01 - Data Exploration.ipynb

{
 "cells": [
  {
   "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",
    "> **Note**: If you've never used the Jupyter Notebooks environment before, there are a few things you should be aware of:\n",
    "> \n",
    "> - Notebooks are made up of *cells*. Some cells (like this one) contain *markdown* text, while others (like the one beneath this one) contain code.\n",
    "> - The notebook is connected to a Python *kernel* (you can see which one at the top right of the page - if you're running this notebook in an Azure Machine Learning compute instance it should be connected to the **Python 3.6 - AzureML** kernel). If you stop the kernel or disconnect from the server (for example, by closing and reopening the notebook, or ending and resuming your session), the output from cells that have been run will still be displayed; but any variables or functions defined in those cells will have been lost - you must rerun the cells before running any subsequent cells that depend on them.\n",
    "> - You can run each code cell by using the **&#9658; Run** button. The **&#9711;** symbol next to the kernel name at the top right will briefly turn to **&#9899;** while the cell runs before turning back to **&#9711;**.\n",
    "> - The output from each code cell will be displayed immediately below the cell.\n",
    "> - Even though the code cells can be run individually, some variables used in the code are global to the notebook. That means that you should run all of the code cells <u>**in order**</u>. There may be dependencies between code cells, so if you skip a cell, subsequent cells might not run correctly.\n",
    "\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": {
    "gather": {
     "logged": 1667468061904
    },
    "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": {
    "gather": {
     "logged": 1667468064179
    },
    "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": {
    "gather": {
     "logged": 1667468064714
    },
    "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": {
    "gather": {
     "logged": 1667468065344
    }
   },
   "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": {
    "gather": {
     "logged": 1667468066178
    }
   },
   "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": {
    "gather": {
     "logged": 1667468066854
    }
   },
   "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": {
    "gather": {
     "logged": 1667468067517
    }
   },
   "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": {
    "gather": {
     "logged": 1667468068131
    }
   },
   "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": {
    "gather": {
     "logged": 1667468068815
    }
   },
   "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": {
    "gather": {
     "logged": 1667468069437
    },
    "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": {
    "gather": {
     "logged": 1667468070559
    }
   },
   "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": 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": {
    "gather": {
     "logged": 1667468071492
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          Vicky\n",
       "StudyHours        1\n",
       "Grade             3\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": {
    "gather": {
     "logged": 1667468072350
    }
   },
   "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",
       "  </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": {
    "gather": {
     "logged": 1667468073309
    }
   },
   "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": {
    "gather": {
     "logged": 1667468074231
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StudyHours    10\n",
       "Grade         50\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": {
    "gather": {
     "logged": 1667468075032
    }
   },
   "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": {
    "gather": {
     "logged": 1667468075952
    }
   },
   "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": {
    "gather": {
     "logged": 1667468076708
    }
   },
   "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": {
    "gather": {
     "logged": 1667468077731
    }
   },
   "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": {
    "gather": {
     "logged": 1667468078478
    }
   },
   "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": {
    "gather": {
     "logged": 1667468079382
    }
   },
   "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": {
    "gather": {
     "logged": 1667468080165
    }
   },
   "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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <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": {
    "gather": {
     "logged": 1667468081112
    }
   },
   "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": {
    "gather": {
     "logged": 1667468081886
    }
   },
   "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": {
    "gather": {
     "logged": 1667468082738
    }
   },
   "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": {
    "gather": {
     "logged": 1667468083592
    }
   },
   "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": {
    "gather": {
     "logged": 1667468084516
    },
    "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": {
    "gather": {
     "logged": 1667468085398
    }
   },
   "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": {
    "gather": {
     "logged": 1667468086562
    }
   },
   "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()"
   ]
  },
  {
   "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",
    "\n",
    "First, we'll create a Pandas **Series** containing the pass/fail indicator (True or False), and then we'll concatenate that series as a new column (axis 1) in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "gather": {
     "logged": 1667468087569
    }
   },
   "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>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": [
    "passes  = pd.Series(df_students['Grade'] >= 60)\n",
    "df_students = pd.concat([df_students, passes.rename(\"Pass\")], axis=1)\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": {
    "gather": {
     "logged": 1667468088544
    }
   },
   "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": [
    "You can aggregate multiple fields in a group using any available aggregation function. For example, you can find the mean study time and grade for the groups of students who passed and failed the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "gather": {
     "logged": 1667468089329
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       StudyHours      Grade\n",
      "Pass                        \n",
      "False    8.783333  38.000000\n",
      "True    14.250000  73.142857\n"
     ]
    }
   ],
   "source": [
    "print(df_students.groupby(df_students.Pass)['StudyHours', 'Grade'].mean())"
   ]
  },
  {
   "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": {
    "gather": {
     "logged": 1667468090144
    }
   },
   "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",
       "    <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": {
    "gather": {
     "logged": 1667468093569
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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": {
    "gather": {
     "logged": 1667468094415
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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": {
    "gather": {
     "logged": 1667468094985
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468095930
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468096718
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f76cc18f610>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": {
    "gather": {
     "logged": 1667468097267
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468098326
    },
    "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"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468099442
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468102225
    },
    "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"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468105743
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468106864
    },
    "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"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468107529
    },
    "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"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAEjCAYAAAAfYCTOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAdlUlEQVR4nO3de7hcdX228fuRqICiQEOBcDCKggcQTTZga21r1UJLELEHAW2pWlJtpdrXVkG9hPRtMbZ9q77YU/AArYq1FJXEClLrobYWmh2BcKiKbdAQUE6CgArUb/+YFdkN2WSSrJk1s+f+XNe+9syambWe4Udmnr2OqSokSZLUjod1HUCSJGkusVxJkiS1yHIlSZLUIsuVJElSiyxXkiRJLbJcSZIktchyJWlkJflkkpNamtdzknx5xv11SZ7fxryb+V2d5Kfbmp+k8WW5kiZQUyy+m+Q7Sb6d5F+TvCpJX58JSRYmqSTztiNDJbk7yV1Jbk3y6SQvmfmcqvq5qjq3z3k98aGeU1X/XFUHbWveTZZ3TpI/2GT+T6uqz7Yxf0njzXIlTa5jqmoX4HHAcuCNwHuHnOHQqno0cBBwDvDuJKe3vZDtKYGStLUsV9KEq6o7qupC4CXASUkOBkhydJIvJbkzyTeSnDHjZZ9vfn+7WfP0Y0kOSPJPzVqoW5J8MMmufWa4par+Bng1cFqSH2kyfDbJrze3n5jkc0nuaOb/t830jVmuaLK8JMlPJ1mf5I1JbgLev3HaJos+LMk1SW5P8v4kOzbz/LUkX5j5xI1rx5IsBV4KvKFZ3srm8R9uZkzyyCTvTLKh+Xlnkkc2j23M9vok30pyY5KX9/PfSdJ4sFxJAqCqLgPWA89pJt0N/CqwK3A08OokL2oe+8nm965V9eiq+iIQ4G3AAuApwH7AGVsZ4+PAPODwzTz2f4FPAbsB+wJnNbk3Zjm0yfK3zf29gN3prZlbOsvyXgocCRwAHAi8ZUsBq2oF8EHgj5rlHbOZp70ZeBbwDODQ5v3MnPdewGOBfYBXAn+WZLctLVvSeLBcSZppA71CQlV9tqrWVtUPqupK4Dzgp2Z7YVVdV1WXVNX3q+pm4E8f6vmzzOM+4JaNGTZxH72itKCqvldVX9jMc2b6AXB6k+e7szzn3VX1jaq6DfhD4IStyfsQXgr8flV9q/lvsQz4lRmP39c8fl9V/QNwF71No5LmAMuVpJn2AW4DSHJEks8kuTnJHcCrgPmzvTDJnkk+nOSGJHcCH3io588yj4cDe2zMsIk30Fs7dllzZN4rtjC7m6vqe1t4zjdm3L6e3lq3Nixo5jfbvG+tqvtn3L8HeHRLy5bUMcuVJACSHEavXG1cI/Qh4EJgv6p6LPCX9MoNQG1mFmc20w+pqscAL5vx/H4dC9wPXLbpA1V1U1WdXFULgN8A/nwLRwhuLuOm9ptxe396a+6gt0l0540PJNlrK+e9gd5ats3NW9IcZ7mSJlySxyRZAnwY+EBVrW0e2gW4raq+l+Rw4MQZL7uZ3ma3J8yYtgu9zVt3JNkH+L2tyLB7kpcCfwa8vapu3cxzfinJvs3d2+kVnB8097+5SZZ+/VaSfZPsTm8/qY37a10BPC3JM5qd3M/Y5HVbWt55wFuS7JFkPvBWemvyJE0Ay5U0uVYm+Q69TWNvpreP1Myj1n4T+P3mOW8FPrLxgaq6h94+Sv/SnCfrWfT2K1oE3AF8ArigjwxXJLkLuA74deB3quqtszz3MODS5vkXAq+tqv9sHjsDOLfJ8st9LHejD9HbSf4/ga8Bf9C8v68Avw/8I/BVHlibt9F7gac2y/vYZub7B8Bq4EpgLbBm47wlzX2p6mfNuSRJkvrhmitJkqQWWa4kSZJaZLmSJElqkeVKkiSpRZYrSZKkFlmuJEmSWmS5kiRJapHlSpIkqUWWK0mSpBZZriRJklpkuZIkSWqR5UqSJKlFlitJkqQWWa4kSZJaZLmSJElqkeVKkiSpRZYrSZKkFs3rOsBM8+fPr4ULFw5s/hs2bABgwYIFA1uGJtl083txpylaMd28l8Xj+17m0GhI2grD/K6fnp6+par22HR6qmrgC+/X1NRUrV69emDzX7ZsGQCnn376wJahSZbm9+j8m9pmad7LCH0+bK05NBqStsIwv+uTTFfV1KbT3SwoSZLUopHaLDhoixYt6jqC5rSTuw7QnpPH/72M/zuQtC1G4bt+ojYLSpIktWW2zYITteZKGrSFp36i6witWbf86K4jSNJYmqh9rjZs2PDDowik9k1z8D7XdR2iFQffdN0DRwyOqWkeOGJQ0uQYhe/6iSpXZ599NmeffXbXMTRnTbHqlNd1HaIVq859HUw9aE33WJlqfiRNllH4rp+ociVJkjRolitJkqQWWa4kSZJaZLmSJElqkeVKkiSpRZYrSZKkFk3USURPngOX9NAoW82Ss77QdYhWLDnpnaw65Se6jrFdvNaDNJlG4bt+osrVggULuo6gOW0xV91wU9chWnHVXk+ExYu7jrFdxju9pG01Ct/1bhaUJElq0USVq5UrV7Jy5cquY2jOWsqZx53VdYhWnHnRWbB0adcxtsvS5kfSZBmF7/qJKldr1qxhzZo1XcfQnHU2Jx5xcdchWnHiFRfDmF8q6uzmR9JkGYXv+okqV5IkSYNmuZIkSWrRwMtVkh2SfCnJqkEvS5IkqWvDWHP1WuDaISxHkiSpcwMtV0n2BY4G3jPI5UiSJI2KQZ9E9J3AG4BdZntCkh8eMb3//vsPNMzee+890Plr0i1i7fo7ug7RirV7HsAh+zy26xjbZVHXASR1YhS+61NVg5lxsgT4+ar6zSQ/DfxuVS15qNdMTU3V6tVetELja+Gpn+g6QmvWLT+66wiSNNKSTFfV1KbT+9osmOSQbVjms4EXJlkHfBj4mSQf2Ib5SJIkjY1+97n68ySXJfnNJH1tK6iq06pq36paCBwP/FNVvWxbg0qSJI2DvspVVT0HeCmwHzCd5ENJXjDQZAOwbNkyli1b1nUMzVlh3fKH3PI9Nta9fQkkXcfYLml+JE2WUfiu73uH9qr6apK3AKuB/w88M0mAN1XVBVt47WeBz25HTkmSpLHQ7z5XT0/yDnrnq/oZ4Jiqekpz+x0DzCdJkjRW+l1zdRa9c1W9qaq+u3FiVW1o1mZJkiSJ/svV0cB3q+q/AZI8DNixqu6pqr8ZWDpJkqQx0+/Rgv8I7DTj/s7NNEmSJM3Qb7nasaru2ninub3zYCJJkiSNr343C96dZFFVrQFIshj47hZeM3KWLJkbh8lrVP0Vp12wtusQrTjtyNfwthdvy7mDR8dfdR1AUidG4bu+r8vfJDmM3lnWN9A7dcxewEuqarrNMF7+RuPOy99I0uSY7fI3fa25qqp/T/Jk4KBm0per6r42A0qSJM0F/e5zBXAY8HR6F5s/IcmvDibS4ExPTzM93erKNmmGFZxw+EVdh2jFCZdfBCtWdB1ju6xofiRNllH4ru9rzVWSvwEOAC4H/ruZXMBfDyjXQKxatQqAxYsXd5xEc9Nv8LYXw3mXHdV1kO32tovfDRcDS5d2HWWb/Ubze3zfgaRtMQrf9f3u0D4FPLX62UFLkiRpgvW7WfAqejuxS5Ik6SH0u+ZqPnBNksuA72+cWFUvHEgqSZKkMdVvuTpjkCE02ebK6QvWLe86gea6ufJvBTzVxyiaK/9/vXynLT9n0Po9FcPnkjwOeFJV/WOSnYEdBhtNkiRp/PS1z1WSk4HzeeCkx/sAHxtUKEmSpHHV72bB3wIOBy4FqKqvJvnRgaUakNNPP73rCJrDFp66qusIrVn4xlVjv9nGQ5ulyfT+7051/vnV79GC36+qezfeSTIPP7skSZIepN9y9bkkbwJ2SvIC4O+AlYOLJUmSNJ76LVenAjcDa+md+PgfgLcMKtSgrFixghVjfkkPja6Vr3ktK1/z2q5jtGLlOa+FMb+SweLmR9JkOeaR13T+Xd/v0YI/AM5ufsbWjTfe2HUEzWGH7Pu1riO05pBvfg2+2XWK7bOm6wCSOjH/Yfdw4433dJqh32sL/heb2ceqqp7QeiJJkqQxtjXXFtxoR+CXgN3bjyNJkjTe+trnqqpunfFzQ1W9Exjv47QlSZIGoN/Ngotm3H0YvTVZ/a71kiRJmhj9FqT/N+P2/cA64JdbTyNJkjTm+j1a8LmDDjIMixYt2vKTpG30oUuP7DpCaz506JGcePj+XcfYLid3HUBSJ758/3xO6Pjzq9/Ngv/noR6vqj9tJ85gHXPMMV1H0Bz2po+e0nWE1rzpqFM4ccwvf+MZ7aTJ9K/3LeRDx4zH5W+mgFfTu2DzPsCrgEXALs3PgyTZL8lnklyT5Ookc+PsipIkSQ+h332u9gUWVdV3AJKcAXyiql72EK+5H3h9Va1JsgswneSSqrpmuxJvhw0bNgCwYMGCriJoDjt4n+sAuOqGJ3acZPsdfNN1MD091mdpn25+j+87kLQtfiR3s2HDhk6/6/tdc7UncO+M+/c202ZVVTdW1Zrm9neAa+mt9erM2Wefzdlnj/VJ5jXCVp3yOlad8rquY7Ri1bmvg6mpLT9xhE3xv0/QJ2kyvHDHazv/ru93zdVfA5cl+Whz/0XAuf0uJMlC4JnApVsTTpIkadz0e7TgHyb5JPCcZtLLq+pL/bw2yaOBvwdeV1V3bubxpcBSgP33H++jk6S5ZuGpn+g6wrZrdsgf6/cgaSz1u1kQYGfgzqp6F7A+yeO39IIkD6dXrD5YVRds7jlVtaKqpqpqao899tiKOJIkSaOnr3KV5HTgjcBpzaSHAx/YwmsCvBe4dlxO1SBJkrS9+l1zdRzwQuBugKrawCynYJjh2cCvAD+T5PLm5+e3OakkSdIY6HeH9nurqpIUQJJHbekFVfUFINsTTpIkadz0W64+kuSvgF2TnAy8Ahi7cxqcfLIXxNDgLDnrnV1HaM2Sk8b/vex11j93HUFSBy783lNYecpPdJphi+Wq2Xfqb4EnA3cCBwFvrapLBpytdZ48VIM0F04eutFVe43/e3nkDQ86OFnSBLi1HtX59/0Wy1WzOfAfquoQYOwKlSRJ0jD1u0P7miSHDTTJEKxcuZKVK1d2HUNz1JnHncWZx53VdYxWnHnRWZx50Xi/l1uPO4Rbjzuk6xiShuzHH76u8+/6fsvVEcC/JflakiuTrE1y5SCDDcKaNWtYs2ZN1zE0R514xMWceMTFXcdoxYlXXMyJV4z3e7nriP256whPTCxNmoPm3dL5d/1DbhZMsn9VfR04ckh5JEmSxtqW9rn6GLCoqq5P8vdV9QvDCCVJkjSutrRZcOZ5qp4wyCCSJElzwZbKVc1yW5IkSZuxpc2Chya5k94arJ2a2zT3q6oeM9B0kiRJY+Yhy1VV7TCsIMOw9957dx1Bc9ja9Qd0HaE1a/cc//fyiPV3dB1BUgdu+cHOHLLPYzvNkKrR2do3NTVVq1ev7jqGhmzhqZ/oOoKkIVu3/OiuI2gTc+mzeFj/fyWZrqqpTaf3e54rSZIk9aHfCzfPGTZzSereXPosljY1UWuuli1bxst3crOjBmPd8iWsW76k6xitWPf2Jax7+3i/l+uXH831/gEiTZyX77SaZcuWdZphosqVJEnSoFmuJEmSWmS5kiRJapHlSpIkqUWWK0mSpBZZriRJklo0Uee5WrJkCaddsLbrGJqjTrvgNV1HaM1pR47/e9n9giu7jiCpA/9y7+N424sP6TTDRJWrxYsX85W/u6nrGJqjzrvsqK4jtOa8Z4z/e9nlsm90HUFSB77y33uwePHiTjO4WVCSJKlFE1WupqenOXCHm7uOoTnqhMMv4oTDL+o6RitOuPwiTrh8vN/Ldw7fj+8cvl/XMSQN2YE73Mz09HSnGSZqs+CqVat49iPgK9/do+somoPe9uJ3A3Nj8+DbLm7eyxhvHrztxU8H3DwoTZpnP+J6Vq26vtNNgxO15kqSJGnQLFeSJEktslxJkiS1aKDlKslRSb6c5Lokpw5yWZIkSaNgYOUqyQ7AnwE/BzwVOCHJUwe1PEmSpFEwyDVXhwPXVdV/VtW9wIeBYwe4PEmSpM6lqgYz4+QXgaOq6teb+78CHFFVr9nkeUuBpc3dg4AvDyTQA+YDtwx4Gdo6jsloclxGj2MymhyX0TOsMXlcVT3o/E6dn+eqqlYAK4a1vCSrq2pqWMvTljkmo8lxGT2OyWhyXEZP12MyyM2CNwAzT4+8bzNNkiRpzhpkufp34ElJHp/kEcDxwIUDXJ4kSVLnBrZZsKruT/Ia4GJgB+B9VXX1oJa3FYa2CVJ9c0xGk+MyehyT0eS4jJ5Ox2RgO7RLkiRNIs/QLkmS1CLLlSRJUosmqlwl2TXJ+Un+I8m1SX6s60yTLsnvJLk6yVVJzkuyY9eZJlGS9yX5VpKrZkzbPcklSb7a/N6ty4yTZpYx+ePm8+vKJB9NsmuXGSfR5sZlxmOvT1JJ5neRbVLNNiZJTmn+vVyd5I+GmWmiyhXwLuCiqnoycChwbcd5JlqSfYDfBqaq6mB6Bz4c322qiXUOcNQm004FPl1VTwI+3dzX8JzDg8fkEuDgqno68BXgtGGH0mbHhST7AT8LfH3YgfTgMUnyXHpXhTm0qp4G/MkwA01MuUryWOAngfcCVNW9VfXtblOJ3hGrOyWZB+wMbOg4z0Sqqs8Dt20y+Vjg3Ob2ucCLhhpqwm1uTKrqU1V1f3P33+idP1BDNMu/FYB3AG8APEpsyGYZk1cDy6vq+81zvjXMTBNTroDHAzcD70/ypSTvSfKorkNNsqq6gd5fE18HbgTuqKpPdZtKM+xZVTc2t28C9uwyjB7kFcAnuw4hSHIscENVXdF1Fv3QgcBzklya5HNJDhvmwiepXM0DFgF/UVXPBO7GzRydavbhOZZe8V0APCrJy7pNpc2p3jlb/It8RCR5M3A/8MGus0y6JDsDbwLe2nUW/S/zgN2BZwG/B3wkSYa18EkqV+uB9VV1aXP/fHplS915PvBfVXVzVd0HXAD8eMeZ9IBvJtkboPk91NXq2rwkvwYsAV5anqhwFBxA7w/EK5Kso7epdk2SvTpNpfXABdVzGfADehdzHoqJKVdVdRPwjSQHNZOeB1zTYST1Ngc+K8nOzV8Uz8ODDEbJhcBJze2TgI93mEVAkqPo7dfzwqq6p+s8gqpaW1U/WlULq2ohvS/1Rc13jrrzMeC5AEkOBB4B3DKshU9MuWqcAnwwyZXAM4AzO84z0Zq1iOcDa4C19P5/9DISHUhyHvBF4KAk65O8ElgOvCDJV+mtZVzeZcZJM8uYvBvYBbgkyeVJ/rLTkBNolnFRh2YZk/cBT2hOz/Bh4KRhrun18jeSJEktmrQ1V5IkSQNluZIkSWqR5UqSJKlF87oOMNP8+fNr4cKFXceQJEnaounp6Vuqao9Np49UuVq4cCGrV6/uOoYkSdIWJbl+c9PdLChJktQiy5UkSVKLLFeSJEktslxJkiS1yHIlSZLUIsuVJElSiyxXkiRJLbJcSZIktchyJUmS1CLLlSRJUossV5IkSS2yXEmSJLXIciVJktQiy5UkSVKLLFeSJEktslxJkiS1yHIlSZLUIsuVJElSiyxXkiRJLbJcSZIktchyJUmS1CLLlSRJUossV5IkSS2yXEmSJLXIciVJktQiy5UkSVKLLFeSJEktslxJkiS1yHIlSZLUIsuVJElSiyxXkiRJLbJcSZIktchyJUmS1CLLlSRJUossV5IkSS2yXEmSJLXIciVJktQiy5UkSVKLLFeSJEktslxJkiS1aF7XAaS5ZPfdd+f222/vOkYr6vTHkGV3dh1DHdhtt9247bbbuo4hjS3LldSi22+/narqOkY7znjs3Hkv2ipJuo4gjTU3C0qSJLXIciVJktSigZWrJO9L8q0kVw1qGdvC1d2SJM1tXX/XD3LN1TnAUQOcvyRJ0sgZWLmqqs8DHm4iSZImSudHCyZZCiwF2H///Ye1zKEsR5LGlZ+T0rbrvFxV1QpgBcDU1NRQjvv28HINil9Imiv8nNQ46/qz2KMFJUmSWmS5kiRJatEgT8VwHvBF4KAk65O8clDLkiRJGhUD2+eqqk4Y1Ly3h/sRSJI0t3X9Xe9mQUmSpBZZriRJklrU+akYpLmm60OA21KnP2bOvBdtnd12263rCNJYs1xJLep6O3/b6oyuE0jS+HGzoCRJUossV5IkSS2yXEmSJLXIciVJktQiy5UkSVKLLFeSJEktslxJkiS1yHIlSZLUIsuVJElSiyxXkiRJLbJcSZIktchyJUmS1CLLlSRJUossV5IkSS2yXEmSJLXIciVJktQiy5UkSVKLLFeSJEktslxJkiS1yHIlSZLUIsuVJElSiyxXkiRJLbJcSZIktchyJUmS1CLLlSRJUossV5IkSS2yXEmSJLXIciVJktQiy5UkSVKLLFeSJEktslxJkiS1yHIlSZLUIsuVJElSiyxXkiRJLbJcSZIktchyJUmS1CLLlSRJUossV5IkSS2yXEmSJLXIciVJktSiVFXXGX4oyc3A9QNezHzglgEvQ1vHMRlNjsvocUxGk+MyeoY1Jo+rqj02nThS5WoYkqyuqqmuc+gBjsloclxGj2MymhyX0dP1mLhZUJIkqUWWK0mSpBZNYrla0XUAPYhjMpocl9HjmIwmx2X0dDomE7fPlSRJ0iBN4porSZKkgZmocpVk1yTnJ/mPJNcm+bGuM026JL+T5OokVyU5L8mOXWeaREnel+RbSa6aMW33JJck+Wrze7cuM06aWcbkj5vPryuTfDTJrl1mnESbG5cZj70+SSWZ30W2STXbmCQ5pfn3cnWSPxpmpokqV8C7gIuq6snAocC1HeeZaEn2AX4bmKqqg4EdgOO7TTWxzgGO2mTaqcCnq+pJwKeb+xqec3jwmFwCHFxVTwe+Apw27FDa7LiQZD/gZ4GvDzuQHjwmSZ4LHAscWlVPA/5kmIEmplwleSzwk8B7Aarq3qr6drepBMwDdkoyD9gZ2NBxnolUVZ8Hbttk8rHAuc3tc4EXDTXUhNvcmFTVp6rq/ubuvwH7Dj3YhJvl3wrAO4A3AO7IPGSzjMmrgeVV9f3mOd8aZqaJKVfA44Gbgfcn+VKS9yR5VNehJllV3UDvr4mvAzcCd1TVp7pNpRn2rKobm9s3AXt2GUYP8grgk12HECQ5Frihqq7oOot+6EDgOUkuTfK5JIcNc+GTVK7mAYuAv6iqZwJ342aOTjX78BxLr/guAB6V5GXdptLmVO+wYv8iHxFJ3gzcD3yw6yyTLsnOwJuAt3adRf/LPGB34FnA7wEfSZJhLXySytV6YH1VXdrcP59e2VJ3ng/8V1XdXFX3ARcAP95xJj3gm0n2Bmh+D3W1ujYvya8BS4CXlufSGQUH0PsD8Yok6+htql2TZK9OU2k9cEH1XAb8gN71BodiYspVVd0EfCPJQc2k5wHXdBhJvc2Bz0qyc/MXxfPwIINRciFwUnP7JODjHWYRkOQoevv1vLCq7uk6j6Cq1lbVj1bVwqpaSO9LfVHznaPufAx4LkCSA4FHMMSLa09MuWqcAnwwyZXAM4AzO84z0Zq1iOcDa4C19P5/9EzHHUhyHvBF4KAk65O8ElgOvCDJV+mtZVzeZcZJM8uYvBvYBbgkyeVJ/rLTkBNolnFRh2YZk/cBT2hOz/Bh4KRhrun1DO2SJEktmrQ1V5IkSQNluZIkSWqR5UqSJKlFlitJkqQWWa4kSZJaZLmSNDaSfCbJkZtMe12Sv5jl+Z9NMjWcdJLUY7mSNE7OA47fZNrxzXRJGgmWK0nj5Hzg6CSPAEiykN51KU9IsjrJ1UmWbe6FSe6acfsXk5zT3N4jyd8n+ffm59mDfhOS5jbLlaSxUVW3AZcBP9dMOh74CPDmqpoCng78VJKnb8Vs3wW8o6oOA34BeE+LkSVNoHldB5CkrbRx0+DHm9+vBH45yVJ6n2l7A08Fruxzfs8Hntq7vCUAj0ny6Kq66yFeI0mzslxJGjcfB96RZBGwM3Ab8LvAYVV1e7O5b8fNvG7mtb5mPv4w4FlV9b0B5ZU0YdwsKGmsNGuUPkPvwqznAY8B7gbuSLInD2wy3NQ3kzwlycOA42ZM/xS9i7oDkOQZAwkuaWJYriSNo/OAQ4HzquoK4EvAfwAfAv5lltecCqwC/hW4ccb03wamklyZ5BrgVQNLLWkipKq2/CxJkiT1xTVXkiRJLbJcSZIktchyJUmS1CLLlSRJUossV5IkSS2yXEmSJLXIciVJktQiy5UkSVKL/gfGM69MWLmyBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468109781
    }
   },
   "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"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468112202
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAEICAYAAAAa+FMDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd3xUVd7H8c8vHUgIJaH3qjQxhI69V1wrYEFFsD5refbZdd1d11W36O6qu4oFFbuL2NvasSBNEgTpEHonBAiBkH6eP2bQCAEC5M6dyXzfr9d5ZeaWOb9DMjM/7rnnHHPOISIiIiLhIcbvAERERETkJ0rORERERMKIkjMRERGRMKLkTERERCSMKDkTERERCSNKzkRERETCiJIzERGfmdnlZvap33GISHhQciYiYcHMVprZbjMrMLPtZjbVzG4ws2p9TplZOzNzZhZ3BDE4M9tlZjvNLM/MvjCzyw739arLOfeKc+70veLo5HW9IhKelJyJSDg5zzmXArQF/gb8Bng2xDEc45xLBroCzwOPmdkfQxyDiEQxJWciEnacc/nOufeAy4CRZtYDwMzOMbPvzWyHma0xs3sqnfZN8Of24JWvgWbW0cwmBa+CbTGzV8ysQTVj2OKcewm4EfitmTUOxpBqZs+a2QYzW2dm95tZbHDf1Wb2rZn9w8y2mdkKMztrz2sG9y8PXh1cYWaXVz4v+HhPO+YE23GZmc0zs/MqvU58sD3HHvq/roiEOyVnIhK2nHPfAWuB44KbdgFXAQ2Ac4AbzeyC4L7jgz8bOOeSnXPTAAP+CrQAjgZaA/ccYhjvAnFAv+Dz54EyoBNwLHA6cF2l4/sDi4E04EHgWQuoB/wbOCt4dXAQMLuKNu9pxzHBdrwGvAhcUemws4ENzrnvD7EtIhIBlJyJSLhbDzQCcM595Zyb65yrcM79APwHOGF/Jzrncpxznznnip1zucBDBzp+P69RCmwBGplZUwKJ0W3OuV3Ouc3Aw8CwSqescs497ZwrB14AmgNNg/sqgB5mVsc5t8E5N7+aYbwMnG1m9YPPrwReOpR2iEjkUHImIuGuJbAVwMz6m9mXZpZrZvnADQSuUFXJzJqa2YRg9+MOAknOfo/fz2vEA+nBGNoC8cCG4KCF7cBTQJNKp2zc88A5Vxh8mOyc20Wgm/aG4PkfmtlR1YnBObcemAJcFOyWPQt45VDaISKRQ8mZiIQtM+tLIDn7NrjpVeA9oLVzLhV4kkDXJYCr4iX+Etze0zlXn0DXoFVx3IEMJdCN+R2wBigG0pxzDYKlvnOue3VeyDn3iXPuNAJX0xYBTx9CHC8QiP8SYJpzbt2hNEJEIoeSMxEJO2ZW38zOBSYALzvn5gZ3pQBbnXNFZtYPGFHptFwC3YYdKm1LAXYC+WbWEvi/Q4ihUfCG/bHAA865POfcBuBT4J/BGGOCgw4O2lUavIo3NHjvWXEwror9HL5pr3YAvANkALcSuAdNRGopJWciEk7eN7MCAleofkfgHrFrKu2/Cbg3eMzdwMQ9O4JdiH8GpgS7HAcAfyKQ0OQDHwJvVSOGOWa2E8ghcKP/7c65uyvtvwpIABYA24A3CFwJO5gY4A4C99BtJXDv2437OfYe4IVgOy4Ntm838CbQvprtEJEIZc5V1RMgIiLhxszuBro456446MEiErEOeyZtEREJHTNrBIwiMFJTRGoxdWuKiIQ5MxtNoKv3I+fcNwc7XkQim7o1RURERMKIrpyJiIiIhJFac89ZWlqaa9eund9hiAgA2cGffTysIlhHH+/qCEErRCRKZWdnb3HOpVe1r9Z0a2ZmZrqsrCy/wxAR4MDzwtZUFcE6PPwMC0ErRCRKmVm2cy6zqn3q1hQREREJI0rORERERMKIkjMRERGRMKLkTERERCSM1JrRmiISTkJwC30IBjNpIICI+EFXzkRERETCiJIzERERkTCibk0R8cCeaVuzf7Y1f3cpSzcVsCx3J/m7SyksKaduQiypdeJp27geXZqm0KheQjWrCNaRnX3g445A1a0QEfGWkjMR8cCsHx+t3VbIW7PW8cXCTcxZm3/QMzs1SWZIpzTO7dWcPm0bYnsmm92nillVb69B3tcgIrIvJWci4pnRL2bxxcJNOKB36wbcfmoXerVKpVOTZBrWS6BOfCy7S8vZtquE5Vt2sXDDDqYuy2PCzNU8P3Ul7dPqcVnf1lzevw0pSfF+N0dEJCS0fJOI1KjNO4poUr8OAL3v/YTL+7dhRP+2tGxQp9qvsau4jP/O3cDr2Wv5bsVW6ifFcfWgdowa0oHUusEkTcs3iUgEO9DyTUrORKRGOOd4/4cN/OGdecz54xkA7Cgqof4RXvGaty6fxybl8PH8jTSql8D/nt6FYX3bEBsbs6fiIw19v5SciYhXDpScqVtTRI5YcVk5f3x3PhNmrqF36wY/bj/SxAygR8tUnryyD/PX5/On9xfwu7fn8eqM1Xx4xK8sIhKeNJWGiByR3IJiho+bzoSZa7j5pI68ccNAT+rp3iKV18YM4LERx7K5oPjH7aXlFZ7UJyLiF105E5HDtnZbIVc++x0b84t4/PIMzu7ZPLhntCf1mRnn9mrB4I5pTJ30C1bmFTLhiak8OvxY2jauV+P1edMKEZED0z1nInJYVm7ZxYinp7OzuIznrulHn7YNQx7DR3M3cOdbc6mocPz9kl6c2aP5wU8SEQkDB7rnTN2aInLINuYXcfkzMygqq+A/Ywb4kpgBnNWzOR/+cggdmiRzw8uzuPf9BermFJGIp+RMRA7J9sISrnx2Bvm7S3nhmn50b5FaxVHZeD6vfnY2ZGfTqmFdXr9+IFcPasf4KSu4+rnv2F5YUjNVoNUBRCT0PE3OzOxMM1tsZjlmdmcV+483s1lmVmZmF1fa3tvMppnZfDP7wcwu8zJOEameotJyrn1+JqvyChl3VR96tqoqMQPIDBYPZWYGCpAQF8M953fn7xf34rsVW/nF41NZnrvzyKvA81aIiOzDs+TMzGKBscBZQDdguJl12+uw1cDVwKt7bS8ErnLOdQfOBB4xswaIiG+cc/zu7XnMWr2dR4b1ZlDHNL9D2sclma15dfQA8neXcsHYKUzJ2eJ3SCIih8zLK2f9gBzn3HLnXAkwARha+QDn3Ern3A9AxV7blzjnlgYfrwc2A+kexioiB/H81JW8OWstt57SudKozPDTt10j3r15MM1T63D1c9/x7ux1fockInJIvEzOWgJrKj1fG9x2SMysH5AALKti3xgzyzKzrNzc3MMOVEQObPryPO7/cCGnHt2UW0/p7Hc4B9W6UV1ev3Egfdo25NYJs3lm8nK/QxIRqbawHhBgZs2Bl4BrnHP7DMFyzo1zzmU65zLT03VhTcQL23aVcOuE72nbqC4PX3YMMTF28JPCQP2keJ6/ph9n92zG/R8u5C//XUhFRe2YOkhEajcvJ6FdB7Su9LxVcFu1mFl94EPgd8656TUcm4hUg3OO37z5A1t3lfDsyL6k1MByTKGUFB/Lo8MzSEuez7hvlpNbUMyDF/ciPjas/18qIlHOy+RsJtDZzNoTSMqGASOqc6KZJQBvAy86597wLkQROZD/fLeGTxds4ndnH02PlvsbmRneYmOMP53fnSYpifzj0yUUFJXy2IgMkuJj/Q5NRKRKnv330TlXBtwCfAIsBCY65+ab2b1mdj6AmfU1s7XAJcBTZjY/ePqlwPHA1WY2O1h6exWriOxrVd4u7v1gPsd1TmPUkPaHeHZWsHgoKytQqsHMuOXkztw3tDufL9zMdS9kUVhSdvAq8LwVIiL70PJNIrIP5xyXPzODuWvz+fSO42meWsfvkGrMG9lr+fUbc8ho05Dx1/SlfoR11YpI7aDlm0TkkLyetZapy/K48+yjalViBnBxn1Y8OjyD2Wu2c8UzM9i2q2ZWExARqSlKzkTkZzbvKOL+DxfQr30jhvdtc5ivMiZYPDRmTKAchnN6NeepK/uwaGMBw8ZNZ3NBUdVV4HkrRET2oW5NEfmZG1/O5otFm/n41uPokJ58mK+yZ7oNDz9fLFjHEXyGTcnZwugXs2haP4lXrutPiwY/v0oYglaISJRSt6aIVMuXizfz0byN3HpK5yNIzCLH4E5pvDSqH1sKirn0qWms2Vrod0giIkrORCSgpKyC+95fQIe0eow+roPf4YRMn7aNeGV0fwqKyrjkyWk1smC6iMiRUHImIgA8P3UFy7fs4g/ndSMhLro+Gnq1asCEMQMoLa/g0qems2RTgd8hiUgUi65PYBGp0uaCIv79RQ6nHNWEk7o28TscXxzdvD6vXT+AGINh46Yzf32+3yGJSJRSciYiPPjxYorLyvn9ud38DsVXnZqkMPH6gSTFxTB8nFaNExF/KDkTiXJz1+bzRvZarh3SnvZp9WroVTOCxUMZGYFSw9ql1eO16wfSoG4CSevzOar44CsJiIjUJC/X1hSRCPDAx4toWDeeW07qVIOvml2Dr7W/Kryro3Wjuky8fiAjnp7Ohvwipo7MZFCnNM/qExGpTFfORKLYt0u38G3OFm45uTMpWsboZ5qlJjHh+gG0blSHa56fyVeLN/sdkohECSVnIlGqosLxwMeLaNmgDlcMONyVAGq3JilJTBgzkE5Nkhn9Yhafzt/od0giEgWUnIlEqf/O28DcdfnccVoXEuNia/jVjZ/m1/eI2U+rBHhVBdC4XgKvXjeA7i1SuemVWbw/Z72ndYqIKDkTiUKl5RX845PFdG2awgXHtvQ7nLCXWjeel0b149g2Dbh1wve8kb3W75BEpBZTciYShSbMXMPKvEJ+c1ZXYmM8vsJVS6QkxfPCtf0Y2LExv3p9Dq/OWO13SCJSSyk5E4kyRaXlPDZpKX3bNYzaCWcPV92EOJ4d2ZeTuqZz19tzeW7KCr9DEpFaSMmZSJSZmLWGTTuKue3ULpjH92zVRknxsTx5ZR9O79aUP72/gCe+WuZ3SCJSyyg5E4kixWXlPPHVMjLbNmRQx8Z+hxOxEuNiGXt5Bucd04IHPl7EI58vwTnnd1giUktoElqRKDIxay0b8ot48OJeump2hOJjY3jkst4kxMbwyOdLKSqt4DdndtW/q4gcMSVnIlGiuKycJ77MIaNNA4Z4Ptv9Ux6/PvCU93UcrIbYGOPvF/ciKT6GJ79eRlFpOX88r5sSNBE5IkrORKLEG9lrWZ9fxF8vCsVVszEevz4wxvs6qlNDTIxx/wU9SIyLZfyUFZSUV3D/0B7EaBSsiBwmJWciUaCkrILHv1xG79YNOL6z1oisaWbGH849mqT4GB7/ahnFpRU8eHEvTVMiIofF0wEBZnammS02sxwzu7OK/ceb2SwzKzOzi/faN9LMlgbLSC/jFKnt3py1lnXbd3PrqZ1D1OU2Lli8rGJcoHhZBdVvhZnxf2d05Y7TuvDmrLXcOuF7SssrvAxPRGop82qEkZnFAkuA04C1wExguHNuQaVj2gH1gV8B7znn3ghubwRkAZmAA7KBPs65bfurLzMz02VlZXnSFpFIVlZewcn//JoGdeN59+bBIUrO9tTh4QjGPe3wcJTk4bbiqa+X8dePFnF6t6Y8OuJYD5bHEpFIZ2bZzrnMqvZ5eeWsH5DjnFvunCsBJgBDKx/gnFvpnPsB2Pu/l2cAnznntgYTss+AMz2MVaTW+mjeRlZvLeSmEzvqRvUQuf6EjtxzXjc+XbCJ0S9mU1hS5ndIIhJBvEzOWgJrKj1fG9xWY+ea2RgzyzKzrNzc3MMOVKS2cs7x5NfL6JBWj9O6NfM7nKhy9eD2PHBRT75dmsvlz8xge2GJ3yGJSISI6ElonXPjnHOZzrnM9PR0v8MRCTvf5mxh/vodjDm+g25O98Flfdvw+OUZzF+3g0uenMaG/N1+hyQiEcDL5Gwd0LrS81bBbV6fKyJBT369jCYpifwio7oXraWmndmjOc9f25cN+UVc/MQ0luXu9DskEQlzXiZnM4HOZtbezBKAYcB71Tz3E+B0M2toZg2B04PbRKSa5q7NZ0pOHqOGtNcN6T4b1DGNCWMGUFRaziVPTmPu2ny/QxKRMOZZcuacKwNuIZBULQQmOufmm9m9ZnY+gJn1NbO1wCXAU2Y2P3juVuA+AgneTODe4DYRqaYnv15GSlIcI/q38TsUAXq0TOX1GwZSJz6WYeOmMTVni98hiUiY8mwqjVDTVBoiP1m5ZRcn//Mrrj+hI7858yi/w5FKNuYXcdX4GazcUsi/hvXmrJ7N/Q5JRHzg11QaIuKTpycvJy42hmsGt/M7FNlLs9QkJl4/kB4t63Pzq7P4z3er/Q5JRMKMkjORWia3oJjXs9dyUUYrmqQk+R2OVKFB3QRevq4/x3dJ57dvzeWxSUupLb0YInLklJyJ1DIvTVtJaXkFY47v4GMUfYLFyyr6BIqXVeBdK+omxPH0VZlc0LsF//h0CX94dx7lFUrQREQLn4vUKkWl5bw8YzWnHNWU9mn1fIxkVgiq8L4Or2uIj43hoUt70yy1Dk9+vYyN+cU8OvxY6iRodK1INNOVM5Fa5J3v17F1VwnXHdfe71CkmmJijDvPOop7h3bni0WbGP70dPJ2Fvsdloj4SMmZSC3hnOPZb1fQvUV9+rdv5Hc4coiuGtiOJy7vw8INO7joiamsytvld0gi4hMlZyK1xDdLt7B0805GDWmvBc4j1Jk9mvHq6P5s313KhY9PZc6a7X6HJCI+UHImUks8++0KmqQkcm6vFn6HIkegT9tGvHnjIOomxjJs3HQmLdrkd0giEmJKzkRqgaWbCvhmSS5XDWxLQpze1pGuY3oyb944iE5NkrnuhSzNhSYSZTRaU6QWGD9lBYlxMYzo39bvUIJGh6AK7+sIQSv2q0lKEhPGDODmV2fx27fmsmZrIb86vSsxMeqyFqntlJyJRLi8ncW8NWsdF2a0olG9BL/DCRoXgiq8ryMErTigeomBudDufnc+j3+1jJV5u/jnJb011YZILaf+D5EI98qM1RSXVTBqSDu/QxEPxMfG8Jdf9OD35xzNR/M2ctm4aWzeUeR3WCLiISVnIhGsuKycF6et4sSu6XRqkuJ3OJVkB4uXVWQHipdV4HkrqsXMuO64Doy7MpOczTsZOnYKC9bv8DssEfGIkjORCPb+nA1s2VnMqCHhNulsZrB4WUVmoHhZBZ634pCc1q0pr98wEOfg4ien8vkCjeQUqY2UnIlEqD2TznZtmsKQTml+hyMh0r1FKu/eMpiO6cmMfimLZyYv16LpIrWMkjORCDVteR4LN+zg2iHtNOlslGlaP4nXrh/AGd2acf+HC7nzzbkUl5X7HZaI1BAlZyIRavy3K2hcL4GhvVv6HYr4oG5CHI9fnsEtJ3Xitaw1DBs3nU0aKCBSKyg5E4lAy3N38vnCzVw+oC1J8ZpWIVrFxBi/OqMrj1+eweKNBZz76Ldkr9rqd1gicoSUnIlEoOenriQhNoYrB4TLpLPip7N7NuftmwZTNyGw5NOrM7SigEgkU3ImEmHyd5fyRvZazu/dgvSURL/DkTDRtVkK7908hIEd07jr7bnc9fZcSsoq/A5LRA6DVggQiTATZ66hsKScawa38zuUA8gKQRXe1xGCVtSo1LrxPHd1X/7x6WKe+GoZC9bv4LERx9KqYV2/QxORQ+DplTMzO9PMFptZjpndWcX+RDN7Lbh/hpm1C26PN7MXzGyumS00s996GadIpCgrr+D5qSvp374R3Vuk+h3OAfQJFi+r6BMoXlaB562ocbExxm/OPIrHL88gZ/NOzvn3t5oPTSTCeJacmVksMBY4C+gGDDezbnsdNgrY5pzrBDwMPBDcfgmQ6JzrSeCz8fo9iZtINPtswSbWbd/NNYPDbdJZCTdn92zOB/8zhFYN63Ddi1n85b8LKS1XN6dIJPDyylk/IMc5t9w5VwJMAIbudcxQ4IXg4zeAUywwYZMD6plZHFAHKAG0VolEveemrKRVwzqc1q2p36EcxJhg8bKKMYHiZRV43gpPtUurx5s3DuKqgW0Z981yLn1qGuu27/Y7LBE5CC+Ts5bAmkrP1wa3VXmMc64MyAcaE0jUdgEbgNXAP5xzGh8uUW3euny+W7mVqwe1IzYm3CedfTpYvKzi6UDxsgo8b4XnkuJjuXdoD8aOyGDppp2c/a/JfDR3g99hicgBhOtozX5AOdACaA/8r5l12PsgMxtjZllmlpWbmxvqGEVCavyUFdRLiOXSvq39DkUi0Dm9At2cbRvX5cZXZnHHxNnsKCr1OywRqYKXydk6oPK3SKvgtiqPCXZhpgJ5wAjgY+dcqXNuMzCFKtYfds6Nc85lOucy09PTPWiCSHjYXFDE+3PWc0lma+onxfsdjkSoPd2ct57SmXdnr+esRyYzbVme32GJyF68TM5mAp3NrL2ZJQDDgPf2OuY9YGTw8cXAJBdYwXc1cDKAmdUDBgCLPIxVJKy9PH01ZRWOkYPa+R2KRLj42BhuP60Lb9wwkIS4GEY8M50/f7iAolKtzSkSLjxLzoL3kN0CfAIsBCY65+ab2b1mdn7wsGeBxmaWA9wB7JluYyyQbGbzCSR5zznnfvAqVpFwVlRazqszVnFy1ya0T6vndzhSSxzbpiEf/nIII/q14enJKzj7X5OZvlxX0UTCgQUuVEW+zMxMlxWCSSlFQu31rDX83xs/8Mp1/RncKc3vcKppz4AFDz9fLFiHh59hIWhFWJi8NJe73p7Lmq27Gd6vNXeedTSpddR9LuIlM8t2zu1zyxZohQCRsOacY/yUlXRtmsKgjo39DucQZISgCu/rCEErwsJxndP59LYTeOTzJTw9eTlfLNzMPed356wezTAL95HBIrVPuI7WFBFgxoqtLNywg2sGt4uwL8nsYPGyiuxA8bIKPG9F2KiTEMtvzz6ad28eQnpKIje9Mosrnp3Bkk0FfocmEnWUnImEsfHfrqBh3XguOHbvKQJFvNGzVSrv3jyYe4d2Z966HZz1r8nc89588gs17YZIqFQrOTOzt8zsHDNTMicSIqvzCvls4SZG9G9DUnys3+FIFImLjeGqge348lcnMrxfa16ctpKT/vkVL0xdSUmZloAS8Vp1k63HCcw9ttTM/mZmXT2MSUSAF6atJNaMKwe08zuUw2D8dDu9V1XYT4MCvKoCz1sR1hrVS+D+C3ry/v8MoXOTZP743nxOeegr3vl+HRUVtX2YhIh/qpWcOec+d85dTuD+2JXA52Y21cyuMTMN6RGpYQVFpbw2cw3n9GpOs9Qkv8ORKNe9RSoTxgzg+Wv6kpIYz22vzebsf09m0qJN1JYR/yLhpNrdlGbWGLgauA74HvgXgWTtM08iE4lib2SvZWdxGdcMbu93KCIAmBkndm3CB/8zhH8PP5bdpeVc+3wWv3h8Kp8vUJImUpOqNZWGmb0NdAVeAs5zzu1ZNfc1M9PkYiI1qKLC8fzUlWS0aUDv1g38DkfkZ2JijPOPacFZPZoxMWsNT3y1jOtezOKoZincfFInzu7ZnNiYaO4MFjly1b1y9rRzrptz7q97EjMzSwTY3wRqInJ4Ji3azKq8Qq4doqtmEr7iY2O4vH9bvvzVifzzkmMoLa/gf/7zPac99DUTs9Zo4IDIEahucnZ/Fdum1WQgIhLwzLfLaZ6axBndm/kdishBxcfGcFGfVnx6+wmMHZFBYnwsv37jBwY/MInHJi1l664Sv0MUiTgH7NY0s2ZAS6COmR3LTwOX6gN1PY5NJOrMXZvP9OVbuevso4iP1cw1EjliY4xzejXn7J7N+GbpFp79dgX/+HQJj07K4cKMVowa0o5OTVL8DlMkIhzsnrMzCAwCaAU8VGl7AXCXRzGJRK2nJy8nOTGOYf3a+B3KEXoqBFV4X0cIWlHrmBkndEnnhC7pLNlUwPhvV/DmrLX857vVnNg1nVFD2jOkU1qErXghElrVWvjczC5yzr0ZgngOmxY+l0i3bvtujn/wS64Z1I7fn9vN73BEasyWncW8Mn01L01fyZadJRzVLIVrh7RnaO8WJMZpgmWJTgda+PyAyZmZXeGce9nM/hfY50Dn3ENVnOYLJWcS6f784QLGT1nJN78+iZYN6vgdjkiNKy4r593Z6xn/7QoWbSwgLTmRkQPbcvmAtjSql+B3eCIhdaDk7GDdmvWCP5NrNiQRqWxHUSn/+W4N5/RsXksSs3HBn2M8rCJYxxjv6ghBK6JKYlwsl2a25pI+rZiSk8fTk5fzz8+WMParHC7KaMW1Q9rTMV1fNyLV6taMBLpyJpHs6W+W8+f/LuT9W4bQs1Wq3+HUgD33E3n4+bLnniUPP8NC0Iqot2RTAc9OXsHbs9dRUlbBKUc1YdRx7RnYobHuS5Na7UBXzqq78PmDZlbfzOLN7AszyzWzK2o2TJHoVFpewfgpKxjQoVEtScxEqq9L0xQeuLgXU35zMree0pnv12xnxNMzOP+xKXw6f6NWHpCoVN2x+qc753YA5xJYW7MT8H9eBSUSTf47dwMb8osYc3wHv0MR8U16SiK3n9aFqXeezF8v7MmOolLGvJTNOf/+lk+UpEmUqW5ytufetHOA151z+R7FIxJVnHOM+2Y5HdPrcWKXJn6HI+K7pPhYhvdrwxd3nMA/LjmGXSVlXP9SNmcrSZMoUt3k7AMzWwT0Ab4ws3SgyLuwRKLDtOV5zF+/g9HHdSBG6xGK/CguNoaL+7TiiztO4J+XHENRaTnXv5TNJU9O4/vV2/wOT8RT1UrOnHN3AoOATOdcKbALGOplYCLR4JnJK0hLTuCCY1v6HYpIWIoLLg/12e3H89cLe7Iyr5BfPD6VW16dxZqthX6HJ+KJg02lUdlRQDszq3zOizUcj0jUWLKpgEmLNnP7qV1IitdEnCIHEhcbw/B+bTjvmBaM+3oZ4yYv59MFm7j5xE7ccGIHTWYrtUp1R2u+BPwDGAL0DZYqh3/udd6ZZrbYzHLM7M4q9iea2WvB/TPMrF2lfb3MbJqZzTezuWaWVM02iUSEJ79aRt2EWK4a2NbvUDzg8HwCCuc8nUYDQtIKOUTJiXHccXpXvvrVSZzerSkPf76Esx6ZzNRlW/wOTaTGVPfKWSbQzR3CnZhmFguMBU4D1gIzzew959yCSoeNArY55ymYHS4AACAASURBVDqZ2TDgAeCy4NW5l4ErnXNzzKwxUFrdukXC3Zqthbw7Zz1XD2pHQ82MLnLImqUm8diIDC7us5m7353PiKdncOGxLbn7vG40qKv3lES26g4ImAc0O8TX7gfkOOeWO+dKgAnse5/aUOCF4OM3gFMsMOvg6cAPzrk5AM65POdc+SHWLxK2xn2znBiD645r73coIhHtxK5N+PT247n5pI68N2c9ZzzyDd8syfU7LJEjUt3kLA1YYGafmNl7e8pBzmkJrKn0fG1wW5XHOOfKgHygMdAFcMH6ZpnZr6uqwMzGmFmWmWXl5urNKJEht6CYiVlruPDYVjRPrQ1LNVWlT7B4WUWfQPGyCjxvhdSApPhY/u+Mo3j7psGkJMVz1fjvuPvdeewu0f/pJTJVt1vzHi+DqEIcP93fVkhg+o5s59wXlQ9yzo0juPxdZmambg2RiDB+ygpKyiu4/oTaPOnsrBBU4X0dIWiF1KCerVL54H+G8ODHixk/ZQXTluXxxBUZdGqS4ndoIoekulNpfE1gZYD44OOZHPxzax3QutLzVsFtVR4TvM8sFcgjcJXtG+fcFudcIfBfIKM6sYqEs/zdpbw8bRVn92xOBy3wLFLjkuJjufu8brw8qj/bCks4/7EpvDt7768ekfBW3dGaowncE/ZUcFNL4J2DnDYT6Gxm7c0sARgG7N0V+h4wMvj4YmBScNDBJ0BPM6sbTNpOABYgEuFenr6KguIybjyho9+hiNRqQzqn8eEvj6NHi1RunTCbu96eS3GZujklMlT3nrObgcHADgDn3FLggGvNBO8hu4VAorUQmOicm29m95rZ+cHDngUam1kOcAdwZ/DcbcBDBBK82cAs59yHh9IwkXCzu6Sc8d+u4IQu6fRoqQXORbzWtH4Sr47uz/UndODVGau5/OkZ5O0s9jsskYOq7j1nxc65ksBAyh+7IA96j5dz7r8EuiQrb7u70uMi4JL9nPsygek0RGqFiVlryNtVwk0n6qqZSKjExcbw27OOpkeLVH71+hyGjp3CsyP70rWZ7kOT8FXdK2dfm9ldQB0zOw14HXjfu7BEapfisnKe+noZfdo2pF/7Rn6HIxJ1zjumBROvH0hJWQUXPj6FSYs2+R2SyH5VNzm7E8gF5gLXE7ga9nuvghKpbV7PWsv6/CJuPaUze65A126jg8XLKkYHipdV4HkrJISOad2A924ZQvv0eox+MZuJWWsOfpKID6y6k/6bWTqAcy4sJxTLzMx0WVlZfochso/isnJO+vtXNEtN4s0bB0VJciYSvnYVl3HDy9lMXrqFX5/ZlRtP6Kj3pYRccIqwKpfCPOCVMwu4x8y2AIuBxWaWa2Z3H+g8EfnJnqtmt53aRV8AImGgXmIcz47sy/nHtODBjxdz3wcLqajQVJkSPg7WrXk7gVGafZ1zjZxzjYD+wGAzu93z6EQiXHFZOY9/mUNGmwYc1znN73BCKDtYvKwiO1C8rALPWyE+SYiL4ZHLenP1oHaMn7KCO9/6QQmahI2Djda8EjjNObdlzwbn3HIzuwL4FHjYy+BEIt2eq2Z/u6hXlF0123Ol3sMvu8xgHdW8NeOwqgj+1Fd27RQTY/zxvG7UT4rj35NyKK+ABy/uRWxMNL1XJRwdLDmLr5yY7eGcyzWzeI9iEqkVSsoqovSqmUjkMDPuOL0rsTExPPz5EsorKvjHJccQF1vd8XIiNe9gyVnJYe4TiXqvZ6+J0qtmIpHn1lM7Exdr/P2TxZQ7ePhSJWjin4MlZ8eY2Y4qthuQ5EE8IrVCUWk5j03K4VhdNROJGDef1InYGONvHy0i1uChS3sToy5O8cEBkzPnXGyoAhGpTV6evooN+UX889JjdNVMJILccEJHyiscf/9kMfUS47j/gh56D0vIVXf5JhGppoKiUsZ+mcNxndMY1FFXzUQizc0ndWJncRlPfLWM5MQ47jzrKCVoElJKzkRq2NOTV7CtsJRfn3GU36GIyGH69Rld2VlUxlPfLCclKY5bTu7sd0gSRZScidSgLTuLeWbycs7p1ZyerVL9DsdHIVitIwQrgmjNkehlZvzp/O7sKi7jH58uIbVOPFcObOd3WBIllJyJ1KDHJuVQXFbB/57Wxe9QfNYnBFV4X0cIWiFhLCbGePDiXuwoKuPu9+bTpH4SZ3Rv5ndYEgU0TlikhqzZWsirM1ZzaWYrOqQn+x2OiNSAuNgYHh1+LL1bN+CX//me7FVb/Q5JooCSM5Ea8sjnSzGDX56ie1NgTLB4WcWYQPGyCjxvhUSAOgmxPDuyLy0a1GHUC1ksy93pd0hSy5nzcOmTUMrMzHRZIbgHRaQq89blc95j3zLm+A789qyj/Q4nDOwZ2ebh58ue0XMefoaFoBUSQVbnFXLhE1NIio/lrZsG0SRF033K4TOzbOdcZlX7dOVM5Ag557jvgwU0rJvAzSd18jscEfFIm8Z1GX91X7buKuGa52ayq7jM75CkllJyJnKEPpm/iRkrtnL7aV2on6QlZ0Vqs16tGjB2RAYLN+zgttdmU1Gh66pS85SciRyB4rJy/vrRQro0TWZ439Z+hyMiIXDSUU24+9xufLZgEw98ssjvcKQW0lQaIkfgxamrWJVXyAvX9tMiySJRZOSgdizL3cVTXy+nY1oyl+o/Z1KD9G0icpjydhbz70lLOaFLOid0Sfc7HBEJITPjj+d147jOadz19lymL8/zOySpRTxNzszsTDNbbGY5ZnZnFfsTzey14P4ZZtZur/1tzGynmf3KyzhFDsc/Pl1CYUk5vz9HozP3lREsXlaRESheVoHnrZAIFhcbw2MjMmjbuC43vJzNyi27/A5JagnPkjMziwXGAmcB3YDhZtZtr8NGAducc52Ah4EH9tr/EPCRVzGKHK7Za7YzYeZqrh7Ujs5NU/wOJwxlB4uXVWQHipdV4HkrJMKl1oln/NV9MeDaF2aSX1jqd0hSC3h55awfkOOcW+6cKwEmAEP3OmYo8ELw8RvAKWaByYvM7AJgBTDfwxhFDll5heP378wlPTmR207VhLMi0a5t43o8dWUma7YWctOr2ZSWV/gdkkQ4L5OzlsCaSs/XBrdVeYxzrgzIBxqbWTLwG+BPB6rAzMaYWZaZZeXm5tZY4CIH8up3q5m3bge/O+doUjR1hogA/do34i+/6MmUnDzu+2CB3+FIhAvXAQH3AA875w64RoZzbpxzLtM5l5merhuyxXtbdhbz948XMbBDY84/poXf4YQx46f59b2qwn5aJcCrKvC8FVKLXJLZmjHHd+DFaat4afoqv8ORCOblVBrrgMpji1sFt1V1zFoziwNSgTygP3CxmT0INAAqzKzIOfeYh/GKHNTfPlpEYUk5913QHfM4MRCRyPObM48iZ/NO7nlvPh3T6jGoU5rfIUkE8vLK2Uygs5m1N7MEYBjw3l7HvAeMDD6+GJjkAo5zzrVzzrUDHgH+osRM/DY1ZwtvZK9l1HHt6dREgwBEZF+xMca/hvWmY3o9bnxlFis0glMOg2fJWfAesluAT4CFwETn3Hwzu9fMzg8e9iyBe8xygDuAfabbEAkHu0vKufOtubRtXJfbTunidzgiEsZSkuJ55qq+xBiMemEm+bs1glMOjTlXO9YFy8zMdFlZWX6HIbXUnz9cwNOTV/Dq6P4M6qhuioPb0+Xr4efLnm5lDz/DQtAKqcWmL8/jimdmMKhTGuNHZmoVEfkZM8t2zmVWtU9/KSIHMWfNdp79dgXD+7VRYiYi1TagQ2Puv6AH3yzJ5c//Xeh3OBJBtLamyAGUlFXwmzd/ID0lkd+efZTf4YhIhBnWrw2LNxXw3JSVdGmawvB+bfwOSSKAkjORAxj7ZQ6LNhbw9FWZ1NecZofgqRBU4X0dIWiFRIHfnX00y3J38Yd35tE+rR4DOjT2OyQJc7rnTGQ/vl+9jYufnMZ5vZrzyLBj/Q5HRCLYjqJSfjF2Clt3lfDuzUNo07iu3yGJz3TPmcghKiwp446Jc2iaksifhvbwOxwRiXD1k+J5ZmRfKlxgBGdBkUZwyv4pOROpwp8/XMjKvF3889LepNZRd+ahGxcsXlYxLlC8rALPWyFRpH1aPZ64PIMVW3bxy/98T3lF7ei5kpqnbk2RvUxatIlrn89izPEduOvso/0OJ0JpKg2R/Xl5+ip+/848fcZEuQN1a2pAgEgluQXF/PqNuRzVLIX/PV2TzYpIzbtiQFuWbCpg3DfL6dwkmUsyWx/8JIkq6tYUCSqvcNw64Xt2Fpfyr2HHkhgX63dIIlJL3X1uN4Z0SuOut+cyc+VWv8ORMKPkTCToX18sZeqyPO4d2oOuzbR2poh4Jy42hrEjMmjVsC43vJTNmq2FfockYUTJmQgweWkuj05aykUZrbhUXQwiEgKpdeN5ZmQmpeUVjH4xi53FZX6HJGFCyZlEvU07irhtwmw6N0nmvgu6+x2OiESRjunJjL08g6Wbd3LbhNlUaASnoORMolxxWTk3vTKL3aXlPH55BnUTNEZGRELruM7p/OGco/l84Sb+/uliv8ORMKBvIolazjl+//Y8sldtY+yIDDo10X1mNScE//sPwTRAuoYhoTJyUDuWbN7JE18to3OTZC7MaOV3SOIjXTmTqPXclJW8nr2WX57ciXN6Nfc7HBGJYmbGn87vzoAOjbjzzbnMWr3N75DER0rOJCpNXprL/R8u4PRuTbntVM1nJiL+i4+N4YnL+9C8QRJjXsxm3fbdfockPlFyJlFnWe5Obnn1e7o0TeHhy3oTE2MHP0kOUZ9g8bKKPoHiZRV43gqRn2lYL4FnR2ZSXFrO6BeyKCzRCM5opORMosrmHUWMHP8d8bHG01dlUi9Rt116Y1aweFnFrEDxsgo8b4XIPjo1SeHfI45l0cYd3PHaHI3gjEJKziRqFBSVcvVzM9m6q4TxV/eldaO6fockIlKlk7o24a6zj+bj+Rt56LMlfocjIabLBhIVSsoquOmVWSzeVMCzIzPp1aqB3yGJiBzQqCHtydm8k8e+zKFpahJXDmjrd0gSIkrOpNYrr3D8+o05TF66hb9f3IsTuzbxOyQRkYMyM+67oAe5BcXc/e48GtaN59xeLfwOS0JA3ZpSq1VUOO56ay7vzF7Pr8/syiVamklEIkh8bAxjL8+gb9tG3P7abL5Zkut3SBICniZnZnammS02sxwzu7OK/Ylm9lpw/wwzaxfcfpqZZZvZ3ODPk72MU2on5xx/fG8+r2Wt4ZendOamEzv5HZKIyCFLio/l6ZGZdGqSwvUvZfO95kCr9TxLzswsFhgLnAV0A4abWbe9DhsFbHPOdQIeBh4Ibt8CnOec6wmMBF7yKk6pnZxz3P/hQl6avorrT+jA7ad29jukKDM6WLysYnSgeFkFnrdCpFpS68TzwrV9SU9J5JrnZ7JkU4HfIYmHzHm0BIqZDQTucc6dEXz+WwDn3F8rHfNJ8JhpZhYHbATSXaWgzMyAPKC5c654f/VlZma6rKwsT9oikaWiwnHfhwt4bspKrhncjrvP7Ubgz0hEJLKtzivkoien4hxMGDOATk2S/Q5JDpOZZTvnMqva52W3ZktgTaXna4PbqjzGOVcG5AON9zrmImBWVYmZmY0xsywzy8rNVT+8BG7+/82bP/DclJWMGtJeiZmI1CptGtfl1ev6A44RT09nxZZdfockHgjrAQFm1p1AV+f1Ve13zo1zzmU65zLT09NDG5yEnZKyCn75n+95PXstt53amd+fc7QSM99kB4uXVWQHipdV4HkrRA5Z56YpvHLdAMoqHMPHTWdVnhK02sbL5GwdUHloXKvgtiqPCXZrphLowsTMWgFvA1c555Z5GKfUAjuLy7juxSw+nLuB359zNLed2kWJma8yg8XLKjIDxcsq8LwVIoela7MUXrmuP0Vl5QwfN53VeYV+hyQ1yMvkbCbQ2czam1kCMAx4b69j3iNwwz/AxcAk55wzswbAh8CdzrkpHsYotcCG/N1c8uQ0puRs4YGLenLdcR38DklExHNHN6/Py6P6s6uknEufmkbOZg0SqC08S86C95DdAnwCLAQmOufmm9m9ZnZ+8LBngcZmlgPcAeyZbuMWoBNwt5nNDhbNHCr7mL8+nwvGTmHN1kLGX92Xy/q28TskEZGQ6dEylQljAl2clz41nXnr8v0OSWqAZ6M1Q02jNaPPZws2cduE76lfJ57xV/fl6Ob1/Q5JfrSnS9nDz5c93dYefoaFoBUiNWLFll1c8cwMduwuZfw1fenbrpHfIclB+DVaU8QT5RWOf366mNEvZtEhPZl3bh6sxExEolr7tHq8fsNA0usncuWzM/hy0Wa/Q5IjoORMIsr2whKufX4mj07K4ZI+rXj9hoE0rZ/kd1giIr5r0aAOE68fSMf0ZK57MYuXpq/yOyQ5TErOJGJkr9rGuY9+y9RlW/jzL3rw4MW9SIqP9TssEZGwkZacyGvXD+SELun84Z153P/BAsor1DEfaeL8DkDkYMorHI9/mcMjXyyleWoSr10/kIw2Df0OSw4oBPd/huAeU93FKpEoOTGOcVf24f4PF/LMtytYtbWQfw3rTd0EfeVHCg0IkLC2dlshd7w2h+9WbuX8Y1pw/y96UD8p3u+wREQiwnNTVnDfBwvo2qw+T13RhzaN6/odkgRpQIBEnIoKx4vTVnLGw98wf30+D116DP8a1luJmYjIIbhmcHuevbov67YVcu6jk5m0aJPfIUk1KDmTsLMsdyeXjZvG3e/OJ6NtQz6+7XguzGilGf8jyphg8bKKMYHiZRV43goRz53UtQkf/M9xtGpYl2ufz+KhTxfrPrQwp25NCRtFpeWM+2Y5j32ZQ534WP5wbjcuymippCwiaZ4zkXBTVFrOH96Zx+vZa+nbriEPXdqb1o3UzemXA3VrKjkT3znn+GjeRv784ULWbd/NOT2b88fzu9EkRVNkRC4lZyLhyDnH29+v44/vzscB9w7tzi+O1X+C/XCg5ExDN8RX89fnc/8HC5m2PI+jmqXwn9EDGNixsd9hiYjUSmbGhRmt6NuuEXdMnM0dE+fw2YJN/On87jTRnJFhQ8mZ+CJn804e/nwJH/6wgQZ147nvgh4M79uauFjdBiki4rXWjeoyYcxAnvpmGY98vpRvc7bw27OOZljf1sTE6Cqa39StKSG1Oq+Qf09ayluz1lInPpZrh7TnuuM6kFpHozBrF3VrikSK5bk7uevtuUxfvpW+7Rryp/N70K2FlsTzmu45E9/NXZvPU98s479zNxAXG8NVA9py44kdaZyc6Hdo4gklZyKRxDnH69lr+ct/F5K/u5RL+rTiV6d3VVenh3TPmfiivMLx9ZLNPDN5BVOX5ZGSGMfo4ztw7eD2Wg+z1ssIQRXe1xGCVoiEBTPj0szWnNGtGY9OWsoL01bywQ8bGH1cB64d0l69GyGmK2dS4zbvKOK1mWuYMHMN67bvpmn9REYNac+wfm00iayISARYlbeLv320iI/mbSQlKY5rBrfn2sHtaFA3we/Qag11a4rnisvK+XpxLm/NWsfnCzdRVuEY3KkxI/q15bRuTUmI043+IiKRZv76fB79IoeP528kOTGOYX1bc9XAdloGqgYoORNPlFc4pi3L47056/h43kZ2FJXRqF4Cl/RpxbB+bWifVs/vEEVEpAYs2riDsV8u46O5Gyh3jlOOasrIQW0Z1DGNWI3uPCxKzqTGFBSVMnnpFr5YuJmvFm8mb1cJ9RJiOaNHM84/pgWDO6URr+kwRAMCRGqljflFvDJjFa/OWE3erhKapyZxwbEtuSijJZ2apPgdXkRRciaHray8gnnrdzB9eR7fLt3CjBV5lJY7UuvEc2LXdM7o3oyTj2pCUnys36FKWFFyJlKbFZWW8/nCTbw1ax1fL8mlvMLRo2V9zujWjNO6N6Vr0xStOnAQSs6k2gpLyliwfgfZq7YxfXkeM1duY2dxGQCdmiRzylFNOPmoJvRp21ATxsoBKDkTiRa5BcW8N2c9789Zz+w12wFo1bAOpx7dlEEdG9O/Q2ON9qyCkjOpUv7uUnI2F7BgQwE/rNnO3HX5LNlUQEXwT6Jjej0GdGjMgA6N6d+hkda6lEOg5EwkGm0uKOKLhZv5fMEmvs3ZQnFZBWbQvUV9BrRvTO82DTimVQNaNawT9VfWlJxFseKyctZvL2LttkJWby1k6aad5GzeydLNBWzaUfzjcY3qJdCrVSq9WjWgV8tUjmndgPQUTRArh0vJmUi0Ky4r5/vV25m+PI9py/L4fvV2SsorAGhQN56eLVPp1qI+HdOT6dQkmY7pyVF1hc235MzMzgT+BcQCzzjn/rbX/kTgRaAPkAdc5pxbGdz3W2AUUA780jn3yYHqirbkrKLCsX13KVt2FpNbECh7Hm/aUcS67btZs3U3mwqKfvbdVTchlk5NkuncJIXOTZPp3CSZLk1T9L8YqWFKzkTk50rKKliyqYA5a7czd20+c9bms2zzzh8TNoD0lETaN65HiwZJtGhQJ1gCj9OSE2lYN6HWjA71ZYUAM4sFxgKnAWuBmWb2nnNuQaXDRgHbnHOdzGwY8ABwmZl1A4YB3YEWwOdm1sU5V+5VvDXNOUd5haOs4qefZeUVFJVVUFRaHixVPC4rZ3dJoBQUl7Fjdyk7ikrZsbuMHUWlFBQFtuXvLqWsYt+vjIS4GNKTE2nVsA6DO6XRulEdWjWsS+uGdWjVqC7N6ydpUVsREQm5hLgYerRMpUfLVOgf2FZWXsHabbvJ2byTZbmBnp3VWwvJXr2ND37YsM/3nBmk1omnUb0EGtdLoGHdQKmXGEdyYmzgZ1IcyYlx1EuIo15iHEnxMSTExZAYF0NCbCwJcTE/ldgY4mMt7C5OeLl8Uz8gxzm3HMDMJgBDgcrJ2VDgnuDjN4DHLPAvNBSY4JwrBlaYWU7w9aZ5GO9BDR07hYKi0kCyVV45+ar4WRJWHixHKik+hvpJ8dSvE0/9pDga1k2gbeN61E+KI7VOPGnJiaSnBMqex/WT4sLuj0yi0VMhqML7OkLQCpGoFhcbQ7u0erRLq8epNP3ZvvIKx5adxazbvpv123eTt7OErbt+XlblFTJ7zXZ2FZexq+Twrt+YQXxsIFGLjTHiY40O6clMvH5gTTTxsHiZnLUE1lR6vpYfc+V9j3HOlZlZPtA4uH36Xue23LsCMxsDjAFo06ZNjQW+Px3T61FSVkFcjBEbExP4GWvB5/bz7Xuex/58e1J8DEnxsSTGxf74OCk+ljrxlZ7HxVInIVaz6ksEGxOCKryvIwStEJH9iI0xmtZPomn9JDLaNDzo8RUVjsLScnYVl7GzuOzHn8WlFRSXVVBSXkFJ2Z5S/rPnxeUVlJb9dLGlcbK/91xH9MLnzrlxwDgI3HPmdX0PXdrb6ypERETkMMTEGMmJgS7Npgc/PKx5eWlmHdC60vNWwW1VHmNmcUAqgYEB1TlXRMLWj/9v8rCKcYHiZRV43goRkX14NlozmGwtAU4hkFjNBEY45+ZXOuZmoKdz7obggIALnXOXmll34FUC95m1AL4AOh9oQEC0jdYUCW8arSkiciC+jNYM3kN2C/AJgak0xjvn5pvZvUCWc+494FngpeAN/1sJjNAkeNxEAoMHyoCbI2mkpoiIiMjh0iS0IuIBXTkTETmQA10503BAERERkTCi5ExEREQkjCg5ExEREQkjSs5EREREwkitGRBgZgXAYr/j8FEasMXvIHwSzW2H6G6/2h69orn90dx2qD3tb+ucS69qR0SvELCXxfsb9RANzCwrWtsfzW2H6G6/2h6dbYfobn80tx2io/3q1hQREREJI0rORERERMJIbUrOon0JvGhufzS3HaK7/Wp79Irm9kdz2yEK2l9rBgSIiIiI1Aa16cqZiIiISMRTciYiIiISRiIuOTOzM81ssZnlmNmdVexPNLPXgvtnmFm70EfpDTNrbWZfmtkCM5tvZrdWccyJZpZvZrOD5W4/YvWCma00s7nBdu2zyr0F/Dv4u//BzDL8iLOmmVnXSr/P2Wa2w8xu2+uYWvV7N7PxZrbZzOZV2tbIzD4zs6XBnw33c+7I4DFLzWxk6KKuGftp+9/NbFHw7/ptM2uwn3MP+B6JBPtp/z1mtq7S3/fZ+zn3gN8P4W4/bX+tUrtXmtns/Zwb0b/7/X2/Rcv7fh/OuYgpQCywDOgAJABzgG57HXMT8GTw8TDgNb/jrsH2Nwcygo9TgCVVtP9E4AO/Y/Wo/SuBtAPsPxv4CDBgADDD75g9+DeIBTYSmLyw1v7egeOBDGBepW0PAncGH98JPFDFeY2A5cGfDYOPG/rdnhpo++lAXPDxA1W1PbjvgO+RSCj7af89wK8Oct5Bvx/CvVTV9r32/xO4uzb+7vf3/RYt7/u9S6RdOesH5DjnljvnSoAJwNC9jhkKvBB8/AZwiplZCGP0jHNug3NuVvBxAbAQaOlvVGFlKPCiC5gONDCz5n4HVcNOAZY551b5HYiXnHPfAFv32lz5vf0CcEEVp54BfOac2+qc2wZ8BpzpWaAeqKrtzrlPnXNlwafTgVYhDyxE9vO7r47qfD+EtQO1Pfg9dinwn5AGFSIH+H6Livf93iItOWsJrKn0fC37Jic/HhP8MMsHGockuhAKdtceC8yoYvdAM5tjZh+ZWfeQBuYtB3xqZtlmNqaK/dX5+4h0w9j/h3Nt/b3v0dQ5tyH4eCPQtIpjouFv4FoCV4ircrD3SCS7JditO34/XVu1/Xd/HLDJObd0P/trze9+r++3qHzfR1pyJoCZJQNvArc553bstXsWgS6vY4BHgXdCHZ+HhjjnMoCzgJvN7Hi/AwolM0sAzgder2J3bf6978MF+jKibh4gM/sdUAa8sp9Daut75AmgI9Ab2ECgey/aDOfAV81qxe/+QN9v0fS+j7TkbB3QutLzVsFtVR5jZnFAKpAXkuhCwMziCfzhvuKce2vvDKntSAAAAlRJREFU/c65Hc65ncHH/wXizSwtxGF6wjm3LvhzM/A2gW6Myqrz9xHJzgJmOec27b2jNv/eK/n/9u6fNYowiOP4d8BCCCKminYGfAdBJKSUICKCYiEIxj9NCus0dnkBdqlUEKwsvUIQ1Fq00UQx6JWCxM7GRuJYPLOwbHbDCQe7z3O/Dyy5bCaww3Nzz2T3eche9Zg6vv5siSn2PWBmt4BLwI2YpA6YoEay5O577r7v7n+Bh7TnVfLYHwGuAs+6YkoY+475bSbrPrfm7D1wxsxOx12E68CoETMCqp0a14A3XR9kuYk1B4+BL+7+oCNmoVpjZ2ZnSWOcfXNqZnNmdqx6TVog/akRNgJuWnIO+FW7HV6Czr+cSx33hnptrwHPW2JeAqtmdiIefa3GuayZ2QVgA7js7r87YiapkSw11o5eoT2vSeaHXJ0Hdt39e9sPSxj7Q+a32az7vnck/O9B2pH3lbQr536c2yR9aAEcJT32GQPvgMW+r3mKua+QbuluAx/iuAisA+sRcw/4TNqp9BZY7vu6p5T7YuT0MfKrxr6euwFb8d7YAZb6vu4p5j9HaraO184VO+6kJvQH8Ie0fuQuae3oa+Ab8AqYj9gl4FHtd+9E/Y+B233nMqXcx6Q1NVXdVzvSTwEv4nVrjeR2dOT/NGp6mzRZn2zmH98fmB9yOtpyj/NPqlqvxRY19ofMbzNR981D/75JREREZEBye6wpIiIiUjQ1ZyIiIiIDouZMREREZEDUnImIiIgMiJozERERkQFRcyYiIiIyIGrORERERAbkH0lNAuxoWg0+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468115707
    },
    "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": {
    "gather": {
     "logged": 1667468116407
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": {
    "gather": {
     "logged": 1667468118538
    }
   },
   "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": {
    "gather": {
     "logged": 1667468120126
    }
   },
   "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": {
    "gather": {
     "logged": 1667468121648
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f76bbb28cd0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468125281
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f76bbb28910>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {
    "gather": {
     "logged": 1667468218051
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f76ba814430>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeMAAAFlCAYAAADYnoD9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deZyVZf3/8ddHBFEUTEG/FiJoaKIo4kiamrs/t3DPLQW1yJ32bPkqWvbVtq+mpFGGpShuSVjkjruRgCyiUoiQUF8FK9IUFfn8/rjuM5w5nBlm7mWuM3Pez8fjPGbu+5z5nOucuc/53Nd1X4u5OyIiIhLPerELICIiUu+UjEVERCJTMhYREYlMyVhERCQyJWMREZHIlIxFREQiWz/WE/fu3dv79+8f6+lFRETa1YwZM5a7e59q90VLxv3792f69Omxnl5ERKRdmdni5u5TM7WIiEhkSsYiIiKRKRmLiIhEFu2asYiIFO/9999nyZIlrFy5MnZR6kb37t3p27cvXbt2bfXfKBmLiHRiS5YsYZNNNqF///6YWezidHruzhtvvMGSJUsYMGBAq/9OzdQiIp3YypUr2XzzzZWI24mZsfnmm7e5JULJWESkk1Mibl9p3u91JmMz+6WZvW5mzzdzv5nZT8xsgZnNMbOhbS6FiIh0aq+99hqnnnoq2267Lbvvvjt77bUX99xzT+p4Y8aM4Yc//GGOJYyrNdeMbwKuA37dzP2HAwOT28eB65OfIiJSY/pf/Ptc4y268sh1PsbdOeaYYxgxYgS33norAIsXL2by5MlNHrdq1SrWX78+uzKts2bs7o8D/2jhIUcDv/bgj8CmZrZVXgUUEZGO7ZFHHqFbt26cc845jfu22WYbLrzwQm666SaGDx/OgQceyEEHHcRbb73FQQcdxNChQxk8eDC//e1vG//miiuuYPvtt2efffZh/vz5jftffvllDjvsMHbffXf23XdfXnrppXZ9fXnI4xTkI8CrZdtLkn1/zyG2iIh0cPPmzWPo0OavYM6cOZM5c+aw2WabsWrVKu655x569uzJ8uXL2XPPPRk+fDgzZ85k4sSJzJo1i1WrVjF06FB23313AEaNGsUNN9zAwIEDmTZtGueddx6PPPJIe728XLRre4CZjQJGAfTr12+t+5trPlnU/dS1d45ZkWvZRESkfZx//vk8+eSTdOvWjfPPP59DDjmEzTbbDAhN2t/85jd5/PHHWW+99Vi6dCmvvfYaTzzxBMceeywbbbQRAMOHDwfgrbfe4umnn+bEE09sjP/uu++2/4vKKI9kvBTYumy7b7JvLe4+DhgH0NDQ4Dk8t4iI1LiddtqJu+++u3F77NixLF++nIaGBgB69OjReN+ECRNYtmwZM2bMoGvXrvTv37/FYUKrV69m0003ZdasWcW9gHaQx9CmycAZSa/qPYEV7q4mahERAeDAAw9k5cqVXH/99Y373n777aqPXbFiBVtssQVdu3Zl6tSpLF4cFjr65Cc/yaRJk3jnnXd48803uffeewHo2bMnAwYM4M477wRCzXr27NkFv6L8tWZo023AM8AOZrbEzM42s3PMrHQlfgqwEFgA/Bw4r7DSiohIh2NmTJo0iccee4wBAwYwbNgwRowYwVVXXbXWY0877TSmT5/O4MGD+fWvf83HPvYxAIYOHcpJJ53ErrvuyuGHH84ee+zR+DcTJkzgxhtvZNddd2WnnXZq0umrozD3OK3FDQ0NXrmesa4Zi4jk68UXX2THHXeMXYy6U+19N7MZ7t5Q7fGagUtERCQyJWMREZHIlIxFREQiUzIWERGJTMlYREQkMiVjERGRyJSMRUSkcFdccQU77bQTu+yyC0OGDGHatGlcffXVzU7+0ZKNN964xfsfffRRjjrqqCb7Ro4cyV133dXm52ov9blWlYhIvRrTK+d4657z4ZlnnuF3v/sdM2fOZIMNNmD58uW89957nHTSSXzmM59pnG+6Iyhqmcf6rRmP6bX2rRZVK2etllVEpIq///3v9O7dmw022ACA3r17c9ddd/G3v/2NAw44gAMOOABoWuO96667GDlyJACvvPIKe+21F4MHD+bb3/5242POOOMMJk2a1Lh92mmntWr2rYcffpjddtuNwYMHc9ZZZzUuLNG/f3+WL18OwPTp09l///0BGDNmDKeffjp77703p59+OvPmzWPYsGEMGTKEXXbZhb/85S/p35xE/SZjERFpF4ceeiivvvoq22+/Peeddx6PPfYYF110ER/+8IeZOnUqU6dObfHvR48ezbnnnsvcuXPZaqutGvefffbZ3HTTTUCY0/rpp5/myCOPBOCJJ55gyJAhjbfJkycDsHLlSkaOHMntt9/O3LlzWbVqVZM5s5vzwgsv8NBDD3Hbbbdxww03MHr0aGbNmsX06dPp27dvyndmDSVjEREp1MYbb8yMGTMYN24cffr04aSTTmpMoq3x1FNPccoppwBw+umnN+7fb7/9+Mtf/sKyZcu47bbbOP744xubkPfdd19mzZrVeCstuTh//nwGDBjA9ttvD8CIESN4/PHH11mG4cOHs+GGGwKw11578b3vfY+rrrqKxYsXN+7PQslYREQK16VLF/bff38uu+wyrrvuuiZLKpaYWePvlcsmlt9X7owzzuCWW25h/PjxnHXWWZnKuP7667N69eqqz1++zOOpp57K5MmT2XDDDTniiCN45JFHMj0vKBmLiEjB5s+f3+S66qxZs9hmm23YZJNNePPNNxv3b7nllrz44ousXr2ae+65p3H/3nvvzcSJE4GwQlO5kSNHcvXVVwMwaNCgdZZlhx12YNGiRSxYsACAm2++mf322w8I14xnzJgBUPVkoWThwoVsu+22XHTRRRx99NHMmTNnnc+7LkrGIiJSqLfeeosRI0YwaNAgdtllF1544QXGjBnDqFGjOOywwxo7cF155ZUcddRRfOITn2hybfiaa65h7NixDB48mKVLlzaJveWWW7Ljjjty5plntqos3bt3Z/z48Zx44okMHjyY9dZbj3POCSsCX3rppYwePZqGhga6dOnSbIw77riDnXfemSFDhvD8889zxhlntPUtWUv9LqFYrUdyLS7L2FzP6Vosq4jUnM6+hOLbb7/N4MGDmTlzJr161c5IEy2hKCIideGhhx5ixx135MILL6ypRJyGJv0QEZEO6eCDD2bx4sWxi5EL1YxFREQiUzIWEenkYvUNqldp3m8lYxGRTqx79+688cYbSsjtxN1544036N69e5v+TteMRUQ6sb59+7JkyRKWLVsWuyh1o3v37m2eIlPJWESkE+vatSsDBgyIXQxZBzVTi4iIRKZkLCIiEpmSsYiISGRKxiIiIpEpGYuIiESmZCwiIhKZkrGIiEhkSsYiIiKRKRmLiIhEpmQsIiISmZKxiIhIZErGIiIikWmhiDyN6dXM/hXtWw4REelQVDMWERGJTMlYREQkMiVjERGRyJSMRUREIlMyFhERiUzJWEREJDIlYxERkcjqYpxx/4t/v9a+Rd0jFERERKSKVtWMzewwM5tvZgvM7OIq9/czs6lm9pyZzTGzI/IvqoiISOe0zmRsZl2AscDhwCDgFDMbVPGwbwN3uPtuwMnAT/MuqIiISGfVmprxMGCBuy909/eAicDRFY9xoGfyey/gb/kVUUREpHNrzTXjjwCvlm0vAT5e8ZgxwANmdiHQAzg4l9KJiIjUgbx6U58C3OTufYEjgJvNbK3YZjbKzKab2fRly5bl9NQiIiIdW2uS8VJg67Ltvsm+cmcDdwC4+zNAd6B3ZSB3H+fuDe7e0KdPn3QlFhER6WRak4yfBQaa2QAz60booDW54jF/BQ4CMLMdCclYVV8REZFWWGcydvdVwAXA/cCLhF7T88zscjMbnjzsy8DnzGw2cBsw0t29qEKLiIh0Jq2a9MPdpwBTKvZdUvb7C8De+RZNRESkPmg6TBERkcjqYjpM6cDG9Gpm/4r2LYeISIFUMxYREYlMyVhERCQyJWMREZHIlIxFREQiUzIWERGJTMlYREQkMiVjERGRyJSMRUREIlMyFhERiUzJWEREJDIlYxERkciUjEVERCJTMhYREYlMqzal1P/i36+1b1H3CAUREZEOTzVjERGRyJSMRUREIlMyFhERiUzJWEREJDIlYxERkciUjEVERCJTMhYREYlMyVhERCQyJWMREZHIlIxFREQiUzIWERGJTMlYREQkMi0UUUO0+ISISH1SzVhERCQyJWMREZHIlIxFREQiUzIWERGJTB24JD9jelXZt6L9yyEi0sGoZiwiIhKZkrGIiEhkSsYiIiKRKRmLiIhEpmQsIiISmZKxiIhIZErGIiIikSkZi4iIRKZkLCIiElmrkrGZHWZm881sgZld3MxjPm1mL5jZPDO7Nd9iioiIdF7rnA7TzLoAY4FDgCXAs2Y22d1fKHvMQOAbwN7u/k8z26KoAouIiHQ2rakZDwMWuPtCd38PmAgcXfGYzwFj3f2fAO7+er7FFBER6bxas1DER4BXy7aXAB+veMz2AGb2FNAFGOPu91UGMrNRwCiAfv36pSmv5EWLOuRP76mIpJTXqk3rAwOB/YG+wONmNtjd/1X+IHcfB4wDaGho8JyeW1rQ/+LfV92/qHs7F0RERJrVmmbqpcDWZdt9k33llgCT3f19d38F+DMhOYuIiMg6tCYZPwsMNLMBZtYNOBmYXPGYSYRaMWbWm9BsvTDHcoqIiHRa62ymdvdVZnYBcD/hevAv3X2emV0OTHf3ycl9h5rZC8AHwFfd/Y0iCy5xVWv+VtO3iEg6rbpm7O5TgCkV+y4p+92BLyU3ERERaQPNwCUiIhJZXr2pRTJT07eI1CvVjEVERCJTMhYREYlMyVhERCQyJWMREZHIlIxFREQiU29qqU8dZVGHjlJOEclENWMREZHIlIxFREQiUzIWERGJTMlYREQkMiVjERGRyJSMRUREIlMyFhERiUzJWEREJDIlYxERkciUjEVERCJTMhYREYlMyVhERCQyJWMREZHIlIxFREQiUzIWERGJTMlYREQkMiVjERGRyJSMRUREIlMyFhERiUzJWEREJDIlYxERkciUjEVERCJbP3YBRIrU/+LfV92/qHs7F0REpAWqGYuIiESmZCwiIhKZmqlF2khN3yKSN9WMRUREIlMyFhERiUzJWEREJDIlYxERkciUjEVERCJTb2qRGlGtl3bWHtpVY155ZLagIpI71YxFREQiUzIWERGJrFXN1GZ2GHAN0AX4hbtf2czjjgfuAvZw9+m5lVJEakazk56o+VsktXXWjM2sCzAWOBwYBJxiZoOqPG4TYDQwLe9CioiIdGataaYeBixw94Xu/h4wETi6yuO+A1wFrMyxfCIiIp1ea5LxR4BXy7aXJPsamdlQYGt3r95+JSIiIs3KPLTJzNYDfgyMbMVjRwGjAPr165f1qUUkjTG9mtm/on3LUab5xTdOXXtnxHKKFKU1NeOlwNZl232TfSWbADsDj5rZImBPYLKZNVQGcvdx7t7g7g19+vRJX2oREZFOpDXJ+FlgoJkNMLNuwMnA5NKd7r7C3Xu7e3937w/8ERiu3tQiIiKts85mandfZWYXAPcThjb90t3nmdnlwHR3n9xyBBGRODQDmXQUrbpm7O5TgCkV+y5p5rH7Zy+WiIhI/dAMXCIiIpEpGYuIiESmZCwiIhKZkrGIiEhkSsYiIiKRKRmLiIhEpmQsIiISmZKxiIhIZErGIiIikSkZi4iIRKZkLCIiEpmSsYiISGRKxiIiIpG1atUmEREJtCyjFEE1YxERkchUMxaRfIzpVWXfivYvh0gHpJqxiIhIZErGIiIikSkZi4iIRKZkLCIiEpmSsYiISGTqTS0i9aWIXt/VYhYVtxZjSmaqGYuIiESmZCwiIhKZkrGIiEhkSsYiIiKRKRmLiIhEpmQsIiISmZKxiIhIZBpnLCISWbU1kgEWdW/ngkg0qhmLiIhEpmQsIiISmZqpRUQ6qWrN32r6rk2qGYuIiESmmrGIiNSPGl0oQzVjERGRyFQzFhGRVqt6HfrKIyOUpHNRzVhERCQyJWMREZHIlIxFREQiUzIWERGJTMlYREQkMiVjERGRyFqVjM3sMDObb2YLzOziKvd/ycxeMLM5ZvawmW2Tf1FFREQ6p3UmYzPrAowFDgcGAaeY2aCKhz0HNLj7LsBdwPfzLqiIiEhn1Zqa8TBggbsvdPf3gInA0eUPcPep7v52svlHoG++xRQREem8WpOMPwK8Wra9JNnXnLOBP2QplIiISD3JdTpMM/sM0ADs18z9o4BRAP369cvzqUVEpDOptqAD1MSiDmvJYfGJ1tSMlwJbl233TfY1YWYHA98Chrv7u9UCufs4d29w94Y+ffq0qaAiIiKdVWuS8bPAQDMbYGbdgJOByeUPMLPdgJ8REvHr+RdTRESk81pnMnb3VcAFwP3Ai8Ad7j7PzC43s+HJw34AbAzcaWazzGxyM+FERESkQquuGbv7FGBKxb5Lyn4/OOdyiYiI1A3NwCUiIhKZkrGIiEhkSsYiIiKRKRmLiIhEpmQsIiISmZKxiIhIZErGIiIikSkZi4iIRKZkLCIiElmuqzaJiIi0Vf+Lf7/WvkXdIxQkItWMRUREIlMyFhERiUzN1CIi0ulUa/qG2m3+Vs1YREQkMiVjERGRyJSMRUREIlMyFhERiUzJWEREJDIlYxERkciUjEVERCJTMhYREYlMyVhERCQyJWMREZHIlIxFREQiUzIWERGJTMlYREQkMiVjERGRyJSMRUREIlMyFhERiUzJWEREJDIlYxERkciUjEVERCJTMhYREYlMyVhERCQyJWMREZHIlIxFREQiUzIWERGJTMlYREQkMiVjERGRyJSMRUREIlMyFhERiUzJWEREJDIlYxERkchalYzN7DAzm29mC8zs4ir3b2Bmtyf3TzOz/nkXVEREpLNaZzI2sy7AWOBwYBBwipkNqnjY2cA/3f2jwP8CV+VdUBERkc6qNTXjYcACd1/o7u8BE4GjKx5zNPCr5Pe7gIPMzPIrpoiISOfVmmT8EeDVsu0lyb6qj3H3VcAKYPM8CigiItLZmbu3/ACzE4DD3P2zyfbpwMfd/YKyxzyfPGZJsv1y8pjlFbFGAaOSzR2A+a0sZ29g+Tof1Tb1HLOouB0lZlFx6zlmUXHrOWZRces5ZlFxWxtzG3fvU+2O9Vvxx0uBrcu2+yb7qj1miZmtD/QC3qgM5O7jgHGteM4mzGy6uze09e8Us33jdpSYRcWt55hFxa3nmEXFreeYRcXNI2ZrmqmfBQaa2QAz6wacDEyueMxkYETy+wnAI76uKreIiIgAragZu/sqM7sAuB/oAvzS3eeZ2eXAdHefDNwI3GxmC4B/EBK2iIiItEJrmqlx9ynAlIp9l5T9vhI4Md+iNdHmpm3FjBK3o8QsKm49xywqbj3HLCpuPccsKm7mmOvswCUiIiLF0nSYIiIikSkZi4iIRNaqa8bSfsxssLvPjV0OEWmZme0DDHT38WbWB9jY3V+JXa6OKpl6+UigP2W5yd1/HKtM7anmk7GZbeTub+cY70PAQKB7aZ+7P54izoHu/oiZHVftfnf/Tcoi/tTMNgBuAia4+4qUcQplZkNbut/dZ+YQfx/Agaeyxkti7gnMc/c3k+2ewI7uPi1j3NyOUTPbGxgDbEP4fBrg7r5tDrE/wdpfdL/OIW4XYMuKuH/NEC+Xz2iVuLsC+yabT7j77AyxLgUaCJMXjQe6ArcAe2ctZ9lzbEHT9yD1e5rEy/V9NbPR7n7Nuva1wb3ASmAusDptuZpT1HGVl5rtwJV8cfyCcLbZL/kgfd7dz8sQ87PAaMLEJbOAPYFn3P3AFLEuc/dLzWx8lbvd3c/KUM6BwFmEHup/Asa7+4Np4yUxuxMW9NiJpgdjqnKa2dQW7vY072lZ7EsIr710QnMMcKe7fzdtzCTuc8DQ0hh4M1uPMDyvxROLFuIVcYy+BHwRmAF8UNrv7mtNotPGuDcD2xGO+1Jcd/eLMsa9ELgUeI01X6Du7rukjJfbZ7Qi7mjgc6w5po4Fxrn7tSnjzQJ2A2a6+27JvjlpX3dF7OHAj4APA68TTsxedPedMsTM/X01s5mVnx0ze670fqSIl8v710zsIl7/cYRFkbYgnDSXTpx7pgro7jV5A6YRZvV6rmzf8xljziUkolnJ9seA38R+rc2UtQtwPGF2sxeBl4DjMsS7E/gO8DJhgpYHgGtiv85myjof6F62vSEwP4e4s6rsm5MhXhHH6LSC3tMXSU6+c467ANg8x3iFfEaBOUCPsu0eGf/3f0p+zswjXkXs2YS5/Z9Ltg8AbqyV9xU4hVCL/SdhwqfSbSrwcIYyXgUcmtexVPRxlRz7O+ZVxppupnb3VysWf/qguce20kp3X2lmmNkG7v6Sme2QJWDSpHw8azf/XZ4y3i7AmYRrJw8Cn3L3mWb2YeAZ1pzZt9VH3f1EMzva3X9lZrcCT6SMVV7ejYAvAf3cfVRSq9/B3X+XIezfCB+clcn2Bqw9BWsaC83sIuD6ZPs8YGGWgAUco1PN7AeE//O7Zc+TtZn+eeC/gL9njFPpVcLCMHnJ/TOaMJr+bz5I9qV1h5n9DNjUzD5HaMn6eYZ45d539zfMbD0zW8/dp5rZ1Rlj5vm+Pk04jnoTavAlbxJOetL6I3BP0mL1Pllrmk0VcVy95u4v5lA2oLavGb+aNAO6mXUlNDFkfeFLzGxTYBLwoJn9E1icMeZvCV9GMyj78szgWsKMZt9093dKO939b2b27Qxx309+/svMdgb+j9C8ktV4wmv/RLK9lFALb3MyNrNrCdeIVwDzzOzBZPsQQnN9VucAPwG+ncR9mDULl6RRxDH68eRn+Ty3DmRqpiV8cb5gZn+iaZIfnjHuQuBRM/t9Rdy0nW6K+IxCOE6nmdk9yfYxhM9ZKu7+QzM7BPg34brxJZ7xUlKZf5nZxsDjwAQzex34T8aYub2v7r44+du9Mpap0o+TmHM9qXrmqIjjarqZ3Z7ELD/2U1WYavmacW/gGuBgwhnSA8Boz3jtrCz+foQFLe7zsE5z2jjPu/vOeZSpSMk1k7uBXQhfTBsTvkBuyBh3urs3lF8rMrPZ7r5rilgjWrrf3X/V0v3trehjNE/J8b4Wd38sY9xLm4l7WZa4Sey8PqPrEa4RriR0CoTQgeu5rGUsgpn1IJTVgNMI78GEGvzu25NQedgR6Ea4tPaftDVZM3sc2N/dc++8VfE8eb3+XPsL1WwyLkIRvWnNbBxwrec0HKnI3rRFMLOngYMIPZ6Hmtl2wG3uPixy0RqZ2dfc/ftlNe8mPGUnJjPbPO/Em/dlj47CzDZr6X53/0fG+Kk7FlXEedLd9zGzN2l6LOXZpFqIAnq9TyesQ3AnoSXnDGB7d/9Gyng3AdsCfyCfVpbCj6s81VwzdVFfnInrgfLef29V2ddW+wAjzewVwgFU+lCm7RV4I1V602ZV4Jf8pcB9wNZmNoEwtGNkloDJdef/AQbRtOd32hOSUtPx9CzlquKPSa/aXxLOsvM4s837sgeQfy2mLG4f4Gus3Uu/rc3qMwif92rXcZ3wJZ3Fw2Z2PKHTTur/k7vvk/zcJGN5mpV7L12a7/VOaClLzd0XmFkXd/8AGJ+MWEiVjIFXklu35JaH8uOqH6HTmQGbAn8FBqQNnPcIlZpLxhT3xQmhJaDxg+juqy2sv5zF4Rn/vtIKd/9DzjGhoC95d3/QzGYSmgGN0EybdeHu8YQvjv8l9CQ9kwyzxbn7vUmtYLC7fyVj2cptT2iiPgu41szuAG5y9z9niNnX3Q/LpXRNXUeVWkwOcScAtwNHEa7JjwCWtTWIu6f+UmylzxM6Gq4ys1ITcNYE9yFCb/ryk9vM4+GB7xM6bubWOYjQn2GHnFty3rawrO4sM/s+oVNXls/pZZDvuP3ScWVmPwfu8bDoEWZ2OKHfQBY3E0a5/D/gcsIlhdT/sw7RTJ1c89nY3f+dMc5vgEdp2pv2AHfP9E/Js/nHzK4k1Fpy7U2b97VtM/tY0iOxaqtClvKa2Qx3393M5rr74PJ9aWMmMZ5x97w7nZRiH0CY9KEHYWjKxe7+TIo4uV72KItburbfOJYzj6bbsv9Vedxn3X2PNsYp7Hgqgpl9h9ACtJCm46uzdrTDzJ5y99wmD0liTgUOcfdVOcbchjAOuiuhNa8X8FN3X5Ay3l6ElsHcxu2XxW78LmlpXxtjPufuu5WO/aQT5xPuvmeaeLVYMwbAwtCbcwhNtc8CPc3sGnf/QYawefemLaL5p6jetE9bvlNtfonw3v2oyn1Zy/tucgL2FwtraS8ldDjLapaZTSbUDht7p6bu/Wi2OfAZ4HTC//9CwnjLIclzpKnt5X3ZoyTXWkyZUi/9v5vZkYRhaS1ep2tGIcdTc8m9MXD6JP9pYLssHYBakGsv3UTevd5LvaoB3gEyd9gDribUMicn8Web2SdziAtQGo1yS7J9GuFYzSLXESo1WzM2s1nuPsTMTiNc070YmJHDl1KuzGwB8PFa7EEL4eyP8GW2PmEquIXk+yWfOzPbg9DcsylhopJewPfd/Y8Z4+bb+9Hsz4SmqvHuvqTivq+7+1UpYm5TbX/ZF18qSdzXCNfiMtdiyuIeRRivvjXhmnRP4DJ3n5wlbl5szUxx3QknuLMJx/4uhNnXUrWUmNndwLnu/nouBW0au4hZ/XLv9Z7877/D2p1N0/amnubuH89jZEaV2JsRKk2l5P444ThN3YHLch6hUsvJeB6hhnErcJ27P5b1H5PUCL5LOJO7j/AmftHdb2nxD1uOmWvzT94drZr7ci+Lm3kMpxU053GtM7NR7j6uYt+V7n5xDrFznZc4idknidXma7rtqYjjKblEdWmpZSipyYxx9xNSxmsg9MN4nnzHbRcqz+uxSUXkOHIaF2xmdxHGGl9HaCEcDTS4+8lZY3cENdtMDfwMWEQ4k308SSqZrhkTplr7mpkdm8Q+jnCGlDoZk3/zT64drUrJ1sKQoyXu/q6Z7U84EcljkYCqcx6niW1mV7v7F8zsXtbuSe/AP4Cfpa0hm9n2hP4CW7r7zhZmOxvu6ee8PsbM/qZQK6EAABGBSURBVOPuE5L4YylLoCnLWHVeYkKPzTTxjFAjuIDQLG1mtopwXTrzcKkkwX+OtZNn2taG3I6nCjuUX6Jx9+fNbMcM8X5F6PGc+6IGeffSTWI2Xo8F8roe+yph+te8anTnEMbtf4RwaeoB4Pw8Aief/a+w9nGaZl2Cz7j7LWb2pWr3p/3ur9lk7O4/IVzfLVmcdJLJovR6jyQsPLDCmk5lmMZfk1te3fGL6k17N9BgZh8FxhGS/q3AERnjNgCDcvpA3pz8/GEz9/cmDCMalDL+z4GvEk70cPc5Sd+EtMn4eGCyma0GDgP+5e5np4xV8h1Cz/SHks4hBxCuS6f1RcJwsz08Wd7PzLYFrjezL7r7/2Ys728JzdQPkc9QvDyPp3JzzOwXNL1mmGXqxreT76gi5NpLN1HE9divAVPM7DFyqIh4GIVxWsYyNedO4AbCwi5Zj9Meyc9qw9tSH7c1m4zNrBdN2/gfIxyYWebB/Z2FVXHeAc5NzupXruNvWpTlmksz8u5oVbLa3VdZGMN4rbtfa2FMYFa5zXns7jOSn83OCmVmWTrMbOTuf6o4AWvz5QVrOpHAZwkJ6UngMjPbLMt1KPKfl/h0wmWUxuFm7r7QzD5DqHlkTcYbufvXM8YoV9Qc2mcC5xKaPiG0iF3f/MPX6Qkz+x9CcstzDnEoaB55z38e9SsIczV0J4eKSAEtV+VWuXuW/3e5KVD9uz+5jp5KzSZjQg3oeUKvRQhfKuMJTcupuPvFyXXjFe7+gZm9DRydpZCW36QHJUX1pn3fzE4hjC/9VLKva8aYUMCcx9bCpB/ufm+Gsi5PmutLSyieQLov/fKJBEo/j2BNK0OWCSpK8xI/QT7zEnf1KuO+3X2ZhaEYWf3OzI7wZPxmDqodT+7umT6nHhYJuAGY4u7zsxaSsHwihFaMxqch+6gHKGYe+SLmUf+w5zsVcN4tV+XuNbPzgHto+j2V5sT5QTM7zN0Xle80szMJI3XSLZLjBSxXlceN6svdrbWvjTE3St6sccn2QOCojDEfIFzfeRHYj3AScVWGeNtUu+Xwfg4iNPufkmwPAL6eQ9z9qt0yxnySMMXmnOT1jwEuz6Gs2xKaU98mXJN6EuifIs4wYKuy7RGEGtJPgM0ylrEHYZz5+knci8iwRCHJEn9tva8Vcd8k9OF4k3DN9J2y7X/ndDztD/w3YQrbrP/74YSlOV9JtocAk7PGLeJGaG35UPIeLCT0Hfh8xpi9CRO0vJbEuyXLcZXE/D45LnkIPJv8LF+SNNN3flmcV6rcFqaMdQTwZ2Bg2b5vEPoP9E1bxlruTf0M8FV3fzLZ3hv4oWeYtMHC2L0ZwBkemkE2Ap529yEZYuYy6UFFzH0I/+jxSc17Y0+u99UDK2jSj7L4PYD1PJmjPMXfzwQOdvd/JNfdJhLGGA8hzHWeqoduWfwtgdLx8yfPMHzGzD6ges3aCGtG51E7zpWZ7QacCpxI+NL8jbtfmzHmDEKt9VFfM2wm9aQPFlYAOoO1OwRlma63FLs0vWRNszA/dw9CTTPzkodm9gdCR8M7PcxzfwJwtrvnPcthZmZ2EKEGfwzh5GkYcKS7/zNtzFpupj4X+FVy7dgIPWlbXNWnFbZz95OS5lrc/W3L3oMrr0kPgMbxgA2EZdnGE5qSbyF0wkkT7w53/7StGW/ceBfhOnKmMXxWzJzHuU760Vyvx9K/3tve4aSLr2neOonQ0nI3cLeFuapTM7NPAz8gzBRnhGk2v+rud6WJ5+5dspRnXczsYXc/aF37WhFne8Ki9acAywlTbJq7Z+20WfK+r91hM0tNZAph/d3ce1MTjvu7CePXX8gSyJqZ478ky8mD5z8/9/mEzqUfM7OlhBOxXDp0JU3z57KmD9KjhJEZ7zf7Ry1w94eTZulHCes7H+jumfof1WwydvdZwK4WVlaCcHZ/Mtl6QL5nZhuy5prhdmQfPvTd5IThy6yZ9OCLGeIdS7geNRPAwzrGWQ76UoeV8o4FRpikIe2E7uWKmPN4NOGSwkWE3sUHJnHTKr1/OxBqnKUJKT5FunWSu5jZ+h7Glh9E01ncsn6mvkXo+fw6NPZJeAhIlYyLkgy/6QH0tjBHcynL9SQMTWmrlwjXyY/yZCISM8vyOao0z8xOJfzvBhKOraczxOvu7lVP8nKwK+Ez9YvkpPSXwERPNx1wEXP8A42tlbPc/T9Jh8ChwNWefkz8h9z94PKWq6RDVB7rWV9PqNj8NNk+Pdn32bYGsjUrdhmwAeE74PWkYpe6ZaDdr4e0oj2+JyFJXEdYVN4ITRevAL/NGPsQQq/sZYTrJ4sI62dmidkn59f/p+TnzORnD2BOTrF3I9S6FgFTgQtyiDk9+TmnbN9zWeNWPEcX4LQc4jwObFK2vQnweIo43wKeIvSifo41k+d8lLCUZJYyzq3YXq9yXy3cCCdMpU6G5dfhZqc5rgjNfRMJY1d/TviCeyXH8m5E6P37bHL7LiGhpo33RcL46q0ILWGbkbG/QDPPsx+hZeg/hLHNH836PuRYtjnJ9/OuyefgfOCxDPFmAjuXbZ8MTMuprLNbsy/mrRZrxjcTlrl6hnCwf4vwDz/WQ205NS9mhaGnzGwRoVntN57hmkHiDjP7GbCpmX2O0DnsF2mDtUPzX25zHietIOcTalaTgQeT7S8TPvgTMpZ1S6B8aNR7yb42cfcrzOxhwhfxA558sgmv+8KMZbzPzO4Hbku2Tyas71pT3P0a4Bozu9AzXs9N4k0CJiW1oqOBLwBbmNn1hNV2HkgTN6nBn0M4UZoL7OX5zJb3HuHE9lusaQZ2si/1iIWFZ44kDMfqT5gEZgKwL6F5vM0tT1bMpB+r3N3N7GjCLIk3mlmWcfYnAHclLRj7ElrDDs0Qr9wHZradu78MjWPta+q6fM114KrotNOF8OXezzO2xyfxjgUecfcVyfamhJrxpIxxhxG+NI8BXiA0KWWZYvMQ1hyE97v7QxlirSY0/53ta5r/Fnr6tYEr429DTnMem9lvWXMidhBr1nMdnfVELIn/LcJQuXuSXccAd7j797LGzpOFseClPgJPZD0+i2DJuuPJ7ye6+51l933P3b+Zw3N8iNCJ6yRv4zXoshi3E/p1PEFY7nSRu38hh7ItBIblcDLfXOypwI3u/nTFfT/xFNd5zWwaIdlN9jUd2DKt5GZhso/7CCcNnyT00p7t2VZC2p6wQMZfCRWwd9LGqoh7EKEPzkLCd8o2wJnuPjWP+HmoxWQ8092HNredMfYsr+g5bTksI1cWqzdhbtXTvI0dZ8quQ8Ca628lK4GXgW+5+8NtjHsM4URhb8IHZyLwC89x/VjLac7jIk/Eyp5jKOGsG0ITdR4Tn2RW1P+/KOWfyyI/s1lVHFPrEy4DZS6bmT0AHOM5zfNcEXtjd38r55i5L8JgZv9F6PX+rLs/YWb9CJWbNk1dWqVz6RaEyZ3eBfCcFrOxMO//DsnmfHfPbV33PNRiM/WuZlbqqGDAhsl25sXAqd58muk9SJpWjyUkvO0Ita5hbY3jLfRMTBLTzoSmqjadyRbY/FfEnMeNPRs9TMqyJM9EnNiIMA52vJn1MbMBXgPDxor6/xfImvm92nZM5cfUKss+/W3JfwiXZqbSdBKJ1L2Ty1obvletnFliU8CkH+7+f4TKR2n7r6SbQzz1rFVttDtrhqINMTPaeuJQpJpLxm2tUbbRdDP7MTA22T6fMO44i9mEZpXLPcVi8q3hYczh7GSYQtoY/yHMRX1rWfPf1wmTlqRRxJzHRZ6I5T5srL3k8f8vgDfze7XtmIo6piYltzyVkmPW76RqcluEoaIVp8ldpHhPvWLlOKtYsSwPVtwCJLmpuWbqIiW1w/8GDk52PQh8N0lUaWNa0olhY4C8m5dqlYV5rZvMeZzs70Po1JRL03+eLIwB3o3QU73UVNc4WYu0nq2ZTMSADQmzmpFs1+RkIlLbrJkVy9w91YplFbFfpJgFSHJTczXjIiVJN/NasxV2Ss66NiPk5mXACHd/PufnqTVFz3lchPeSE6fSOPMe6/oDqa7gFqyaZS1PouN5nNglJ7RfZ+252dMs91fYpB8FyHvFsnJFLUCSm7pKxpbjmpZlxgFfKvXKs7BW8DjgExlidgQtrZ6UZWWlIlUOGzuLMKZVpLVKyesoqiTjnJ5jAmEI4pGE5uURhLkR0iif9OMyQj+PWpX3imXYmrXRNyHnBW3yVm/N1LMJa1rOoGyMmSdL96WNWdkjMWsvxY7AOticx0mHs77AxwjDxowwbOzBqAWTDqWF66UQvuQz93q3Aua7T2LkNnKkCGb2EGG44ZXA5oSm6j3cPXXFxsz2a+l+b2G51vZWVzVj8l3TsmShmf03YbISCM0qC3N+jprT0Zopk+bpKckwFyVgSaWder3nOt99mVqveQ0nDOMbTfge7UmozafWXLK1MM3oKVli5y3VTEkd2L1mdp6ZbWVmm5VuGWOeBfQBfpPc+iT7pPbMNLNMtQuR5rj7B+4+mzBHfRbl891/hTADX57zdNcUM3sz6eH+f8C/CBP/XAd8D5hvZn9MJu1IE7unmX3DzK4zs0MtuIBQYfp0Xq8hD/XWTF1tPKl7TrNRSW0zs5cIa1gvYk1P4Fw63YjUoopm9Y1o2us983DBopW3NqSZLcwKntUvT3WVjPNkZle7+xfKOgg0UUsdA+qdmfVz979amLpzLZXjHEViMLNLWrjb3f077VaYGmNmn3f3n6X4u8Jn9ctLvV0zxsx2Zu0hA2kGfpeuEf+wbF9z0xlKXJOAoe6+2MzudvfjYxdIpIpqHSJ7EBaL2Zww9KcupUnEifaY1S8XdZWMkxmY9ick4ymEieOfJN0sLH3NbE93H5vE/hPherETxghK7Sg/OdIlCalJ7v6j0u8W1jAfTViEYSJhMgxpu0Jn9ctTXSVjwqoluxLW2z3TzLYkTIeYxtcI81GXdCNMtdiDMNXindX+SKJoaepGkZqRdCj9EnAaYf3ioZ59Wda61ZFGfdRbMn7H3Veb2SoLCzy8DmydMlY3d3+1bPtJd38DeEMzO9WcXcvOhjesOFOuqbNjqV9m9gPgOMKkQYPrZWpdCeqqA5eZ/RT4JqFG+2XgLWCWu5+ZItYCd/9oM/e97O7bZSqsiNQVC2uPvwusovpUmzpp7MTqJhmXZmAq1WbNrD/Q093npIw3AXjU3X9esf/zhDU9a2pAuYiI1K66ScbQtJt7DrG2IPTSfReYmezeHdiAsOj4a3k8j4iIdH71lox/BVzn7s/mGPNAoLTE1zx3fySv2CIiUh/qLRlrBiYREak5dZGMNQOTiIjUsnpJxjPdfWjyu2ZgEhGRmlIvqzZpBiYREalZ9ZKMNQOTiIjUrHpppv6ANR22NqSDLSMmIiKdW10kYxERkVpWL83UIiIiNUvJWEREJDIlY5EOzMzczMrXwf2KmY2JWCQRSUHJWKRjexc4zsx6xy6IiKSnZCzSsa0irH/7xco7zOxTZjbNzJ4zs4fMbMtk/xgz+5WZPWFmi83sODP7vpnNNbP7zKxr8rjdzewxM5thZveb2Vbt+9JE6oeSsUjHNxY4zcx6Vex/EtjT3XcDJgJfK7tvO+BAYDhwCzA1WdHsHeDIJCFfC5zg7rsDvwSuKPZliNSv9WMXQESycfd/m9mvgYsIybSkL3B7UqPtBrxSdt8f3P19M5sLdAHuS/bPBfoDOwA7Aw+GpcDpAvy9yNchUs9UMxbpHK4GzgZ6lO27lrBk6GDg80D3svveBXD31cD7vmbCgdWEk3QjLAk6JLkNdvdDi34RIvVKyVikE3D3fwB3EBJySS9gafL7iDaGnA/0MbO9AMysq5nttI6/EZGUlIxFOo8fAeW9qscAd5rZDGB5WwK5+3vACcBVZjYbmAV8IqdyikgFTYcpIiISmWrGIiIikSkZi4iIRKZkLCIiEpmSsYiISGRKxiIiIpEpGYuIiESmZCwiIhKZkrGIiEhk/x9BfVsPz4uRQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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."
   ]
  },
  {
   "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)\n",
    "\n",
    "## Challenge: Analyze Flight Data\n",
    "\n",
    "If this notebook has inspired you to try exploring data for yourself, why not take on the challenge of a real-world dataset containing flight records from the US Department of Transportation? You'll find the challenge in the [/challenges/01 - Flights Challenge.ipynb](./challenges/01%20-%20Flights%20Challenge.ipynb) notebook!\n",
    "\n",
    "> **Note**: The time to complete this optional challenge is not included in the estimated time for this exercise - you can spend as little or as much time on it as you like!"
   ]
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python38-azureml"
  },
  "kernelspec": {
   "display_name": "Python 3.8 - AzureML",
   "language": "python",
   "name": "python38-azureml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "nteract": {
   "version": "nteract-front-end@1.0.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}