02-visualize-data (1).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Exploring data with Python - visualize data\n",
        "\n",
        "In this notebook, we'll apply a few techniques to analyze data with basic statistics and visualize it using graphs.\n",
        "\n",
        "## Loading our data\n",
        "\n",
        "Before we begin, let's load the same data about study hours that we analyzed in the previous notebook. We'll also recalculate who passed the class in the same way as last time.\n",
        "\n",
        "Run the code in the cell below by clicking the **&#9658; Run** button to see the data."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load data from a text file\n",
        "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/grades.csv\n",
        "df_students = pd.read_csv('grades.csv',delimiter=',',header='infer')\n",
        "\n",
        "# Remove any rows with missing data\n",
        "df_students = df_students.dropna(axis=0, how='any')\n",
        "\n",
        "# Calculate who passed, assuming '60' is the grade needed to pass\n",
        "passes  = pd.Series(df_students['Grade'] >= 60)\n",
        "\n",
        "# Save who passed to the Pandas dataframe\n",
        "df_students = pd.concat([df_students, passes.rename(\"Pass\")], axis=1)\n",
        "\n",
        "\n",
        "# Print the result out into this notebook\n",
        "df_students "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "--2023-08-01 16:43:51--  https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/grades.csv\r\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\r\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\r\nHTTP request sent, awaiting response... 200 OK\r\nLength: 322 [text/plain]\r\nSaving to: ‘grades.csv.2’\r\n\r\n\rgrades.csv.2          0%[                    ]       0  --.-KB/s               \rgrades.csv.2        100%[===================>]     322  --.-KB/s    in 0s      \r\n\r\n2023-08-01 16:43:51 (19.0 MB/s) - ‘grades.csv.2’ saved [322/322]\r\n\r\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "         Name  StudyHours  Grade   Pass\n0         Dan       10.00   50.0  False\n1       Joann       11.50   50.0  False\n2       Pedro        9.00   47.0  False\n3       Rosie       16.00   97.0   True\n4       Ethan        9.25   49.0  False\n5       Vicky        1.00    3.0  False\n6    Frederic       11.50   53.0  False\n7      Jimmie        9.00   42.0  False\n8      Rhonda        8.50   26.0  False\n9    Giovanni       14.50   74.0   True\n10  Francesca       15.50   82.0   True\n11      Rajab       13.75   62.0   True\n12    Naiyana        9.00   37.0  False\n13       Kian        8.00   15.0  False\n14      Jenny       15.50   70.0   True\n15     Jakeem        8.00   27.0  False\n16     Helena        9.00   36.0  False\n17      Ismat        6.00   35.0  False\n18      Anila       10.00   48.0  False\n19       Skye       12.00   52.0  False\n20     Daniel       12.50   63.0   True\n21      Aisha       12.00   64.0   True",
            "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>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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.\n",
        "\n",
        "**Note**: This first graph may take 1 to 2 minutes to render. Subsequent graphs will render more quickly."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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 many 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 axes (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)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "A plot is technically contained within 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."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 800x300 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1000x400 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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, which plots a bar chart of study hours."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "df_students.plot.bar(x='Name', y='StudyHours', color='teal', figsize=(6,4))"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f57046718e0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 600x400 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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 section is <u>not</u> intended to teach you statistics&#8212;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"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1000x400 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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 analysis 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 occurring 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 those cases, the dataset is described as *bimodal* or even *multimodal*."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Minimum:3.00\nMean:49.18\nMedian:49.50\nMode:50.00\nMaximum:97.00\n\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1000x400 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "tags": []
      }
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1000x400 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "The box plot shows the distribution of the grade values in a format different from the histogram. The *box* part of the plot shows where the inner two *quartiles* of the data reside. 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",
        "For learning, it can be 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.)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Minimum:3.00\nMean:49.18\nMedian:49.50\nMode:50.00\nMaximum:97.00\n\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1000x400 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
        "tags": []
      }
    },
    {
      "cell_type": "markdown",
      "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.\n",
        "\n",
        "What does this mean? *Samples* refer to the data we have on hand, such as information about these 22 students' study habits and grades. The *population* refers to all possible data we could collect, such as every student's grades and study habits across every educational institution throughout the history of time. Usually we're interested in the population, but it's simply not practical to collect all of that data. Instead, we need to try estimate what the population is like from the small amount of data (samples) that we have.\n",
        "\n",
        "If we have enough samples, we can calculate something called a *probability density function*, which estimates the distribution of grades for the full population.\n",
        "\n",
        "The **pyplot** class from Matplotlib provides a helpful plot function to show this density."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1000x400 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
        "## Summary\n",
        "\n",
        "Well done! There were a number of new concepts in here, so let's summarize. \n",
        "\n",
        "Here we have:\n",
        "\n",
        "1. Made graphs with Matplotlib.\n",
        "2. Seen how to customize these graphs.\n",
        "3. Calculated basic statistics, such as medians.\n",
        "4. Looked at the spread of data using box plots and histograms.\n",
        "5. Learned about samples versus populations.\n",
        "6. Estimated what the population of grades might look like from a sample of grades.\n",
        "\n",
        "In our next notebook, we'll look at spotting unusual data and finding relationships between data.\n",
        "\n",
        "## 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)"
      ],
      "metadata": {}
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "conda-env-azureml_py38-py"
    },
    "kernelspec": {
      "name": "conda-env-azureml_py38-py",
      "language": "python",
      "display_name": "azureml_py38"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}