Pie&AI_NumpyPandas_1.ipynb

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      "name": "Pie&AI_NumpyPandas-1",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Numpy "
      ],
      "metadata": {
        "id": "15e-NCHyWApF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "tlRZ1xWIJtgS"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.array([1,2,3,4])\n",
        "print(type(a))\n",
        " "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7LQU734OeDA",
        "outputId": "bbb5f64d-d832-46e2-922e-59921baa25d1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = np.array([[1,2,3,4],[4,5,6,7]])\n",
        "print(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pIt4WynJO10j",
        "outputId": "5acb0b69-041d-4849-a68b-39cc645afcd5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3 4]\n",
            " [4 5 6 7]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = np.array([[1,2,3,4],[4,5,6,7],[4,5,6,7]])\n",
        "print(c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhHZ0KGjPBMI",
        "outputId": "c774a502-4b40-42a1-aedb-1c03e0a3069e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3 4]\n",
            " [4 5 6 7]\n",
            " [4 5 6 7]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "help(np.zeros)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNMBVKs1PRaQ",
        "outputId": "1bf2db3b-2669-47c7-857a-02aebf523fec"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on built-in function zeros in module numpy:\n",
            "\n",
            "zeros(...)\n",
            "    zeros(shape, dtype=float, order='C')\n",
            "    \n",
            "    Return a new array of given shape and type, filled with zeros.\n",
            "    \n",
            "    Parameters\n",
            "    ----------\n",
            "    shape : int or tuple of ints\n",
            "        Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n",
            "    dtype : data-type, optional\n",
            "        The desired data-type for the array, e.g., `numpy.int8`.  Default is\n",
            "        `numpy.float64`.\n",
            "    order : {'C', 'F'}, optional, default: 'C'\n",
            "        Whether to store multi-dimensional data in row-major\n",
            "        (C-style) or column-major (Fortran-style) order in\n",
            "        memory.\n",
            "    \n",
            "    Returns\n",
            "    -------\n",
            "    out : ndarray\n",
            "        Array of zeros with the given shape, dtype, and order.\n",
            "    \n",
            "    See Also\n",
            "    --------\n",
            "    zeros_like : Return an array of zeros with shape and type of input.\n",
            "    empty : Return a new uninitialized array.\n",
            "    ones : Return a new array setting values to one.\n",
            "    full : Return a new array of given shape filled with value.\n",
            "    \n",
            "    Examples\n",
            "    --------\n",
            "    >>> np.zeros(5)\n",
            "    array([ 0.,  0.,  0.,  0.,  0.])\n",
            "    \n",
            "    >>> np.zeros((5,), dtype=int)\n",
            "    array([0, 0, 0, 0, 0])\n",
            "    \n",
            "    >>> np.zeros((2, 1))\n",
            "    array([[ 0.],\n",
            "           [ 0.]])\n",
            "    \n",
            "    >>> s = (2,2)\n",
            "    >>> np.zeros(s)\n",
            "    array([[ 0.,  0.],\n",
            "           [ 0.,  0.]])\n",
            "    \n",
            "    >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype\n",
            "    array([(0, 0), (0, 0)],\n",
            "          dtype=[('x', '<i4'), ('y', '<i4')])\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = np.zeros([2,2])\n",
        "print(d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Uid68NRPfPL",
        "outputId": "3b32a720-6bee-47f6-e378-2af86a88604c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0.]\n",
            " [0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "e = np.ones([4,7])\n",
        "print(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qEOsZLy8PpBY",
        "outputId": "5e8dbeba-396b-446f-95e6-36a10e1d8a58"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 1. 1. 1. 1. 1. 1.]\n",
            " [1. 1. 1. 1. 1. 1. 1.]\n",
            " [1. 1. 1. 1. 1. 1. 1.]\n",
            " [1. 1. 1. 1. 1. 1. 1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "help(np.eye)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87eb8r5pPwEm",
        "outputId": "407f9172-41e6-410f-e02d-78ddfcf5db5b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on function eye in module numpy:\n",
            "\n",
            "eye(N, M=None, k=0, dtype=<class 'float'>, order='C')\n",
            "    Return a 2-D array with ones on the diagonal and zeros elsewhere.\n",
            "    \n",
            "    Parameters\n",
            "    ----------\n",
            "    N : int\n",
            "      Number of rows in the output.\n",
            "    M : int, optional\n",
            "      Number of columns in the output. If None, defaults to `N`.\n",
            "    k : int, optional\n",
            "      Index of the diagonal: 0 (the default) refers to the main diagonal,\n",
            "      a positive value refers to an upper diagonal, and a negative value\n",
            "      to a lower diagonal.\n",
            "    dtype : data-type, optional\n",
            "      Data-type of the returned array.\n",
            "    order : {'C', 'F'}, optional\n",
            "        Whether the output should be stored in row-major (C-style) or\n",
            "        column-major (Fortran-style) order in memory.\n",
            "    \n",
            "        .. versionadded:: 1.14.0\n",
            "    \n",
            "    Returns\n",
            "    -------\n",
            "    I : ndarray of shape (N,M)\n",
            "      An array where all elements are equal to zero, except for the `k`-th\n",
            "      diagonal, whose values are equal to one.\n",
            "    \n",
            "    See Also\n",
            "    --------\n",
            "    identity : (almost) equivalent function\n",
            "    diag : diagonal 2-D array from a 1-D array specified by the user.\n",
            "    \n",
            "    Examples\n",
            "    --------\n",
            "    >>> np.eye(2, dtype=int)\n",
            "    array([[1, 0],\n",
            "           [0, 1]])\n",
            "    >>> np.eye(3, k=1)\n",
            "    array([[0.,  1.,  0.],\n",
            "           [0.,  0.,  1.],\n",
            "           [0.,  0.,  0.]])\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.eye(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BQcCwaBTP5Rt",
        "outputId": "1c9856e4-be27-441a-dc32-ddba83a73b1f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g = np.arange(10,20,0.5)\n",
        "print(g)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRyx2MsCQDAn",
        "outputId": "9455605d-7aa1-4f4f-fc76-5eaf9a2c35c1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10.  10.5 11.  11.5 12.  12.5 13.  13.5 14.  14.5 15.  15.5 16.  16.5\n",
            " 17.  17.5 18.  18.5 19.  19.5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h = np.linspace(0,99,10)\n",
        "print(h)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XTK5MP7QX0u",
        "outputId": "63a82add-86e0-49e8-9cb3-25ae096468f7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0. 11. 22. 33. 44. 55. 66. 77. 88. 99.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "na = np.array([[1,2,3,4],[5,6,7,8]])\n",
        "print(na.ndim)\n",
        "print(na.shape)\n",
        "print(na.size)\n",
        "print(na.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UjqONvjQviH",
        "outputId": "24b1f066-7e54-4019-aff7-232e919f02f5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "(2, 4)\n",
            "8\n",
            "int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nb = np.array([[1,2,3,4],[5,6,7,8]])\n",
        "print(nb)\n",
        "print(nb[0][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYE9dg-4RLCL",
        "outputId": "e04aff2c-3991-4c72-f3d9-b27809a82e73"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3 4]\n",
            " [5 6 7 8]]\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nc = np.arange(10)\n",
        "print(nc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lfys86KuRZBB",
        "outputId": "f620d126-8938-42da-858a-a31a838b3601"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(nc[0:5])\n",
        "print(nc[2:])\n",
        "print(nc[2:8:2])\n",
        "print(nc[:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zoftXi3fRg-4",
        "outputId": "0efda21c-0bfb-4e7b-919f-2e733d84944a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 3 4]\n",
            "[2 3 4 5 6 7 8 9]\n",
            "[2 4 6]\n",
            "[0 1 2 3 4 5 6 7 8 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.arange(6)\n",
        "print(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhLFLJv0R8kZ",
        "outputId": "173e4531-d7c1-4daf-c9fc-0fabd5aaa4da"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 3 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = a.reshape(2,3)\n",
        "print(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F7VAFjDnSDMB",
        "outputId": "5ffc7a5c-b1b3-41f4-a91d-a093f017b00f"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 1 2]\n",
            " [3 4 5]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.array([[6,7,2],[0,1,5]])\n",
        "print(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jp8MRzBESPLK",
        "outputId": "3b6bc7ea-280b-4ce9-a911-84ed429494cc"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6 7 2]\n",
            " [0 1 5]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.amin(a,0))\n",
        "print(np.amax(a,0))\n",
        "print(np.median(a))\n",
        "print(np.mean(a))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JB1Gr06gSsmj",
        "outputId": "88b1557a-25ff-4af9-ac8d-d5791cf831e4"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2]\n",
            "[6 7 5]\n",
            "3.5\n",
            "3.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Listas tradicionales vs Numpy Arrays"
      ],
      "metadata": {
        "id": "bc3Tyv8nTNBF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weights = [65.4,78.8,80.5]\n",
        "heights = [1.70,1.65,1.95]\n",
        "\n",
        "print('Persona 1: ',weights[0],heights[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-f_7Y-6WTQgT",
        "outputId": "1f3f2bec-031a-4106-9bfa-cc560c499a7b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Persona 1:  65.4 1.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#IMC\n",
        "# Formular = pesor / altura **2"
      ],
      "metadata": {
        "id": "TJYRGZhLTpaz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np_weights = np.array(weights)\n",
        "np_heights = np.array(heights)\n",
        "imc = np_weights / np_heights ** 2\n",
        "print(imc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyPIDo6MTvBk",
        "outputId": "b01f1134-fae9-4992-daa9-0abad5257212"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[22.62975779 28.94398531 21.17028271]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "listExample = [1,2,3]\n",
        "np_example = np.array([1,2,3])\n",
        "\n",
        "print('Lista',listExample,type(listExample))\n",
        "print('NP Array',np_example, type(np_example))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5thckQ7kUT8d",
        "outputId": "41b9afd4-1e72-4971-fe16-d255ad40ba83"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lista [1, 2, 3] <class 'list'>\n",
            "NP Array [1 2 3] <class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(listExample+listExample)\n",
        "print(np_example+np_example)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_G5z93RmUyXN",
        "outputId": "4ecf4bfb-68a2-470d-f9c0-8562451b72d3"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2, 3, 1, 2, 3]\n",
            "[2 4 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20LrTq4EVEPA",
        "outputId": "15626667-ccdc-4306-9152-077ae05990d5"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([22.62975779, 28.94398531, 21.17028271])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_imc = np.round(imc, 2)\n",
        "print(n_imc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0Iv-40JVG4-",
        "outputId": "91cf33c7-5cce-4ce1-f6a3-13e4a6d051f1"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[22.63 28.94 21.17]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(n_imc > 22)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnZ5vSOFVZ2o",
        "outputId": "a4440b36-785d-4465-a04b-a64b8c92fd5f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ True  True False]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_imc[n_imc > 22]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqcQ3CeTVxoy",
        "outputId": "ee644d26-7b64-4c2e-c249-d5263583f4dd"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([22.63, 28.94])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pandas"
      ],
      "metadata": {
        "id": "GOecKQnKWeiP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "world = {\n",
        "    'country':['Chile','Colombia','Mexico'],\n",
        "    'capital':['Santiago','Bogota','CDMX'],\n",
        "    'population':[15.76,17.51,20.53]\n",
        "}"
      ],
      "metadata": {
        "id": "0d4BuclgWeS7"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(world)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lH3eox9PV8Ms",
        "outputId": "3aa82695-a621-42bc-f3fd-0a8ec03e68f6"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'country': ['Chile', 'Colombia', 'Mexico'], 'capital': ['Santiago', 'Bogota', 'CDMX'], 'population': [15.76, 17.51, 20.53]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "c8s2S0F5XWlk"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "world_df = pd.DataFrame(world)\n",
        "print(type(world_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wbNvRfOXcyD",
        "outputId": "143455a4-ad6c-4cb5-a5ec-b916cc910c65"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "world_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "G1iNL6noXrd4",
        "outputId": "263ff522-02bd-4a45-bb6f-ffb268ec425c"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-93d867af-3df7-4af9-b161-efbb22b2de75\">\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>country</th>\n",
              "      <th>capital</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Chile</td>\n",
              "      <td>Santiago</td>\n",
              "      <td>15.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>Bogota</td>\n",
              "      <td>17.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>CDMX</td>\n",
              "      <td>20.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93d867af-3df7-4af9-b161-efbb22b2de75')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "\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-93d867af-3df7-4af9-b161-efbb22b2de75 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-93d867af-3df7-4af9-b161-efbb22b2de75');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    country   capital  population\n",
              "0     Chile  Santiago       15.76\n",
              "1  Colombia    Bogota       17.51\n",
              "2    Mexico      CDMX       20.53"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "world_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "mfIbAFkrX-84",
        "outputId": "056ba364-7155-4700-83a4-421941fc2a52"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c9f689cd-f0f5-44ae-990f-99d53498c504\">\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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>capital</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Chile</td>\n",
              "      <td>Santiago</td>\n",
              "      <td>15.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>Bogota</td>\n",
              "      <td>17.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>CDMX</td>\n",
              "      <td>20.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9f689cd-f0f5-44ae-990f-99d53498c504')\"\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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-c9f689cd-f0f5-44ae-990f-99d53498c504 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-c9f689cd-f0f5-44ae-990f-99d53498c504');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    country   capital  population\n",
              "0     Chile  Santiago       15.76\n",
              "1  Colombia    Bogota       17.51\n",
              "2    Mexico      CDMX       20.53"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(world_df.index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6hPN35nGYMf0",
        "outputId": "044d4086-df5d-4dae-dc63-8042b2b79054"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RangeIndex(start=0, stop=3, step=1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(world_df.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W64iuOKnYRuL",
        "outputId": "adf7bc17-a84f-4002-a261-279d1cd2a48a"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Chile' 'Santiago' 15.76]\n",
            " ['Colombia' 'Bogota' 17.51]\n",
            " ['Mexico' 'CDMX' 20.53]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "world_df.index = ['CH','COL','MX']\n"
      ],
      "metadata": {
        "id": "tk-NW9glYYYU"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "world_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "NkIi5FOUYoub",
        "outputId": "6e6a76b5-2638-483a-8458-53949b42cdf3"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d59301ad-2a90-4958-84a1-eddb7ef57b1e\">\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>country</th>\n",
              "      <th>capital</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CH</th>\n",
              "      <td>Chile</td>\n",
              "      <td>Santiago</td>\n",
              "      <td>15.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COL</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>Bogota</td>\n",
              "      <td>17.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MX</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>CDMX</td>\n",
              "      <td>20.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d59301ad-2a90-4958-84a1-eddb7ef57b1e')\"\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",
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              "    }\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-d59301ad-2a90-4958-84a1-eddb7ef57b1e 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-d59301ad-2a90-4958-84a1-eddb7ef57b1e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      country   capital  population\n",
              "CH      Chile  Santiago       15.76\n",
              "COL  Colombia    Bogota       17.51\n",
              "MX     Mexico      CDMX       20.53"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(world_df['capital']) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1mrPiyOSYuB-",
        "outputId": "247e881d-a4d4-4601-c544-028376daf86e"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(world_df[['capital']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LTd-XqyDZBYM",
        "outputId": "dacc0a97-68d8-45a5-b5fe-5fe632762913"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "world_df[['capital']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "tu__3FQiY7Bk",
        "outputId": "ff63ca55-b90d-419a-c41d-f89937c144f2"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th>CH</th>\n",
              "      <td>Santiago</td>\n",
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              "      <td>Bogota</td>\n",
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              "    <tr>\n",
              "      <th>MX</th>\n",
              "      <td>CDMX</td>\n",
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              "  </tbody>\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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              "\n",
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              "          document.querySelector('#df-cb52e644-398b-4ea0-81a6-90a1a24384d7 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-cb52e644-398b-4ea0-81a6-90a1a24384d7');\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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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      capital\n",
              "CH   Santiago\n",
              "COL    Bogota\n",
              "MX       CDMX"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "world_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "xf_d2oyJZeb3",
        "outputId": "ec60a2d1-f711-44df-f08e-cdc45ccfa18b"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5862aec6-1110-48b3-a791-2f7ea07c561b\">\n",
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              "      <td>17.51</td>\n",
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              "    <tr>\n",
              "      <th>MX</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>CDMX</td>\n",
              "      <td>20.53</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "      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-5862aec6-1110-48b3-a791-2f7ea07c561b 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-5862aec6-1110-48b3-a791-2f7ea07c561b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      country   capital  population\n",
              "CH      Chile  Santiago       15.76\n",
              "COL  Colombia    Bogota       17.51\n",
              "MX     Mexico      CDMX       20.53"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Métodos loc vs iloc "
      ],
      "metadata": {
        "id": "Txer-VexZN6X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "world_df.loc[['CH','MX']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "NC_Uioz6ZKZn",
        "outputId": "64e2b4ef-3660-44a4-f213-305bb7f0e437"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   country   capital  population\n",
              "CH   Chile  Santiago       15.76\n",
              "MX  Mexico      CDMX       20.53"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
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        "world_df.iloc[[0,2]]"
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          "height": 112
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        "id": "fAgd4UKYZ1iz",
        "outputId": "8b7dc848-2851-41e0-c8cd-eb4eb985e0af"
      },
      "execution_count": 83,
      "outputs": [
        {
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          "data": {
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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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              "   country   capital  population\n",
              "CH   Chile  Santiago       15.76\n",
              "MX  Mexico      CDMX       20.53"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
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    {
      "cell_type": "code",
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        "world_df.iloc[[0,2],[0,2]]"
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        "id": "tX7mimtIaHqK",
        "outputId": "8a30ef73-cb76-4982-aa53-694a1030f38e"
      },
      "execution_count": 84,
      "outputs": [
        {
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          "data": {
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "   country  population\n",
              "CH   Chile       15.76\n",
              "MX  Mexico       20.53"
            ]
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          "metadata": {},
          "execution_count": 84
        }
      ]
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    {
      "cell_type": "code",
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        "world_df"
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      "metadata": {
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          "height": 143
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        "id": "P6NR5rwdaOHC",
        "outputId": "6aaafed0-abb0-42f7-cdb1-9d01cc2a60ea"
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      "execution_count": 85,
      "outputs": [
        {
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          "data": {
            "text/html": [
              "\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-7ef3d570-b85b-42d6-a418-f9dd98c088b5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      country   capital  population\n",
              "CH      Chile  Santiago       15.76\n",
              "COL  Colombia    Bogota       17.51\n",
              "MX     Mexico      CDMX       20.53"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "world_df['population']>16"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1X6VML5zadr_",
        "outputId": "31b884df-245e-4cb8-f758-5f05add2aab9"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CH     False\n",
              "COL     True\n",
              "MX      True\n",
              "Name: population, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "world_df[world_df['population']>16]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "81mJyzowanmo",
        "outputId": "968abc37-523a-4323-fa84-b39b44138c43"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>capital</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>COL</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>Bogota</td>\n",
              "      <td>17.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MX</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>CDMX</td>\n",
              "      <td>20.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b6143b8a-bca9-4921-81e3-7a0f182ffd1f');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      country capital  population\n",
              "COL  Colombia  Bogota       17.51\n",
              "MX     Mexico    CDMX       20.53"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(world_df.count())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7f_2BtYawUN",
        "outputId": "8fa97d76-ae37-408b-ab11-0938268e37d6"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "country       3\n",
            "capital       3\n",
            "population    3\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(world_df.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exzjmHhOa8hv",
        "outputId": "567adbd8-bcc1-4de2-9cd3-54a85fbbaa68"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "population    17.933333\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(world_df.sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltMH0HZ-bCn6",
        "outputId": "b0e39abe-bf1b-4e25-9e3e-77cd01d847d0"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "country       ChileColombiaMexico\n",
            "capital        SantiagoBogotaCDMX\n",
            "population                   53.8\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pandas + CSV\n",
        "\n",
        "fifa_df = pd.read_csv('fifa_data.csv')"
      ],
      "metadata": {
        "id": "H8n2E_JhbM69"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fifa_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "aK_sCfaFb5ba",
        "outputId": "da80917a-012c-465f-bec1-e7051fe224aa"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>158023</td>\n",
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              "      <td>31</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/158023.png</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>https://cdn.sofifa.org/flags/52.png</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/241.png</td>\n",
              "      <td>€110.5M</td>\n",
              "      <td>€565K</td>\n",
              "      <td>2202</td>\n",
              "      <td>Left</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>2021</td>\n",
              "      <td>5'7</td>\n",
              "      <td>159lbs</td>\n",
              "      <td>88+2</td>\n",
              "      <td>88+2</td>\n",
              "      <td>88+2</td>\n",
              "      <td>92+2</td>\n",
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              "      <td>93+2</td>\n",
              "      <td>93+2</td>\n",
              "      <td>92+2</td>\n",
              "      <td>93+2</td>\n",
              "      <td>93+2</td>\n",
              "      <td>93+2</td>\n",
              "      <td>91+2</td>\n",
              "      <td>...</td>\n",
              "      <td>59+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>59+2</td>\n",
              "      <td>84.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>87.0</td>\n",
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              "      <td>91.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>€226.5M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>20801</td>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>33</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/20801.png</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>https://cdn.sofifa.org/flags/38.png</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>Juventus</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/45.png</td>\n",
              "      <td>€77M</td>\n",
              "      <td>€405K</td>\n",
              "      <td>2228</td>\n",
              "      <td>Right</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>High/ Low</td>\n",
              "      <td>C. Ronaldo</td>\n",
              "      <td>Yes</td>\n",
              "      <td>ST</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Jul 10, 2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2022</td>\n",
              "      <td>6'2</td>\n",
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              "      <td>91+3</td>\n",
              "      <td>91+3</td>\n",
              "      <td>89+3</td>\n",
              "      <td>90+3</td>\n",
              "      <td>90+3</td>\n",
              "      <td>90+3</td>\n",
              "      <td>89+3</td>\n",
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              "      <td>88+3</td>\n",
              "      <td>88+3</td>\n",
              "      <td>88+3</td>\n",
              "      <td>...</td>\n",
              "      <td>61+3</td>\n",
              "      <td>53+3</td>\n",
              "      <td>53+3</td>\n",
              "      <td>53+3</td>\n",
              "      <td>61+3</td>\n",
              "      <td>84.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>€127.1M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>190871</td>\n",
              "      <td>Neymar Jr</td>\n",
              "      <td>26</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/190871.png</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>https://cdn.sofifa.org/flags/54.png</td>\n",
              "      <td>92</td>\n",
              "      <td>93</td>\n",
              "      <td>Paris Saint-Germain</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/73.png</td>\n",
              "      <td>€118.5M</td>\n",
              "      <td>€290K</td>\n",
              "      <td>2143</td>\n",
              "      <td>Right</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>High/ Medium</td>\n",
              "      <td>Neymar</td>\n",
              "      <td>Yes</td>\n",
              "      <td>LW</td>\n",
              "      <td>10.0</td>\n",
              "      <td>Aug 3, 2017</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2022</td>\n",
              "      <td>5'9</td>\n",
              "      <td>150lbs</td>\n",
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              "      <td>84+3</td>\n",
              "      <td>84+3</td>\n",
              "      <td>89+3</td>\n",
              "      <td>89+3</td>\n",
              "      <td>89+3</td>\n",
              "      <td>89+3</td>\n",
              "      <td>89+3</td>\n",
              "      <td>89+3</td>\n",
              "      <td>89+3</td>\n",
              "      <td>89+3</td>\n",
              "      <td>88+3</td>\n",
              "      <td>...</td>\n",
              "      <td>60+3</td>\n",
              "      <td>47+3</td>\n",
              "      <td>47+3</td>\n",
              "      <td>47+3</td>\n",
              "      <td>60+3</td>\n",
              "      <td>79.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>€228.1M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>193080</td>\n",
              "      <td>De Gea</td>\n",
              "      <td>27</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/193080.png</td>\n",
              "      <td>Spain</td>\n",
              "      <td>https://cdn.sofifa.org/flags/45.png</td>\n",
              "      <td>91</td>\n",
              "      <td>93</td>\n",
              "      <td>Manchester United</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/11.png</td>\n",
              "      <td>€72M</td>\n",
              "      <td>€260K</td>\n",
              "      <td>1471</td>\n",
              "      <td>Right</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Lean</td>\n",
              "      <td>Yes</td>\n",
              "      <td>GK</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Jul 1, 2011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020</td>\n",
              "      <td>6'4</td>\n",
              "      <td>168lbs</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>€138.6M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>192985</td>\n",
              "      <td>K. De Bruyne</td>\n",
              "      <td>27</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/192985.png</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>https://cdn.sofifa.org/flags/7.png</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>Manchester City</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/10.png</td>\n",
              "      <td>€102M</td>\n",
              "      <td>€355K</td>\n",
              "      <td>2281</td>\n",
              "      <td>Right</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>High/ High</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Yes</td>\n",
              "      <td>RCM</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Aug 30, 2015</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2023</td>\n",
              "      <td>5'11</td>\n",
              "      <td>154lbs</td>\n",
              "      <td>82+3</td>\n",
              "      <td>82+3</td>\n",
              "      <td>82+3</td>\n",
              "      <td>87+3</td>\n",
              "      <td>87+3</td>\n",
              "      <td>87+3</td>\n",
              "      <td>87+3</td>\n",
              "      <td>87+3</td>\n",
              "      <td>88+3</td>\n",
              "      <td>88+3</td>\n",
              "      <td>88+3</td>\n",
              "      <td>88+3</td>\n",
              "      <td>...</td>\n",
              "      <td>73+3</td>\n",
              "      <td>66+3</td>\n",
              "      <td>66+3</td>\n",
              "      <td>66+3</td>\n",
              "      <td>73+3</td>\n",
              "      <td>93.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>€196.4M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18202</th>\n",
              "      <td>18202</td>\n",
              "      <td>238813</td>\n",
              "      <td>J. Lundstram</td>\n",
              "      <td>19</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/238813.png</td>\n",
              "      <td>England</td>\n",
              "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
              "      <td>47</td>\n",
              "      <td>65</td>\n",
              "      <td>Crewe Alexandra</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/121.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1307</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Lean</td>\n",
              "      <td>No</td>\n",
              "      <td>CM</td>\n",
              "      <td>22.0</td>\n",
              "      <td>May 3, 2017</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>5'9</td>\n",
              "      <td>134lbs</td>\n",
              "      <td>42+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>...</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>34.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>€143K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18203</th>\n",
              "      <td>18203</td>\n",
              "      <td>243165</td>\n",
              "      <td>N. Christoffersson</td>\n",
              "      <td>19</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/243165.png</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>https://cdn.sofifa.org/flags/46.png</td>\n",
              "      <td>47</td>\n",
              "      <td>63</td>\n",
              "      <td>Trelleborgs FF</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/703.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1098</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Normal</td>\n",
              "      <td>No</td>\n",
              "      <td>ST</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Mar 19, 2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020</td>\n",
              "      <td>6'3</td>\n",
              "      <td>170lbs</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>39+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>39+2</td>\n",
              "      <td>40+2</td>\n",
              "      <td>40+2</td>\n",
              "      <td>40+2</td>\n",
              "      <td>38+2</td>\n",
              "      <td>...</td>\n",
              "      <td>29+2</td>\n",
              "      <td>32+2</td>\n",
              "      <td>32+2</td>\n",
              "      <td>32+2</td>\n",
              "      <td>29+2</td>\n",
              "      <td>23.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>€113K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18204</th>\n",
              "      <td>18204</td>\n",
              "      <td>241638</td>\n",
              "      <td>B. Worman</td>\n",
              "      <td>16</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/241638.png</td>\n",
              "      <td>England</td>\n",
              "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
              "      <td>47</td>\n",
              "      <td>67</td>\n",
              "      <td>Cambridge United</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/1944.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1189</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Normal</td>\n",
              "      <td>No</td>\n",
              "      <td>ST</td>\n",
              "      <td>33.0</td>\n",
              "      <td>Jul 1, 2017</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021</td>\n",
              "      <td>5'8</td>\n",
              "      <td>148lbs</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>...</td>\n",
              "      <td>33+2</td>\n",
              "      <td>28+2</td>\n",
              "      <td>28+2</td>\n",
              "      <td>28+2</td>\n",
              "      <td>33+2</td>\n",
              "      <td>25.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>€165K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18205</th>\n",
              "      <td>18205</td>\n",
              "      <td>246268</td>\n",
              "      <td>D. Walker-Rice</td>\n",
              "      <td>17</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/246268.png</td>\n",
              "      <td>England</td>\n",
              "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
              "      <td>47</td>\n",
              "      <td>66</td>\n",
              "      <td>Tranmere Rovers</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/15048.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1228</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Lean</td>\n",
              "      <td>No</td>\n",
              "      <td>RW</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Apr 24, 2018</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>5'10</td>\n",
              "      <td>154lbs</td>\n",
              "      <td>47+2</td>\n",
              "      <td>47+2</td>\n",
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              "      <td>47+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>...</td>\n",
              "      <td>35+2</td>\n",
              "      <td>31+2</td>\n",
              "      <td>31+2</td>\n",
              "      <td>31+2</td>\n",
              "      <td>35+2</td>\n",
              "      <td>44.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>€143K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18206</th>\n",
              "      <td>18206</td>\n",
              "      <td>246269</td>\n",
              "      <td>G. Nugent</td>\n",
              "      <td>16</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/246269.png</td>\n",
              "      <td>England</td>\n",
              "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
              "      <td>46</td>\n",
              "      <td>66</td>\n",
              "      <td>Tranmere Rovers</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/15048.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1321</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Lean</td>\n",
              "      <td>No</td>\n",
              "      <td>CM</td>\n",
              "      <td>33.0</td>\n",
              "      <td>Oct 30, 2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>5'10</td>\n",
              "      <td>176lbs</td>\n",
              "      <td>43+2</td>\n",
              "      <td>43+2</td>\n",
              "      <td>43+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>...</td>\n",
              "      <td>46+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>41.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>€165K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>18207 rows × 89 columns</p>\n",
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            "text/plain": [
              "       Unnamed: 0      ID  ... GKReflexes  Release Clause\n",
              "0               0  158023  ...        8.0         €226.5M\n",
              "1               1   20801  ...       11.0         €127.1M\n",
              "2               2  190871  ...       11.0         €228.1M\n",
              "3               3  193080  ...       94.0         €138.6M\n",
              "4               4  192985  ...       13.0         €196.4M\n",
              "...           ...     ...  ...        ...             ...\n",
              "18202       18202  238813  ...        9.0           €143K\n",
              "18203       18203  243165  ...       12.0           €113K\n",
              "18204       18204  241638  ...       13.0           €165K\n",
              "18205       18205  246268  ...        9.0           €143K\n",
              "18206       18206  246269  ...        9.0           €165K\n",
              "\n",
              "[18207 rows x 89 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fifa_df.tail(10)"
      ],
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          "height": 717
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        "id": "BMDfqXOhcExi",
        "outputId": "78faee5e-26e0-4d2c-9a42-2ce1a9f3d755"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Reactions</th>\n",
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              "      <th>ShotPower</th>\n",
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              "      <th>18197</th>\n",
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              "      <td>246167</td>\n",
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              "      <td>18</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/246167.png</td>\n",
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              "      <td>https://cdn.sofifa.org/flags/25.png</td>\n",
              "      <td>47</td>\n",
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              "      <td>https://cdn.sofifa.org/teams/2/light/422.png</td>\n",
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              "      <td>1362</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>Medium/ Medium</td>\n",
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              "      <td>14.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>47</td>\n",
              "      <td>70</td>\n",
              "      <td>Burton Albion</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/15015.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>792</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Lean</td>\n",
              "      <td>No</td>\n",
              "      <td>GK</td>\n",
              "      <td>22.0</td>\n",
              "      <td>Nov 10, 2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021</td>\n",
              "      <td>5'11</td>\n",
              "      <td>154lbs</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>€165K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18199</th>\n",
              "      <td>18199</td>\n",
              "      <td>244677</td>\n",
              "      <td>M. Baldisimo</td>\n",
              "      <td>18</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/244677.png</td>\n",
              "      <td>Canada</td>\n",
              "      <td>https://cdn.sofifa.org/flags/70.png</td>\n",
              "      <td>47</td>\n",
              "      <td>69</td>\n",
              "      <td>Vancouver Whitecaps FC</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/101112.png</td>\n",
              "      <td>€70K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1303</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ High</td>\n",
              "      <td>Lean</td>\n",
              "      <td>No</td>\n",
              "      <td>CM</td>\n",
              "      <td>65.0</td>\n",
              "      <td>Jul 17, 2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021</td>\n",
              "      <td>5'6</td>\n",
              "      <td>150lbs</td>\n",
              "      <td>42+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>43+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>43+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>...</td>\n",
              "      <td>47+2</td>\n",
              "      <td>48+2</td>\n",
              "      <td>48+2</td>\n",
              "      <td>48+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>€175K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18200</th>\n",
              "      <td>18200</td>\n",
              "      <td>231381</td>\n",
              "      <td>J. Young</td>\n",
              "      <td>18</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/231381.png</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>https://cdn.sofifa.org/flags/42.png</td>\n",
              "      <td>47</td>\n",
              "      <td>62</td>\n",
              "      <td>Swindon Town</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/1934.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1203</td>\n",
              "      <td>Left</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Lean</td>\n",
              "      <td>No</td>\n",
              "      <td>ST</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Oct 17, 2015</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>5'9</td>\n",
              "      <td>157lbs</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>41+2</td>\n",
              "      <td>...</td>\n",
              "      <td>30+2</td>\n",
              "      <td>27+2</td>\n",
              "      <td>27+2</td>\n",
              "      <td>27+2</td>\n",
              "      <td>30+2</td>\n",
              "      <td>28.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>€143K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18201</th>\n",
              "      <td>18201</td>\n",
              "      <td>243413</td>\n",
              "      <td>D. Walsh</td>\n",
              "      <td>18</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/243413.png</td>\n",
              "      <td>Republic of Ireland</td>\n",
              "      <td>https://cdn.sofifa.org/flags/25.png</td>\n",
              "      <td>47</td>\n",
              "      <td>68</td>\n",
              "      <td>Waterford FC</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/753.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1098</td>\n",
              "      <td>Left</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Lean</td>\n",
              "      <td>No</td>\n",
              "      <td>RB</td>\n",
              "      <td>29.0</td>\n",
              "      <td>Apr 20, 2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018</td>\n",
              "      <td>6'1</td>\n",
              "      <td>168lbs</td>\n",
              "      <td>32+2</td>\n",
              "      <td>32+2</td>\n",
              "      <td>32+2</td>\n",
              "      <td>29+2</td>\n",
              "      <td>30+2</td>\n",
              "      <td>30+2</td>\n",
              "      <td>30+2</td>\n",
              "      <td>29+2</td>\n",
              "      <td>28+2</td>\n",
              "      <td>28+2</td>\n",
              "      <td>28+2</td>\n",
              "      <td>30+2</td>\n",
              "      <td>...</td>\n",
              "      <td>42+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>22.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>€153K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18202</th>\n",
              "      <td>18202</td>\n",
              "      <td>238813</td>\n",
              "      <td>J. Lundstram</td>\n",
              "      <td>19</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/238813.png</td>\n",
              "      <td>England</td>\n",
              "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
              "      <td>47</td>\n",
              "      <td>65</td>\n",
              "      <td>Crewe Alexandra</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/121.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1307</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Lean</td>\n",
              "      <td>No</td>\n",
              "      <td>CM</td>\n",
              "      <td>22.0</td>\n",
              "      <td>May 3, 2017</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>5'9</td>\n",
              "      <td>134lbs</td>\n",
              "      <td>42+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>...</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>34.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>€143K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18203</th>\n",
              "      <td>18203</td>\n",
              "      <td>243165</td>\n",
              "      <td>N. Christoffersson</td>\n",
              "      <td>19</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/243165.png</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>https://cdn.sofifa.org/flags/46.png</td>\n",
              "      <td>47</td>\n",
              "      <td>63</td>\n",
              "      <td>Trelleborgs FF</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/703.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1098</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Normal</td>\n",
              "      <td>No</td>\n",
              "      <td>ST</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Mar 19, 2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020</td>\n",
              "      <td>6'3</td>\n",
              "      <td>170lbs</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>39+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>42+2</td>\n",
              "      <td>39+2</td>\n",
              "      <td>40+2</td>\n",
              "      <td>40+2</td>\n",
              "      <td>40+2</td>\n",
              "      <td>38+2</td>\n",
              "      <td>...</td>\n",
              "      <td>29+2</td>\n",
              "      <td>32+2</td>\n",
              "      <td>32+2</td>\n",
              "      <td>32+2</td>\n",
              "      <td>29+2</td>\n",
              "      <td>23.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>€113K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18204</th>\n",
              "      <td>18204</td>\n",
              "      <td>241638</td>\n",
              "      <td>B. Worman</td>\n",
              "      <td>16</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/241638.png</td>\n",
              "      <td>England</td>\n",
              "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
              "      <td>47</td>\n",
              "      <td>67</td>\n",
              "      <td>Cambridge United</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/1944.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1189</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Normal</td>\n",
              "      <td>No</td>\n",
              "      <td>ST</td>\n",
              "      <td>33.0</td>\n",
              "      <td>Jul 1, 2017</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021</td>\n",
              "      <td>5'8</td>\n",
              "      <td>148lbs</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>...</td>\n",
              "      <td>33+2</td>\n",
              "      <td>28+2</td>\n",
              "      <td>28+2</td>\n",
              "      <td>28+2</td>\n",
              "      <td>33+2</td>\n",
              "      <td>25.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>€165K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18205</th>\n",
              "      <td>18205</td>\n",
              "      <td>246268</td>\n",
              "      <td>D. Walker-Rice</td>\n",
              "      <td>17</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/246268.png</td>\n",
              "      <td>England</td>\n",
              "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
              "      <td>47</td>\n",
              "      <td>66</td>\n",
              "      <td>Tranmere Rovers</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/15048.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1228</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Lean</td>\n",
              "      <td>No</td>\n",
              "      <td>RW</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Apr 24, 2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>5'10</td>\n",
              "      <td>154lbs</td>\n",
              "      <td>47+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>...</td>\n",
              "      <td>35+2</td>\n",
              "      <td>31+2</td>\n",
              "      <td>31+2</td>\n",
              "      <td>31+2</td>\n",
              "      <td>35+2</td>\n",
              "      <td>44.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>€143K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18206</th>\n",
              "      <td>18206</td>\n",
              "      <td>246269</td>\n",
              "      <td>G. Nugent</td>\n",
              "      <td>16</td>\n",
              "      <td>https://cdn.sofifa.org/players/4/19/246269.png</td>\n",
              "      <td>England</td>\n",
              "      <td>https://cdn.sofifa.org/flags/14.png</td>\n",
              "      <td>46</td>\n",
              "      <td>66</td>\n",
              "      <td>Tranmere Rovers</td>\n",
              "      <td>https://cdn.sofifa.org/teams/2/light/15048.png</td>\n",
              "      <td>€60K</td>\n",
              "      <td>€1K</td>\n",
              "      <td>1321</td>\n",
              "      <td>Right</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Medium/ Medium</td>\n",
              "      <td>Lean</td>\n",
              "      <td>No</td>\n",
              "      <td>CM</td>\n",
              "      <td>33.0</td>\n",
              "      <td>Oct 30, 2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>5'10</td>\n",
              "      <td>176lbs</td>\n",
              "      <td>43+2</td>\n",
              "      <td>43+2</td>\n",
              "      <td>43+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>44+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>45+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>...</td>\n",
              "      <td>46+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>47+2</td>\n",
              "      <td>46+2</td>\n",
              "      <td>41.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>€165K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 89 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c732cc78-aa63-4eab-8250-26dda8b3fae7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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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              "          const element = document.querySelector('#df-c732cc78-aa63-4eab-8250-26dda8b3fae7');\n",
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              "                                                     [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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              "  "
            ],
            "text/plain": [
              "       Unnamed: 0      ID  ... GKReflexes  Release Clause\n",
              "18197       18197  246167  ...       15.0            €88K\n",
              "18198       18198  242844  ...       48.0           €165K\n",
              "18199       18199  244677  ...       15.0           €175K\n",
              "18200       18200  231381  ...       11.0           €143K\n",
              "18201       18201  243413  ...       13.0           €153K\n",
              "18202       18202  238813  ...        9.0           €143K\n",
              "18203       18203  243165  ...       12.0           €113K\n",
              "18204       18204  241638  ...       13.0           €165K\n",
              "18205       18205  246268  ...        9.0           €143K\n",
              "18206       18206  246269  ...        9.0           €165K\n",
              "\n",
              "[10 rows x 89 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fifa_df['Overall']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dM2qiDCjcXLE",
        "outputId": "341c66ff-fc89-4ec9-afcd-cbdacd91f087"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        94\n",
              "1        94\n",
              "2        92\n",
              "3        91\n",
              "4        91\n",
              "         ..\n",
              "18202    47\n",
              "18203    47\n",
              "18204    47\n",
              "18205    47\n",
              "18206    46\n",
              "Name: Overall, Length: 18207, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fifa_df.Overall "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCnBwwM8ccb1",
        "outputId": "4c7c9eca-547e-4820-c5ce-33c31ee94e45"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        94\n",
              "1        94\n",
              "2        92\n",
              "3        91\n",
              "4        91\n",
              "         ..\n",
              "18202    47\n",
              "18203    47\n",
              "18204    47\n",
              "18205    47\n",
              "18206    46\n",
              "Name: Overall, Length: 18207, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(8,7))\n",
        "\n",
        "plt.hist(fifa_df.Overall)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "c-XMvBNqcn4T",
        "outputId": "26f7e4af-07d3-442b-b837-8082d4305090"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}