01_numpy_pandas.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OmarFloresPTY/my-google-colabs/blob/master/01_numpy_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QOj2wbhLRVD"
      },
      "source": [
        "# Manipulación y Transformación de Datos con Pandas y Numpy 📓🧮🐍"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCckof4ALRVI"
      },
      "source": [
        "## 1. Uso de la librería Numpy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gZTfHqfkLRVL"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18BDjqCcLRVP"
      },
      "source": [
        "### 1.1 Aprendiendo sobre los arrays y matrices {bidi,tridi}.mensionales"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AYSGCe1fLRVQ",
        "outputId": "81050fec-828e-4091-b9d1-b0e2a43c02ba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lista = [1,2,3,4,5,6,7,8,9]\n",
        "lista"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NEU447RKLRVT"
      },
      "outputs": [],
      "source": [
        "arr = np.array(lista)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "it8eQRglLRVW",
        "outputId": "4f0bef59-1c53-47b7-c8b3-1061d4abcbc9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(arr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "8YtzOAR4LRVX",
        "outputId": "14f378c1-5b07-4191-8d46-a80faed7ee2b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6],\n",
              "       [7, 8, 9]])"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "matriz = [[1,2,3],[4,5,6],[7,8,9]]\n",
        "matriz = np.array(matriz)\n",
        "matriz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c0TFahiVLRVZ",
        "outputId": "dd2fa1c1-b6b0-48ee-b378-529f8e904765"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LG9MVYXJLRVb",
        "outputId": "9e065f7f-4e09-409c-c821-b96fd3195356"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arr[0]\n",
        "#arr[1]\n",
        "#arr[8]\n",
        "#arr[9]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IaKsyPXiLRVc",
        "outputId": "75d18120-1bc7-405c-83c3-20e0d52bedaf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(arr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iOzEg4SsLRVe",
        "outputId": "d179ae31-aac7-4971-f40e-b822f07f2ce6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arr[0] + arr[5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UcHHc4dYLRVf",
        "outputId": "59a4cf59-aff4-48dc-dd0a-34935d6d9ca8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "matriz[0][2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k77VHsn9LRVg",
        "outputId": "6fb1141f-9c8b-4579-cfd8-eead86d5c039"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 4, 7])"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#arr[0:4]\n",
        "arr[::3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K1wY4WMfLRVg",
        "outputId": "c017cd4f-88a3-429b-f391-09b5bed0ef25"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[4, 5],\n",
              "       [7, 8]])"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "matriz[1:,0:2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bfEbbEPoLRVh"
      },
      "outputs": [],
      "source": [
        "matriz_tridimensional = np.array([\n",
        "    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],\n",
        "    [[10, 11, 12], [13, 14, 15], [16, 17, 18]],\n",
        "    [[19, 20, 21], [22, 23, 24], [25, 26, 27]]\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "5sb_q09wLRVh",
        "outputId": "13fe6eca-ba7b-4417-d042-a87ea3af9ec9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[[ 1,  2,  3],\n",
              "        [ 4,  5,  6],\n",
              "        [ 7,  8,  9]],\n",
              "\n",
              "       [[10, 11, 12],\n",
              "        [13, 14, 15],\n",
              "        [16, 17, 18]],\n",
              "\n",
              "       [[19, 20, 21],\n",
              "        [22, 23, 24],\n",
              "        [25, 26, 27]]])"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "matriz_tridimensional"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kXmr-wq3LRVi",
        "outputId": "1fedcef6-c4ec-433a-b77a-3d2e437fafa7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6],\n",
              "       [7, 8, 9]])"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#matriz_tridimensional[0]\n",
        "matriz_tridimensional[2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NeUdC8qhLRVi",
        "outputId": "43a2e0d1-9c49-4924-ccd8-014181009110"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "27"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "matriz_tridimensional[2][2][2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H4pyhgMALRVj",
        "outputId": "925d665b-393b-4738-9a44-d2156a56673c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[[10, 11],\n",
              "        [13, 14],\n",
              "        [16, 17]],\n",
              "\n",
              "       [[19, 20],\n",
              "        [22, 23],\n",
              "        [25, 26]]])"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "matriz_tridimensional[1:,:,0:2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Me1ZxJBVLRVj"
      },
      "source": [
        "### 1.2 Tipos de datos en Numpy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CD5s7DWWLRVj"
      },
      "source": [
        "#### Tipo de dato int32"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bIR63y2PLRVk"
      },
      "outputs": [],
      "source": [
        "arr=np.array([0,1,2,3,4]) #método manual para crear un array del 0-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jK4BPiNoLRVk"
      },
      "outputs": [],
      "source": [
        "arr=np.arange(0,5) #método .arange(min,max-1) para crear un array del 0-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rDwyVmWLLRVl",
        "outputId": "a1ba1316-3f5c-45fc-873a-2afe78cc66f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4])"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "84w1-xSSLRVm",
        "outputId": "3a2ee9f2-f5cd-41eb-f998-9c01774b4c5a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dtype('int32')"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arr.dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b3mIGL4qLRVm"
      },
      "source": [
        "#### Tipo de dato float64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "duUT3cTQLRVn"
      },
      "outputs": [],
      "source": [
        "arr=np.array([1,2,3,4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3QP7ivL5LRVn"
      },
      "outputs": [],
      "source": [
        "arr = np.array([1,2,3,4], dtype='float64') #método No.1 para castear un tipo de datos a otro tipo de datos numpy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NKywtOLpLRVn",
        "outputId": "4b961d17-937c-485c-8ee9-d68717b5a7a9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1., 2., 3., 4.])"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arr.astype(np.float64) #método No.2 para castear un tipo de datos a otro tipo de datos en numpy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQZv1vgiLRVo"
      },
      "source": [
        "#### Tipo de dato boole64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sIHyBjCULRVo"
      },
      "outputs": [],
      "source": [
        "arr = np.array([0,1,2,3,4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mrs61snvLRVo"
      },
      "outputs": [],
      "source": [
        "arr=arr.astype(np.bool_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZT-ZXJ_gLRVp",
        "outputId": "67cb815c-c2ce-45a2-e8be-7d6a03ca1749"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([False,  True,  True,  True,  True])"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arr"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hmaz48odLRVp"
      },
      "source": [
        "#### Tipo de dato string64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "blZn2nrwLRVp"
      },
      "outputs": [],
      "source": [
        "arr = np.array([0,1,2,3,4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pKSO3z_TLRVp"
      },
      "outputs": [],
      "source": [
        "arr = arr.astype(np.string_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "Vkn6fSJoLRVq",
        "outputId": "8144dcca-0225-4841-9836-324aa49d0d35"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4], dtype=int8)"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XqdUiNzrLRVq"
      },
      "outputs": [],
      "source": [
        "arr = np.array(['0','1','2','3','4'])\n",
        "#arr = np.array(['Hello','1','2','3','4']) #No puede convertir un texto a entero."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "CdBbVeh_LRVq"
      },
      "outputs": [],
      "source": [
        "arr = arr.astype(np.int8) #Investigar int8 y int32"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdGNFn-RLRVr"
      },
      "source": [
        "Si quieres conocer todos los tipos de datos de numpy, visita su sitio web oficial: https://numpy.org/doc/stable/user/basics.types.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jE7km1DpLRVr"
      },
      "source": [
        "### 1.3 Dimensiones"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VaWZmxrnLRVr"
      },
      "source": [
        "* Scalar = Un único valor por separado (unidad).\n",
        "* Vector = Una colección unidemensional de elementos del mismo tipo de datos.\n",
        "* Matrix = Una colección bidimensional de elementos del mismo tipo de datos. (Filas y Columnas)\n",
        "* Tensor = Una colección de n-dimension de elementos del mismo tipo de datos.\n",
        "\n",
        "<img src=\"https://mblogthumb-phinf.pstatic.net/MjAyMDA1MjVfMjQ0/MDAxNTkwMzcxNTgxMjgx.zVM_8O3aWx1DUjeKFx-FRXeHS1KVHWfdSiOBE2--1k4g.6z_U8ssbX1lwelBEqUxjtyutk2y6HSNTvk5a7pTYIWkg.PNG.nabilera1/image.png?type=w800\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3QKAemB-LRVr",
        "outputId": "a553cc59-6252-4d00-ac01-2553023a3de6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "42\n"
          ]
        }
      ],
      "source": [
        "scalar = np.array(42)\n",
        "print(scalar)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fPgvqVnNLRVs",
        "outputId": "d04ac3b7-dca4-43f2-a11a-3628d777955c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scalar.ndim #Nos permite saber cuantas dimensiones tiene nuestro objeto numpy creado."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yZ3rVZbYLRVs",
        "outputId": "e7a73fa7-3297-45c5-fcc1-21b52e7b5a61"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2 3]\n"
          ]
        }
      ],
      "source": [
        "vector = np.array([1,2,3])\n",
        "print(vector)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-RHsJaBQLRVt",
        "outputId": "cbc89874-4cef-4217-a8f4-e633d7c5b56e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vector.ndim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JaUQWahgLRVt",
        "outputId": "dbb71d04-cd39-4879-c64e-edaf96f7522b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n"
          ]
        }
      ],
      "source": [
        "matrix = np.array([[1,2,3],[4,5,6]])\n",
        "print(matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xNM1Zb9kLRVt",
        "outputId": "1bd06655-6753-4134-c32c-3ad6ac9cf4dc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "matrix.ndim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uOobWQzxLRVt",
        "outputId": "fc3843b2-ed84-40f7-f5eb-479126bcbfcf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[[1 2 3]\n",
            "  [4 5 6]]\n",
            "\n",
            " [[1 2 3]\n",
            "  [4 5 6]]]\n"
          ]
        }
      ],
      "source": [
        "tensor = np.array([[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]]])\n",
        "print(tensor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hKceO97uLRVu",
        "outputId": "c91600d2-15ca-433c-d25d-603bdbacdeb5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tensor.ndim"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1XxCV3ILRVu"
      },
      "source": [
        "### 1.4 Creando arrays\n",
        "Creando arrays con las diferentes funciones que tiene numpy, hasta el momento hemos creado esta estructura de datos pasando como parámetro una lista."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pc2jtKr-LRVu"
      },
      "source": [
        "#### range(min,max)\n",
        "* El método range(min,max)es un método creado por python pero no por numpy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "iRcsvU_9LRVv",
        "outputId": "64652c11-bc6c-4ab4-88b5-604d62c0842b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list(range(0,10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnWjnwQPLRVv"
      },
      "source": [
        "#### np.arange(min,max-1,pasos)\n",
        "* El método np.arange(min,max-1,pasos) sí es un método creado por numpy para crear arrays desde un rango dado."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "Vd0rSPUCLRVv",
        "outputId": "02f16d5c-6850-44ed-d1f6-c8c8e877e4d5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.arange(0,20,2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEgilHzRLRVw"
      },
      "source": [
        "#### np.zeros(n) (vector)\n",
        "* El método np.zeros(n) crea un array con la cantidad de ceros indicados en n."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HN1MvZ0RLRVw",
        "outputId": "db56a7cc-79cd-40b7-b064-402c86267bae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0., 0., 0.])"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.zeros(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g2J8wkCwLRVw"
      },
      "source": [
        "#### np.zeros((n,m)) (matriz)\n",
        "* Crea una matriz de ceros con la sintaxis np.zeros((n,m)) ==> n y m son pasados como parámetros."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "-OZ2bekvLRVx",
        "outputId": "120cbd41-5e0f-40a3-a06c-e3b76c7a7559"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#np.zeros((3,3))\n",
        "np.zeros((10,10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCgB7BocLRVx"
      },
      "source": [
        "#### np.ones((n,m))\n",
        "* Crea una matriz de unos con la sintaxis np.ones((n,m)) ==> n y m son pasados como parámetros."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QQJdtu8cLRVx",
        "outputId": "6656818c-cf98-458c-8a86-c097cf9963c7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.]])"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.ones((10,10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUXTD1EoLRVy"
      },
      "source": [
        "#### np.linspace(min,max,cant)\n",
        "* Crea una distribución normalizada y ordenada de datos del 0 al 10 generados a partir de 100 datos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "2yex8q0HLRVy",
        "outputId": "e852462c-1d0e-4ee2-849e-fcbb56ef2d26"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
              "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
              "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
              "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
              "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
              "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
              "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
              "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
              "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
              "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
              "        5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
              "        5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
              "        6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
              "        6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
              "        7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
              "        7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
              "        8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
              "        8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
              "        9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
              "        9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.linspace(0,10,100) #Crea una distribución normalizada y ordenada de datos del 0 al 10 generados a partir de 100 datos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VTFmfeq3LRVy"
      },
      "source": [
        "#### np.eye(n)\n",
        "* Genera una matriz identidad NxN donde N es el valor pasado como parámetro."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l9GSGYouLRVy",
        "outputId": "79c9ca4a-387b-4a9b-ab74-2434328f1a19"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0.],\n",
              "       [0., 1., 0., 0.],\n",
              "       [0., 0., 1., 0.],\n",
              "       [0., 0., 0., 1.]])"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.eye(4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "peP6tZudLRVz"
      },
      "source": [
        "#### np.random\n",
        "* np.random.rand() genera un escalar del 0 al 1.\n",
        "* np.random.rand(n) generara un array con n cantidad de elementos del 0 al 1.\n",
        "* np.rand(n,m) genera una matriz de n,m cantidad de elementos del 0 al 1.\n",
        "* np.randint(min,max) genera un número aleatorio entre el min y el max.\n",
        "* np.randint(min,max,(n,m)) genera un matriz de n,m de elementos aleatorios entre el min y el max.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QxLO_yi8LRVz",
        "outputId": "04bc17c7-b1b4-47bc-944f-95778cf7e3c2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6906089723726002"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.random.rand()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W3IIRMr0LRVz",
        "outputId": "884aa24c-42fb-4ff4-9ed7-573778ab5056"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.96685689, 0.63863924, 0.42806768, 0.03635644])"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.random.rand(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OtZpN5rFLRV0",
        "outputId": "7ee41c58-687f-40a5-8378-94becf65935a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.02808761, 0.43323258, 0.50477401, 0.67823693],\n",
              "       [0.52756445, 0.22223687, 0.14752001, 0.8626257 ],\n",
              "       [0.05345123, 0.54952941, 0.33329937, 0.81996029],\n",
              "       [0.37559612, 0.80355444, 0.33105405, 0.64180839]])"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.random.rand(4,4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ea44MxKaLRV0",
        "outputId": "acd0630e-c710-4309-f8dd-a949dc8b3665"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.random.randint(1,15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t6qyYgLoLRV1",
        "outputId": "a383ab94-e845-435e-da47-68149a520736",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[17, 14, 80, 33, 45, 75, 35, 61, 96, 99],\n",
              "       [ 4, 49, 36, 31, 55, 77, 36, 71,  1, 24],\n",
              "       [88, 75,  6, 12, 70, 22, 86,  8, 33, 50],\n",
              "       [ 7, 45, 68, 94, 83, 46, 83, 45, 18, 85],\n",
              "       [39, 29, 55, 96,  5, 37, 61, 77, 54,  3],\n",
              "       [77,  3, 67, 46,  5, 45, 65, 76, 79, 60],\n",
              "       [31, 67,  1, 85, 13,  9,  1, 80, 59,  4],\n",
              "       [88, 98, 74, 66,  8, 85, 37, 65, 68, 17],\n",
              "       [49, 83, 84, 38, 42, 24, 77,  6, 35, 37],\n",
              "       [79, 39, 93, 67, 77, 44, 32, 63, 34, 25]])"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "np.random.randint(1,100,(10,10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "940jF-MqLRV1"
      },
      "source": [
        "### 1.5 Shape y Reshape\n",
        "El método shape nos permite ver como está construido el objeto numpy, eso quiere decir que nos muestra las dimensiones y el reshape nos da la opción de ordenarlos de diferente forma siempre y cuando se respete la cantidad de elementos en el objeto numpy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RovB-ckxLRV1"
      },
      "outputs": [],
      "source": [
        "arr = np.random.randint(1,10,(3,2))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-8aA83CXaxb",
        "outputId": "aa371d5a-0987-4118-b576-bdab6192ffe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GELwHPYXnLy",
        "outputId": "376d2ff4-8f8c-4135-a96f-c2cd50d7e2a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6, 8],\n",
              "       [1, 7],\n",
              "       [7, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr.reshape(1,6)\n",
        "#arr.reshape(2,3)\n",
        "#arr.reshape(6,1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5i9JX6sXqPA",
        "outputId": "f827ec79-df2f-4beb-fbda-bf291800b8d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6, 8, 1, 7, 7, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.reshape(arr,(1,6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3TrscFphf4N",
        "outputId": "3a7a106d-eb49-47a2-9806-c62da7ba9b21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6, 8, 1, 7, 7, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.reshape(arr,(2,3),'C')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6J7AN_Ohois",
        "outputId": "e512a1e8-92a6-4c16-a369-e6adf18d1364"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6, 8, 1],\n",
              "       [7, 7, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.reshape(arr,(2,3),'F')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CACoePmSiBsR",
        "outputId": "b97790d6-377b-46b1-e1b1-4af598cec948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6, 7, 7],\n",
              "       [1, 8, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.reshape(arr,(2,3),'A')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mv9LZI2JiI4N",
        "outputId": "4423bb43-4cb8-4d0d-c080-59d87ed20d5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6, 8, 1],\n",
              "       [7, 7, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.6 Funciones Principales de Numpy"
      ],
      "metadata": {
        "id": "4eaUNDC5j8al"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.random.randint(1,20,10)"
      ],
      "metadata": {
        "id": "ZzsnWC50kAK_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgV8lxLVlNh0",
        "outputId": "b6675ca9-1fba-4fd5-93a4-5290e8efd0d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  1,  9,  5, 11, 14,  2,  6,  9, 17])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz = arr.reshape(2,5)"
      ],
      "metadata": {
        "id": "VxOMLBS-lAjR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matriz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTvIojMQlrbD",
        "outputId": "3393da96-6f9d-4c99-c704-95e834bb0cfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  1,  9,  5, 11],\n",
              "       [14,  2,  6,  9, 17]])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### max()\n",
        "max() nos trae el valor máximo de un array o de una matriz, este método se le puede pasar dos parámetro como lo son el 0 y el 1.\n",
        "* max(0): Trae los valores máximos de cada columna. Devuelve un array.\n",
        "* max(1): Trae los valores máximos de cada fila. Devuelve un array."
      ],
      "metadata": {
        "id": "QmUkS6ItxEFC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCFpSXOQlT7q",
        "outputId": "478ad888-b3d1-444b-e282-029f496032a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CO443MJlfum",
        "outputId": "8764d49f-5a34-4d6d-e13d-b22ac3eb0d19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz.max(1) #Realiza la búsqueda del mayor por fila\n",
        "#matriz.max(0) #Realiza la búsqueda del mayor por columna"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJv9UN4hmASD",
        "outputId": "e87446d3-f6a6-4b4c-80fe-0b21cb6b0f3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([14,  2,  9,  9, 17])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### argmax()\n",
        "argmax() a diferencia del max() este solo trae la posición del valor mayor dentro de la array o matriz, este método se le puede pasar dos paráremtros como lo son el 0 y el 1.\n",
        "* argmax(0): Trae los indices de los valores máximos de cada columna. Devuelve un array.\n",
        "* argmax(1): Trae los indices de los valores máximos de cada fila. Devuelve un array."
      ],
      "metadata": {
        "id": "SE-36Lgixvxb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matriz.argmax()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVPiZJNAmjrf",
        "outputId": "d572b5fe-dd88-4a56-cf69-944c66aaff6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz.argmax(0) #Por columna\n",
        "#matriz.argmax(1) #Por fila"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dHr09K1mx6y",
        "outputId": "3a98e11c-be9c-4a40-fa8a-704255c968da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### min()\n",
        "min() nos trae el valor mínimo de un array o de una matriz, este método se le puede pasar dos parámetro como lo son el 0 y el 1.\n",
        "* min(0): Trae los valores mínimos de cada columna. Devuelve un array.\n",
        "* min(1): Trae los valores mínimos de cada fila. Devuelve un array."
      ],
      "metadata": {
        "id": "_tEp9QQzyeTo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvi_TgLZwSIU",
        "outputId": "f5d81784-a0dd-4241-8c92-3bd996d78520"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "16orzxGQwZg1",
        "outputId": "0319a329-0284-475b-a24e-371b611a70cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#matriz.min(0)\n",
        "matriz.min(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDCDeMcJweKB",
        "outputId": "e641dcf8-b46a-471e-9f9d-2f711f84ed1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### argmin()\n",
        "argmin() a diferencia del min() este solo trae la posición del valor menor dentro de la array o matriz, este método se le puede pasar dos paráremtros como lo son el 0 y el 1.\n",
        "* argmin(0): Trae los indices de los valores mínimos de cada columna. Devuelve un array.\n",
        "* argmax(1): Trae los indices de los valores mínimos de cada fila. Devuelve un array."
      ],
      "metadata": {
        "id": "fUu8P5dVzBdw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matriz.argmin()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbaDDOcTwr5g",
        "outputId": "602f57b4-4974-4839-d898-72bdd5d0b69c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz.argmin(0)\n",
        "#matriz.argmin(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_2NjtFkwwb8",
        "outputId": "4973cd3e-8bd5-4c38-c21f-ef9170936f95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ptp()\n",
        "Calcula la diferencia entre el valor máximo y el mínimo de cada array o matriz. Al igual que los otros métodos podemos aplicarlo a las filas y a las columnas"
      ],
      "metadata": {
        "id": "52uwNYKizTSy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr.ptp()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1NTZEC1zWKw",
        "outputId": "27e151fe-3a73-460d-8888-8e0749061011"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz.ptp()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKHLaUOezqQt",
        "outputId": "082c48c5-7574-49d5-ca1b-ce78679e4733"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FigcWNIz23H",
        "outputId": "98f55a5a-19bf-4e78-ecd1-0cad44316a8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  1,  9,  5, 11],\n",
              "       [14,  2,  6,  9, 17]])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz.ptp(0)\n",
        "#matriz.ptp(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZIWKk3pDzxMl",
        "outputId": "bb1fb764-6a34-4ff7-f0b9-c1657a789085"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12,  1,  3,  4,  6])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### percentile()\n",
        "Los percentiles son valores que dividen un conjunto de datos estadísticos, de forma que un porcentaje de los mismos quede por debajo de dicho valor."
      ],
      "metadata": {
        "id": "iwuNom3t0UqZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr.sort() #sort() es un método que nos permite ordenar los elementos.\n",
        "print(arr)\n",
        "matriz.sort()\n",
        "print(f'=====================\\n{matriz}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fNnRHXV1sBI",
        "outputId": "270af648-86fb-49d7-a005-4e9e33bf98f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 1  2  2  5  6  9  9 11 14 17]\n",
            "=====================\n",
            "[[ 1  2  2  5  6]\n",
            " [ 9  9 11 14 17]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.percentile(arr,50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cd7zpm8Z1LIK",
        "outputId": "5f744fba-5a90-4521-fb9f-522fa7c78dd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.5"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.percentile(matriz,50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFUEqsXi1E1Q",
        "outputId": "541bf225-2672-4428-852b-5ee821f31a46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.5"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### median()\n",
        "Calcula la mediana de un conjunto de datos.\n",
        "Al igual que todas la funciones que hemos visto hasta aquí nos permite realizar estas operaciones de forma general, por fila y columna"
      ],
      "metadata": {
        "id": "dGWAB0oO4ttT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.median(arr)"
      ],
      "metadata": {
        "id": "rgi3Z5_N4yy_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.median(matriz,1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQzSHWsu41kY",
        "outputId": "4555307e-613e-41cd-acb7-54afbd942521"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2., 11.])"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### std()\n",
        "Nos permite conocer la desviación estándar de un conjunto de datos.\n",
        "Se puede calcular de forma general o por filas o por columnas."
      ],
      "metadata": {
        "id": "sog7KevF5Jcq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.std(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIZqlaG35ZpQ",
        "outputId": "73619b16-254c-4ae6-dfff-d15e3ede2013"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.102940328869229"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.std(matriz,0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxJ0632x5dL3",
        "outputId": "3af9b666-c586-4004-c919-f1583318f0b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4. , 3.5, 4.5, 4.5, 5.5])"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### var()\n",
        "Podemos tener la varianza en un conjunto de datos. Se puede calcular de forma general o por filas o por columnas."
      ],
      "metadata": {
        "id": "uAQrRIJY5pZd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1DyUpp8s5yr3",
        "outputId": "eda464bb-7bbd-4dd6-d428-481b7fc618cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.04"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(matriz,0)"
      ],
      "metadata": {
        "id": "-DHx1EiQ51Jt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### mean()\n",
        "Calcula la media de un conjunto de datos. Se puede calcular de forma general o por filas o por columnas."
      ],
      "metadata": {
        "id": "Lk_RWuNY6DR5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLUSpZSC6N6O",
        "outputId": "2af9244a-b577-4aa7-8cad-10bc65dd8905"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.6"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(matriz)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GrIlK4yK6TUJ",
        "outputId": "24c8d02b-abe3-41ea-8e4b-ea598a595491"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.6"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(matriz,0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1hZ4-Jf36YES",
        "outputId": "f7ad9e95-632b-474a-fe2d-db1e2e43bb40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 5. ,  5.5,  6.5,  9.5, 11.5])"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### concatener(array1,array2)\n",
        "Nos permite concatenear arrays."
      ],
      "metadata": {
        "id": "p45-IvPe6aPo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.random.randint(1,10,(2,2))\n",
        "b = np.random.randint(1,10,(2,2))"
      ],
      "metadata": {
        "id": "GoAlotjS6scq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a,b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QdE02yCK7TLN",
        "outputId": "94e05323-5086-42cd-9187-5e6295050da8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[7, 4],\n",
              "        [2, 8]]),\n",
              " array([[8, 4],\n",
              "        [5, 1]]))"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.concatenate((a,b))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aex7_H9e7YXh",
        "outputId": "e107248b-a819-4cca-bb1e-d30db40ef819"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7, 4],\n",
              "       [2, 8],\n",
              "       [8, 4],\n",
              "       [5, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.concatenate((a,b),axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rPX81V07pse",
        "outputId": "e2db1c7e-4171-43c2-e76e-0780d02d60de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7, 4, 8, 4],\n",
              "       [2, 8, 5, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### expand_dim()\n",
        "Nos permite expandir la dimensión de un array."
      ],
      "metadata": {
        "id": "BTpu2pSk8ozS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c = np.array([1,2])\n",
        "c.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcXn6J_48P7W",
        "outputId": "d1a27be6-61b1-434f-b38b-1049e21bff68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = np.expand_dims(c,axis=0)\n",
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WifV0ZSt9oe_",
        "outputId": "dbf515b8-f85a-4b9f-d276-cd8e7cc744df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = np.expand_dims(c,axis=1)\n",
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3T9hmjL9AhM",
        "outputId": "600e34fb-676d-4fa1-baa9-4c108fd47d26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1],\n",
              "       [2]])"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.concatenate((a,c),axis=0)\n",
        "#np.concatenate((a,c),axis=1) #Nos genera un error porque aunque sean de la misma dimensión, no son del mismo tamaño de la matriz."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYgtEMl096tt",
        "outputId": "5cc427d5-8617-41f1-ee5d-91980b7c3dc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6, 3],\n",
              "       [7, 7],\n",
              "       [1, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### T (Transpuesta de una matriz)"
      ],
      "metadata": {
        "id": "_EMZgbub_akg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = np.random.randint(2,10,(2,3))\n",
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGa89aJx_dwZ",
        "outputId": "fb822673-58ef-4ca5-e6c9-8fa2dadef3d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9, 4, 6],\n",
              "       [7, 7, 8]])"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0KgpwmD_8s8",
        "outputId": "162154a5-c8c5-423d-83c6-2eeda19d9fd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9, 7],\n",
              "       [4, 7],\n",
              "       [6, 8]])"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.7 Copy"
      ],
      "metadata": {
        "id": "sSJOODWIINvN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A continuación veremos la mala práctica de copiar un conjunto de datos en numpy."
      ],
      "metadata": {
        "id": "lJtC3CrhJfeu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.arange(0,11)\n",
        "arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-to_RW-9IQKQ",
        "outputId": "1b5cdd46-d04f-4202-fbe1-a443724d5ffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trozo = arr[0:6]\n",
        "trozo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4fhN3KII5IQ",
        "outputId": "9c968cc5-5231-41f0-8696-ff72ae891cf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trozo[:] = 0\n",
        "trozo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzwQbYDHJJBv",
        "outputId": "a4e4e74b-a37c-4629-d8ff-9d06a381e6a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KwLeCgMtJSIh",
        "outputId": "0529e547-d1d4-4e5e-8dd3-f247be9c7360"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  0,  0,  0,  0,  0,  6,  7,  8,  9, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La forma correcta de hacerlo es la siguiente"
      ],
      "metadata": {
        "id": "SLcq_sQkJmgG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr_copy = arr.copy()"
      ],
      "metadata": {
        "id": "y8u0UAwlJqpl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr_copy[:] = 100\n",
        "arr_copy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaMNhlwTJ5EQ",
        "outputId": "203767cb-7b85-43ab-fc40-a3193861d7fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([100, 100, 100, 100, 100, 100, 100, 100, 100, 100])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.8 Condiciones"
      ],
      "metadata": {
        "id": "hCfzvSFLhCkj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.linspace(1,10,10,dtype='int8')"
      ],
      "metadata": {
        "id": "qS09LdVUhGtN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SiEr9shjiwuN",
        "outputId": "e2557476-f728-41dd-ee78-845c9dfb68ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10], dtype=int8)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indices_cond = arr > 5"
      ],
      "metadata": {
        "id": "g8A1fG9Di2h2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "indices_cond"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4BaVamljAh_",
        "outputId": "7a05d6cd-44af-4f7c-94b3-cc2149d9612f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False, False, False,  True,  True,  True,  True,\n",
              "        True])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr[indices_cond]\n",
        "#arr[arr > 5] #Es exactamente lo mismo, es otra forma de evaluar la condición."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5xPshdCjIWT",
        "outputId": "dd29275a-38ee-43a1-ebab-151098515bc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 6,  7,  8,  9, 10], dtype=int8)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr[(arr > 5) & (arr<9)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLqgYB4VjeON",
        "outputId": "b67cc266-d7ac-4db2-8856-c8cea77062de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 7, 8], dtype=int8)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr[arr > 5] = 99\n",
        "#arr"
      ],
      "metadata": {
        "id": "3b9t7fisjscW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr[arr==99]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z6Ev079mj6vM",
        "outputId": "a7041abb-9f54-4555-d46f-5f372c9ec7c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([99, 99, 99, 99, 99], dtype=int8)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.9 Operaciones"
      ],
      "metadata": {
        "id": "83Gc5kr0mLoD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista = [1,2]"
      ],
      "metadata": {
        "id": "nSSoktgnmO81"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lista * 2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYKdl2MbmX7G",
        "outputId": "475bb626-f1b7-44fc-ad48-e81e8e94db03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 1, 2]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr=np.arange(0,10)"
      ],
      "metadata": {
        "id": "PmPumFggmgiE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr2 = arr.copy()"
      ],
      "metadata": {
        "id": "EVy46j8-oUms"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kU905Ul8oQ8h",
        "outputId": "8a748fbe-07dd-4696-ed73-3c18feefcbe0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz = arr.reshape(2,5)"
      ],
      "metadata": {
        "id": "lYjOxkOwp_Qh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matriz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JgPN2tv7qDOn",
        "outputId": "30108ce7-73c3-4619-d40b-7da119ca0314"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2, 3, 4],\n",
              "       [5, 6, 7, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz2 = matriz.copy()"
      ],
      "metadata": {
        "id": "P7hcjFeNqGrz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matriz2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRioBdLoqKwr",
        "outputId": "69d9a8b0-43c9-4d2b-d755-4880bc7b1978"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2, 3, 4],\n",
              "       [5, 6, 7, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Multiplicación"
      ],
      "metadata": {
        "id": "MSabx7zYody-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr * 2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E5De4c8oogTG",
        "outputId": "04ef8dac-e8e6-40ee-f52a-9ef578a4d5a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr * arr2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjskBiZGpjXh",
        "outputId": "a1e1a771-8e09-4e8c-e9dc-99f37f1194cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz * matriz2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_HGoIZ5qaGH",
        "outputId": "7f4e2ad2-99cc-43e3-bf98-8865d02b5cf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  4,  9, 16],\n",
              "       [25, 36, 49, 64, 81]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Suma"
      ],
      "metadata": {
        "id": "G8aw0ak4opdC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr + 2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuVUW3XoonMJ",
        "outputId": "abe8ebf2-52e6-4ba3-845b-f87d79729377"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr + arr2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xEjywLOpYs8",
        "outputId": "0c8ccb39-d22c-4781-8c37-dba00a2702e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz + matriz2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NT45u-I9qPMO",
        "outputId": "93ed50bf-c8d6-42b1-98a0-72683ef78b2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  2,  4,  6,  8],\n",
              "       [10, 12, 14, 16, 18]])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* División"
      ],
      "metadata": {
        "id": "If7W6hBxoyq3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lo interesante de este problema es que si existe un elemento que es cero, en lugar de saltar un error, numpy nos indica en que posición se proporcionó el error y sigue con las operaciones en cada elemento."
      ],
      "metadata": {
        "id": "fgQGcNBso2TZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "1/arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wYrUVvLosat",
        "outputId": "3a137bda-1cd6-4ec7-9da8-71f069dd91b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-54-016353831300>:1: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  1/arr\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([       inf, 1.        , 0.5       , 0.33333333, 0.25      ,\n",
              "       0.2       , 0.16666667, 0.14285714, 0.125     , 0.11111111])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr / arr2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pUr53jtGpo3r",
        "outputId": "756eca51-2c20-480d-af84-9bd0b4b157dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-55-5a1b3fd1a087>:1: RuntimeWarning: invalid value encountered in true_divide\n",
            "  arr / arr2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz / matriz2"
      ],
      "metadata": {
        "id": "KzbRvhH8qify"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Potencia"
      ],
      "metadata": {
        "id": "GrCd3OALpRlC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr ** 2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7NtTkJ_pOZP",
        "outputId": "0ca62e3a-1008-4842-e14e-8ac7d17ca7a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Producto Punto"
      ],
      "metadata": {
        "id": "9UIKIHWXqnx5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.matmul(matriz,matriz2.T)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q056rM_5qqqQ",
        "outputId": "fea18b29-2cee-4fc1-d228-8b3c3cd7864a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 30,  80],\n",
              "       [ 80, 255]])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz @ matriz2.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nbSZTDuJwVR0",
        "outputId": "6aa2f83a-10e4-4677-8d69-311f121e7ec7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 30,  80],\n",
              "       [ 80, 255]])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Uso de la libreria Pandas\n",
        "Pandas es una librería de Python especializada en el manejo y análisis de estructuras de datos. El nombre viene de “Panel data”.\n",
        "* Velocidad,Poco código, Múltiples formatos de archivos,Alineación inteligente\n",
        "* Pandas Series: Es muy parecido a un array unidimensional de numpy, es indexado, puedo utilizar slices,puedo entrar a los objetos en específicos, optimizado y puede manejar diferentes tipos de datos.\n",
        "* Pandas DataFrame: Es un objeto hecho de arrayz pero matricial (filas y columnas) cada fila tiene un índice. Optimiza mucho la memoria."
      ],
      "metadata": {
        "id": "-8Be2JiqK5HD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "KNLGl8B0K7tY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.1 Pandas Series"
      ],
      "metadata": {
        "id": "KYimquGaUTUB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "psg_player = pd.Series(['Navas','Mbappe','Neymar','Messi'], index=[1,7,10,30])\n",
        "psg_player"
      ],
      "metadata": {
        "id": "35B9iUAgLLXo",
        "outputId": "0f76ad35-3e28-4191-f7e6-d4b594611e56",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1      Navas\n",
              "7     Mbappe\n",
              "10    Neymar\n",
              "30     Messi\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "psg_player[7]"
      ],
      "metadata": {
        "id": "jb157-DhQyAu",
        "outputId": "84ed75ef-1d87-4640-adc9-001494844e66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Mbappe'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "psg_player = pd.Series(['Navas','Mbappe','Neymar','Messi'])\n",
        "psg_player"
      ],
      "metadata": {
        "id": "NRDLTW_9QJsC",
        "outputId": "d2685a6d-49a6-4dc5-83d7-17df73848a05",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     Navas\n",
              "1    Mbappe\n",
              "2    Neymar\n",
              "3     Messi\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "psg_player[2:]"
      ],
      "metadata": {
        "id": "57ROqYZVRLX3",
        "outputId": "329a44df-3495-4549-e5b1-63e0dd16af3f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    Neymar\n",
              "3     Messi\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary = {1:'Navas',7:'Mbappe',10:'Neymar',30:'Messi'}\n",
        "psg_player = pd.Series(dictionary)\n",
        "psg_player"
      ],
      "metadata": {
        "id": "_PNRyiTdQOvq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "psg_player[7]"
      ],
      "metadata": {
        "id": "657JBESFRdhB",
        "outputId": "47882d9c-9fc8-42e8-c30b-4acc7fe248eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Mbappe'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.2 Pandas DataFrame"
      ],
      "metadata": {
        "id": "gOwKD0T5UYDq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary = {'Jugador':['Navas','Mbappe','Neymar','Messi'],\n",
        " 'Altura':[183.0,170.0,170.0,165.0],\n",
        " 'Goles':[2,200,200,200]}"
      ],
      "metadata": {
        "id": "_zM9fauNR-jN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_player = pd.DataFrame(dictionary,index=[1,7,10,30])\n",
        "df_player"
      ],
      "metadata": {
        "id": "ltWMii9wSvIv",
        "outputId": "12f070a8-153a-4bb0-dc29-4d0883de8d90",
        "colab": {
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          "height": 175
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Jugador  Altura  Goles\n",
              "1    Navas   183.0      2\n",
              "7   Mbappe   170.0    200\n",
              "10  Neymar   170.0    200\n",
              "30   Messi   165.0    200"
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      "cell_type": "code",
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        "df_player = pd.DataFrame(dictionary)\n",
        "df_player"
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      "metadata": {
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          "data": {
            "text/plain": [
              "  Jugador  Altura  Goles\n",
              "0   Navas   183.0      2\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-35387e7f-2dc0-4b42-a478-2e535d155e38');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "lz1UrbiIUQto"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_player.columns"
      ],
      "metadata": {
        "id": "qjEmfoMNUGeF",
        "outputId": "b9a67b0f-125c-4670-f715-acbb655f6ec6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Jugador', 'Altura', 'Goles'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(df_player)"
      ],
      "metadata": {
        "id": "X1dS1T0ZXXjA",
        "outputId": "701921a4-b433-417e-d3c1-de20fd707d59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Jugador', 'Altura', 'Goles']"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_player.index"
      ],
      "metadata": {
        "id": "nDkM1X5OULIZ",
        "outputId": "7a0089c8-dc1d-4e53-c73d-444e27b11d29",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([1, 7, 10, 30], dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.3 Leer archivos CSV y JSON con Pandas"
      ],
      "metadata": {
        "id": "PCwYyqKm6yJn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "2VZ2wDFZ7CFo",
        "outputId": "efb94d96-a280-448f-a866-4df5f3b93a8e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir '/content/drive/MyDrive/Pandas'"
      ],
      "metadata": {
        "id": "I_fHQkRB7v2v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_books = pd.read_csv('/content/drive/MyDrive/Pandas/bestsellers-with-categories_e591527f-ae45-4fa5-b0d1-d50142128fa6.csv')"
      ],
      "metadata": {
        "id": "Cabc5giv-IVB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* El parámetro **sep** de la función read_csv permite cambiar las separaciones que vienen en el csv, por lo general los csv trabajan con comas (,) pero de no ser así podemos cambiar estos separadores.\n",
        "* El parámetro **header** permite mover el encabezado del dataframe bajando por filas.\n",
        "* El parámetro **names** nos permite cambiarles el nombre a las columnas, nos permite personalizar.\n",
        "\n"
      ],
      "metadata": {
        "id": "Svlp0yl6BjHc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books = pd.read_csv('/content/drive/MyDrive/Pandas/bestsellers-with-categories_e591527f-ae45-4fa5-b0d1-d50142128fa6.csv',sep='|',header=1)"
      ],
      "metadata": {
        "id": "j0MAf7oiBTIj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_books"
      ],
      "metadata": {
        "id": "B5_phnxwCZyU",
        "outputId": "ad6f16fd-438b-478c-8640-9804c812bf77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                                  Name  \\\n",
              "0                        10-Day Green Smoothie Cleanse   \n",
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              "2              12 Rules for Life: An Antidote to Chaos   \n",
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              "..                                                 ...   \n",
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              "2          Jordan B. Peterson          4.7    18979     15  2018  Non Fiction  \n",
              "3               George Orwell          4.7    21424      6  2017      Fiction  \n",
              "4    National Geographic Kids          4.8     7665     12  2019  Non Fiction  \n",
              "..                        ...          ...      ...    ...   ...          ...  \n",
              "545               Jeff Kinney          4.9     9413      8  2019      Fiction  \n",
              "546               Jen Sincero          4.7    14331      8  2016  Non Fiction  \n",
              "547               Jen Sincero          4.7    14331      8  2017  Non Fiction  \n",
              "548               Jen Sincero          4.7    14331      8  2018  Non Fiction  \n",
              "549               Jen Sincero          4.7    14331      8  2019  Non Fiction  \n",
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    {
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      "source": [
        "df_books = pd.read_csv('/content/drive/MyDrive/Pandas/bestsellers-with-categories_e591527f-ae45-4fa5-b0d1-d50142128fa6.csv',sep=',',header=0,)"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
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              "                                                  Name  \\\n",
              "0                        10-Day Green Smoothie Cleanse   \n",
              "1                                    11/22/63: A Novel   \n",
              "2              12 Rules for Life: An Antidote to Chaos   \n",
              "3                               1984 (Signet Classics)   \n",
              "4    5,000 Awesome Facts (About Everything!) (Natio...   \n",
              "..                                                 ...   \n",
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              "2          Jordan B. Peterson          4.7    18979     15  2018  Non Fiction  \n",
              "3               George Orwell          4.7    21424      6  2017      Fiction  \n",
              "4    National Geographic Kids          4.8     7665     12  2019  Non Fiction  \n",
              "..                        ...          ...      ...    ...   ...          ...  \n",
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              "546               Jen Sincero          4.7    14331      8  2016  Non Fiction  \n",
              "547               Jen Sincero          4.7    14331      8  2017  Non Fiction  \n",
              "548               Jen Sincero          4.7    14331      8  2018  Non Fiction  \n",
              "549               Jen Sincero          4.7    14331      8  2019  Non Fiction  \n",
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Author</th>\n",
              "      <th>User Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Price</th>\n",
              "      <th>Year</th>\n",
              "      <th>Genre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10-Day Green Smoothie Cleanse</td>\n",
              "      <td>JJ Smith</td>\n",
              "      <td>4.7</td>\n",
              "      <td>17350</td>\n",
              "      <td>8</td>\n",
              "      <td>2016</td>\n",
              "      <td>Non Fiction</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11/22/63: A Novel</td>\n",
              "      <td>Stephen King</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2052</td>\n",
              "      <td>22</td>\n",
              "      <td>2011</td>\n",
              "      <td>Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12 Rules for Life: An Antidote to Chaos</td>\n",
              "      <td>Jordan B. Peterson</td>\n",
              "      <td>4.7</td>\n",
              "      <td>18979</td>\n",
              "      <td>15</td>\n",
              "      <td>2018</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1984 (Signet Classics)</td>\n",
              "      <td>George Orwell</td>\n",
              "      <td>4.7</td>\n",
              "      <td>21424</td>\n",
              "      <td>6</td>\n",
              "      <td>2017</td>\n",
              "      <td>Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5,000 Awesome Facts (About Everything!) (Natio...</td>\n",
              "      <td>National Geographic Kids</td>\n",
              "      <td>4.8</td>\n",
              "      <td>7665</td>\n",
              "      <td>12</td>\n",
              "      <td>2019</td>\n",
              "      <td>Non Fiction</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>545</th>\n",
              "      <td>Wrecking Ball (Diary of a Wimpy Kid Book 14)</td>\n",
              "      <td>Jeff Kinney</td>\n",
              "      <td>4.9</td>\n",
              "      <td>9413</td>\n",
              "      <td>8</td>\n",
              "      <td>2019</td>\n",
              "      <td>Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "      <td>14331</td>\n",
              "      <td>8</td>\n",
              "      <td>2016</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>547</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "      <td>14331</td>\n",
              "      <td>8</td>\n",
              "      <td>2017</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>548</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "      <td>14331</td>\n",
              "      <td>8</td>\n",
              "      <td>2018</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "      <td>14331</td>\n",
              "      <td>8</td>\n",
              "      <td>2019</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>550 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e98cea7b-2227-43a4-807e-5b5915879ab7')\"\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "boD6JsJZDY7R",
        "outputId": "f538cae8-9341-4dcd-f419-28d1fb6ad4ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Name', 'Author', 'User Rating', 'Reviews', 'Price', 'Year', 'Genre'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books = pd.read_csv('/content/drive/MyDrive/Pandas/bestsellers-with-categories_e591527f-ae45-4fa5-b0d1-d50142128fa6.csv',\n",
        "                       sep=',',header=0,names=['Namesss', 'Authorrrr', 'User Ratinggg', 'Reviewsss', 'Priceee', 'Yearrr', 'Genreeee'])"
      ],
      "metadata": {
        "id": "sHJCjKoODfi6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_books"
      ],
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        "id": "tqqtJiscD7wq",
        "outputId": "8b8abe48-2e47-4121-e0be-f3f05fdec3c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Namesss  \\\n",
              "0                        10-Day Green Smoothie Cleanse   \n",
              "1                                    11/22/63: A Novel   \n",
              "2              12 Rules for Life: An Antidote to Chaos   \n",
              "3                               1984 (Signet Classics)   \n",
              "4    5,000 Awesome Facts (About Everything!) (Natio...   \n",
              "..                                                 ...   \n",
              "545       Wrecking Ball (Diary of a Wimpy Kid Book 14)   \n",
              "546  You Are a Badass: How to Stop Doubting Your Gr...   \n",
              "547  You Are a Badass: How to Stop Doubting Your Gr...   \n",
              "548  You Are a Badass: How to Stop Doubting Your Gr...   \n",
              "549  You Are a Badass: How to Stop Doubting Your Gr...   \n",
              "\n",
              "                    Authorrrr  User Ratinggg  Reviewsss  Priceee  Yearrr  \\\n",
              "0                    JJ Smith            4.7      17350        8    2016   \n",
              "1                Stephen King            4.6       2052       22    2011   \n",
              "2          Jordan B. Peterson            4.7      18979       15    2018   \n",
              "3               George Orwell            4.7      21424        6    2017   \n",
              "4    National Geographic Kids            4.8       7665       12    2019   \n",
              "..                        ...            ...        ...      ...     ...   \n",
              "545               Jeff Kinney            4.9       9413        8    2019   \n",
              "546               Jen Sincero            4.7      14331        8    2016   \n",
              "547               Jen Sincero            4.7      14331        8    2017   \n",
              "548               Jen Sincero            4.7      14331        8    2018   \n",
              "549               Jen Sincero            4.7      14331        8    2019   \n",
              "\n",
              "        Genreeee  \n",
              "0    Non Fiction  \n",
              "1        Fiction  \n",
              "2    Non Fiction  \n",
              "3        Fiction  \n",
              "4    Non Fiction  \n",
              "..           ...  \n",
              "545      Fiction  \n",
              "546  Non Fiction  \n",
              "547  Non Fiction  \n",
              "548  Non Fiction  \n",
              "549  Non Fiction  \n",
              "\n",
              "[550 rows x 7 columns]"
            ],
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.read_json('/content/drive/MyDrive/Pandas/hpcharactersdataraw_3d934e85-dfa4-42ec-8520-fadfbecae574.json')"
      ],
      "metadata": {
        "id": "Q3LZPtYCEyxy",
        "outputId": "4f0d4fa6-1b98-4551-fb5e-7c7350987616",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Name                                               Link  \\\n",
              "0              Mrs. Abbott  https://www.hp-lexicon.org/character/abbott-fa...   \n",
              "1            Hannah Abbott  https://www.hp-lexicon.org/character/abbott-fa...   \n",
              "2            Abel Treetops  https://www.hp-lexicon.org/character/abel-tree...   \n",
              "3         Euan Abercrombie  https://www.hp-lexicon.org/character/abercromb...   \n",
              "4     Aberforth Dumbledore  https://www.hp-lexicon.org/character/dumbledor...   \n",
              "...                    ...                                                ...   \n",
              "1935        Georgi Zdravko  https://www.hp-lexicon.org/character/georgi-zd...   \n",
              "1936                Zograf       https://www.hp-lexicon.org/character/zograf/   \n",
              "1937                 Zonko        https://www.hp-lexicon.org/character/zonko/   \n",
              "1938     Valentina Vázquez  https://www.hp-lexicon.org/character/valentina...   \n",
              "1939         Zygmunt Budge  https://www.hp-lexicon.org/character/zygmunt-b...   \n",
              "\n",
              "                                                  Descr  Gender Species/Race  \\\n",
              "0     Mrs. Abbott was the mother of Hannah Abbott, a...  Female        Witch   \n",
              "1     Hannah Abbott is a Hufflepuff student in Harry...  Female        Witch   \n",
              "2     Abel Treetops was a wizard from Cincinnati who...    Male       Wizard   \n",
              "3     Euan Abercrombie was a small boy with prominen...    Male       Wizard   \n",
              "4     Aberforth Dumbledore was a tall, thin, grumpy-...    Male       Wizard   \n",
              "...                                                 ...     ...          ...   \n",
              "1935  Georgi Zdravko played Keeper for the Bulgarian...    Male       Wizard   \n",
              "1936  Zograf played Keeper for the Bulgarian Nationa...     NaN       Wizard   \n",
              "1937  Founder(?) of Zonko’s Joke Shop. Possibly a re...     NaN      Unknown   \n",
              "1938  Valentina Vázquez was President of the Argenti...  Female        Witch   \n",
              "1939  Zygmunt Budge is considered to be one of the m...    Male       Wizard   \n",
              "\n",
              "            Blood                 School  \\\n",
              "0     Muggle-born                Unknown   \n",
              "1      Half-blood  Hogwarts - Hufflepuff   \n",
              "2         Unknown                Unknown   \n",
              "3         Unknown  Hogwarts - Gryffindor   \n",
              "4      Half-blood     Hogwarts - Student   \n",
              "...           ...                    ...   \n",
              "1935      Unknown                Unknown   \n",
              "1936      Unknown                Unknown   \n",
              "1937      Unknown                Unknown   \n",
              "1938      Unknown                Unknown   \n",
              "1939      Unknown     Hogwarts - Student   \n",
              "\n",
              "                                         Profession  \n",
              "0                                           Unknown  \n",
              "1                    Landlady of the Leaky Cauldron  \n",
              "2                                           Unknown  \n",
              "3                                           Unknown  \n",
              "4                                            Barman  \n",
              "...                                             ...  \n",
              "1935                      Quidditch player (Seeker)  \n",
              "1936                      Quidditch player (Keeper)  \n",
              "1937                                        Unknown  \n",
              "1938  President of the Argentinian Council of Magic  \n",
              "1939    Potions Master, Potioneer, Inventor, Author  \n",
              "\n",
              "[1940 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-61c86979-1939-4ade-a067-1b44b7d03fdd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Link</th>\n",
              "      <th>Descr</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Species/Race</th>\n",
              "      <th>Blood</th>\n",
              "      <th>School</th>\n",
              "      <th>Profession</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mrs. Abbott</td>\n",
              "      <td>https://www.hp-lexicon.org/character/abbott-fa...</td>\n",
              "      <td>Mrs. Abbott was the mother of Hannah Abbott, a...</td>\n",
              "      <td>Female</td>\n",
              "      <td>Witch</td>\n",
              "      <td>Muggle-born</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hannah Abbott</td>\n",
              "      <td>https://www.hp-lexicon.org/character/abbott-fa...</td>\n",
              "      <td>Hannah Abbott is a Hufflepuff student in Harry...</td>\n",
              "      <td>Female</td>\n",
              "      <td>Witch</td>\n",
              "      <td>Half-blood</td>\n",
              "      <td>Hogwarts - Hufflepuff</td>\n",
              "      <td>Landlady of the Leaky Cauldron</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abel Treetops</td>\n",
              "      <td>https://www.hp-lexicon.org/character/abel-tree...</td>\n",
              "      <td>Abel Treetops was a wizard from Cincinnati who...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Wizard</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Euan Abercrombie</td>\n",
              "      <td>https://www.hp-lexicon.org/character/abercromb...</td>\n",
              "      <td>Euan Abercrombie was a small boy with prominen...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Wizard</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Hogwarts - Gryffindor</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aberforth Dumbledore</td>\n",
              "      <td>https://www.hp-lexicon.org/character/dumbledor...</td>\n",
              "      <td>Aberforth Dumbledore was a tall, thin, grumpy-...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Wizard</td>\n",
              "      <td>Half-blood</td>\n",
              "      <td>Hogwarts - Student</td>\n",
              "      <td>Barman</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1935</th>\n",
              "      <td>Georgi Zdravko</td>\n",
              "      <td>https://www.hp-lexicon.org/character/georgi-zd...</td>\n",
              "      <td>Georgi Zdravko played Keeper for the Bulgarian...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Wizard</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Quidditch player (Seeker)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1936</th>\n",
              "      <td>Zograf</td>\n",
              "      <td>https://www.hp-lexicon.org/character/zograf/</td>\n",
              "      <td>Zograf played Keeper for the Bulgarian Nationa...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wizard</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Quidditch player (Keeper)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1937</th>\n",
              "      <td>Zonko</td>\n",
              "      <td>https://www.hp-lexicon.org/character/zonko/</td>\n",
              "      <td>Founder(?) of Zonko’s Joke Shop. Possibly a re...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1938</th>\n",
              "      <td>Valentina Vázquez</td>\n",
              "      <td>https://www.hp-lexicon.org/character/valentina...</td>\n",
              "      <td>Valentina Vázquez was President of the Argenti...</td>\n",
              "      <td>Female</td>\n",
              "      <td>Witch</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>President of the Argentinian Council of Magic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1939</th>\n",
              "      <td>Zygmunt Budge</td>\n",
              "      <td>https://www.hp-lexicon.org/character/zygmunt-b...</td>\n",
              "      <td>Zygmunt Budge is considered to be one of the m...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Wizard</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Hogwarts - Student</td>\n",
              "      <td>Potions Master, Potioneer, Inventor, Author</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1940 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-61c86979-1939-4ade-a067-1b44b7d03fdd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-61c86979-1939-4ade-a067-1b44b7d03fdd 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-61c86979-1939-4ade-a067-1b44b7d03fdd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Otra forma de leer el .JSON es utilizar la nomenclatura key:value cambiando el tipo con el parámetro typ='Series'"
      ],
      "metadata": {
        "id": "CJpXY02wFFMs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "read_json = pd.read_json('/content/drive/MyDrive/Pandas/hpcharactersdataraw_3d934e85-dfa4-42ec-8520-fadfbecae574.json',typ='Series')"
      ],
      "metadata": {
        "id": "d6BpqpZ3KJuH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "read_json"
      ],
      "metadata": {
        "id": "vdbSbbpvKSoc",
        "outputId": "67d54210-a9f3-478d-d320-2d939e01c17b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       {'Name': 'Mrs. Abbott', 'Link': 'https://www.h...\n",
              "1       {'Name': 'Hannah Abbott', 'Link': 'https://www...\n",
              "2       {'Name': 'Abel Treetops', 'Link': 'https://www...\n",
              "3       {'Name': 'Euan Abercrombie', 'Link': 'https://...\n",
              "4       {'Name': 'Aberforth Dumbledore', 'Link': 'http...\n",
              "                              ...                        \n",
              "1935    {'Name': 'Georgi Zdravko', 'Link': 'https://ww...\n",
              "1936    {'Name': 'Zograf', 'Link': 'https://www.hp-lex...\n",
              "1937    {'Name': 'Zonko', 'Link': 'https://www.hp-lexi...\n",
              "1938    {'Name': 'Valentina Vázquez', 'Link': 'https:/...\n",
              "1939    {'Name': 'Zygmunt Budge', 'Link': 'https://www...\n",
              "Length: 1940, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "read_json[0]"
      ],
      "metadata": {
        "id": "n5bJ1bl9KVmj",
        "outputId": "84ad2d3c-e6c0-4c37-80d8-31a8ea549374",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Name': 'Mrs. Abbott',\n",
              " 'Link': 'https://www.hp-lexicon.org/character/abbott-family/mrs-abbott/',\n",
              " 'Descr': 'Mrs. Abbott was the mother of Hannah Abbott, a Hufflepuff in Harry’s year. Mrs. Abbott was found dead in the fall of 1996, having been murdered by Death Eaters (HBP11). ',\n",
              " 'Gender': 'Female',\n",
              " 'Species/Race': 'Witch',\n",
              " 'Blood': 'Muggle-born',\n",
              " 'School': 'Unknown',\n",
              " 'Profession': 'Unknown'}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.4 iloc y loc"
      ],
      "metadata": {
        "id": "LAwlCXyLxxv2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books[0:4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "DQeeil9gx2ZI",
        "outputId": "8d52b532-29f1-4811-b743-e87765d89b4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      Name              Author  User Rating  \\\n",
              "0            10-Day Green Smoothie Cleanse            JJ Smith          4.7   \n",
              "1                        11/22/63: A Novel        Stephen King          4.6   \n",
              "2  12 Rules for Life: An Antidote to Chaos  Jordan B. Peterson          4.7   \n",
              "3                   1984 (Signet Classics)       George Orwell          4.7   \n",
              "\n",
              "   Reviews  Price  Year        Genre  \n",
              "0    17350      8  2016  Non Fiction  \n",
              "1     2052     22  2011      Fiction  \n",
              "2    18979     15  2018  Non Fiction  \n",
              "3    21424      6  2017      Fiction  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d7beb59c-61fb-41d2-850e-d492418493c4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Author</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10-Day Green Smoothie Cleanse</td>\n",
              "      <td>JJ Smith</td>\n",
              "      <td>4.7</td>\n",
              "      <td>17350</td>\n",
              "      <td>8</td>\n",
              "      <td>2016</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11/22/63: A Novel</td>\n",
              "      <td>Stephen King</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2052</td>\n",
              "      <td>22</td>\n",
              "      <td>2011</td>\n",
              "      <td>Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12 Rules for Life: An Antidote to Chaos</td>\n",
              "      <td>Jordan B. Peterson</td>\n",
              "      <td>4.7</td>\n",
              "      <td>18979</td>\n",
              "      <td>15</td>\n",
              "      <td>2018</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
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              "0                          10-Day Green Smoothie Cleanse\n",
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              "                                                  Name  \\\n",
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              "      <td>Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "      <td>14331</td>\n",
              "      <td>8</td>\n",
              "      <td>2016</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>547</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "      <td>14331</td>\n",
              "      <td>8</td>\n",
              "      <td>2017</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>548</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "      <td>14331</td>\n",
              "      <td>8</td>\n",
              "      <td>2018</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "      <td>14331</td>\n",
              "      <td>8</td>\n",
              "      <td>2019</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>550 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-579695f8-7651-4145-8ee1-5f7d19421faf')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.loc[0:4,['Name','Author']] #Es un slice mucho más complejo o personalizado que el primero que vimos en este apartado."
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "fY4c_2TRzeNU",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Name                    Author\n",
              "0                      10-Day Green Smoothie Cleanse                  JJ Smith\n",
              "1                                  11/22/63: A Novel              Stephen King\n",
              "2            12 Rules for Life: An Antidote to Chaos        Jordan B. Peterson\n",
              "3                             1984 (Signet Classics)             George Orwell\n",
              "4  5,000 Awesome Facts (About Everything!) (Natio...  National Geographic Kids"
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              "      <td>Stephen King</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12 Rules for Life: An Antidote to Chaos</td>\n",
              "      <td>Jordan B. Peterson</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1984 (Signet Classics)</td>\n",
              "      <td>George Orwell</td>\n",
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              "      <th>4</th>\n",
              "      <td>5,000 Awesome Facts (About Everything!) (Natio...</td>\n",
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              "\n",
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          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Operaciones matematicas por columnas gracias a loc."
      ],
      "metadata": {
        "id": "JAmObw9M0eEr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.loc[:,['Reviews']] * -1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "_XAcEuGs0Nmz",
        "outputId": "2de53c38-4863-4774-fcfc-64360d19fff6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Reviews\n",
              "0     -17350\n",
              "1      -2052\n",
              "2     -18979\n",
              "3     -21424\n",
              "4      -7665\n",
              "..       ...\n",
              "545    -9413\n",
              "546   -14331\n",
              "547   -14331\n",
              "548   -14331\n",
              "549   -14331\n",
              "\n",
              "[550 rows x 1 columns]"
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              "      <th>545</th>\n",
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              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>-14331</td>\n",
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              "    <tr>\n",
              "      <th>547</th>\n",
              "      <td>-14331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>548</th>\n",
              "      <td>-14331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>-14331</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>550 rows × 1 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7db6b5c2-701b-45cd-87c8-6fb6b91b1ef3');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.loc[:,['Author']] == 'JJ Smith' #Nos dice quien de todas las filas tiene como nombre de autor JJ Smith"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Zd8jpNeu0kfH",
        "outputId": "057d4433-5890-4f13-81da-83144b9ffc40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Author\n",
              "0      True\n",
              "1     False\n",
              "2     False\n",
              "3     False\n",
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              "..      ...\n",
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              "      <th>0</th>\n",
              "      <td>10-Day Green Smoothie Cleanse</td>\n",
              "      <td>JJ Smith</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11/22/63: A Novel</td>\n",
              "      <td>Stephen King</td>\n",
              "      <td>4.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12 Rules for Life: An Antidote to Chaos</td>\n",
              "      <td>Jordan B. Peterson</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1984 (Signet Classics)</td>\n",
              "      <td>George Orwell</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5,000 Awesome Facts (About Everything!) (Natio...</td>\n",
              "      <td>National Geographic Kids</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>545</th>\n",
              "      <td>Wrecking Ball (Diary of a Wimpy Kid Book 14)</td>\n",
              "      <td>Jeff Kinney</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>547</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>548</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>550 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa40f883-ac2a-4cf7-846d-ab5b0d317413')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.iloc[1,3] * -1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oIKqEZOT14Ho",
        "outputId": "5176f681-64b3-4572-e30a-d75318e4204e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-2052"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.iloc[:2,2:]"
      ],
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          "height": 112
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        "id": "yhOgs3i92I9i",
        "outputId": "a6aed8d6-1f84-43c7-8e0f-11015133ef50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   User Rating  Reviews  Price  Year        Genre\n",
              "0          4.7    17350      8  2016  Non Fiction\n",
              "1          4.6     2052     22  2011      Fiction"
            ],
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.5 Agregar o eliminar datos de mi DataFrame"
      ],
      "metadata": {
        "id": "JZ4eTYk9-uEb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "OCF9vd_G-yOf",
        "outputId": "b4322c7d-59f9-4453-ea3b-2e7a041da6b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Name        Author  User Rating  Reviews  Price  \\\n",
              "0  10-Day Green Smoothie Cleanse      JJ Smith          4.7    17350      8   \n",
              "1              11/22/63: A Novel  Stephen King          4.6     2052     22   \n",
              "\n",
              "   Year        Genre  \n",
              "0  2016  Non Fiction  \n",
              "1  2011      Fiction  "
            ],
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              "\n",
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              "      <th>0</th>\n",
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              "      <td>8</td>\n",
              "      <td>2016</td>\n",
              "      <td>Non Fiction</td>\n",
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              "      <th>1</th>\n",
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              "      <td>22</td>\n",
              "      <td>2011</td>\n",
              "      <td>Fiction</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91c9533d-d770-4a95-b37a-cb251d8a3dc7')\"\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91c9533d-d770-4a95-b37a-cb251d8a3dc7 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-91c9533d-d770-4a95-b37a-cb251d8a3dc7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Eliminar las columnas de un dataFrame\n",
        "\n",
        "axis=(1=Columnas,0=filas)"
      ],
      "metadata": {
        "id": "Z54UDbje_D_p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.drop('Genre',axis=1).head(2) #con drop solo eliminamos la salida, pero no del dataFrame"
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              "                            Name        Author  User Rating  Reviews  Price  \\\n",
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      "cell_type": "code",
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        "df_books.drop('Genre',axis=1,inplace=True)#Ahora sí lo eliminamos del dataFrame"
      ],
      "metadata": {
        "id": "ofBLU-Ew_yCr"
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              "                            Name        Author  User Rating  Reviews  Price  \\\n",
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        "df_books = df_books.drop('Year',axis=1)#Ahora sí lo eliminamos del dataFrame"
      ],
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        "id": "MbgHeG_PAJwn"
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              "                            Name        Author  User Rating  Reviews  Price\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "del df_books['Price'] #No es una buena práctica"
      ],
      "metadata": {
        "id": "KWT04bgBAlj-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "df_books.head(2)"
      ],
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        },
        "id": "4hQmLcosAtpd",
        "outputId": "256caa6f-278d-43c2-cd4c-b17467a9a53c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                      Name              Author  User Rating  \\\n",
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              "2  12 Rules for Life: An Antidote to Chaos  Jordan B. Peterson          4.7   \n",
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              "                                                Name  \\\n",
              "3                             1984 (Signet Classics)   \n",
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    {
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              "                                                 Name            Author  \\\n",
              "10                          A Man Called Ove: A Novel   Fredrik Backman   \n",
              "11  A Patriot's History of the United States: From...  Larry Schweikart   \n",
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              "      <td>A Man Called Ove: A Novel</td>\n",
              "      <td>Fredrik Backman</td>\n",
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              "      <td>23848</td>\n",
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              "      <th>11</th>\n",
              "      <td>A Patriot's History of the United States: From...</td>\n",
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              "      <td>4.6</td>\n",
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              "\n",
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          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.drop(range(0,10),axis=0).head(2)"
      ],
      "metadata": {
        "id": "A4lb3_1ELUCE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agregar columnas en un dataFrame"
      ],
      "metadata": {
        "id": "PNeXWfJQO5KY"
      }
    },
    {
      "cell_type": "code",
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        "df_books.head(2)"
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          "height": 112
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        "id": "ZdjeJ_6-O9Oj",
        "outputId": "fc7047ce-b4b4-4587-b649-0ce714233000"
      },
      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "                            Name        Author  User Rating  Reviews\n",
              "0  10-Day Green Smoothie Cleanse      JJ Smith          4.7    17350\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books['Nueva_Columna'] = np.nan #nan es un valor no númerico, la columna agregada modifica todo el dataFrame real."
      ],
      "metadata": {
        "id": "O7yRZKsPPCOr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df_books.head(2)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 112
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        "id": "4-5ONyVTPMhm",
        "outputId": "91e879d2-e0f5-4132-ff8e-8ea59c99a343"
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              "                            Name        Author  User Rating  Reviews  \\\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
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        "data=np.arange(0,df_books.shape[0])\n",
        "data"
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        "id": "qcLfjVWoQPLI",
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      "execution_count": null,
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              "       299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,\n",
              "       312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,\n",
              "       325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,\n",
              "       338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,\n",
              "       351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,\n",
              "       364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,\n",
              "       377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,\n",
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              "       416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,\n",
              "       429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,\n",
              "       442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,\n",
              "       455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,\n",
              "       468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,\n",
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              "       520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532,\n",
              "       533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545,\n",
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            "<ipython-input-69-2d66bcf3bac0>:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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              "  "
            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2.6 Manejo de datos nulos"
      ],
      "metadata": {
        "id": "wZPWdv_HSFut"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dict_example = {'col1':[1,2,3,np.nan],\n",
        " 'col2':[3,np.nan,6,7],\n",
        " 'col3':['a','b','c',None]}\n"
      ],
      "metadata": {
        "id": "TpmaBLInSImb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(dict_example)"
      ],
      "metadata": {
        "id": "vR6q-CnzyoS5"
      },
      "execution_count": null,
      "outputs": []
    },
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        {
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            "text/plain": [
              "   col1  col2  col3\n",
              "0   1.0   3.0     a\n",
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        {
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              "    col1   col2   col3\n",
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              "3   True  False   True"
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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        "df.fillna('Missing')"
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              "      col1     col2     col3\n",
              "0      1.0      3.0        a\n",
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              "2      3.0      6.0        c\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
      "source": [
        "df.fillna(df.mean()) #Se cambian los datos nulos por la media o cualquier otra operación matemáticas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "uwsglvkE0eHf",
        "outputId": "0160d719-777c-4df1-b6bc-3bb507005907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-87-a2478f315f9e>:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df.fillna(df.mean())\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   col1      col2  col3\n",
              "0   1.0  3.000000     a\n",
              "1   2.0  5.333333     b\n",
              "2   3.0  6.000000     c\n",
              "3   2.0  7.000000  None"
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-faf608a1-0788-4b9e-a814-17aad386019e');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.interpolate() #Interpola los datos nulos."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Qie52Mj302f7",
        "outputId": "b9b1555f-8c68-456b-9f70-711de05fec28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   col1  col2  col3\n",
              "0   1.0   3.0     a\n",
              "1   2.0   4.5     b\n",
              "2   3.0   6.0     c\n",
              "3   3.0   7.0  None"
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              "  "
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          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna() #Elimina los datos nulos."
      ],
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          "height": 112
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        "id": "qGu0O2S41Ka1",
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              "   col1  col2 col3\n",
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              "\n",
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "### 2.7 Filtrado por condiciones"
      ],
      "metadata": {
        "id": "qhNVDWO-4CnG"
      }
    },
    {
      "cell_type": "code",
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        "df_books.head(2)"
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          "height": 112
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        "id": "p8hZ4iNl4IKZ",
        "outputId": "103a1d8a-02ad-4580-9837-f0d9065acf81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Name        Author  User Rating  Reviews  Price  \\\n",
              "0  10-Day Green Smoothie Cleanse      JJ Smith          4.7    17350      8   \n",
              "1              11/22/63: A Novel  Stephen King          4.6     2052     22   \n",
              "\n",
              "   Year        Genre  \n",
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    {
      "cell_type": "code",
      "source": [
        "df_books['Year'] > 2016"
      ],
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        "id": "-xwCjIb34ZRw",
        "outputId": "3aa0c848-354b-4973-d7d4-30c9c7ec362c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      False\n",
              "1      False\n",
              "2       True\n",
              "3       True\n",
              "4       True\n",
              "       ...  \n",
              "545     True\n",
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              "Name: Year, Length: 550, dtype: bool"
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    {
      "cell_type": "code",
      "source": [
        "mayor_a_2016 = df_books['Year'] > 2016"
      ],
      "metadata": {
        "id": "RQRMNEHh-m2C"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
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        "df_books[mayor_a_2016]"
      ],
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        "id": "t_cj8hZ0-qG6",
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      "execution_count": null,
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            "text/plain": [
              "                                                  Name  \\\n",
              "2              12 Rules for Life: An Antidote to Chaos   \n",
              "3                               1984 (Signet Classics)   \n",
              "4    5,000 Awesome Facts (About Everything!) (Natio...   \n",
              "7                       A Gentleman in Moscow: A Novel   \n",
              "8        A Higher Loyalty: Truth, Lies, and Leadership   \n",
              "..                                                 ...   \n",
              "544                                             Wonder   \n",
              "545       Wrecking Ball (Diary of a Wimpy Kid Book 14)   \n",
              "547  You Are a Badass: How to Stop Doubting Your Gr...   \n",
              "548  You Are a Badass: How to Stop Doubting Your Gr...   \n",
              "549  You Are a Badass: How to Stop Doubting Your Gr...   \n",
              "\n",
              "                       Author  User Rating  Reviews  Price  Year        Genre  \n",
              "2          Jordan B. Peterson          4.7    18979     15  2018  Non Fiction  \n",
              "3               George Orwell          4.7    21424      6  2017      Fiction  \n",
              "4    National Geographic Kids          4.8     7665     12  2019  Non Fiction  \n",
              "7                 Amor Towles          4.7    19699     15  2017      Fiction  \n",
              "8                 James Comey          4.7     5983      3  2018  Non Fiction  \n",
              "..                        ...          ...      ...    ...   ...          ...  \n",
              "544             R. J. Palacio          4.8    21625      9  2017      Fiction  \n",
              "545               Jeff Kinney          4.9     9413      8  2019      Fiction  \n",
              "547               Jen Sincero          4.7    14331      8  2017  Non Fiction  \n",
              "548               Jen Sincero          4.7    14331      8  2018  Non Fiction  \n",
              "549               Jen Sincero          4.7    14331      8  2019  Non Fiction  \n",
              "\n",
              "[150 rows x 7 columns]"
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12 Rules for Life: An Antidote to Chaos</td>\n",
              "      <td>Jordan B. Peterson</td>\n",
              "      <td>4.7</td>\n",
              "      <td>18979</td>\n",
              "      <td>15</td>\n",
              "      <td>2018</td>\n",
              "      <td>Non Fiction</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1984 (Signet Classics)</td>\n",
              "      <td>George Orwell</td>\n",
              "      <td>4.7</td>\n",
              "      <td>21424</td>\n",
              "      <td>6</td>\n",
              "      <td>2017</td>\n",
              "      <td>Fiction</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>National Geographic Kids</td>\n",
              "      <td>4.8</td>\n",
              "      <td>7665</td>\n",
              "      <td>12</td>\n",
              "      <td>2019</td>\n",
              "      <td>Non Fiction</td>\n",
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              "    <tr>\n",
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              "      <td>A Gentleman in Moscow: A Novel</td>\n",
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              "      <td>4.7</td>\n",
              "      <td>19699</td>\n",
              "      <td>15</td>\n",
              "      <td>2017</td>\n",
              "      <td>Fiction</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>A Higher Loyalty: Truth, Lies, and Leadership</td>\n",
              "      <td>James Comey</td>\n",
              "      <td>4.7</td>\n",
              "      <td>5983</td>\n",
              "      <td>3</td>\n",
              "      <td>2018</td>\n",
              "      <td>Non Fiction</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",
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              "    <tr>\n",
              "      <th>544</th>\n",
              "      <td>Wonder</td>\n",
              "      <td>R. J. Palacio</td>\n",
              "      <td>4.8</td>\n",
              "      <td>21625</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "      <td>Fiction</td>\n",
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              "    <tr>\n",
              "      <th>545</th>\n",
              "      <td>Wrecking Ball (Diary of a Wimpy Kid Book 14)</td>\n",
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              "      <td>9413</td>\n",
              "      <td>8</td>\n",
              "      <td>2019</td>\n",
              "      <td>Fiction</td>\n",
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              "    <tr>\n",
              "      <th>547</th>\n",
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              "      <td>14331</td>\n",
              "      <td>8</td>\n",
              "      <td>2017</td>\n",
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              "      <td>4.7</td>\n",
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              "      <td>8</td>\n",
              "      <td>2018</td>\n",
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              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>You Are a Badass: How to Stop Doubting Your Gr...</td>\n",
              "      <td>Jen Sincero</td>\n",
              "      <td>4.7</td>\n",
              "      <td>14331</td>\n",
              "      <td>8</td>\n",
              "      <td>2019</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
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              "<p>150 rows × 7 columns</p>\n",
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    {
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      "source": [
        "genre_fiction = df_books['Genre'] == 'Fiction'"
      ],
      "metadata": {
        "id": "1IejnwPc_nhh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "df_books[genre_fiction & mayor_a_2016]"
      ],
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              "                                                  Name             Author  \\\n",
              "3                               1984 (Signet Classics)      George Orwell   \n",
              "7                       A Gentleman in Moscow: A Novel        Amor Towles   \n",
              "10                           A Man Called Ove: A Novel    Fredrik Backman   \n",
              "13                    A Wrinkle in Time (Time Quintet)  Madeleine L'Engle   \n",
              "40            Brown Bear, Brown Bear, What Do You See?    Bill Martin Jr.   \n",
              "..                                                 ...                ...   \n",
              "509                              To Kill a Mockingbird         Harper Lee   \n",
              "529  What Should Danny Do? (The Power to Choose Ser...          Adir Levy   \n",
              "534                            Where the Crawdads Sing        Delia Owens   \n",
              "544                                             Wonder      R. J. Palacio   \n",
              "545       Wrecking Ball (Diary of a Wimpy Kid Book 14)        Jeff Kinney   \n",
              "\n",
              "     User Rating  Reviews  Price  Year    Genre  \n",
              "3            4.7    21424      6  2017  Fiction  \n",
              "7            4.7    19699     15  2017  Fiction  \n",
              "10           4.6    23848      8  2017  Fiction  \n",
              "13           4.5     5153      5  2018  Fiction  \n",
              "40           4.9    14344      5  2017  Fiction  \n",
              "..           ...      ...    ...   ...      ...  \n",
              "509          4.8    26234      7  2019  Fiction  \n",
              "529          4.8     8170     13  2019  Fiction  \n",
              "534          4.8    87841     15  2019  Fiction  \n",
              "544          4.8    21625      9  2017  Fiction  \n",
              "545          4.9     9413      8  2019  Fiction  \n",
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    {
      "cell_type": "code",
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        "df_books[~mayor_a_2016] #El símbolo ~ permite negar la condición en pandas."
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  Name               Author  \\\n",
              "0                        10-Day Green Smoothie Cleanse             JJ Smith   \n",
              "1                                    11/22/63: A Novel         Stephen King   \n",
              "5        A Dance with Dragons (A Song of Ice and Fire)  George R. R. Martin   \n",
              "6    A Game of Thrones / A Clash of Kings / A Storm...  George R. R. Martin   \n",
              "9                            A Man Called Ove: A Novel      Fredrik Backman   \n",
              "..                                                 ...                  ...   \n",
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        "###2.8 Funciones principales de Pandas"
      ],
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              "                                                  Name  \\\n",
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              "\n",
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              "          const element = document.querySelector('#df-c8e31946-40b9-49e8-bc6e-3b486b2c1448');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "info() nos muestra la información del dataFrame"
      ],
      "metadata": {
        "id": "aTHLCJBdE_Wd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.info()"
      ],
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        "id": "up79sWWcEv3S",
        "outputId": "f7e72b0a-a002-4845-a120-8d2c0af9586c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 550 entries, 0 to 549\n",
            "Data columns (total 7 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Name         550 non-null    object \n",
            " 1   Author       550 non-null    object \n",
            " 2   User Rating  550 non-null    float64\n",
            " 3   Reviews      550 non-null    int64  \n",
            " 4   Price        550 non-null    int64  \n",
            " 5   Year         550 non-null    int64  \n",
            " 6   Genre        550 non-null    object \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 30.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "describe() de las columnas numéricas, me dará ciertos datos estadísticos."
      ],
      "metadata": {
        "id": "a6OAk4KdFeaM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.describe()"
      ],
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        "id": "ufiOFQGOFGSl",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       User Rating       Reviews       Price         Year\n",
              "count   550.000000    550.000000  550.000000   550.000000\n",
              "mean      4.618364  11953.281818   13.100000  2014.000000\n",
              "std       0.226980  11731.132017   10.842262     3.165156\n",
              "min       3.300000     37.000000    0.000000  2009.000000\n",
              "25%       4.500000   4058.000000    7.000000  2011.000000\n",
              "50%       4.700000   8580.000000   11.000000  2014.000000\n",
              "75%       4.800000  17253.250000   16.000000  2017.000000\n",
              "max       4.900000  87841.000000  105.000000  2019.000000"
            ],
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              "      <td>550.000000</td>\n",
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              "      <td>550.000000</td>\n",
              "      <td>550.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.618364</td>\n",
              "      <td>11953.281818</td>\n",
              "      <td>13.100000</td>\n",
              "      <td>2014.000000</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.226980</td>\n",
              "      <td>11731.132017</td>\n",
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              "      <td>3.165156</td>\n",
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              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>2009.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>7.000000</td>\n",
              "      <td>2011.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>8580.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>2014.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.800000</td>\n",
              "      <td>17253.250000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.900000</td>\n",
              "      <td>87841.000000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>2019.000000</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.tail(2) #lo mismo que head, solo que nos devuelve los últimos dos registros."
      ],
      "metadata": {
        "id": "mhVWx3TfFSfe",
        "outputId": "def70c71-238d-4c18-f059-83ef8e6f91c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  Name       Author  \\\n",
              "548  You Are a Badass: How to Stop Doubting Your Gr...  Jen Sincero   \n",
              "549  You Are a Badass: How to Stop Doubting Your Gr...  Jen Sincero   \n",
              "\n",
              "     User Rating  Reviews  Price  Year        Genre  \n",
              "548          4.7    14331      8  2018  Non Fiction  \n",
              "549          4.7    14331      8  2019  Non Fiction  "
            ],
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              "\n",
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              "        const buttonEl =\n",
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              "\n",
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              "          const element = document.querySelector('#df-3c062dc1-334d-43b9-9d8b-a39835c04bc1');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.memory_usage(deep=True) #Para saber cuanto ocupa en memoria el dataFrame"
      ],
      "metadata": {
        "id": "ruNmp68nFsV2",
        "outputId": "17b68d75-9f2e-4db2-9f57-47786e99abb8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index            128\n",
              "Name           59737\n",
              "Author         39078\n",
              "User Rating     4400\n",
              "Reviews         4400\n",
              "Price           4400\n",
              "Year            4400\n",
              "Genre          36440\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "el método value_count() nos permite contar cuantos datos hay de esa columna."
      ],
      "metadata": {
        "id": "RaAIqUnIG0XK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books['Author'].value_counts()"
      ],
      "metadata": {
        "id": "UbcpoA1cGjqn",
        "outputId": "a44cf338-98e6-4e84-e559-7e8371657523",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Jeff Kinney                           12\n",
              "Gary Chapman                          11\n",
              "Rick Riordan                          11\n",
              "Suzanne Collins                       11\n",
              "American Psychological Association    10\n",
              "                                      ..\n",
              "Keith Richards                         1\n",
              "Chris Cleave                           1\n",
              "Alice Schertle                         1\n",
              "Celeste Ng                             1\n",
              "Adam Gasiewski                         1\n",
              "Name: Author, Length: 248, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "drop_duplicates() nos permite eliminar filas duplicadas"
      ],
      "metadata": {
        "id": "7NswMDiFHenT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.iloc[0]"
      ],
      "metadata": {
        "id": "CTeVgrL7HETj",
        "outputId": "f30f77d1-fd48-45a6-a735-9b5f5786f48a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name           10-Day Green Smoothie Cleanse\n",
              "Author                              JJ Smith\n",
              "User Rating                              4.7\n",
              "Reviews                                17350\n",
              "Price                                      8\n",
              "Year                                    2016\n",
              "Genre                            Non Fiction\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {},
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            "<ipython-input-109-c6130e308d71>:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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              "                                                  Name  \\\n",
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          "metadata": {},
          "execution_count": 111
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.drop_duplicates(keep='last') #con keep le decimos que elimine el primer duplicado y solo deje el último."
      ],
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        "id": "SzUBRqOiHtQ-",
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              "                                                  Name  \\\n",
              "1                                    11/22/63: A Novel   \n",
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              "3                               1984 (Signet Classics)   \n",
              "4    5,000 Awesome Facts (About Everything!) (Natio...   \n",
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              "..                        ...          ...      ...    ...   ...          ...  \n",
              "546               Jen Sincero          4.7    14331      8  2016  Non Fiction  \n",
              "547               Jen Sincero          4.7    14331      8  2017  Non Fiction  \n",
              "548               Jen Sincero          4.7    14331      8  2018  Non Fiction  \n",
              "549               Jen Sincero          4.7    14331      8  2019  Non Fiction  \n",
              "0                    JJ Smith          4.7    17350      8  2016  Non Fiction  \n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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    {
      "cell_type": "markdown",
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        "sort_values('name_columns') ordena por columnas.\n",
        "\n",
        "* ascending = False (ordena de mayor a menor)\n",
        "\n",
        "* ascending = True (ordena de menor a mayor)"
      ],
      "metadata": {
        "id": "CnDT3bjmIFaV"
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    },
    {
      "cell_type": "code",
      "source": [
        "df_books.sort_values('Year',ascending=False)"
      ],
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        "id": "svuWNxqmH59S",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  Name                 Author  \\\n",
              "179                       If Animals Kissed Good Night      Ann Whitford Paul   \n",
              "294  School Zone - Big Preschool Workbook - Ages 4 ...            School Zone   \n",
              "263    P is for Potty! (Sesame Street) (Lift-the-Flap)        Naomi Kleinberg   \n",
              "489                   The Wonderful Things You Will Be  Emily Winfield Martin   \n",
              "267             Player's Handbook (Dungeons & Dragons)       Wizards RPG Team   \n",
              "..                                                 ...                    ...   \n",
              "160                  Have a Little Faith: A True Story            Mitch Albom   \n",
              "512                             True Compass: A Memoir      Edward M. Kennedy   \n",
              "513               Twilight (The Twilight Saga, Book 1)        Stephenie Meyer   \n",
              "78   Divine Soul Mind Body Healing and Transmission...           Zhi Gang Sha   \n",
              "459        The Shack: Where Tragedy Confronts Eternity       William P. Young   \n",
              "\n",
              "     User Rating  Reviews  Price  Year        Genre  \n",
              "179          4.8    16643      4  2019      Fiction  \n",
              "294          4.8    23047      6  2019  Non Fiction  \n",
              "263          4.7    10820      5  2019  Non Fiction  \n",
              "489          4.9     8842     10  2019      Fiction  \n",
              "267          4.8    16990     27  2019      Fiction  \n",
              "..           ...      ...    ...   ...          ...  \n",
              "160          4.8     1930      4  2009  Non Fiction  \n",
              "512          4.5      438     15  2009  Non Fiction  \n",
              "513          4.7    11676      9  2009      Fiction  \n",
              "78           4.6       37      6  2009  Non Fiction  \n",
              "459          4.6    19720      8  2009      Fiction  \n",
              "\n",
              "[551 rows x 7 columns]"
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              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Divine Soul Mind Body Healing and Transmission...</td>\n",
              "      <td>Zhi Gang Sha</td>\n",
              "      <td>4.6</td>\n",
              "      <td>37</td>\n",
              "      <td>6</td>\n",
              "      <td>2009</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>459</th>\n",
              "      <td>The Shack: Where Tragedy Confronts Eternity</td>\n",
              "      <td>William P. Young</td>\n",
              "      <td>4.6</td>\n",
              "      <td>19720</td>\n",
              "      <td>8</td>\n",
              "      <td>2009</td>\n",
              "      <td>Fiction</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>551 rows × 7 columns</p>\n",
              "</div>\n",
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              "  "
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.9 Groupby"
      ],
      "metadata": {
        "id": "nZSI7iArtm-J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books.head(2)"
      ],
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          "height": 112
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        "id": "sKkH6Ncitsh9",
        "outputId": "2235932f-df25-4ebb-b817-7a8b5206137f"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Name        Author  User Rating  Reviews  Price  \\\n",
              "0  10-Day Green Smoothie Cleanse      JJ Smith          4.7    17350      8   \n",
              "1              11/22/63: A Novel  Stephen King          4.6     2052     22   \n",
              "\n",
              "   Year        Genre  \n",
              "0  2016  Non Fiction  \n",
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              "                                                     [key], {});\n",
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    {
      "cell_type": "code",
      "source": [
        "df_books.groupby('Author').count()"
      ],
      "metadata": {
        "colab": {
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        "id": "J_QT_EUauWx_",
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "                            Name  User Rating  Reviews  Price  Year  Genre\n",
              "Author                                                                    \n",
              "Abraham Verghese               2            2        2      2     2      2\n",
              "Adam Gasiewski                 1            1        1      1     1      1\n",
              "Adam Mansbach                  1            1        1      1     1      1\n",
              "Adir Levy                      1            1        1      1     1      1\n",
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              "...                          ...          ...      ...    ...   ...    ...\n",
              "Walter Isaacson                3            3        3      3     3      3\n",
              "William Davis                  2            2        2      2     2      2\n",
              "William P. Young               2            2        2      2     2      2\n",
              "Wizards RPG Team               3            3        3      3     3      3\n",
              "Zhi Gang Sha                   2            2        2      2     2      2\n",
              "\n",
              "[248 rows x 6 columns]"
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              "      <td>1</td>\n",
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              "      <th>Walter Isaacson</th>\n",
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
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              "<p>248 rows × 6 columns</p>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "df_books.groupby('Author').mean()"
      ],
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          "base_uri": "https://localhost:8080/",
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        },
        "id": "M0SHZOKJun9H",
        "outputId": "b03b3c5b-782d-41b6-c231-2d0f378eaa60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-b6a0de8d28d9>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df_books.groupby('Author').mean()\n"
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              "                            User Rating       Reviews      Price         Year\n",
              "Author                                                                       \n",
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              "Adam Gasiewski                 4.400000   3113.000000   6.000000  2017.000000\n",
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              "Adir Levy                      4.800000   8170.000000  13.000000  2019.000000\n",
              "Admiral William H. McRaven     4.700000  10199.000000  11.000000  2017.000000\n",
              "...                                 ...           ...        ...          ...\n",
              "Walter Isaacson                4.566667   6222.666667  20.333333  2013.333333\n",
              "William Davis                  4.400000   7497.000000   6.000000  2012.500000\n",
              "William P. Young               4.600000  19720.000000   8.000000  2013.000000\n",
              "Wizards RPG Team               4.800000  16990.000000  27.000000  2018.000000\n",
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        "id": "wiRhKukzurKD",
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              "                                                                         Name  \\\n",
              "Author                                                                          \n",
              "Abraham Verghese                                            Cutting for Stone   \n",
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              "Adam Mansbach                                            Go the F**k to Sleep   \n",
              "Adir Levy                   What Should Danny Do? (The Power to Choose Ser...   \n",
              "Admiral William H. McRaven  Make Your Bed: Little Things That Can Change Y...   \n",
              "...                                                                       ...   \n",
              "Walter Isaacson                                             Leonardo da Vinci   \n",
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              "William P. Young                  The Shack: Where Tragedy Confronts Eternity   \n",
              "Wizards RPG Team                       Player's Handbook (Dungeons & Dragons)   \n",
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              "Adam Gasiewski                      4.4     3113      6  2017  Non Fiction  \n",
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              "Adir Levy                           4.8     8170     13  2019      Fiction  \n",
              "Admiral William H. McRaven          4.7    10199     11  2017  Non Fiction  \n",
              "...                                 ...      ...    ...   ...          ...  \n",
              "Walter Isaacson                     4.5     3014     20  2011  Non Fiction  \n",
              "William Davis                       4.4     7497      6  2012  Non Fiction  \n",
              "William P. Young                    4.6    19720      8  2009      Fiction  \n",
              "Wizards RPG Team                    4.8    16990     27  2017      Fiction  \n",
              "Zhi Gang Sha                        4.6       37      6  2009  Non Fiction  \n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
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        "df_books.groupby('Author').max()"
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              "                                                                         Name  \\\n",
              "Author                                                                          \n",
              "Abraham Verghese                                            Cutting for Stone   \n",
              "Adam Gasiewski              Milk and Vine: Inspirational Quotes From Class...   \n",
              "Adam Mansbach                                            Go the F**k to Sleep   \n",
              "Adir Levy                   What Should Danny Do? (The Power to Choose Ser...   \n",
              "Admiral William H. McRaven  Make Your Bed: Little Things That Can Change Y...   \n",
              "...                                                                       ...   \n",
              "Walter Isaacson                                                    Steve Jobs   \n",
              "William Davis               Wheat Belly: Lose the Wheat, Lose the Weight, ...   \n",
              "William P. Young                  The Shack: Where Tragedy Confronts Eternity   \n",
              "Wizards RPG Team                       Player's Handbook (Dungeons & Dragons)   \n",
              "Zhi Gang Sha                Soul Healing Miracles: Ancient and New Sacred ...   \n",
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              "                            User Rating  Reviews  Price  Year        Genre  \n",
              "Author                                                                      \n",
              "Abraham Verghese                    4.6     4866     11  2011      Fiction  \n",
              "Adam Gasiewski                      4.4     3113      6  2017  Non Fiction  \n",
              "Adam Mansbach                       4.8     9568      9  2011      Fiction  \n",
              "Adir Levy                           4.8     8170     13  2019      Fiction  \n",
              "Admiral William H. McRaven          4.7    10199     11  2017  Non Fiction  \n",
              "...                                 ...      ...    ...   ...          ...  \n",
              "Walter Isaacson                     4.6     7827     21  2017  Non Fiction  \n",
              "William Davis                       4.4     7497      6  2013  Non Fiction  \n",
              "William P. Young                    4.6    19720      8  2017      Fiction  \n",
              "Wizards RPG Team                    4.8    16990     27  2019      Fiction  \n",
              "Zhi Gang Sha                        4.6      220     17  2013  Non Fiction  \n",
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              "      <td>Non Fiction</td>\n",
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        "df_books.groupby('Author').sum()"
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          "text": [
            "<ipython-input-11-af849977d3fa>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df_books.groupby('Author').sum()\n"
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              "                            User Rating  Reviews  Price  Year\n",
              "Author                                                       \n",
              "Abraham Verghese                    9.2     9732     22  4021\n",
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              "Adir Levy                           4.8     8170     13  2019\n",
              "Admiral William H. McRaven          4.7    10199     11  2017\n",
              "...                                 ...      ...    ...   ...\n",
              "Walter Isaacson                    13.7    18668     61  6040\n",
              "William Davis                       8.8    14994     12  4025\n",
              "William P. Young                    9.2    39440     16  4026\n",
              "Wizards RPG Team                   14.4    50970     81  6054\n",
              "Zhi Gang Sha                        9.2      257     23  4022\n",
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        "df_books.groupby('Author').sum().loc['William Davis']"
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          "text": [
            "<ipython-input-12-4b4655dcf6dc>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df_books.groupby('Author').sum().loc['William Davis']\n"
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              "User Rating        8.8\n",
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              "Name: William Davis, dtype: float64"
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            "<ipython-input-13-ecb9159ebf61>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df_books.groupby('Author').sum().reset_index()\n"
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              "..                          ...          ...      ...    ...   ...\n",
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              "245            William P. Young          9.2    39440     16  4026\n",
              "246            Wizards RPG Team         14.4    50970     81  6054\n",
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              "                                                                         Name  \\\n",
              "                                                                          min   \n",
              "Author                                                                          \n",
              "Abraham Verghese                                            Cutting for Stone   \n",
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              "Adam Mansbach                                            Go the F**k to Sleep   \n",
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              "Walter Isaacson                                             Leonardo da Vinci   \n",
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              "...                                                                       ...   \n",
              "Walter Isaacson                                                    Steve Jobs   \n",
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              "Wizards RPG Team                       Player's Handbook (Dungeons & Dragons)   \n",
              "Zhi Gang Sha                Soul Healing Miracles: Ancient and New Sacred ...   \n",
              "\n",
              "                           User Rating      Reviews        Price      Year  \\\n",
              "                                   min  max     min    max   min max   min   \n",
              "Author                                                                       \n",
              "Abraham Verghese                   4.6  4.6    4866   4866    11  11  2010   \n",
              "Adam Gasiewski                     4.4  4.4    3113   3113     6   6  2017   \n",
              "Adam Mansbach                      4.8  4.8    9568   9568     9   9  2011   \n",
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              "...                                ...  ...     ...    ...   ...  ..   ...   \n",
              "Walter Isaacson                    4.5  4.6    3014   7827    20  21  2011   \n",
              "William Davis                      4.4  4.4    7497   7497     6   6  2012   \n",
              "William P. Young                   4.6  4.6   19720  19720     8   8  2009   \n",
              "Wizards RPG Team                   4.8  4.8   16990  16990    27  27  2017   \n",
              "Zhi Gang Sha                       4.6  4.6      37    220     6  17  2009   \n",
              "\n",
              "                                        Genre               \n",
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              "Abraham Verghese            2011      Fiction      Fiction  \n",
              "Adam Gasiewski              2017  Non Fiction  Non Fiction  \n",
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              "Adir Levy                   2019      Fiction      Fiction  \n",
              "Admiral William H. McRaven  2017  Non Fiction  Non Fiction  \n",
              "...                          ...          ...          ...  \n",
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              "      <td>Fiction</td>\n",
              "      <td>Fiction</td>\n",
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              "      <td>10199</td>\n",
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              "      <td>11</td>\n",
              "      <td>2017</td>\n",
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              "      <td>Non Fiction</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>21</td>\n",
              "      <td>2011</td>\n",
              "      <td>2017</td>\n",
              "      <td>Non Fiction</td>\n",
              "      <td>Non Fiction</td>\n",
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              "    <tr>\n",
              "      <th>William Davis</th>\n",
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              "      <td>Wheat Belly: Lose the Wheat, Lose the Weight, ...</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4.4</td>\n",
              "      <td>7497</td>\n",
              "      <td>7497</td>\n",
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              "      <td>6</td>\n",
              "      <td>2012</td>\n",
              "      <td>2013</td>\n",
              "      <td>Non Fiction</td>\n",
              "      <td>Non Fiction</td>\n",
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              "    <tr>\n",
              "      <th>William P. Young</th>\n",
              "      <td>The Shack: Where Tragedy Confronts Eternity</td>\n",
              "      <td>The Shack: Where Tragedy Confronts Eternity</td>\n",
              "      <td>4.6</td>\n",
              "      <td>4.6</td>\n",
              "      <td>19720</td>\n",
              "      <td>19720</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
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              "      <td>2017</td>\n",
              "      <td>Fiction</td>\n",
              "      <td>Fiction</td>\n",
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              "    <tr>\n",
              "      <th>Wizards RPG Team</th>\n",
              "      <td>Player's Handbook (Dungeons &amp; Dragons)</td>\n",
              "      <td>Player's Handbook (Dungeons &amp; Dragons)</td>\n",
              "      <td>4.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>16990</td>\n",
              "      <td>16990</td>\n",
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              "      <td>27</td>\n",
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              "      <td>Fiction</td>\n",
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              "    <tr>\n",
              "      <th>Zhi Gang Sha</th>\n",
              "      <td>Divine Soul Mind Body Healing and Transmission...</td>\n",
              "      <td>Soul Healing Miracles: Ancient and New Sacred ...</td>\n",
              "      <td>4.6</td>\n",
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              "      <td>Non Fiction</td>\n",
              "      <td>Non Fiction</td>\n",
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              "                           Reviews        User Rating\n",
              "                               min    max         sum\n",
              "Author                                               \n",
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              "William P. Young             19720  19720         9.2\n",
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          "data": {
            "text/plain": [
              "                       Name  User Rating  Reviews  Price  Genre\n",
              "Author           Year                                          \n",
              "Abraham Verghese 2010     1            1        1      1      1\n",
              "                 2011     1            1        1      1      1\n",
              "Adam Gasiewski   2017     1            1        1      1      1\n",
              "Adam Mansbach    2011     1            1        1      1      1\n",
              "Adir Levy        2019     1            1        1      1      1\n",
              "...                     ...          ...      ...    ...    ...\n",
              "Wizards RPG Team 2017     1            1        1      1      1\n",
              "                 2018     1            1        1      1      1\n",
              "                 2019     1            1        1      1      1\n",
              "Zhi Gang Sha     2009     1            1        1      1      1\n",
              "                 2013     1            1        1      1      1\n",
              "\n",
              "[493 rows x 5 columns]"
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adam Mansbach</th>\n",
              "      <th>2011</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adir Levy</th>\n",
              "      <th>2019</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Wizards RPG Team</th>\n",
              "      <th>2017</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Zhi Gang Sha</th>\n",
              "      <th>2009</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>493 rows × 5 columns</p>\n",
              "</div>\n",
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              "  "
            ]
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.10 Combinando DataFrames\n",
        "Las Operaciones de conjuntos que podemos realizar con los DataFrames son:\n",
        "\n",
        "<img src=\"https://www.golinuxcloud.com/wp-content/uploads/types_joins.png\" width=600>"
      ],
      "metadata": {
        "id": "7k806t1o0p5z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.DataFrame({'A':['A0','A1','A2','A3'],\n",
        " 'B':['B0','B1','B2','B3'],\n",
        " 'C':['C0','C1','C2','C3'],\n",
        " 'D':['D0','D1','D2','D3']})\n",
        "\n",
        "df2 = pd.DataFrame({'A':['A4','A5','A6','A7'],\n",
        " 'B':['B4','B5','B6','B'],\n",
        " 'C':['C4','C5','C6','C'],\n",
        " 'D':['D4','D5','D6','D']})"
      ],
      "metadata": {
        "id": "AW6CEvqd0t9F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Concatenar diferentes dataFrames"
      ],
      "metadata": {
        "id": "-90blibzITe1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([df1,df2])"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "kBmpg4XZIQZX",
        "outputId": "afe9b8d9-ca99-47f6-af38-c9aba0e12228"
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      "execution_count": null,
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              "    A   B   C   D\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "pd.concat([df1,df2],ignore_index=True) #Reorganiza los indices cuando se concatenan los dataFrames."
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "T2DEbatJTn3o",
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              "\n",
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              "          const element = document.querySelector('#df-a33e5b3e-ecf3-4d30-93dc-dbef20e1d5a4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([df1,df2],axis=1) #Concatena columnas"
      ],
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          "height": 175
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        "id": "z_13BcBPUaQq",
        "outputId": "11eb43e6-10d1-4b83-c793-11805e1b41ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    A   B   C   D   A   B   C   D\n",
              "0  A0  B0  C0  D0  A4  B4  C4  D4\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-934082c5-f336-4dcf-b022-899f8d8bcc94');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizando Merge"
      ],
      "metadata": {
        "id": "ZFY09iAfYw7m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "izq = pd.DataFrame({'key':['k0','k1','k2','k3'],\n",
        "'A':['A0','A1','A2','A3'],\n",
        "'B':['B0','B1','B2','B3']})\n",
        "izq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Pff4iHfGY0UG",
        "outputId": "d8d53171-f7bd-4211-e327-210466c0a633"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  key   A   B\n",
              "0  k0  A0  B0\n",
              "1  k1  A1  B1\n",
              "2  k2  A2  B2\n",
              "3  k3  A3  B3"
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>key</th>\n",
              "      <th>A</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>k0</td>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>k1</td>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>k2</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>k3</td>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd7de37a-bdea-484f-914f-4158d3c36f9c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd7de37a-bdea-484f-914f-4158d3c36f9c 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-bd7de37a-bdea-484f-914f-4158d3c36f9c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "der = pd.DataFrame({'key':['k0','k1','k2','k3'],\n",
        "'C':['C0','C1','C2','C3'],\n",
        "'D':['D0','D1','D2','D4']})\n",
        "der"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ETUToi9VbO3l",
        "outputId": "e8cfe18c-1631-4fac-dcd7-9ec444c8fdfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  key   C   D\n",
              "0  k0  C0  D0\n",
              "1  k1  C1  D1\n",
              "2  k2  C2  D2\n",
              "3  k3  C3  D4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f0357f8-4528-4807-9d4d-378210094075\">\n",
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              "      <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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              "\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>key</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>k0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>k1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>k2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>k3</td>\n",
              "      <td>C3</td>\n",
              "      <td>D4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f0357f8-4528-4807-9d4d-378210094075')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-5f0357f8-4528-4807-9d4d-378210094075 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-5f0357f8-4528-4807-9d4d-378210094075');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "izq.merge(der, on='key')  #Full Join"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "0abWCqwwgnca",
        "outputId": "e764d612-01a0-4d43-cb7b-607a76015c87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  key   A   B   C    D\n",
              "0  k0  A0  B0  C0   D0\n",
              "1  k1  A1  B1  C1   D1\n",
              "2  k2  A2  B2  C2   D2\n",
              "3  k3  A3  B3  C3  D34"
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              "\n",
              "  <div id=\"df-aae119d0-5c14-42f6-b80e-d06dbb9d159e\">\n",
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              "      <div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>key</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>k0</td>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>k1</td>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>k2</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>k3</td>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "      <td>C3</td>\n",
              "      <td>D34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aae119d0-5c14-42f6-b80e-d06dbb9d159e')\"\n",
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              "              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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              "  </svg>\n",
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              "      \n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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-aae119d0-5c14-42f6-b80e-d06dbb9d159e 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-aae119d0-5c14-42f6-b80e-d06dbb9d159e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "der = pd.DataFrame({'key_2':['k0','k1','k2','k3'],\n",
        "'C':['C0','C1','C2','C3'],\n",
        "'D':['D0','D1','D2','D4']})\n",
        "der"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "SDfcrrtphHuK",
        "outputId": "909dc0cd-1f10-47c7-c214-b2aa0eaeef06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  key_2   C    D\n",
              "0    k0  C0   D0\n",
              "1    k1  C1   D1\n",
              "2    k2  C2   D2\n",
              "3    k3  C3  D34"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70dcf72a-7b60-4901-b23f-f176296d19f2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>key_2</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>k0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>k1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>k2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>k3</td>\n",
              "      <td>C3</td>\n",
              "      <td>D34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70dcf72a-7b60-4901-b23f-f176296d19f2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-70dcf72a-7b60-4901-b23f-f176296d19f2 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-70dcf72a-7b60-4901-b23f-f176296d19f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "izq.merge(der, left_on='key',right_on='key_2') #Full Join"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Rl2QDCp1hPm3",
        "outputId": "c001bf90-466f-4d17-b631-4fb33d92bac0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  key   A   B key_2   C    D\n",
              "0  k0  A0  B0    k0  C0   D0\n",
              "1  k1  A1  B1    k1  C1   D1\n",
              "2  k2  A2  B2    k2  C2   D2\n",
              "3  k3  A3  B3    k3  C3  D34"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-52135e38-e684-4da3-973b-451ab3091eea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>key</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>key_2</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>k0</td>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "      <td>k0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>k1</td>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "      <td>k1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>k2</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>k2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>k3</td>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "      <td>k3</td>\n",
              "      <td>C3</td>\n",
              "      <td>D34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52135e38-e684-4da3-973b-451ab3091eea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-52135e38-e684-4da3-973b-451ab3091eea 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-52135e38-e684-4da3-973b-451ab3091eea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "der = pd.DataFrame({'key_2':['k0','k1','k2',np.nan],\n",
        "'C':['C0','C1','C2','C3'],\n",
        "'D':['D0','D1','D2','D3']})\n",
        "der"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "QQM1zKo-kBqf",
        "outputId": "59ab7904-7a3b-406b-ac96-21fae820323b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  key_2   C   D\n",
              "0    k0  C0  D0\n",
              "1    k1  C1  D1\n",
              "2    k2  C2  D2\n",
              "3   NaN  C3  D3"
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              "\n",
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              "        text-align: right;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>key_2</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>k1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>k2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df5cbc91-9aea-4709-b762-d3068cdd9507')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-df5cbc91-9aea-4709-b762-d3068cdd9507 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-df5cbc91-9aea-4709-b762-d3068cdd9507');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "izq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "YAUscNRFkU9m",
        "outputId": "e91aa715-e118-4153-e62c-62a3f9dacada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  key   A   B\n",
              "0  k0  A0  B0\n",
              "1  k1  A1  B1\n",
              "2  k2  A2  B2\n",
              "3  k3  A3  B3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ab4fb0a2-56f8-4c73-8fdd-f4398b04b5b9\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "\n",
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              "</style>\n",
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              "      <th></th>\n",
              "      <th>key</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>k0</td>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>k1</td>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>k2</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>k3</td>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab4fb0a2-56f8-4c73-8fdd-f4398b04b5b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      </button>\n",
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              "\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ab4fb0a2-56f8-4c73-8fdd-f4398b04b5b9 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-ab4fb0a2-56f8-4c73-8fdd-f4398b04b5b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "izq.merge(der, left_on='key',right_on='key_2',how='inner') #Inner join"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "vV3ZCeBZkJq2",
        "outputId": "5bc9ba68-8b3e-4a4d-ef93-ee89855e51de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  key   A   B key_2   C   D\n",
              "0  k0  A0  B0    k0  C0  D0\n",
              "1  k1  A1  B1    k1  C1  D1\n",
              "2  k2  A2  B2    k2  C2  D2"
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              "\n",
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              "\n",
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              "</style>\n",
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              "      <th></th>\n",
              "      <th>key</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>key_2</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>k0</td>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "      <td>k0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>k1</td>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "      <td>k1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>k2</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>k2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b143cc1e-63bf-49b0-91b9-c5101c397729')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-b143cc1e-63bf-49b0-91b9-c5101c397729 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-b143cc1e-63bf-49b0-91b9-c5101c397729');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "izq.merge(der, left_on='key',right_on='key_2',how='left') #Left join"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "BHq8pt1pk76y",
        "outputId": "4e0b5458-fbe6-466b-8c42-242fd5ac4886"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  key   A   B key_2    C    D\n",
              "0  k0  A0  B0    k0   C0   D0\n",
              "1  k1  A1  B1    k1   C1   D1\n",
              "2  k2  A2  B2    k2   C2   D2\n",
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              "      <th></th>\n",
              "      <th>key</th>\n",
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              "      <td>k1</td>\n",
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              "      <td>k2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>k3</td>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3dfef14-bc67-48af-b16c-a863b46909b8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "      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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c3dfef14-bc67-48af-b16c-a863b46909b8 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-c3dfef14-bc67-48af-b16c-a863b46909b8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "izq.merge(der, left_on='key',right_on='key_2',how='right') #Right join"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Ag-uY_SslM5X",
        "outputId": "5a6be066-0628-4dbb-927b-54e20757bb32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   key    A    B key_2   C   D\n",
              "0   k0   A0   B0    k0  C0  D0\n",
              "1   k1   A1   B1    k1  C1  D1\n",
              "2   k2   A2   B2    k2  C2  D2\n",
              "3  NaN  NaN  NaN   NaN  C3  D3"
            ],
            "text/html": [
              "\n",
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              "      <th>key</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>k0</td>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "      <td>k0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>k1</td>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "      <td>k1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>k2</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>k2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a2fcb67-8f6a-40a7-a335-4a2b3b693f20')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8a2fcb67-8f6a-40a7-a335-4a2b3b693f20 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-8a2fcb67-8f6a-40a7-a335-4a2b3b693f20');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comando Join, buscará el match con los índices y únicamente con ellos."
      ],
      "metadata": {
        "id": "n-l8UOSlxklW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "izq = pd.DataFrame({'A':['A0','A1','A2'],\n",
        "'B':['B0','B1','B2']},index=['k0','k1','k2'])\n",
        "der = pd.DataFrame({'C':['C0','C1','C2'],\n",
        "'D':['D0','D1','D2']},index=['k0','k2','k3'])"
      ],
      "metadata": {
        "id": "QdSPefuxzWf8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "izq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "H6SANuPBLamz",
        "outputId": "b6155521-df6b-4fd8-cad3-9ba96547634f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     A   B\n",
              "k0  A0  B0\n",
              "k1  A1  B1\n",
              "k2  A2  B2"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>A</th>\n",
              "      <th>B</th>\n",
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              "      <th>k0</th>\n",
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              "      <td>B0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>k1</th>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>k2</th>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71b08eeb-277d-491d-912f-186a0d5397ff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-71b08eeb-277d-491d-912f-186a0d5397ff 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-71b08eeb-277d-491d-912f-186a0d5397ff');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "der"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "NAQJ9Qe8L_A-",
        "outputId": "fd1670bb-3c32-4cca-cc55-9d621c3a1f18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     C   D\n",
              "k0  C0  D0\n",
              "k2  C1  D1\n",
              "k3  C2  D2"
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              "      <th>k2</th>\n",
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              "      <td>D1</td>\n",
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              "    <tr>\n",
              "      <th>k3</th>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f5bb24f-e807-44e2-8966-2f5d4e0181f4')\"\n",
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              "          document.querySelector('#df-2f5bb24f-e807-44e2-8966-2f5d4e0181f4 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2f5bb24f-e807-44e2-8966-2f5d4e0181f4');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "izq.join(der) #lef join con índices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "jeXlBLZ2LFML",
        "outputId": "a69a3801-862c-4825-b2db-20bd9f0be19c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     A   B    C    D\n",
              "k0  A0  B0   C0   D0\n",
              "k1  A1  B1  NaN  NaN\n",
              "k2  A2  B2   C1   D1"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>k1</th>\n",
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              "      <td>B1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>k2</th>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbc6931c-e747-478d-b3de-419169b8a20f')\"\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\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-dbc6931c-e747-478d-b3de-419169b8a20f button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dbc6931c-e747-478d-b3de-419169b8a20f');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "izq.join(der,how='inner') #inner join con índices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "wAo11wfDMJm2",
        "outputId": "f90da1d6-e84c-4e44-ab46-5c85a64df16f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     A   B   C   D\n",
              "k0  A0  B0  C0  D0\n",
              "k2  A2  B2  C1  D1"
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              "<style scoped>\n",
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              "      <td>A0</td>\n",
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              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
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              "    <tr>\n",
              "      <th>k2</th>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
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              "      A    B    C    D\n",
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    {
      "cell_type": "markdown",
      "source": [
        "### 2.11 Pivot Table\n",
        "Cambiar la estructura de nuestro DataFrame de acuerdo a nuestras necesidades.\n",
        "\n"
      ],
      "metadata": {
        "id": "QQQj1kcjg8b5"
      }
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    {
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      "execution_count": null,
      "outputs": [
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              "                                                Name  \\\n",
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              "                     Author  User Rating  Reviews  Price  Year        Genre  \n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 26
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    {
      "cell_type": "code",
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        "df_books.pivot_table(index='Author', columns='Genre',values='User Rating')"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Genre                       Fiction  Non Fiction\n",
              "Author                                          \n",
              "Abraham Verghese                4.6          NaN\n",
              "Adam Gasiewski                  NaN     4.400000\n",
              "Adam Mansbach                   4.8          NaN\n",
              "Adir Levy                       4.8          NaN\n",
              "Admiral William H. McRaven      NaN     4.700000\n",
              "...                             ...          ...\n",
              "Walter Isaacson                 NaN     4.566667\n",
              "William Davis                   NaN     4.400000\n",
              "William P. Young                4.6          NaN\n",
              "Wizards RPG Team                4.8          NaN\n",
              "Zhi Gang Sha                    NaN     4.600000\n",
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              "[248 rows x 2 columns]"
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              "<p>248 rows × 2 columns</p>\n",
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    },
    {
      "cell_type": "code",
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        "df_books.pivot_table(index='Genre',columns='Year', values='User Rating',aggfunc='sum')"
      ],
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        "id": "K77M3oTlicGF",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year          2009   2010   2011   2012   2013   2014   2015   2016   2017  \\\n",
              "Genre                                                                        \n",
              "Fiction      110.2   92.3   97.0   94.4  109.1  134.3   79.1   89.6  113.7   \n",
              "Non Fiction  119.0  135.6  130.9  132.2  118.6   96.8  153.3  144.3  119.3   \n",
              "\n",
              "Year          2018   2019  \n",
              "Genre                      \n",
              "Fiction       99.5   96.4  \n",
              "Non Fiction  133.9  140.6  "
            ],
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              "      <td>110.2</td>\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 29
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2.12 Melt\n",
        "El método melt toma las columnas del DataFrame y las pasa a filas, con dos nuevas columnas para especificar la antigua columna y el valor que traía."
      ],
      "metadata": {
        "id": "9oONvKaKos2z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_books[['Name','Genre']].head(5)"
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      "metadata": {
        "id": "Oe1JMAl2oxoc",
        "outputId": "eab40bed-d019-4b3e-8207-a7da78108fe7",
        "colab": {
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          "height": 206
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Name        Genre\n",
              "0                      10-Day Green Smoothie Cleanse  Non Fiction\n",
              "1                                  11/22/63: A Novel      Fiction\n",
              "2            12 Rules for Life: An Antidote to Chaos  Non Fiction\n",
              "3                             1984 (Signet Classics)      Fiction\n",
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              "  variable                                              value\n",
              "0     Name                      10-Day Green Smoothie Cleanse\n",
              "1     Name                                  11/22/63: A Novel\n",
              "2     Name            12 Rules for Life: An Antidote to Chaos\n",
              "3     Name                             1984 (Signet Classics)\n",
              "4     Name  5,000 Awesome Facts (About Everything!) (Natio...\n",
              "5    Genre                                        Non Fiction\n",
              "6    Genre                                            Fiction\n",
              "7    Genre                                        Non Fiction\n",
              "8    Genre                                            Fiction\n",
              "9    Genre                                        Non Fiction"
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            "text/plain": [
              "     Year variable        value\n",
              "0    2016    Genre  Non Fiction\n",
              "1    2011    Genre      Fiction\n",
              "2    2018    Genre  Non Fiction\n",
              "3    2017    Genre      Fiction\n",
              "4    2019    Genre  Non Fiction\n",
              "..    ...      ...          ...\n",
              "545  2019    Genre      Fiction\n",
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              "547  2017    Genre  Non Fiction\n",
              "548  2018    Genre  Non Fiction\n",
              "549  2019    Genre  Non Fiction\n",
              "\n",
              "[550 rows x 3 columns]"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2.13 Apply\n",
        "Es un método que nos permite agregar funciones al dataFrame, se podría decir que se parece a las funciones de orden superior, pero en DataFrames.\n"
      ],
      "metadata": {
        "id": "pEPIzjOEq4Ia"
      }
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        "df_books.head(5)"
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              "                                                Name  \\\n",
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        "def two_times(value):\n",
        "  return value * 2"
      ],
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        "id": "m4jQ4eHarCcI"
      },
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      "outputs": []
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              "0      9.4\n",
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              "      ... \n",
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      "metadata": {
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      "execution_count": null,
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    {
      "cell_type": "code",
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        "df_books['Rating_2'] = df_books['User Rating'].apply(lambda x:x*3)"
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      "metadata": {
        "id": "heHOfE7XsHIz"
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              "                                                Name  \\\n",
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        "df_books['Rating_2'] = df_books.apply(lambda x: x['User Rating'] * 2 if x['Genre'] == 'Fiction' else x['User Rating'],axis=1)"
      ],
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              "                                                  Name  \\\n",
              "0                        10-Day Green Smoothie Cleanse   \n",
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              "..                                                 ...   \n",
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              "549  You Are a Badass: How to Stop Doubting Your Gr...   \n",
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              "                       Author  User Rating  Reviews  Price  Year        Genre  \\\n",
              "0                    JJ Smith          4.7    17350      8  2016  Non Fiction   \n",
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              "4    National Geographic Kids          4.8     7665     12  2019  Non Fiction   \n",
              "..                        ...          ...      ...    ...   ...          ...   \n",
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              "548               Jen Sincero          4.7    14331      8  2018  Non Fiction   \n",
              "549               Jen Sincero          4.7    14331      8  2019  Non Fiction   \n",
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