Curso_Numpy_Pandas.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJftkellT_HL"
      },
      "source": [
        "# Dimensionalidades"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fWQ-R__VSRhA",
        "outputId": "08ca63f1-a00f-41e3-ba0e-59f150fa655b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "42\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "escalar = np.array(42)\n",
        "print(type(escalar))\n",
        "print(escalar)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHwWlY5cg4vF",
        "outputId": "93c3d5f2-c26b-49b5-c830-a6892fd08a45"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[30 29 35 31 33 36 42]\n"
          ]
        }
      ],
      "source": [
        "vector = np.array([30,29,35,31,33,36,42])\n",
        "print(vector)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2q0YTODYhCSh",
        "outputId": "533790d4-e432-4748-c11a-4ba362c33a28"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]\n",
            " [7 8 9]]\n"
          ]
        }
      ],
      "source": [
        "matrix = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
        "print(matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kuA9R4VVjfe7",
        "outputId": "4a633af2-2323-42da-8b50-34a344603a79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[[1 2]\n",
            "  [3 4]\n",
            "  [5 6]\n",
            "  [7 8]]]\n"
          ]
        }
      ],
      "source": [
        "tensor = np.array([[[1,2],[3,4],[5,6], [7,8]]])\n",
        "print(tensor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZStoAPuvlrp_",
        "outputId": "0c877f0c-c2f7-4d84-c70a-055540a27d5e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n"
          ]
        }
      ],
      "source": [
        "array_arange = np.arange(10)\n",
        "print(array_arange)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6HSzCmckl6wa",
        "outputId": "66de6efe-9262-4c17-8bdd-e0047ae5d554"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 1.]]\n"
          ]
        }
      ],
      "source": [
        "eye_matrix = np.eye(6)\n",
        "print(eye_matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDZHIlxnmJzz",
        "outputId": "903bb958-cba0-49a9-841f-dca006f5ae44"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 0 0 0 0 0 0]\n",
            " [0 2 0 0 0 0 0]\n",
            " [0 0 3 0 0 0 0]\n",
            " [0 0 0 4 0 0 0]\n",
            " [0 0 0 0 5 0 0]\n",
            " [0 0 0 0 0 6 0]\n",
            " [0 0 0 0 0 0 7]]\n"
          ]
        }
      ],
      "source": [
        "diag = np.diag([1,2,3,4,5,6,7])\n",
        "print(diag)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCFbTThtmng-",
        "outputId": "af3a13a4-d0d3-423b-b14f-4aca20c61a37"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[0.77629614 0.62932673 0.92080471]\n",
            " [0.04501054 0.47904574 0.57064826]]\n"
          ]
        }
      ],
      "source": [
        "random = np.random.random((2,3))\n",
        "print(random)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y_pyqw0a_071"
      },
      "source": [
        "# Arrays"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eep-ytz_3pf",
        "outputId": "ab364310-1cd8-46cf-920a-f610b5ca928e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2\n",
            "(2, 3)\n",
            "int64\n"
          ]
        }
      ],
      "source": [
        "array = np.array([[1,2,3],[4,5,6]])\n",
        "print(array.ndim)\n",
        "print(array.shape)\n",
        "print(array.dtype)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNpUDvybCgpW",
        "outputId": "d38420f1-0cc6-4a9c-c045-90e9a2e15cfb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3\n"
          ]
        }
      ],
      "source": [
        "z = np.array(3, dtype=np.uint8)\n",
        "print(z)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYEU66TFDLv9",
        "outputId": "1f30bd40-f27f-4ac7-df77-837420ede10b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1. 2. 3.]\n"
          ]
        }
      ],
      "source": [
        "double_array = np.array([1,2,3], dtype='d')\n",
        "print(double_array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FF4ZojF9Dl7-",
        "outputId": "f5706c30-2fd0-4a44-e325-446690304d5b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3.0\n"
          ]
        }
      ],
      "source": [
        "z = z.astype(np.float64)\n",
        "print(z)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vGqb4G8EOEz",
        "outputId": "6a87b94d-a85e-44c3-d36e-108ffb400838"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "21\n"
          ]
        }
      ],
      "source": [
        "array = np.array([[1,2,3],[4,5,6]])\n",
        "add = np.sum(array)\n",
        "print(add)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJrVyoGuEl4G",
        "outputId": "fb681364-6733-4a1d-b795-83ca158119fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "21\n"
          ]
        }
      ],
      "source": [
        "array = np.array([[1,2,3],[4,5,6]])\n",
        "sum = np.sum(array)\n",
        "print(sum)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FztRSz_FE8QX",
        "outputId": "0e17e73a-feae-444c-92ec-14818387b257"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3.5\n"
          ]
        }
      ],
      "source": [
        "mean = np.mean(array)\n",
        "print(mean)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5a5R7YRFCIY",
        "outputId": "12226199-8ff5-40c0-ccd0-1affe814ce0c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.707825127659933\n"
          ]
        }
      ],
      "source": [
        "std = np.std(array)\n",
        "print(std)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zDxxN0p_YuJV"
      },
      "source": [
        "# Indexación y Slicing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dO_dx9dTY8W3",
        "outputId": "ee72652b-3861-44b8-9414-332e7647884e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "20\n",
            "50\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "array = np.array([10,20,30,40,50])\n",
        "print(array[1])\n",
        "print(array[-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJORgMhNd-pc",
        "outputId": "2f40010b-fd2e-4183-a16b-f2b322d17db3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[20 30 40]\n"
          ]
        }
      ],
      "source": [
        "print(array[1:4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hgYu7T8SeFPg",
        "outputId": "c3b3e7ee-e0d3-4937-8282-80021b9287eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[20 30 40 50]\n"
          ]
        }
      ],
      "source": [
        "print(array[1:7])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HqkNLEr0eK9m",
        "outputId": "e813fdcd-58b5-46d7-ca11-0f8aa58119d4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[]\n"
          ]
        }
      ],
      "source": [
        "print(array[-1:-7])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLYWGTCQejyj",
        "outputId": "3c2972f5-65fc-4466-a1f7-d4c8132e40cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[False False  True  True  True]\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "array = np.array([10,20,30,40,50])\n",
        "bool_index = array > 25\n",
        "print(bool_index)\n",
        "print(type(bool_index))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZB58zavmfDcK",
        "outputId": "5d6e638e-3ca5-43af-b7ab-1decf7d449cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[30 10 40]\n"
          ]
        }
      ],
      "source": [
        "index = [2,0,3]\n",
        "print(array[index])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7tn_-rWf-93",
        "outputId": "c6a7b4b0-c198-4023-8723-1d5427728ada"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[8 5 1]\n",
            " [9 2 7]\n",
            " [8 3 6]]\n",
            "5\n"
          ]
        }
      ],
      "source": [
        "array = np.random.randint(1, 10, size=(3,3))\n",
        "print(array)\n",
        "print(array[0,1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxO7jTXogohW",
        "outputId": "8379e80b-9e1b-4abf-c3ea-c6dd5024b8cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[8 5]\n",
            " [9 2]]\n"
          ]
        }
      ],
      "source": [
        "print(array[:2,:2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKZ6zzGvmfpA"
      },
      "source": [
        "# Broadcasting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkQw7C90mfJq",
        "outputId": "b1318bed-f472-4a85-dfb4-77876dc2a24d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 90. 180. 270.]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "prices = np.array([100,200,300])\n",
        "discount = np.array([0.9])\n",
        "discount_prices = prices * discount\n",
        "print(discount_prices)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7wT79pwojiO",
        "outputId": "f2a21b47-c11a-4c7a-efbf-7525046a9b38"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[127 100 308]\n",
            " [458 108 493]\n",
            " [194 379 188]]\n",
            "[[137 120 338]\n",
            " [468 128 523]\n",
            " [204 399 218]]\n"
          ]
        }
      ],
      "source": [
        "prices = np.random.randint(100,500, size=(3,3))\n",
        "discount = np.array([10,20,30])\n",
        "discount_prices = prices + discount\n",
        "print(prices)\n",
        "print(discount_prices)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxRlHkNvpPSn",
        "outputId": "60b759e9-ed24-45f1-9468-5229dec1bef2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "False\n"
          ]
        }
      ],
      "source": [
        "array = np.array([1,2,3,4,5])\n",
        "print(np.all(array > 9))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y03TSXbXpe9D",
        "outputId": "4db826bd-53e6-4c2f-8520-83f10bb54434"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "print(np.any(array > 0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TZHE47xq5bm",
        "outputId": "38eb6256-fea2-4a39-927b-62e3002ff181"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2 3 1 2 3]\n",
            "[1 2 3 4 5 6]\n"
          ]
        }
      ],
      "source": [
        "array_a = np.array([1,2,3])\n",
        "array_b = np.array([4,5,6])\n",
        "concatenated_a = np.concatenate((array_a, array_a))\n",
        "concatenated_ab = np.concatenate((array_a, array_b))\n",
        "print(concatenated_a)\n",
        "print(concatenated_ab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGSSUKDbrsG5",
        "outputId": "b45a349b-5af1-4413-c829-2a10b45d9878"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n"
          ]
        }
      ],
      "source": [
        "stacked_v = np.vstack((array_a, array_b))\n",
        "print(stacked_v)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tXE73CyCr4hF",
        "outputId": "ae73c360-611f-4c7c-944a-942b1b3889e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5 6]\n"
          ]
        }
      ],
      "source": [
        "satecked_h = np.hstack((array_a, array_b))\n",
        "print(satecked_h)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8zGbhZVts4zW",
        "outputId": "546c8c28-553f-4491-f3cc-35425d161e71"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5]\n",
            "[array([1]), array([2]), array([3]), array([4]), array([5])]\n"
          ]
        }
      ],
      "source": [
        "array_c = np.arange(1,10)\n",
        "split_array = np.split(array,5)\n",
        "print(array)\n",
        "print(split_array)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQmcW41MyDx4"
      },
      "source": [
        "# Unique & Count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jbfDUwy0P5x",
        "outputId": "f7c2ebc2-03ae-4943-ef95-9b883f194f0c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['bueno' 'excelente' 'malo']\n"
          ]
        }
      ],
      "source": [
        "survey_responses = np.array([\"bueno\", \"excelente\", \"malo\",\n",
        "                             \"bueno\", \"malo\", \"excelente\",\n",
        "                             \"bueno\", \"bueno\", \"malo\",\n",
        "                             \"excelente\"])\n",
        "print(np.unique(survey_responses))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHqtwN2J8vGO",
        "outputId": "d6c0a5ed-736a-4d53-a3c5-47b20a611500"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['bueno' 'excelente' 'malo']\n",
            "[4 5 3]\n"
          ]
        }
      ],
      "source": [
        "unique_elements, counts = np.unique(survey_responses, return_counts=True)\n",
        "print(unique_elements)\n",
        "print(counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09Wt5upj9eAE",
        "outputId": "b6d2b23e-b412-4e09-8f52-037cd6f898cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n",
            "[1 2]\n",
            "[ 0 10 11  3  4  5  6  7  8  9]\n",
            "[10 11]\n"
          ]
        }
      ],
      "source": [
        "array_x = np.arange(10)\n",
        "view_y = array_x[1:3]\n",
        "print(array_x)\n",
        "print(view_y)\n",
        "array_x[1:3] = [10,11]\n",
        "print(array_x)\n",
        "print(view_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awy7XoZt_JKZ",
        "outputId": "7e71f7e2-0b55-450d-9001-2acf9ac99633"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n",
            "[1 2]\n",
            "[ 0 10 11  3  4  5  6  7  8  9]\n",
            "[1 2]\n"
          ]
        }
      ],
      "source": [
        "array_x = np.arange(10)\n",
        "copy_x = array_x[[1,2]]\n",
        "print(array_x)\n",
        "print(copy_x)\n",
        "array_x[1:3] = [10,11]\n",
        "print(array_x)\n",
        "print(copy_x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6IWzW9PBoyI"
      },
      "source": [
        "**texto en negrita**# 7 Transformación de Arrays"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xW0IC_o7A6XN",
        "outputId": "bdc3d5dd-7d96-482b-e2d4-b244330b8d94"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]\n",
            " [7 8 9]]\n",
            "[[1 4 7]\n",
            " [2 5 8]\n",
            " [3 6 9]]\n"
          ]
        }
      ],
      "source": [
        "matrix = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
        "transposed_matrix = matrix.T\n",
        "print(matrix)\n",
        "print(transposed_matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_S_o3WLF8xW",
        "outputId": "3c4859c1-1b6e-4ccc-908e-7b2eb4f2eae3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
            "[[ 1  2  3  4]\n",
            " [ 5  6  7  8]\n",
            " [ 9 10 11 12]]\n"
          ]
        }
      ],
      "source": [
        "array = np.arange(1,13)\n",
        "reshaped_array = array.reshape(3,4)\n",
        "print(array)\n",
        "print(reshaped_array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Ziv7pbDHbYM",
        "outputId": "de81cf6e-ecde-4ae3-b2e9-720ce5252a64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
            "[12 11 10  9  8  7  6  5  4  3  2  1]\n"
          ]
        }
      ],
      "source": [
        "reversed_array = array[::-1]\n",
        "print(array)\n",
        "print(reversed_array )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ymz9wOQxIjAH",
        "outputId": "d1b5c04a-182f-4be5-bfb5-152c5f7a6786"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]\n",
            " [7 8 9]]\n",
            "[1 2 3 4 5 6 7 8 9]\n"
          ]
        }
      ],
      "source": [
        "matrix = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
        "flattened_array = matrix.flatten()\n",
        "print(matrix)\n",
        "print(flattened_array)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbacxokKL6zn"
      },
      "source": [
        "# Ejercicio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s3ptt3FFL3PI"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Paso 1: Crear arrays con datos de ventas mensuales\n",
        "meses = np.array(['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio',\n",
        "                  'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'])\n",
        "ventas_A = np.array([150, 200, 250, 300, 220, 210, 180, 190, 230, 240, 280, 300])\n",
        "ventas_B = np.array([180, 210, 230, 250, 270, 260, 240, 250, 270, 290, 310, 330])\n",
        "ventas_C = np.array([200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400, 420])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B9CxPXkxDr8T",
        "outputId": "5ffa534b-7d27-45fe-efdf-025cdfde3de4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Media de ventas Producto A: 229.16666666666666\n",
            "Suma de ventas Producto A: 2750\n",
            "Media de ventas Producto B: 257.5\n",
            "Suma de ventas Producto B: 3090\n",
            "Media de ventas Producto C: 310.0\n",
            "Suma de ventas Producto C: 3720\n",
            "Total de ventas por mes: [ 530  630  720  810  770  770  740  780  860  910  990 1050]\n",
            "Promedio de ventas por producto: [229.16666667 257.5        310.        ]\n",
            "Mes con mayor ventas: Diciembre\n",
            "Mes con menor ventas: Enero\n",
            "Ventas Matrix:\n",
            " [[150 200 250 300 220 210 180 190 230 240 280 300]\n",
            " [180 210 230 250 270 260 240 250 270 290 310 330]\n",
            " [200 220 240 260 280 300 320 340 360 380 400 420]]\n",
            "Ventas Reshaped (3, 4, 3):\n",
            " [[[150 200 250]\n",
            "  [300 220 210]\n",
            "  [180 190 230]\n",
            "  [240 280 300]]\n",
            "\n",
            " [[180 210 230]\n",
            "  [250 270 260]\n",
            "  [240 250 270]\n",
            "  [290 310 330]]\n",
            "\n",
            " [[200 220 240]\n",
            "  [260 280 300]\n",
            "  [320 340 360]\n",
            "  [380 400 420]]]\n",
            "Ventas Transposed:\n",
            " [[150 180 200]\n",
            " [200 210 220]\n",
            " [250 230 240]\n",
            " [300 250 260]\n",
            " [220 270 280]\n",
            " [210 260 300]\n",
            " [180 240 320]\n",
            " [190 250 340]\n",
            " [230 270 360]\n",
            " [240 290 380]\n",
            " [280 310 400]\n",
            " [300 330 420]]\n",
            "Ventas Invertidas:\n",
            " [[300 280 240 230 190 180 210 220 300 250 200 150]\n",
            " [330 310 290 270 250 240 260 270 250 230 210 180]\n",
            " [420 400 380 360 340 320 300 280 260 240 220 200]]\n",
            "Ventas Aplanadas:\n",
            " [150 200 250 300 220 210 180 190 230 240 280 300 180 210 230 250 270 260\n",
            " 240 250 270 290 310 330 200 220 240 260 280 300 320 340 360 380 400 420]\n",
            "Elementos únicos en las ventas: [150 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340\n",
            " 360 380 400 420]\n",
            "Conteos de elementos únicos en las ventas: [1 2 1 2 2 2 2 3 3 2 2 2 1 3 1 1 1 1 1 1 1 1]\n",
            "Ventas del primer trimestre:\n",
            " [[150 200 250]\n",
            " [180 210 230]\n",
            " [200 220 240]]\n",
            "Meses con ventas totales superiores a 800: ['Abril' 'Septiembre' 'Octubre' 'Noviembre' 'Diciembre']\n",
            "Ventas totales superiores a 800: [ 810  860  910  990 1050]\n",
            "Ventas en meses seleccionados:\n",
            " [[150 250 220 180 230 280]\n",
            " [180 230 270 240 270 310]\n",
            " [200 240 280 320 360 400]]\n"
          ]
        }
      ],
      "source": [
        "# Paso 2: Transformaciones básicas con NumPy\n",
        "# Estadísticas básicas\n",
        "media_A = np.mean(ventas_A)\n",
        "suma_A = np.sum(ventas_A)\n",
        "media_B = np.mean(ventas_B)\n",
        "suma_B = np.sum(ventas_B)\n",
        "media_C = np.mean(ventas_C)\n",
        "suma_C = np.sum(ventas_C)\n",
        "\n",
        "print(f\"Media de ventas Producto A: {media_A}\")\n",
        "print(f\"Suma de ventas Producto A: {suma_A}\")\n",
        "print(f\"Media de ventas Producto B: {media_B}\")\n",
        "print(f\"Suma de ventas Producto B: {suma_B}\")\n",
        "print(f\"Media de ventas Producto C: {media_C}\")\n",
        "print(f\"Suma de ventas Producto C: {suma_C}\")\n",
        "\n",
        "# Paso 3: Manipulación y análisis de datos\n",
        "# Calcular el total de ventas por mes\n",
        "total_ventas_por_mes = ventas_A + ventas_B + ventas_C\n",
        "\n",
        "# Calcular el promedio de ventas por producto\n",
        "promedio_ventas_productos = np.array([media_A, media_B, media_C])\n",
        "\n",
        "# Identificar el mes con mayor y menor ventas\n",
        "mes_mayor_ventas = meses[np.argmax(total_ventas_por_mes)]\n",
        "mes_menor_ventas = meses[np.argmin(total_ventas_por_mes)]\n",
        "\n",
        "print(\"Total de ventas por mes:\", total_ventas_por_mes)\n",
        "print(\"Promedio de ventas por producto:\", promedio_ventas_productos)\n",
        "print(f\"Mes con mayor ventas: {mes_mayor_ventas}\")\n",
        "print(f\"Mes con menor ventas: {mes_menor_ventas}\")\n",
        "\n",
        "# Paso 4: Operaciones avanzadas con NumPy\n",
        "# Reshape y Transposición\n",
        "ventas_matrix = np.array([ventas_A, ventas_B, ventas_C])\n",
        "ventas_reshaped = ventas_matrix.reshape(3, 4, 3)\n",
        "ventas_transposed = ventas_matrix.T\n",
        "\n",
        "print(\"Ventas Matrix:\\n\", ventas_matrix)\n",
        "print(\"Ventas Reshaped (3, 4, 3):\\n\", ventas_reshaped)\n",
        "print(\"Ventas Transposed:\\n\", ventas_transposed)\n",
        "\n",
        "# Invertir arrays y aplanar matrices\n",
        "ventas_inverted = ventas_matrix[:, ::-1]\n",
        "ventas_flattened = ventas_matrix.flatten()\n",
        "\n",
        "print(\"Ventas Invertidas:\\n\", ventas_inverted)\n",
        "print(\"Ventas Aplanadas:\\n\", ventas_flattened)\n",
        "\n",
        "# Paso 5: Análisis de elementos únicos y sus conteos\n",
        "unique_ventas, counts_ventas = np.unique(ventas_flattened, return_counts=True)\n",
        "print(\"Elementos únicos en las ventas:\", unique_ventas)\n",
        "print(\"Conteos de elementos únicos en las ventas:\", counts_ventas)\n",
        "\n",
        "# Paso 6: Indexación y slicing\n",
        "# Seleccionar ventas del primer trimestre\n",
        "ventas_primer_trimestre = ventas_matrix[:, :3]\n",
        "print(\"Ventas del primer trimestre:\\n\", ventas_primer_trimestre)\n",
        "\n",
        "# Indexación booleana para seleccionar meses con ventas totales superiores a 800\n",
        "meses_altas_ventas = meses[total_ventas_por_mes > 800]\n",
        "ventas_altas = total_ventas_por_mes[total_ventas_por_mes > 800]\n",
        "print(\"Meses con ventas totales superiores a 800:\", meses_altas_ventas)\n",
        "print(\"Ventas totales superiores a 800:\", ventas_altas)\n",
        "\n",
        "# Selección avanzada\n",
        "indices = [0, 2, 4, 6, 8, 10]\n",
        "ventas_indices_seleccionados = ventas_matrix[:, indices]\n",
        "print(\"Ventas en meses seleccionados:\\n\", ventas_indices_seleccionados)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DD9z-gvyVPeH"
      },
      "source": [
        "# Cálculos Matriciales"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0758eaEVVNe",
        "outputId": "4caf1081-d194-45ed-e9bd-82535907e645"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2]\n",
            " [3 4]]\n",
            "[[5 6]\n",
            " [7 8]]\n",
            "[[ 6  8]\n",
            " [10 12]]\n"
          ]
        }
      ],
      "source": [
        "A = np.array([[1,2],[3,4]])\n",
        "B = np.array([[5,6],[7,8]])\n",
        "\n",
        "#Suma\n",
        "sum = A + B\n",
        "print(A)\n",
        "print(B)\n",
        "print(sum)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7B793DAgYL5-",
        "outputId": "17a5999e-71ec-43fb-cae1-b87200f724ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[19 22]\n",
            " [43 50]]\n"
          ]
        }
      ],
      "source": [
        "#Multiplicación\n",
        "product = np.dot(A, B)\n",
        "print(product)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDIgvq1YZlYP",
        "outputId": "7b0cee65-906d-479c-a5fe-bfdc5c604ae9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2]\n",
            " [3 4]]\n",
            "-2.0000000000000004\n",
            "[[-2.   1. ]\n",
            " [ 1.5 -0.5]]\n"
          ]
        }
      ],
      "source": [
        "A = np.array([[1,2],[3,4]])\n",
        "det_A = np.linalg.det(A)\n",
        "inv_A = np.linalg.inv(A)\n",
        "print(A)\n",
        "print(det_A)\n",
        "print(inv_A)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X54AdRsraCmh",
        "outputId": "8c83a534-2338-4c98-efc3-0392b2b99452"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[-7.  8.]\n"
          ]
        }
      ],
      "source": [
        "#Resolver Ax = b\n",
        "b = np.array([9,11])\n",
        "x = np.linalg.solve(A,b)\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJ63tV3Ccbz3"
      },
      "source": [
        "# Guardar y visualizar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVEpzGqIcgn9",
        "outputId": "e26dff5c-16ec-45c8-8570-183fc65b4120"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[277 163 201 267 231 278 145 300 192 190 282 248]\n"
          ]
        }
      ],
      "source": [
        "sales = np.random.randint(100, 301, size = 12)\n",
        "print(sales)\n",
        "np.save('sales.npy', sales)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I3n2zagTdWyW",
        "outputId": "6b21f7e3-f0ff-4c7c-c099-0763b0233cb1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[277 163 201 267 231 278 145 300 192 190 282 248]\n"
          ]
        }
      ],
      "source": [
        "loaded_sales = np.load('sales.npy')\n",
        "print(loaded_sales)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vLJ-FGI1eC16"
      },
      "outputs": [],
      "source": [
        "np.savetxt('sales.csv', sales, delimiter=',')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uhf4bonbe0D7",
        "outputId": "988f6cef-3cbe-4eea-d84b-e9c51705ba85"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[277. 163. 201. 267. 231. 278. 145. 300. 192. 190. 282. 248.]\n"
          ]
        }
      ],
      "source": [
        "loaded_sales_csv = np.loadtxt('sales.csv', delimiter=',')\n",
        "print(loaded_sales_csv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "8O7J6XGsfhoB",
        "outputId": "82b214d4-53a1-48f0-a6d5-13bf543b95ae"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.bar(months,sales, color = 'b', label = 'Ventas generadas')\n",
        "plt.title('Ventas por mes')\n",
        "plt.xlabel('Meses')\n",
        "plt.ylabel('Ventas')\n",
        "plt.xticks(rotation = 45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R861s2E2BcTU"
      },
      "source": [
        "# Pandas: Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0CkOXAWBfA9",
        "outputId": "e035e31e-1a3c-4f8b-8be4-2364427a5d74"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0         536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1         536365     71053                  WHITE METAL LANTERN         6   \n",
            "2         536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3         536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4         536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "...          ...       ...                                  ...       ...   \n",
            "541904    581587     22613          PACK OF 20 SPACEBOY NAPKINS        12   \n",
            "541905    581587     22899         CHILDREN'S APRON DOLLY GIRL          6   \n",
            "541906    581587     23254        CHILDRENS CUTLERY DOLLY GIRL          4   \n",
            "541907    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
            "541908    581587     22138        BAKING SET 9 PIECE RETROSPOT          3   \n",
            "\n",
            "                InvoiceDate  UnitPrice  CustomerID         Country  \n",
            "0       2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
            "1       2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "2       2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
            "3       2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "4       2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "...                     ...        ...         ...             ...  \n",
            "541904  2011-12-09 12:50:00       0.85     12680.0          France  \n",
            "541905  2011-12-09 12:50:00       2.10     12680.0          France  \n",
            "541906  2011-12-09 12:50:00       4.15     12680.0          France  \n",
            "541907  2011-12-09 12:50:00       4.15     12680.0          France  \n",
            "541908  2011-12-09 12:50:00       4.95     12680.0          France  \n",
            "\n",
            "[541909 rows x 8 columns]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "path = '/content/online_retail.csv'\n",
        "retail_data = pd.read_csv(path)\n",
        "\n",
        "#Excel\n",
        "#data_excel = pd.read_excel(path)\n",
        "\n",
        "#JSON\n",
        "#data_json = pd.read_json(path)\n",
        "\n",
        "#print(type(retail_data))\n",
        "print(retail_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7-UGd80H4yw"
      },
      "source": [
        "# Estructuras y funciones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxxdTiAIH5Lu",
        "outputId": "3463a8fd-60f3-432e-9562-21870c4acb34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['InvoiceNo', 'StockCode', 'Description', 'Quantity', 'InvoiceDate',\n",
            "       'UnitPrice', 'CustomerID', 'Country'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# Nombres de columnas\n",
        "columns_names = retail_data.columns\n",
        "print(columns_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jNOZrPYJ44k",
        "outputId": "99aff14b-97e1-4368-e165-b5c7e3e4fa13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Número de filas 541909\n",
            "Número de columnas 8\n"
          ]
        }
      ],
      "source": [
        "num_rows, num_columns = retail_data.shape\n",
        "print('Número de filas', num_rows)\n",
        "print('Número de columnas', num_columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MQLOJOSOMTzT",
        "outputId": "6a5ea71e-000d-47ce-8fae-2096d2cdf401"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0          6\n",
            "1          6\n",
            "2          8\n",
            "3          6\n",
            "4          6\n",
            "          ..\n",
            "541904    12\n",
            "541905     6\n",
            "541906     4\n",
            "541907     4\n",
            "541908     3\n",
            "Name: Quantity, Length: 541909, dtype: int64\n",
            "8\n"
          ]
        }
      ],
      "source": [
        "daily_sales = retail_data['Quantity']\n",
        "print(daily_sales)\n",
        "print(daily_sales[2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSmlt8brMwYA",
        "outputId": "a35b476b-09a6-4da0-dca1-d6b07dc68126"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "            Quantity      UnitPrice     CustomerID\n",
            "count  541909.000000  541909.000000  406829.000000\n",
            "mean        9.552250       4.611114   15287.690570\n",
            "std       218.081158      96.759853    1713.600303\n",
            "min    -80995.000000  -11062.060000   12346.000000\n",
            "25%         1.000000       1.250000   13953.000000\n",
            "50%         3.000000       2.080000   15152.000000\n",
            "75%        10.000000       4.130000   16791.000000\n",
            "max     80995.000000   38970.000000   18287.000000\n"
          ]
        }
      ],
      "source": [
        "summary = retail_data.describe()\n",
        "print(summary)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KV2pdJLM_UW",
        "outputId": "c9b44194-b743-4f2d-98aa-860f24e7053c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "La media es 9.55224954743324\n"
          ]
        }
      ],
      "source": [
        "mean_value = daily_sales.mean()\n",
        "print('La media es', mean_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SlWqvd1KNJT6",
        "outputId": "c811989b-9024-4925-a364-90601b677b8d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "La mediana es 9.55224954743324\n"
          ]
        }
      ],
      "source": [
        "median_value = daily_sales.median()\n",
        "print('La mediana es', mean_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcSuUBpxNN7w",
        "outputId": "f35eb50c-54e5-49e2-e151-8371dbce57c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "La suma total es 9.55224954743324\n"
          ]
        }
      ],
      "source": [
        "sum_value = daily_sales.sum()\n",
        "print('La suma total es', mean_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ke8tfvXNNYUy",
        "outputId": "75ea51a8-3e71-4e14-c7e5-7dfebcfc4fc7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "541909\n"
          ]
        }
      ],
      "source": [
        "count_values = daily_sales.count()\n",
        "print(count_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPwwObRPN75H",
        "outputId": "fb7b6746-0fa6-46b6-9c7a-89b5c085e0a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Suma de Quantity: 120.0\n",
            "Conteo de Quantity: 4\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "daily_sales = pd.Series([10, 20, None, 40, 50])\n",
        "\n",
        "# Suma de Quantity\n",
        "total_sum = daily_sales.sum()\n",
        "print(\"Suma de Quantity:\", total_sum)\n",
        "\n",
        "# Conteo de Quantity\n",
        "count_values = daily_sales.count()\n",
        "print(\"Conteo de Quantity:\", count_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "rN4vwtK8PAHD",
        "outputId": "9388dfb1-b2ea-4a31-cede-3fd121ca93fa"
      },
      "outputs": [
        {
          "data": {
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              "type": "dataframe",
              "variable_name": "retail_data"
            },
            "text/html": [
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
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              "      <td>3.39</td>\n",
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              "      <td>536365</td>\n",
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              "      <td>8</td>\n",
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              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "      <th>3</th>\n",
              "      <td>536365</td>\n",
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              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>536365</td>\n",
              "      <td>22752</td>\n",
              "      <td>SET 7 BABUSHKA NESTING BOXES</td>\n",
              "      <td>2</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>7.65</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>536365</td>\n",
              "      <td>21730</td>\n",
              "      <td>GLASS STAR FROSTED T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>4.25</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>536366</td>\n",
              "      <td>22633</td>\n",
              "      <td>HAND WARMER UNION JACK</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:28:00</td>\n",
              "      <td>1.85</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  InvoiceNo StockCode                          Description  Quantity  \\\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                  WHITE METAL LANTERN         6   \n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
              "5    536365     22752         SET 7 BABUSHKA NESTING BOXES         2   \n",
              "6    536365     21730    GLASS STAR FROSTED T-LIGHT HOLDER         6   \n",
              "7    536366     22633               HAND WARMER UNION JACK         6   \n",
              "\n",
              "           InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0  2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
              "1  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "2  2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
              "3  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "4  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "5  2010-12-01 08:26:00       7.65     17850.0  United Kingdom  \n",
              "6  2010-12-01 08:26:00       4.25     17850.0  United Kingdom  \n",
              "7  2010-12-01 08:28:00       1.85     17850.0  United Kingdom  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "retail_data.head(8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mnaDSILuTLEn"
      },
      "source": [
        "# iloc & loc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNsyyJXeTKrS",
        "outputId": "482863f3-d435-4505-87d4-df2e226cf4ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "InvoiceNo                                  536365\n",
            "StockCode                                  85123A\n",
            "Description    WHITE HANGING HEART T-LIGHT HOLDER\n",
            "Quantity                                        6\n",
            "InvoiceDate                   2010-12-01 08:26:00\n",
            "UnitPrice                                    2.55\n",
            "CustomerID                                17850.0\n",
            "Country                            United Kingdom\n",
            "Name: 0, dtype: object\n"
          ]
        }
      ],
      "source": [
        "first_row = retail_data.iloc[0]\n",
        "print(first_row)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NS-2UhzCT70W",
        "outputId": "2e00930a-331b-46f9-f161-d610497defcb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  InvoiceNo StockCode                        Description  Quantity  \\\n",
            "6    536365     21730  GLASS STAR FROSTED T-LIGHT HOLDER         6   \n",
            "7    536366     22633             HAND WARMER UNION JACK         6   \n",
            "\n",
            "           InvoiceDate  UnitPrice  CustomerID         Country  \n",
            "6  2010-12-01 08:26:00       4.25     17850.0  United Kingdom  \n",
            "7  2010-12-01 08:28:00       1.85     17850.0  United Kingdom  \n"
          ]
        }
      ],
      "source": [
        "first_five_row = retail_data.iloc[6:8]\n",
        "print(first_five_row)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUddkGwhUUVA",
        "outputId": "e3a9232a-8e7f-4dac-a053-a952ba3fad6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  InvoiceNo StockCode\n",
            "0    536365    85123A\n",
            "1    536365     71053\n",
            "2    536365    84406B\n"
          ]
        }
      ],
      "source": [
        "subset = retail_data.iloc[:3, :2]\n",
        "print(subset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufIsgQqAUjFi",
        "outputId": "37afd31a-2530-4f95-ad37-f3b7ac6f6df9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6\n"
          ]
        }
      ],
      "source": [
        "retail_value = retail_data.iloc[1, 3]\n",
        "print(retail_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghJEEVLZVZZ3",
        "outputId": "97c095f9-16d3-43a7-af15-aa5413f5ecc1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "InvoiceNo                                   536365\n",
            "StockCode                                   84029G\n",
            "Description    KNITTED UNION FLAG HOT WATER BOTTLE\n",
            "Quantity                                         6\n",
            "InvoiceDate                    2010-12-01 08:26:00\n",
            "UnitPrice                                     3.39\n",
            "CustomerID                                 17850.0\n",
            "Country                             United Kingdom\n",
            "Name: 3, dtype: object\n"
          ]
        }
      ],
      "source": [
        "row_index_3 = retail_data.loc[3]\n",
        "print(row_index_3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTtG71qwVd6_",
        "outputId": "b4b9f4c5-5147-4680-f105-d435d56c18c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1    536365     71053                  WHITE METAL LANTERN         6   \n",
            "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "\n",
            "           InvoiceDate  UnitPrice  CustomerID         Country  \n",
            "0  2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
            "1  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "2  2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
            "3  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "4  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n"
          ]
        }
      ],
      "source": [
        "row_index_0_to_4 = retail_data.loc[0:4]\n",
        "print(row_index_0_to_4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NSbxoUe3Vr_x",
        "outputId": "2f9c0894-4687-4c1b-ba3d-5e53f81db0d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0          6\n",
            "1          6\n",
            "2          8\n",
            "3          6\n",
            "4          6\n",
            "          ..\n",
            "541904    12\n",
            "541905     6\n",
            "541906     4\n",
            "541907     4\n",
            "541908     3\n",
            "Name: Quantity, Length: 541909, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "quantity_column = retail_data.loc[:,'Quantity']\n",
        "print(quantity_column)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BtrlO83V81k",
        "outputId": "dad76261-b0e3-4e86-919e-b0c3771aa9de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        Quantity  UnitPrice\n",
            "0              6       2.55\n",
            "1              6       3.39\n",
            "2              8       2.75\n",
            "3              6       3.39\n",
            "4              6       3.39\n",
            "...          ...        ...\n",
            "541904        12       0.85\n",
            "541905         6       2.10\n",
            "541906         4       4.15\n",
            "541907         4       4.15\n",
            "541908         3       4.95\n",
            "\n",
            "[541909 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "quantity_unitprices_column = retail_data.loc[:,['Quantity','UnitPrice']]\n",
        "print(quantity_unitprices_column)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HttwsTREcFot"
      },
      "source": [
        "# Datos Faltantes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7PsuEumcH9X",
        "outputId": "529d47b1-6910-4217-c0fc-f521679fb00f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        InvoiceNo  StockCode  Description  Quantity  InvoiceDate  UnitPrice  \\\n",
            "0           False      False        False     False        False      False   \n",
            "1           False      False        False     False        False      False   \n",
            "2           False      False        False     False        False      False   \n",
            "3           False      False        False     False        False      False   \n",
            "4           False      False        False     False        False      False   \n",
            "...           ...        ...          ...       ...          ...        ...   \n",
            "541904      False      False        False     False        False      False   \n",
            "541905      False      False        False     False        False      False   \n",
            "541906      False      False        False     False        False      False   \n",
            "541907      False      False        False     False        False      False   \n",
            "541908      False      False        False     False        False      False   \n",
            "\n",
            "        CustomerID  Country  \n",
            "0            False    False  \n",
            "1            False    False  \n",
            "2            False    False  \n",
            "3            False    False  \n",
            "4            False    False  \n",
            "...            ...      ...  \n",
            "541904       False    False  \n",
            "541905       False    False  \n",
            "541906       False    False  \n",
            "541907       False    False  \n",
            "541908       False    False  \n",
            "\n",
            "[541909 rows x 8 columns]\n"
          ]
        }
      ],
      "source": [
        "missing_data = retail_data.isna()\n",
        "print(missing_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BauZ_l4uduF1",
        "outputId": "02289d42-70e2-4987-9811-573539deecf3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   InvoiceNo  StockCode  Description  Quantity  InvoiceDate  UnitPrice  \\\n",
            "0      False      False        False     False        False      False   \n",
            "1      False      False        False     False        False      False   \n",
            "2      False      False        False     False        False      False   \n",
            "3      False      False        False     False        False      False   \n",
            "4      False      False        False     False        False      False   \n",
            "\n",
            "   CustomerID  Country  \n",
            "0       False    False  \n",
            "1       False    False  \n",
            "2       False    False  \n",
            "3       False    False  \n",
            "4       False    False  \n"
          ]
        }
      ],
      "source": [
        "print(missing_data.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfaQlO-cdyjG",
        "outputId": "9703f5ba-e51f-4bdc-a2a1-7607f2eef5f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Conteo de datos faltantes por columna:\n",
            " InvoiceNo           0\n",
            "StockCode           0\n",
            "Description      1454\n",
            "Quantity            0\n",
            "InvoiceDate         0\n",
            "UnitPrice           0\n",
            "CustomerID     135080\n",
            "Country             0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "missing_data_count = retail_data.isna().sum()\n",
        "print('Conteo de datos faltantes por columna:\\n',missing_data_count )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdFoJQZNek_H",
        "outputId": "cfed15a1-1daf-40f9-f386-487fc6fd1ea6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Datos sin filas con valores faltantes:\n",
            "        InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0         536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1         536365     71053                  WHITE METAL LANTERN         6   \n",
            "2         536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3         536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4         536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "...          ...       ...                                  ...       ...   \n",
            "541904    581587     22613          PACK OF 20 SPACEBOY NAPKINS        12   \n",
            "541905    581587     22899         CHILDREN'S APRON DOLLY GIRL          6   \n",
            "541906    581587     23254        CHILDRENS CUTLERY DOLLY GIRL          4   \n",
            "541907    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
            "541908    581587     22138        BAKING SET 9 PIECE RETROSPOT          3   \n",
            "\n",
            "                InvoiceDate  UnitPrice  CustomerID         Country  \n",
            "0       2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
            "1       2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "2       2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
            "3       2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "4       2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "...                     ...        ...         ...             ...  \n",
            "541904  2011-12-09 12:50:00       0.85     12680.0          France  \n",
            "541905  2011-12-09 12:50:00       2.10     12680.0          France  \n",
            "541906  2011-12-09 12:50:00       4.15     12680.0          France  \n",
            "541907  2011-12-09 12:50:00       4.15     12680.0          France  \n",
            "541908  2011-12-09 12:50:00       4.95     12680.0          France  \n",
            "\n",
            "[406829 rows x 8 columns]\n"
          ]
        }
      ],
      "source": [
        "no_missing_rows = retail_data.dropna()\n",
        "print('Datos sin filas con valores faltantes:\\n', no_missing_rows)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cjmwDyQEe8Yw",
        "outputId": "51123485-ce1f-4fc1-f26c-83c2ac2ae9e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DAtos sin columnas con valores faltantes:\n",
            "        InvoiceNo StockCode  Quantity          InvoiceDate  UnitPrice  \\\n",
            "0         536365    85123A         6  2010-12-01 08:26:00       2.55   \n",
            "1         536365     71053         6  2010-12-01 08:26:00       3.39   \n",
            "2         536365    84406B         8  2010-12-01 08:26:00       2.75   \n",
            "3         536365    84029G         6  2010-12-01 08:26:00       3.39   \n",
            "4         536365    84029E         6  2010-12-01 08:26:00       3.39   \n",
            "...          ...       ...       ...                  ...        ...   \n",
            "541904    581587     22613        12  2011-12-09 12:50:00       0.85   \n",
            "541905    581587     22899         6  2011-12-09 12:50:00       2.10   \n",
            "541906    581587     23254         4  2011-12-09 12:50:00       4.15   \n",
            "541907    581587     23255         4  2011-12-09 12:50:00       4.15   \n",
            "541908    581587     22138         3  2011-12-09 12:50:00       4.95   \n",
            "\n",
            "               Country  \n",
            "0       United Kingdom  \n",
            "1       United Kingdom  \n",
            "2       United Kingdom  \n",
            "3       United Kingdom  \n",
            "4       United Kingdom  \n",
            "...                ...  \n",
            "541904          France  \n",
            "541905          France  \n",
            "541906          France  \n",
            "541907          France  \n",
            "541908          France  \n",
            "\n",
            "[541909 rows x 6 columns]\n"
          ]
        }
      ],
      "source": [
        "no_missing_columns = retail_data.dropna(axis=1)\n",
        "print('DAtos sin columnas con valores faltantes:\\n', no_missing_columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVB3VGdKfkao",
        "outputId": "f92a2a35-1aa6-48a2-f7bc-364c97e3cc95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "InvoiceNo      0\n",
            "StockCode      0\n",
            "Description    0\n",
            "Quantity       0\n",
            "InvoiceDate    0\n",
            "UnitPrice      0\n",
            "CustomerID     0\n",
            "Country        0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "retail_data_filled_zeros = retail_data.fillna(0)\n",
        "retail_data_filled_zeros_count = retail_data_filled_zeros.isna().sum()\n",
        "print(retail_data_filled_zeros_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjnFkFD3gV3i",
        "outputId": "b74c6f8c-3d7b-4776-c99e-c05c34fe16af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0         2.55\n",
            "1         3.39\n",
            "2         2.75\n",
            "3         3.39\n",
            "4         3.39\n",
            "          ... \n",
            "541904    0.85\n",
            "541905    2.10\n",
            "541906    4.15\n",
            "541907    4.15\n",
            "541908    4.95\n",
            "Name: UnitPrice, Length: 541909, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "mean_unit_price = retail_data['UnitPrice'].mean()\n",
        "retail_data_filled_mean = retail_data['UnitPrice'].fillna(mean_unit_price)\n",
        "print(retail_data_filled_mean)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXxEF97hj1ES"
      },
      "source": [
        "# Manipulación de columnas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjNQlLW2j3XO",
        "outputId": "f6417e53-99ee-4843-9ad8-e92bd71cfc39"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1    536365     71053                  WHITE METAL LANTERN         6   \n",
            "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "\n",
            "           InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
            "0  2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30  \n",
            "1  2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "2  2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00  \n",
            "3  2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "4  2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('online_retail.csv')\n",
        "\n",
        "df['TotalPrice'] = df['Quantity'] * df['UnitPrice']\n",
        "\n",
        "print(df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmjCSEr8khkO",
        "outputId": "89686706-d1c5-4e32-abe3-6b953647af17"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    False\n",
            "1     True\n",
            "2     True\n",
            "3     True\n",
            "4     True\n",
            "5    False\n",
            "6     True\n",
            "7    False\n",
            "8    False\n",
            "9     True\n",
            "Name: HighValue, dtype: bool\n"
          ]
        }
      ],
      "source": [
        "df['HighValue'] = df['TotalPrice'] > 16\n",
        "print(df['HighValue'].head(10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bDydG0ZPlebf",
        "outputId": "8d6c0206-64a8-4754-d4dc-56a3dbcd26b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 10 columns):\n",
            " #   Column       Non-Null Count   Dtype  \n",
            "---  ------       --------------   -----  \n",
            " 0   InvoiceNo    541909 non-null  object \n",
            " 1   StockCode    541909 non-null  object \n",
            " 2   Description  540455 non-null  object \n",
            " 3   Quantity     541909 non-null  int64  \n",
            " 4   InvoiceDate  541909 non-null  object \n",
            " 5   UnitPrice    541909 non-null  float64\n",
            " 6   CustomerID   406829 non-null  float64\n",
            " 7   Country      541909 non-null  object \n",
            " 8   TotalPrice   541909 non-null  float64\n",
            " 9   HighValue    541909 non-null  bool   \n",
            "dtypes: bool(1), float64(3), int64(1), object(5)\n",
            "memory usage: 37.7+ MB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "print(df.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CuQ7_0o7lpRT",
        "outputId": "73fbdf7a-e7e1-47dd-9639-007f83e9d483"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 10 columns):\n",
            " #   Column       Non-Null Count   Dtype         \n",
            "---  ------       --------------   -----         \n",
            " 0   InvoiceNo    541909 non-null  object        \n",
            " 1   StockCode    541909 non-null  object        \n",
            " 2   Description  540455 non-null  object        \n",
            " 3   Quantity     541909 non-null  int64         \n",
            " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
            " 5   UnitPrice    541909 non-null  float64       \n",
            " 6   CustomerID   406829 non-null  float64       \n",
            " 7   Country      541909 non-null  object        \n",
            " 8   TotalPrice   541909 non-null  float64       \n",
            " 9   HighValue    541909 non-null  bool          \n",
            "dtypes: bool(1), datetime64[ns](1), float64(3), int64(1), object(4)\n",
            "memory usage: 37.7+ MB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])\n",
        "print(df.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDkT1VeNneGW",
        "outputId": "cfd964e3-88a8-4970-d1b6-066f2a2da8ab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  InvoiceNo StockCode                         Description  Quantity  \\\n",
            "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1    536365     71053                 WHITE METAL LANTERN         6   \n",
            "2    536365    84406B      CREAM CUPID HEARTS COAT HANGER         8   \n",
            "\n",
            "          InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \\\n",
            "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30   \n",
            "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34   \n",
            "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00   \n",
            "\n",
            "   HighValue  DiscountedPrice  \n",
            "0      False            2.295  \n",
            "1       True            3.051  \n",
            "2       True            2.475  \n"
          ]
        }
      ],
      "source": [
        "df['DiscountedPrice'] = df['UnitPrice'].apply(lambda x: x* 0.9)\n",
        "print(df.head(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0ToaaGCn8Jf",
        "outputId": "01c5dd6f-d553-41e9-c72f-ddb8d4a39d85"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1    536365     71053                  WHITE METAL LANTERN         6   \n",
            "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "5    536365     22752         SET 7 BABUSHKA NESTING BOXES         2   \n",
            "6    536365     21730    GLASS STAR FROSTED T-LIGHT HOLDER         6   \n",
            "7    536366     22633               HAND WARMER UNION JACK         6   \n",
            "8    536366     22632            HAND WARMER RED POLKA DOT         6   \n",
            "9    536367     84879        ASSORTED COLOUR BIRD ORNAMENT        32   \n",
            "\n",
            "          InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \\\n",
            "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30   \n",
            "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34   \n",
            "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00   \n",
            "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34   \n",
            "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34   \n",
            "5 2010-12-01 08:26:00       7.65     17850.0  United Kingdom       15.30   \n",
            "6 2010-12-01 08:26:00       4.25     17850.0  United Kingdom       25.50   \n",
            "7 2010-12-01 08:28:00       1.85     17850.0  United Kingdom       11.10   \n",
            "8 2010-12-01 08:28:00       1.85     17850.0  United Kingdom       11.10   \n",
            "9 2010-12-01 08:34:00       1.69     13047.0  United Kingdom       54.08   \n",
            "\n",
            "   HighValue  DiscountedPrice PriceCategory  \n",
            "0      False            2.295        Medium  \n",
            "1       True            3.051        Medium  \n",
            "2       True            2.475        Medium  \n",
            "3       True            3.051        Medium  \n",
            "4       True            3.051        Medium  \n",
            "5      False            6.885        Medium  \n",
            "6       True            3.825        Medium  \n",
            "7      False            1.665        Medium  \n",
            "8      False            1.665        Medium  \n",
            "9       True            1.521        Medium  \n"
          ]
        }
      ],
      "source": [
        "def categorize_price(price):\n",
        "  if price > 50:\n",
        "    return 'High'\n",
        "  elif price < 20:\n",
        "    return 'Medium'\n",
        "  else:\n",
        "    return 'Low'\n",
        "\n",
        "df['PriceCategory'] = df['UnitPrice'].apply(categorize_price)\n",
        "print(df.head(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFaMmDl0xeq4"
      },
      "source": [
        "# Groupby"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMe1UqOpxggI",
        "outputId": "8cc10529-3d70-4f21-bb98-67ec0da08b36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Country\n",
            "United Kingdom          495478\n",
            "Germany                   9495\n",
            "France                    8557\n",
            "EIRE                      8196\n",
            "Spain                     2533\n",
            "Netherlands               2371\n",
            "Belgium                   2069\n",
            "Switzerland               2002\n",
            "Portugal                  1519\n",
            "Australia                 1259\n",
            "Norway                    1086\n",
            "Italy                      803\n",
            "Channel Islands            758\n",
            "Finland                    695\n",
            "Cyprus                     622\n",
            "Sweden                     462\n",
            "Unspecified                446\n",
            "Austria                    401\n",
            "Denmark                    389\n",
            "Japan                      358\n",
            "Poland                     341\n",
            "Israel                     297\n",
            "USA                        291\n",
            "Hong Kong                  288\n",
            "Singapore                  229\n",
            "Iceland                    182\n",
            "Canada                     151\n",
            "Greece                     146\n",
            "Malta                      127\n",
            "United Arab Emirates        68\n",
            "European Community          61\n",
            "RSA                         58\n",
            "Lebanon                     45\n",
            "Lithuania                   35\n",
            "Brazil                      32\n",
            "Czech Republic              30\n",
            "Bahrain                     19\n",
            "Saudi Arabia                10\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "country_count = df['Country'].value_counts()\n",
        "print(country_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JIAJE-6Oyj9Z",
        "outputId": "06dca25b-0979-4419-ac7d-d0495c1a0513"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Country\n",
            "Australia                 83653\n",
            "Austria                    4827\n",
            "Bahrain                     260\n",
            "Belgium                   23152\n",
            "Brazil                      356\n",
            "Canada                     2763\n",
            "Channel Islands            9479\n",
            "Cyprus                     6317\n",
            "Czech Republic              592\n",
            "Denmark                    8188\n",
            "EIRE                     142637\n",
            "European Community          497\n",
            "Finland                   10666\n",
            "France                   110480\n",
            "Germany                  117448\n",
            "Greece                     1556\n",
            "Hong Kong                  4769\n",
            "Iceland                    2458\n",
            "Israel                     4353\n",
            "Italy                      7999\n",
            "Japan                     25218\n",
            "Lebanon                     386\n",
            "Lithuania                   652\n",
            "Malta                       944\n",
            "Netherlands              200128\n",
            "Norway                    19247\n",
            "Poland                     3653\n",
            "Portugal                  16180\n",
            "RSA                         352\n",
            "Saudi Arabia                 75\n",
            "Singapore                  5234\n",
            "Spain                     26824\n",
            "Sweden                    35637\n",
            "Switzerland               30325\n",
            "USA                        1034\n",
            "United Arab Emirates        982\n",
            "United Kingdom          4263829\n",
            "Unspecified                3300\n",
            "Name: Quantity, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "country_group = df.groupby('Country')['Quantity'].sum()\n",
        "print(country_group)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NQySa8hey-gn",
        "outputId": "931fb8ce-ad5a-4064-82ac-5edb4a70917e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                            mean          sum\n",
            "Country                                      \n",
            "Australia               3.220612     4054.750\n",
            "Austria                 4.243192     1701.520\n",
            "Bahrain                 4.556316       86.570\n",
            "Belgium                 3.644335     7540.130\n",
            "Brazil                  4.456250      142.600\n",
            "Canada                  6.030331      910.580\n",
            "Channel Islands         4.932124     3738.550\n",
            "Cyprus                  6.302363     3920.070\n",
            "Czech Republic          2.938333       88.150\n",
            "Denmark                 3.256941     1266.950\n",
            "EIRE                    5.911077    48447.190\n",
            "European Community      4.820492      294.050\n",
            "Finland                 5.448705     3786.850\n",
            "France                  5.028864    43031.990\n",
            "Germany                 3.966930    37666.000\n",
            "Greece                  4.885548      713.290\n",
            "Hong Kong              42.505208    12241.500\n",
            "Iceland                 2.644011      481.210\n",
            "Israel                  3.633131     1079.040\n",
            "Italy                   4.831121     3879.390\n",
            "Japan                   2.276145      814.860\n",
            "Lebanon                 5.387556      242.440\n",
            "Lithuania               2.841143       99.440\n",
            "Malta                   5.244173      666.010\n",
            "Netherlands             2.738317     6492.550\n",
            "Norway                  6.012026     6529.060\n",
            "Poland                  4.170880     1422.270\n",
            "Portugal                8.582976    13037.540\n",
            "RSA                     4.277586      248.100\n",
            "Saudi Arabia            2.411000       24.110\n",
            "Singapore             109.645808    25108.890\n",
            "Spain                   4.987544    12633.450\n",
            "Sweden                  3.910887     1806.830\n",
            "Switzerland             3.403442     6813.690\n",
            "USA                     2.216426      644.980\n",
            "United Arab Emirates    3.380735      229.890\n",
            "United Kingdom          4.532422  2245715.474\n",
            "Unspecified             2.699574     1204.010\n"
          ]
        }
      ],
      "source": [
        "country_stats = df.groupby('Country')['UnitPrice'].agg(['mean', 'sum'])\n",
        "print(country_stats)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "itnMw4K1zWZ-",
        "outputId": "278fe970-64ac-4410-d3cc-4bead1a5b7d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Country      StockCode\n",
            "Australia    15036        600\n",
            "             15056BL        3\n",
            "             16161P       400\n",
            "             16169E        25\n",
            "             20665          6\n",
            "                         ... \n",
            "Unspecified  85180A         2\n",
            "             85180B         1\n",
            "             85212         12\n",
            "             85213         12\n",
            "             85227         10\n",
            "Name: Quantity, Length: 19839, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "country_stock_group = df.groupby(['Country', 'StockCode'])['Quantity'].sum()\n",
        "print(country_stock_group)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3O_zGiCe0BKI",
        "outputId": "f96494a9-8d69-4e50-f3e2-d5b2f9833a09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Country\n",
            "Australia                137077.270\n",
            "Austria                   10154.320\n",
            "Bahrain                     548.400\n",
            "Belgium                   40910.960\n",
            "Brazil                     1143.600\n",
            "Canada                     3666.380\n",
            "Channel Islands           20086.290\n",
            "Cyprus                    12946.290\n",
            "Czech Republic              707.720\n",
            "Denmark                   18768.140\n",
            "EIRE                     263276.820\n",
            "European Community         1291.750\n",
            "Finland                   22326.740\n",
            "France                   197403.900\n",
            "Germany                  221698.210\n",
            "Greece                     4710.520\n",
            "Hong Kong                 10117.040\n",
            "Iceland                    4310.000\n",
            "Israel                     7907.820\n",
            "Italy                     16890.510\n",
            "Japan                     35340.620\n",
            "Lebanon                    1693.880\n",
            "Lithuania                  1661.060\n",
            "Malta                      2505.470\n",
            "Netherlands              284661.540\n",
            "Norway                    35163.460\n",
            "Poland                     7213.140\n",
            "Portugal                  29367.020\n",
            "RSA                        1002.310\n",
            "Saudi Arabia                131.170\n",
            "Singapore                  9120.390\n",
            "Spain                     54774.580\n",
            "Sweden                    36595.910\n",
            "Switzerland               56385.350\n",
            "USA                        1730.920\n",
            "United Arab Emirates       1902.280\n",
            "United Kingdom          8187806.364\n",
            "Unspecified                4749.790\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "def total_revenue(group):\n",
        "  return (group['Quantity'] * group['UnitPrice']).sum()\n",
        "\n",
        "revenue_per_country = df.groupby('Country').apply(total_revenue)\n",
        "print(revenue_per_country)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3DJZIwii_ET6"
      },
      "source": [
        "# Filtrado de datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5qc9HMv_LHa",
        "outputId": "47e255a6-f28c-4589-8f77-67e5c9b9df36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1    536365     71053                  WHITE METAL LANTERN         6   \n",
            "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "\n",
            "          InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
            "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30  \n",
            "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00  \n",
            "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Leer el dataset de ventas desde un archivo CSV\n",
        "file_path = \"/content/online_retail.csv\"\n",
        "sales_data = pd.read_csv(file_path)\n",
        "\n",
        "# Convertir la columna 'InvoiceDate' a tipo datetime\n",
        "sales_data['InvoiceDate'] = pd.to_datetime(sales_data['InvoiceDate'])\n",
        "\n",
        "# Eliminar filas con valores faltantes en las columnas críticas\n",
        "sales_data.dropna(subset=['CustomerID', 'InvoiceDate'], inplace=True)\n",
        "\n",
        "# Crear una nueva columna 'TotalPrice'\n",
        "sales_data['TotalPrice'] = sales_data['Quantity'] * sales_data['UnitPrice']\n",
        "\n",
        "print(sales_data.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HO2obI00RloX",
        "outputId": "feee6bbd-60b7-436e-a03c-b1f056d5dae3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0         536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1         536365     71053                  WHITE METAL LANTERN         6   \n",
            "2         536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3         536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4         536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "...          ...       ...                                  ...       ...   \n",
            "541889    581585     22466       FAIRY TALE COTTAGE NIGHT LIGHT        12   \n",
            "541890    581586     22061  LARGE CAKE STAND  HANGING STRAWBERY         8   \n",
            "541891    581586     23275     SET OF 3 HANGING OWLS OLLIE BEAK        24   \n",
            "541892    581586     21217        RED RETROSPOT ROUND CAKE TINS        24   \n",
            "541893    581586     20685                DOORMAT RED RETROSPOT        10   \n",
            "\n",
            "               InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
            "0      2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30  \n",
            "1      2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "2      2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00  \n",
            "3      2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "4      2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "...                    ...        ...         ...             ...         ...  \n",
            "541889 2011-12-09 12:31:00       1.95     15804.0  United Kingdom       23.40  \n",
            "541890 2011-12-09 12:49:00       2.95     13113.0  United Kingdom       23.60  \n",
            "541891 2011-12-09 12:49:00       1.25     13113.0  United Kingdom       30.00  \n",
            "541892 2011-12-09 12:49:00       8.95     13113.0  United Kingdom      214.80  \n",
            "541893 2011-12-09 12:49:00       7.08     13113.0  United Kingdom       70.80  \n",
            "\n",
            "[361878 rows x 9 columns]\n"
          ]
        }
      ],
      "source": [
        "#Filtrar ventas en el Reino Unido - UK\n",
        "uk_sales = sales_data[sales_data['Country'] == 'United Kingdom']\n",
        "print(uk_sales)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSm9J1LISqhC",
        "outputId": "08d2e93a-7cdd-48e8-8f3f-3de7bac12e26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       InvoiceNo StockCode                         Description  Quantity  \\\n",
            "31        536370     10002         INFLATABLE POLITICAL GLOBE         48   \n",
            "46        536371     22086     PAPER CHAIN KIT 50'S CHRISTMAS         80   \n",
            "82        536376     22114   HOT WATER BOTTLE TEA AND SYMPATHY        48   \n",
            "83        536376     21733    RED HANGING HEART T-LIGHT HOLDER        64   \n",
            "96        536378     21212     PACK OF 72 RETROSPOT CAKE CASES       120   \n",
            "...          ...       ...                                 ...       ...   \n",
            "541746    581571     23167      SMALL CERAMIC TOP STORAGE JAR         96   \n",
            "541747    581571     21314       SMALL GLASS HEART TRINKET POT        48   \n",
            "541751    581572     23328  SET 6 SCHOOL MILK BOTTLES IN CRATE        48   \n",
            "541867    581584     20832    RED FLOCK LOVE HEART PHOTO FRAME        72   \n",
            "541868    581584     85038     6 CHOCOLATE LOVE HEART T-LIGHTS        48   \n",
            "\n",
            "               InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
            "31     2010-12-01 08:45:00       0.85     12583.0          France       40.80  \n",
            "46     2010-12-01 09:00:00       2.55     13748.0  United Kingdom      204.00  \n",
            "82     2010-12-01 09:32:00       3.45     15291.0  United Kingdom      165.60  \n",
            "83     2010-12-01 09:32:00       2.55     15291.0  United Kingdom      163.20  \n",
            "96     2010-12-01 09:37:00       0.42     14688.0  United Kingdom       50.40  \n",
            "...                    ...        ...         ...             ...         ...  \n",
            "541746 2011-12-09 12:00:00       0.69     15311.0  United Kingdom       66.24  \n",
            "541747 2011-12-09 12:00:00       1.85     15311.0  United Kingdom       88.80  \n",
            "541751 2011-12-09 12:08:00       3.39     16705.0  United Kingdom      162.72  \n",
            "541867 2011-12-09 12:25:00       0.72     13777.0  United Kingdom       51.84  \n",
            "541868 2011-12-09 12:25:00       1.85     13777.0  United Kingdom       88.80  \n",
            "\n",
            "[18591 rows x 9 columns]\n"
          ]
        }
      ],
      "source": [
        "high_quantity_sales = sales_data[sales_data['Quantity'] > 40]\n",
        "print(high_quantity_sales)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p89QdTFcTtCJ",
        "outputId": "a77dbe0a-1426-4c17-840e-9f5a834fff4c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       InvoiceNo StockCode                     Description  Quantity  \\\n",
            "181       536387     22466   FAIRY TALE COTTAGE NIGHTLIGHT       432   \n",
            "182       536387     21731   RED TOADSTOOL LED NIGHT LIGHT       432   \n",
            "730       536437     17021          NAMASTE SWAGAT INCENSE       600   \n",
            "870       536477     21137        BLACK RECORD COVER FRAME       480   \n",
            "2364      536584    84029E  RED WOOLLY HOTTIE WHITE HEART.       384   \n",
            "...          ...       ...                             ...       ...   \n",
            "540060    581457     22189         CREAM HEART CARD HOLDER       324   \n",
            "540061    581457     23543             WALL ART KEEP CALM        698   \n",
            "540070    581458     22197                  POPCORN HOLDER      1500   \n",
            "540071    581459     22197                  POPCORN HOLDER      1200   \n",
            "540421    581483     23843     PAPER CRAFT , LITTLE BIRDIE     80995   \n",
            "\n",
            "               InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
            "181    2010-12-01 09:58:00       1.45     16029.0  United Kingdom      626.40  \n",
            "182    2010-12-01 09:58:00       1.25     16029.0  United Kingdom      540.00  \n",
            "730    2010-12-01 12:12:00       0.24     13694.0  United Kingdom      144.00  \n",
            "870    2010-12-01 12:27:00       3.39     16210.0  United Kingdom     1627.20  \n",
            "2364   2010-12-01 16:22:00       2.95     13777.0  United Kingdom     1132.80  \n",
            "...                    ...        ...         ...             ...         ...  \n",
            "540060 2011-12-08 18:43:00       3.39     18102.0  United Kingdom     1098.36  \n",
            "540061 2011-12-08 18:43:00       4.15     18102.0  United Kingdom     2896.70  \n",
            "540070 2011-12-08 18:45:00       0.72     17949.0  United Kingdom     1080.00  \n",
            "540071 2011-12-08 18:46:00       0.72     17949.0  United Kingdom      864.00  \n",
            "540421 2011-12-09 09:15:00       2.08     16446.0  United Kingdom   168469.60  \n",
            "\n",
            "[727 rows x 9 columns]\n"
          ]
        }
      ],
      "source": [
        "uk_high_quantity_sales = sales_data[(sales_data['Country'] == 'United Kingdom') &\n",
        "                                    (sales_data['Quantity'] > 300)]\n",
        "print(uk_high_quantity_sales)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nh5T8JInUgyo",
        "outputId": "5b75c1a4-9f0a-462e-a324-4d02e0c079f8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       InvoiceNo StockCode                      Description  Quantity  \\\n",
            "42481     539993     22386          JUMBO BAG PINK POLKADOT        10   \n",
            "42482     539993     21499               BLUE POLKADOT WRAP        25   \n",
            "42483     539993     21498              RED RETROSPOT WRAP         25   \n",
            "42484     539993     22379         RECYCLING BAG RETROSPOT          5   \n",
            "42485     539993     20718        RED RETROSPOT SHOPPER BAG        10   \n",
            "...          ...       ...                              ...       ...   \n",
            "541904    581587     22613      PACK OF 20 SPACEBOY NAPKINS        12   \n",
            "541905    581587     22899     CHILDREN'S APRON DOLLY GIRL          6   \n",
            "541906    581587     23254    CHILDRENS CUTLERY DOLLY GIRL          4   \n",
            "541907    581587     23255  CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
            "541908    581587     22138    BAKING SET 9 PIECE RETROSPOT          3   \n",
            "\n",
            "               InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
            "42481  2011-01-04 10:00:00       1.95     13313.0  United Kingdom       19.50  \n",
            "42482  2011-01-04 10:00:00       0.42     13313.0  United Kingdom       10.50  \n",
            "42483  2011-01-04 10:00:00       0.42     13313.0  United Kingdom       10.50  \n",
            "42484  2011-01-04 10:00:00       2.10     13313.0  United Kingdom       10.50  \n",
            "42485  2011-01-04 10:00:00       1.25     13313.0  United Kingdom       12.50  \n",
            "...                    ...        ...         ...             ...         ...  \n",
            "541904 2011-12-09 12:50:00       0.85     12680.0          France       10.20  \n",
            "541905 2011-12-09 12:50:00       2.10     12680.0          France       12.60  \n",
            "541906 2011-12-09 12:50:00       4.15     12680.0          France       16.60  \n",
            "541907 2011-12-09 12:50:00       4.15     12680.0          France       16.60  \n",
            "541908 2011-12-09 12:50:00       4.95     12680.0          France       14.85  \n",
            "\n",
            "[379979 rows x 9 columns]\n"
          ]
        }
      ],
      "source": [
        "sales_2011 = sales_data[sales_data['InvoiceDate'].dt.year == 2011]\n",
        "print(sales_2011)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jy1iyfsqUy0G",
        "outputId": "c4a57a33-506b-4c86-f730-05537eb0688b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0        536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1        536365     71053                  WHITE METAL LANTERN         6   \n",
            "2        536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3        536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4        536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "...         ...       ...                                  ...       ...   \n",
            "42448    539988     84380    SET OF 3 BUTTERFLY COOKIE CUTTERS         1   \n",
            "42449    539988    84849D                HOT BATHS SOAP HOLDER         1   \n",
            "42450    539988    84849B               FAIRY SOAP SOAP HOLDER         1   \n",
            "42451    539988     22854          CREAM SWEETHEART EGG HOLDER         2   \n",
            "42452    539988    47559B                  TEA TIME OVEN GLOVE         2   \n",
            "\n",
            "              InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
            "0     2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30  \n",
            "1     2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "2     2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00  \n",
            "3     2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "4     2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "...                   ...        ...         ...             ...         ...  \n",
            "42448 2010-12-23 16:06:00       1.25     18116.0  United Kingdom        1.25  \n",
            "42449 2010-12-23 16:06:00       1.69     18116.0  United Kingdom        1.69  \n",
            "42450 2010-12-23 16:06:00       1.69     18116.0  United Kingdom        1.69  \n",
            "42451 2010-12-23 16:06:00       4.95     18116.0  United Kingdom        9.90  \n",
            "42452 2010-12-23 16:06:00       1.25     18116.0  United Kingdom        2.50  \n",
            "\n",
            "[26850 rows x 9 columns]\n"
          ]
        }
      ],
      "source": [
        "sales_2010 = sales_data[sales_data['InvoiceDate'].dt.year == 2010]\n",
        "print(sales_2010)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdloxsO_VQxF",
        "outputId": "491117a3-2b2d-410b-f246-5f50e2df8915"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0        536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1        536365     71053                  WHITE METAL LANTERN         6   \n",
            "2        536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3        536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4        536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "...         ...       ...                                  ...       ...   \n",
            "42448    539988     84380    SET OF 3 BUTTERFLY COOKIE CUTTERS         1   \n",
            "42449    539988    84849D                HOT BATHS SOAP HOLDER         1   \n",
            "42450    539988    84849B               FAIRY SOAP SOAP HOLDER         1   \n",
            "42451    539988     22854          CREAM SWEETHEART EGG HOLDER         2   \n",
            "42452    539988    47559B                  TEA TIME OVEN GLOVE         2   \n",
            "\n",
            "              InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
            "0     2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30  \n",
            "1     2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "2     2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00  \n",
            "3     2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "4     2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34  \n",
            "...                   ...        ...         ...             ...         ...  \n",
            "42448 2010-12-23 16:06:00       1.25     18116.0  United Kingdom        1.25  \n",
            "42449 2010-12-23 16:06:00       1.69     18116.0  United Kingdom        1.69  \n",
            "42450 2010-12-23 16:06:00       1.69     18116.0  United Kingdom        1.69  \n",
            "42451 2010-12-23 16:06:00       4.95     18116.0  United Kingdom        9.90  \n",
            "42452 2010-12-23 16:06:00       1.25     18116.0  United Kingdom        2.50  \n",
            "\n",
            "[26850 rows x 9 columns]\n"
          ]
        }
      ],
      "source": [
        "sales_dec_2010 = sales_data[(sales_data['InvoiceDate'].dt.year == 2010) &\n",
        "                            (sales_data['InvoiceDate'].dt.month == 12)]\n",
        "print(sales_dec_2010)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tb_pFxQRZ26x"
      },
      "source": [
        "# Pivot Table y Reshape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XPoIXq-JZ5HD",
        "outputId": "d9beaafb-eb2e-40bf-d2fa-7fda35b6a9f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "StockCode              10002   10080   10120  10123C  10124A  10124G   10125  \\\n",
            "Country                                                                        \n",
            "Australia                NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Austria                  NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Bahrain                  NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Belgium                  NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Brazil                   NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Canada                   NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Channel Islands          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Cyprus                   NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Czech Republic           NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Denmark                  NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "EIRE                  $12.00     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "European Community       NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Finland                  NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "France               $372.00     NaN  $10.00     NaN     NaN     NaN $470.00   \n",
            "Germany                $1.00     NaN     NaN     NaN     NaN     NaN $120.00   \n",
            "Greece                   NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Iceland                  NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Israel                   NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Italy                    NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Japan                  $1.00     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Lebanon                  NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Lithuania                NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Malta                    NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Netherlands              NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Norway                   NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Poland                   NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Portugal                 NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "RSA                      NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Saudi Arabia             NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Singapore                NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Spain                 $24.00     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Sweden                   NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "Switzerland           $12.00     NaN     NaN     NaN     NaN     NaN  $20.00   \n",
            "USA                      NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "United Arab Emirates     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "United Kingdom       $401.00 $291.00 $183.00   $5.00  $16.00  $17.00 $616.00   \n",
            "Unspecified              NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
            "\n",
            "StockCode                10133     10135   11001  ...  90214Y  90214Z  \\\n",
            "Country                                           ...                   \n",
            "Australia                  NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Austria                    NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Bahrain                    NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Belgium                    NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Brazil                     NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Canada                  $40.00    $20.00     NaN  ...     NaN     NaN   \n",
            "Channel Islands            NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Cyprus                     NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Czech Republic             NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Denmark                    NaN       NaN     NaN  ...     NaN     NaN   \n",
            "EIRE                    $40.00    $60.00  $48.00  ...     NaN     NaN   \n",
            "European Community         NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Finland                    NaN       NaN     NaN  ...     NaN     NaN   \n",
            "France                     NaN    $10.00  $16.00  ...     NaN     NaN   \n",
            "Germany                    NaN   $200.00  $32.00  ...     NaN     NaN   \n",
            "Greece                     NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Iceland                    NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Israel                  $20.00       NaN     NaN  ...     NaN     NaN   \n",
            "Italy                      NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Japan                      NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Lebanon                    NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Lithuania                  NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Malta                      NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Netherlands                NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Norway                     NaN    $10.00  $16.00  ...     NaN     NaN   \n",
            "Poland                     NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Portugal                $20.00       NaN  $16.00  ...     NaN     NaN   \n",
            "RSA                        NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Saudi Arabia               NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Singapore                  NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Spain                      NaN       NaN  $16.00  ...     NaN     NaN   \n",
            "Sweden                     NaN       NaN     NaN  ...     NaN     NaN   \n",
            "Switzerland             $10.00       NaN     NaN  ...     NaN     NaN   \n",
            "USA                        NaN       NaN     NaN  ...     NaN     NaN   \n",
            "United Arab Emirates       NaN       NaN     NaN  ...     NaN     NaN   \n",
            "United Kingdom       $2,244.00 $1,637.00 $923.00  ...  $61.00  $12.00   \n",
            "Unspecified                NaN       NaN     NaN  ...     NaN     NaN   \n",
            "\n",
            "StockCode             BANK CHARGES     C2    CRUK          D    DOT         M  \\\n",
            "Country                                                                         \n",
            "Australia                      NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Austria                        NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Bahrain                        NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Belgium                        NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Brazil                         NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Canada                         NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Channel Islands                NaN  $3.00     NaN        NaN    NaN     $0.00   \n",
            "Cyprus                         NaN    NaN     NaN        NaN    NaN     $0.00   \n",
            "Czech Republic                 NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Denmark                        NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "EIRE                           NaN $97.00     NaN     $-1.00    NaN    $-1.00   \n",
            "European Community             NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Finland                        NaN    NaN     NaN        NaN    NaN     $2.00   \n",
            "France                         NaN  $3.00     NaN        NaN    NaN   $-16.00   \n",
            "Germany                        NaN    NaN     NaN        NaN    NaN     $2.00   \n",
            "Greece                         NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Iceland                        NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Israel                         NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Italy                          NaN    NaN     NaN     $-1.00    NaN    $-1.00   \n",
            "Japan                          NaN    NaN     NaN        NaN    NaN    $-3.00   \n",
            "Lebanon                        NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Lithuania                      NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Malta                          NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Netherlands                    NaN    NaN     NaN     $-1.00    NaN  $-480.00   \n",
            "Norway                         NaN    NaN     NaN        NaN    NaN     $0.00   \n",
            "Poland                         NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Portugal                       NaN    NaN     NaN        NaN    NaN     $1.00   \n",
            "RSA                            NaN    NaN     NaN        NaN    NaN     $1.00   \n",
            "Saudi Arabia                   NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "Singapore                      NaN    NaN     NaN        NaN    NaN     $0.00   \n",
            "Spain                          NaN    NaN     NaN        NaN    NaN    $-3.00   \n",
            "Sweden                         NaN    NaN     NaN        NaN    NaN     $1.00   \n",
            "Switzerland                    NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "USA                            NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "United Arab Emirates           NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "United Kingdom              $12.00 $30.00 $-16.00 $-1,191.00 $16.00 $3,681.00   \n",
            "Unspecified                    NaN    NaN     NaN        NaN    NaN       NaN   \n",
            "\n",
            "StockCode             PADS      POST  \n",
            "Country                               \n",
            "Australia              NaN     $0.00  \n",
            "Austria                NaN    $37.00  \n",
            "Bahrain                NaN       NaN  \n",
            "Belgium                NaN   $272.00  \n",
            "Brazil                 NaN       NaN  \n",
            "Canada                 NaN     $1.00  \n",
            "Channel Islands        NaN       NaN  \n",
            "Cyprus                 NaN     $1.00  \n",
            "Czech Republic         NaN     $0.00  \n",
            "Denmark                NaN    $41.00  \n",
            "EIRE                   NaN       NaN  \n",
            "European Community     NaN     $9.00  \n",
            "Finland                NaN    $89.00  \n",
            "France                 NaN   $803.00  \n",
            "Germany                NaN $1,104.00  \n",
            "Greece                 NaN     $6.00  \n",
            "Iceland                NaN       NaN  \n",
            "Israel                 NaN       NaN  \n",
            "Italy                  NaN    $44.00  \n",
            "Japan                  NaN       NaN  \n",
            "Lebanon                NaN       NaN  \n",
            "Lithuania              NaN       NaN  \n",
            "Malta                  NaN    $10.00  \n",
            "Netherlands            NaN    $99.00  \n",
            "Norway                 NaN    $57.00  \n",
            "Poland                 NaN     $9.00  \n",
            "Portugal               NaN    $97.00  \n",
            "RSA                    NaN       NaN  \n",
            "Saudi Arabia           NaN       NaN  \n",
            "Singapore              NaN       NaN  \n",
            "Spain                  NaN   $209.00  \n",
            "Sweden                 NaN    $37.00  \n",
            "Switzerland            NaN    $97.00  \n",
            "USA                    NaN       NaN  \n",
            "United Arab Emirates   NaN     $1.00  \n",
            "United Kingdom       $4.00   $-21.00  \n",
            "Unspecified            NaN       NaN  \n",
            "\n",
            "[37 rows x 3684 columns]\n"
          ]
        }
      ],
      "source": [
        "pivot_table = pd.pivot_table(sales_data, values = 'Quantity', index = 'Country',\n",
        "                             columns='StockCode', aggfunc='sum')\n",
        "print(pivot_table)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzebm6u-cKyQ",
        "outputId": "bcf5672b-1845-4691-c4e8-aff82707a73c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A  B  C\n",
            "0  foo  1  4\n",
            "1  bar  2  5\n",
            "2  baz  3  6\n"
          ]
        }
      ],
      "source": [
        "df = pd.DataFrame({\n",
        "    'A': ['foo', 'bar', 'baz'],\n",
        "    'B': [1, 2, 3],\n",
        "    'C': [4, 5, 6]\n",
        "})\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xu8HOVhDcnoB",
        "outputId": "f2d8204d-feda-4faf-f832-3ed4043b86a2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0  A    foo\n",
            "   B      1\n",
            "   C      4\n",
            "1  A    bar\n",
            "   B      2\n",
            "   C      5\n",
            "2  A    baz\n",
            "   B      3\n",
            "   C      6\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "df_stacked = df.stack()\n",
        "print(df_stacked)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGmQa8Nhc3y9",
        "outputId": "f7e6f56f-7868-4bc5-a01e-d61669165f0e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     A  B  C\n",
            "0  foo  1  4\n",
            "1  bar  2  5\n",
            "2  baz  3  6\n"
          ]
        }
      ],
      "source": [
        "df_unstacked = df_stacked.unstack()\n",
        "print(df_unstacked)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XeK48SxxUuHr"
      },
      "source": [
        "# Uniones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5myeQS4Uw3j",
        "outputId": "3d1d523d-cb83-4411-f5b4-368b69146516"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  key  value1\n",
            "0   A       1\n",
            "1   B       2\n",
            "2   C       3\n",
            "  key  value2\n",
            "0   B       4\n",
            "1   C       5\n",
            "2   D       6\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Crear DataFrames de ejemplo\n",
        "df1 = pd.DataFrame({\n",
        "    'key': ['A', 'B', 'C'],\n",
        "    'value1': [1, 2, 3]\n",
        "})\n",
        "\n",
        "df2 = pd.DataFrame({\n",
        "    'key': ['B', 'C', 'D'],\n",
        "    'value2': [4, 5, 6]\n",
        "})\n",
        "print(df1)\n",
        "print(df2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N9mfk3TuUz4j",
        "outputId": "9c1ab028-452f-4a5b-9f18-117abe205607"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  key  value1  value2\n",
            "0   B       2       4\n",
            "1   C       3       5\n"
          ]
        }
      ],
      "source": [
        "inner_merged = pd.merge(df1, df2, on='key', how='inner')\n",
        "print(inner_merged)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UqbTjZzU1ax",
        "outputId": "211f1523-e140-40e8-d502-26f32ea9e99f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  key  value1  value2\n",
            "0   A     1.0     NaN\n",
            "1   B     2.0     4.0\n",
            "2   C     3.0     5.0\n",
            "3   D     NaN     6.0\n"
          ]
        }
      ],
      "source": [
        "outer_merged = pd.merge(df1, df2, on='key', how= 'outer')\n",
        "print(outer_merged)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zV9e_FCjU3X5",
        "outputId": "ef9688ed-b65e-45f6-f82c-8154d52dfc8e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  key  value1  value2\n",
            "0   A       1     NaN\n",
            "1   B       2     4.0\n",
            "2   C       3     5.0\n"
          ]
        }
      ],
      "source": [
        "left_merged = pd.merge(df1,df2, on='key', how = 'left')\n",
        "print(left_merged)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0U0NHy4DU719",
        "outputId": "8dc720ff-aeba-4f7d-e8f6-1fae2330939e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  key  value1  value2\n",
            "0   B     2.0       4\n",
            "1   C     3.0       5\n",
            "2   D     NaN       6\n"
          ]
        }
      ],
      "source": [
        "right_merged = pd.merge(df1,df2, on='key', how = 'right')\n",
        "print(right_merged)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4ZcuxZQmNPY"
      },
      "source": [
        "# Series de Tiempo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "Ggd0mM7LmPrB",
        "outputId": "053cff91-0bdf-442f-b68b-84fc3f6a69c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Primeras filas del dataset original:\n"
          ]
        },
        {
          "data": {
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              "      <th>InvoiceNo</th>\n",
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              "      <td>536365</td>\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  InvoiceNo StockCode                          Description  Quantity  \\\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                  WHITE METAL LANTERN         6   \n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
              "\n",
              "           InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0  2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
              "1  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "2  2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
              "3  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "4  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Cargar el dataset desde el archivo CSV\n",
        "file_path = 'online_retail.csv'\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "# Mostrar las primeras filas para entender la estructura del dataset\n",
        "print(\"Primeras filas del dataset original:\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EBp0xWalpaUq",
        "outputId": "53794566-b5a3-4428-afe7-700c6b9c243e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype  \n",
            "---  ------       --------------   -----  \n",
            " 0   InvoiceNo    541909 non-null  object \n",
            " 1   StockCode    541909 non-null  object \n",
            " 2   Description  540455 non-null  object \n",
            " 3   Quantity     541909 non-null  int64  \n",
            " 4   InvoiceDate  541909 non-null  object \n",
            " 5   UnitPrice    541909 non-null  float64\n",
            " 6   CustomerID   406829 non-null  float64\n",
            " 7   Country      541909 non-null  object \n",
            "dtypes: float64(2), int64(1), object(5)\n",
            "memory usage: 33.1+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "NFETmNh_pbo6"
      },
      "outputs": [],
      "source": [
        "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnsZJ6o0pc2F",
        "outputId": "b0582a6a-4909-49d1-b0bb-af406f905c15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype         \n",
            "---  ------       --------------   -----         \n",
            " 0   InvoiceNo    541909 non-null  object        \n",
            " 1   StockCode    541909 non-null  object        \n",
            " 2   Description  540455 non-null  object        \n",
            " 3   Quantity     541909 non-null  int64         \n",
            " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
            " 5   UnitPrice    541909 non-null  float64       \n",
            " 6   CustomerID   406829 non-null  float64       \n",
            " 7   Country      541909 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
            "memory usage: 33.1+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "0zmeWAbYpeHg"
      },
      "outputs": [],
      "source": [
        "df.dropna(subset=['InvoiceDate'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "OAr_e1cbpffG"
      },
      "outputs": [],
      "source": [
        "df.set_index('InvoiceDate', inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QPmS5aVIpiEv",
        "outputId": "fa9592af-10a1-47b6-c862-86e2411a5592"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "InvoiceDate\n",
            "2010-12-01 08:26:00    2010\n",
            "2010-12-01 08:26:00    2010\n",
            "2010-12-01 08:26:00    2010\n",
            "2010-12-01 08:26:00    2010\n",
            "2010-12-01 08:26:00    2010\n",
            "                       ... \n",
            "2011-12-09 12:50:00    2011\n",
            "2011-12-09 12:50:00    2011\n",
            "2011-12-09 12:50:00    2011\n",
            "2011-12-09 12:50:00    2011\n",
            "2011-12-09 12:50:00    2011\n",
            "Name: Year, Length: 541909, dtype: int32\n",
            "InvoiceDate\n",
            "2010-12-01 08:26:00     8\n",
            "2010-12-01 08:26:00     8\n",
            "2010-12-01 08:26:00     8\n",
            "2010-12-01 08:26:00     8\n",
            "2010-12-01 08:26:00     8\n",
            "                       ..\n",
            "2011-12-09 12:50:00    12\n",
            "2011-12-09 12:50:00    12\n",
            "2011-12-09 12:50:00    12\n",
            "2011-12-09 12:50:00    12\n",
            "2011-12-09 12:50:00    12\n",
            "Name: Hour, Length: 541909, dtype: int32\n"
          ]
        }
      ],
      "source": [
        "df['Year'] = df.index.year\n",
        "df['Month'] = df.index.month\n",
        "df['Day'] = df.index.day\n",
        "df['Weekdy'] = df.index.weekday #Error\n",
        "df['Hour'] = df.index.hour\n",
        "\n",
        "\n",
        "print(df['Year'])\n",
        "print(df['Hour'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "EAaG-q5gplN-",
        "outputId": "26dfdada-61a2-4e53-c24d-9126702a5cb8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-305e217d-7109-4835-b7ed-fa59cffe5489\" 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>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
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              "      <th>Weekdy</th>\n",
              "      <th>Hour</th>\n",
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              "    <tr>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-12-01 08:26:00</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2010</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>2010-12-01 08:26:00</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2010</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-01 08:26:00</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2010</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-01 08:26:00</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2010</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-01 08:26:00</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2010</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09 12:50:00</th>\n",
              "      <td>581587</td>\n",
              "      <td>22613</td>\n",
              "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
              "      <td>12</td>\n",
              "      <td>0.85</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>France</td>\n",
              "      <td>2011</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09 12:50:00</th>\n",
              "      <td>581587</td>\n",
              "      <td>22899</td>\n",
              "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
              "      <td>6</td>\n",
              "      <td>2.10</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>France</td>\n",
              "      <td>2011</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09 12:50:00</th>\n",
              "      <td>581587</td>\n",
              "      <td>23254</td>\n",
              "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
              "      <td>4</td>\n",
              "      <td>4.15</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>France</td>\n",
              "      <td>2011</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09 12:50:00</th>\n",
              "      <td>581587</td>\n",
              "      <td>23255</td>\n",
              "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
              "      <td>4</td>\n",
              "      <td>4.15</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>France</td>\n",
              "      <td>2011</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09 12:50:00</th>\n",
              "      <td>581587</td>\n",
              "      <td>22138</td>\n",
              "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
              "      <td>3</td>\n",
              "      <td>4.95</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>France</td>\n",
              "      <td>2011</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>541909 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-305e217d-7109-4835-b7ed-fa59cffe5489')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-305e217d-7109-4835-b7ed-fa59cffe5489 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-305e217d-7109-4835-b7ed-fa59cffe5489');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7686adae-cee5-41cf-80f0-f48fcc04a4da\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7686adae-cee5-41cf-80f0-f48fcc04a4da')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                    InvoiceNo StockCode                          Description  \\\n",
              "InvoiceDate                                                                    \n",
              "2010-12-01 08:26:00    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER   \n",
              "2010-12-01 08:26:00    536365     71053                  WHITE METAL LANTERN   \n",
              "2010-12-01 08:26:00    536365    84406B       CREAM CUPID HEARTS COAT HANGER   \n",
              "2010-12-01 08:26:00    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE   \n",
              "2010-12-01 08:26:00    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.   \n",
              "...                       ...       ...                                  ...   \n",
              "2011-12-09 12:50:00    581587     22613          PACK OF 20 SPACEBOY NAPKINS   \n",
              "2011-12-09 12:50:00    581587     22899         CHILDREN'S APRON DOLLY GIRL    \n",
              "2011-12-09 12:50:00    581587     23254        CHILDRENS CUTLERY DOLLY GIRL    \n",
              "2011-12-09 12:50:00    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE   \n",
              "2011-12-09 12:50:00    581587     22138        BAKING SET 9 PIECE RETROSPOT    \n",
              "\n",
              "                     Quantity  UnitPrice  CustomerID         Country  Year  \\\n",
              "InvoiceDate                                                                  \n",
              "2010-12-01 08:26:00         6       2.55     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         8       2.75     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "...                       ...        ...         ...             ...   ...   \n",
              "2011-12-09 12:50:00        12       0.85     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         6       2.10     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         4       4.15     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         4       4.15     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         3       4.95     12680.0          France  2011   \n",
              "\n",
              "                     Month  Day  Weekdy  Hour  \n",
              "InvoiceDate                                    \n",
              "2010-12-01 08:26:00     12    1       2     8  \n",
              "2010-12-01 08:26:00     12    1       2     8  \n",
              "2010-12-01 08:26:00     12    1       2     8  \n",
              "2010-12-01 08:26:00     12    1       2     8  \n",
              "2010-12-01 08:26:00     12    1       2     8  \n",
              "...                    ...  ...     ...   ...  \n",
              "2011-12-09 12:50:00     12    9       4    12  \n",
              "2011-12-09 12:50:00     12    9       4    12  \n",
              "2011-12-09 12:50:00     12    9       4    12  \n",
              "2011-12-09 12:50:00     12    9       4    12  \n",
              "2011-12-09 12:50:00     12    9       4    12  \n",
              "\n",
              "[541909 rows x 12 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "XdxveiZupmor"
      },
      "outputs": [],
      "source": [
        "df = df.drop(columns=['Weekdy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "LiMDtPuGpoYi",
        "outputId": "be2f4023-51c9-4a7d-f91b-ed06ada5593b"
      },
      "outputs": [
        {
          "data": {
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            "text/html": [
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              "      <th>InvoiceNo</th>\n",
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              "      <td>6</td>\n",
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              "      <td>17850.0</td>\n",
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              "      <td>6</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
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              "      <td>2011</td>\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                    InvoiceNo StockCode                          Description  \\\n",
              "InvoiceDate                                                                    \n",
              "2010-12-01 08:26:00    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER   \n",
              "2010-12-01 08:26:00    536365     71053                  WHITE METAL LANTERN   \n",
              "2010-12-01 08:26:00    536365    84406B       CREAM CUPID HEARTS COAT HANGER   \n",
              "2010-12-01 08:26:00    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE   \n",
              "2010-12-01 08:26:00    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.   \n",
              "...                       ...       ...                                  ...   \n",
              "2011-12-09 12:50:00    581587     22613          PACK OF 20 SPACEBOY NAPKINS   \n",
              "2011-12-09 12:50:00    581587     22899         CHILDREN'S APRON DOLLY GIRL    \n",
              "2011-12-09 12:50:00    581587     23254        CHILDRENS CUTLERY DOLLY GIRL    \n",
              "2011-12-09 12:50:00    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE   \n",
              "2011-12-09 12:50:00    581587     22138        BAKING SET 9 PIECE RETROSPOT    \n",
              "\n",
              "                     Quantity  UnitPrice  CustomerID         Country  Year  \\\n",
              "InvoiceDate                                                                  \n",
              "2010-12-01 08:26:00         6       2.55     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         8       2.75     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "...                       ...        ...         ...             ...   ...   \n",
              "2011-12-09 12:50:00        12       0.85     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         6       2.10     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         4       4.15     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         4       4.15     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         3       4.95     12680.0          France  2011   \n",
              "\n",
              "                     Month  Day  Hour  \n",
              "InvoiceDate                            \n",
              "2010-12-01 08:26:00     12    1     8  \n",
              "2010-12-01 08:26:00     12    1     8  \n",
              "2010-12-01 08:26:00     12    1     8  \n",
              "2010-12-01 08:26:00     12    1     8  \n",
              "2010-12-01 08:26:00     12    1     8  \n",
              "...                    ...  ...   ...  \n",
              "2011-12-09 12:50:00     12    9    12  \n",
              "2011-12-09 12:50:00     12    9    12  \n",
              "2011-12-09 12:50:00     12    9    12  \n",
              "2011-12-09 12:50:00     12    9    12  \n",
              "2011-12-09 12:50:00     12    9    12  \n",
              "\n",
              "[541909 rows x 11 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "tDHEg49PpqWT"
      },
      "outputs": [],
      "source": [
        "df['Weekday'] = df.index.weekday"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "O2e3ytBSprz1",
        "outputId": "74a2459d-6453-4959-c525-0c75f7e1ee37"
      },
      "outputs": [
        {
          "data": {
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            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
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              "      <th>2010-12-01 08:26:00</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
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              "      <td>17850.0</td>\n",
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              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2010</td>\n",
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              "      <td>1</td>\n",
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              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
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              "      <td>8</td>\n",
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              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "      <td>536365</td>\n",
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              "      <td>6</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2010</td>\n",
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              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
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              "      <td>17850.0</td>\n",
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              "      <td>9</td>\n",
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              "      <td>France</td>\n",
              "      <td>2011</td>\n",
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              "      <td>12</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "\n",
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            ],
            "text/plain": [
              "                    InvoiceNo StockCode                          Description  \\\n",
              "InvoiceDate                                                                    \n",
              "2010-12-01 08:26:00    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER   \n",
              "2010-12-01 08:26:00    536365     71053                  WHITE METAL LANTERN   \n",
              "2010-12-01 08:26:00    536365    84406B       CREAM CUPID HEARTS COAT HANGER   \n",
              "2010-12-01 08:26:00    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE   \n",
              "2010-12-01 08:26:00    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.   \n",
              "...                       ...       ...                                  ...   \n",
              "2011-12-09 12:50:00    581587     22613          PACK OF 20 SPACEBOY NAPKINS   \n",
              "2011-12-09 12:50:00    581587     22899         CHILDREN'S APRON DOLLY GIRL    \n",
              "2011-12-09 12:50:00    581587     23254        CHILDRENS CUTLERY DOLLY GIRL    \n",
              "2011-12-09 12:50:00    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE   \n",
              "2011-12-09 12:50:00    581587     22138        BAKING SET 9 PIECE RETROSPOT    \n",
              "\n",
              "                     Quantity  UnitPrice  CustomerID         Country  Year  \\\n",
              "InvoiceDate                                                                  \n",
              "2010-12-01 08:26:00         6       2.55     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         8       2.75     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "...                       ...        ...         ...             ...   ...   \n",
              "2011-12-09 12:50:00        12       0.85     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         6       2.10     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         4       4.15     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         4       4.15     12680.0          France  2011   \n",
              "2011-12-09 12:50:00         3       4.95     12680.0          France  2011   \n",
              "\n",
              "                     Month  Day  Hour  Weekday  \n",
              "InvoiceDate                                     \n",
              "2010-12-01 08:26:00     12    1     8        2  \n",
              "2010-12-01 08:26:00     12    1     8        2  \n",
              "2010-12-01 08:26:00     12    1     8        2  \n",
              "2010-12-01 08:26:00     12    1     8        2  \n",
              "2010-12-01 08:26:00     12    1     8        2  \n",
              "...                    ...  ...   ...      ...  \n",
              "2011-12-09 12:50:00     12    9    12        4  \n",
              "2011-12-09 12:50:00     12    9    12        4  \n",
              "2011-12-09 12:50:00     12    9    12        4  \n",
              "2011-12-09 12:50:00     12    9    12        4  \n",
              "2011-12-09 12:50:00     12    9    12        4  \n",
              "\n",
              "[541909 rows x 12 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "jye7NOofps8m",
        "outputId": "bb255532-1793-4e4d-b877-5b295fec5517"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "variable_name": "df_2011"
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              "      <th>InvoiceNo</th>\n",
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              "      <th>2011-01-04 10:00:00</th>\n",
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            "text/plain": [
              "                    InvoiceNo StockCode                Description  Quantity  \\\n",
              "InvoiceDate                                                                    \n",
              "2011-01-04 10:00:00    539993     22386    JUMBO BAG PINK POLKADOT        10   \n",
              "2011-01-04 10:00:00    539993     21499         BLUE POLKADOT WRAP        25   \n",
              "2011-01-04 10:00:00    539993     21498        RED RETROSPOT WRAP         25   \n",
              "2011-01-04 10:00:00    539993     22379   RECYCLING BAG RETROSPOT          5   \n",
              "2011-01-04 10:00:00    539993     20718  RED RETROSPOT SHOPPER BAG        10   \n",
              "\n",
              "                     UnitPrice  CustomerID         Country  Year  Month  Day  \\\n",
              "InvoiceDate                                                                    \n",
              "2011-01-04 10:00:00       1.95     13313.0  United Kingdom  2011      1    4   \n",
              "2011-01-04 10:00:00       0.42     13313.0  United Kingdom  2011      1    4   \n",
              "2011-01-04 10:00:00       0.42     13313.0  United Kingdom  2011      1    4   \n",
              "2011-01-04 10:00:00       2.10     13313.0  United Kingdom  2011      1    4   \n",
              "2011-01-04 10:00:00       1.25     13313.0  United Kingdom  2011      1    4   \n",
              "\n",
              "                     Hour  Weekday  \n",
              "InvoiceDate                         \n",
              "2011-01-04 10:00:00    10        1  \n",
              "2011-01-04 10:00:00    10        1  \n",
              "2011-01-04 10:00:00    10        1  \n",
              "2011-01-04 10:00:00    10        1  \n",
              "2011-01-04 10:00:00    10        1  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_2011 = df.loc['2011']\n",
        "\n",
        "df_2011.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
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          "height": 394
        },
        "id": "Y7HldPompuRp",
        "outputId": "de328997-b286-4e38-dca5-d75610d8fbf4"
      },
      "outputs": [
        {
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            "text/plain": [
              "                    InvoiceNo StockCode                         Description  \\\n",
              "InvoiceDate                                                                   \n",
              "2011-12-01 08:12:00   C579889     23245          SET OF 3 REGENCY CAKE TINS   \n",
              "2011-12-01 08:14:00   C579890     84947   ANTIQUE SILVER TEA GLASS ENGRAVED   \n",
              "2011-12-01 08:14:00   C579890     23374             RED SPOT PAPER GIFT BAG   \n",
              "2011-12-01 08:14:00   C579890     84945  MULTI COLOUR SILVER T-LIGHT HOLDER   \n",
              "2011-12-01 08:18:00   C579891     23485       BOTANICAL GARDENS WALL CLOCK    \n",
              "\n",
              "                     Quantity  UnitPrice  CustomerID         Country  Year  \\\n",
              "InvoiceDate                                                                  \n",
              "2011-12-01 08:12:00        -8       4.15     13853.0  United Kingdom  2011   \n",
              "2011-12-01 08:14:00        -1       1.25     15197.0  United Kingdom  2011   \n",
              "2011-12-01 08:14:00        -1       0.82     15197.0  United Kingdom  2011   \n",
              "2011-12-01 08:14:00        -2       0.85     15197.0  United Kingdom  2011   \n",
              "2011-12-01 08:18:00        -1      25.00     13644.0  United Kingdom  2011   \n",
              "\n",
              "                     Month  Day  Hour  Weekday  \n",
              "InvoiceDate                                     \n",
              "2011-12-01 08:12:00     12    1     8        3  \n",
              "2011-12-01 08:14:00     12    1     8        3  \n",
              "2011-12-01 08:14:00     12    1     8        3  \n",
              "2011-12-01 08:14:00     12    1     8        3  \n",
              "2011-12-01 08:18:00     12    1     8        3  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_2011_dec = df.loc['2011-12']\n",
        "\n",
        "df_2011_dec.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "DERKLZGKpv7j",
        "outputId": "f8f3d598-0cf8-437c-8e47-d651e5340f89"
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      "outputs": [
        {
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              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d57cc63a-8353-42cd-a4a5-75d430845a02 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                    InvoiceNo StockCode                          Description  \\\n",
              "InvoiceDate                                                                    \n",
              "2010-12-01 08:26:00    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER   \n",
              "2010-12-01 08:26:00    536365     71053                  WHITE METAL LANTERN   \n",
              "2010-12-01 08:26:00    536365    84406B       CREAM CUPID HEARTS COAT HANGER   \n",
              "2010-12-01 08:26:00    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE   \n",
              "2010-12-01 08:26:00    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.   \n",
              "\n",
              "                     Quantity  UnitPrice  CustomerID         Country  Year  \\\n",
              "InvoiceDate                                                                  \n",
              "2010-12-01 08:26:00         6       2.55     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         8       2.75     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "2010-12-01 08:26:00         6       3.39     17850.0  United Kingdom  2010   \n",
              "\n",
              "                     Month  Day  Hour  Weekday  \n",
              "InvoiceDate                                     \n",
              "2010-12-01 08:26:00     12    1     8        2  \n",
              "2010-12-01 08:26:00     12    1     8        2  \n",
              "2010-12-01 08:26:00     12    1     8        2  \n",
              "2010-12-01 08:26:00     12    1     8        2  \n",
              "2010-12-01 08:26:00     12    1     8        2  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_dec_range = df.loc['2010-12-01':'2010-12-15']\n",
        "df_dec_range.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4HvaKVy_pxMF",
        "outputId": "c1aa4eb7-fb30-4d8c-b99e-9007d483d871"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatetimeIndex(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04',\n",
              "               '2024-01-05', '2024-01-06', '2024-01-07', '2024-01-08',\n",
              "               '2024-01-09', '2024-01-10',\n",
              "               ...\n",
              "               '2024-12-22', '2024-12-23', '2024-12-24', '2024-12-25',\n",
              "               '2024-12-26', '2024-12-27', '2024-12-28', '2024-12-29',\n",
              "               '2024-12-30', '2024-12-31'],\n",
              "              dtype='datetime64[ns]', length=366, freq='D')"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "date_range_new = pd.date_range(start='2024-01-01', end='2024-12-31', freq='D')\n",
        "date_range_new"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CD10tif3pyTz",
        "outputId": "e13d416e-baeb-4f18-a0cd-39e00cabb504"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_dates\",\n  \"rows\": 366,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-01-01 00:00:00\",\n        \"max\": \"2024-12-31 00:00:00\",\n        \"num_unique_values\": 366,\n        \"samples\": [\n          \"2024-07-12 00:00:00\",\n          \"2024-02-03 00:00:00\",\n          \"2024-01-16 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_dates"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-d34b2626-237c-4b26-9b8c-f77937abc296\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2024-01-01</td>\n",
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              "      <td>2024-01-02</td>\n",
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              "      <td>2024-01-04</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-01-05</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "\n",
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              "        document.querySelector('#df-d34b2626-237c-4b26-9b8c-f77937abc296 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-d34b2626-237c-4b26-9b8c-f77937abc296');\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",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-24987586-99ca-41a4-840d-ba75d693fcfc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        Date\n",
              "0 2024-01-01\n",
              "1 2024-01-02\n",
              "2 2024-01-03\n",
              "3 2024-01-04\n",
              "4 2024-01-05"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_dates = pd.DataFrame(date_range_new, columns=['Date'])\n",
        "df_dates.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCqllbiJYQWZ"
      },
      "source": [
        "# Creación de dataframes en Pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YyXm4ecvb5KJ",
        "outputId": "0575f7a1-8d25-4a45-e7e2-6234fbdfd6a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1    536365     71053                  WHITE METAL LANTERN         6   \n",
            "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "\n",
            "           InvoiceDate  UnitPrice  CustomerID         Country  \n",
            "0  2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
            "1  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "2  2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
            "3  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "4  2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "file_path = '/content/online_retail.csv'\n",
        "sales_data = pd.read_csv(file_path)\n",
        "print(sales_data.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDVzZ5GUds0e",
        "outputId": "cb4e7d1d-b663-4a58-f472-401a8f3e8855"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   A  B  c\n",
            "0  1  2  3\n",
            "1  4  5  6\n",
            "2  7  8  9\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "data = np.array([[1,2,3], [4,5,6], [7,8,9]])\n",
        "\n",
        "dt_from_array = pd.DataFrame(data, columns=['A','B', 'c'])\n",
        "print(dt_from_array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gFYhjs_Rea_5",
        "outputId": "68560ffe-749c-4835-a96a-fff1aef18a99"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   ID  Name  Age\n",
            "0   1  Jhon   22\n",
            "1   2  Anna   24\n"
          ]
        }
      ],
      "source": [
        "data = [[1,'Jhon', 22], [2,'Anna', 24]]\n",
        "\n",
        "df_from_list = pd.DataFrame(data, columns = ['ID', 'Name', 'Age'])\n",
        "print(df_from_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytAQ_b8uexXI",
        "outputId": "2aed6152-45ca-4ec7-8087-20d8ae6b0496"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   ID  Name  Age\n",
            "0   1  Jhon   22\n"
          ]
        }
      ],
      "source": [
        "data = [{'ID': 1,\n",
        "         'Name': 'Jhon',\n",
        "         'Age' : 22}]\n",
        "dt_from_dict_list = pd.DataFrame(data)\n",
        "print(dt_from_dict_list )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfPWQwJqf61J",
        "outputId": "9bc04d7f-2c98-4fa5-e29a-47b79f0bbaa6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   ID  Name  Age\n",
            "0   1  Jhon   22\n",
            "1   2  Anna   24\n",
            "2   3  Mike   21\n"
          ]
        }
      ],
      "source": [
        "data = {'ID': [1,2,3], 'Name': ['Jhon', 'Anna', 'Mike'], 'Age': [22,24,21]}\n",
        "\n",
        "df_from_dict = pd.DataFrame(data)\n",
        "print(df_from_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kCeB0FfZg0S0",
        "outputId": "6268c442-5562-4bf2-d9ad-246fd4338591"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   ID  Name  Age\n",
            "0   1  Jhon   22\n",
            "1   2  Anna   24\n",
            "2   3  Mike   21\n"
          ]
        }
      ],
      "source": [
        "data = {'ID': pd.Series([1,2,3]),\n",
        "        'Name': pd.Series(['Jhon', 'Anna', 'Mike']),\n",
        "        'Age': pd.Series([22,24,21])}\n",
        "\n",
        "df_from_series_dict = pd.DataFrame(data)\n",
        "print(df_from_series_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RjJmtNdwhbGJ"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "file_path = \"/content/drive/My Drive/online_retail.csv\"\n",
        "\n",
        "sales_data = pd.read_csv(file_path)\n",
        "print(sales_data.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LcOH41Xvh7_-"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aDq9k7UnmlJV"
      },
      "source": [
        "# Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9yv9WGhtm6_r",
        "outputId": "f420f029-3080-462b-ca55-32101b38d26f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "pip install matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "yT97UG-znCZj",
        "outputId": "a71706e3-7e78-44a1-f73a-99812c056624"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "month = np.array(['E', 'F', 'M', 'A', 'Ma'])\n",
        "sales = np.array([20,25,30,28,35])\n",
        "\n",
        "#Configurar el tamaño del gráfico\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "#Crear el gráfico\n",
        "plt.plot(month, sales, marker='o', color='blue')\n",
        "\n",
        "plt.title('Ventas mensuales de un producto')\n",
        "plt.xlabel('Meses')\n",
        "plt.ylabel('Ventas en miles de unidades')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "VBOrTtYirL4H",
        "outputId": "c9e31db5-ef39-4295-90ba-901c3e98f56b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Configurar el tamaño del gráfico\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "hours = [2,3,4,5,6,7,8]\n",
        "exam = [55,60,65,70,75,80,85]\n",
        "\n",
        "plt.scatter(hours, exam, color='green')\n",
        "\n",
        "plt.title('Relación entre horas estudiadas y el puntaje')\n",
        "plt.xlabel('Horas')\n",
        "plt.ylabel('Puntaje')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "URmypzsywqGr"
      },
      "source": [
        "# Personalización"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "OnrOSttnxnsC",
        "outputId": "e35cc87a-e7f6-4b62-b25c-341a88fa1cdc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Configurar el tamaño del gráfico\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "hours = [2,3,4,5,6,7,8]\n",
        "exam_scores_student_1 = [55, 60, 65, 70, 75, 80, 85]\n",
        "exam_scores_student_2 = [50, 58, 63, 69, 74, 78, 83]\n",
        "\n",
        "#Crear gráfico de dispersión de dos estudiantes\n",
        "plt.scatter(hours, exam_scores_student_1, marker='o', color='green', label = 'Estudiante 1')\n",
        "plt.scatter(hours, exam_scores_student_2, marker='s', color='red', label = 'Estudiante 2')\n",
        "\n",
        "plt.title('Relación entre horas estudiadas y el puntaje de dos estudiantes')\n",
        "plt.xlabel('Horas')\n",
        "plt.ylabel('Puntaje')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "NUxS6C6M1GQE",
        "outputId": "b83e0fd6-25a2-45c5-cfab-e062ff9eceac"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Configurar el tamaño del gráfico\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "hours = [2,3,4,5,6,7,8]\n",
        "exam_scores_student_1 = [55, 60, 65, 70, 75, 80, 85]\n",
        "exam_scores_student_2 = [50, 58, 63, 69, 74, 78, 83]\n",
        "\n",
        "#Crear gráfico de dispersión de dos estudiantes\n",
        "plt.plot(hours, exam_scores_student_1, marker='o', color='green', linestyle='-', linewidth = 2 , label = 'Estudiante 1')\n",
        "plt.plot(hours, exam_scores_student_2, marker='s', color='red', linestyle='--', linewidth = 2, label = 'Estudiante 2')\n",
        "\n",
        "plt.title('Relación entre horas estudiadas y el puntaje de dos estudiantes')\n",
        "plt.xlabel('Horas')\n",
        "plt.ylabel('Puntaje')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01EyNbSEBQEX"
      },
      "source": [
        "# Barras y Pastel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "tZTM9861BTJZ",
        "outputId": "8927bbbf-a75b-485f-8592-d828329ddd7f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "categories = ['Producto A', 'Producto B', 'Producto C']\n",
        "sales = [120,150,90]\n",
        "\n",
        "#Creación del gráfico de barras verticales\n",
        "plt.bar(categories, sales, color='skyblue', label='Ventas mensuales')\n",
        "\n",
        "#Anotación con flecha para destacar un punto específico\n",
        "plt.annotate('Máximo de ventas', xy=('Producto B', 150),xytext=('Producto C', 160),\n",
        "             arrowprops= dict(facecolor = 'black', shrink= 0.05))\n",
        "\n",
        "plt.title('Ventas de productos en un mes')\n",
        "plt.xlabel('Productos')\n",
        "plt.ylabel('Ventas')\n",
        "\n",
        "#plt.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "NcsprWXdENaD",
        "outputId": "dbae0bb5-3f4d-4c99-c3d6-959de03e2d91"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "categories = ['Producto A', 'Producto B', 'Producto C']\n",
        "sales = [120,150,90]\n",
        "\n",
        "#Creación del gráfico de barras verticales\n",
        "plt.barh(categories, sales, color='skyblue', label='Ventas mensuales')\n",
        "\n",
        "\n",
        "plt.title('Ventas de productos en un mes')\n",
        "plt.ylabel('Productos')\n",
        "plt.xlabel('Ventas')\n",
        "\n",
        "#plt.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "08y7ZjDxBXcn",
        "outputId": "4b401330-baac-45fb-9e0b-d123597dea04"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Configurar el tamaño de la figura\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Datos de ejemplo: Participación de mercado por producto\n",
        "products = ['Producto A', 'Producto B', 'Producto C']\n",
        "market_share = [50, 35, 15]\n",
        "\n",
        "# Creación del diagrama de pastel\n",
        "plt.pie(market_share, labels=products, autopct='%1.1f%%', startangle= 140,\n",
        "        colors=['gold', 'lightcoral', 'orange'] )\n",
        "\n",
        "#Título\n",
        "plt.title('Participación de Mercado por Producto')\n",
        "\n",
        "# Asegurar que el gráfico sea un círculo\n",
        "plt.axis('equal')\n",
        "\n",
        "# Mostrar el gráfico\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6b9HeF33SjtP"
      },
      "source": [
        "# Gráficos de Histograma y Boxplot para distribuciones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lb8z1snLSmqt",
        "outputId": "4eb13071-482c-488f-d1b2-a31950b6088b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[177.45553169 178.83906076 181.88289816 169.5200205  170.07417256\n",
            " 171.84403311 162.06802896 169.89863075 169.6047942  173.37033625\n",
            " 162.73566637 181.25408883 179.50687689 161.65885819 173.69773821\n",
            " 158.37410557 170.7754144  159.51454595 168.11787151 168.27631534\n",
            " 166.85218875 168.01743821 171.84705935 167.8277882  167.90496428\n",
            " 166.76009035 180.3016     164.99491393 170.61388675 167.26440615\n",
            " 173.1968298  153.02649651 156.94524986 168.68294722 161.28931073\n",
            " 155.88712902 172.55022578 157.23175676 186.40273001 176.22336322\n",
            " 172.17448316 181.43577536 176.82169916 163.77455897 166.80958797\n",
            " 170.97091448 168.74763573 178.15314511 178.025117   162.75405103\n",
            " 152.21866389 138.11567281 163.68208796 171.23540497 174.25235486\n",
            " 171.51560742 162.41822879 188.04154637 162.12072889 186.71251663\n",
            " 172.3207514  165.48002304 160.35546325 181.47527982 162.10167999\n",
            " 174.18460061 188.24537486 173.10548041 151.23105068 183.8447082\n",
            " 179.18080111 151.32948357 191.27336313 193.21298972 178.61825456\n",
            " 141.94855874 164.9465612  174.77272691 175.58889073 172.51473634\n",
            " 173.94164077 170.26227185 159.38104272 175.52325273 154.21576342\n",
            " 142.30971586 175.01310529 164.69511883 182.03016029 180.05225754\n",
            " 150.85853809 166.83145152 174.14479838 177.1469129  176.39782155\n",
            " 172.95738147 170.64393448 152.07084027 151.28297108 180.30875815\n",
            " 174.6984162  176.4015149  155.23435957 175.90651628 183.82988746\n",
            " 174.52519077 187.80354919 161.80700767 178.21648453 153.94932196\n",
            " 166.85465159 191.83707248 168.88417451 178.06257835 176.04962356\n",
            " 172.00274311 172.06907948 171.22865302 164.99557516 153.36067011\n",
            " 171.5371747  176.60269244 157.05774031 151.19555085 190.78925867\n",
            " 174.52112436 166.88175909 177.65562442 177.02166474 165.73006497\n",
            " 175.57166116 156.50582356 155.23508686 167.70073992 152.1339787\n",
            " 166.73320046 157.69819496 151.65994993 169.57332321 179.85289878\n",
            " 155.59035492 168.56369786 164.0808513  170.7621951  185.57579952\n",
            " 170.61461155 148.13934056 189.76904386 173.23076538 177.77141816\n",
            " 177.74136558 177.93465494 174.96642817 183.67891058 178.08490205\n",
            " 173.5182702  158.52907462 186.10647482 176.88432139 165.9569269\n",
            " 161.24843672 171.60544429 156.57371826 171.88050742 158.66185118\n",
            " 183.20316417 155.49811154 169.5882835  152.4112122  178.2666704\n",
            " 174.20532874 170.56319574 167.09290932 172.30684027 182.05613951\n",
            " 167.94855845 163.68707242 171.99734908 175.68699867 152.49800283\n",
            " 172.68845932 165.22552655 168.74730495 184.62291352 150.36334215\n",
            " 157.88868042 184.27810198 171.11684717 167.99870169 181.18916751\n",
            " 161.27444835 168.08502223 178.17680517 181.16823098 175.85602091\n",
            " 177.16430662 156.92064755 176.17098718 166.84802901 149.43859688]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "data = np.random.normal(170, 10, 200)\n",
        "print(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "BvRJVDEJSoZ9",
        "outputId": "d14b4dc1-1f43-40a5-f847-8495326f5c02"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(data, color='salmon', bins=20, edgecolor='black', alpha=0.6)\n",
        "plt.title('Distribución de alturas')\n",
        "plt.xlabel('Altura (cm)')\n",
        "plt.ylabel('Densidad')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zof5F9AYZNOd",
        "outputId": "e8b19836-61a0-45ef-f7e2-047f1697785d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[array([38.82026173, 32.00078604, 34.89368992, 41.204466  , 39.33778995,\n",
            "       25.1136106 , 34.75044209, 29.24321396, 29.48390574, 32.05299251,\n",
            "       30.72021786, 37.27136753, 33.80518863, 30.60837508, 32.21931616,\n",
            "       31.66837164, 37.47039537, 28.97420868, 31.56533851, 25.7295213 ,\n",
            "       17.23505092, 33.26809298, 34.32218099, 26.2891749 , 41.34877312,\n",
            "       22.72817163, 30.22879259, 29.06408075, 37.66389607, 37.34679385,\n",
            "       30.77473713, 31.8908126 , 25.56107126, 20.09601766, 28.26043925,\n",
            "       30.78174485, 36.1514534 , 36.01189924, 28.06336591, 28.48848625,\n",
            "       24.75723517, 22.89991031, 21.46864905, 39.75387698, 27.45173909,\n",
            "       27.80962849, 23.7360232 , 33.88745178, 21.93051076, 28.9362986 ,\n",
            "       25.52266719, 31.93451249, 27.44597431, 24.09683908, 29.85908886,\n",
            "       32.14165935, 30.33258611, 31.51235949, 26.82838953, 28.18629417,\n",
            "       26.63769776, 28.20223419, 25.93426859, 21.36858699, 30.88713071,\n",
            "       27.99109532, 21.84900827, 32.31391128, 25.46350818, 30.25972698,\n",
            "       33.64545281, 30.64491455, 35.69700342, 23.8258709 , 32.01170821,\n",
            "       26.57594955, 25.64601425, 27.10575168, 28.44223734, 30.28082671,\n",
            "       24.1742508 , 34.50413243, 32.3283122 , 22.31878157, 37.44126097,\n",
            "       39.47944588, 35.89389786, 29.10037582, 24.64623689, 35.27225863,\n",
            "       27.98411527, 36.11222535, 31.04137489, 34.88319518, 31.78183199,\n",
            "       33.53286584, 30.0525001 , 38.92935247, 30.63456046, 32.00994682]), array([49.41575349, 33.26120469, 33.64757501, 44.84698354, 34.13438297,\n",
            "       49.71810593, 37.9319051 , 36.26272594, 49.61471013, 47.40257396,\n",
            "       49.3377948 , 44.53022329, 35.69387157, 49.55032477, 38.65998315,\n",
            "       44.01228198, 44.73625984, 39.22494953, 43.07039685, 44.61103336,\n",
            "       41.88212766, 34.50299605, 41.49119087, 46.63192948, 36.5271607 ,\n",
            "       39.2518273 , 37.82423224, 49.24631864, 43.36147379, 42.03730918,\n",
            "       36.15041963, 42.69624596, 36.6283367 , 40.15915279, 36.82076961,\n",
            "       43.38216647, 42.88295408, 38.95850622, 41.98003356, 34.53469246,\n",
            "       32.54371204, 42.19695851, 40.83336748, 43.17515718, 51.91572387,\n",
            "       44.72239743, 35.43588887, 45.58508144, 33.42046295, 37.69207698,\n",
            "       39.65879197, 48.56671361, 36.27622589, 35.86780731, 39.50773738,\n",
            "       36.68260857, 45.63317961, 34.60034246, 34.26265674, 37.81089978,\n",
            "       37.50983775, 49.64766027, 44.74710403, 40.43775621, 33.87282241,\n",
            "       44.22181488, 34.99892326, 32.27614452, 45.94014896, 41.58471306,\n",
            "       44.60429412, 41.59363826, 44.28415306, 36.74487203, 34.82878579,\n",
            "       43.40797259, 35.98295168, 36.55225111, 37.72233748, 40.0873958 ,\n",
            "       38.23003044, 33.12524353, 36.78190799, 28.88298424, 43.12615726,\n",
            "       31.98971172, 34.4780833 , 40.2608254 , 36.30218502, 47.71507298,\n",
            "       33.53571545, 41.33525435, 39.80358591, 34.15953251, 42.6163833 ,\n",
            "       39.14226834, 43.85895276, 44.11752077, 50.81617975, 46.68263975]), array([33.15409081, 33.80310411, 40.49829798, 38.27631865, 38.20065763,\n",
            "       26.91521978, 34.87836938, 31.30984545, 36.399623  , 34.50924805,\n",
            "       39.55089454, 36.58609108, 38.93163981, 32.66790452, 30.27776872,\n",
            "       32.94975153, 34.91489793, 36.89575868, 46.29654475, 34.78871424,\n",
            "       30.220275  , 33.27009112, 32.68202013, 37.40740737, 27.29601493,\n",
            "       35.31630997, 35.78253269, 36.16090518, 32.01341966, 33.81039135,\n",
            "       27.87969546, 32.53340058, 32.28569262, 37.08025023, 29.21908784,\n",
            "       38.90599051, 42.47242272, 24.65007487, 37.13129365, 38.38454018,\n",
            "       31.81281487, 33.01364093, 34.33559711, 33.5110456 , 33.45493515,\n",
            "       26.61998097, 40.76165782, 40.39809296, 30.9331787 , 27.66787836,\n",
            "       37.60532438, 32.12106015, 35.70976582, 33.40335791, 38.45769376,\n",
            "       38.47374572, 31.37201311, 28.08318022, 27.08530801, 38.0518969 ,\n",
            "       29.05570371, 32.46591823, 32.01842981, 34.73716352, 25.31860097,\n",
            "       35.94389298, 37.61945512, 35.44211044, 33.44556914, 35.48700083,\n",
            "       36.99523173, 21.13703622, 44.77956154, 36.95046661, 31.73795709,\n",
            "       33.04523312, 37.46870889, 34.4194803 , 24.84657766, 45.32246431,\n",
            "       34.44729671, 40.10086356, 31.53975076, 42.68188527, 36.43171844,\n",
            "       38.04421917, 29.77373317, 41.05572645, 38.44909082, 41.50923115,\n",
            "       31.8595622 , 32.59486441, 46.51958349, 29.69992089, 34.3202515 ,\n",
            "       40.68445681, 35.48862484, 37.9147684 , 33.00275485, 36.85027944])]\n"
          ]
        }
      ],
      "source": [
        "np.random.seed(0)\n",
        "ages = [np.random.normal(30, 5, 100),\n",
        "        np.random.normal(40, 5, 100),\n",
        "        np.random.normal(35, 5, 100)]\n",
        "print(ages)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "cY8OUbEDaK7f",
        "outputId": "823a02c0-474f-47de-978d-4791e52c0d8e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.boxplot(ages, patch_artist=True, notch=True, vert=True,\n",
        "            labels=['Grupo 1','Grupo 2','Grupo 3'])\n",
        "\n",
        "plt.title('Distribución de edades por grupo')\n",
        "plt.xlabel('Grupo')\n",
        "plt.ylabel('Edad (años)')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-dEiZ6MfeKa"
      },
      "source": [
        "# Subplots y Layouts Avanzados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6weH2o3ofiqc"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import matplotlib.gridspec as gridspec\n",
        "\n",
        "x = np.linspace(0, 10, 100)\n",
        "y = np.sin(x)\n",
        "\n",
        "data = np.random.randn(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "GtsOLGPBfize",
        "outputId": "9544e525-f4e3-4155-e884-396edd50920f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "gs = gridspec.GridSpec(2,2, height_ratios=[2,1],width_ratios=[1,1])\n",
        "fig = plt.figure(figsize =(10,8))\n",
        "\n",
        "# Primer subplot grande, ocupa toda la primera fila\n",
        "ax1 = fig.add_subplot(gs[0,:])\n",
        "ax1.plot(x,y, color = 'blue')\n",
        "ax1.set_title('Seno de X')\n",
        "ax1.set_xlabel('x')\n",
        "ax1.set_ylabel('sin(x)')\n",
        "\n",
        "# Segundo subplot, ocupa la esquina inferior izquierda\n",
        "ax2 = fig.add_subplot(gs[1,0])\n",
        "ax2.hist(data, bins = 20, color='purple', edgecolor='black')\n",
        "ax1.set_title('Histograma')\n",
        "ax1.set_xlabel('Valor')\n",
        "ax1.set_ylabel('Frecuencia')\n",
        "\n",
        "# Tercer subplot, ubicado en la esquian inferior derecha\n",
        "ax3 = fig.add_subplot(gs[1,1])\n",
        "ax3.scatter(x,y, color = 'red')\n",
        "ax1.set_title('Dispersión de Seno')\n",
        "ax1.set_xlabel('x')\n",
        "ax1.set_ylabel('Frecuencia')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1-RD6zjog-h"
      },
      "source": [
        "\n",
        "# Series de tiempo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LqFguzB_oiwt"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib.dates import DateFormatter\n",
        "\n",
        "dates = pd.date_range(start= '2023-01-01', periods=100)\n",
        "\n",
        "values = np.random.rand(100).cumsum()\n",
        "\n",
        "data = pd.DataFrame({'Date': dates, 'Value': values})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "n0sC9cdwojpc",
        "outputId": "6aaa4477-0c60-4b64-cce5-97c098238474"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Crear Gráfico de líneas\n",
        "fig, ax = plt.subplots(figsize=(12,6))\n",
        "\n",
        "ax.plot(data['Date'], data['Value'], color = 'green')\n",
        "\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "plt.title('Serie de tiempo con formato en las fechas')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Value')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "F7hLG_QCumDa",
        "outputId": "9500aa6e-3a9e-4ee3-c4c0-7478d4b282ac"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dates = pd.date_range(start='2023-01-01', periods=12, freq='M')\n",
        "sales = np.random.randint(1000, 5000, size=12)\n",
        "\n",
        "sales_data = pd.DataFrame=({'Date': dates, 'Sales': sales})\n",
        "\n",
        "plt.plot(sales_data['Date'],sales_data['Sales'], marker='o',\n",
        "         linestyle = '-', label = 'Ventas mensuales')\n",
        "\n",
        "plt.gca().xaxis.set_major_formatter(DateFormatter('%b %Y'))\n",
        "\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "plt.title('Análisis de ventas mensuales')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Sales')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
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      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}