Assignment_Data_Toolkit.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Shahab-16/bostonhousepricing/blob/main/Assignment_Data_Toolkit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "oPcJHKmeRvFg"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q1->Demonstrate three different methods for creating identical 2D arrays in NumPy.Provide the code for each\n",
        "method and the final output after each method"
      ],
      "metadata": {
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    {
      "cell_type": "code",
      "source": [
        "#Method1\n",
        "\n",
        "row=int(input(\"Enter the value of row : \"))\n",
        "col=int(input(\"Enter the value of column : \"))\n",
        "val=int(input(\"Enter the value of val : \"))\n",
        "\n",
        "ans1=np.full((row,col),val)\n",
        "print(ans1)"
      ],
      "metadata": {
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        },
        "id": "qmu5nRreSN8H",
        "outputId": "46a2e2d0-3bf9-4099-86b0-403e9d098d50"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the value of row : 3\n",
            "Enter the value of column : 3\n",
            "Enter the value of val : 6\n",
            "[[6 6 6]\n",
            " [6 6 6]\n",
            " [6 6 6]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Method2\n",
        "\n",
        "row=int(input(\"Enter the value of row : \"))\n",
        "col=int(input(\"Enter the value of column : \"))\n",
        "val=int(input(\"Enter the value of val : \"))\n",
        "\n",
        "ans2=np.ones((row,col))*val\n",
        "print(ans2)\n",
        "\n"
      ],
      "metadata": {
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        },
        "id": "ue_xOCspSN-V",
        "outputId": "0fd27234-d3f8-422b-9a79-060b923798bd"
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      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the value of row : 3\n",
            "Enter the value of column : 3\n",
            "Enter the value of val : 7\n",
            "[[7. 7. 7.]\n",
            " [7. 7. 7.]\n",
            " [7. 7. 7.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Method 3\n",
        "\n",
        "row=int(input(\"Enter the value of row : \"))\n",
        "col=int(input(\"Enter the value of column : \"))\n",
        "val=int(input(\"Enter the value of val : \"))\n",
        "\n",
        "ans3=np.zeros((row,col))+val\n",
        "print(ans3)"
      ],
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        "outputId": "c3c89f0c-161a-4844-84dc-0811bea17275"
      },
      "execution_count": 8,
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          "text": [
            "Enter the value of row : 3\n",
            "Enter the value of column : 4\n",
            "Enter the value of val : 7\n",
            "[[7. 7. 7. 7.]\n",
            " [7. 7. 7. 7.]\n",
            " [7. 7. 7. 7.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q2->Using the Numpy function, generate an array of 100 evenly spaced numbers between 1 and 10 and\n",
        "Reshape that 1D array into a 2D array"
      ],
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      "cell_type": "code",
      "source": [
        "row=int(input(\"Enter the value of row : \"))\n",
        "col=int(input(\"Enter the value of column : \"))\n",
        "if row*col==100:\n",
        "  print(np.linspace(1,10,100).reshape(10,10))\n",
        "else:\n",
        "  print(\"Enter valid row and col\")"
      ],
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      "execution_count": 11,
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          "text": [
            "Enter the value of row : 10\n",
            "Enter the value of column : 10\n",
            "[[ 1.          1.09090909  1.18181818  1.27272727  1.36363636  1.45454545\n",
            "   1.54545455  1.63636364  1.72727273  1.81818182]\n",
            " [ 1.90909091  2.          2.09090909  2.18181818  2.27272727  2.36363636\n",
            "   2.45454545  2.54545455  2.63636364  2.72727273]\n",
            " [ 2.81818182  2.90909091  3.          3.09090909  3.18181818  3.27272727\n",
            "   3.36363636  3.45454545  3.54545455  3.63636364]\n",
            " [ 3.72727273  3.81818182  3.90909091  4.          4.09090909  4.18181818\n",
            "   4.27272727  4.36363636  4.45454545  4.54545455]\n",
            " [ 4.63636364  4.72727273  4.81818182  4.90909091  5.          5.09090909\n",
            "   5.18181818  5.27272727  5.36363636  5.45454545]\n",
            " [ 5.54545455  5.63636364  5.72727273  5.81818182  5.90909091  6.\n",
            "   6.09090909  6.18181818  6.27272727  6.36363636]\n",
            " [ 6.45454545  6.54545455  6.63636364  6.72727273  6.81818182  6.90909091\n",
            "   7.          7.09090909  7.18181818  7.27272727]\n",
            " [ 7.36363636  7.45454545  7.54545455  7.63636364  7.72727273  7.81818182\n",
            "   7.90909091  8.          8.09090909  8.18181818]\n",
            " [ 8.27272727  8.36363636  8.45454545  8.54545455  8.63636364  8.72727273\n",
            "   8.81818182  8.90909091  9.          9.09090909]\n",
            " [ 9.18181818  9.27272727  9.36363636  9.45454545  9.54545455  9.63636364\n",
            "   9.72727273  9.81818182  9.90909091 10.        ]]\n"
          ]
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      ]
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    {
      "cell_type": "markdown",
      "source": [
        "Q3->Explain the following terms.\n",
        "\n",
        "a-> The difference in np.array, np.asarray and np.asanyarrayX\n",
        "\n",
        "b-> The difference between Deep copy and shallow copy."
      ],
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    {
      "cell_type": "code",
      "source": [
        "#a-> np.array is used to create a array in numpy,np.asarray is used to create a copy of numpy array and np.asanyarray is used to create a array but passes through the subclasses of ndarray\n",
        "\n",
        "#b-> Deep copy-: In deep copy the object is just copies and if we change the original object the copied doen't changes where as in shallow copy it is passed through reference so it affects the other object also\n"
      ],
      "metadata": {
        "id": "0gtA5M5cSOHZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q4->Generate a 3x3 array with random floating-point numbers between 5 and 10 then, round each number in\n",
        "the array to 2 decimal places"
      ],
      "metadata": {
        "id": "oNO4oKIMaTJA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print((np.round(np.random.uniform(5,10,9),2).reshape(3,3)))\n"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "5VPGIh6cSOLl",
        "outputId": "5c6ec996-e40e-4c4f-9752-0ea86ce530c8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[9.28 7.8  5.31]\n",
            " [6.4  5.78 8.52]\n",
            " [5.8  5.88 6.87]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q5-> Create a NumPy array with random integers between 1 and 10 of shape (5,6) After creating the array\n",
        "perform the following operations:\n",
        "\n",
        " a->Extract all even integers from array.\n",
        "\n",
        " b->Extract all odd integers from array"
      ],
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      "cell_type": "code",
      "source": [
        "x=np.random.randint(1,10,(5,6))\n",
        "even=[]\n",
        "odd=[]\n",
        "\n",
        "for i in x:\n",
        "  for j in i:\n",
        "    if (j%2==0):\n",
        "      even.append(j)\n",
        "    else:\n",
        "      odd.append(j)\n",
        "print(\"Even\",even)\n",
        "print(\"Odd\",odd)"
      ],
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        "id": "41zASRbGSOOe",
        "outputId": "d205a65e-d76e-4b70-e637-b2bbf22d14f4"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Even [4, 6, 8, 2, 4, 2, 4, 6, 4, 2, 4, 8, 2, 2]\n",
            "Odd [9, 5, 5, 5, 9, 9, 1, 5, 1, 9, 1, 1, 1, 5, 3, 7]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q->6 Create a 3D NumPy array of shape (3, 3, 3) containing random integers Petween 1 and 10. Perform the\n",
        "following operations:\n",
        "\n",
        " a-> Find the indices of the maximum values along each depth level (third axis).\n",
        "\n",
        " b-> Perform element-wise multiplication of between both array"
      ],
      "metadata": {
        "id": "d5JBKI6DfEMY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y=np.random.randint(1,10,(3,3,3))"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UOTdgXkSOS9",
        "outputId": "c47e14cc-c956-4787-b39b-36d14b83a9e6"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[9, 3, 4],\n",
              "        [3, 7, 5],\n",
              "        [3, 3, 5]],\n",
              "\n",
              "       [[4, 5, 8],\n",
              "        [6, 8, 9],\n",
              "        [2, 1, 6]],\n",
              "\n",
              "       [[1, 6, 5],\n",
              "        [2, 8, 9],\n",
              "        [7, 9, 7]]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q->7 Clean and transform the 'Phone' column in the sample dataset to remove non-numeric characters and convert it to a numeric data type.Also display the table attributes and data types of each column."
      ],
      "metadata": {
        "id": "haHKBfxsglJf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"People Data.csv\")\n",
        "print(df.shape)\n",
        "df.info()\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VwgVZD3KSOVV",
        "outputId": "be3e0bc7-b602-48a5-f356-714654d66b15"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1000, 10)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 10 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Index          1000 non-null   int64 \n",
            " 1   User Id        1000 non-null   object\n",
            " 2   First Name     1000 non-null   object\n",
            " 3   Last Name      1000 non-null   object\n",
            " 4   Gender         1000 non-null   object\n",
            " 5   Email          1000 non-null   object\n",
            " 6   Phone          979 non-null    object\n",
            " 7   Date of birth  1000 non-null   object\n",
            " 8   Job Title      1000 non-null   object\n",
            " 9   Salary         1000 non-null   int64 \n",
            "dtypes: int64(2), object(8)\n",
            "memory usage: 78.2+ KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index             0\n",
              "User Id           0\n",
              "First Name        0\n",
              "Last Name         0\n",
              "Gender            0\n",
              "Email             0\n",
              "Phone            21\n",
              "Date of birth     0\n",
              "Job Title         0\n",
              "Salary            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df[~df['Phone'].isnull()]"
      ],
      "metadata": {
        "id": "-22_3s5qSOXl"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Phone'] = df['Phone'].str.replace(r'\\D', '', regex=True).astype(int)"
      ],
      "metadata": {
        "id": "WF-ly2esSOZ5"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "id": "G2ITvQ4kSOcF",
        "outputId": "806a2a81-ac4b-4888-880e-2611d477f7d6"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Index          User Id First Name Last Name  Gender  \\\n",
              "0      1  8717bbf45cCDbEe     Shelia   Mahoney    Male   \n",
              "2      3  810Ce0F276Badec     Sheryl    Lowery  Female   \n",
              "4      5  9afFEafAe1CBBB9    Lindsey      Rice  Female   \n",
              "5      6  aF75e6dDEBC5b66     Sherry  Caldwell    Male   \n",
              "6      7  efeb05c7Cc94EA3     Ernest   Hoffman    Male   \n",
              "\n",
              "                    Email           Phone Date of birth  \\\n",
              "0     pwarner@example.org      8571398239    27-01-2014   \n",
              "2     fhoward@example.org      5997820605    25-11-2013   \n",
              "4        elin@example.net  39041716353010    15-04-1923   \n",
              "5   kaitlin13@example.net      8537800927    06-08-1917   \n",
              "6  jeffharvey@example.com   9365574807895    22-12-1984   \n",
              "\n",
              "                   Job Title  Salary  \n",
              "0          Probation officer   90000  \n",
              "2                       Copy   50000  \n",
              "4        Biomedical engineer  100000  \n",
              "5  Higher education lecturer   50000  \n",
              "6             Health visitor   60000  "
            ],
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              "      <th></th>\n",
              "      <th>Index</th>\n",
              "      <th>User Id</th>\n",
              "      <th>First Name</th>\n",
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              "      <td>15-04-1923</td>\n",
              "      <td>Biomedical engineer</td>\n",
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              "      <td>Sherry</td>\n",
              "      <td>Caldwell</td>\n",
              "      <td>Male</td>\n",
              "      <td>kaitlin13@example.net</td>\n",
              "      <td>8537800927</td>\n",
              "      <td>06-08-1917</td>\n",
              "      <td>Higher education lecturer</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
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              "      <th>6</th>\n",
              "      <td>7</td>\n",
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              "      <td>Ernest</td>\n",
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              "      <td>jeffharvey@example.com</td>\n",
              "      <td>9365574807895</td>\n",
              "      <td>22-12-1984</td>\n",
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              "    .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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-b579fc38-2426-433b-ba2a-746c5e0149f0 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-b579fc38-2426-433b-ba2a-746c5e0149f0');\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",
              "\n",
              "\n",
              "<div id=\"df-4887adf5-3a12-4fb3-b562-b486e4249f4a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4887adf5-3a12-4fb3-b562-b486e4249f4a')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4887adf5-3a12-4fb3-b562-b486e4249f4a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 979,\n  \"fields\": [\n    {\n      \"column\": \"Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 285,\n        \"min\": 1,\n        \"max\": 1000,\n        \"num_unique_values\": 979,\n        \"samples\": [\n          217,\n          565,\n          192\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User Id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 979,\n        \"samples\": [\n          \"aBa137daedc833c\",\n          \"42069E2a97adFCb\",\n          \"26A154Aa4318fFd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"First Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 520,\n        \"samples\": [\n          \"Marie\",\n          \"Brandy\",\n          \"Cheryl\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Last Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 618,\n        \"samples\": [\n          \"Dawson\",\n          \"Booth\",\n          \"Hodge\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 979,\n        \"samples\": [\n          \"melody43@example.net\",\n          \"jeanne07@example.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Phone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 419643657018319,\n        \"min\": 1081,\n        \"max\": 1944627061259599,\n        \"num_unique_values\": 979,\n        \"samples\": [\n          5003439851714,\n          7035447090\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of birth\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 971,\n        \"samples\": [\n          \"05-04-1984\",\n          \"26-05-1929\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Job Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 513,\n        \"samples\": [\n          \"Surveyor, planning and development\",\n          \"Geographical information systems officer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16101,\n        \"min\": 50000,\n        \"max\": 100000,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50000,\n          85000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()\n",
        "df.reset_index(inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDSXcwxXSOef",
        "outputId": "d3b68814-6845-473b-f5ab-69e4822e0a92"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 979 entries, 0 to 999\n",
            "Data columns (total 10 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Index          979 non-null    int64 \n",
            " 1   User Id        979 non-null    object\n",
            " 2   First Name     979 non-null    object\n",
            " 3   Last Name      979 non-null    object\n",
            " 4   Gender         979 non-null    object\n",
            " 5   Email          979 non-null    object\n",
            " 6   Phone          979 non-null    int64 \n",
            " 7   Date of birth  979 non-null    object\n",
            " 8   Job Title      979 non-null    object\n",
            " 9   Salary         979 non-null    int64 \n",
            "dtypes: int64(3), object(7)\n",
            "memory usage: 116.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['Index'],inplace=True)"
      ],
      "metadata": {
        "id": "W6ljdMAYl6HJ"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['index'],inplace=True)"
      ],
      "metadata": {
        "id": "l3QPlzC95NRK"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xBaV33qWmFk6",
        "outputId": "09b5c3d9-3552-4d78-a25e-3fe1e864e5ba"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           User Id First Name Last Name  Gender                   Email  \\\n",
              "0  8717bbf45cCDbEe     Shelia   Mahoney    Male     pwarner@example.org   \n",
              "1  810Ce0F276Badec     Sheryl    Lowery  Female     fhoward@example.org   \n",
              "2  9afFEafAe1CBBB9    Lindsey      Rice  Female        elin@example.net   \n",
              "3  aF75e6dDEBC5b66     Sherry  Caldwell    Male   kaitlin13@example.net   \n",
              "4  efeb05c7Cc94EA3     Ernest   Hoffman    Male  jeffharvey@example.com   \n",
              "\n",
              "            Phone Date of birth                  Job Title  Salary  \n",
              "0      8571398239    27-01-2014          Probation officer   90000  \n",
              "1      5997820605    25-11-2013                       Copy   50000  \n",
              "2  39041716353010    15-04-1923        Biomedical engineer  100000  \n",
              "3      8537800927    06-08-1917  Higher education lecturer   50000  \n",
              "4   9365574807895    22-12-1984             Health visitor   60000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc5376c4-86a6-4482-bd74-48797763a8d0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User Id</th>\n",
              "      <th>First Name</th>\n",
              "      <th>Last Name</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Email</th>\n",
              "      <th>Phone</th>\n",
              "      <th>Date of birth</th>\n",
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              "      <th>0</th>\n",
              "      <td>8717bbf45cCDbEe</td>\n",
              "      <td>Shelia</td>\n",
              "      <td>Mahoney</td>\n",
              "      <td>Male</td>\n",
              "      <td>pwarner@example.org</td>\n",
              "      <td>8571398239</td>\n",
              "      <td>27-01-2014</td>\n",
              "      <td>Probation officer</td>\n",
              "      <td>90000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>810Ce0F276Badec</td>\n",
              "      <td>Sheryl</td>\n",
              "      <td>Lowery</td>\n",
              "      <td>Female</td>\n",
              "      <td>fhoward@example.org</td>\n",
              "      <td>5997820605</td>\n",
              "      <td>25-11-2013</td>\n",
              "      <td>Copy</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9afFEafAe1CBBB9</td>\n",
              "      <td>Lindsey</td>\n",
              "      <td>Rice</td>\n",
              "      <td>Female</td>\n",
              "      <td>elin@example.net</td>\n",
              "      <td>39041716353010</td>\n",
              "      <td>15-04-1923</td>\n",
              "      <td>Biomedical engineer</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>aF75e6dDEBC5b66</td>\n",
              "      <td>Sherry</td>\n",
              "      <td>Caldwell</td>\n",
              "      <td>Male</td>\n",
              "      <td>kaitlin13@example.net</td>\n",
              "      <td>8537800927</td>\n",
              "      <td>06-08-1917</td>\n",
              "      <td>Higher education lecturer</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>efeb05c7Cc94EA3</td>\n",
              "      <td>Ernest</td>\n",
              "      <td>Hoffman</td>\n",
              "      <td>Male</td>\n",
              "      <td>jeffharvey@example.com</td>\n",
              "      <td>9365574807895</td>\n",
              "      <td>22-12-1984</td>\n",
              "      <td>Health visitor</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc5376c4-86a6-4482-bd74-48797763a8d0')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-dc5376c4-86a6-4482-bd74-48797763a8d0 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-dc5376c4-86a6-4482-bd74-48797763a8d0');\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",
              "\n",
              "\n",
              "<div id=\"df-98d874df-3b65-47df-a185-752c607abcdf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98d874df-3b65-47df-a185-752c607abcdf')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-98d874df-3b65-47df-a185-752c607abcdf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 979,\n  \"fields\": [\n    {\n      \"column\": \"User Id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 979,\n        \"samples\": [\n          \"aBa137daedc833c\",\n          \"42069E2a97adFCb\",\n          \"26A154Aa4318fFd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"First Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 520,\n        \"samples\": [\n          \"Marie\",\n          \"Brandy\",\n          \"Cheryl\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Last Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 618,\n        \"samples\": [\n          \"Dawson\",\n          \"Booth\",\n          \"Hodge\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 979,\n        \"samples\": [\n          \"melody43@example.net\",\n          \"jeanne07@example.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Phone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 419643657018319,\n        \"min\": 1081,\n        \"max\": 1944627061259599,\n        \"num_unique_values\": 979,\n        \"samples\": [\n          5003439851714,\n          7035447090\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of birth\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 971,\n        \"samples\": [\n          \"05-04-1984\",\n          \"26-05-1929\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Job Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 513,\n        \"samples\": [\n          \"Surveyor, planning and development\",\n          \"Geographical information systems officer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16101,\n        \"min\": 50000,\n        \"max\": 100000,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50000,\n          85000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question -: 8**"
      ],
      "metadata": {
        "id": "t6r7VX-ok3zo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#a->\n",
        "df.iloc[51:]\n",
        "df_copy=df[['Last Name','Gender','Email','Phone','Salary']]"
      ],
      "metadata": {
        "id": "69Z1HEb0lCdI"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_copy.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZakM3aCxlD_I",
        "outputId": "0b5cbbd5-9c32-45d4-bd05-625b76d49f00"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Last Name  Gender                   Email           Phone  Salary\n",
              "0   Mahoney    Male     pwarner@example.org      8571398239   90000\n",
              "1    Lowery  Female     fhoward@example.org      5997820605   50000\n",
              "2      Rice  Female        elin@example.net  39041716353010  100000\n",
              "3  Caldwell    Male   kaitlin13@example.net      8537800927   50000\n",
              "4   Hoffman    Male  jeffharvey@example.com   9365574807895   60000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6a59631c-34d5-465f-a449-3558b27efc2b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Last Name</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Email</th>\n",
              "      <th>Phone</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mahoney</td>\n",
              "      <td>Male</td>\n",
              "      <td>pwarner@example.org</td>\n",
              "      <td>8571398239</td>\n",
              "      <td>90000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Lowery</td>\n",
              "      <td>Female</td>\n",
              "      <td>fhoward@example.org</td>\n",
              "      <td>5997820605</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rice</td>\n",
              "      <td>Female</td>\n",
              "      <td>elin@example.net</td>\n",
              "      <td>39041716353010</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Caldwell</td>\n",
              "      <td>Male</td>\n",
              "      <td>kaitlin13@example.net</td>\n",
              "      <td>8537800927</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hoffman</td>\n",
              "      <td>Male</td>\n",
              "      <td>jeffharvey@example.com</td>\n",
              "      <td>9365574807895</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6a59631c-34d5-465f-a449-3558b27efc2b')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-6a59631c-34d5-465f-a449-3558b27efc2b 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-6a59631c-34d5-465f-a449-3558b27efc2b');\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",
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              "\n",
              "\n",
              "<div id=\"df-b1f70c30-8409-4bcc-836b-8f03bbc5159e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b1f70c30-8409-4bcc-836b-8f03bbc5159e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b1f70c30-8409-4bcc-836b-8f03bbc5159e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_copy",
              "summary": "{\n  \"name\": \"df_copy\",\n  \"rows\": 979,\n  \"fields\": [\n    {\n      \"column\": \"Last Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 618,\n        \"samples\": [\n          \"Dawson\",\n          \"Booth\",\n          \"Hodge\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 979,\n        \"samples\": [\n          \"melody43@example.net\",\n          \"jeanne07@example.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Phone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 419643657018319,\n        \"min\": 1081,\n        \"max\": 1944627061259599,\n        \"num_unique_values\": 979,\n        \"samples\": [\n          5003439851714,\n          7035447090\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16101,\n        \"min\": 50000,\n        \"max\": 100000,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50000,\n          85000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_copy.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "yCHIyo_XlEAw",
        "outputId": "3ac8ca72-c0b5-4c22-fb4e-f16120f896d0"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Last Name  Gender                      Email           Phone  Salary\n",
              "0   Mahoney    Male        pwarner@example.org      8571398239   90000\n",
              "1    Lowery  Female        fhoward@example.org      5997820605   50000\n",
              "2      Rice  Female           elin@example.net  39041716353010  100000\n",
              "3  Caldwell    Male      kaitlin13@example.net      8537800927   50000\n",
              "4   Hoffman    Male     jeffharvey@example.com   9365574807895   60000\n",
              "5  Andersen    Male       alicia33@example.org      4709522945   65000\n",
              "6      Mays    Male         jake50@example.com       138204758   50000\n",
              "7  Mitchell    Male  lanechristina@example.net  56090350684985   50000\n",
              "8     Ayala    Male         ncantu@example.com      8629884096   60000\n",
              "9      Huff    Male       kristy41@example.net  10418593844272  100000"
            ],
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              "      <th>1</th>\n",
              "      <td>Lowery</td>\n",
              "      <td>Female</td>\n",
              "      <td>fhoward@example.org</td>\n",
              "      <td>5997820605</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rice</td>\n",
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              "      <td>39041716353010</td>\n",
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              "      <th>3</th>\n",
              "      <td>Caldwell</td>\n",
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              "      <td>8537800927</td>\n",
              "      <td>50000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>9365574807895</td>\n",
              "      <td>60000</td>\n",
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              "    <tr>\n",
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              "      <td>Andersen</td>\n",
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              "      <td>alicia33@example.org</td>\n",
              "      <td>4709522945</td>\n",
              "      <td>65000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Mays</td>\n",
              "      <td>Male</td>\n",
              "      <td>jake50@example.com</td>\n",
              "      <td>138204758</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Mitchell</td>\n",
              "      <td>Male</td>\n",
              "      <td>lanechristina@example.net</td>\n",
              "      <td>56090350684985</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Ayala</td>\n",
              "      <td>Male</td>\n",
              "      <td>ncantu@example.com</td>\n",
              "      <td>8629884096</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Huff</td>\n",
              "      <td>Male</td>\n",
              "      <td>kristy41@example.net</td>\n",
              "      <td>10418593844272</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "        document.querySelector('#df-9a569bce-1c90-4a49-a8d8-51acfc8690c5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "        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",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7a379e07-b581-4393-a5b7-ff8b926bcbda button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_copy",
              "summary": "{\n  \"name\": \"df_copy\",\n  \"rows\": 979,\n  \"fields\": [\n    {\n      \"column\": \"Last Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 618,\n        \"samples\": [\n          \"Dawson\",\n          \"Booth\",\n          \"Hodge\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 979,\n        \"samples\": [\n          \"melody43@example.net\",\n          \"jeanne07@example.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Phone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 419643657018319,\n        \"min\": 1081,\n        \"max\": 1944627061259599,\n        \"num_unique_values\": 979,\n        \"samples\": [\n          5003439851714,\n          7035447090\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16101,\n        \"min\": 50000,\n        \"max\": 100000,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50000,\n          85000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_copy['Salary'].tail(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NwJ2xrjlECy",
        "outputId": "31de2e8d-2c81-4476-bb12-1a22f2e698fe"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "974     90000\n",
              "975     50000\n",
              "976     60000\n",
              "977    100000\n",
              "978     90000\n",
              "Name: Salary, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Question -> 9**"
      ],
      "metadata": {
        "id": "-TJGQtIRnxxK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['Last Name']=='Duke') & (df['Gender']=='Female') & (df['Salary']<85000)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "AJvLFknhlEGK",
        "outputId": "01d8c706-9993-4888-d678-3e0f76fb46bf"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             User Id First Name Last Name  Gender                     Email  \\\n",
              "39   99A502C175C4EBd     Olivia      Duke  Female       diana26@example.net   \n",
              "193  DF17975CC0a0373    Katrina      Duke  Female       robin78@example.com   \n",
              "437  dcE1B7DE83c1076      Traci      Duke  Female  perryhoffman@example.org   \n",
              "708  c9b482D7aa3e682     Lonnie      Duke  Female   kevinkramer@example.net   \n",
              "\n",
              "                Phone Date of birth        Job Title  Salary  \n",
              "39   1366475860704350    13-10-1934          Dentist   60000  \n",
              "193        7404340212    21-09-1935  Producer, radio   50000  \n",
              "437    19035960995489    11-02-1997        Herbalist   50000  \n",
              "708        9826926257    12-05-2015     Nurse, adult   70000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f3edefa-976c-40bd-9224-7174b627042d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User Id</th>\n",
              "      <th>First Name</th>\n",
              "      <th>Last Name</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Email</th>\n",
              "      <th>Phone</th>\n",
              "      <th>Date of birth</th>\n",
              "      <th>Job Title</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>99A502C175C4EBd</td>\n",
              "      <td>Olivia</td>\n",
              "      <td>Duke</td>\n",
              "      <td>Female</td>\n",
              "      <td>diana26@example.net</td>\n",
              "      <td>1366475860704350</td>\n",
              "      <td>13-10-1934</td>\n",
              "      <td>Dentist</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>DF17975CC0a0373</td>\n",
              "      <td>Katrina</td>\n",
              "      <td>Duke</td>\n",
              "      <td>Female</td>\n",
              "      <td>robin78@example.com</td>\n",
              "      <td>7404340212</td>\n",
              "      <td>21-09-1935</td>\n",
              "      <td>Producer, radio</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>dcE1B7DE83c1076</td>\n",
              "      <td>Traci</td>\n",
              "      <td>Duke</td>\n",
              "      <td>Female</td>\n",
              "      <td>perryhoffman@example.org</td>\n",
              "      <td>19035960995489</td>\n",
              "      <td>11-02-1997</td>\n",
              "      <td>Herbalist</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>708</th>\n",
              "      <td>c9b482D7aa3e682</td>\n",
              "      <td>Lonnie</td>\n",
              "      <td>Duke</td>\n",
              "      <td>Female</td>\n",
              "      <td>kevinkramer@example.net</td>\n",
              "      <td>9826926257</td>\n",
              "      <td>12-05-2015</td>\n",
              "      <td>Nurse, adult</td>\n",
              "      <td>70000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f3edefa-976c-40bd-9224-7174b627042d')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-5f3edefa-976c-40bd-9224-7174b627042d 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-5f3edefa-976c-40bd-9224-7174b627042d');\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",
              "\n",
              "\n",
              "<div id=\"df-acd79152-0478-4f6c-8e16-d8283f7782d0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-acd79152-0478-4f6c-8e16-d8283f7782d0')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-acd79152-0478-4f6c-8e16-d8283f7782d0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_copy[(df_copy['Last Name']=='Duke') & (df_copy['Gender']=='Female') & (df_copy['Salary']<85000)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "51xXsR0_lEIC",
        "outputId": "6bf9e7e5-71d1-4183-d14b-4998231581f4"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Last Name  Gender                     Email             Phone  Salary\n",
              "39       Duke  Female       diana26@example.net  1366475860704350   60000\n",
              "193      Duke  Female       robin78@example.com        7404340212   50000\n",
              "437      Duke  Female  perryhoffman@example.org    19035960995489   50000\n",
              "708      Duke  Female   kevinkramer@example.net        9826926257   70000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7f752fce-e473-45cb-9942-016f16525517\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Last Name</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Email</th>\n",
              "      <th>Phone</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Duke</td>\n",
              "      <td>Female</td>\n",
              "      <td>diana26@example.net</td>\n",
              "      <td>1366475860704350</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>Duke</td>\n",
              "      <td>Female</td>\n",
              "      <td>robin78@example.com</td>\n",
              "      <td>7404340212</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>Duke</td>\n",
              "      <td>Female</td>\n",
              "      <td>perryhoffman@example.org</td>\n",
              "      <td>19035960995489</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>708</th>\n",
              "      <td>Duke</td>\n",
              "      <td>Female</td>\n",
              "      <td>kevinkramer@example.net</td>\n",
              "      <td>9826926257</td>\n",
              "      <td>70000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f752fce-e473-45cb-9942-016f16525517')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-7f752fce-e473-45cb-9942-016f16525517 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-7f752fce-e473-45cb-9942-016f16525517');\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",
              "\n",
              "\n",
              "<div id=\"df-6b176875-b9d9-4602-8f94-d7cb9b00b1df\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6b176875-b9d9-4602-8f94-d7cb9b00b1df')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6b176875-b9d9-4602-8f94-d7cb9b00b1df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_copy[(df_copy['Last Name']=='Duke') & (df_copy['Gender']=='Female') & (df_copy['Salary']<85000)]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Last Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Duke\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"robin78@example.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Phone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 680121547150870,\n        \"min\": 7404340212,\n        \"max\": 1366475860704350,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7404340212\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9574,\n        \"min\": 50000,\n        \"max\": 70000,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          60000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question -> 10**"
      ],
      "metadata": {
        "id": "SbK2kPJSpLP0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_series = pd.Series(np.random.randint(1, 7, size=35))\n",
        "data = pd.DataFrame(random_series.values.reshape(7, 5))\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Xkmz2xBwlEJ8",
        "outputId": "5144c281-dfc3-419b-f941-62f5bbe09598"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0  1  2  3  4\n",
              "0  6  1  2  6  6\n",
              "1  3  3  6  6  5\n",
              "2  2  4  1  3  5\n",
              "3  4  1  5  1  6\n",
              "4  5  1  5  6  1\n",
              "5  6  1  4  4  4\n",
              "6  6  5  3  3  4"
            ],
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              "        document.querySelector('#df-f10eaffa-defe-41c1-9637-0701c5d17c05 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
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              "        const element = document.querySelector('#df-f10eaffa-defe-41c1-9637-0701c5d17c05');\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",
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              "\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c087410f-1f92-4352-96f7-5c54ccb41d09 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_55719e8d-5023-4dde-8b0c-7785753c9331\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_55719e8d-5023-4dde-8b0c-7785753c9331 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          5,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          6,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 11**"
      ],
      "metadata": {
        "id": "iBWauJp62iqb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=pd.Series(np.random.randint(10,50,50))\n",
        "y=pd.Series(np.random.randint(100,1000,50))\n",
        "ans=pd.DataFrame({'col1':x,'col2':y})\n",
        "print(ans)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bf3w93volELt",
        "outputId": "55f15101-ca9b-4c78-b8a6-e78f1bb60de2"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    col1  col2\n",
            "0     31   738\n",
            "1     14   393\n",
            "2     20   186\n",
            "3     38   483\n",
            "4     15   974\n",
            "5     32   125\n",
            "6     12   457\n",
            "7     26   618\n",
            "8     14   662\n",
            "9     13   678\n",
            "10    24   275\n",
            "11    26   562\n",
            "12    41   722\n",
            "13    37   865\n",
            "14    36   721\n",
            "15    15   819\n",
            "16    42   876\n",
            "17    38   939\n",
            "18    27   363\n",
            "19    32   532\n",
            "20    42   245\n",
            "21    25   503\n",
            "22    33   567\n",
            "23    21   161\n",
            "24    10   233\n",
            "25    46   707\n",
            "26    44   858\n",
            "27    28   628\n",
            "28    29   608\n",
            "29    13   757\n",
            "30    24   442\n",
            "31    16   551\n",
            "32    22   835\n",
            "33    22   542\n",
            "34    23   543\n",
            "35    10   731\n",
            "36    28   587\n",
            "37    18   804\n",
            "38    19   344\n",
            "39    42   905\n",
            "40    22   786\n",
            "41    20   293\n",
            "42    11   121\n",
            "43    42   738\n",
            "44    43   989\n",
            "45    44   520\n",
            "46    16   345\n",
            "47    18   919\n",
            "48    23   162\n",
            "49    18   527\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 12**"
      ],
      "metadata": {
        "id": "YzGJLqdT4DFr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output=df.copy()\n",
        "output.drop(columns=['Email','Phone','Date of birth'],inplace=True)"
      ],
      "metadata": {
        "id": "U-NhE65YlEPp"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Up0JdrsVlERc",
        "outputId": "bceb30db-60df-46bd-ee36-b094d6bc1c96"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 979 entries, 0 to 978\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   User Id     979 non-null    object\n",
            " 1   First Name  979 non-null    object\n",
            " 2   Last Name   979 non-null    object\n",
            " 3   Gender      979 non-null    object\n",
            " 4   Job Title   979 non-null    object\n",
            " 5   Salary      979 non-null    int64 \n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 46.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(output.isnull().sum())\n",
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "XBzGTdW4lEU7",
        "outputId": "3e08474c-031f-40eb-810c-09addd3278ac"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "User Id       0\n",
            "First Name    0\n",
            "Last Name     0\n",
            "Gender        0\n",
            "Job Title     0\n",
            "Salary        0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             User Id First Name Last Name  Gender  \\\n",
              "0    8717bbf45cCDbEe     Shelia   Mahoney    Male   \n",
              "1    810Ce0F276Badec     Sheryl    Lowery  Female   \n",
              "2    9afFEafAe1CBBB9    Lindsey      Rice  Female   \n",
              "3    aF75e6dDEBC5b66     Sherry  Caldwell    Male   \n",
              "4    efeb05c7Cc94EA3     Ernest   Hoffman    Male   \n",
              "..               ...        ...       ...     ...   \n",
              "974  fedF4c7Fd9e7cFa       Kurt    Bryant  Female   \n",
              "975  ECddaFEDdEc4FAB      Donna     Barry  Female   \n",
              "976  2adde51d8B8979E      Cathy  Mckinney  Female   \n",
              "977  Fb2FE369D1E171A   Jermaine    Phelps    Male   \n",
              "978  8b756f6231DDC6e        Lee      Tran  Female   \n",
              "\n",
              "                           Job Title  Salary  \n",
              "0                  Probation officer   90000  \n",
              "1                               Copy   50000  \n",
              "2                Biomedical engineer  100000  \n",
              "3          Higher education lecturer   50000  \n",
              "4                     Health visitor   60000  \n",
              "..                               ...     ...  \n",
              "974                Personnel officer   90000  \n",
              "975          Education administrator   50000  \n",
              "976  Commercial/residential surveyor   60000  \n",
              "977                 Ambulance person  100000  \n",
              "978       Nurse, learning disability   90000  \n",
              "\n",
              "[979 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa38ed76-a9cf-4f0d-9342-9340947c8040\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User Id</th>\n",
              "      <th>First Name</th>\n",
              "      <th>Last Name</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Job Title</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8717bbf45cCDbEe</td>\n",
              "      <td>Shelia</td>\n",
              "      <td>Mahoney</td>\n",
              "      <td>Male</td>\n",
              "      <td>Probation officer</td>\n",
              "      <td>90000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>810Ce0F276Badec</td>\n",
              "      <td>Sheryl</td>\n",
              "      <td>Lowery</td>\n",
              "      <td>Female</td>\n",
              "      <td>Copy</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9afFEafAe1CBBB9</td>\n",
              "      <td>Lindsey</td>\n",
              "      <td>Rice</td>\n",
              "      <td>Female</td>\n",
              "      <td>Biomedical engineer</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>aF75e6dDEBC5b66</td>\n",
              "      <td>Sherry</td>\n",
              "      <td>Caldwell</td>\n",
              "      <td>Male</td>\n",
              "      <td>Higher education lecturer</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>efeb05c7Cc94EA3</td>\n",
              "      <td>Ernest</td>\n",
              "      <td>Hoffman</td>\n",
              "      <td>Male</td>\n",
              "      <td>Health visitor</td>\n",
              "      <td>60000</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>974</th>\n",
              "      <td>fedF4c7Fd9e7cFa</td>\n",
              "      <td>Kurt</td>\n",
              "      <td>Bryant</td>\n",
              "      <td>Female</td>\n",
              "      <td>Personnel officer</td>\n",
              "      <td>90000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>975</th>\n",
              "      <td>ECddaFEDdEc4FAB</td>\n",
              "      <td>Donna</td>\n",
              "      <td>Barry</td>\n",
              "      <td>Female</td>\n",
              "      <td>Education administrator</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>976</th>\n",
              "      <td>2adde51d8B8979E</td>\n",
              "      <td>Cathy</td>\n",
              "      <td>Mckinney</td>\n",
              "      <td>Female</td>\n",
              "      <td>Commercial/residential surveyor</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>977</th>\n",
              "      <td>Fb2FE369D1E171A</td>\n",
              "      <td>Jermaine</td>\n",
              "      <td>Phelps</td>\n",
              "      <td>Male</td>\n",
              "      <td>Ambulance person</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>978</th>\n",
              "      <td>8b756f6231DDC6e</td>\n",
              "      <td>Lee</td>\n",
              "      <td>Tran</td>\n",
              "      <td>Female</td>\n",
              "      <td>Nurse, learning disability</td>\n",
              "      <td>90000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>979 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa38ed76-a9cf-4f0d-9342-9340947c8040')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-aa38ed76-a9cf-4f0d-9342-9340947c8040 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-aa38ed76-a9cf-4f0d-9342-9340947c8040');\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",
              "\n",
              "\n",
              "<div id=\"df-69377756-4da2-46f3-b0b2-4541700d7817\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69377756-4da2-46f3-b0b2-4541700d7817')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-69377756-4da2-46f3-b0b2-4541700d7817 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8e1c569d-cdde-497e-b5a4-9833d32473b2\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('output')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8e1c569d-cdde-497e-b5a4-9833d32473b2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('output');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "output",
              "summary": "{\n  \"name\": \"output\",\n  \"rows\": 979,\n  \"fields\": [\n    {\n      \"column\": \"User Id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 979,\n        \"samples\": [\n          \"aBa137daedc833c\",\n          \"42069E2a97adFCb\",\n          \"26A154Aa4318fFd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"First Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 520,\n        \"samples\": [\n          \"Marie\",\n          \"Brandy\",\n          \"Cheryl\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Last Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 618,\n        \"samples\": [\n          \"Dawson\",\n          \"Booth\",\n          \"Hodge\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Job Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 513,\n        \"samples\": [\n          \"Surveyor, planning and development\",\n          \"Geographical information systems officer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16101,\n        \"min\": 50000,\n        \"max\": 100000,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50000,\n          85000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 13**"
      ],
      "metadata": {
        "id": "rdW7MiMm6RD2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.random.uniform(0,1,100)\n",
        "y=np.random.uniform(0,1,100)\n",
        "\n",
        "print(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPfoCwAT6eEk",
        "outputId": "86805602-fff8-4d7c-dd81-ae0e264ec2b5"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.59199476 0.76941435 0.54626277 0.31765568 0.4746689  0.27504078\n",
            " 0.31651634 0.19570205 0.05920174 0.2591186  0.75303964 0.99882911\n",
            " 0.04585834 0.86163499 0.34752227 0.77777068 0.63416154 0.2246118\n",
            " 0.39150179 0.71725033 0.95518262 0.58533251 0.85424127 0.69917687\n",
            " 0.47842878 0.2678544  0.93647387 0.87257588 0.7208811  0.03984164\n",
            " 0.17262334 0.85064259 0.49796642 0.48985883 0.50682511 0.06959269\n",
            " 0.17769503 0.42073356 0.59527733 0.26054338 0.77723403 0.67689222\n",
            " 0.99950718 0.15062968 0.43380867 0.08767047 0.13936679 0.07928908\n",
            " 0.92784102 0.92453882 0.50797906 0.978047   0.25061204 0.29977597\n",
            " 0.95298697 0.90977933 0.19656711 0.39668742 0.76641727 0.9679271\n",
            " 0.49876428 0.45101377 0.98848788 0.79296901 0.69799634 0.326271\n",
            " 0.50818542 0.22101197 0.25980402 0.09393221 0.83901655 0.14385036\n",
            " 0.24721557 0.32506594 0.53285077 0.31189311 0.47886843 0.91042728\n",
            " 0.8572976  0.74948924 0.18968877 0.53006054 0.94887069 0.37671049\n",
            " 0.78645686 0.11501069 0.41036059 0.70594281 0.54048455 0.18312718\n",
            " 0.43306011 0.27999139 0.02235946 0.5468104  0.8755175  0.69270862\n",
            " 0.48157315 0.74728581 0.91432451 0.61473487] [0.50373147 0.41337408 0.43199567 0.93277661 0.25684754 0.56903392\n",
            " 0.31276784 0.30201704 0.65043464 0.91322627 0.28009849 0.21306507\n",
            " 0.00532938 0.14110371 0.8963975  0.08395486 0.92668908 0.96562988\n",
            " 0.33636058 0.01040785 0.36865772 0.92571149 0.31345582 0.85825553\n",
            " 0.52345556 0.33346279 0.83411002 0.48294608 0.53238125 0.94315067\n",
            " 0.65317233 0.43708685 0.31611223 0.84686403 0.54476104 0.96353043\n",
            " 0.66955096 0.59387422 0.38507529 0.1131623  0.65503514 0.88677141\n",
            " 0.96548736 0.69086834 0.5190087  0.87924922 0.96958263 0.27507464\n",
            " 0.18619495 0.15715643 0.07609199 0.98195364 0.12472559 0.02185389\n",
            " 0.30871664 0.69209344 0.06331862 0.77992672 0.81382164 0.36546653\n",
            " 0.52291912 0.07397304 0.9409925  0.86330418 0.97355149 0.50173211\n",
            " 0.30543511 0.4746599  0.38432714 0.36330252 0.35696038 0.47633784\n",
            " 0.63696114 0.34575201 0.62362523 0.44380703 0.77747296 0.19977937\n",
            " 0.56581721 0.64287394 0.97317017 0.34732552 0.44543537 0.57789033\n",
            " 0.55200389 0.77138349 0.87475043 0.99447084 0.91216341 0.28073387\n",
            " 0.82525686 0.18445776 0.60072611 0.44740535 0.82664303 0.90525418\n",
            " 0.07317067 0.00776555 0.13176021 0.45616662]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x,y,c='red',marker='o')\n",
        "plt.xlabel('X_axis')\n",
        "plt.ylabel('Y_axis')\n",
        "plt.axvline(x=0.5, color='green', linestyle=':', label='x=0.5')\n",
        "plt.axhline(y=0.5, color='blue', linestyle='--', label='y=0.5')\n",
        "plt.title(\"Advanced Scatter Plot of Random Values\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "3U39C0Sh8BG3",
        "outputId": "157aa237-12cb-441b-a834-aec4da8c7eaa"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7c3c17c1be80>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question->14"
      ],
      "metadata": {
        "id": "HHJNt9x2-vA-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates = pd.date_range('2024-01-01', periods=100)\n",
        "temperature = np.random.randint(20, 30, size=100)\n",
        "humidity = np.random.randint(40, 60, size=100)\n",
        "\n",
        "df = pd.DataFrame({'Date': dates, 'Temperature': temperature, 'Humidity': humidity})\n",
        "\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "color = 'tab:red'\n",
        "ax1.set_xlabel('Date')\n",
        "ax1.set_ylabel('Temperature (°C)', color=color)\n",
        "ax1.plot(df['Date'], df['Temperature'], color=color)\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "color = 'tab:blue'\n",
        "ax2.set_ylabel('Humidity (%)', color=color)\n",
        "ax2.plot(df['Date'], df['Humidity'], color=color)\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "plt.title('Temperature and Humidity Over Time')\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "q1eundJq8BLR",
        "outputId": "b355afd7-5f90-4280-d207-f4f63be3c95d"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 15"
      ],
      "metadata": {
        "id": "CoWvf1jc_3Bz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "variable=np.round(np.random.rand(1000),1)"
      ],
      "metadata": {
        "id": "UQSa6eGr_5cL"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "variable"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDPGw_GJ_5n1",
        "outputId": "6b748d61-cb29-47ca-fc6c-78bb2fb9bded"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.4, 0. , 0.7, 0.9, 0.2, 0.5, 0.2, 0.6, 0.5, 0.8, 1. , 0.5, 0.1,\n",
              "       0.5, 0.9, 0.2, 0.5, 0.1, 0.2, 0.8, 0.7, 0.1, 0.7, 0.6, 0.3, 0.1,\n",
              "       0.9, 0.2, 0.4, 0.9, 0.3, 0.5, 0.4, 0.2, 0.5, 0. , 0.3, 0.2, 0.3,\n",
              "       0. , 0.6, 0.5, 0.4, 0.8, 0.9, 0.9, 0.6, 0.3, 1. , 0.8, 0.3, 0.7,\n",
              "       0.4, 0.7, 0.6, 0.8, 0.2, 0.6, 0.8, 0.2, 0.8, 0.5, 0.5, 0.9, 0.1,\n",
              "       0.5, 0.8, 0.4, 0.8, 0.2, 0.3, 0.4, 0.2, 0.2, 0.9, 0.2, 0.2, 0.8,\n",
              "       0.1, 0.9, 0.8, 0.9, 0. , 0.7, 0.7, 1. , 0.5, 0. , 0.1, 0.7, 0.1,\n",
              "       1. , 0.9, 0.9, 0.5, 0.3, 0.4, 0.9, 0.7, 0.1, 0.1, 0.4, 0.4, 0.3,\n",
              "       0.6, 0.2, 0.5, 0. , 0.7, 0.1, 0.4, 0. , 0.3, 1. , 0.4, 0.6, 1. ,\n",
              "       0.5, 0.9, 0.1, 0.3, 0.1, 0.2, 0.2, 0.5, 0.8, 0.8, 0.8, 0.4, 0.4,\n",
              "       0. , 0.3, 0.5, 0.8, 0.9, 0.8, 0.7, 0.1, 0. , 0.4, 0.3, 0.6, 0.3,\n",
              "       0.9, 0.5, 0.4, 0.3, 0.4, 1. , 0.5, 0.2, 0.5, 0.7, 0.7, 0.7, 0.4,\n",
              "       0.6, 0.4, 0.1, 0.2, 0.9, 0.8, 0.8, 0.4, 0.3, 0.2, 0.3, 0. , 0.1,\n",
              "       0.3, 0.5, 0.6, 0.6, 0.7, 0.9, 0.8, 0.4, 0.5, 0.1, 0.3, 0.6, 0.1,\n",
              "       0.6, 0.1, 0.7, 0.5, 0.1, 0.4, 0.1, 0.1, 0.9, 0.8, 0.1, 0.6, 0.1,\n",
              "       0.7, 0.7, 0.3, 0.4, 0.1, 0. , 0.6, 0.5, 0.5, 1. , 0.4, 0.7, 0.5,\n",
              "       0.1, 0.7, 0.9, 0.1, 0.9, 0.7, 0.3, 0.4, 0.7, 0.9, 0.9, 0.4, 0.6,\n",
              "       0.8, 0.6, 0. , 0.3, 0.4, 0.6, 0.1, 0.9, 0. , 0.6, 0.8, 0.7, 0.2,\n",
              "       0.3, 0.2, 0.2, 0.7, 0.7, 0.4, 0.9, 0.8, 0.7, 0.3, 0.7, 0.1, 0.4,\n",
              "       0.4, 0.1, 0.8, 0.7, 0.3, 0.6, 0.6, 0.1, 0.9, 0.9, 0.4, 0.2, 0.2,\n",
              "       0.4, 0.8, 0.4, 0. , 0.3, 0.9, 0.6, 0.8, 0.2, 0. , 0.1, 0.2, 0.7,\n",
              "       0.2, 0.1, 0.4, 0.6, 0.4, 0.3, 0.6, 0.4, 0.8, 0.1, 0.9, 0.5, 0.6,\n",
              "       0.1, 0.4, 0.7, 0.1, 0.4, 0.2, 0. , 0.8, 0.3, 0.9, 1. , 0.2, 0.3,\n",
              "       0.2, 0.1, 0.3, 0.1, 0.3, 0. , 1. , 0.5, 0.2, 0.5, 0.4, 0. , 0.2,\n",
              "       0.9, 0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.9, 0.3, 0.8, 0.6, 0.6,\n",
              "       0.8, 0.1, 0.7, 1. , 0.7, 0.3, 0.1, 0.8, 0. , 1. , 0.1, 0.7, 0.1,\n",
              "       0.5, 0.1, 0.9, 0.9, 0.2, 0.1, 0.7, 0.2, 0.8, 0.7, 0.2, 0.2, 0.5,\n",
              "       0.4, 0.3, 0.4, 0.4, 1. , 0.7, 0.2, 0.6, 0.3, 0.7, 0.1, 0.2, 0.4,\n",
              "       0.2, 0.1, 0.6, 0.2, 0.9, 0.5, 0.2, 0.3, 0.9, 0.3, 0.8, 0.2, 0.6,\n",
              "       0.3, 0.1, 0.1, 0.1, 0.8, 0.1, 0.1, 0.4, 0.9, 0.8, 0.4, 1. , 0.2,\n",
              "       0.5, 0.2, 0.1, 0.2, 0.6, 0.2, 0.6, 1. , 0.8, 0.3, 0.1, 0.8, 0.3,\n",
              "       0.8, 0.2, 0.6, 0.1, 0.3, 1. , 0.7, 0. , 0.1, 0.6, 0.5, 0.4, 0.9,\n",
              "       1. , 0.1, 0.4, 0.5, 0.5, 0.8, 0.8, 0.8, 0.1, 0. , 0.4, 0.7, 0.9,\n",
              "       0.8, 0.7, 0.9, 0.1, 0.6, 0.9, 1. , 0. , 1. , 0.6, 0.9, 0.9, 0. ,\n",
              "       0.1, 0.4, 0.6, 0. , 0.1, 0.9, 0. , 0.4, 0.9, 0.3, 0.7, 0. , 1. ,\n",
              "       0.9, 0.1, 0.3, 0.8, 0.7, 0.8, 0.5, 0.7, 0.9, 0.9, 0.3, 0.8, 0.9,\n",
              "       0.8, 0.8, 0.2, 0.4, 0.4, 0.2, 0.4, 0. , 0.1, 0.1, 0.3, 0.9, 0.5,\n",
              "       0.5, 0.5, 0.8, 0.6, 0.1, 0.8, 0.7, 0.4, 0.5, 0.3, 0.1, 0.7, 0.2,\n",
              "       0.7, 0.5, 0.2, 1. , 0.9, 0.8, 1. , 0.6, 0.7, 0.6, 0.5, 0.3, 0.5,\n",
              "       1. , 0.2, 0.4, 0.3, 0.9, 0.9, 0.4, 0.8, 0.9, 0.8, 0.6, 0.8, 0.4,\n",
              "       0.5, 0.7, 0.1, 0.6, 0.9, 0.5, 0.2, 0.6, 0. , 0. , 0.3, 0.3, 0.1,\n",
              "       0.6, 0.4, 0.4, 0.6, 1. , 0.5, 0.4, 0.7, 0.5, 0.1, 0.8, 0. , 0.1,\n",
              "       0.4, 0.6, 0.1, 0. , 0.5, 0.6, 0.6, 0.9, 0.7, 0.4, 0.8, 0.1, 0.8,\n",
              "       0.8, 0.1, 0.7, 0.1, 0.3, 0.8, 0.6, 0. , 0.4, 0.3, 1. , 0.4, 0.7,\n",
              "       1. , 0.2, 0.6, 0.9, 0.2, 0.8, 0.2, 0.6, 0.4, 1. , 0.1, 0.4, 0.8,\n",
              "       0.9, 0.8, 0.3, 1. , 0.5, 0.6, 0.1, 0.1, 0.5, 0.2, 0.7, 0. , 0.2,\n",
              "       0.1, 0.7, 0.3, 0.5, 0.9, 0. , 0.6, 0.8, 0.6, 0.5, 0.3, 0.8, 0.7,\n",
              "       0.5, 0.6, 0.2, 0.5, 0.7, 0.2, 0.5, 0.7, 0.7, 0.5, 0.1, 0.7, 0.4,\n",
              "       0.1, 0.3, 0.1, 0.6, 0.4, 0.2, 0.9, 0.5, 1. , 1. , 0.5, 0.5, 0.7,\n",
              "       0.7, 0.8, 0.7, 0.5, 0.4, 0.9, 0.7, 0.4, 0.9, 0.4, 0.4, 0. , 0.1,\n",
              "       0.5, 0. , 1. , 0.7, 0.5, 0.4, 0.1, 0.6, 0.6, 0.8, 0.2, 0.3, 0.3,\n",
              "       0.3, 0.4, 0.9, 0. , 0.2, 0.5, 0.6, 0.4, 0.7, 0.6, 0.4, 0.5, 0.2,\n",
              "       0.1, 0.4, 0. , 0.3, 0.4, 0.6, 0.3, 0.4, 0.4, 0.8, 0.5, 0.6, 0.7,\n",
              "       0.9, 0.9, 0.6, 0.3, 0.8, 0.2, 0.9, 0.9, 0.9, 0.7, 0.8, 0.6, 0.1,\n",
              "       0.3, 0.3, 0.5, 0.8, 0.7, 0.6, 0.5, 0.9, 0.2, 0.4, 0.6, 0.4, 0.1,\n",
              "       0. , 0.2, 1. , 0.9, 0.7, 0.3, 0.3, 0.7, 1. , 0.4, 0.6, 0.3, 0.4,\n",
              "       0.2, 0.1, 0.4, 0.7, 0.7, 0.2, 0. , 0.4, 0.8, 0.4, 0.7, 0.1, 0.5,\n",
              "       0.5, 0.5, 0.6, 1. , 0.9, 0.6, 0.9, 0.4, 0.1, 0.7, 0.7, 0.6, 0.5,\n",
              "       0.7, 0.5, 0.5, 0.5, 0.1, 0.9, 0. , 1. , 0.7, 0.2, 0.8, 0.8, 0.8,\n",
              "       0. , 0.5, 0.5, 0.1, 0.2, 0.9, 0.9, 0.9, 0.1, 0.2, 0.3, 0.8, 0.9,\n",
              "       0.2, 0. , 0.7, 0.4, 0.4, 0.5, 0.6, 0.3, 0.9, 0.1, 0.9, 0.3, 0.3,\n",
              "       1. , 0.8, 0.7, 0.1, 0.7, 0.9, 0.9, 0.2, 0.6, 0.6, 0.5, 0.3, 0.2,\n",
              "       0.1, 0.1, 0.7, 0.6, 0.2, 0.5, 0.6, 0.1, 0.4, 0.1, 0.3, 0.6, 0.1,\n",
              "       0.8, 0.7, 0.4, 0.8, 0.7, 0.2, 0.8, 0.4, 0.6, 0.4, 0.7, 0.7, 0.5,\n",
              "       0.4, 0.1, 0.4, 0.4, 0.9, 0.6, 0.6, 0. , 0.3, 0.8, 0.3, 0.4, 0.7,\n",
              "       0.6, 0.9, 0.7, 0.4, 0.7, 0.9, 0.5, 0.7, 0.3, 0.6, 0.1, 0.3, 0.4,\n",
              "       0.1, 0.1, 0.6, 0.7, 0.6, 0.8, 0.5, 0.6, 0.1, 0.1, 0.3, 0.4, 0.4,\n",
              "       0. , 0.3, 0.1, 0.4, 0.6, 0.8, 0.4, 0.9, 0.2, 1. , 0.1, 1. , 1. ,\n",
              "       0.1, 0.5, 0.2, 0.8, 0.4, 0.8, 0.1, 0.9, 0.2, 0.7, 0.6, 0.4, 0.9,\n",
              "       0.9, 0.7, 0.4, 0.8, 0.6, 0.7, 0.8, 0.9, 0.4, 0.9, 0.8, 0.8, 0.6,\n",
              "       0. , 0.9, 0.5, 0.5, 0.5, 0.9, 1. , 0.4, 0.5, 0.2, 0.8, 0.9, 0.6,\n",
              "       0.4, 0.2, 0.4, 0.7, 0.4, 1. , 0.7, 0.9, 0.1, 0. , 0.9, 0.4, 0.1,\n",
              "       0.8, 0.2, 0.6, 0.9, 0.5, 0.5, 0.2, 0.4, 0.7, 0.8, 0.7, 0.4, 0.6,\n",
              "       0.7, 0.2, 1. , 0.8, 0.2, 0.7, 0.7, 0.3, 1. , 0.7, 0. , 0.9, 0. ,\n",
              "       0.6, 1. , 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0.6, 0.1, 0.7, 0.5, 0.3,\n",
              "       0.9, 0.8, 0.5, 0.7, 0.9, 0.6, 0.8, 0.7, 0.4, 0.6, 0.7, 0.6, 0.3,\n",
              "       0.7, 0.6, 1. , 0.1, 0.8, 0.2, 0.3, 0.4, 0.5, 0.2, 0.1, 0. ])"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade matplotlib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQ49n61NCWr2",
        "outputId": "d8daa30a-de27-464e-bac5-d5c2fd80aabe"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Collecting matplotlib\n",
            "  Downloading matplotlib-3.9.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.3/8.3 MB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.51.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.0)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
            "Installing collected packages: matplotlib\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.7.1\n",
            "    Uninstalling matplotlib-3.7.1:\n",
            "      Successfully uninstalled matplotlib-3.7.1\n",
            "Successfully installed matplotlib-3.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(variable,bins=30,kde=True)\n",
        "plt.title('Histogram with PDF overlay')\n",
        "plt.xlabel('Values')\n",
        "plt.ylabel('Probability')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "TiaWqBqPCkYk",
        "outputId": "3f33856c-160f-41d0-827e-30cba6ce1cf3"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Probability')"
            ]
          },
          "metadata": {},
          "execution_count": 173
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 17"
      ],
      "metadata": {
        "id": "l22SJ4F8DtE9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.random.rand(20)\n",
        "y=np.random.rand(20)\n",
        "sns.scatterplot(x=x,y=y,c='blue')\n",
        "plt.legend()\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"Y\")\n",
        "plt.title(\"Quadrant-wise Scatter Plot\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "YOD5M3UBD2zU",
        "outputId": "ae5c9055-8349-4964-9da9-7b7877972738"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Quadrant-wise Scatter Plot')"
            ]
          },
          "metadata": {},
          "execution_count": 181
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question->18"
      ],
      "metadata": {
        "id": "Wb_3-NnVFkyH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from bokeh.plotting import figure, show\n",
        "from bokeh.io import output_notebook\n",
        "\n",
        "# Output to notebook (for Jupyter or Colab)\n",
        "output_notebook()\n",
        "\n",
        "# Define the data\n",
        "x = range(0, 10)\n",
        "y = [i for i in map(lambda x: round((x**2) * 0.1, 2), x)]  # Sine wave function\n",
        "\n",
        "# Create a figure\n",
        "p = figure(title='Sine Wave Function', x_axis_label='X-axis', y_axis_label='Y-axis')\n",
        "\n",
        "# Add a line plot\n",
        "p.line(x, y, line_width=2)\n",
        "\n",
        "# Add grid lines\n",
        "p.grid.grid_line_alpha = 0.3\n",
        "p.grid.grid_line_color = 'navy'\n",
        "p.grid.grid_line_dash = [6, 4]  # Dashed grid lines\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "21rvRRyFFniV"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question ->20"
      ],
      "metadata": {
        "id": "Jol_9pr2Ge22"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "\n",
        "np.random.seed(0)\n",
        "x = np.linspace(0, 10, 100)\n",
        "y = np.random.randn(100)\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name='Random Data'))\n",
        "fig.update_layout(title='Simple Line Plot', xaxis_title='X-axis', yaxis_title='Y-axis')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "RkeAEnf_HGkO",
        "outputId": "911506b0-b39d-4ee0-f8ed-a9667991583d"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      "cell_type": "markdown",
      "source": [
        "Question->21"
      ],
      "metadata": {
        "id": "hrV88Z5xHXYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "import numpy as np\n",
        "\n",
        "np.random.seed(0)\n",
        "labels = ['Category A', 'Category B', 'Category C', 'Category D']\n",
        "values = np.random.randint(1, 100, size=len(labels))\n",
        "fig = px.pie(values=values, names=labels, title='Interactive Pie Chart', labels={'label': 'Category'})\n",
        "fig.update_traces(textinfo='percent+label', pull=[0.1, 0, 0, 0])\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "6RVzaTEaHcwu",
        "outputId": "1d7088c4-dde5-4a87-810f-e009005ece59"
      },
      "execution_count": 187,
      "outputs": [
        {
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