240971_NamyaSheth (2).ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Numpy exercise"
      ],
      "metadata": {
        "id": "xjMGftWslX3r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "you should make use of google or documentation if you get stuck on any question. Even you are cofused on understanding some part, reach out to any of us mentors. Try to learn as much as you can."
      ],
      "metadata": {
        "id": "1SaoZmRAleak"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "import numpy"
      ],
      "metadata": {
        "id": "l0oCQweGuVqr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "Zi9PzqXbuXFJ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Array Creation**"
      ],
      "metadata": {
        "id": "Zf7qoUlemfsm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a numpy array of size 10, filled with zeros.\n"
      ],
      "metadata": {
        "id": "sVr_HdsZm1rO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.zeros(10)"
      ],
      "metadata": {
        "id": "96N_Osg8m9QK"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a numpy array with values ranging from 10 to 49\n"
      ],
      "metadata": {
        "id": "zLZG2XCZm-Cg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr1 = np.array(range(10,49))"
      ],
      "metadata": {
        "id": "Jb3ER9tqnBZy"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a numpy matrix of 2*2 integers, filled with ones.\n"
      ],
      "metadata": {
        "id": "-x_82qwgnMj3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr2 = np.ones([2,2])"
      ],
      "metadata": {
        "id": "zn6jVJNOnNJa"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy array, create a new numpy array with the same shape and type as X, filled with ones.\n"
      ],
      "metadata": {
        "id": "SviHusp0nTrI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#assuming X to be arr2 here,\n",
        "arr3 = np.ones(arr2.shape, arr2.dtype)"
      ],
      "metadata": {
        "id": "xFxQejOhnUSD"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy matrix, create a new numpy matrix with the same shape and type as X, filled with zeros.\n"
      ],
      "metadata": {
        "id": "AcCekhA2nZdq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#assuming X to be arr2 here,\n",
        "arr4 = np.zeros(arr2.shape, arr2.dtype)"
      ],
      "metadata": {
        "id": "6xmGS9MTnaBL"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a numpy matrix of 4*4 integers, filled with fives.\n"
      ],
      "metadata": {
        "id": "_-POa2N7ncPa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.full((4,4),5)"
      ],
      "metadata": {
        "id": "5FvaaJTknesz"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy matrix, create a new numpy matrix with the same shape and type as X, filled with sevens.\n"
      ],
      "metadata": {
        "id": "9zWmiEJDnfNZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#assuming X to be arr3\n",
        "a = np.full(arr3.shape,7)"
      ],
      "metadata": {
        "id": "RzcYCBk9niw3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a 3*3 identity numpy matrix with ones on the diagonal and zeros elsewhere.\n"
      ],
      "metadata": {
        "id": "dZ4hdBzznlpH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.identity(3)"
      ],
      "metadata": {
        "id": "LoA1E5Y0nmH_"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a numpy array, filled with 3 random integer values between 1 and 10. hint: use randint\n"
      ],
      "metadata": {
        "id": "63pIFnHwnq47"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.random.randint(1,10,3)"
      ],
      "metadata": {
        "id": "P0WiZbIinr-m"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a 3 x 3 x 3 numpy matrix, filled with random float values.\n"
      ],
      "metadata": {
        "id": "KdG19zE2n3CK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.random.rand(3,3,3)"
      ],
      "metadata": {
        "id": "Bdxi6ad1n3kv"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X python list convert it to an Y numpy array\n"
      ],
      "metadata": {
        "id": "XrDgmMuLoAeQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = [1,2,3,4,5]\n",
        "Y = np.array(X)"
      ],
      "metadata": {
        "id": "_qjTftGqoBVC"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy array, make a copy and store it on Y.\n"
      ],
      "metadata": {
        "id": "9BZUrsPyoE94"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array([1,2,3,4,5])\n",
        "Y = X.copy()"
      ],
      "metadata": {
        "id": "BEkqlkcZoFmZ"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a numpy array with numbers from 1 to 10\n"
      ],
      "metadata": {
        "id": "djmwx3hJoIeM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.array(range(1,11))"
      ],
      "metadata": {
        "id": "nkH8A-2YoL3y"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a numpy array with the odd numbers between 1 to 100\n"
      ],
      "metadata": {
        "id": "rumL2xGdoRWP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.array(range(3,101,2))"
      ],
      "metadata": {
        "id": "tEDYMPkLoTKF"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a numpy array with numbers from 1 to 100, in descending order.\n"
      ],
      "metadata": {
        "id": "XKEpIMPMoWEH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.array(range(100,1,-1))"
      ],
      "metadata": {
        "id": "Hd4E6ZUZoXhM"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a 3*3 numpy matrix, filled with values ranging from 0 to 8\n"
      ],
      "metadata": {
        "id": "OjVz9LDIob6e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a=np.arange(9).reshape(3,3)"
      ],
      "metadata": {
        "id": "cymvUDrVocmB"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Array Indexation**"
      ],
      "metadata": {
        "id": "xq3phoxioiBg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following is the array X, run the following cell."
      ],
      "metadata": {
        "id": "PAGbCdHOp2_d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(['A','B','C','D','E'])"
      ],
      "metadata": {
        "id": "ItTSFkNRqAxf"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy array, show it's first and last element\n"
      ],
      "metadata": {
        "id": "zC2VllkXqDGx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X[0], X[-1])"
      ],
      "metadata": {
        "id": "vgZPW6RsqJaj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5c6e48e-0287-4304-aef7-9253f1995937"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A E\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy array, show it's first three elements\n"
      ],
      "metadata": {
        "id": "TBIdhFd9qJtQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range (3):\n",
        "  print(X[i], end=' ')\n",
        "\n",
        "#or\n",
        "\n",
        "print('\\n', X[0:3])\n"
      ],
      "metadata": {
        "id": "5RvD8q3Rsdvd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eadfbe0a-c482-48ad-91e2-1e9beb1c2090"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A B C \n",
            " ['A' 'B' 'C']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy array, show the elements in reverse position\n"
      ],
      "metadata": {
        "id": "1k2sqOdgsb1l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X[::-1])"
      ],
      "metadata": {
        "id": "49VcB5p4rWz_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb5aad8d-d869-4574-ab54-6fe847d8cccb"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['E' 'D' 'C' 'B' 'A']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy array, show the elements in an odd position. hint: you can manioulate X if you want to.\n"
      ],
      "metadata": {
        "id": "154sBbW4s3xz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X[0::2])"
      ],
      "metadata": {
        "id": "ZztyoUeztnqm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d3f50a4-7f82-4adf-9e68-3e8f3fa9df56"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['A' 'C' 'E']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the following is X now, run this cell below"
      ],
      "metadata": {
        "id": "OenkPTXktn6G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array([\n",
        "    [1,   2,  3,  4],\n",
        "    [5,   6,  7,  8],\n",
        "    [9,  10, 11, 12],\n",
        "    [13, 14, 15, 16]\n",
        "])"
      ],
      "metadata": {
        "id": "4Eg1mu5Kt3TL"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy matrix, show the first row elements and last row elements.\n"
      ],
      "metadata": {
        "id": "AVvPVCfHt6iY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X[0])\n",
        "print(X[-1])"
      ],
      "metadata": {
        "id": "AyEDT4ZYuFQD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e395e90f-3da8-45c0-e610-0232574ae21d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3 4]\n",
            "[13 14 15 16]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy matrix, show the last element on last row\n"
      ],
      "metadata": {
        "id": "HQee_E8huFlr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X[-1][-1]"
      ],
      "metadata": {
        "id": "y5XanU0vutwA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04403e5b-9db2-48ee-ea06-bc3d93e7046c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy matrix, show the middle row elements\n",
        "\n",
        "the following is wrong! it is a common mistake many students make\n",
        "```\n",
        "X[1:-1][1:-1]\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "gM29jchtuxo7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X[1:-1,1:-1]"
      ],
      "metadata": {
        "id": "ZR-2Lr21uypK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7aac9a2-f1f7-4b27-8a23-159d124cd06a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 6,  7],\n",
              "       [10, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy matrix, show the first two elements on the first two rows\n",
        "```\n",
        "#X[:2][:2] is wrong!\n",
        "```"
      ],
      "metadata": {
        "id": "oBPA7UkN3h3W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X[:2,:2]"
      ],
      "metadata": {
        "id": "3XgbHYsgBahH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abdc4354-fccf-41ac-dc5d-dc90231505be"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2],\n",
              "       [5, 6]])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy matrix, show the last two elements on the last two rows\n"
      ],
      "metadata": {
        "id": "gzHrT1iGBpiB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X[-2:,-2:]"
      ],
      "metadata": {
        "id": "dDqN6R2hBqI-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "242278d5-92c9-47d9-97ff-e4aae6d3c654"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11, 12],\n",
              "       [15, 16]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Array manipulation**"
      ],
      "metadata": {
        "id": "Y_85S02VBqdV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "this the array X, you have to perform operations on"
      ],
      "metadata": {
        "id": "A-TKXFvTDDby"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = [0, 10, -5, 40, -3]"
      ],
      "metadata": {
        "id": "yx36J5fhDmDj"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert the given integer numpy array to float\n"
      ],
      "metadata": {
        "id": "g_gKXi74Bu7S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(X, dtype=float)"
      ],
      "metadata": {
        "id": "741x7uWcCy9t"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reverse the given numpy array (first element becomes last)\n"
      ],
      "metadata": {
        "id": "YW3DvQAZDra8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = X[::-1]"
      ],
      "metadata": {
        "id": "rbpjNT8WDuSp"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Order (sort) the given numpy array\n"
      ],
      "metadata": {
        "id": "WrgCmYpDDw2i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X.sort()"
      ],
      "metadata": {
        "id": "E86is04SDxxw"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy array, set the fifth element equal to 1\n"
      ],
      "metadata": {
        "id": "SGk3xZ5cD3rF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X[4]=1"
      ],
      "metadata": {
        "id": "WzBhVE1TD4OD"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "now for the following X"
      ],
      "metadata": {
        "id": "xQCHdxDuD-6E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array([\n",
        "    [1,   2,  3,  4],\n",
        "    [5,   6,  7,  8],\n",
        "    [9,  10, 11, 12],\n",
        "    [13, 14, 15, 16]\n",
        "])"
      ],
      "metadata": {
        "id": "rFiW6j4LEBcH"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy matrix, change the last row with all 1\n"
      ],
      "metadata": {
        "id": "Qz9HU_XpEEI2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X[-1] = np.ones(4)"
      ],
      "metadata": {
        "id": "Yzel4w9bEGyI"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy matrix, change the last item on the last row with a 0\n"
      ],
      "metadata": {
        "id": "r4hFmci3EK_1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X[-1][-1] = 0"
      ],
      "metadata": {
        "id": "CQ8AmCdSELr4"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the X numpy matrix, add 5 to every element\n"
      ],
      "metadata": {
        "id": "8q2N_zvzEOz5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = X+5"
      ],
      "metadata": {
        "id": "83SP9EunEPYB"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PANDAS DATAFRAME"
      ],
      "metadata": {
        "id": "mdudmaTYF0QL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "import pandas and matplotlib"
      ],
      "metadata": {
        "id": "7Bma98zHF7Ku"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "02u09bcSGDN9"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create an empty pandas DataFrame\n"
      ],
      "metadata": {
        "id": "o_lIAdsAGDmp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df0 = pd.DataFrame"
      ],
      "metadata": {
        "id": "KNvISqUHGHNL"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a marvel_df pandas DataFrame with the given marvel data\n"
      ],
      "metadata": {
        "id": "75Dm4VKrGHlH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_data = [\n",
        "    ['Spider-Man', 'male', 1962],\n",
        "    ['Captain America', 'male', 1941],\n",
        "    ['Wolverine', 'male', 1974],\n",
        "    ['Iron Man', 'male', 1963],\n",
        "    ['Thor', 'male', 1963],\n",
        "    ['Thing', 'male', 1961],\n",
        "    ['Mister Fantastic', 'male', 1961],\n",
        "    ['Hulk', 'male', 1962],\n",
        "    ['Beast', 'male', 1963],\n",
        "    ['Invisible Woman', 'female', 1961],\n",
        "    ['Storm', 'female', 1975],\n",
        "    ['Namor', 'male', 1939],\n",
        "    ['Hawkeye', 'male', 1964],\n",
        "    ['Daredevil', 'male', 1964],\n",
        "    ['Doctor Strange', 'male', 1963],\n",
        "    ['Hank Pym', 'male', 1962],\n",
        "    ['Scarlet Witch', 'female', 1964],\n",
        "    ['Wasp', 'female', 1963],\n",
        "    ['Black Widow', 'female', 1964],\n",
        "    ['Vision', 'male', 1968]\n",
        "]"
      ],
      "metadata": {
        "id": "Kq9Hgq9xGMzp"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df = pd.DataFrame(marvel_data)"
      ],
      "metadata": {
        "id": "R3THVuvUGPrj"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Add column names to the marvel_df \\\n",
        "name them - 'name' 'sex' and 'first_appearance'"
      ],
      "metadata": {
        "id": "N3I_Z_gUGRAK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df.columns = [\"Name\", 'Sex', 'First Appearance']"
      ],
      "metadata": {
        "id": "KrfmVJE9GdD3"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Add index names to the marvel_df (use the character name as index)\n"
      ],
      "metadata": {
        "id": "oIRf7mvaGkUM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df[\"Names\"] = marvel_df['Name']\n",
        "marvel_df.set_index('Names', inplace=True)"
      ],
      "metadata": {
        "id": "7ZKcxNVUGoE8"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop the name column as it's now the index\n"
      ],
      "metadata": {
        "id": "3lrAXfzjGodn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df.drop(\"Name\", axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "CB47WxvKGrW7"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop 'Namor' and 'Hank Pym' rows\n"
      ],
      "metadata": {
        "id": "yFBlidmMGsjQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df.drop('Namor', axis=0, inplace=True)\n",
        "marvel_df.drop('Hank Pym', axis=0, inplace=True)"
      ],
      "metadata": {
        "id": "i_06meFoGrc0"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DataFrame selection, slicing and indexation**"
      ],
      "metadata": {
        "id": "EVaXSBEqGvGb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Show the first 5 elements on marvel_df\\\n",
        "you can do it in many ways\n"
      ],
      "metadata": {
        "id": "6rx_tIpFG5-x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df[:5]"
      ],
      "metadata": {
        "id": "AaryJtvJHCCW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "8dd6dfc4-f616-4289-efc4-d2435121e19f"
      },
      "execution_count": 66,
      "outputs": [
        {
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              "                  Sex  First Appearance\n",
              "Names                                  \n",
              "Spider-Man       male              1962\n",
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              "Wolverine        male              1974\n",
              "Iron Man         male              1963\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"marvel_df[:5]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Names\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Captain America\",\n          \"Thor\",\n          \"Wolverine\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"First Appearance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1941,\n        \"max\": 1974,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1941\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "Show the last 5 elements on marvel_df\n"
      ],
      "metadata": {
        "id": "vliP0iF5HFG-"
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      "cell_type": "code",
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          "height": 237
        },
        "outputId": "8cd21116-c467-4020-aa0a-8ae303efcb68"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Sex  First Appearance\n",
              "Names                                   \n",
              "Doctor Strange    male              1963\n",
              "Scarlet Witch   female              1964\n",
              "Wasp            female              1963\n",
              "Black Widow     female              1964\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
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              "  .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",
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              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "  @keyframes spin {\n",
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              "      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",
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              "        document.querySelector('#df-be745197-6949-4342-ae53-d2df47274d66 button');\n",
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              "        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\": \"marvel_df[-5:]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Names\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Scarlet Witch\",\n          \"Vision\",\n          \"Wasp\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\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\": \"First Appearance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1963,\n        \"max\": 1968,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1963,\n          1964\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Show just the sex of the first 5 elements on marvel_df\n"
      ],
      "metadata": {
        "id": "wudebhkCHGHp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df['Sex'][:5]"
      ],
      "metadata": {
        "id": "E1tVdY-vHIpS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "c72a46e8-d0cc-458f-810a-9c8c80364f49"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Names\n",
              "Spider-Man         male\n",
              "Captain America    male\n",
              "Wolverine          male\n",
              "Iron Man           male\n",
              "Thor               male\n",
              "Name: Sex, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sex</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Names</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Spider-Man</th>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Captain America</th>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wolverine</th>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iron Man</th>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thor</th>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Show the first_appearance of all middle elements on marvel_df\n"
      ],
      "metadata": {
        "id": "bzQ0x_8zHI3T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df['First Appearance'][1:-2]"
      ],
      "metadata": {
        "id": "1kBrf8L7HK71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "da96e0dd-6492-4d76-9900-99bb02fb2b47"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Names\n",
              "Captain America     1941\n",
              "Wolverine           1974\n",
              "Iron Man            1963\n",
              "Thor                1963\n",
              "Thing               1961\n",
              "Mister Fantastic    1961\n",
              "Hulk                1962\n",
              "Beast               1963\n",
              "Invisible Woman     1961\n",
              "Storm               1975\n",
              "Hawkeye             1964\n",
              "Daredevil           1964\n",
              "Doctor Strange      1963\n",
              "Scarlet Witch       1964\n",
              "Wasp                1963\n",
              "Name: First Appearance, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>First Appearance</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Names</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Captain America</th>\n",
              "      <td>1941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wolverine</th>\n",
              "      <td>1974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iron Man</th>\n",
              "      <td>1963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thor</th>\n",
              "      <td>1963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thing</th>\n",
              "      <td>1961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mister Fantastic</th>\n",
              "      <td>1961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hulk</th>\n",
              "      <td>1962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beast</th>\n",
              "      <td>1963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Invisible Woman</th>\n",
              "      <td>1961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Storm</th>\n",
              "      <td>1975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hawkeye</th>\n",
              "      <td>1964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daredevil</th>\n",
              "      <td>1964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doctor Strange</th>\n",
              "      <td>1963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scarlet Witch</th>\n",
              "      <td>1964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wasp</th>\n",
              "      <td>1963</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Show the first and last elements on marvel_df\n"
      ],
      "metadata": {
        "id": "14xvaGHsHLKi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(marvel_df.iloc[0])\n",
        "print(marvel_df.iloc[-1])"
      ],
      "metadata": {
        "id": "lPXLftYXHNYA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e06b69c5-9605-4c1d-db18-386db909f4d7"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex                 male\n",
            "First Appearance    1962\n",
            "Name: Spider-Man, dtype: object\n",
            "Sex                 male\n",
            "First Appearance    1968\n",
            "Name: Vision, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DataFrame manipulation and operations**"
      ],
      "metadata": {
        "id": "AlZwRFUwHNkZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Modify the first_appearance of 'Vision' to year 1964\n"
      ],
      "metadata": {
        "id": "DKkPvwUqHUex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df.at[\"Vision\", 'First Appearance'] = 1964"
      ],
      "metadata": {
        "id": "F2qMqJbVHXgK"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Add a new column to marvel_df called 'years_since' with the years since first_appearance.\\\n",
        "hint: values would be 2024 - coulmn year\n"
      ],
      "metadata": {
        "id": "S5EvMnuMHWUQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df[\"Years_Since\"] = 2024 - marvel_df['First Appearance']"
      ],
      "metadata": {
        "id": "A89uFuvHHh_-"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DataFrame summary statistics**"
      ],
      "metadata": {
        "id": "8ozUmak_Hici"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Show basic statistics of marvel_df\\\n",
        "hint: describe"
      ],
      "metadata": {
        "id": "BA7E4P03Hp4K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df.describe()"
      ],
      "metadata": {
        "id": "5Nfqdo3hHxh8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "05e658a3-fd77-4f5a-8256-40f4286e952c"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       First Appearance  Years_Since\n",
              "count         18.000000    18.000000\n",
              "mean        1962.888889    61.111111\n",
              "std            6.720372     6.720372\n",
              "min         1941.000000    49.000000\n",
              "25%         1962.000000    60.000000\n",
              "50%         1963.000000    61.000000\n",
              "75%         1964.000000    62.000000\n",
              "max         1975.000000    83.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c61243d-8ac4-4730-bb5e-c984f80758a8\" 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",
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              "    }\n",
              "\n",
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              "        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>First Appearance</th>\n",
              "      <th>Years_Since</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1962.888889</td>\n",
              "      <td>61.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.720372</td>\n",
              "      <td>6.720372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1941.000000</td>\n",
              "      <td>49.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1962.000000</td>\n",
              "      <td>60.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1963.000000</td>\n",
              "      <td>61.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1964.000000</td>\n",
              "      <td>62.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1975.000000</td>\n",
              "      <td>83.000000</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-1c61243d-8ac4-4730-bb5e-c984f80758a8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\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",
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              "      display: none;\n",
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              "      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",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1c61243d-8ac4-4730-bb5e-c984f80758a8 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-1c61243d-8ac4-4730-bb5e-c984f80758a8');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-b4a1f438-73ad-4678-9819-67da3e8277f9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b4a1f438-73ad-4678-9819-67da3e8277f9')\"\n",
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              "            style=\"display:none;\">\n",
              "\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",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    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-b4a1f438-73ad-4678-9819-67da3e8277f9 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\": \"marvel_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"First Appearance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 902.2442499732654,\n        \"min\": 6.720371916256605,\n        \"max\": 1975.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1962.888888888889,\n          1963.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Years_Since\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.285502838830418,\n        \"min\": 6.720371916256603,\n        \"max\": 83.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          61.111111111111114,\n          61.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the marvel_df pandas DataFrame, show the mean value of first_appearance\n"
      ],
      "metadata": {
        "id": "IMkTqT6ZH0Cn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df[\"First Appearance\"].mean()"
      ],
      "metadata": {
        "id": "ClMFohM7H22H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b96c95e-2152-43f1-c37a-bad027e951a3"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1962.888888888889"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the marvel_df pandas DataFrame, show the min value of first_appearance\n"
      ],
      "metadata": {
        "id": "ZiTu1H4EH3TW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df[\"First Appearance\"].min()\n"
      ],
      "metadata": {
        "id": "AYiG6X7YH67c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5ccf6ef-e718-45a5-8543-34b05a3b1ea5"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1941"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the marvel_df pandas DataFrame, get the characters with the min value of first_appearance\n"
      ],
      "metadata": {
        "id": "nvoyBXvdH7LY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df[marvel_df['First Appearance']==marvel_df['First Appearance'].min()]"
      ],
      "metadata": {
        "id": "496I3MpvIGYx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "ca31dccc-6ca3-4e5f-ad3e-c5de7c76b199"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Sex  First Appearance  Years_Since\n",
              "Names                                               \n",
              "Captain America  male              1941           83"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83921258-ed94-436c-984c-47e1324f46ec\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sex</th>\n",
              "      <th>First Appearance</th>\n",
              "      <th>Years_Since</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Names</th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Captain America</th>\n",
              "      <td>male</td>\n",
              "      <td>1941</td>\n",
              "      <td>83</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-83921258-ed94-436c-984c-47e1324f46ec')\"\n",
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              "            style=\"display:none;\">\n",
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              "  <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-83921258-ed94-436c-984c-47e1324f46ec 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-83921258-ed94-436c-984c-47e1324f46ec');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"marvel_df[marvel_df['First Appearance']==marvel_df['First Appearance']\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Names\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Captain America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"First Appearance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1941,\n        \"max\": 1941,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1941\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Years_Since\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 83,\n        \"max\": 83,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DataFrame basic plottings**"
      ],
      "metadata": {
        "id": "WF8avBQHIGpO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reset index names of marvel_df\\\n",
        "hint: use .reset_index()"
      ],
      "metadata": {
        "id": "MiUVrrmZITwF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marvel_df.reset_index()"
      ],
      "metadata": {
        "id": "hJkWjyWkIdVB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "5203b01d-b4e1-40bb-b899-d58b571f96bd"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Names     Sex  First Appearance  Years_Since\n",
              "0         Spider-Man    male              1962           62\n",
              "1    Captain America    male              1941           83\n",
              "2          Wolverine    male              1974           50\n",
              "3           Iron Man    male              1963           61\n",
              "4               Thor    male              1963           61\n",
              "5              Thing    male              1961           63\n",
              "6   Mister Fantastic    male              1961           63\n",
              "7               Hulk    male              1962           62\n",
              "8              Beast    male              1963           61\n",
              "9    Invisible Woman  female              1961           63\n",
              "10             Storm  female              1975           49\n",
              "11           Hawkeye    male              1964           60\n",
              "12         Daredevil    male              1964           60\n",
              "13    Doctor Strange    male              1963           61\n",
              "14     Scarlet Witch  female              1964           60\n",
              "15              Wasp  female              1963           61\n",
              "16       Black Widow  female              1964           60\n",
              "17            Vision    male              1964           60"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Names</th>\n",
              "      <th>Sex</th>\n",
              "      <th>First Appearance</th>\n",
              "      <th>Years_Since</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Spider-Man</td>\n",
              "      <td>male</td>\n",
              "      <td>1962</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Captain America</td>\n",
              "      <td>male</td>\n",
              "      <td>1941</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Wolverine</td>\n",
              "      <td>male</td>\n",
              "      <td>1974</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Iron Man</td>\n",
              "      <td>male</td>\n",
              "      <td>1963</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thor</td>\n",
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              "      <td>1963</td>\n",
              "      <td>61</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
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              "      <td>1961</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Mister Fantastic</td>\n",
              "      <td>male</td>\n",
              "      <td>1961</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Hulk</td>\n",
              "      <td>male</td>\n",
              "      <td>1962</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Beast</td>\n",
              "      <td>male</td>\n",
              "      <td>1963</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Invisible Woman</td>\n",
              "      <td>female</td>\n",
              "      <td>1961</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Storm</td>\n",
              "      <td>female</td>\n",
              "      <td>1975</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Hawkeye</td>\n",
              "      <td>male</td>\n",
              "      <td>1964</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Daredevil</td>\n",
              "      <td>male</td>\n",
              "      <td>1964</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Doctor Strange</td>\n",
              "      <td>male</td>\n",
              "      <td>1963</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Scarlet Witch</td>\n",
              "      <td>female</td>\n",
              "      <td>1964</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Wasp</td>\n",
              "      <td>female</td>\n",
              "      <td>1963</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Black Widow</td>\n",
              "      <td>female</td>\n",
              "      <td>1964</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Vision</td>\n",
              "      <td>male</td>\n",
              "      <td>1964</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"marvel_df\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Names\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Spider-Man\",\n          \"Captain America\",\n          \"Beast\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\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\": \"First Appearance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1941,\n        \"max\": 1975,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1962,\n          1941\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Years_Since\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 49,\n        \"max\": 83,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          62,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot the values of first_appearance\n"
      ],
      "metadata": {
        "id": "2Miimz0MIdr8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(marvel_df['First Appearance'])"
      ],
      "metadata": {
        "id": "fDBPJFdZIfwY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "025f07d3-5d66-46c4-9dc7-cfb4e8b022fe"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ad612865030>]"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "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": [
        "Plot a histogram (plot.hist) with values of first_appearance\n"
      ],
      "metadata": {
        "id": "nnuTvn_rIf8s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(marvel_df['First Appearance'])"
      ],
      "metadata": {
        "id": "fd2EJnfLIiBl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "fe6461f6-01e8-458a-e8e5-c82a2d28cf03"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 1.,  0.,  0.,  0.,  0.,  3., 12.,  0.,  0.,  2.]),\n",
              " array([1941. , 1944.4, 1947.8, 1951.2, 1954.6, 1958. , 1961.4, 1964.8,\n",
              "        1968.2, 1971.6, 1975. ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pI_f0GRHsEy-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}