</td>\n",
       "      <td>-13.901600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.50</td>\n",
       "      <td>82.0</td>\n",
       "      <td>79.941687</td>\n",
       "      <td>-2.058313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.50</td>\n",
       "      <td>74.0</td>\n",
       "      <td>73.628262</td>\n",
       "      <td>-0.371738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.50</td>\n",
       "      <td>70.0</td>\n",
       "      <td>79.941687</td>\n",
       "      <td>9.941687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>57.844698</td>\n",
       "      <td>-6.155302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.50</td>\n",
       "      <td>63.0</td>\n",
       "      <td>61.001410</td>\n",
       "      <td>-1.998590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13.75</td>\n",
       "      <td>62.0</td>\n",
       "      <td>68.893193</td>\n",
       "      <td>6.893193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.50</td>\n",
       "      <td>53.0</td>\n",
       "      <td>54.687985</td>\n",
       "      <td>1.687985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12.00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>57.844698</td>\n",
       "      <td>5.844698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>54.687985</td>\n",
       "      <td>4.687985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>45.217846</td>\n",
       "      <td>-4.782154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.25</td>\n",
       "      <td>49.0</td>\n",
       "      <td>40.482777</td>\n",
       "      <td>-8.517223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10.00</td>\n",
       "      <td>48.0</td>\n",
       "      <td>45.217846</td>\n",
       "      <td>-2.782154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.00</td>\n",
       "      <td>47.0</td>\n",
       "      <td>38.904421</td>\n",
       "      <td>-8.095579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.00</td>\n",
       "      <td>42.0</td>\n",
       "      <td>38.904421</td>\n",
       "      <td>-3.095579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.904421</td>\n",
       "      <td>1.904421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9.00</td>\n",
       "      <td>36.0</td>\n",
       "      <td>38.904421</td>\n",
       "      <td>2.904421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.00</td>\n",
       "      <td>35.0</td>\n",
       "      <td>19.964144</td>\n",
       "      <td>-15.035856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32.590995</td>\n",
       "      <td>5.590995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.50</td>\n",
       "      <td>26.0</td>\n",
       "      <td>35.747708</td>\n",
       "      <td>9.747708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>32.590995</td>\n",
       "      <td>17.590995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    StudyHours  Grade         fx      error\n",
       "3        16.00   97.0  83.098400 -13.901600\n",
       "10       15.50   82.0  79.941687  -2.058313\n",
       "9        14.50   74.0  73.628262  -0.371738\n",
       "14       15.50   70.0  79.941687   9.941687\n",
       "21       12.00   64.0  57.844698  -6.155302\n",
       "20       12.50   63.0  61.001410  -1.998590\n",
       "11       13.75   62.0  68.893193   6.893193\n",
       "6        11.50   53.0  54.687985   1.687985\n",
       "19       12.00   52.0  57.844698   5.844698\n",
       "1        11.50   50.0  54.687985   4.687985\n",
       "0        10.00   50.0  45.217846  -4.782154\n",
       "4         9.25   49.0  40.482777  -8.517223\n",
       "18       10.00   48.0  45.217846  -2.782154\n",
       "2         9.00   47.0  38.904421  -8.095579\n",
       "7         9.00   42.0  38.904421  -3.095579\n",
       "12        9.00   37.0  38.904421   1.904421\n",
       "16        9.00   36.0  38.904421   2.904421\n",
       "17        6.00   35.0  19.964144 -15.035856\n",
       "15        8.00   27.0  32.590995   5.590995\n",
       "8         8.50   26.0  35.747708   9.747708\n",
       "13        8.00   15.0  32.590995  17.590995"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the original x,y values, the f(x) value, and the error\n",
    "df_regression[['StudyHours', 'Grade', 'fx', 'error']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the regression coefficients for prediction\n",
    "\n",
    "Now that you have the regression coefficients for the study time and grade relationship, you can use them in a function to estimate the expected grade for a given amount of study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Studying for 14 hours per week may result in a grade of 70\n"
     ]
    }
   ],
   "source": [
    "# Define a function based on our regression coefficients\n",
    "def f(x):\n",
    "    m = 6.3134\n",
    "    b = -17.9164\n",
    "    return m*x + b\n",
    "\n",
    "study_time = 14\n",
    "\n",
    "# Get f(x) for study time\n",
    "prediction = f(study_time)\n",
    "\n",
    "# Grade can't be less than 0 or more than 100\n",
    "expected_grade = max(0,min(100,prediction))\n",
    "\n",
    "#Print the estimated grade\n",
    "print ('Studying for {} hours per week may result in a grade of {:.0f}'.format(study_time, expected_grade))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So by applying statistics to sample data, you've determined a relationship between study time and grade; and encapsulated that relationship in a general function that can be used to predict a grade for a given amount of study time.\n",
    "\n",
    "This technique is in fact the basic premise of machine learning. You can take a set of sample data that includes one or more *features* (in this case, the number of hours studied) and a known *label* value (in this case, the grade achieved) and use the sample data to derive a function that calculates predicted label values for any given set of features."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading\n",
    "\n",
    "To learn more about the Python packages you explored in this notebook, see the following documentation:\n",
    "\n",
    "- [NumPy](https://numpy.org/doc/stable/)\n",
    "- [Pandas](https://pandas.pydata.org/pandas-docs/stable/)\n",
    "- [Matplotlib](https://matplotlib.org/contents.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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