Pandas_wwm.ipynb

pandas_wwm.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Pandas_wwm.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNsJaL19RZY2VQwTfOlvRbP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/marisbotero/f7ed14f9531de1fb0602f6dd303d67cd/pandas_wwm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1H5WJgj20r7r",
        "colab_type": "text"
      },
      "source": [
        "# Women Who code Medellín\n",
        "\n",
        "\n",
        "## 🐼Pandas para machine Learning🤓\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RTSV5s5XMm1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "83c8dd82-e941-407b-dda4-f319b22d3743"
      },
      "source": [
        "from IPython.display import Image\n",
        "Image(filename='pandas.PNG', width=200)\n",
        "# Resource: https://bookdata.readthedocs.io/en/latest/base/01_pandas.html"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 200
            }
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OO4sgh2r1lTx",
        "colab_type": "text"
      },
      "source": [
        "## 🌳Manipulación de Datos con Pandas\n",
        "Pandas es un paquete creado sobre Numpy, los DataFrame son conjunto de datos multidimensionales con etiquetas de fila y columna. Ademas de ofrecer una interfaz de almacenamiento para datos etiquetados, Pandas implementa potentes operaciones de datos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ktK6hLR5KMI",
        "colab_type": "text"
      },
      "source": [
        "Las características principales de la librería Pandas son:\n",
        "\n",
        "\n",
        "> \n",
        "\n",
        "\n",
        "⭐Objeto DataFrame rápido y eficiente con indexación predeterminada y\n",
        "personalizada.\n",
        "\n",
        "⭐Herramientas para cargar datos en objetos de datos en memoria desde diferentes\n",
        "formatos de archivo.\n",
        "\n",
        "⭐Alineación de datos y manejo integrado de datos faltantes.\n",
        "\n",
        "⭐Remodelación y giro de conjuntos de fechas.\n",
        "\n",
        "⭐Etiquetado, corte, indexación y subconjunto de grandes conjuntos de datos.\n",
        "\n",
        "⭐Las columnas de una estructura de datos se pueden eliminar o insertar.\n",
        "\n",
        "⭐Agrupa por datos para agregación y transformaciones.\n",
        "\n",
        "⭐Alto rendimiento de fusión y unión de datos.\n",
        "\n",
        "⭐Funcionalidad de la serie de tiempo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6du2cUMM9EHd",
        "colab_type": "text"
      },
      "source": [
        "## 📝Instalando Pandas\n",
        "\n",
        "La instalación de Pandas requiere en el sistema el paquete Numpy.\n",
        "\n",
        "Para instalarlo se usa el comando:\n",
        "```\n",
        "# Instalacion con pip\n",
        "pip install pandas\n",
        "\n",
        "# Instalacion con conda\n",
        "conda install pandas\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2L0z8xrVjPM",
        "colab_type": "text"
      },
      "source": [
        "## 💎Si no se tiene pandas instalado\n",
        "!pip install pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvSnuV0E0L95",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importando librerias\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrI8sYjm0f5a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4652ac2b-9e0b-4af5-fb88-2816aae391aa"
      },
      "source": [
        "# Version de Pandas\n",
        "pd.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.0.5'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YkI_-cOW-7Iq",
        "colab_type": "text"
      },
      "source": [
        "## 🍀Series\n",
        "Las series cuanta con una columna de indexación"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFbM7ZVj-7rf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sr =pd.Series([10,9,8,7,6])"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-ABZgrc_F3Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "6d67fba0-3208-45ec-ce38-0834ac6df9f5"
      },
      "source": [
        "sr.values"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10,  9,  8,  7,  6])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LE82QsBN_MzJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "58774748-343e-4212-d713-87dd5200020e"
      },
      "source": [
        "sr.index"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=5, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEZEuLYrNZ2j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "fc992caa-f1d4-4021-d8c7-154d0697ae4b"
      },
      "source": [
        "sr.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAkw64zUNaRx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "294f26aa-790a-4a84-d162-956e4ab3d1c9"
      },
      "source": [
        "sr[3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kNz_dM2KNjBx",
        "colab_type": "text"
      },
      "source": [
        "## 💙Dataframe\n",
        "\n",
        "La manera en que se crea este DataFrame será la misma para todas las estructuras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iEa8z5kNhz0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(np.array([[1,2,3], [4,5,6]]))"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bvo1KdRcPk2_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "b0be5579-618e-439a-8ee9-34938911b6c5"
      },
      "source": [
        "df"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   0  1  2\n",
              "0  1  2  3\n",
              "1  4  5  6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEf9oQFwPz33",
        "colab_type": "text"
      },
      "source": [
        "Una vez creado el DataFrame se puede explorarlo con todas las instrucciones con las\n",
        "que Pandas cuenta. Lo primero que se debe hacer es conocer la forma de los datos para\n",
        "ello se utiliza la instrucción shape. Con esta instrucción se puede conocer las\n",
        "dimensiones del DataFrame, es decir el ancho y altura"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v69CwS8cPpeC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "b0c81c3b-c077-453d-c599-f457fa337951"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fb5NpZw2P9UI",
        "colab_type": "text"
      },
      "source": [
        "Por otra parte, se puede utilizar la función len() en combinación con la instrucción index para conocer la altura del DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wggIodKvP5nk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "2c22c5e3-eda5-4e56-ae9b-e869f381d637"
      },
      "source": [
        "print('Altura del dataframe')\n",
        "len(df.index)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Altura del dataframe\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lvLhKvSRSmYq",
        "colab_type": "text"
      },
      "source": [
        "# 🌹Carguemos un set de datos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGas9vEdSpgS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "3a2b81fb-b9a1-4c84-bb22-713e72f3c86d"
      },
      "source": [
        "from IPython.display import Image\n",
        "Image(filename='base_01_pandas_5_0.png', width=500)\n",
        "# Resource: https://bookdata.readthedocs.io/en/latest/base/01_pandas.html"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 500
            }
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrBfNgJQS6Mn",
        "colab_type": "text"
      },
      "source": [
        "### ⛵Titanic Dataset\n",
        "\n",
        "Trabajemos con el famoso conjunto de datos del [titanic](https://www.kaggle.com/c/titanic/data).\n",
        "\n",
        "- PassengerId -- A numerical id assigned to each passenger.\n",
        "- Survived -- Whether the passenger survived (1), or didn't (0).\n",
        "- Pclass -- The class the passenger was in.\n",
        "- Name -- the name of the passenger.\n",
        "- Sex -- The gender of the passenger -- male or female.\n",
        "- Age -- The age of the passenger.  Fractional.\n",
        "- SibSp -- The number of siblings and spouses the passenger had on board.\n",
        "- Parch -- The number of parents and children the passenger had on board.\n",
        "- Ticket -- The ticket number of the passenger.\n",
        "- Fare -- How much the passenger paid for the ticket.\n",
        "- Cabin -- Which cabin the passenger was in.\n",
        "- Embarked -- Where the passenger boarded the Titanic."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWLuvVFPSuUI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "f53faee1-533a-4a23-b0f8-51154d1b6b7e"
      },
      "source": [
        "# Cargamos el archivo\n",
        "titanic = pd.read_csv(\"train.csv\")\n",
        "titanic.head()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOjWixP1TAYW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "29ca2dac-e1d9-4e8f-c60e-a9f5ebbca859"
      },
      "source": [
        "titanic.index"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=891, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RF4WHg4QVW4R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "5c266072-8655-479f-c398-7bcf842c1063"
      },
      "source": [
        "titanic.columns"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O9Ti7Kj0VYul",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "de248f08-e350-4f21-dc1e-81636218ba99"
      },
      "source": [
        "type(titanic.PassengerId)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NzS2jmDVaxF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "62018839-c0e7-433a-d423-52ae935dffad"
      },
      "source": [
        "type(titanic)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7twRRvHVeS-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "outputId": "1ec1d6bc-ca9f-42b8-f9ef-b4a16522893e"
      },
      "source": [
        "titanic.values"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 3, ..., 7.25, nan, 'S'],\n",
              "       [2, 1, 1, ..., 71.2833, 'C85', 'C'],\n",
              "       [3, 1, 3, ..., 7.925, nan, 'S'],\n",
              "       ...,\n",
              "       [889, 0, 3, ..., 23.45, nan, 'S'],\n",
              "       [890, 1, 1, ..., 30.0, 'C148', 'C'],\n",
              "       [891, 0, 3, ..., 7.75, nan, 'Q']], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oC8lI5TnVj3B",
        "colab_type": "text"
      },
      "source": [
        "### 💛Indexers: loc, and iloc\n",
        "\n",
        "Pandas proporciona algunos atributos especiales *indexer* que exponen explícitamente ciertos esquemas de indexación.\n",
        "\n",
        "Estos no son métodos funcionales, sino atributos que exponen una interfaz de corte particular a los datos.\n",
        "\n",
        "Primero, el atributo ``loc`` permite indexar y segmentar que siempre hace referencia al índice explícito.\n",
        "\n",
        "El atributo ``iloc`` permite indexar y segmentar que siempre hace referencia al índice implícito de estilo Python."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-jD4NkzVgVG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "outputId": "2b56cfc9-cecd-482c-80af-d52ad9ced21f"
      },
      "source": [
        "titanic.loc[10:15,'PassengerId':'Age']"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
              "      <td>male</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
              "      <td>female</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  ...     Sex   Age\n",
              "10           11         1  ...  female   4.0\n",
              "11           12         1  ...  female  58.0\n",
              "12           13         0  ...    male  20.0\n",
              "13           14         0  ...    male  39.0\n",
              "14           15         0  ...  female  14.0\n",
              "15           16         1  ...  female  55.0\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZsqoRk8VqHs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "e50d952c-5701-44ed-a1f4-05e872b83e2b"
      },
      "source": [
        "titanic.iloc[10:15,0:5]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass                                  Name     Sex\n",
              "10           11         1       3       Sandstrom, Miss. Marguerite Rut  female\n",
              "11           12         1       1              Bonnell, Miss. Elizabeth  female\n",
              "12           13         0       3        Saundercock, Mr. William Henry    male\n",
              "13           14         0       3           Andersson, Mr. Anders Johan    male\n",
              "14           15         0       3  Vestrom, Miss. Hulda Amanda Adolfina  female"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KU8Yri8RZOn8",
        "colab_type": "text"
      },
      "source": [
        "### 💛Selección de datos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tXakc9HZLvm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "0885b2fc-1d99-4065-e571-fe23fac1cf21"
      },
      "source": [
        "# segmentación por índice implícito\n",
        "titanic[10:15]"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>PP 9549</td>\n",
              "      <td>16.7000</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113783</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>C103</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5. 2151</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
              "      <td>male</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>347082</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>350406</td>\n",
              "      <td>7.8542</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "10           11         1       3  ...  16.7000    G6         S\n",
              "11           12         1       1  ...  26.5500  C103         S\n",
              "12           13         0       3  ...   8.0500   NaN         S\n",
              "13           14         0       3  ...  31.2750   NaN         S\n",
              "14           15         0       3  ...   7.8542   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiiCfCwBZRZq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "2ccb42d2-0872-4fc6-ee27-604dce0b9ccb"
      },
      "source": [
        "# indexación elegante\n",
        "titanic[['PassengerId','Age']][10:15]"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId   Age\n",
              "10           11   4.0\n",
              "11           12  58.0\n",
              "12           13  20.0\n",
              "13           14  39.0\n",
              "14           15  14.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KNfAsKukjNZr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "175e622c-e671-4a7d-cb4d-6e35b78b3f1a"
      },
      "source": [
        "# masking\n",
        "titanic[(titanic.Age > 18) & (titanic['Age'] < 50)][10:15]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Beesley, Mr. Lawrence</td>\n",
              "      <td>male</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>248698</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>D56</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sloper, Mr. William Thompson</td>\n",
              "      <td>male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113788</td>\n",
              "      <td>35.5000</td>\n",
              "      <td>A6</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>347077</td>\n",
              "      <td>31.3875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Fortune, Mr. Charles Alexander</td>\n",
              "      <td>male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>19950</td>\n",
              "      <td>263.0000</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Uruchurtu, Don. Manuel E</td>\n",
              "      <td>male</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17601</td>\n",
              "      <td>27.7208</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass  ...      Fare        Cabin  Embarked\n",
              "21           22         1       2  ...   13.0000          D56         S\n",
              "23           24         1       1  ...   35.5000           A6         S\n",
              "25           26         1       3  ...   31.3875          NaN         S\n",
              "27           28         0       1  ...  263.0000  C23 C25 C27         S\n",
              "30           31         0       1  ...   27.7208          NaN         C\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6veUXKejPtr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "46d7e0c9-a04c-4798-bc48-5986b90fd262"
      },
      "source": [
        "titanic['Age'][10:15]"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10     4.0\n",
              "11    58.0\n",
              "12    20.0\n",
              "13    39.0\n",
              "14    14.0\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SiaOMoX9jfk5",
        "colab_type": "text"
      },
      "source": [
        "## 🌟Anatomia de un dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9i2CBpnjSDS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "bb9770c7-f880-41ee-8340-162490f92597"
      },
      "source": [
        "from IPython.display import Image\n",
        "Image(filename='AnatomyDataFrame.png', width=500)\n",
        "#Resource: https://cvw.cac.cornell.edu/PyDataSci1/arrays_dataframes"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 500
            }
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVPSUCm-joty",
        "colab_type": "text"
      },
      "source": [
        "### ⭐concat\n",
        "\n",
        "Pandas tiene una función, pd.concat(), que tiene una sintaxis similar a np.concatenate pero contiene una diferentes argumentos.\n",
        "\n",
        "pd.concat() puede ser usado para una concatenacion simple de Series o DataFrames:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pd0VKwWgjm-w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1 = titanic[(titanic.Age > 18) & (titanic['Age'] < 50)][10:15][['PassengerId','Survived']]\n",
        "df1.reset_index(inplace = True)\n",
        "#df1"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2N8Y6pPkjvZq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2 = titanic.iloc[10:15,2:5]\n",
        "df2.reset_index(inplace = True)\n",
        "#df2"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCDlqtRnOFhf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "10b98acf-c3f1-4d4d-c3c3-de19a79e0967"
      },
      "source": [
        "df1"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "      <th>0</th>\n",
              "      <td>21</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23</td>\n",
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              "      <th>2</th>\n",
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              "      <th>4</th>\n",
              "      <td>30</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  PassengerId  Survived\n",
              "0     21           22         1\n",
              "1     23           24         1\n",
              "2     25           26         1\n",
              "3     27           28         0\n",
              "4     30           31         0"
            ]
          },
          "metadata": {
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          "execution_count": 54
        }
      ]
    },
    {
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        "outputId": "6c436373-d124-42f1-92b2-9cbecf99874a"
      },
      "source": [
        "df2"
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      "execution_count": 55,
      "outputs": [
        {
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              "      <th>4</th>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>"
            ],
            "text/plain": [
              "   index  Pclass                                  Name     Sex\n",
              "0     10       3       Sandstrom, Miss. Marguerite Rut  female\n",
              "1     11       1              Bonnell, Miss. Elizabeth  female\n",
              "2     12       3        Saundercock, Mr. William Henry    male\n",
              "3     13       3           Andersson, Mr. Anders Johan    male\n",
              "4     14       3  Vestrom, Miss. Hulda Amanda Adolfina  female"
            ]
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        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euCR5dPtjzVy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "54a91fe6-f6dc-4109-b48a-ca7f75659625"
      },
      "source": [
        "pd.concat([df1, df2],sort=True, axis=1)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>index</th>\n",
              "      <th>Pclass</th>\n",
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              "      <th>Sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21</td>\n",
              "      <td>22</td>\n",
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              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
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              "      <td>female</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
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              "      <th>4</th>\n",
              "      <td>30</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  PassengerId  ...                                  Name     Sex\n",
              "0     21           22  ...       Sandstrom, Miss. Marguerite Rut  female\n",
              "1     23           24  ...              Bonnell, Miss. Elizabeth  female\n",
              "2     25           26  ...        Saundercock, Mr. William Henry    male\n",
              "3     27           28  ...           Andersson, Mr. Anders Johan    male\n",
              "4     30           31  ...  Vestrom, Miss. Hulda Amanda Adolfina  female\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6Da3y8Zj1wN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "a49c4a7a-755e-4476-ff93-ccf584c14656"
      },
      "source": [
        "pd.concat([df1, df2],ignore_index=True,sort=True, axis=0)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>31.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   Name  PassengerId  ...  Survived index\n",
              "0                                   NaN         22.0  ...       1.0    21\n",
              "1                                   NaN         24.0  ...       1.0    23\n",
              "2                                   NaN         26.0  ...       1.0    25\n",
              "3                                   NaN         28.0  ...       0.0    27\n",
              "4                                   NaN         31.0  ...       0.0    30\n",
              "5       Sandstrom, Miss. Marguerite Rut          NaN  ...       NaN    10\n",
              "6              Bonnell, Miss. Elizabeth          NaN  ...       NaN    11\n",
              "7        Saundercock, Mr. William Henry          NaN  ...       NaN    12\n",
              "8           Andersson, Mr. Anders Johan          NaN  ...       NaN    13\n",
              "9  Vestrom, Miss. Hulda Amanda Adolfina          NaN  ...       NaN    14\n",
              "\n",
              "[10 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mwk12vKdnIU5",
        "colab_type": "text"
      },
      "source": [
        "### ⭐Merge and join\n",
        "\n",
        "Ambas funciones permiten que los datos de diferentes dataframes se combinen en uno solo de acuerdo con una regla de \"cruce\" o \"búsqueda\".\n",
        "\n",
        "Aunque tanto `merge` como` join` hacen cosas similares, la forma en que lo hacen es diferente.\n",
        "\n",
        "La función `merge` es la función predeterminada de pandas para unir datos. Básicamente es contraparte de *pandas de la unión de SQL*, y requiere la especificación de qué columnas de ambos dataframes se compararán. A Merge no le importa en absoluto los índices definidos en ellos.\n",
        "\n",
        "Por otro lado, la función `join` de Panda es más conveniente (incluso utiliza merge internamente), unir es básicamente hacer una fusión aprovechando los índices de ambos marcos de datos.\n",
        "\n",
        "La siguiente figura resume los diferentes 4 tipos de combinaciones: _inner, outer, left and right_.\n",
        "\n",
        "![MERGE](http://www.datasciencemadesimple.com/wp-content/uploads/2017/09/join-or-merge-in-python-pandas-1.png)\n",
        "\n",
        "La función merge también está disponible como método en la clase `DataFrame`.\n",
        "La sintaxis básica es:\n",
        "\n",
        "```\n",
        "new_joined_df = df.merge (another_df, left_on = \"col_in_df\", right_on = \"col_in_another_df\",\n",
        "                          how=\"inner\"|\"left\"|\"right\"|\"outer\")\n",
        "```\n",
        "\n",
        "El primer argumento (`another_df`), así como` left_on` y `right_on` son argumentos obligatorios.\n",
        "`left_on` especifica un nombre de columna en el dataframe `df` cuyos valores deben coincidir con\n",
        "los de la columna `another_df` 'especificados en `right_on`.\n",
        "\n",
        "El argumento `how` es opcional y por defecto es `inner`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcKLwsYlm_P8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "staff_df = pd.DataFrame([{'Name': 'Kelly', 'Role': 'Director of HR'},\n",
        "                         {'Name': 'Sally', 'Role': 'Course liasion'},\n",
        "                         {'Name': 'James', 'Role': 'Grader'}])\n",
        "staff_df = staff_df.set_index('Name')\n",
        "student_df = pd.DataFrame([{'Name': 'James', 'School': 'Business'},\n",
        "                           {'Name': 'Mike', 'School': 'Law'},\n",
        "                           {'Name': 'Sally', 'School': 'Engineering'}])\n",
        "student_df = student_df.set_index('Name')"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iR8t95hNQvFR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "71cdf2f4-b0f9-47e0-e8ae-e87568d7b0db"
      },
      "source": [
        "staff_df"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Role</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Kelly</td>\n",
              "      <td>Director of HR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sally</td>\n",
              "      <td>Course liasion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>James</td>\n",
              "      <td>Grader</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name            Role\n",
              "0  Kelly  Director of HR\n",
              "1  Sally  Course liasion\n",
              "2  James          Grader"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "student_df"
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      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>School</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>James</td>\n",
              "      <td>Business</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mike</td>\n",
              "      <td>Law</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sally</td>\n",
              "      <td>Engineering</td>\n",
              "    </tr>\n",
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              "    Name       School\n",
              "0  James     Business\n",
              "1   Mike          Law\n",
              "2  Sally  Engineering"
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          "execution_count": 57
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      "source": [
        "pd.merge(staff_df, student_df,left_index=True, right_index=True)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sally</th>\n",
              "      <td>Course liasion</td>\n",
              "      <td>Engineering</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>James</th>\n",
              "      <td>Grader</td>\n",
              "      <td>Business</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "                 Role       School\n",
              "Name                              \n",
              "Sally  Course liasion  Engineering\n",
              "James          Grader     Business"
            ]
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          "metadata": {
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          "execution_count": 40
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    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "db543a97-bc06-42bb-958a-86303f62d48d"
      },
      "source": [
        "pd.merge(staff_df, student_df, how='outer', left_index=True, right_index=True)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr>\n",
              "      <th>Mike</th>\n",
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              "                 Role       School\n",
              "Name                              \n",
              "James          Grader     Business\n",
              "Kelly  Director of HR          NaN\n",
              "Mike              NaN          Law\n",
              "Sally  Course liasion  Engineering"
            ]
          },
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          },
          "execution_count": 41
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      "cell_type": "code",
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        },
        "outputId": "c791e799-983b-4be8-e6c9-12914c890adf"
      },
      "source": [
        "pd.merge(staff_df, student_df, how='left', left_index=True, right_index=True)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "                 Role       School\n",
              "Name                              \n",
              "Kelly  Director of HR          NaN\n",
              "Sally  Course liasion  Engineering\n",
              "James          Grader     Business"
            ]
          },
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          "execution_count": 42
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    {
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        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "staff_df = staff_df.reset_index()\n",
        "student_df = student_df.reset_index()"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6TgXpXfjF8K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "b7d35875-ba05-48bd-925e-75cfe4a76a36"
      },
      "source": [
        "pd.merge(staff_df, student_df, how=\"left\",on=\"Name\")"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr>\n",
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              "      <td>Kelly</td>\n",
              "      <td>Director of HR</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>Sally</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>James</td>\n",
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              "      <td>Business</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name            Role       School\n",
              "0  Kelly  Director of HR          NaN\n",
              "1  Sally  Course liasion  Engineering\n",
              "2  James          Grader     Business"
            ]
          },
          "metadata": {
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          "execution_count": 45
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    {
      "cell_type": "code",
      "metadata": {
        "id": "7Ee0ZZBThr2L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "staff_df = pd.DataFrame([{'First Name': 'Kelly', 'Last Name': 'Desjardins', 'Role': 'Director of HR'},\n",
        "                         {'First Name': 'Sally', 'Last Name': 'Brooks', 'Role': 'Course liasion'},\n",
        "                         {'First Name': 'James', 'Last Name': 'Wilde', 'Role': 'Grader'}])"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "030UPP9phzNE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "student_df = pd.DataFrame([{'First Name': 'James', 'Last Name': 'Hammond', 'School': 'Business'},\n",
        "                           {'First Name': 'Mike', 'Last Name': 'Smith', 'School': 'Law'},\n",
        "                           {'First Name': 'Sally', 'Last Name': 'Brooks', 'School': 'Engineering'}])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s5ftfF5rh2Mr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "2f361c17-7458-49ce-8c47-aec7e6c93c52"
      },
      "source": [
        "pd.merge(staff_df, student_df, how='inner', left_on=['First Name','Last Name'], right_on=['First Name','Last Name'])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "      <th>Role</th>\n",
              "      <th>School</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sally</td>\n",
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              "      <td>Engineering</td>\n",
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              "</table>\n",
              "</div>"
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            "text/plain": [
              "  First Name Last Name            Role       School\n",
              "0      Sally    Brooks  Course liasion  Engineering"
            ]
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          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mKeapLTJRvWf",
        "colab_type": "text"
      },
      "source": [
        "###⭐ join\n",
        "```DataFrame.join ()``` es un método conveniente para combinar las columnas de dos DataFrames potencialmente indexados de manera diferente en un único DataFrame de resultado. Aquí hay un ejemplo muy básico:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXNTeX2CSlmF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\n",
        "                     'B': ['B0', 'B1', 'B2']},\n",
        "                      index=['K0', 'K1', 'K2'])"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGbBNXs7THH_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\n",
        "                       'D': ['D0', 'D2', 'D3']},\n",
        "                       index=['K0', 'K2', 'K3'])"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4eqbMgQTM4F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "result = left.join(right)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yD-lC-KgTPV2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "dd521287-ff95-42ef-e434-473d0af74cf9"
      },
      "source": [
        "left"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>K0</th>\n",
              "      <td>A0</td>\n",
              "      <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>"
            ],
            "text/plain": [
              "     A   B\n",
              "K0  A0  B0\n",
              "K1  A1  B1\n",
              "K2  A2  B2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4xaciNRTQ5B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "a8f85ec8-b857-426e-9458-7d6e99ca406b"
      },
      "source": [
        "right"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>K0</th>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K2</th>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K3</th>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     C   D\n",
              "K0  C0  D0\n",
              "K2  C2  D2\n",
              "K3  C3  D3"
            ]
          },
          "metadata": {
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          },
          "execution_count": 70
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "YHTkWV-STS1h",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 137
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        "outputId": "b2a3c9b3-b484-4f00-f6af-796fa7019aae"
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      "source": [
        "result"
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      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></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>K0</th>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K1</th>\n",
              "      <td>A1</td>\n",
              "      <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>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A   B    C    D\n",
              "K0  A0  B0   C0   D0\n",
              "K1  A1  B1  NaN  NaN\n",
              "K2  A2  B2   C2   D2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1MytAtTk8Tm",
        "colab_type": "text"
      },
      "source": [
        "### ⭐Agregación y Agrupamiento\n",
        "Un análisis esencial de grandes datos es un resumen eficiente: agregaciones informáticas como ``sum()``, ``mean()``, ``median()``, ``min()`` y ``max()``, en el que un solo número da una idea de la naturaleza de un conjunto de datos potencialmente grande.\n",
        "\n",
        "En esta sección, exploraremos las agregaciones en Pandas, desde operaciones simples similares a las que hemos visto en los arreglos NumPy, hasta operaciones más sofisticadas basadas en el concepto de ``groupby``."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiKrZ3JIk7N8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "10c38271-c4dc-4cf7-995f-2f8754866af0"
      },
      "source": [
        "titanic['Age'].mean()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lOBrn_MlBXI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83
        },
        "outputId": "af4e8b77-9c17-4c34-f970-986ca3e23acd"
      },
      "source": [
        "titanic.groupby('Sex').size()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "female    314\n",
              "male      577\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePPzQo0RlDgu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "c9980d59-c226-455c-8a7a-e0f89cd1ba23"
      },
      "source": [
        "titanic.groupby('Sex').agg({'Age':['min','mean','max']})"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">Age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>mean</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.75</td>\n",
              "      <td>27.915709</td>\n",
              "      <td>63.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.42</td>\n",
              "      <td>30.726645</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Age                 \n",
              "         min       mean   max\n",
              "Sex                          \n",
              "female  0.75  27.915709  63.0\n",
              "male    0.42  30.726645  80.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WaLU9lnbpjoh",
        "colab_type": "text"
      },
      "source": [
        "## 🍀Exploración de Datos del Titanic\n",
        "\n",
        "Para esta fase inicial conocer algunas funciones que nos provee pandas para facilitarnos la vida son fundamentales.  Dentro de esta fase se desea identificar el dataset que tipo de datos maneja, si tenemos nulos en nuestro conjunto de datos, entre otros.\n",
        "\n",
        "Estas son algunas funciones que nos sirven para esta fase:\n",
        "\n",
        "```python\n",
        "df.head()\n",
        "df.tail()\n",
        "df.info()\n",
        "df.shape\n",
        "df.columns\n",
        "df.describe()\n",
        "df.value_counts()\n",
        "df.unique()\n",
        "df.nunique()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OyzNZ-ouljCS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "4288e274-f9ad-4733-82bd-ec6a77a1e385"
      },
      "source": [
        "# Visualiza las primeras 2 filas del titanic\n",
        "titanic.head(2)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "\n",
              "[2 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrtJDBctVFpi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "4bf28653-0920-4a10-8ed0-fbc7bc25fb34"
      },
      "source": [
        "# Visualiza las ultimas 2 filas del titanic\n",
        "titanic.tail(2)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.00</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...   Fare Cabin  Embarked\n",
              "889          890         1       1  ...  30.00  C148         C\n",
              "890          891         0       3  ...   7.75   NaN         Q\n",
              "\n",
              "[2 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I16VTfpiVH_t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "44c839be-f56f-40bf-e411-ba82bc8f4da5"
      },
      "source": [
        "# Conocer cuantas filas y columnas tiene nuestro dataset\n",
        "titanic.shape"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rthY3bl6VKAj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "2465c630-74ff-443e-adf1-a7bd7a2ded8b"
      },
      "source": [
        "# Tipos de datos que tiene nuestro dataset y que cantidad de nulos tiene\n",
        "titanic.info()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwYo2m10VL-J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "8eb82f35-132d-4bef-e3cb-10a3880d75f6"
      },
      "source": [
        "# Otra manera de visualizar los nulos por columna\n",
        "titanic.isnull().sum()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfiBdYpLVOjL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "295eac9a-ca9e-43cc-da8d-75b6f416891b"
      },
      "source": [
        "# Otra manera de conocer los tipos de datos\n",
        "titanic.dtypes"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LDoCL0fZVR7z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "9d939b5b-26d3-44b8-8b46-00b870b3fa98"
      },
      "source": [
        "# Resumen de las columnas numericas\n",
        "titanic.describe()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-GiKDcNNVUM4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "8d1b6087-0888-475a-ddf5-888fd0419ace"
      },
      "source": [
        "# Resumen de las columnas categoricas\n",
        "titanic.describe(include = ['O'])"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>204</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>891</td>\n",
              "      <td>2</td>\n",
              "      <td>681</td>\n",
              "      <td>147</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Smith, Mr. James Clinch</td>\n",
              "      <td>male</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>577</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>644</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Name   Sex    Ticket        Cabin Embarked\n",
              "count                       891   891       891          204      889\n",
              "unique                      891     2       681          147        3\n",
              "top     Smith, Mr. James Clinch  male  CA. 2343  C23 C25 C27        S\n",
              "freq                          1   577         7            4      644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-WnTsLnVXVW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "1321aecd-fb12-4a67-f825-7df4e2cf9586"
      },
      "source": [
        "# Valores unicos por columna sin tener en cuenta nulos\n",
        "titanic.nunique()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    891\n",
              "Survived         2\n",
              "Pclass           3\n",
              "Name           891\n",
              "Sex              2\n",
              "Age             88\n",
              "SibSp            7\n",
              "Parch            7\n",
              "Ticket         681\n",
              "Fare           248\n",
              "Cabin          147\n",
              "Embarked         3\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6V0vykaVebg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "679ed519-bd3a-4e7e-9f53-c5e9dd7ee3d3"
      },
      "source": [
        "# Valores unicos por columna teniendo en cuenta nulos\n",
        "titanic.nunique(dropna=False)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    891\n",
              "Survived         2\n",
              "Pclass           3\n",
              "Name           891\n",
              "Sex              2\n",
              "Age             89\n",
              "SibSp            7\n",
              "Parch            7\n",
              "Ticket         681\n",
              "Fare           248\n",
              "Cabin          148\n",
              "Embarked         4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NO3cKfZgVjyq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "c9065989-7ac2-40b0-f7c0-d6e4d7d27ddc"
      },
      "source": [
        "# Conteo de valores por columna de no nulos\n",
        "titanic.count()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    891\n",
              "Survived       891\n",
              "Pclass         891\n",
              "Name           891\n",
              "Sex            891\n",
              "Age            714\n",
              "SibSp          891\n",
              "Parch          891\n",
              "Ticket         891\n",
              "Fare           891\n",
              "Cabin          204\n",
              "Embarked       889\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GhOODfTGVnIy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "17a6d909-aaff-44d8-ccb4-60be84e2b4f7"
      },
      "source": [
        "# Valor mas frecuente en la serie\n",
        "titanic['Embarked'].mode()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    S\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIPf6j-qVrrr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "6edcd53f-c70a-4838-851f-e6b5a47a8d1c"
      },
      "source": [
        "# Valores unicos de la columna 'Embarked'\n",
        "titanic['Embarked'].unique()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['S', 'C', 'Q', nan], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ABwt_69Vvxt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83
        },
        "outputId": "f85d21c7-900c-460a-8c1c-2e7e9b31993c"
      },
      "source": [
        "# Cuantos registros tenemos por categoria de la columna 'Embarked' sin nulos\n",
        "titanic['Embarked'].value_counts()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJ9K37ppV1-m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "450651d3-7df6-47ab-8d14-e6d53f397601"
      },
      "source": [
        "# Cuales son los nombres de las columnas del dataset\n",
        "titanic.columns"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3deY4zZnV6qs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "95a9a3ee-c79e-44ed-c3fe-77942a4cd4e8"
      },
      "source": [
        "titanic.columns = ['Passenger_Id', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
        "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n",
        "titanic = titanic.rename(columns={'Passenger_Id':'PassengerId'})\n",
        "titanic.head()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JjzOuUiV9SW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "?titanic.rename"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJsCn4v6WtSs",
        "colab_type": "text"
      },
      "source": [
        "#🌻Ejercicio EDA y Manipulacion datos con Pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czX54hhrW3VJ",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Creamos un nuevo Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXI9-NF8WBso",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(data={\"Pais\":['Mexico','Argentina','Espana','Colombia'],\n",
        "                        \"Poblacion\":[127212000, 45167000, 47099000, 48922000]})"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8IbgiurW-I4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "1c57a955-8899-44e0-967f-6f9bf7ac5a7e"
      },
      "source": [
        "df"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion\n",
              "0     Mexico  127212000\n",
              "1  Argentina   45167000\n",
              "2     Espana   47099000\n",
              "3   Colombia   48922000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lzKyYUs_XG4d",
        "colab_type": "text"
      },
      "source": [
        "Ordenamos por columna"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzxJd2r8XC95",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "6c07a040-d17f-4ff3-8973-a0c58cd89827"
      },
      "source": [
        "df.sort_values([\"Poblacion\"], ascending=False)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion\n",
              "0     Mexico  127212000\n",
              "3   Colombia   48922000\n",
              "2     Espana   47099000\n",
              "1  Argentina   45167000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "quZURymZXK21",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "c503d5bc-9e5a-4b8a-f64b-5229f90f0027"
      },
      "source": [
        "df = df.sort_values([\"Pais\"])\n",
        "df"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion\n",
              "1  Argentina   45167000\n",
              "3   Colombia   48922000\n",
              "2     Espana   47099000\n",
              "0     Mexico  127212000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Acz2MlttXYNb",
        "colab_type": "text"
      },
      "source": [
        "Agregar una columna"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ovgarwzJXTWV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "2d5c1ce9-7bc5-49ae-8a7b-b4d83ca922d4"
      },
      "source": [
        "df[\"Superficie\"] = [1964375, 2780400, 505944, 1142748]\n",
        "df"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "      <td>1964375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "      <td>2780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "      <td>505944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "      <td>1142748</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion  Superficie\n",
              "1  Argentina   45167000     1964375\n",
              "3   Colombia   48922000     2780400\n",
              "2     Espana   47099000      505944\n",
              "0     Mexico  127212000     1142748"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ta2uuVkYXgSy",
        "colab_type": "text"
      },
      "source": [
        "Asigno mismo valor a todas las filas en una columna nueva"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7naZV4qXauY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "dcd96b80-1aff-48da-ec7d-f70c56492ceb"
      },
      "source": [
        "df['Deporte']= 'Futbol'\n",
        "df"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "      <th>Deporte</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "      <td>1964375</td>\n",
              "      <td>Futbol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "      <td>2780400</td>\n",
              "      <td>Futbol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "      <td>505944</td>\n",
              "      <td>Futbol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "      <td>1142748</td>\n",
              "      <td>Futbol</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion  Superficie Deporte\n",
              "1  Argentina   45167000     1964375  Futbol\n",
              "3   Colombia   48922000     2780400  Futbol\n",
              "2     Espana   47099000      505944  Futbol\n",
              "0     Mexico  127212000     1142748  Futbol"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3wHDWFLXnWl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "329d3298-0ba9-4802-9bc6-ed7c9afb9cff"
      },
      "source": [
        "df = df.drop(['Deporte'],axis=1)\n",
        "df"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "      <td>1964375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "      <td>2780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "      <td>505944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "      <td>1142748</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion  Superficie\n",
              "1  Argentina   45167000     1964375\n",
              "3   Colombia   48922000     2780400\n",
              "2     Espana   47099000      505944\n",
              "0     Mexico  127212000     1142748"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7SzKEBWwX0-V",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Eliminar multiples columnas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1FvwSuVXvef",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "f495c190-0cd6-4d7b-e0b8-34f15c73edec"
      },
      "source": [
        "df.drop(['Superficie','Pais'], axis=1)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Poblacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45167000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48922000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47099000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>127212000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Poblacion\n",
              "1   45167000\n",
              "3   48922000\n",
              "2   47099000\n",
              "0  127212000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbSsK3XcX36c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "4f55289e-92dd-4001-a39e-28df55879851"
      },
      "source": [
        "df # recuerda! como no lo asignamos, el df anterior sigue \"intacto\""
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "      <td>1964375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "      <td>2780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "      <td>505944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "      <td>1142748</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion  Superficie\n",
              "1  Argentina   45167000     1964375\n",
              "3   Colombia   48922000     2780400\n",
              "2     Espana   47099000      505944\n",
              "0     Mexico  127212000     1142748"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGtPGM2Sa3S3",
        "colab_type": "text"
      },
      "source": [
        "Agregar una fila nueva al final"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PLQKwPnYayEr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "5d957c1e-c4be-4111-9cfb-2f4e50dd4b91"
      },
      "source": [
        "cantidad_filas = len(df) # obtengo la cantidad de filas\n",
        "cantidad_filas"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vtzYZT-ya7P5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "4d305d67-e067-4bf1-e0b6-e26f62e551b2"
      },
      "source": [
        "df.loc[cantidad_filas] = [\"Hargentina\", 0, 916445] # Está mal escrito el pais! (lo sé)\n",
        "df"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "      <td>1964375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "      <td>2780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "      <td>505944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "      <td>1142748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hargentina</td>\n",
              "      <td>0</td>\n",
              "      <td>916445</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Pais  Poblacion  Superficie\n",
              "1   Argentina   45167000     1964375\n",
              "3    Colombia   48922000     2780400\n",
              "2      Espana   47099000      505944\n",
              "0      Mexico  127212000     1142748\n",
              "4  Hargentina          0      916445"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-TECK1IbI6b",
        "colab_type": "text"
      },
      "source": [
        "Actualizo la fila entera"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYV4JNola-FS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "373bdd32-1aaa-463a-87ac-baa030ef6eaa"
      },
      "source": [
        "df.loc[4] = [\"Argentina\", 0, 916445]\n",
        "df"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "      <td>1964375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "      <td>2780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "      <td>505944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "      <td>1142748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>0</td>\n",
              "      <td>916445</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion  Superficie\n",
              "1  Argentina   45167000     1964375\n",
              "3   Colombia   48922000     2780400\n",
              "2     Espana   47099000      505944\n",
              "0     Mexico  127212000     1142748\n",
              "4  Argentina          0      916445"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YRo6aKrtbQ5s",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Actualizo una celda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36Mf2yQubM9E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "cc928414-0d5d-4916-ffd3-6baa454d6e1d"
      },
      "source": [
        "df.at[4,'Poblacion'] = 32423000\n",
        "df"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "      <td>1964375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "      <td>2780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "      <td>505944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "      <td>1142748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>32423000</td>\n",
              "      <td>916445</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion  Superficie\n",
              "1  Argentina   45167000     1964375\n",
              "3   Colombia   48922000     2780400\n",
              "2     Espana   47099000      505944\n",
              "0     Mexico  127212000     1142748\n",
              "4  Argentina   32423000      916445"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uG711PuJbZAl",
        "colab_type": "text"
      },
      "source": [
        "Eliminar una fila"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ModM6K1lbUIu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "ec001318-51a7-4337-be04-31fed40f5491"
      },
      "source": [
        "df.drop([3])"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "      <td>1964375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "      <td>505944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "      <td>1142748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>32423000</td>\n",
              "      <td>916445</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion  Superficie\n",
              "1  Argentina   45167000     1964375\n",
              "2     Espana   47099000      505944\n",
              "0     Mexico  127212000     1142748\n",
              "4  Argentina   32423000      916445"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKgwb8Xlbh3e",
        "colab_type": "text"
      },
      "source": [
        "### 💙Filtrar\n",
        "\n",
        "Paises con mas de 46 millones de habitantes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQGTk0VFbbRf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "ad5b9041-24d6-433a-f2a1-38bb263d3b9d"
      },
      "source": [
        "mas_de_46 = df[ df['Poblacion'] > 46000000 ]\n",
        "mas_de_46"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "      <td>2780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "      <td>505944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "      <td>1142748</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Pais  Poblacion  Superficie\n",
              "3  Colombia   48922000     2780400\n",
              "2    Espana   47099000      505944\n",
              "0    Mexico  127212000     1142748"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ZZFsK6VbvDx",
        "colab_type": "text"
      },
      "source": [
        "### 💙Busco por un valor específico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxh2vrb-bqAP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "35b85526-4860-463d-d900-65f68fcbbec2"
      },
      "source": [
        "por_nombre = df[ df['Pais'] == 'Colombia' ]\n",
        "por_nombre"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "      <td>2780400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Pais  Poblacion  Superficie\n",
              "3  Colombia   48922000     2780400"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FbxuUbhNb4aV",
        "colab_type": "text"
      },
      "source": [
        "### 💙Aplicar operaciones entre columnas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5WAAJS_Pbxtg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "f278b4c1-783d-424c-af70-42ca6e686799"
      },
      "source": [
        "df['Habit_x_km2'] = (df['Poblacion'] / df['Superficie']).astype(int)\n",
        "df.sort_values(['Habit_x_km2'])"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pais</th>\n",
              "      <th>Poblacion</th>\n",
              "      <th>Superficie</th>\n",
              "      <th>Habit_x_km2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>48922000</td>\n",
              "      <td>2780400</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>45167000</td>\n",
              "      <td>1964375</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>32423000</td>\n",
              "      <td>916445</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Espana</td>\n",
              "      <td>47099000</td>\n",
              "      <td>505944</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>127212000</td>\n",
              "      <td>1142748</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Pais  Poblacion  Superficie  Habit_x_km2\n",
              "3   Colombia   48922000     2780400           17\n",
              "1  Argentina   45167000     1964375           22\n",
              "4  Argentina   32423000      916445           35\n",
              "2     Espana   47099000      505944           93\n",
              "0     Mexico  127212000     1142748          111"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ApD_LDncYj3",
        "colab_type": "text"
      },
      "source": [
        "### 💙Pivotar una Tabla"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyplMujgb68F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "2b96210d-f8b8-4162-c09d-23c562a8bea1"
      },
      "source": [
        "tabla_t = pd.pivot_table(df, columns='Pais').fillna(0)\n",
        "tabla_t"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Pais</th>\n",
              "      <th>Argentina</th>\n",
              "      <th>Colombia</th>\n",
              "      <th>Espana</th>\n",
              "      <th>Mexico</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Habit_x_km2</th>\n",
              "      <td>28.5</td>\n",
              "      <td>17.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>111.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Poblacion</th>\n",
              "      <td>38795000.0</td>\n",
              "      <td>48922000.0</td>\n",
              "      <td>47099000.0</td>\n",
              "      <td>127212000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Superficie</th>\n",
              "      <td>1440410.0</td>\n",
              "      <td>2780400.0</td>\n",
              "      <td>505944.0</td>\n",
              "      <td>1142748.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Pais          Argentina    Colombia      Espana       Mexico\n",
              "Habit_x_km2        28.5        17.0        93.0        111.0\n",
              "Poblacion    38795000.0  48922000.0  47099000.0  127212000.0\n",
              "Superficie    1440410.0   2780400.0    505944.0    1142748.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fg33Fs3Kcmza",
        "colab_type": "text"
      },
      "source": [
        "### 💙Transponer una tabla"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "he8yvRRccfHC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "10ce81c9-61b8-47f2-f86f-58b35a3048f8"
      },
      "source": [
        "\n",
        "df.T"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>3</th>\n",
              "      <th>2</th>\n",
              "      <th>0</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pais</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Espana</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>Argentina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Poblacion</th>\n",
              "      <td>45167000</td>\n",
              "      <td>48922000</td>\n",
              "      <td>47099000</td>\n",
              "      <td>127212000</td>\n",
              "      <td>32423000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Superficie</th>\n",
              "      <td>1964375</td>\n",
              "      <td>2780400</td>\n",
              "      <td>505944</td>\n",
              "      <td>1142748</td>\n",
              "      <td>916445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Habit_x_km2</th>\n",
              "      <td>22</td>\n",
              "      <td>17</td>\n",
              "      <td>93</td>\n",
              "      <td>111</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     1         3         2          0          4\n",
              "Pais         Argentina  Colombia    Espana     Mexico  Argentina\n",
              "Poblacion     45167000  48922000  47099000  127212000   32423000\n",
              "Superficie     1964375   2780400    505944    1142748     916445\n",
              "Habit_x_km2         22        17        93        111         35"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gFuVOUwctfH",
        "colab_type": "text"
      },
      "source": [
        "## 💙Visualización"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M06J6YIccpKa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "2f342ede-ec46-488e-c878-a55b6cf68d1d"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "df.set_index('Pais')['Poblacion'].plot(kind='bar');"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M35EE_U0czXv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "07faa590-bacf-4eb7-9ab8-0ff15aa89da2"
      },
      "source": [
        "df.set_index('Pais')['Habit_x_km2'].plot(kind='area');"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5DOwHGzdDNM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "0dfbb691-50e9-46e9-c175-39c06c3178d9"
      },
      "source": [
        "df.set_index('Pais').plot.barh(stacked=True);"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6OUFjmldHED",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "9a76161a-6176-4b06-ff4d-d040a2913599"
      },
      "source": [
        "\n",
        "df.set_index('Pais')['Superficie'].plot.pie();"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bozQ9hhGdMH1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "outputId": "45861596-e988-453f-fcb7-b6ed5b331e2a"
      },
      "source": [
        "df.plot.scatter(x='Habit_x_km2', y='Superficie');"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Pry1bKVdUjG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}