Scipy_Pandas_Seaborn.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/JebaMercy14/AI-Spartificial-Course/blob/main/Scipy_Pandas_Seaborn.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": "markdown",
      "source": [
        "## SciPy\n",
        "\n",
        "SciPy is a scientific computation library that uses NumPy underneath.\n",
        "\n",
        "It provides more utility functions for optimization, stats and signal processing.\n",
        "\n",
        "https://github.com/scipy/scipy"
      ],
      "metadata": {
        "id": "7-bTbF_Vi2w0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import constants\n",
        "\n",
        "# How many cubic meters are in one liter:\n",
        "print(constants.liter)"
      ],
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          "base_uri": "https://localhost:8080/"
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      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(constants.pi)"
      ],
      "metadata": {
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        },
        "id": "8pPosE1njh4r",
        "outputId": "0635fa35-52cc-48fc-ad3a-55d013e02127"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.141592653589793\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dir(constants))"
      ],
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        },
        "id": "LONmvUdxjjXf",
        "outputId": "22b64361-8604-49e8-977d-ad3271ae0883"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Avogadro', 'Boltzmann', 'Btu', 'Btu_IT', 'Btu_th', 'ConstantWarning', 'G', 'Julian_year', 'N_A', 'Planck', 'R', 'Rydberg', 'Stefan_Boltzmann', 'Wien', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_obsolete_constants', 'acre', 'alpha', 'angstrom', 'arcmin', 'arcminute', 'arcsec', 'arcsecond', 'astronomical_unit', 'atm', 'atmosphere', 'atomic_mass', 'atto', 'au', 'bar', 'barrel', 'bbl', 'blob', 'c', 'calorie', 'calorie_IT', 'calorie_th', 'carat', 'centi', 'codata', 'constants', 'convert_temperature', 'day', 'deci', 'degree', 'degree_Fahrenheit', 'deka', 'dyn', 'dyne', 'e', 'eV', 'electron_mass', 'electron_volt', 'elementary_charge', 'epsilon_0', 'erg', 'exa', 'exbi', 'femto', 'fermi', 'find', 'fine_structure', 'fluid_ounce', 'fluid_ounce_US', 'fluid_ounce_imp', 'foot', 'g', 'gallon', 'gallon_US', 'gallon_imp', 'gas_constant', 'gibi', 'giga', 'golden', 'golden_ratio', 'grain', 'gram', 'gravitational_constant', 'h', 'hbar', 'hectare', 'hecto', 'horsepower', 'hour', 'hp', 'inch', 'k', 'kgf', 'kibi', 'kilo', 'kilogram_force', 'kmh', 'knot', 'lambda2nu', 'lb', 'lbf', 'light_year', 'liter', 'litre', 'long_ton', 'm_e', 'm_n', 'm_p', 'm_u', 'mach', 'mebi', 'mega', 'metric_ton', 'micro', 'micron', 'mil', 'mile', 'milli', 'minute', 'mmHg', 'mph', 'mu_0', 'nano', 'nautical_mile', 'neutron_mass', 'nu2lambda', 'ounce', 'oz', 'parsec', 'pebi', 'peta', 'physical_constants', 'pi', 'pico', 'point', 'pound', 'pound_force', 'precision', 'proton_mass', 'psi', 'pt', 'short_ton', 'sigma', 'slinch', 'slug', 'speed_of_light', 'speed_of_sound', 'stone', 'survey_foot', 'survey_mile', 'tebi', 'tera', 'test', 'ton_TNT', 'torr', 'troy_ounce', 'troy_pound', 'u', 'unit', 'value', 'week', 'yard', 'year', 'yobi', 'yotta', 'zebi', 'zepto', 'zero_Celsius', 'zetta']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in meter (e.g. centi returns 0.01)\n",
        "print(constants.yotta)    #1e+24\n",
        "print(constants.zetta)    #1e+21\n",
        "print(constants.exa)      #1e+18\n",
        "print(constants.peta)     #1000000000000000.0\n",
        "print(constants.tera)     #1000000000000.0\n",
        "print(constants.giga)     #1000000000.0\n",
        "print(constants.mega)     #1000000.0\n",
        "print(constants.kilo)     #1000.0\n",
        "print(constants.hecto)    #100.0\n",
        "print(constants.deka)     #10.0\n",
        "print(constants.deci)     #0.1\n",
        "print(constants.centi)    #0.01\n",
        "print(constants.milli)    #0.001\n",
        "print(constants.micro)    #1e-06\n",
        "print(constants.nano)     #1e-09\n",
        "print(constants.pico)     #1e-12\n",
        "print(constants.femto)    #1e-15\n",
        "print(constants.atto)     #1e-18\n",
        "print(constants.zepto)    #1e-21"
      ],
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      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1e+24\n",
            "1e+21\n",
            "1e+18\n",
            "1000000000000000.0\n",
            "1000000000000.0\n",
            "1000000000.0\n",
            "1000000.0\n",
            "1000.0\n",
            "100.0\n",
            "10.0\n",
            "0.1\n",
            "0.01\n",
            "0.001\n",
            "1e-06\n",
            "1e-09\n",
            "1e-12\n",
            "1e-15\n",
            "1e-18\n",
            "1e-21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in bytes (e.g. kibi returns 1024)\n",
        "print(constants.kibi)    #1024\n",
        "print(constants.mebi)    #1048576\n",
        "print(constants.gibi)    #1073741824\n",
        "print(constants.tebi)    #1099511627776\n",
        "print(constants.pebi)    #1125899906842624\n",
        "print(constants.exbi)    #1152921504606846976\n",
        "print(constants.zebi)    #1180591620717411303424\n",
        "print(constants.yobi)    #1208925819614629174706176"
      ],
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      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1024\n",
            "1048576\n",
            "1073741824\n",
            "1099511627776\n",
            "1125899906842624\n",
            "1152921504606846976\n",
            "1180591620717411303424\n",
            "1208925819614629174706176\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in kg (e.g. gram returns 0.001)\n",
        "print(constants.gram)        #0.001\n",
        "print(constants.metric_ton)  #1000.0\n",
        "print(constants.grain)       #6.479891e-05\n",
        "print(constants.lb)          #0.45359236999999997\n",
        "print(constants.pound)       #0.45359236999999997\n",
        "print(constants.oz)          #0.028349523124999998\n",
        "print(constants.ounce)       #0.028349523124999998\n",
        "print(constants.stone)       #6.3502931799999995\n",
        "print(constants.long_ton)    #1016.0469088\n",
        "print(constants.short_ton)   #907.1847399999999\n",
        "print(constants.troy_ounce)  #0.031103476799999998\n",
        "print(constants.troy_pound)  #0.37324172159999996\n",
        "print(constants.carat)       #0.0002\n",
        "print(constants.atomic_mass) #1.66053904e-27\n",
        "print(constants.m_u)         #1.66053904e-27\n",
        "print(constants.u)           #1.66053904e-27"
      ],
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.001\n",
            "1000.0\n",
            "6.479891e-05\n",
            "0.45359236999999997\n",
            "0.45359236999999997\n",
            "0.028349523124999998\n",
            "0.028349523124999998\n",
            "6.3502931799999995\n",
            "1016.0469088\n",
            "907.1847399999999\n",
            "0.031103476799999998\n",
            "0.37324172159999996\n",
            "0.0002\n",
            "1.6605390666e-27\n",
            "1.6605390666e-27\n",
            "1.6605390666e-27\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in radians (e.g. degree returns 0.017453292519943295)\n",
        "\n",
        "print(constants.degree)     #0.017453292519943295\n",
        "print(constants.arcmin)     #0.0002908882086657216\n",
        "print(constants.arcminute)  #0.0002908882086657216\n",
        "print(constants.arcsec)     #4.84813681109536e-06\n",
        "print(constants.arcsecond)"
      ],
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      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.017453292519943295\n",
            "0.0002908882086657216\n",
            "0.0002908882086657216\n",
            "4.84813681109536e-06\n",
            "4.84813681109536e-06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in seconds (e.g. hour returns 3600.0)\n",
        "\n",
        "print(constants.minute)      #60.0\n",
        "print(constants.hour)        #3600.0\n",
        "print(constants.day)         #86400.0\n",
        "print(constants.week)        #604800.0\n",
        "print(constants.year)        #31536000.0\n",
        "print(constants.Julian_year) #31557600.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pd6kMgoVkVEu",
        "outputId": "96f919ad-f4d4-423a-82f5-a3d17f8fbb2c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60.0\n",
            "3600.0\n",
            "86400.0\n",
            "604800.0\n",
            "31536000.0\n",
            "31557600.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in meters (e.g. nautical_mile returns 1852.0)\n",
        "\n",
        "print(constants.inch)              #0.0254\n",
        "print(constants.foot)              #0.30479999999999996\n",
        "print(constants.yard)              #0.9143999999999999\n",
        "print(constants.mile)              #1609.3439999999998\n",
        "print(constants.mil)               #2.5399999999999997e-05\n",
        "print(constants.pt)                #0.00035277777777777776\n",
        "print(constants.point)             #0.00035277777777777776\n",
        "print(constants.survey_foot)       #0.3048006096012192\n",
        "print(constants.survey_mile)       #1609.3472186944373\n",
        "print(constants.nautical_mile)     #1852.0\n",
        "print(constants.fermi)             #1e-15\n",
        "print(constants.angstrom)          #1e-10\n",
        "print(constants.micron)            #1e-06\n",
        "print(constants.au)                #149597870691.0\n",
        "print(constants.astronomical_unit) #149597870691.0\n",
        "print(constants.light_year)        #9460730472580800.0\n",
        "print(constants.parsec)            #3.0856775813057292e+16"
      ],
      "metadata": {
        "id": "penUvDOQkakK",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ee9f2ca-b33b-4022-f8c5-549df9850382"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0254\n",
            "0.30479999999999996\n",
            "0.9143999999999999\n",
            "1609.3439999999998\n",
            "2.5399999999999997e-05\n",
            "0.00035277777777777776\n",
            "0.00035277777777777776\n",
            "0.3048006096012192\n",
            "1609.3472186944373\n",
            "1852.0\n",
            "1e-15\n",
            "1e-10\n",
            "1e-06\n",
            "149597870700.0\n",
            "149597870700.0\n",
            "9460730472580800.0\n",
            "3.085677581491367e+16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in pascals (e.g. psi returns 6894.757293168361)\n",
        "\n",
        "print(constants.atm)         #101325.0\n",
        "print(constants.atmosphere)  #101325.0\n",
        "print(constants.bar)         #100000.0\n",
        "print(constants.torr)        #133.32236842105263\n",
        "print(constants.mmHg)        #133.32236842105263\n",
        "print(constants.psi)         #6894.757293168361"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xoDiAcZjkerR",
        "outputId": "f010d5c2-1c46-44fb-d1dc-3127137fc5d4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "101325.0\n",
            "101325.0\n",
            "100000.0\n",
            "133.32236842105263\n",
            "133.32236842105263\n",
            "6894.757293168361\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in square meters(e.g. hectare returns 10000.0)\n",
        "\n",
        "print(constants.hectare) #10000.0\n",
        "print(constants.acre)    #4046.8564223999992"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZ4xXyOOksgg",
        "outputId": "ab0bfaa7-c793-4283-8a46-748bd160c2a6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10000.0\n",
            "4046.8564223999992\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in cubic meters (e.g. liter returns 0.001)\n",
        "\n",
        "print(constants.liter)            #0.001\n",
        "print(constants.litre)            #0.001\n",
        "print(constants.gallon)           #0.0037854117839999997\n",
        "print(constants.gallon_US)        #0.0037854117839999997\n",
        "print(constants.gallon_imp)       #0.00454609\n",
        "print(constants.fluid_ounce)      #2.9573529562499998e-05\n",
        "print(constants.fluid_ounce_US)   #2.9573529562499998e-05\n",
        "print(constants.fluid_ounce_imp)  #2.84130625e-05\n",
        "print(constants.barrel)           #0.15898729492799998\n",
        "print(constants.bbl)              #0.15898729492799998"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSQ9_IB0kzW0",
        "outputId": "f7cc0d05-7651-4dcb-d90d-d10626f6cff5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.001\n",
            "0.001\n",
            "0.0037854117839999997\n",
            "0.0037854117839999997\n",
            "0.00454609\n",
            "2.9573529562499998e-05\n",
            "2.9573529562499998e-05\n",
            "2.84130625e-05\n",
            "0.15898729492799998\n",
            "0.15898729492799998\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in meters per second (e.g. speed_of_sound returns 340.5)\n",
        "\n",
        "print(constants.kmh)            #0.2777777777777778\n",
        "print(constants.mph)            #0.44703999999999994\n",
        "print(constants.mach)           #340.5\n",
        "print(constants.speed_of_sound) #340.5\n",
        "print(constants.knot)           #0.5144444444444445"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dAFhoB2Ok6zb",
        "outputId": "e21a3711-d289-4bb9-e154-225d861d2388"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2777777777777778\n",
            "0.44703999999999994\n",
            "340.5\n",
            "340.5\n",
            "0.5144444444444445\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the specified unit in Kelvin (e.g. zero_Celsius returns 273.15)\n",
        "\n",
        "print(constants.zero_Celsius)      #273.15\n",
        "print(constants.degree_Fahrenheit) #0.5555555555555556"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIWxxA_elCCs",
        "outputId": "8716e80e-99bb-4230-e6df-343096fb9e12"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "273.15\n",
            "0.5555555555555556\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "7x + 2y = 8\n",
        "\n",
        "4x + 5y = 10"
      ],
      "metadata": {
        "id": "-DTYXm7Nlg6b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import linalg\n",
        "import numpy as np\n",
        "  \n",
        "# The function takes two arrays\n",
        "a = np.array([[7, 2], [4, 5]])\n",
        "b = np.array([8, 10])\n",
        "  \n",
        "# Solving the linear equations\n",
        "res = linalg.solve(a, b)\n",
        "print(res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8HK1t1WklfaO",
        "outputId": "c650d6e6-152b-4f4c-881d-9ae7a762b410"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.74074074 1.40740741]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the matrix\n",
        "x = np.array([[7, 2], [4, 5]])\n",
        "  \n",
        "# Finding the inverse of\n",
        "# matrix x\n",
        "y = linalg.inv(x)\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5a5SzHyLlrhl",
        "outputId": "45e94eab-3698-46a7-9a7e-8d1502868a9a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.18518519 -0.07407407]\n",
            " [-0.14814815  0.25925926]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the matrix\n",
        "x = np.array([[8 , 2] , [3 , 5] , [1 , 3]])\n",
        "  \n",
        "# finding the pseudo inverse of matrix x\n",
        "y = linalg.pinv(x)\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKKMmcLClxAR",
        "outputId": "67b61f35-986a-4750-f202-54a84024fb47"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.14251208 -0.03381643 -0.03864734]\n",
            " [-0.07487923  0.16183575  0.11352657]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the matrix A\n",
        "A = np.array([[9 , 6] , [4 , 5]])\n",
        "  \n",
        "# Finding the determinant of matrix A\n",
        "D = linalg.det(A)\n",
        "print(D)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dOc7u44Fl5iX",
        "outputId": "2448482d-b179-4856-b6af-28a36a0b2916"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The Singular-Value Decomposition is a matrix decomposition method for reducing a matrix to its constituent parts to make specific subsequent matrix calculations simpler.\n",
        "\n",
        "# Initializing the matrix M\n",
        "M = np.array([[1 , 5] , [6 , 10]]) \n",
        "  \n",
        "# Passing the values to the \n",
        "# eigen function\n",
        "x , y , z = linalg.svd(M)\n",
        "print(x , y , z)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x2MPLkr4l_QG",
        "outputId": "72893a71-ad3d-42b7-f945-0b66c7669d2c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.38684104 -0.92214641]\n",
            " [-0.92214641  0.38684104]] [12.62901571  1.58365469] [[-0.46873958 -0.88333641]\n",
            " [ 0.88333641 -0.46873958]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the matrix M\n",
        "M = np.array([[9 , 3] , [2 , 4]])\n",
        "  \n",
        "# Passing the values to the eigen\n",
        "# function\n",
        "val , vect = linalg.eig(M)\n",
        "  \n",
        "# Display the Eigen values and Eigen\n",
        "# vectors\n",
        "print(val)\n",
        "print(vect)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJKCwhOZmJeO",
        "outputId": "2e8100f3-9a9b-457b-d8fb-5702a52d37d0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10.+0.j  3.+0.j]\n",
            "[[ 0.9486833  -0.4472136 ]\n",
            " [ 0.31622777  0.89442719]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the input array \n",
        "x = np.array([6 , 3])\n",
        "  \n",
        "# Calculating the L2 norm\n",
        "a = linalg.norm(x)\n",
        "  \n",
        "# Calculating the L1 norm\n",
        "b = linalg.norm(x , 1)\n",
        "  \n",
        "# Displaying the norm values\n",
        "print(a)\n",
        "print(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WOihTLnmUOp",
        "outputId": "3a3e92f5-528a-49d3-985c-50cef27484f6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6.708203932499369\n",
            "9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initializing the matrix \n",
        "x = np.array([[16 , 4] , [100 , 25]])\n",
        "  \n",
        "# Calculate and print the matrix \n",
        "# square root\n",
        "r = linalg.sqrtm(x)\n",
        "print(r)\n",
        "print(\"\\n\")\n",
        "  \n",
        "# Calculate and print the matrix \n",
        "# exponential\n",
        "e = linalg.expm(x)\n",
        "print(e)\n",
        "print(\"\\n\")\n",
        "  \n",
        "# Calculate and print the matrix\n",
        "# sine\n",
        "s = linalg.sinm(x)\n",
        "print(s)\n",
        "print(\"\\n\")\n",
        "  \n",
        "# Calculate and print the matrix \n",
        "# cosine\n",
        "c = linalg.cosm(x)\n",
        "print(c)\n",
        "print(\"\\n\")\n",
        "  \n",
        "# Calculate and print the matrix \n",
        "# tangent\n",
        "t = linalg.tanm(x)\n",
        "print(t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvns-guGmZ6b",
        "outputId": "6dee6b4d-d37d-4303-e5d5-e9505e8e568e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 2.49878019  0.62469505]\n",
            " [15.61737619  3.90434405]]\n",
            "\n",
            "\n",
            "[[2.49695022e+17 6.24237555e+16]\n",
            " [1.56059389e+18 3.90148472e+17]]\n",
            "\n",
            "\n",
            "[[-0.06190153 -0.01547538]\n",
            " [-0.38688456 -0.09672114]]\n",
            "\n",
            "\n",
            "[[ 0.22445296 -0.19388676]\n",
            " [-4.84716897 -0.21179224]]\n",
            "\n",
            "\n",
            "[[0.0626953  0.01567382]\n",
            " [0.39184561 0.0979614 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pandas\n",
        "\n",
        "Series, **Dataframe**"
      ],
      "metadata": {
        "id": "tKOBvFavmgMy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "\n",
        "data = {\n",
        "    'apples': [3, 2, 0, 1], \n",
        "    'oranges': [0, 3, 7, 2]\n",
        "}"
      ],
      "metadata": {
        "id": "FGIjSBRSmw3w"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "purchases = pd.DataFrame(data)\n",
        "\n",
        "purchases"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "-R8rA3kom2sE",
        "outputId": "8c7fb2dd-c1a6-4b16-ad49-4c679d86aada"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   apples  oranges\n",
              "0       3        0\n",
              "1       2        3\n",
              "2       0        7\n",
              "3       1        2"
            ],
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              "      <th>3</th>\n",
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              "    }\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",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-0ce4a043-13d9-42be-8177-fc39d51b5b80 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-0ce4a043-13d9-42be-8177-fc39d51b5b80');\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",
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              "          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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "purchases = pd.DataFrame(data, index=['June', 'Robert', 'Lily', 'David'])\n",
        "\n",
        "purchases"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "TpI2sE7OnDLT",
        "outputId": "a7461759-36a4-48fb-c7ad-38005a4516b7"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        apples  oranges\n",
              "June         3        0\n",
              "Robert       2        3\n",
              "Lily         0        7\n",
              "David        1        2"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>June</th>\n",
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              "  </tbody>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5454935f-b836-4380-8226-bfa919d78674 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-5454935f-b836-4380-8226-bfa919d78674');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "purchases.loc['June']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mz1aC_2wnG93",
        "outputId": "cad54a93-c732-4368-f338-ea1417e1dcec"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "apples     3\n",
              "oranges    0\n",
              "Name: June, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/Ecommerce-Purchases.csv')\n",
        "\n",
        "df"
      ],
      "metadata": {
        "id": "gaSAVECTny7x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "73225eda-4e9e-44b2-b6f1-a208e7d347d4"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                Address    Lot AM or PM  \\\n",
              "0     16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...  46 in       PM   \n",
              "1     9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...  28 rn       PM   \n",
              "2                      Unit 0065 Box 5052\\nDPO AP 27450  94 vE       PM   \n",
              "3                 7780 Julia Fords\\nNew Stacy, WA 45798  36 vm       PM   \n",
              "4     23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...  20 IE       AM   \n",
              "...                                                 ...    ...      ...   \n",
              "9995      966 Castaneda Locks\\nWest Juliafurt, CO 96415  92 XI       PM   \n",
              "9996  832 Curtis Dam Suite 785\\nNorth Edwardburgh, T...  41 JY       AM   \n",
              "9997              Unit 4434 Box 6343\\nDPO AE 28026-0283  74 Zh       AM   \n",
              "9998               0096 English Rest\\nRoystad, IA 12457  74 cL       PM   \n",
              "9999     40674 Barrett Stravenue\\nGrimesville, WI 79682  64 Hr       AM   \n",
              "\n",
              "                                           Browser Info  \\\n",
              "0     Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...   \n",
              "1     Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...   \n",
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              "4     Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...   \n",
              "...                                                 ...   \n",
              "9995  Mozilla/5.0 (Windows NT 5.1) AppleWebKit/5352 ...   \n",
              "9996  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
              "9997  Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_7...   \n",
              "9998  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_8;...   \n",
              "9999  Mozilla/5.0 (X11; Linux i686; rv:1.9.5.20) Gec...   \n",
              "\n",
              "                              Company       Credit Card CC Exp Date  \\\n",
              "0                     Martinez-Herman  6011929061123406       02/20   \n",
              "1     Fletcher, Richards and Whitaker  3337758169645356       11/18   \n",
              "2          Simpson, Williams and Pham      675957666125       08/19   \n",
              "3     Williams, Marshall and Buchanan  6011578504430710       02/24   \n",
              "4           Brown, Watson and Andrews  6011456623207998       10/25   \n",
              "...                               ...               ...         ...   \n",
              "9995                    Randall-Sloan   342945015358701       03/22   \n",
              "9996         Hale, Collins and Wilson   210033169205009       07/25   \n",
              "9997                     Anderson Ltd  6011539787356311       05/21   \n",
              "9998                         Cook Inc   180003348082930       11/17   \n",
              "9999                       Greene Inc  4139972901927273       02/19   \n",
              "\n",
              "      CC Security Code                  CC Provider  \\\n",
              "0                  900                 JCB 16 digit   \n",
              "1                  561                   Mastercard   \n",
              "2                  699                 JCB 16 digit   \n",
              "3                  384                     Discover   \n",
              "4                  678  Diners Club / Carte Blanche   \n",
              "...                ...                          ...   \n",
              "9995               838                 JCB 15 digit   \n",
              "9996               207                 JCB 16 digit   \n",
              "9997                 1                VISA 16 digit   \n",
              "9998               987             American Express   \n",
              "9999               302                 JCB 15 digit   \n",
              "\n",
              "                               Email                                     Job  \\\n",
              "0                  pdunlap@yahoo.com  Scientist, product/process development   \n",
              "1                 anthony41@reed.com                       Drilling engineer   \n",
              "2     amymiller@morales-harrison.com                Customer service manager   \n",
              "3        brent16@olson-robinson.info                       Drilling engineer   \n",
              "4        christopherwright@gmail.com                             Fine artist   \n",
              "...                              ...                                     ...   \n",
              "9995          iscott@wade-garner.com                              Printmaker   \n",
              "9996              mary85@hotmail.com                         Energy engineer   \n",
              "9997               tyler16@gmail.com                      Veterinary surgeon   \n",
              "9998         elizabethmoore@reid.net                Local government officer   \n",
              "9999           rachelford@vaughn.com                  Embryologist, clinical   \n",
              "\n",
              "           IP Address Language  Purchase Price  \n",
              "0     149.146.147.205       el           98.14  \n",
              "1        15.160.41.51       fr           70.73  \n",
              "2      132.207.160.22       de            0.95  \n",
              "3        30.250.74.19       es           78.04  \n",
              "4        24.140.33.94       es           77.82  \n",
              "...               ...      ...             ...  \n",
              "9995    29.73.197.114       it           82.21  \n",
              "9996   121.133.168.51       pt           25.63  \n",
              "9997    156.210.0.254       el           83.98  \n",
              "9998     55.78.26.143       es           38.84  \n",
              "9999  176.119.198.199       el           67.59  \n",
              "\n",
              "[10000 rows x 14 columns]"
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              "      <th>Address</th>\n",
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              "      <th>AM or PM</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...</td>\n",
              "      <td>46 in</td>\n",
              "      <td>PM</td>\n",
              "      <td>Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...</td>\n",
              "      <td>Martinez-Herman</td>\n",
              "      <td>6011929061123406</td>\n",
              "      <td>02/20</td>\n",
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              "      <td>pdunlap@yahoo.com</td>\n",
              "      <td>Scientist, product/process development</td>\n",
              "      <td>149.146.147.205</td>\n",
              "      <td>el</td>\n",
              "      <td>98.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...</td>\n",
              "      <td>28 rn</td>\n",
              "      <td>PM</td>\n",
              "      <td>Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...</td>\n",
              "      <td>Fletcher, Richards and Whitaker</td>\n",
              "      <td>3337758169645356</td>\n",
              "      <td>11/18</td>\n",
              "      <td>561</td>\n",
              "      <td>Mastercard</td>\n",
              "      <td>anthony41@reed.com</td>\n",
              "      <td>Drilling engineer</td>\n",
              "      <td>15.160.41.51</td>\n",
              "      <td>fr</td>\n",
              "      <td>70.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Unit 0065 Box 5052\\nDPO AP 27450</td>\n",
              "      <td>94 vE</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
              "      <td>Simpson, Williams and Pham</td>\n",
              "      <td>675957666125</td>\n",
              "      <td>08/19</td>\n",
              "      <td>699</td>\n",
              "      <td>JCB 16 digit</td>\n",
              "      <td>amymiller@morales-harrison.com</td>\n",
              "      <td>Customer service manager</td>\n",
              "      <td>132.207.160.22</td>\n",
              "      <td>de</td>\n",
              "      <td>0.95</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7780 Julia Fords\\nNew Stacy, WA 45798</td>\n",
              "      <td>36 vm</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...</td>\n",
              "      <td>Williams, Marshall and Buchanan</td>\n",
              "      <td>6011578504430710</td>\n",
              "      <td>02/24</td>\n",
              "      <td>384</td>\n",
              "      <td>Discover</td>\n",
              "      <td>brent16@olson-robinson.info</td>\n",
              "      <td>Drilling engineer</td>\n",
              "      <td>30.250.74.19</td>\n",
              "      <td>es</td>\n",
              "      <td>78.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...</td>\n",
              "      <td>20 IE</td>\n",
              "      <td>AM</td>\n",
              "      <td>Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...</td>\n",
              "      <td>Brown, Watson and Andrews</td>\n",
              "      <td>6011456623207998</td>\n",
              "      <td>10/25</td>\n",
              "      <td>678</td>\n",
              "      <td>Diners Club / Carte Blanche</td>\n",
              "      <td>christopherwright@gmail.com</td>\n",
              "      <td>Fine artist</td>\n",
              "      <td>24.140.33.94</td>\n",
              "      <td>es</td>\n",
              "      <td>77.82</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>966 Castaneda Locks\\nWest Juliafurt, CO 96415</td>\n",
              "      <td>92 XI</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Windows NT 5.1) AppleWebKit/5352 ...</td>\n",
              "      <td>Randall-Sloan</td>\n",
              "      <td>342945015358701</td>\n",
              "      <td>03/22</td>\n",
              "      <td>838</td>\n",
              "      <td>JCB 15 digit</td>\n",
              "      <td>iscott@wade-garner.com</td>\n",
              "      <td>Printmaker</td>\n",
              "      <td>29.73.197.114</td>\n",
              "      <td>it</td>\n",
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              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>832 Curtis Dam Suite 785\\nNorth Edwardburgh, T...</td>\n",
              "      <td>41 JY</td>\n",
              "      <td>AM</td>\n",
              "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
              "      <td>Hale, Collins and Wilson</td>\n",
              "      <td>210033169205009</td>\n",
              "      <td>07/25</td>\n",
              "      <td>207</td>\n",
              "      <td>JCB 16 digit</td>\n",
              "      <td>mary85@hotmail.com</td>\n",
              "      <td>Energy engineer</td>\n",
              "      <td>121.133.168.51</td>\n",
              "      <td>pt</td>\n",
              "      <td>25.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>Unit 4434 Box 6343\\nDPO AE 28026-0283</td>\n",
              "      <td>74 Zh</td>\n",
              "      <td>AM</td>\n",
              "      <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_7...</td>\n",
              "      <td>Anderson Ltd</td>\n",
              "      <td>6011539787356311</td>\n",
              "      <td>05/21</td>\n",
              "      <td>1</td>\n",
              "      <td>VISA 16 digit</td>\n",
              "      <td>tyler16@gmail.com</td>\n",
              "      <td>Veterinary surgeon</td>\n",
              "      <td>156.210.0.254</td>\n",
              "      <td>el</td>\n",
              "      <td>83.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>0096 English Rest\\nRoystad, IA 12457</td>\n",
              "      <td>74 cL</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_8;...</td>\n",
              "      <td>Cook Inc</td>\n",
              "      <td>180003348082930</td>\n",
              "      <td>11/17</td>\n",
              "      <td>987</td>\n",
              "      <td>American Express</td>\n",
              "      <td>elizabethmoore@reid.net</td>\n",
              "      <td>Local government officer</td>\n",
              "      <td>55.78.26.143</td>\n",
              "      <td>es</td>\n",
              "      <td>38.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>40674 Barrett Stravenue\\nGrimesville, WI 79682</td>\n",
              "      <td>64 Hr</td>\n",
              "      <td>AM</td>\n",
              "      <td>Mozilla/5.0 (X11; Linux i686; rv:1.9.5.20) Gec...</td>\n",
              "      <td>Greene Inc</td>\n",
              "      <td>4139972901927273</td>\n",
              "      <td>02/19</td>\n",
              "      <td>302</td>\n",
              "      <td>JCB 15 digit</td>\n",
              "      <td>rachelford@vaughn.com</td>\n",
              "      <td>Embryologist, clinical</td>\n",
              "      <td>176.119.198.199</td>\n",
              "      <td>el</td>\n",
              "      <td>67.59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e5ff636-e47a-420d-a622-14747ff466cf')\"\n",
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              "    .colab-df-convert {\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "q3nItR1MoDeA",
        "outputId": "b1d93668-58fe-4d3e-c766-df39cdd71f01"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             Address    Lot AM or PM  \\\n",
              "0  16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...  46 in       PM   \n",
              "1  9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...  28 rn       PM   \n",
              "2                   Unit 0065 Box 5052\\nDPO AP 27450  94 vE       PM   \n",
              "3              7780 Julia Fords\\nNew Stacy, WA 45798  36 vm       PM   \n",
              "4  23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...  20 IE       AM   \n",
              "\n",
              "                                        Browser Info  \\\n",
              "0  Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...   \n",
              "1  Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...   \n",
              "2  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
              "3  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...   \n",
              "4  Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...   \n",
              "\n",
              "                           Company       Credit Card CC Exp Date  \\\n",
              "0                  Martinez-Herman  6011929061123406       02/20   \n",
              "1  Fletcher, Richards and Whitaker  3337758169645356       11/18   \n",
              "2       Simpson, Williams and Pham      675957666125       08/19   \n",
              "3  Williams, Marshall and Buchanan  6011578504430710       02/24   \n",
              "4        Brown, Watson and Andrews  6011456623207998       10/25   \n",
              "\n",
              "   CC Security Code                  CC Provider  \\\n",
              "0               900                 JCB 16 digit   \n",
              "1               561                   Mastercard   \n",
              "2               699                 JCB 16 digit   \n",
              "3               384                     Discover   \n",
              "4               678  Diners Club / Carte Blanche   \n",
              "\n",
              "                            Email                                     Job  \\\n",
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    {
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        "df.tail(5)"
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      "execution_count": 29,
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              "          const element = document.querySelector('#df-c8612bff-5345-4602-a8d8-92050935eae7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xl-j25GWoMBX",
        "outputId": "535018d6-afe4-48ef-8c74-915010411bfc"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Address           10000 non-null  object \n",
            " 1   Lot               10000 non-null  object \n",
            " 2   AM or PM          10000 non-null  object \n",
            " 3   Browser Info      10000 non-null  object \n",
            " 4   Company           10000 non-null  object \n",
            " 5   Credit Card       10000 non-null  int64  \n",
            " 6   CC Exp Date       10000 non-null  object \n",
            " 7   CC Security Code  10000 non-null  int64  \n",
            " 8   CC Provider       10000 non-null  object \n",
            " 9   Email             10000 non-null  object \n",
            " 10  Job               10000 non-null  object \n",
            " 11  IP Address        10000 non-null  object \n",
            " 12  Language          10000 non-null  object \n",
            " 13  Purchase Price    10000 non-null  float64\n",
            "dtypes: float64(1), int64(2), object(11)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m6fpNOb0oQTP",
        "outputId": "68ac47d3-66d1-4c9c-bf5b-832ba4264011"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "ujQ1MbNJoaTf"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxAdefwNofkI",
        "outputId": "83d9d441-1abb-4866-beac-92fe9e08e8db"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another important argument for drop_duplicates() is keep, which has three possible options:\n",
        "\n",
        "first: (default) Drop duplicates except for the first occurrence.\n",
        "\n",
        "last: Drop duplicates except for the last occurrence.\n",
        "\n",
        "False: Drop all duplicates."
      ],
      "metadata": {
        "id": "9JThgoTUpP_h"
      }
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    {
      "cell_type": "code",
      "source": [
        "temp_df = df.append(df)\n",
        "\n",
        "temp_df.drop_duplicates(inplace=True, keep=False)\n",
        "temp_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ct9Z2yBcpOri",
        "outputId": "c9e71702-4c7c-439c-bf7b-5b97cdbaaba7"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-yFllND4p1AZ",
        "outputId": "4fd6d9b3-cb6f-4e90-95ab-bc9d1a5bb0ff"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Address', 'Lot', 'AM or PM', 'Browser Info', 'Company', 'Credit Card',\n",
              "       'CC Exp Date', 'CC Security Code', 'CC Provider', 'Email', 'Job',\n",
              "       'IP Address', 'Language', 'Purchase Price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns={\n",
        "        'CC Exp Date': 'cc-exp-date', \n",
        "        'CC Security Code': 'cc-security-code'\n",
        "    }, inplace=True)\n",
        "\n",
        "\n",
        "df.columns"
      ],
      "metadata": {
        "id": "4hBqBzZjp76Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76cee4c9-5d8d-42d8-b351-89303f75280b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Address', 'Lot', 'AM or PM', 'Browser Info', 'Company', 'Credit Card',\n",
              "       'cc-exp-date', 'cc-security-code', 'CC Provider', 'Email', 'Job',\n",
              "       'IP Address', 'Language', 'Purchase Price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns = [col.lower() for col in df]\n",
        "\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M7-4giwIqMkq",
        "outputId": "e9f74af0-4144-40b2-89bf-99119b4dac66"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['address', 'lot', 'am or pm', 'browser info', 'company', 'credit card',\n",
              "       'cc-exp-date', 'cc-security-code', 'cc provider', 'email', 'job',\n",
              "       'ip address', 'language', 'purchase price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull()"
      ],
      "metadata": {
        "id": "JzSLHb3gqVkM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "outputId": "650ab235-7d2d-4b1c-83fe-8abe4913eb1d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      address    lot  am or pm  browser info  company  credit card  \\\n",
              "0       False  False     False         False    False        False   \n",
              "1       False  False     False         False    False        False   \n",
              "2       False  False     False         False    False        False   \n",
              "3       False  False     False         False    False        False   \n",
              "4       False  False     False         False    False        False   \n",
              "...       ...    ...       ...           ...      ...          ...   \n",
              "9995    False  False     False         False    False        False   \n",
              "9996    False  False     False         False    False        False   \n",
              "9997    False  False     False         False    False        False   \n",
              "9998    False  False     False         False    False        False   \n",
              "9999    False  False     False         False    False        False   \n",
              "\n",
              "      cc-exp-date  cc-security-code  cc provider  email    job  ip address  \\\n",
              "0           False             False        False  False  False       False   \n",
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              "2           False             False        False  False  False       False   \n",
              "3           False             False        False  False  False       False   \n",
              "4           False             False        False  False  False       False   \n",
              "...           ...               ...          ...    ...    ...         ...   \n",
              "9995        False             False        False  False  False       False   \n",
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              "9997        False             False        False  False  False       False   \n",
              "9998        False             False        False  False  False       False   \n",
              "9999        False             False        False  False  False       False   \n",
              "\n",
              "      language  purchase price  \n",
              "0        False           False  \n",
              "1        False           False  \n",
              "2        False           False  \n",
              "3        False           False  \n",
              "4        False           False  \n",
              "...        ...             ...  \n",
              "9995     False           False  \n",
              "9996     False           False  \n",
              "9997     False           False  \n",
              "9998     False           False  \n",
              "9999     False           False  \n",
              "\n",
              "[10000 rows x 14 columns]"
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              "          const element = document.querySelector('#df-dac8640c-5fe1-4030-a441-7c40a3b7cf2e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kftpLpIBqZTs",
        "outputId": "1c6025f6-64f9-4a2e-a321-fe9a8d335e4b"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "address             0\n",
              "lot                 0\n",
              "am or pm            0\n",
              "browser info        0\n",
              "company             0\n",
              "credit card         0\n",
              "cc-exp-date         0\n",
              "cc-security-code    0\n",
              "cc provider         0\n",
              "email               0\n",
              "job                 0\n",
              "ip address          0\n",
              "language            0\n",
              "purchase price      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna()"
      ],
      "metadata": {
        "id": "vOFnYx8NqhUc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4eba5101-fec9-4f80-c91e-f13f6157ac81"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                address    lot am or pm  \\\n",
              "0     16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...  46 in       PM   \n",
              "1     9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...  28 rn       PM   \n",
              "2                      Unit 0065 Box 5052\\nDPO AP 27450  94 vE       PM   \n",
              "3                 7780 Julia Fords\\nNew Stacy, WA 45798  36 vm       PM   \n",
              "4     23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...  20 IE       AM   \n",
              "...                                                 ...    ...      ...   \n",
              "9995      966 Castaneda Locks\\nWest Juliafurt, CO 96415  92 XI       PM   \n",
              "9996  832 Curtis Dam Suite 785\\nNorth Edwardburgh, T...  41 JY       AM   \n",
              "9997              Unit 4434 Box 6343\\nDPO AE 28026-0283  74 Zh       AM   \n",
              "9998               0096 English Rest\\nRoystad, IA 12457  74 cL       PM   \n",
              "9999     40674 Barrett Stravenue\\nGrimesville, WI 79682  64 Hr       AM   \n",
              "\n",
              "                                           browser info  \\\n",
              "0     Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...   \n",
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              "...                                                 ...   \n",
              "9995  Mozilla/5.0 (Windows NT 5.1) AppleWebKit/5352 ...   \n",
              "9996  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
              "9997  Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_7...   \n",
              "9998  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_8;...   \n",
              "9999  Mozilla/5.0 (X11; Linux i686; rv:1.9.5.20) Gec...   \n",
              "\n",
              "                              company       credit card cc-exp-date  \\\n",
              "0                     Martinez-Herman  6011929061123406       02/20   \n",
              "1     Fletcher, Richards and Whitaker  3337758169645356       11/18   \n",
              "2          Simpson, Williams and Pham      675957666125       08/19   \n",
              "3     Williams, Marshall and Buchanan  6011578504430710       02/24   \n",
              "4           Brown, Watson and Andrews  6011456623207998       10/25   \n",
              "...                               ...               ...         ...   \n",
              "9995                    Randall-Sloan   342945015358701       03/22   \n",
              "9996         Hale, Collins and Wilson   210033169205009       07/25   \n",
              "9997                     Anderson Ltd  6011539787356311       05/21   \n",
              "9998                         Cook Inc   180003348082930       11/17   \n",
              "9999                       Greene Inc  4139972901927273       02/19   \n",
              "\n",
              "      cc-security-code                  cc provider  \\\n",
              "0                  900                 JCB 16 digit   \n",
              "1                  561                   Mastercard   \n",
              "2                  699                 JCB 16 digit   \n",
              "3                  384                     Discover   \n",
              "4                  678  Diners Club / Carte Blanche   \n",
              "...                ...                          ...   \n",
              "9995               838                 JCB 15 digit   \n",
              "9996               207                 JCB 16 digit   \n",
              "9997                 1                VISA 16 digit   \n",
              "9998               987             American Express   \n",
              "9999               302                 JCB 15 digit   \n",
              "\n",
              "                               email                                     job  \\\n",
              "0                  pdunlap@yahoo.com  Scientist, product/process development   \n",
              "1                 anthony41@reed.com                       Drilling engineer   \n",
              "2     amymiller@morales-harrison.com                Customer service manager   \n",
              "3        brent16@olson-robinson.info                       Drilling engineer   \n",
              "4        christopherwright@gmail.com                             Fine artist   \n",
              "...                              ...                                     ...   \n",
              "9995          iscott@wade-garner.com                              Printmaker   \n",
              "9996              mary85@hotmail.com                         Energy engineer   \n",
              "9997               tyler16@gmail.com                      Veterinary surgeon   \n",
              "9998         elizabethmoore@reid.net                Local government officer   \n",
              "9999           rachelford@vaughn.com                  Embryologist, clinical   \n",
              "\n",
              "           ip address language  purchase price  \n",
              "0     149.146.147.205       el           98.14  \n",
              "1        15.160.41.51       fr           70.73  \n",
              "2      132.207.160.22       de            0.95  \n",
              "3        30.250.74.19       es           78.04  \n",
              "4        24.140.33.94       es           77.82  \n",
              "...               ...      ...             ...  \n",
              "9995    29.73.197.114       it           82.21  \n",
              "9996   121.133.168.51       pt           25.63  \n",
              "9997    156.210.0.254       el           83.98  \n",
              "9998     55.78.26.143       es           38.84  \n",
              "9999  176.119.198.199       el           67.59  \n",
              "\n",
              "[10000 rows x 14 columns]"
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              "      <td>16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...</td>\n",
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              "      <td>9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...</td>\n",
              "      <td>28 rn</td>\n",
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              "      <td>fr</td>\n",
              "      <td>70.73</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Unit 0065 Box 5052\\nDPO AP 27450</td>\n",
              "      <td>94 vE</td>\n",
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              "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
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              "      <td>7780 Julia Fords\\nNew Stacy, WA 45798</td>\n",
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              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...</td>\n",
              "      <td>Williams, Marshall and Buchanan</td>\n",
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              "      <td>02/24</td>\n",
              "      <td>384</td>\n",
              "      <td>Discover</td>\n",
              "      <td>brent16@olson-robinson.info</td>\n",
              "      <td>Drilling engineer</td>\n",
              "      <td>30.250.74.19</td>\n",
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              "      <th>9996</th>\n",
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              "      <td>mary85@hotmail.com</td>\n",
              "      <td>Energy engineer</td>\n",
              "      <td>121.133.168.51</td>\n",
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              "      <td>Unit 4434 Box 6343\\nDPO AE 28026-0283</td>\n",
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              "      <td>05/21</td>\n",
              "      <td>1</td>\n",
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              "      <td>83.98</td>\n",
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              "    <tr>\n",
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              "      <td>0096 English Rest\\nRoystad, IA 12457</td>\n",
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              "      <td>11/17</td>\n",
              "      <td>987</td>\n",
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              "      <td>Local government officer</td>\n",
              "      <td>55.78.26.143</td>\n",
              "      <td>es</td>\n",
              "      <td>38.84</td>\n",
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              "    <tr>\n",
              "      <th>9999</th>\n",
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              "      <td>64 Hr</td>\n",
              "      <td>AM</td>\n",
              "      <td>Mozilla/5.0 (X11; Linux i686; rv:1.9.5.20) Gec...</td>\n",
              "      <td>Greene Inc</td>\n",
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              "      <td>302</td>\n",
              "      <td>JCB 15 digit</td>\n",
              "      <td>rachelford@vaughn.com</td>\n",
              "      <td>Embryologist, clinical</td>\n",
              "      <td>176.119.198.199</td>\n",
              "      <td>el</td>\n",
              "      <td>67.59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8045f8dc-a049-4c98-b63a-c84185b0df78')\"\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
          "execution_count": 40
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    {
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        "df.describe()"
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      "metadata": {
        "id": "5CYkQ5Efqw-o",
        "colab": {
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          "height": 300
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      },
      "execution_count": 41,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "        credit card  cc-security-code  purchase price\n",
              "count  1.000000e+04      10000.000000    10000.000000\n",
              "mean   2.341374e+15        907.217800       50.347302\n",
              "std    2.256103e+15       1589.693035       29.015836\n",
              "min    6.040186e+10          0.000000        0.000000\n",
              "25%    3.056322e+13        280.000000       25.150000\n",
              "50%    8.699942e+14        548.000000       50.505000\n",
              "75%    4.492298e+15        816.000000       75.770000\n",
              "max    6.012000e+15       9993.000000       99.990000"
            ],
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              "      <th>credit card</th>\n",
              "      <th>cc-security-code</th>\n",
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              "      <th>count</th>\n",
              "      <td>1.000000e+04</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.341374e+15</td>\n",
              "      <td>907.217800</td>\n",
              "      <td>50.347302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.256103e+15</td>\n",
              "      <td>1589.693035</td>\n",
              "      <td>29.015836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.040186e+10</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.056322e+13</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>25.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.699942e+14</td>\n",
              "      <td>548.000000</td>\n",
              "      <td>50.505000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.492298e+15</td>\n",
              "      <td>816.000000</td>\n",
              "      <td>75.770000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.012000e+15</td>\n",
              "      <td>9993.000000</td>\n",
              "      <td>99.990000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['am or pm'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DO2mXeJZq281",
        "outputId": "34d1172c-5e8b-4878-a325-782170c15a7b"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     10000\n",
              "unique        2\n",
              "top          PM\n",
              "freq       5068\n",
              "Name: am or pm, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['am or pm'].value_counts()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "aXKvGDHfq_Ov",
        "outputId": "cfa8c512-07f0-4ed9-85d0-25d5f65f1ad3"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PM    5068\n",
              "AM    4932\n",
              "Name: am or pm, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['company'].value_counts().head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8TRPHg7rLOp",
        "outputId": "e8008101-f53f-446a-abe2-263e47d3cf55"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brown Ltd         15\n",
              "Smith Group       15\n",
              "Smith PLC         13\n",
              "Smith LLC         13\n",
              "Williams LLC      12\n",
              "Smith and Sons    11\n",
              "Davis and Sons    11\n",
              "Brown Group       10\n",
              "Johnson LLC       10\n",
              "Johnson Ltd       10\n",
              "Name: company, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
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          "height": 143
        },
        "id": "dKWV2uhQrlxO",
        "outputId": "f4db61a3-d167-401d-f15e-0fe54c3a1fce"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  credit card  cc-security-code  purchase price\n",
              "credit card          1.000000         -0.018379       -0.005470\n",
              "cc-security-code    -0.018379          1.000000        0.002856\n",
              "purchase price      -0.005470          0.002856        1.000000"
            ],
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              "    </tr>\n",
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              "      <td>0.002856</td>\n",
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              "    <tr>\n",
              "      <th>purchase price</th>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-dd5320be-3f22-4ad8-a696-f54314f688cb');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.heatmap(df.corr(), cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "kq9v8RrorTxJ",
        "outputId": "fd486fa5-1926-4e4b-97d9-00affc664aca"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f24e90f3130>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "subset = df[['company', 'cc-security-code']]\n",
        "\n",
        "subset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZZ77zXzBrz80",
        "outputId": "f1fd71c9-6d31-4a0f-d4e8-2ca9f95fdf69"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           company  cc-security-code\n",
              "0                  Martinez-Herman               900\n",
              "1  Fletcher, Richards and Whitaker               561\n",
              "2       Simpson, Williams and Pham               699\n",
              "3  Williams, Marshall and Buchanan               384\n",
              "4        Brown, Watson and Andrews               678"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2423b9f1-c30b-4143-9c47-ecb3325598cb\">\n",
              "    <div 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>company</th>\n",
              "      <th>cc-security-code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Martinez-Herman</td>\n",
              "      <td>900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "address             16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...\n",
              "lot                                                             46 in\n",
              "am or pm                                                           PM\n",
              "browser info        Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...\n",
              "company                                               Martinez-Herman\n",
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              "cc-exp-date                                                     02/20\n",
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              "email                                               pdunlap@yahoo.com\n",
              "job                            Scientist, product/process development\n",
              "ip address                                            149.146.147.205\n",
              "language                                                           el\n",
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              "      <td>Customer service manager</td>\n",
              "      <td>132.207.160.22</td>\n",
              "      <td>de</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7780 Julia Fords\\nNew Stacy, WA 45798</td>\n",
              "      <td>36 vm</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...</td>\n",
              "      <td>Williams, Marshall and Buchanan</td>\n",
              "      <td>6011578504430710</td>\n",
              "      <td>02/24</td>\n",
              "      <td>384</td>\n",
              "      <td>Discover</td>\n",
              "      <td>brent16@olson-robinson.info</td>\n",
              "      <td>Drilling engineer</td>\n",
              "      <td>30.250.74.19</td>\n",
              "      <td>es</td>\n",
              "      <td>78.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...</td>\n",
              "      <td>20 IE</td>\n",
              "      <td>AM</td>\n",
              "      <td>Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...</td>\n",
              "      <td>Brown, Watson and Andrews</td>\n",
              "      <td>6011456623207998</td>\n",
              "      <td>10/25</td>\n",
              "      <td>678</td>\n",
              "      <td>Diners Club / Carte Blanche</td>\n",
              "      <td>christopherwright@gmail.com</td>\n",
              "      <td>Fine artist</td>\n",
              "      <td>24.140.33.94</td>\n",
              "      <td>es</td>\n",
              "      <td>77.82</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.address)\n",
        "print('************************************************')\n",
        "print(df.loc[2, 'address'])\n",
        "print('************************************************')\n",
        "df.iloc[2,0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "KLHy4xhcDXWs",
        "outputId": "8cb71211-94ee-4279-8380-9e2340b4f1af"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...\n",
            "1       9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...\n",
            "2                        Unit 0065 Box 5052\\nDPO AP 27450\n",
            "3                   7780 Julia Fords\\nNew Stacy, WA 45798\n",
            "4       23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...\n",
            "                              ...                        \n",
            "9995        966 Castaneda Locks\\nWest Juliafurt, CO 96415\n",
            "9996    832 Curtis Dam Suite 785\\nNorth Edwardburgh, T...\n",
            "9997                Unit 4434 Box 6343\\nDPO AE 28026-0283\n",
            "9998                 0096 English Rest\\nRoystad, IA 12457\n",
            "9999       40674 Barrett Stravenue\\nGrimesville, WI 79682\n",
            "Name: address, Length: 10000, dtype: object\n",
            "************************************************\n",
            "Unit 0065 Box 5052\n",
            "DPO AP 27450\n",
            "************************************************\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Unit 0065 Box 5052\\nDPO AP 27450'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df.iloc[:, 2] == 'PM']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "T7A1yOrwFmOr",
        "outputId": "1fa3a792-b5aa-42d6-85cb-bc3fdc54a165"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                address    lot am or pm  \\\n",
              "0     16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...  46 in       PM   \n",
              "1     9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...  28 rn       PM   \n",
              "2                      Unit 0065 Box 5052\\nDPO AP 27450  94 vE       PM   \n",
              "3                 7780 Julia Fords\\nNew Stacy, WA 45798  36 vm       PM   \n",
              "5     7502 Powell Mission Apt. 768\\nTravisland, VA 3...  21 XT       PM   \n",
              "...                                                 ...    ...      ...   \n",
              "9992  885 Allen Mountains Apt. 230\\nWallhaven, LA 16995  40 vH       PM   \n",
              "9993  7555 Larson Locks Suite 229\\nEllisburgh, MA 34...  72 jg       PM   \n",
              "9994       6276 Rojas Hollow\\nLake Louis, WY 56410-7837  93 Ex       PM   \n",
              "9995      966 Castaneda Locks\\nWest Juliafurt, CO 96415  92 XI       PM   \n",
              "9998               0096 English Rest\\nRoystad, IA 12457  74 cL       PM   \n",
              "\n",
              "                                           browser info  \\\n",
              "0     Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...   \n",
              "1     Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...   \n",
              "2     Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
              "3     Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...   \n",
              "5     Mozilla/5.0 (Macintosh; U; PPC Mac OS X 10_8_5...   \n",
              "...                                                 ...   \n",
              "9992  Mozilla/5.0 (Macintosh; PPC Mac OS X 10_6_5) A...   \n",
              "9993  Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_8...   \n",
              "9994  Opera/9.68.(X11; Linux x86_64; sl-SI) Presto/2...   \n",
              "9995  Mozilla/5.0 (Windows NT 5.1) AppleWebKit/5352 ...   \n",
              "9998  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_8;...   \n",
              "\n",
              "                              company       credit card cc-exp-date  \\\n",
              "0                     Martinez-Herman  6011929061123406       02/20   \n",
              "1     Fletcher, Richards and Whitaker  3337758169645356       11/18   \n",
              "2          Simpson, Williams and Pham      675957666125       08/19   \n",
              "3     Williams, Marshall and Buchanan  6011578504430710       02/24   \n",
              "5                      Silva-Anderson    30246185196287       07/25   \n",
              "...                               ...               ...         ...   \n",
              "9992                        Wells Ltd  4664825258997302       10/20   \n",
              "9993                   Colon and Sons    30025560104631       10/25   \n",
              "9994                     Ritter-Smith  3112186784121077       01/25   \n",
              "9995                    Randall-Sloan   342945015358701       03/22   \n",
              "9998                         Cook Inc   180003348082930       11/17   \n",
              "\n",
              "      cc-security-code       cc provider                           email  \\\n",
              "0                  900      JCB 16 digit               pdunlap@yahoo.com   \n",
              "1                  561        Mastercard              anthony41@reed.com   \n",
              "2                  699      JCB 16 digit  amymiller@morales-harrison.com   \n",
              "3                  384          Discover     brent16@olson-robinson.info   \n",
              "5                 7169          Discover               ynguyen@gmail.com   \n",
              "...                ...               ...                             ...   \n",
              "9992               431          Discover               bberry@wright.net   \n",
              "9993               629           Maestro       chelseawilliams@lopez.biz   \n",
              "9994              1823           Maestro              iroberts@gmail.com   \n",
              "9995               838      JCB 15 digit          iscott@wade-garner.com   \n",
              "9998               987  American Express         elizabethmoore@reid.net   \n",
              "\n",
              "                                         job       ip address language  \\\n",
              "0     Scientist, product/process development  149.146.147.205       el   \n",
              "1                          Drilling engineer     15.160.41.51       fr   \n",
              "2                   Customer service manager   132.207.160.22       de   \n",
              "3                          Drilling engineer     30.250.74.19       es   \n",
              "5                          Fish farm manager    55.96.152.147       ru   \n",
              "...                                      ...              ...      ...   \n",
              "9992                            Set designer    174.173.51.32       de   \n",
              "9993            Designer, exhibition/display    177.46.82.128       el   \n",
              "9994               Education officer, museum    242.44.112.18       zh   \n",
              "9995                              Printmaker    29.73.197.114       it   \n",
              "9998                Local government officer     55.78.26.143       es   \n",
              "\n",
              "      purchase price  \n",
              "0              98.14  \n",
              "1              70.73  \n",
              "2               0.95  \n",
              "3              78.04  \n",
              "5              25.15  \n",
              "...              ...  \n",
              "9992           67.96  \n",
              "9993           65.61  \n",
              "9994           31.85  \n",
              "9995           82.21  \n",
              "9998           38.84  \n",
              "\n",
              "[5068 rows x 14 columns]"
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              "      <th>address</th>\n",
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              "      <th>0</th>\n",
              "      <td>16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...</td>\n",
              "      <td>46 in</td>\n",
              "      <td>PM</td>\n",
              "      <td>Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...</td>\n",
              "      <td>Martinez-Herman</td>\n",
              "      <td>6011929061123406</td>\n",
              "      <td>02/20</td>\n",
              "      <td>900</td>\n",
              "      <td>JCB 16 digit</td>\n",
              "      <td>pdunlap@yahoo.com</td>\n",
              "      <td>Scientist, product/process development</td>\n",
              "      <td>149.146.147.205</td>\n",
              "      <td>el</td>\n",
              "      <td>98.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...</td>\n",
              "      <td>28 rn</td>\n",
              "      <td>PM</td>\n",
              "      <td>Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...</td>\n",
              "      <td>Fletcher, Richards and Whitaker</td>\n",
              "      <td>3337758169645356</td>\n",
              "      <td>11/18</td>\n",
              "      <td>561</td>\n",
              "      <td>Mastercard</td>\n",
              "      <td>anthony41@reed.com</td>\n",
              "      <td>Drilling engineer</td>\n",
              "      <td>15.160.41.51</td>\n",
              "      <td>fr</td>\n",
              "      <td>70.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Unit 0065 Box 5052\\nDPO AP 27450</td>\n",
              "      <td>94 vE</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
              "      <td>Simpson, Williams and Pham</td>\n",
              "      <td>675957666125</td>\n",
              "      <td>08/19</td>\n",
              "      <td>699</td>\n",
              "      <td>JCB 16 digit</td>\n",
              "      <td>amymiller@morales-harrison.com</td>\n",
              "      <td>Customer service manager</td>\n",
              "      <td>132.207.160.22</td>\n",
              "      <td>de</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7780 Julia Fords\\nNew Stacy, WA 45798</td>\n",
              "      <td>36 vm</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...</td>\n",
              "      <td>Williams, Marshall and Buchanan</td>\n",
              "      <td>6011578504430710</td>\n",
              "      <td>02/24</td>\n",
              "      <td>384</td>\n",
              "      <td>Discover</td>\n",
              "      <td>brent16@olson-robinson.info</td>\n",
              "      <td>Drilling engineer</td>\n",
              "      <td>30.250.74.19</td>\n",
              "      <td>es</td>\n",
              "      <td>78.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7502 Powell Mission Apt. 768\\nTravisland, VA 3...</td>\n",
              "      <td>21 XT</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Macintosh; U; PPC Mac OS X 10_8_5...</td>\n",
              "      <td>Silva-Anderson</td>\n",
              "      <td>30246185196287</td>\n",
              "      <td>07/25</td>\n",
              "      <td>7169</td>\n",
              "      <td>Discover</td>\n",
              "      <td>ynguyen@gmail.com</td>\n",
              "      <td>Fish farm manager</td>\n",
              "      <td>55.96.152.147</td>\n",
              "      <td>ru</td>\n",
              "      <td>25.15</td>\n",
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              "      <th>9992</th>\n",
              "      <td>885 Allen Mountains Apt. 230\\nWallhaven, LA 16995</td>\n",
              "      <td>40 vH</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Macintosh; PPC Mac OS X 10_6_5) A...</td>\n",
              "      <td>Wells Ltd</td>\n",
              "      <td>4664825258997302</td>\n",
              "      <td>10/20</td>\n",
              "      <td>431</td>\n",
              "      <td>Discover</td>\n",
              "      <td>bberry@wright.net</td>\n",
              "      <td>Set designer</td>\n",
              "      <td>174.173.51.32</td>\n",
              "      <td>de</td>\n",
              "      <td>67.96</td>\n",
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              "    <tr>\n",
              "      <th>9993</th>\n",
              "      <td>7555 Larson Locks Suite 229\\nEllisburgh, MA 34...</td>\n",
              "      <td>72 jg</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_8...</td>\n",
              "      <td>Colon and Sons</td>\n",
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              "      <td>10/25</td>\n",
              "      <td>629</td>\n",
              "      <td>Maestro</td>\n",
              "      <td>chelseawilliams@lopez.biz</td>\n",
              "      <td>Designer, exhibition/display</td>\n",
              "      <td>177.46.82.128</td>\n",
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              "      <td>65.61</td>\n",
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              "      <th>9994</th>\n",
              "      <td>6276 Rojas Hollow\\nLake Louis, WY 56410-7837</td>\n",
              "      <td>93 Ex</td>\n",
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              "      <td>Opera/9.68.(X11; Linux x86_64; sl-SI) Presto/2...</td>\n",
              "      <td>Ritter-Smith</td>\n",
              "      <td>3112186784121077</td>\n",
              "      <td>01/25</td>\n",
              "      <td>1823</td>\n",
              "      <td>Maestro</td>\n",
              "      <td>iroberts@gmail.com</td>\n",
              "      <td>Education officer, museum</td>\n",
              "      <td>242.44.112.18</td>\n",
              "      <td>zh</td>\n",
              "      <td>31.85</td>\n",
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              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>966 Castaneda Locks\\nWest Juliafurt, CO 96415</td>\n",
              "      <td>92 XI</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Windows NT 5.1) AppleWebKit/5352 ...</td>\n",
              "      <td>Randall-Sloan</td>\n",
              "      <td>342945015358701</td>\n",
              "      <td>03/22</td>\n",
              "      <td>838</td>\n",
              "      <td>JCB 15 digit</td>\n",
              "      <td>iscott@wade-garner.com</td>\n",
              "      <td>Printmaker</td>\n",
              "      <td>29.73.197.114</td>\n",
              "      <td>it</td>\n",
              "      <td>82.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>0096 English Rest\\nRoystad, IA 12457</td>\n",
              "      <td>74 cL</td>\n",
              "      <td>PM</td>\n",
              "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_8;...</td>\n",
              "      <td>Cook Inc</td>\n",
              "      <td>180003348082930</td>\n",
              "      <td>11/17</td>\n",
              "      <td>987</td>\n",
              "      <td>American Express</td>\n",
              "      <td>elizabethmoore@reid.net</td>\n",
              "      <td>Local government officer</td>\n",
              "      <td>55.78.26.143</td>\n",
              "      <td>es</td>\n",
              "      <td>38.84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5068 rows × 14 columns</p>\n",
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    {
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      "source": [
        "new = df[\"company\"].isin([\"Silva-Anderson\", 'Wells Ltd'])\n",
        "df[new]"
      ],
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        "colab": {
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              "                                                address    lot am or pm  \\\n",
              "5     7502 Powell Mission Apt. 768\\nTravisland, VA 3...  21 XT       PM   \n",
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              "9992  4664825258997302       10/20               431    Discover   \n",
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              "2897  alexis18@wise.com  Education administrator   51.132.24.17       it   \n",
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      "source": [
        "filter1 = df[\"company\"].isin([\"Silva-Anderson\", 'Wells Ltd'])\n",
        "filter2 = df['am or pm'] == 'PM'\n",
        "\n",
        "df[filter1 & filter2]"
      ],
      "metadata": {
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          "height": 367
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        "id": "9ZBMPtz9Gy5w",
        "outputId": "36c0e4ab-af62-4644-96de-01b48e660593"
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      "execution_count": 58,
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              "5     7502 Powell Mission Apt. 768\\nTravisland, VA 3...  21 XT       PM   \n",
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              "9992  Mozilla/5.0 (Macintosh; PPC Mac OS X 10_6_5) A...       Wells Ltd   \n",
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              "      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-458b7e9c-8fb1-4d49-b501-f931f58c2433 button.colab-df-convert');\n",
              "        buttonEl.style.display =\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def filter_price(x):\n",
        "  if x > 20.0:\n",
        "    return 'good'\n",
        "  else:\n",
        "    return 'bad'"
      ],
      "metadata": {
        "id": "-DZC_wRcHlzN"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "good_prices = df['purchase price'].apply(filter_price) \n",
        "good_prices[filter2].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0ZvrHV0H1wA",
        "outputId": "0c68532c-4b05-4c2f-8567-c701172889cb"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5068"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P421i_a90g-r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from scipy import linalg\n",
        "import numpy as np\n",
        "\n",
        "arr_input = np.array([[3,4],[7,2]])\n",
        "arr_output = np.array([12,10])\n",
        "\n",
        "res = linalg.solve(arr_input, arr_output)\n",
        "print(res)"
      ],
      "metadata": {
        "id": "DgUXQU_y0kyo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "587a3de8-9712-4fea-f46c-59b68bbcf5fb"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.72727273 2.45454545]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to find determinant\n",
        "A = np.array([[3,4],[7,2]])\n",
        "D = linalg.det(A)\n",
        "print(D)"
      ],
      "metadata": {
        "id": "mqF-RDXB3y12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1df08f78-3ee4-42de-fd14-acca53085a80"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-22.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# the eigen vector and eigen value\n",
        "M = np.array([[3,4],[7,2]])\n",
        "\n",
        "# Passing the values to the eigen\n",
        "# function\n",
        "val , vect = linalg.eig(M)\n",
        "  \n",
        "# Display the Eigen values and Eigen\n",
        "# vectors\n",
        "print(val)\n",
        "print(vect)"
      ],
      "metadata": {
        "id": "CqPQQ9Kz36qr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28fcdc4a-b695-4707-df3b-5b77be39e13b"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 7.81507291+0.j -2.81507291+0.j]\n",
            "[[ 0.63899943 -0.56673444]\n",
            " [ 0.76920721  0.82390052]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to find the sin, cos and tan of the matrix\n",
        "x = np.array([[3,4],[7,2]])\n",
        "s = linalg.sinm(x)\n",
        "print(s)\n",
        "print(\"\\n\")\n",
        "  \n",
        "# Calculate and print the matrix \n",
        "# cosine\n",
        "c = linalg.cosm(x)\n",
        "print(c)\n",
        "print(\"\\n\")\n",
        "  \n",
        "# Calculate and print the matrix \n",
        "# tangent\n",
        "t = linalg.tanm(x)\n",
        "print(t)"
      ],
      "metadata": {
        "id": "7KQn6VmZ4g-R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "027c7f86-aaf6-416f-a792-d63f8eadc3c8"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.40133446 0.49669752]\n",
            " [0.86922065 0.27716008]]\n",
            "\n",
            "\n",
            "[[-0.40775221  0.37104412]\n",
            " [ 0.64932721 -0.50051324]]\n",
            "\n",
            "\n",
            "[[14.20576543  9.53874508]\n",
            " [16.69280388 11.82107916]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5mvtWcbU4ohp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q2. Given below is the Cars dataset, which contains all details about the cars included in the dataset. So, let's explore the dataset and perform following operations.\n",
        "\n",
        "A. Read and explore the following dataset.\n",
        "\n",
        "URL for the dataset : https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv\n",
        "\n",
        "To get clear tabular view : https://github.com/selva86/datasets/blob/master/Cars93_miss.csv\n",
        "\n",
        "\n",
        "B. Display top and last 15 rows of the dataset.\n",
        "\n",
        "C. Figure out different Manufacturer present in the dataset and how many cars each manufacturer has in the market. Store the result in the form of dictionary, Manufacturer name as key and number of cars as value.\n",
        "\n",
        "D. Find the total of cars having seating capacity more than 4.\n",
        "\n",
        "E. Drop all rows containing NaN values.\n",
        "\n",
        "F. Drop columns named 'Turn.circle' and 'Weight'.\n"
      ],
      "metadata": {
        "id": "eyxX1rEm4qGP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Read and Explore the dataset\n",
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')\n",
        "data"
      ],
      "metadata": {
        "id": "cHZUicDH-APp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "outputId": "58384e62-ed33-4a29-98fd-48268813090e"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Manufacturer    Model     Type  Min.Price  Price  Max.Price  MPG.city  \\\n",
              "0         Acura  Integra    Small       12.9   15.9       18.8      25.0   \n",
              "1           NaN   Legend  Midsize       29.2   33.9       38.7      18.0   \n",
              "2          Audi       90  Compact       25.9   29.1       32.3      20.0   \n",
              "3          Audi      100  Midsize        NaN   37.7       44.6      19.0   \n",
              "4           BMW     535i  Midsize        NaN   30.0        NaN      22.0   \n",
              "..          ...      ...      ...        ...    ...        ...       ...   \n",
              "88   Volkswagen  Eurovan      Van       16.6   19.7       22.7      17.0   \n",
              "89   Volkswagen   Passat  Compact       17.6   20.0       22.4      21.0   \n",
              "90   Volkswagen  Corrado   Sporty       22.9   23.3       23.7      18.0   \n",
              "91        Volvo      240  Compact       21.8   22.7       23.5      21.0   \n",
              "92          NaN      850  Midsize       24.8   26.7       28.5      20.0   \n",
              "\n",
              "    MPG.highway             AirBags DriveTrain  ... Passengers  Length  \\\n",
              "0          31.0                None      Front  ...        5.0   177.0   \n",
              "1          25.0  Driver & Passenger      Front  ...        5.0   195.0   \n",
              "2          26.0         Driver only      Front  ...        5.0   180.0   \n",
              "3          26.0  Driver & Passenger        NaN  ...        6.0   193.0   \n",
              "4          30.0                 NaN       Rear  ...        4.0   186.0   \n",
              "..          ...                 ...        ...  ...        ...     ...   \n",
              "88         21.0                None      Front  ...        7.0   187.0   \n",
              "89         30.0                None      Front  ...        5.0   180.0   \n",
              "90         25.0                None      Front  ...        4.0   159.0   \n",
              "91         28.0         Driver only       Rear  ...        5.0   190.0   \n",
              "92         28.0  Driver & Passenger      Front  ...        5.0   184.0   \n",
              "\n",
              "    Wheelbase  Width  Turn.circle Rear.seat.room  Luggage.room  Weight  \\\n",
              "0       102.0   68.0         37.0           26.5           NaN  2705.0   \n",
              "1       115.0   71.0         38.0           30.0          15.0  3560.0   \n",
              "2       102.0   67.0         37.0           28.0          14.0  3375.0   \n",
              "3       106.0    NaN         37.0           31.0          17.0  3405.0   \n",
              "4       109.0   69.0         39.0           27.0          13.0  3640.0   \n",
              "..        ...    ...          ...            ...           ...     ...   \n",
              "88      115.0   72.0         38.0           34.0           NaN  3960.0   \n",
              "89      103.0   67.0         35.0           31.5          14.0  2985.0   \n",
              "90       97.0   66.0         36.0           26.0          15.0  2810.0   \n",
              "91      104.0   67.0         37.0           29.5          14.0  2985.0   \n",
              "92      105.0   69.0         38.0           30.0          15.0  3245.0   \n",
              "\n",
              "     Origin                Make  \n",
              "0   non-USA       Acura Integra  \n",
              "1   non-USA        Acura Legend  \n",
              "2   non-USA             Audi 90  \n",
              "3   non-USA            Audi 100  \n",
              "4   non-USA            BMW 535i  \n",
              "..      ...                 ...  \n",
              "88      NaN  Volkswagen Eurovan  \n",
              "89  non-USA   Volkswagen Passat  \n",
              "90  non-USA  Volkswagen Corrado  \n",
              "91  non-USA           Volvo 240  \n",
              "92  non-USA           Volvo 850  \n",
              "\n",
              "[93 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2ec03df8-f50c-4bcf-9a0c-b0b4df8728c2\">\n",
              "    <div 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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Manufacturer</th>\n",
              "      <th>Model</th>\n",
              "      <th>Type</th>\n",
              "      <th>Min.Price</th>\n",
              "      <th>Price</th>\n",
              "      <th>Max.Price</th>\n",
              "      <th>MPG.city</th>\n",
              "      <th>MPG.highway</th>\n",
              "      <th>AirBags</th>\n",
              "      <th>DriveTrain</th>\n",
              "      <th>...</th>\n",
              "      <th>Passengers</th>\n",
              "      <th>Length</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Width</th>\n",
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              "      <th>Rear.seat.room</th>\n",
              "      <th>Luggage.room</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Origin</th>\n",
              "      <th>Make</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acura</td>\n",
              "      <td>Integra</td>\n",
              "      <td>Small</td>\n",
              "      <td>12.9</td>\n",
              "      <td>15.9</td>\n",
              "      <td>18.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2705.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Acura Integra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Legend</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>29.2</td>\n",
              "      <td>33.9</td>\n",
              "      <td>38.7</td>\n",
              "      <td>18.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3560.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Acura Legend</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Audi</td>\n",
              "      <td>90</td>\n",
              "      <td>Compact</td>\n",
              "      <td>25.9</td>\n",
              "      <td>29.1</td>\n",
              "      <td>32.3</td>\n",
              "      <td>20.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3375.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Audi 90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Audi</td>\n",
              "      <td>100</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.7</td>\n",
              "      <td>44.6</td>\n",
              "      <td>19.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3405.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Audi 100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>535i</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>3640.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>BMW 535i</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Eurovan</td>\n",
              "      <td>Van</td>\n",
              "      <td>16.6</td>\n",
              "      <td>19.7</td>\n",
              "      <td>22.7</td>\n",
              "      <td>17.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3960.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Volkswagen Eurovan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Passat</td>\n",
              "      <td>Compact</td>\n",
              "      <td>17.6</td>\n",
              "      <td>20.0</td>\n",
              "      <td>22.4</td>\n",
              "      <td>21.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>31.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2985.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Passat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Corrado</td>\n",
              "      <td>Sporty</td>\n",
              "      <td>22.9</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.7</td>\n",
              "      <td>18.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2810.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Corrado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Volvo</td>\n",
              "      <td>240</td>\n",
              "      <td>Compact</td>\n",
              "      <td>21.8</td>\n",
              "      <td>22.7</td>\n",
              "      <td>23.5</td>\n",
              "      <td>21.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2985.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volvo 240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>NaN</td>\n",
              "      <td>850</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>24.8</td>\n",
              "      <td>26.7</td>\n",
              "      <td>28.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3245.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volvo 850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93 rows × 27 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ec03df8-f50c-4bcf-9a0c-b0b4df8728c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display top and last 15 rows of the dataset.\n",
        "data.head(15)"
      ],
      "metadata": {
        "id": "TUmFuv52-VlW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 874
        },
        "outputId": "247c0014-c884-4bab-d244-e1ed0c161f57"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Manufacturer       Model     Type  Min.Price  Price  Max.Price  MPG.city  \\\n",
              "0         Acura     Integra    Small       12.9   15.9       18.8      25.0   \n",
              "1           NaN      Legend  Midsize       29.2   33.9       38.7      18.0   \n",
              "2          Audi          90  Compact       25.9   29.1       32.3      20.0   \n",
              "3          Audi         100  Midsize        NaN   37.7       44.6      19.0   \n",
              "4           BMW        535i  Midsize        NaN   30.0        NaN      22.0   \n",
              "5         Buick     Century  Midsize       14.2   15.7       17.3      22.0   \n",
              "6         Buick     LeSabre    Large       19.9   20.8        NaN      19.0   \n",
              "7         Buick  Roadmaster    Large       22.6   23.7       24.9      16.0   \n",
              "8         Buick     Riviera  Midsize       26.3   26.3       26.3      19.0   \n",
              "9      Cadillac     DeVille    Large       33.0   34.7       36.3      16.0   \n",
              "10     Cadillac     Seville  Midsize       37.5   40.1       42.7      16.0   \n",
              "11    Chevrolet    Cavalier  Compact        8.5   13.4       18.3      25.0   \n",
              "12    Chevrolet     Corsica  Compact       11.4   11.4       11.4      25.0   \n",
              "13    Chevrolet      Camaro   Sporty       13.4   15.1       16.8      19.0   \n",
              "14    Chevrolet      Lumina  Midsize       13.4   15.9       18.4       NaN   \n",
              "\n",
              "    MPG.highway             AirBags DriveTrain  ... Passengers  Length  \\\n",
              "0          31.0                None      Front  ...        5.0   177.0   \n",
              "1          25.0  Driver & Passenger      Front  ...        5.0   195.0   \n",
              "2          26.0         Driver only      Front  ...        5.0   180.0   \n",
              "3          26.0  Driver & Passenger        NaN  ...        6.0   193.0   \n",
              "4          30.0                 NaN       Rear  ...        4.0   186.0   \n",
              "5          31.0         Driver only        NaN  ...        6.0   189.0   \n",
              "6          28.0         Driver only      Front  ...        6.0   200.0   \n",
              "7          25.0         Driver only       Rear  ...        6.0   216.0   \n",
              "8          27.0         Driver only      Front  ...        5.0   198.0   \n",
              "9          25.0         Driver only      Front  ...        6.0   206.0   \n",
              "10         25.0  Driver & Passenger      Front  ...        5.0   204.0   \n",
              "11         36.0                 NaN        NaN  ...        5.0   182.0   \n",
              "12         34.0         Driver only      Front  ...        5.0   184.0   \n",
              "13         28.0  Driver & Passenger       Rear  ...        4.0   193.0   \n",
              "14         29.0                None      Front  ...        6.0     NaN   \n",
              "\n",
              "    Wheelbase  Width  Turn.circle Rear.seat.room  Luggage.room  Weight  \\\n",
              "0       102.0   68.0         37.0           26.5           NaN  2705.0   \n",
              "1       115.0   71.0         38.0           30.0          15.0  3560.0   \n",
              "2       102.0   67.0         37.0           28.0          14.0  3375.0   \n",
              "3       106.0    NaN         37.0           31.0          17.0  3405.0   \n",
              "4       109.0   69.0         39.0           27.0          13.0  3640.0   \n",
              "5       105.0   69.0         41.0           28.0          16.0     NaN   \n",
              "6       111.0   74.0         42.0           30.5          17.0  3470.0   \n",
              "7       116.0   78.0         45.0           30.5          21.0  4105.0   \n",
              "8       108.0    NaN         41.0           26.5          14.0  3495.0   \n",
              "9       114.0   73.0         43.0           35.0          18.0  3620.0   \n",
              "10      111.0   74.0         44.0           31.0           NaN  3935.0   \n",
              "11      101.0   66.0         38.0           25.0          13.0  2490.0   \n",
              "12      103.0   68.0         39.0           26.0           NaN  2785.0   \n",
              "13      101.0   74.0         43.0           25.0          13.0  3240.0   \n",
              "14      108.0   71.0          NaN           28.5          16.0  3195.0   \n",
              "\n",
              "     Origin                Make  \n",
              "0   non-USA       Acura Integra  \n",
              "1   non-USA        Acura Legend  \n",
              "2   non-USA             Audi 90  \n",
              "3   non-USA            Audi 100  \n",
              "4   non-USA            BMW 535i  \n",
              "5       USA       Buick Century  \n",
              "6       USA       Buick LeSabre  \n",
              "7       USA    Buick Roadmaster  \n",
              "8       USA       Buick Riviera  \n",
              "9       USA    Cadillac DeVille  \n",
              "10      USA    Cadillac Seville  \n",
              "11      USA  Chevrolet Cavalier  \n",
              "12      USA   Chevrolet Corsica  \n",
              "13      USA    Chevrolet Camaro  \n",
              "14      USA    Chevrolet Lumina  \n",
              "\n",
              "[15 rows x 27 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Manufacturer</th>\n",
              "      <th>Model</th>\n",
              "      <th>Type</th>\n",
              "      <th>Min.Price</th>\n",
              "      <th>Price</th>\n",
              "      <th>Max.Price</th>\n",
              "      <th>MPG.city</th>\n",
              "      <th>MPG.highway</th>\n",
              "      <th>AirBags</th>\n",
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              "      <th>Rear.seat.room</th>\n",
              "      <th>Luggage.room</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Origin</th>\n",
              "      <th>Make</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acura</td>\n",
              "      <td>Integra</td>\n",
              "      <td>Small</td>\n",
              "      <td>12.9</td>\n",
              "      <td>15.9</td>\n",
              "      <td>18.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2705.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Acura Integra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Legend</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>29.2</td>\n",
              "      <td>33.9</td>\n",
              "      <td>38.7</td>\n",
              "      <td>18.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3560.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Acura Legend</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Audi</td>\n",
              "      <td>90</td>\n",
              "      <td>Compact</td>\n",
              "      <td>25.9</td>\n",
              "      <td>29.1</td>\n",
              "      <td>32.3</td>\n",
              "      <td>20.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3375.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Audi 90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Audi</td>\n",
              "      <td>100</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.7</td>\n",
              "      <td>44.6</td>\n",
              "      <td>19.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3405.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Audi 100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>535i</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>3640.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>BMW 535i</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Buick</td>\n",
              "      <td>Century</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>14.2</td>\n",
              "      <td>15.7</td>\n",
              "      <td>17.3</td>\n",
              "      <td>22.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA</td>\n",
              "      <td>Buick Century</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Buick</td>\n",
              "      <td>LeSabre</td>\n",
              "      <td>Large</td>\n",
              "      <td>19.9</td>\n",
              "      <td>20.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>30.5</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3470.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Buick LeSabre</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Buick</td>\n",
              "      <td>Roadmaster</td>\n",
              "      <td>Large</td>\n",
              "      <td>22.6</td>\n",
              "      <td>23.7</td>\n",
              "      <td>24.9</td>\n",
              "      <td>16.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>30.5</td>\n",
              "      <td>21.0</td>\n",
              "      <td>4105.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Buick Roadmaster</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Buick</td>\n",
              "      <td>Riviera</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>26.3</td>\n",
              "      <td>26.3</td>\n",
              "      <td>26.3</td>\n",
              "      <td>19.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3495.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Buick Riviera</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Cadillac</td>\n",
              "      <td>DeVille</td>\n",
              "      <td>Large</td>\n",
              "      <td>33.0</td>\n",
              "      <td>34.7</td>\n",
              "      <td>36.3</td>\n",
              "      <td>16.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>206.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>3620.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Cadillac DeVille</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Cadillac</td>\n",
              "      <td>Seville</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>37.5</td>\n",
              "      <td>40.1</td>\n",
              "      <td>42.7</td>\n",
              "      <td>16.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3935.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Cadillac Seville</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Cavalier</td>\n",
              "      <td>Compact</td>\n",
              "      <td>8.5</td>\n",
              "      <td>13.4</td>\n",
              "      <td>18.3</td>\n",
              "      <td>25.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2490.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Chevrolet Cavalier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Corsica</td>\n",
              "      <td>Compact</td>\n",
              "      <td>11.4</td>\n",
              "      <td>11.4</td>\n",
              "      <td>11.4</td>\n",
              "      <td>25.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2785.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Chevrolet Corsica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Camaro</td>\n",
              "      <td>Sporty</td>\n",
              "      <td>13.4</td>\n",
              "      <td>15.1</td>\n",
              "      <td>16.8</td>\n",
              "      <td>19.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>3240.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Chevrolet Camaro</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Lumina</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>13.4</td>\n",
              "      <td>15.9</td>\n",
              "      <td>18.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>108.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.5</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3195.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Chevrolet Lumina</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15 rows × 27 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78706196-e96b-47a3-9986-54472cb2da46')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 856
        },
        "id": "saJtUKe6K2Gk",
        "outputId": "c69e1be1-2f60-4236-9d7a-84ecd090ad48"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Manufacturer    Model     Type  Min.Price  Price  Max.Price  MPG.city  \\\n",
              "78       Saturn       SL    Small        9.2    NaN       12.9       NaN   \n",
              "79       Subaru    Justy    Small        7.3    8.4        9.5      33.0   \n",
              "80       Subaru   Loyale    Small       10.5   10.9       11.3      25.0   \n",
              "81       Subaru   Legacy  Compact       16.3   19.5       22.7      23.0   \n",
              "82       Suzuki    Swift      NaN        7.3    8.6        NaN      39.0   \n",
              "83       Toyota   Tercel    Small        NaN    9.8       11.8      32.0   \n",
              "84       Toyota   Celica   Sporty       14.2   18.4       22.6      25.0   \n",
              "85       Toyota    Camry  Midsize       15.2    NaN       21.2      22.0   \n",
              "86       Toyota   Previa      Van        NaN   22.7       26.6      18.0   \n",
              "87   Volkswagen      Fox    Small        8.7    9.1        9.5      25.0   \n",
              "88   Volkswagen  Eurovan      Van       16.6   19.7       22.7      17.0   \n",
              "89   Volkswagen   Passat  Compact       17.6   20.0       22.4      21.0   \n",
              "90   Volkswagen  Corrado   Sporty       22.9   23.3       23.7      18.0   \n",
              "91        Volvo      240  Compact       21.8   22.7       23.5      21.0   \n",
              "92          NaN      850  Midsize       24.8   26.7       28.5      20.0   \n",
              "\n",
              "    MPG.highway             AirBags DriveTrain  ... Passengers  Length  \\\n",
              "78         38.0         Driver only      Front  ...        5.0   176.0   \n",
              "79         37.0                None        4WD  ...        4.0   146.0   \n",
              "80         30.0                None        4WD  ...        5.0   175.0   \n",
              "81         30.0         Driver only        4WD  ...        5.0   179.0   \n",
              "82         43.0                None      Front  ...        4.0   161.0   \n",
              "83         37.0         Driver only      Front  ...        NaN   162.0   \n",
              "84         32.0                 NaN      Front  ...        4.0   174.0   \n",
              "85         29.0         Driver only        NaN  ...        5.0   188.0   \n",
              "86         22.0         Driver only        4WD  ...        7.0   187.0   \n",
              "87         33.0                None      Front  ...        4.0   163.0   \n",
              "88         21.0                None      Front  ...        7.0   187.0   \n",
              "89         30.0                None      Front  ...        5.0   180.0   \n",
              "90         25.0                None      Front  ...        4.0   159.0   \n",
              "91         28.0         Driver only       Rear  ...        5.0   190.0   \n",
              "92         28.0  Driver & Passenger      Front  ...        5.0   184.0   \n",
              "\n",
              "    Wheelbase  Width  Turn.circle Rear.seat.room  Luggage.room  Weight  \\\n",
              "78      102.0   68.0         40.0           26.5           NaN  2495.0   \n",
              "79       90.0   60.0         32.0           23.5          10.0  2045.0   \n",
              "80       97.0   65.0         35.0           27.5          15.0  2490.0   \n",
              "81      102.0   67.0         37.0           27.0          14.0  3085.0   \n",
              "82       93.0    NaN         34.0           27.5          10.0  1965.0   \n",
              "83       94.0    NaN         36.0           24.0          11.0  2055.0   \n",
              "84       99.0   69.0          NaN           23.0          13.0  2950.0   \n",
              "85      103.0   70.0         38.0           28.5          15.0  3030.0   \n",
              "86      113.0   71.0         41.0           35.0           NaN  3785.0   \n",
              "87       93.0   63.0         34.0           26.0          10.0  2240.0   \n",
              "88      115.0   72.0         38.0           34.0           NaN  3960.0   \n",
              "89      103.0   67.0         35.0           31.5          14.0  2985.0   \n",
              "90       97.0   66.0         36.0           26.0          15.0  2810.0   \n",
              "91      104.0   67.0         37.0           29.5          14.0  2985.0   \n",
              "92      105.0   69.0         38.0           30.0          15.0  3245.0   \n",
              "\n",
              "     Origin                Make  \n",
              "78      USA           Saturn SL  \n",
              "79  non-USA        Subaru Justy  \n",
              "80  non-USA       Subaru Loyale  \n",
              "81  non-USA       Subaru Legacy  \n",
              "82  non-USA        Suzuki Swift  \n",
              "83  non-USA       Toyota Tercel  \n",
              "84  non-USA       Toyota Celica  \n",
              "85  non-USA        Toyota Camry  \n",
              "86  non-USA       Toyota Previa  \n",
              "87  non-USA      Volkswagen Fox  \n",
              "88      NaN  Volkswagen Eurovan  \n",
              "89  non-USA   Volkswagen Passat  \n",
              "90  non-USA  Volkswagen Corrado  \n",
              "91  non-USA           Volvo 240  \n",
              "92  non-USA           Volvo 850  \n",
              "\n",
              "[15 rows x 27 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Manufacturer</th>\n",
              "      <th>Model</th>\n",
              "      <th>Type</th>\n",
              "      <th>Min.Price</th>\n",
              "      <th>Price</th>\n",
              "      <th>Max.Price</th>\n",
              "      <th>MPG.city</th>\n",
              "      <th>MPG.highway</th>\n",
              "      <th>AirBags</th>\n",
              "      <th>DriveTrain</th>\n",
              "      <th>...</th>\n",
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              "      <th>Length</th>\n",
              "      <th>Wheelbase</th>\n",
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              "      <th>Turn.circle</th>\n",
              "      <th>Rear.seat.room</th>\n",
              "      <th>Luggage.room</th>\n",
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              "      <th>Make</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Saturn</td>\n",
              "      <td>SL</td>\n",
              "      <td>Small</td>\n",
              "      <td>9.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2495.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Saturn SL</td>\n",
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              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Subaru</td>\n",
              "      <td>Justy</td>\n",
              "      <td>Small</td>\n",
              "      <td>7.3</td>\n",
              "      <td>8.4</td>\n",
              "      <td>9.5</td>\n",
              "      <td>33.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4WD</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>23.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2045.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Subaru Justy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Subaru</td>\n",
              "      <td>Loyale</td>\n",
              "      <td>Small</td>\n",
              "      <td>10.5</td>\n",
              "      <td>10.9</td>\n",
              "      <td>11.3</td>\n",
              "      <td>25.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4WD</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>27.5</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2490.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Subaru Loyale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>Subaru</td>\n",
              "      <td>Legacy</td>\n",
              "      <td>Compact</td>\n",
              "      <td>16.3</td>\n",
              "      <td>19.5</td>\n",
              "      <td>22.7</td>\n",
              "      <td>23.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>4WD</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>179.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3085.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Subaru Legacy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Suzuki</td>\n",
              "      <td>Swift</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.3</td>\n",
              "      <td>8.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.0</td>\n",
              "      <td>27.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1965.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Suzuki Swift</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>Tercel</td>\n",
              "      <td>Small</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.8</td>\n",
              "      <td>11.8</td>\n",
              "      <td>32.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>162.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2055.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Toyota Tercel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>Celica</td>\n",
              "      <td>Sporty</td>\n",
              "      <td>14.2</td>\n",
              "      <td>18.4</td>\n",
              "      <td>22.6</td>\n",
              "      <td>25.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2950.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Toyota Celica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>Camry</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>15.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.2</td>\n",
              "      <td>22.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>28.5</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3030.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Toyota Camry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>Previa</td>\n",
              "      <td>Van</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.7</td>\n",
              "      <td>26.6</td>\n",
              "      <td>18.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>4WD</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3785.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Toyota Previa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Fox</td>\n",
              "      <td>Small</td>\n",
              "      <td>8.7</td>\n",
              "      <td>9.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>25.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>163.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2240.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Fox</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Eurovan</td>\n",
              "      <td>Van</td>\n",
              "      <td>16.6</td>\n",
              "      <td>19.7</td>\n",
              "      <td>22.7</td>\n",
              "      <td>17.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3960.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Volkswagen Eurovan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Passat</td>\n",
              "      <td>Compact</td>\n",
              "      <td>17.6</td>\n",
              "      <td>20.0</td>\n",
              "      <td>22.4</td>\n",
              "      <td>21.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>31.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2985.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Passat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Corrado</td>\n",
              "      <td>Sporty</td>\n",
              "      <td>22.9</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.7</td>\n",
              "      <td>18.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2810.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Corrado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Volvo</td>\n",
              "      <td>240</td>\n",
              "      <td>Compact</td>\n",
              "      <td>21.8</td>\n",
              "      <td>22.7</td>\n",
              "      <td>23.5</td>\n",
              "      <td>21.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2985.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volvo 240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>NaN</td>\n",
              "      <td>850</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>24.8</td>\n",
              "      <td>26.7</td>\n",
              "      <td>28.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3245.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volvo 850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15 rows × 27 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-621ffe17-0b5b-4f50-a479-d2574954ecaf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\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",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-621ffe17-0b5b-4f50-a479-d2574954ecaf');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Figure out different Manufacturer present in the dataset and how many cars each manufacturer has in the market.\n",
        "manu_cars = {}\n",
        "\n",
        "index = data['Manufacturer'].value_counts().index\n",
        "number_of_cars = data['Manufacturer'].value_counts().values\n",
        "\n",
        "for i in range(len(index)):\n",
        "    manu_cars[index[i]] = number_of_cars[i]\n",
        "\n",
        "manu_cars"
      ],
      "metadata": {
        "id": "h8kZ3F3R-dA1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db76e808-29ed-431a-a596-427a6762da09"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Ford': 8,\n",
              " 'Chevrolet': 8,\n",
              " 'Dodge': 6,\n",
              " 'Pontiac': 5,\n",
              " 'Mazda': 5,\n",
              " 'Oldsmobile': 4,\n",
              " 'Nissan': 4,\n",
              " 'Toyota': 4,\n",
              " 'Hyundai': 4,\n",
              " 'Buick': 4,\n",
              " 'Volkswagen': 4,\n",
              " 'Honda': 3,\n",
              " 'Subaru': 3,\n",
              " 'Eagle': 2,\n",
              " 'Geo': 2,\n",
              " 'Chrysler': 2,\n",
              " 'Audi': 2,\n",
              " 'Cadillac': 2,\n",
              " 'Mercedes-Benz': 2,\n",
              " 'Mercury': 2,\n",
              " 'Mitsubishi': 2,\n",
              " 'Lincoln': 2,\n",
              " 'Saturn': 1,\n",
              " 'Suzuki': 1,\n",
              " 'Acura': 1,\n",
              " 'Saab': 1,\n",
              " 'Plymouth': 1,\n",
              " 'Lexus': 1,\n",
              " 'Infiniti': 1,\n",
              " 'BMW': 1,\n",
              " 'Volvo': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the total of cars having seating capacity more than 4.\n",
        "sum(data['Passengers'] > 4.0)"
      ],
      "metadata": {
        "id": "aX53cp34-kVH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "350f1585-bd40-4047-9e84-882a92e952ed"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop all rows containing NaN values.\n",
        "data.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "dPykEJBw-npm"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zFBpbXxgLYnF",
        "outputId": "d727ac52-4d53-4313-a0b6-bc9ba757e57e"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Manufacturer       Model     Type  Min.Price  Price  Max.Price  MPG.city  \\\n",
              "2          Audi          90  Compact       25.9   29.1       32.3      20.0   \n",
              "7         Buick  Roadmaster    Large       22.6   23.7       24.9      16.0   \n",
              "17    Chevrolet     Caprice    Large       18.0   18.8       19.6      17.0   \n",
              "20     Chrysler     LeBaron  Compact       14.5   15.8       17.1      23.0   \n",
              "21     Chrysler    Imperial    Large       29.5   29.5       29.5      20.0   \n",
              "28        Eagle      Summit    Small        7.9   12.2       16.5      29.0   \n",
              "43      Hyundai       Excel    Small        6.8    8.0        9.2      29.0   \n",
              "52        Mazda         323    Small        7.4    8.3        9.1      29.0   \n",
              "53        Mazda     Protege    Small       10.9   11.6       12.3      28.0   \n",
              "54        Mazda         626  Compact       14.3   16.5       18.7      26.0   \n",
              "60      Mercury      Cougar  Midsize       14.9   14.9       14.9      19.0   \n",
              "64       Nissan      Altima  Compact       13.0   15.7       18.3      24.0   \n",
              "67   Oldsmobile     Achieva  Compact       13.0   13.5       14.0      24.0   \n",
              "75      Pontiac  Grand_Prix  Midsize       15.4   18.5       21.6      19.0   \n",
              "76      Pontiac  Bonneville    Large       19.4   24.4       29.4      19.0   \n",
              "79       Subaru       Justy    Small        7.3    8.4        9.5      33.0   \n",
              "80       Subaru      Loyale    Small       10.5   10.9       11.3      25.0   \n",
              "87   Volkswagen         Fox    Small        8.7    9.1        9.5      25.0   \n",
              "89   Volkswagen      Passat  Compact       17.6   20.0       22.4      21.0   \n",
              "90   Volkswagen     Corrado   Sporty       22.9   23.3       23.7      18.0   \n",
              "\n",
              "    MPG.highway             AirBags DriveTrain  ... Passengers  Length  \\\n",
              "2          26.0         Driver only      Front  ...        5.0   180.0   \n",
              "7          25.0         Driver only       Rear  ...        6.0   216.0   \n",
              "17         26.0         Driver only       Rear  ...        6.0   214.0   \n",
              "20         28.0  Driver & Passenger      Front  ...        6.0   183.0   \n",
              "21         26.0         Driver only      Front  ...        6.0   203.0   \n",
              "28         33.0                None      Front  ...        5.0   174.0   \n",
              "43         33.0                None      Front  ...        5.0   168.0   \n",
              "52         37.0                None      Front  ...        4.0   164.0   \n",
              "53         36.0                None      Front  ...        5.0   172.0   \n",
              "54         34.0         Driver only      Front  ...        5.0   184.0   \n",
              "60         26.0                None       Rear  ...        5.0   199.0   \n",
              "64         30.0         Driver only      Front  ...        5.0   181.0   \n",
              "67         31.0                None      Front  ...        5.0   188.0   \n",
              "75         27.0                None      Front  ...        5.0   195.0   \n",
              "76         28.0  Driver & Passenger      Front  ...        6.0   177.0   \n",
              "79         37.0                None        4WD  ...        4.0   146.0   \n",
              "80         30.0                None        4WD  ...        5.0   175.0   \n",
              "87         33.0                None      Front  ...        4.0   163.0   \n",
              "89         30.0                None      Front  ...        5.0   180.0   \n",
              "90         25.0                None      Front  ...        4.0   159.0   \n",
              "\n",
              "    Wheelbase  Width  Turn.circle Rear.seat.room  Luggage.room  Weight  \\\n",
              "2       102.0   67.0         37.0           28.0          14.0  3375.0   \n",
              "7       116.0   78.0         45.0           30.5          21.0  4105.0   \n",
              "17      116.0   77.0         42.0           29.5          20.0  3910.0   \n",
              "20      104.0   68.0         41.0           30.5          14.0  3085.0   \n",
              "21      110.0   69.0         44.0           36.0          17.0  3570.0   \n",
              "28       98.0   66.0         36.0           26.5          11.0  2295.0   \n",
              "43       94.0   63.0         35.0           26.0          11.0  2345.0   \n",
              "52       97.0   66.0         34.0           27.0          16.0  2325.0   \n",
              "53       98.0   66.0         36.0           26.5          13.0  2440.0   \n",
              "54      103.0   69.0         40.0           29.5          14.0  2970.0   \n",
              "60      113.0   73.0         38.0           28.0          15.0  3610.0   \n",
              "64      103.0   67.0         40.0           28.5          14.0  3050.0   \n",
              "67      103.0   67.0         39.0           28.0          14.0  2910.0   \n",
              "75      108.0   72.0         41.0           28.5          16.0  3450.0   \n",
              "76      111.0   74.0         43.0           30.5          18.0  3495.0   \n",
              "79       90.0   60.0         32.0           23.5          10.0  2045.0   \n",
              "80       97.0   65.0         35.0           27.5          15.0  2490.0   \n",
              "87       93.0   63.0         34.0           26.0          10.0  2240.0   \n",
              "89      103.0   67.0         35.0           31.5          14.0  2985.0   \n",
              "90       97.0   66.0         36.0           26.0          15.0  2810.0   \n",
              "\n",
              "     Origin                Make  \n",
              "2   non-USA             Audi 90  \n",
              "7       USA    Buick Roadmaster  \n",
              "17      USA   Chevrolet Caprice  \n",
              "20      USA    Chrysler LeBaron  \n",
              "21      USA   Chrysler Imperial  \n",
              "28      USA        Eagle Summit  \n",
              "43  non-USA       Hyundai Excel  \n",
              "52  non-USA           Mazda 323  \n",
              "53  non-USA       Mazda Protege  \n",
              "54  non-USA           Mazda 626  \n",
              "60      USA      Mercury Cougar  \n",
              "64  non-USA       Nissan Altima  \n",
              "67      USA  Oldsmobile Achieva  \n",
              "75      USA  Pontiac Grand_Prix  \n",
              "76      USA  Pontiac Bonneville  \n",
              "79  non-USA        Subaru Justy  \n",
              "80  non-USA       Subaru Loyale  \n",
              "87  non-USA      Volkswagen Fox  \n",
              "89  non-USA   Volkswagen Passat  \n",
              "90  non-USA  Volkswagen Corrado  \n",
              "\n",
              "[20 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-35bdb7d6-9098-4492-a48c-d6a020aab665\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Manufacturer</th>\n",
              "      <th>Model</th>\n",
              "      <th>Type</th>\n",
              "      <th>Min.Price</th>\n",
              "      <th>Price</th>\n",
              "      <th>Max.Price</th>\n",
              "      <th>MPG.city</th>\n",
              "      <th>MPG.highway</th>\n",
              "      <th>AirBags</th>\n",
              "      <th>DriveTrain</th>\n",
              "      <th>...</th>\n",
              "      <th>Passengers</th>\n",
              "      <th>Length</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Width</th>\n",
              "      <th>Turn.circle</th>\n",
              "      <th>Rear.seat.room</th>\n",
              "      <th>Luggage.room</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Origin</th>\n",
              "      <th>Make</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Audi</td>\n",
              "      <td>90</td>\n",
              "      <td>Compact</td>\n",
              "      <td>25.9</td>\n",
              "      <td>29.1</td>\n",
              "      <td>32.3</td>\n",
              "      <td>20.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3375.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Audi 90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Buick</td>\n",
              "      <td>Roadmaster</td>\n",
              "      <td>Large</td>\n",
              "      <td>22.6</td>\n",
              "      <td>23.7</td>\n",
              "      <td>24.9</td>\n",
              "      <td>16.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>30.5</td>\n",
              "      <td>21.0</td>\n",
              "      <td>4105.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Buick Roadmaster</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Caprice</td>\n",
              "      <td>Large</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.8</td>\n",
              "      <td>19.6</td>\n",
              "      <td>17.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>214.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3910.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Chevrolet Caprice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Chrysler</td>\n",
              "      <td>LeBaron</td>\n",
              "      <td>Compact</td>\n",
              "      <td>14.5</td>\n",
              "      <td>15.8</td>\n",
              "      <td>17.1</td>\n",
              "      <td>23.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>30.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3085.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Chrysler LeBaron</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Chrysler</td>\n",
              "      <td>Imperial</td>\n",
              "      <td>Large</td>\n",
              "      <td>29.5</td>\n",
              "      <td>29.5</td>\n",
              "      <td>29.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3570.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Chrysler Imperial</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Eagle</td>\n",
              "      <td>Summit</td>\n",
              "      <td>Small</td>\n",
              "      <td>7.9</td>\n",
              "      <td>12.2</td>\n",
              "      <td>16.5</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2295.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Eagle Summit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Hyundai</td>\n",
              "      <td>Excel</td>\n",
              "      <td>Small</td>\n",
              "      <td>6.8</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2345.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Hyundai Excel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Mazda</td>\n",
              "      <td>323</td>\n",
              "      <td>Small</td>\n",
              "      <td>7.4</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>29.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>164.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2325.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Mazda 323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Mazda</td>\n",
              "      <td>Protege</td>\n",
              "      <td>Small</td>\n",
              "      <td>10.9</td>\n",
              "      <td>11.6</td>\n",
              "      <td>12.3</td>\n",
              "      <td>28.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2440.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Mazda Protege</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Mazda</td>\n",
              "      <td>626</td>\n",
              "      <td>Compact</td>\n",
              "      <td>14.3</td>\n",
              "      <td>16.5</td>\n",
              "      <td>18.7</td>\n",
              "      <td>26.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2970.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Mazda 626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Mercury</td>\n",
              "      <td>Cougar</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>14.9</td>\n",
              "      <td>14.9</td>\n",
              "      <td>14.9</td>\n",
              "      <td>19.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>199.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3610.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Mercury Cougar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Nissan</td>\n",
              "      <td>Altima</td>\n",
              "      <td>Compact</td>\n",
              "      <td>13.0</td>\n",
              "      <td>15.7</td>\n",
              "      <td>18.3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>28.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3050.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Nissan Altima</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Oldsmobile</td>\n",
              "      <td>Achieva</td>\n",
              "      <td>Compact</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2910.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Oldsmobile Achieva</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Pontiac</td>\n",
              "      <td>Grand_Prix</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>15.4</td>\n",
              "      <td>18.5</td>\n",
              "      <td>21.6</td>\n",
              "      <td>19.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>28.5</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Pontiac Grand_Prix</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Pontiac</td>\n",
              "      <td>Bonneville</td>\n",
              "      <td>Large</td>\n",
              "      <td>19.4</td>\n",
              "      <td>24.4</td>\n",
              "      <td>29.4</td>\n",
              "      <td>19.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>30.5</td>\n",
              "      <td>18.0</td>\n",
              "      <td>3495.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Pontiac Bonneville</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Subaru</td>\n",
              "      <td>Justy</td>\n",
              "      <td>Small</td>\n",
              "      <td>7.3</td>\n",
              "      <td>8.4</td>\n",
              "      <td>9.5</td>\n",
              "      <td>33.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4WD</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>23.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2045.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Subaru Justy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Subaru</td>\n",
              "      <td>Loyale</td>\n",
              "      <td>Small</td>\n",
              "      <td>10.5</td>\n",
              "      <td>10.9</td>\n",
              "      <td>11.3</td>\n",
              "      <td>25.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4WD</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>27.5</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2490.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Subaru Loyale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Fox</td>\n",
              "      <td>Small</td>\n",
              "      <td>8.7</td>\n",
              "      <td>9.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>25.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>163.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2240.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Fox</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Passat</td>\n",
              "      <td>Compact</td>\n",
              "      <td>17.6</td>\n",
              "      <td>20.0</td>\n",
              "      <td>22.4</td>\n",
              "      <td>21.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>31.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2985.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Passat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Corrado</td>\n",
              "      <td>Sporty</td>\n",
              "      <td>22.9</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.7</td>\n",
              "      <td>18.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2810.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Corrado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20 rows × 27 columns</p>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop columns named 'Turn.circle' and 'Weight'.\n",
        "data.drop(columns=['Turn.circle', 'Weight'], inplace = True)\n",
        "data"
      ],
      "metadata": {
        "id": "dDTT-M_2-qo9",
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          "height": 1000
        },
        "outputId": "6a7ff5ae-c55e-4ee1-a96b-0643ba7d8053"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Manufacturer       Model     Type  Min.Price  Price  Max.Price  MPG.city  \\\n",
              "2          Audi          90  Compact       25.9   29.1       32.3      20.0   \n",
              "7         Buick  Roadmaster    Large       22.6   23.7       24.9      16.0   \n",
              "17    Chevrolet     Caprice    Large       18.0   18.8       19.6      17.0   \n",
              "20     Chrysler     LeBaron  Compact       14.5   15.8       17.1      23.0   \n",
              "21     Chrysler    Imperial    Large       29.5   29.5       29.5      20.0   \n",
              "28        Eagle      Summit    Small        7.9   12.2       16.5      29.0   \n",
              "43      Hyundai       Excel    Small        6.8    8.0        9.2      29.0   \n",
              "52        Mazda         323    Small        7.4    8.3        9.1      29.0   \n",
              "53        Mazda     Protege    Small       10.9   11.6       12.3      28.0   \n",
              "54        Mazda         626  Compact       14.3   16.5       18.7      26.0   \n",
              "60      Mercury      Cougar  Midsize       14.9   14.9       14.9      19.0   \n",
              "64       Nissan      Altima  Compact       13.0   15.7       18.3      24.0   \n",
              "67   Oldsmobile     Achieva  Compact       13.0   13.5       14.0      24.0   \n",
              "75      Pontiac  Grand_Prix  Midsize       15.4   18.5       21.6      19.0   \n",
              "76      Pontiac  Bonneville    Large       19.4   24.4       29.4      19.0   \n",
              "79       Subaru       Justy    Small        7.3    8.4        9.5      33.0   \n",
              "80       Subaru      Loyale    Small       10.5   10.9       11.3      25.0   \n",
              "87   Volkswagen         Fox    Small        8.7    9.1        9.5      25.0   \n",
              "89   Volkswagen      Passat  Compact       17.6   20.0       22.4      21.0   \n",
              "90   Volkswagen     Corrado   Sporty       22.9   23.3       23.7      18.0   \n",
              "\n",
              "    MPG.highway             AirBags DriveTrain  ... Man.trans.avail  \\\n",
              "2          26.0         Driver only      Front  ...             Yes   \n",
              "7          25.0         Driver only       Rear  ...              No   \n",
              "17         26.0         Driver only       Rear  ...              No   \n",
              "20         28.0  Driver & Passenger      Front  ...              No   \n",
              "21         26.0         Driver only      Front  ...              No   \n",
              "28         33.0                None      Front  ...             Yes   \n",
              "43         33.0                None      Front  ...             Yes   \n",
              "52         37.0                None      Front  ...             Yes   \n",
              "53         36.0                None      Front  ...             Yes   \n",
              "54         34.0         Driver only      Front  ...             Yes   \n",
              "60         26.0                None       Rear  ...              No   \n",
              "64         30.0         Driver only      Front  ...             Yes   \n",
              "67         31.0                None      Front  ...              No   \n",
              "75         27.0                None      Front  ...             Yes   \n",
              "76         28.0  Driver & Passenger      Front  ...              No   \n",
              "79         37.0                None        4WD  ...             Yes   \n",
              "80         30.0                None        4WD  ...             Yes   \n",
              "87         33.0                None      Front  ...             Yes   \n",
              "89         30.0                None      Front  ...             Yes   \n",
              "90         25.0                None      Front  ...             Yes   \n",
              "\n",
              "    Fuel.tank.capacity  Passengers  Length  Wheelbase Width  Rear.seat.room  \\\n",
              "2                 16.9         5.0   180.0      102.0  67.0            28.0   \n",
              "7                 23.0         6.0   216.0      116.0  78.0            30.5   \n",
              "17                23.0         6.0   214.0      116.0  77.0            29.5   \n",
              "20                16.0         6.0   183.0      104.0  68.0            30.5   \n",
              "21                16.0         6.0   203.0      110.0  69.0            36.0   \n",
              "28                13.2         5.0   174.0       98.0  66.0            26.5   \n",
              "43                11.9         5.0   168.0       94.0  63.0            26.0   \n",
              "52                13.2         4.0   164.0       97.0  66.0            27.0   \n",
              "53                14.5         5.0   172.0       98.0  66.0            26.5   \n",
              "54                15.5         5.0   184.0      103.0  69.0            29.5   \n",
              "60                18.0         5.0   199.0      113.0  73.0            28.0   \n",
              "64                15.9         5.0   181.0      103.0  67.0            28.5   \n",
              "67                15.2         5.0   188.0      103.0  67.0            28.0   \n",
              "75                16.5         5.0   195.0      108.0  72.0            28.5   \n",
              "76                18.0         6.0   177.0      111.0  74.0            30.5   \n",
              "79                 9.2         4.0   146.0       90.0  60.0            23.5   \n",
              "80                15.9         5.0   175.0       97.0  65.0            27.5   \n",
              "87                12.4         4.0   163.0       93.0  63.0            26.0   \n",
              "89                18.5         5.0   180.0      103.0  67.0            31.5   \n",
              "90                18.5         4.0   159.0       97.0  66.0            26.0   \n",
              "\n",
              "    Luggage.room   Origin                Make  \n",
              "2           14.0  non-USA             Audi 90  \n",
              "7           21.0      USA    Buick Roadmaster  \n",
              "17          20.0      USA   Chevrolet Caprice  \n",
              "20          14.0      USA    Chrysler LeBaron  \n",
              "21          17.0      USA   Chrysler Imperial  \n",
              "28          11.0      USA        Eagle Summit  \n",
              "43          11.0  non-USA       Hyundai Excel  \n",
              "52          16.0  non-USA           Mazda 323  \n",
              "53          13.0  non-USA       Mazda Protege  \n",
              "54          14.0  non-USA           Mazda 626  \n",
              "60          15.0      USA      Mercury Cougar  \n",
              "64          14.0  non-USA       Nissan Altima  \n",
              "67          14.0      USA  Oldsmobile Achieva  \n",
              "75          16.0      USA  Pontiac Grand_Prix  \n",
              "76          18.0      USA  Pontiac Bonneville  \n",
              "79          10.0  non-USA        Subaru Justy  \n",
              "80          15.0  non-USA       Subaru Loyale  \n",
              "87          10.0  non-USA      Volkswagen Fox  \n",
              "89          14.0  non-USA   Volkswagen Passat  \n",
              "90          15.0  non-USA  Volkswagen Corrado  \n",
              "\n",
              "[20 rows x 25 columns]"
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              "      <th></th>\n",
              "      <th>Manufacturer</th>\n",
              "      <th>Model</th>\n",
              "      <th>Type</th>\n",
              "      <th>Min.Price</th>\n",
              "      <th>Price</th>\n",
              "      <th>Max.Price</th>\n",
              "      <th>MPG.city</th>\n",
              "      <th>MPG.highway</th>\n",
              "      <th>AirBags</th>\n",
              "      <th>DriveTrain</th>\n",
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              "      <th>Luggage.room</th>\n",
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              "      <th>Make</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Audi</td>\n",
              "      <td>90</td>\n",
              "      <td>Compact</td>\n",
              "      <td>25.9</td>\n",
              "      <td>29.1</td>\n",
              "      <td>32.3</td>\n",
              "      <td>20.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>16.9</td>\n",
              "      <td>5.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Audi 90</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Buick</td>\n",
              "      <td>Roadmaster</td>\n",
              "      <td>Large</td>\n",
              "      <td>22.6</td>\n",
              "      <td>23.7</td>\n",
              "      <td>24.9</td>\n",
              "      <td>16.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Rear</td>\n",
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              "      <td>No</td>\n",
              "      <td>23.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>30.5</td>\n",
              "      <td>21.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Buick Roadmaster</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Caprice</td>\n",
              "      <td>Large</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.8</td>\n",
              "      <td>19.6</td>\n",
              "      <td>17.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>23.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>214.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Chevrolet Caprice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Chrysler</td>\n",
              "      <td>LeBaron</td>\n",
              "      <td>Compact</td>\n",
              "      <td>14.5</td>\n",
              "      <td>15.8</td>\n",
              "      <td>17.1</td>\n",
              "      <td>23.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>30.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Chrysler LeBaron</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Chrysler</td>\n",
              "      <td>Imperial</td>\n",
              "      <td>Large</td>\n",
              "      <td>29.5</td>\n",
              "      <td>29.5</td>\n",
              "      <td>29.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Chrysler Imperial</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Eagle</td>\n",
              "      <td>Summit</td>\n",
              "      <td>Small</td>\n",
              "      <td>7.9</td>\n",
              "      <td>12.2</td>\n",
              "      <td>16.5</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>13.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>11.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Eagle Summit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Hyundai</td>\n",
              "      <td>Excel</td>\n",
              "      <td>Small</td>\n",
              "      <td>6.8</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11.9</td>\n",
              "      <td>5.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Hyundai Excel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Mazda</td>\n",
              "      <td>323</td>\n",
              "      <td>Small</td>\n",
              "      <td>7.4</td>\n",
              "      <td>8.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>29.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>13.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>164.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Mazda 323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Mazda</td>\n",
              "      <td>Protege</td>\n",
              "      <td>Small</td>\n",
              "      <td>10.9</td>\n",
              "      <td>11.6</td>\n",
              "      <td>12.3</td>\n",
              "      <td>28.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>14.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>13.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Mazda Protege</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Mazda</td>\n",
              "      <td>626</td>\n",
              "      <td>Compact</td>\n",
              "      <td>14.3</td>\n",
              "      <td>16.5</td>\n",
              "      <td>18.7</td>\n",
              "      <td>26.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>15.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Mazda 626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Mercury</td>\n",
              "      <td>Cougar</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>14.9</td>\n",
              "      <td>14.9</td>\n",
              "      <td>14.9</td>\n",
              "      <td>19.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>18.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>199.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Mercury Cougar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Nissan</td>\n",
              "      <td>Altima</td>\n",
              "      <td>Compact</td>\n",
              "      <td>13.0</td>\n",
              "      <td>15.7</td>\n",
              "      <td>18.3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>15.9</td>\n",
              "      <td>5.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>28.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Nissan Altima</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Oldsmobile</td>\n",
              "      <td>Achieva</td>\n",
              "      <td>Compact</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>15.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Oldsmobile Achieva</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Pontiac</td>\n",
              "      <td>Grand_Prix</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>15.4</td>\n",
              "      <td>18.5</td>\n",
              "      <td>21.6</td>\n",
              "      <td>19.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>16.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>28.5</td>\n",
              "      <td>16.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Pontiac Grand_Prix</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Pontiac</td>\n",
              "      <td>Bonneville</td>\n",
              "      <td>Large</td>\n",
              "      <td>19.4</td>\n",
              "      <td>24.4</td>\n",
              "      <td>29.4</td>\n",
              "      <td>19.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>18.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>30.5</td>\n",
              "      <td>18.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Pontiac Bonneville</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Subaru</td>\n",
              "      <td>Justy</td>\n",
              "      <td>Small</td>\n",
              "      <td>7.3</td>\n",
              "      <td>8.4</td>\n",
              "      <td>9.5</td>\n",
              "      <td>33.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4WD</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>9.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>23.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Subaru Justy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Subaru</td>\n",
              "      <td>Loyale</td>\n",
              "      <td>Small</td>\n",
              "      <td>10.5</td>\n",
              "      <td>10.9</td>\n",
              "      <td>11.3</td>\n",
              "      <td>25.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>None</td>\n",
              "      <td>4WD</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>15.9</td>\n",
              "      <td>5.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>27.5</td>\n",
              "      <td>15.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Subaru Loyale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Fox</td>\n",
              "      <td>Small</td>\n",
              "      <td>8.7</td>\n",
              "      <td>9.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>25.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>12.4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>163.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Fox</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Passat</td>\n",
              "      <td>Compact</td>\n",
              "      <td>17.6</td>\n",
              "      <td>20.0</td>\n",
              "      <td>22.4</td>\n",
              "      <td>21.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>18.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>31.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Passat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Corrado</td>\n",
              "      <td>Sporty</td>\n",
              "      <td>22.9</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.7</td>\n",
              "      <td>18.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>18.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Corrado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20 rows × 25 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-001e8469-ed23-42ad-b704-792850d92ef4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-001e8469-ed23-42ad-b704-792850d92ef4 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-001e8469-ed23-42ad-b704-792850d92ef4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EY4WlJUm3zsa"
      },
      "execution_count": 73,
      "outputs": []
    }
  ]
}