Introduccion_PreProcesamientoWoAns.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qJeDOsJY9cJ2"
   },
   "source": [
    "# Introducción al Pre-procesamiento \n",
    "\n",
    "<img src=\"http://tanukamandal.com/wp-content/uploads/2018/05/data-preparation-1-570x350.png\" width=\"800\" align=\"middle\">\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SXOGYR4r-nfD"
   },
   "source": [
    "## Contenido de la sección\n",
    "\n",
    "\n",
    "1.   Introducción al Pre-procesamiento de Datos\n",
    "          1.1. Tipos de PP.\n",
    "2.   Exploración Visual de los Datos\n",
    "          2.1. Histogramas.\n",
    "            2.1.1. Joyplot\n",
    "          2.2. Diagramas de Dispersión.\n",
    "\n",
    "### Literatura recomendada:\n",
    "\n",
    "1. García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining (pp. 195-243). Cham, Switzerland: Springer International Publishing.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MM7-5Y56beuy"
   },
   "source": [
    "### Introducción al Pre-procesamiento (PP) de Datos.\n",
    "\n",
    "El **preprocesamiento** de datos dentro de la Ciencia de Datos es FUNDAMENTAL. \n",
    "\n",
    "Según el Foro Económico Mundial, **¡para 2025 generaremos alrededor de 463 exabytes ($463*10^{18}$ bytes = 463 milliones de TB) de datos a nivel mundial por día!**. Entonces, deberiamos preguntarnos\n",
    "\n",
    " * ¿Todos los datos son adecuados para ser utilizados por los algoritmos de aprendizaje automático?\n",
    " * ¿Cómo decidimos eso?\n",
    "\n",
    "El **preprocesamiento** es el primer paso a realizarse antes de comenzar a aprender cualquier tipo de modelo, ya que la calidad de los datos y la información útil que puede derivarse de estos afectará directamente la capacidad de nuestros modelos.\n",
    "\n",
    "* **Garbage In, Garbage Out**\n",
    "\n",
    "![](https://upload.wikimedia.org/wikipedia/commons/thumb/b/ba/Data_visualization_process_v1.png/525px-Data_visualization_process_v1.png)\n",
    "\n",
    "Tomado de: https://www.kdnuggets.com/2016/03/data-science-process.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w05UuB7ccIof"
   },
   "source": [
    "---\n",
    "### Entonces, ¿qué es el PP de datos?\n",
    "\n",
    "Cuando hablamos de datos, generalmente pensamos en conjuntos de datos en **forma  tabular o tablas de excel** (filas y columnas) con valores que una computadora es capáz de asimilar (números continuos) y sin datos faltantes.  Por ejemplo, consideremos un conjunto de datos de aprobación crediticia.\n",
    "Dicho conjunto consta de:\n",
    "\n",
    "650 clientes (filas), 15 variables explicativas (nominales/categóricas y númericas) así como una clase (en total 16 columnas).\n",
    "\n",
    "* pregunta: **¿Una computadora puede analizar variables nominales?**\n",
    "\n",
    "* En el caso negativo, **¿cómo podriamos convertir la variable para que la computadora pueda interpretarla?**\n",
    "\n",
    "* **¿Qué se debe hacer cuando una variable presenta un dato faltante?**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 946,
     "status": "ok",
     "timestamp": 1585069012960,
     "user": {
      "displayName": "Guillermo Santamaria",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhFOdraf7be0jgGxdadpJk6jB59MMNn-k2Hl7rJ=s64",
      "userId": "14757125121333916384"
     },
     "user_tz": 360
    },
    "id": "3CzugePw1Rbr",
    "outputId": "29d72317-8505-44ca-f76f-aadb7ea52fdb"
   },
   "outputs": [
    {
     "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>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43</td>\n",
       "      <td>560</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280</td>\n",
       "      <td>824</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>b</td>\n",
       "      <td>21.08</td>\n",
       "      <td>10.085</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>e</td>\n",
       "      <td>h</td>\n",
       "      <td>1.25</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>260</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>a</td>\n",
       "      <td>22.67</td>\n",
       "      <td>0.750</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>2.00</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>200</td>\n",
       "      <td>394</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>a</td>\n",
       "      <td>25.25</td>\n",
       "      <td>13.500</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>2.00</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>b</td>\n",
       "      <td>17.92</td>\n",
       "      <td>0.205</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>aa</td>\n",
       "      <td>v</td>\n",
       "      <td>0.04</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280</td>\n",
       "      <td>750</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>b</td>\n",
       "      <td>35</td>\n",
       "      <td>3.375</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>h</td>\n",
       "      <td>8.29</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>690 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    A1     A2      A3 A4 A5  A6  A7    A8 A9 A10  A11 A12 A13  A14  A15 class\n",
       "0    b  30.83   0.000  u  g   w   v  1.25  t   t    1   f   g  202    0     +\n",
       "1    a  58.67   4.460  u  g   q   h  3.04  t   t    6   f   g   43  560     +\n",
       "2    a   24.5   0.500  u  g   q   h  1.50  t   f    0   f   g  280  824     +\n",
       "3    b  27.83   1.540  u  g   w   v  3.75  t   t    5   t   g  100    3     +\n",
       "4    b  20.17   5.625  u  g   w   v  1.71  t   f    0   f   s  120    0     +\n",
       "..  ..    ...     ... .. ..  ..  ..   ... ..  ..  ...  ..  ..  ...  ...   ...\n",
       "685  b  21.08  10.085  y  p   e   h  1.25  f   f    0   f   g  260    0     -\n",
       "686  a  22.67   0.750  u  g   c   v  2.00  f   t    2   t   g  200  394     -\n",
       "687  a  25.25  13.500  y  p  ff  ff  2.00  f   t    1   t   g  200    1     -\n",
       "688  b  17.92   0.205  u  g  aa   v  0.04  f   f    0   f   g  280  750     -\n",
       "689  b     35   3.375  u  g   c   h  8.29  f   f    0   t   g    0    0     -\n",
       "\n",
       "[690 rows x 16 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#from google.colab import data_table\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/MauricioRR-Tec/Machine-Learning/master/Notebooks/Module_2/Datasets/creditApproval_dataset.csv'\n",
    "df1 = pd.read_csv(url)\n",
    "\n",
    "#data_table.DataTable(df1, include_index=True, num_rows_per_page=10)\n",
    "#data_table.DataTable? #How to display help regarding a function\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LySnndA0DTKo"
   },
   "source": [
    "### ¿Cómo identificarían si una variable, por ejemplo **A4** tiene valores faltantes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "THmZ6a6ukDCs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([206, 270, 330, 456, 592, 622]),)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Here your answer...\n",
    "import numpy as np\n",
    "\n",
    "## Mostrar en pantalla\n",
    "np.where(df1['A4'] == '?')\n",
    "\n",
    "# ['u' 'y' '?' 'l'] \n",
    "# [False False  True False]\n",
    "#[206, 270, 330, 456, 592, 622] indices en donde se encuentran"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W0y8lK6MzYbF"
   },
   "source": [
    "---\n",
    "Si bien mucha información se encuentra en un formato tabular o algún otro compatible, no siempre es el caso. \n",
    "Los **datos pueden tener muchas formas diferentes: \n",
    "tablas estructuradas, imágenes, archivos de audio, videos, etc**.\n",
    "\n",
    "Las máquinas no entienden los datos de texto, imagen o video **tal como son**, entienden los 1 y los 0. Por lo tanto, no es suficiente con hacer una presentación de diapositivas de todas nuestras imágenes y esperar que nuestro modelo de aprendizaje automático se entrene solo con eso.\n",
    "\n",
    "![](https://imgs.xkcd.com/comics/machine_learning.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "go1vPF-6EQze"
   },
   "source": [
    "#### Por ejemplo, en el caso de una imagen correspondiente al dígito **6** escrito a mano\n",
    "\n",
    "#### ¿Cómo podemos representarla para que una máquina entienda una imagen?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 653,
     "status": "ok",
     "timestamp": 1578451770480,
     "user": {
      "displayName": "Guillermo Santamaria",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCj19tufD_ATO44T3LbwDQPFnIaHb3WO7AwHbNK=s64",
      "userId": "14757125121333916384"
     },
     "user_tz": 360
    },
    "id": "foUnJi7SDe8p",
    "outputId": "4baae7bb-c83e-4b9a-e931-bc6012958e5b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11591a4c0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import image as img\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "image = img.imread(\"https://i.stack.imgur.com/Ds5Rc.png\")\n",
    "\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kpYrgRt5EDPh"
   },
   "source": [
    "Representamos la imagen en una matriz, o mejor dicho, varias matrices correspondientes a la escala de colores en Rojo, Verde y Azul (RGB por sus siglas en inglés). Por lo tanto, una imagen corresponde a 3 matrices $z_i\\in Z$, de tamaño $m\\times n$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 410,
     "status": "ok",
     "timestamp": 1578453468226,
     "user": {
      "displayName": "Guillermo Santamaria",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCj19tufD_ATO44T3LbwDQPFnIaHb3WO7AwHbNK=s64",
      "userId": "14757125121333916384"
     },
     "user_tz": 360
    },
    "id": "sRacvvkUcTI4",
    "outputId": "47e66f3d-496a-40f4-a94a-c33ab1793cdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(599, 602, 3)\n",
      "[[[0.99607843 0.99607843 0.99607843]\n",
      "  [0.99607843 0.99607843 0.99607843]\n",
      "  [0.99607843 0.99607843 0.99607843]\n",
      "  ...\n",
      "  [0.9647059  0.9647059  0.9647059 ]\n",
      "  [0.93333334 0.93333334 0.93333334]\n",
      "  [0.8784314  0.8784314  0.8784314 ]]\n",
      "\n",
      " [[0.99607843 0.99607843 0.99607843]\n",
      "  [0.99607843 0.99607843 0.99607843]\n",
      "  [0.99607843 0.99607843 0.99607843]\n",
      "  ...\n",
      "  [0.972549   0.972549   0.972549  ]\n",
      "  [0.93333334 0.93333334 0.93333334]\n",
      "  [0.8784314  0.8784314  0.8784314 ]]\n",
      "\n",
      " [[0.99607843 0.99607843 0.99607843]\n",
      "  [0.99607843 0.99607843 0.99607843]\n",
      "  [0.99607843 0.99607843 0.99607843]\n",
      "  ...\n",
      "  [0.99215686 0.99215686 0.99215686]\n",
      "  [0.93333334 0.93333334 0.93333334]\n",
      "  [0.88235295 0.88235295 0.88235295]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.96862745 0.96862745 0.96862745]\n",
      "  [0.9764706  0.9764706  0.9764706 ]\n",
      "  [0.99215686 0.99215686 0.99215686]\n",
      "  ...\n",
      "  [1.         1.         1.        ]\n",
      "  [0.9882353  0.9843137  0.9882353 ]\n",
      "  [0.827451   0.827451   0.827451  ]]\n",
      "\n",
      " [[0.96862745 0.96862745 0.96862745]\n",
      "  [0.9764706  0.9764706  0.9764706 ]\n",
      "  [0.99215686 0.99215686 0.99215686]\n",
      "  ...\n",
      "  [1.         1.         1.        ]\n",
      "  [0.9882353  0.9843137  0.9882353 ]\n",
      "  [0.827451   0.827451   0.827451  ]]\n",
      "\n",
      " [[0.96862745 0.96862745 0.96862745]\n",
      "  [0.9764706  0.9764706  0.9764706 ]\n",
      "  [0.99215686 0.99215686 0.99215686]\n",
      "  ...\n",
      "  [1.         1.         1.        ]\n",
      "  [0.9882353  0.9843137  0.9882353 ]\n",
      "  [0.827451   0.827451   0.827451  ]]]\n"
     ]
    }
   ],
   "source": [
    "print(type(image)) #El objeto devuelto es un arreglo multidimensional MxNxD\n",
    "print(image.shape) #Funciones de numpy.ndarray, devuelve el tamaño del arreglo.\n",
    "print(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "StpjbIir6ZB5"
   },
   "source": [
    "---\n",
    "### ¿Cuáles son los problemas básicos que deben resolverse en el PP de datos?\n",
    "\n",
    "> En cualquier proceso de Machine Learning, **el PP es el paso en el que los datos se transforman o codifican** para llevarlos a un estado tal que ahora la máquina pueda analizarlos fácilmente. \n",
    "\n",
    "Estos son algunos de los tipos de problemas básicos así como la familia de técnicas de PP a la que pertenecen:\n",
    "* ¿Cómo limpio los datos? ***Limpieza de datos***.\n",
    "* ¿Cómo unifico y escalo los datos? ***Normalización de datos***.\n",
    "* ¿Cómo proporciono datos precisos? ***Transformación de datos***.\n",
    "* ¿Cómo manejo los datos faltantes? ***Imputación de datos perdidos***.\n",
    "* ¿Cómo incorporo y ajusto datos? ***Integración de datos***.\n",
    "* ¿Cómo detecto y manejo el ruido? ***Análisis del ruido***.\n",
    "\n",
    "![](https://docs.google.com/uc?export=download&id=1-Dro_Qk1I63w2OAtjrFXRN2HcXMC07lo)\n",
    "\n",
    "---\n",
    "#### a.1. Limpieza de Datos.\n",
    "\n",
    "Involucra operaciones como\n",
    "* **corrección** de datos \n",
    "* **filtrar datos** especificios o incorrectos \n",
    "* **reducir detalles** innecesarios. \n",
    "\n",
    "Es un concepto general que se traslapa con otras técnicas de PP como la imputación de datos faltantes y análisis del ruido (aunque ambas categorías se ven por separado para un análisis más profundo). \n",
    "\n",
    "Otras operaciones son \n",
    "* la **detección de discrepancias** y **datos sucios** (fragmentos de los datos originales que no tienen sentido). \n",
    "\n",
    "Esto típicamente requiere intervención humana ya que es necesaria la comprensión de los datos originales.\n",
    "\n",
    "#### Ejemplo:\n",
    "\n",
    "https://archive.ics.uci.edu/ml/datasets/Credit+Approval\n",
    "\n",
    "![](https://docs.google.com/uc?export=download&id=1SY3zCno1dL3qukcBjWz8xIa7gkfbqm7D)\n",
    "\n",
    "<!-- <img src=\"https://docs.google.com/uc?export=download&id=1SY3zCno1dL3qukcBjWz8xIa7gkfbqm7D\" width=\"800\" align=\"middle\"> -->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 691,
     "status": "ok",
     "timestamp": 1578594676059,
     "user": {
      "displayName": "Guillermo Santamaria",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCj19tufD_ATO44T3LbwDQPFnIaHb3WO7AwHbNK=s64",
      "userId": "14757125121333916384"
     },
     "user_tz": 360
    },
    "id": "MddtqUJqSQLZ",
    "outputId": "7dde2201-99d3-4a09-fdad-d47992ed7c1e"
   },
   "outputs": [
    {
     "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>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43</td>\n",
       "      <td>560</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280</td>\n",
       "      <td>824</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>b</td>\n",
       "      <td>21.08</td>\n",
       "      <td>10.085</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>e</td>\n",
       "      <td>h</td>\n",
       "      <td>1.25</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>260</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>a</td>\n",
       "      <td>22.67</td>\n",
       "      <td>0.750</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>2.00</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>200</td>\n",
       "      <td>394</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>a</td>\n",
       "      <td>25.25</td>\n",
       "      <td>13.500</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>2.00</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>b</td>\n",
       "      <td>17.92</td>\n",
       "      <td>0.205</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>aa</td>\n",
       "      <td>v</td>\n",
       "      <td>0.04</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280</td>\n",
       "      <td>750</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>b</td>\n",
       "      <td>35</td>\n",
       "      <td>3.375</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>h</td>\n",
       "      <td>8.29</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>690 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    A1     A2      A3 A4 A5  A6  A7    A8 A9 A10  A11 A12 A13  A14  A15 class\n",
       "0    b  30.83   0.000  u  g   w   v  1.25  t   t    1   f   g  202    0     +\n",
       "1    a  58.67   4.460  u  g   q   h  3.04  t   t    6   f   g   43  560     +\n",
       "2    a   24.5   0.500  u  g   q   h  1.50  t   f    0   f   g  280  824     +\n",
       "3    b  27.83   1.540  u  g   w   v  3.75  t   t    5   t   g  100    3     +\n",
       "4    b  20.17   5.625  u  g   w   v  1.71  t   f    0   f   s  120    0     +\n",
       "..  ..    ...     ... .. ..  ..  ..   ... ..  ..  ...  ..  ..  ...  ...   ...\n",
       "685  b  21.08  10.085  y  p   e   h  1.25  f   f    0   f   g  260    0     -\n",
       "686  a  22.67   0.750  u  g   c   v  2.00  f   t    2   t   g  200  394     -\n",
       "687  a  25.25  13.500  y  p  ff  ff  2.00  f   t    1   t   g  200    1     -\n",
       "688  b  17.92   0.205  u  g  aa   v  0.04  f   f    0   f   g  280  750     -\n",
       "689  b     35   3.375  u  g   c   h  8.29  f   f    0   t   g    0    0     -\n",
       "\n",
       "[690 rows x 16 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#from google.colab import data_table\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/MauricioRR-Tec/Machine-Learning/master/Notebooks/Module_2/Datasets/creditApproval_dataset.csv'\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "#data_table.DataTable(df1, include_index=True, num_rows_per_page=10)\n",
    "#data_table.DataTable? #How to display help regarding a function\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iOEwinEAfWMM"
   },
   "source": [
    "### Ejercicio:\n",
    "\n",
    "1. Ahora queremos remover (por alguna razón) los registros de la matriz donde la variable A4 == 'u'\n",
    "\n",
    "2. También podriamos querer acotarnos a un rango de valores, por ejemplo para la\n",
    "variable 'A3' entre 4 y 10\n",
    "\n",
    "3. En particular, queremos cambiar el valor de la columna ``class`` de $\\{-,+\\}$ a $\\{-1,1\\}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OCNTZuewag-p"
   },
   "outputs": [
    {
     "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>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b</td>\n",
       "      <td>42.5</td>\n",
       "      <td>4.915</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.165</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>52</td>\n",
       "      <td>1442</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>b</td>\n",
       "      <td>36.67</td>\n",
       "      <td>4.415</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>v</td>\n",
       "      <td>0.250</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>10</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>320</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>b</td>\n",
       "      <td>44.83</td>\n",
       "      <td>7.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>1.625</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>160</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>b</td>\n",
       "      <td>?</td>\n",
       "      <td>5.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>aa</td>\n",
       "      <td>v</td>\n",
       "      <td>8.500</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>4.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>i</td>\n",
       "      <td>h</td>\n",
       "      <td>5.750</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>a</td>\n",
       "      <td>40.33</td>\n",
       "      <td>7.540</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>8.000</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>14</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>2300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>b</td>\n",
       "      <td>34.5</td>\n",
       "      <td>4.040</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>i</td>\n",
       "      <td>bb</td>\n",
       "      <td>8.500</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>7</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>b</td>\n",
       "      <td>28.25</td>\n",
       "      <td>5.040</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>c</td>\n",
       "      <td>bb</td>\n",
       "      <td>1.500</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>8</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>144</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>b</td>\n",
       "      <td>24.33</td>\n",
       "      <td>6.625</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>d</td>\n",
       "      <td>v</td>\n",
       "      <td>5.500</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>s</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>a</td>\n",
       "      <td>18.92</td>\n",
       "      <td>9.250</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>1.000</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>4</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>80</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>a</td>\n",
       "      <td>22.5</td>\n",
       "      <td>8.460</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>v</td>\n",
       "      <td>2.460</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>b</td>\n",
       "      <td>20.5</td>\n",
       "      <td>10.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>2.500</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>b</td>\n",
       "      <td>18.08</td>\n",
       "      <td>6.750</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>m</td>\n",
       "      <td>v</td>\n",
       "      <td>0.040</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>a</td>\n",
       "      <td>18.17</td>\n",
       "      <td>10.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>0.165</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>b</td>\n",
       "      <td>?</td>\n",
       "      <td>4.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>i</td>\n",
       "      <td>v</td>\n",
       "      <td>0.085</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>411</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>b</td>\n",
       "      <td>26.75</td>\n",
       "      <td>4.500</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>c</td>\n",
       "      <td>bb</td>\n",
       "      <td>2.500</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>200</td>\n",
       "      <td>1210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>a</td>\n",
       "      <td>56.83</td>\n",
       "      <td>4.250</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>5.000</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>b</td>\n",
       "      <td>34</td>\n",
       "      <td>5.500</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>1.500</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>b</td>\n",
       "      <td>20.75</td>\n",
       "      <td>5.085</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>j</td>\n",
       "      <td>v</td>\n",
       "      <td>0.290</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>140</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>b</td>\n",
       "      <td>34</td>\n",
       "      <td>5.085</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>i</td>\n",
       "      <td>bb</td>\n",
       "      <td>1.085</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>480</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>a</td>\n",
       "      <td>40.33</td>\n",
       "      <td>8.125</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>v</td>\n",
       "      <td>0.165</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>?</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>b</td>\n",
       "      <td>21.08</td>\n",
       "      <td>4.125</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>i</td>\n",
       "      <td>h</td>\n",
       "      <td>0.040</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>140</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>b</td>\n",
       "      <td>19.17</td>\n",
       "      <td>4.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>i</td>\n",
       "      <td>v</td>\n",
       "      <td>1.000</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>360</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>a</td>\n",
       "      <td>18.83</td>\n",
       "      <td>4.415</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>c</td>\n",
       "      <td>h</td>\n",
       "      <td>3.000</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>b</td>\n",
       "      <td>21</td>\n",
       "      <td>4.790</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>2.250</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>80</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>b</td>\n",
       "      <td>13.75</td>\n",
       "      <td>4.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.750</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>120</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>b</td>\n",
       "      <td>19.67</td>\n",
       "      <td>10.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>h</td>\n",
       "      <td>0.835</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>b</td>\n",
       "      <td>32.67</td>\n",
       "      <td>9.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>h</td>\n",
       "      <td>5.250</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>b</td>\n",
       "      <td>22</td>\n",
       "      <td>7.835</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>i</td>\n",
       "      <td>bb</td>\n",
       "      <td>0.165</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>a</td>\n",
       "      <td>21.08</td>\n",
       "      <td>5.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>0.000</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>b</td>\n",
       "      <td>32.08</td>\n",
       "      <td>4.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>cc</td>\n",
       "      <td>v</td>\n",
       "      <td>1.500</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A1     A2      A3 A4 A5  A6  A7     A8 A9 A10  A11 A12 A13  A14   A15  \\\n",
       "9    b   42.5   4.915  y  p   w   v  3.165  t   f    0   t   g   52  1442   \n",
       "15   b  36.67   4.415  y  p   k   v  0.250  t   t   10   t   g  320     0   \n",
       "74   b  44.83   7.000  y  p   c   v  1.625  f   f    0   f   g  160     2   \n",
       "92   b      ?   5.000  y  p  aa   v  8.500  t   f    0   f   g    0     0   \n",
       "104  b  27.83   4.000  y  p   i   h  5.750  t   t    2   t   g   75     0   \n",
       "147  a  40.33   7.540  y  p   q   h  8.000  t   t   14   f   g    0  2300   \n",
       "194  b   34.5   4.040  y  p   i  bb  8.500  t   t    7   t   g  195     0   \n",
       "195  b  28.25   5.040  y  p   c  bb  1.500  t   t    8   t   g  144     7   \n",
       "211  b  24.33   6.625  y  p   d   v  5.500  t   f    0   t   s  100     0   \n",
       "219  a  18.92   9.250  y  p   c   v  1.000  t   t    4   t   g   80   500   \n",
       "227  a   22.5   8.460  y  p   x   v  2.460  f   f    0   f   g  164     0   \n",
       "240  b   20.5  10.000  y  p   c   v  2.500  t   f    0   f   s   40     0   \n",
       "272  b  18.08   6.750  y  p   m   v  0.040  f   f    0   f   g  140     0   \n",
       "277  a  18.17  10.000  y  p   q   h  0.165  f   f    0   f   g  340     0   \n",
       "329  b      ?   4.000  y  p   i   v  0.085  f   f    0   t   g  411     0   \n",
       "347  b  26.75   4.500  y  p   c  bb  2.500  f   f    0   f   g  200  1210   \n",
       "383  a  56.83   4.250  y  p  ff  ff  5.000  f   f    0   t   g    0     4   \n",
       "385  b     34   5.500  y  p   c   v  1.500  f   f    0   t   g   60     0   \n",
       "400  b  20.75   5.085  y  p   j   v  0.290  f   f    0   f   g  140   184   \n",
       "404  b     34   5.085  y  p   i  bb  1.085  f   f    0   t   g  480     0   \n",
       "406  a  40.33   8.125  y  p   k   v  0.165  f   t    2   f   g    ?    18   \n",
       "471  b  21.08   4.125  y  p   i   h  0.040  f   f    0   f   g  140   100   \n",
       "473  b  19.17   4.000  y  p   i   v  1.000  f   f    0   t   g  360  1000   \n",
       "494  a  18.83   4.415  y  p   c   h  3.000  t   f    0   f   g  240     0   \n",
       "509  b     21   4.790  y  p   w   v  2.250  t   t    1   t   g   80   300   \n",
       "510  b  13.75   4.000  y  p   w   v  1.750  t   t    2   t   g  120  1000   \n",
       "542  b  19.67  10.000  y  p   k   h  0.835  t   f    0   t   g  140     0   \n",
       "583  b  32.67   9.000  y  p   w   h  5.250  t   f    0   t   g  154     0   \n",
       "626  b     22   7.835  y  p   i  bb  0.165  f   f    0   t   g    ?     0   \n",
       "655  a  21.08   5.000  y  p  ff  ff  0.000  f   f    0   f   g    0     0   \n",
       "663  b  32.08   4.000  y  p  cc   v  1.500  f   f    0   t   g  120     0   \n",
       "\n",
       "    class  \n",
       "9       1  \n",
       "15      1  \n",
       "74      0  \n",
       "92      0  \n",
       "104     0  \n",
       "147     1  \n",
       "194     1  \n",
       "195     1  \n",
       "211     1  \n",
       "219     1  \n",
       "227     1  \n",
       "240     1  \n",
       "272     0  \n",
       "277     0  \n",
       "329     0  \n",
       "347     0  \n",
       "383     0  \n",
       "385     0  \n",
       "400     0  \n",
       "404     0  \n",
       "406     0  \n",
       "471     0  \n",
       "473     0  \n",
       "494     1  \n",
       "509     1  \n",
       "510     1  \n",
       "542     0  \n",
       "583     1  \n",
       "626     0  \n",
       "655     0  \n",
       "663     0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ahora queremos remover (por alguna razón) los registros de la matriz donde\n",
    "#la variable A4 == 'u'\n",
    "df = df.drop(df.iloc[np.where(df['A4'] == 'u')[0]].index)\n",
    "\n",
    "#También podriamos querer acotarnos a un rango de valores, por ejemplo para la\n",
    "#variable 'A3' entre 4 y 10\n",
    "df = df.drop(df.iloc[np.where(df['A3'] < 4)[0]].index)\n",
    "df = df.drop(df.iloc[np.where(df['A3'] > 10)[0]].index)\n",
    "\n",
    "#Queremos cambiar el valor de la columna class de {−,+} a {−1,1}\n",
    "df.loc[df['class'] == '+', 'class'] = 1\n",
    "df.loc[df['class'] == '-', 'class'] = 0\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f3gOmDngpDp-"
   },
   "source": [
    "---\n",
    "#### a.2. Normalización de los Datos.\n",
    "\n",
    "La unidad de medida utilizada puede afectar el análisis de datos.\n",
    "\n",
    ">> Por ejemplo: típicamente los datos atmosféricos en Cuernavaca, reflejan una  temperatura acotada entre 8 a 40 °C, mientras que la presión atmosférica esta data entre 950 a 1050 mbar. \n",
    ">> <img src=\"https://upload.wikimedia.org/wikipedia/commons/9/9e/Dosen-barometer.jpg\" width=\"400\">\n",
    ">> \n",
    ">> * ¿Qué variable predomina más? \n",
    ">> * ¿Qué cambio produce un mayor efecto, una diferencia de 5°C de temperatura o 20 mbar de presión?\n",
    "\n",
    "La normalización de los datos intenta dar a todos los atributos el mismo peso, expresando cada variable en las mismas unidades de medida y así como una escala en común (determinada).\n",
    "\n",
    "#### Ejercicio:\n",
    "\n",
    "1. Escalar los valores de las columnas A3 y A15 entre 0 y 1.\n",
    "\n",
    "Utilice la siguiente formula:\n",
    "\n",
    "$$x_{new} = \\frac{x-a}{b-a}$$\n",
    "\n",
    "tal que\n",
    "\n",
    "$$a=\\min(x),\\ b=\\max(x)$$\n",
    "\n",
    "2. Escalar las mismas columnas pero entre dos valores arbitrarios $q$ y $z$, tal que $q<z$.\n",
    "\n",
    "Utilice la siguiente formula:\n",
    "\n",
    "$$x_{qz}= (z-q)\\cdot x_{new}+q$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pnH-prxZpIRd"
   },
   "outputs": [
    {
     "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>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>0.159286</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43</td>\n",
       "      <td>0.00560</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280</td>\n",
       "      <td>0.00824</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>0.055000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>0.200893</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>b</td>\n",
       "      <td>21.08</td>\n",
       "      <td>0.360179</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>e</td>\n",
       "      <td>h</td>\n",
       "      <td>1.25</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>260</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>a</td>\n",
       "      <td>22.67</td>\n",
       "      <td>0.026786</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>2.00</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>200</td>\n",
       "      <td>0.00394</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>a</td>\n",
       "      <td>25.25</td>\n",
       "      <td>0.482143</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>2.00</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>200</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>b</td>\n",
       "      <td>17.92</td>\n",
       "      <td>0.007321</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>aa</td>\n",
       "      <td>v</td>\n",
       "      <td>0.04</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280</td>\n",
       "      <td>0.00750</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>b</td>\n",
       "      <td>35</td>\n",
       "      <td>0.120536</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>h</td>\n",
       "      <td>8.29</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>690 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    A1     A2        A3 A4 A5  A6  A7    A8 A9 A10  A11 A12 A13  A14      A15  \\\n",
       "0    b  30.83  0.000000  u  g   w   v  1.25  t   t    1   f   g  202  0.00000   \n",
       "1    a  58.67  0.159286  u  g   q   h  3.04  t   t    6   f   g   43  0.00560   \n",
       "2    a   24.5  0.017857  u  g   q   h  1.50  t   f    0   f   g  280  0.00824   \n",
       "3    b  27.83  0.055000  u  g   w   v  3.75  t   t    5   t   g  100  0.00003   \n",
       "4    b  20.17  0.200893  u  g   w   v  1.71  t   f    0   f   s  120  0.00000   \n",
       "..  ..    ...       ... .. ..  ..  ..   ... ..  ..  ...  ..  ..  ...      ...   \n",
       "685  b  21.08  0.360179  y  p   e   h  1.25  f   f    0   f   g  260  0.00000   \n",
       "686  a  22.67  0.026786  u  g   c   v  2.00  f   t    2   t   g  200  0.00394   \n",
       "687  a  25.25  0.482143  y  p  ff  ff  2.00  f   t    1   t   g  200  0.00001   \n",
       "688  b  17.92  0.007321  u  g  aa   v  0.04  f   f    0   f   g  280  0.00750   \n",
       "689  b     35  0.120536  u  g   c   h  8.29  f   f    0   t   g    0  0.00000   \n",
       "\n",
       "    class  \n",
       "0       +  \n",
       "1       +  \n",
       "2       +  \n",
       "3       +  \n",
       "4       +  \n",
       "..    ...  \n",
       "685     -  \n",
       "686     -  \n",
       "687     -  \n",
       "688     -  \n",
       "689     -  \n",
       "\n",
       "[690 rows x 16 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#from google.colab import data_table\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/MauricioRR-Tec/Machine-Learning/master/Notebooks/Module_2/Datasets/creditApproval_dataset.csv'\n",
    "df = pd.read_csv(url)\n",
    "#Your answer here\n",
    "df['A3'] = df['A3'].apply(lambda x: (x - min(df['A3']))/(max(df['A3']) - min(df['A3'])))\n",
    "df['A15'] = df['A15'].apply(lambda x: (x - min(df['A15']))/(max(df['A15']) - min(df['A15'])))\n",
    "\n",
    "#valor maximo... indice en donde se encuentra dicho valor...\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Usar\n",
    "def scale(x):\n",
    "    tmp_min = x.min()\n",
    "    tmp_max = x.max()\n",
    "    x_new = (x - tmp_min)/(tmp_max - tmp_min)\n",
    "    return x_new\n",
    "\n",
    "#En esta sección escalaremos los valores usando minimos y maximos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Usar\n",
    "def scale(x,q,z):\n",
    "    tmp_min = x.min()\n",
    "    tmp_max = x.max()\n",
    "    x_new = (z-q)*((x - tmp_min)/(tmp_max - tmp_min))+q\n",
    "    return x_new\n",
    "\n",
    "#Mientras que en esta sección escalaremos los valores usando valores especificos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XW0rzCPQ1Xf1"
   },
   "source": [
    "---\n",
    "#### a.3. Transformación de Datos\n",
    "\n",
    "En este paso los datos se **convierten o consolidan** para que el resultado del modelado sea más *eficiente/efectivo*. \n",
    "\n",
    "Entre las técnicas de transformación de datos se encuentra\n",
    "* el **suavizamiento**.\n",
    "    * <img src=\"https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2016/12/Zoomed-Moving-Average-Transform.png\" width=\"500\">\n",
    "* **construcción de características**.\n",
    "![](https://docs.google.com/uc?export=download&id=1a4zaszYOY3Rc7YX5Ggo1gBykOz_nwnML)\n",
    "* **agregación o resumen** de datos.\n",
    "* **discretización**.\n",
    "* **generalización**. \n",
    "\n",
    "Sin embargo, la mayoría de estas se pueden considerar como áreas independientes.\n",
    "\n",
    "Las tareas que requieren supervisión humana son las técnicas clásicas de transformación de datos como: \n",
    "* la generación de informes\n",
    "* nuevos atributos que agregan los existentes \n",
    "* generalización de conceptos, especialmente en atributos categóricos\n",
    "    * ej. la sustitución de fechas completas por solo números de año.\n",
    "\n",
    "#### Ejercicio: One-Hot Encoding\n",
    "\n",
    "One-Hot Encoding es la transformación de variables nominales/categóricas en númericas. Esta transformación consiste en convertir cada valor $v_i$ de una variable $V$ ($v_i\\in V$), en columnas. De tal suerte, que si la variable $V$ tiene tres valores ($|V|=3$), esta será reemplazada por V1, V2 y V3, en donde se pondrá un 1 si $V1 = v_i$, de otra forma 0; se aplica el mismo principio para V2 y V3.\n",
    "\n",
    "![](https://docs.google.com/uc?export=download&id=18CttdKmICOmEvQ82JEJwQbdnxO3myjnU)\n",
    "\n",
    "Por ejemplo, si tenemos ``df.A1=['red', 'red', 'green']``\n",
    "\n",
    "Cada valor de A1 se representara por un vector de 0's y 1's. Esto es ``[1,0],[1,0],[0,1]``. \n",
    "\n",
    "Ahora, en lugar de tener ``df.A1``, tenemos ``df.A1_red,df.A1_ref``.\n",
    "\n",
    "**Actividad**: Explicar el One-Hot-Encoding a las variables A4 y A5 del conjunto de aprobación de crédito."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "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>?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ?\n",
       "0  1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Nota: get dummies ayuda a?\n",
    "pd.get_dummies(pd.Series(list('?')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "urMOkdcP1nG9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A1     A2      A3 A5  A6  A7    A8 A9 A10  A11 A12 A13  A14  A15 class  \\\n",
      "0    b  30.83   0.000  g   w   v  1.25  t   t    1   f   g  202    0     +   \n",
      "1    a  58.67   4.460  g   q   h  3.04  t   t    6   f   g   43  560     +   \n",
      "2    a   24.5   0.500  g   q   h  1.50  t   f    0   f   g  280  824     +   \n",
      "3    b  27.83   1.540  g   w   v  3.75  t   t    5   t   g  100    3     +   \n",
      "4    b  20.17   5.625  g   w   v  1.71  t   f    0   f   s  120    0     +   \n",
      "..  ..    ...     ... ..  ..  ..   ... ..  ..  ...  ..  ..  ...  ...   ...   \n",
      "685  b  21.08  10.085  p   e   h  1.25  f   f    0   f   g  260    0     -   \n",
      "686  a  22.67   0.750  g   c   v  2.00  f   t    2   t   g  200  394     -   \n",
      "687  a  25.25  13.500  p  ff  ff  2.00  f   t    1   t   g  200    1     -   \n",
      "688  b  17.92   0.205  g  aa   v  0.04  f   f    0   f   g  280  750     -   \n",
      "689  b     35   3.375  g   c   h  8.29  f   f    0   t   g    0    0     -   \n",
      "\n",
      "     A4_?  A4_l  A4_u  A4_y  \n",
      "0       0     0     1     0  \n",
      "1       0     0     1     0  \n",
      "2       0     0     1     0  \n",
      "3       0     0     1     0  \n",
      "4       0     0     1     0  \n",
      "..    ...   ...   ...   ...  \n",
      "685     0     0     0     1  \n",
      "686     0     0     1     0  \n",
      "687     0     0     0     1  \n",
      "688     0     0     1     0  \n",
      "689     0     0     1     0  \n",
      "\n",
      "[690 rows x 19 columns]\n",
      "object\n",
      "category\n",
      "    A1     A2      A3 A5  A6  A7    A8 A9 A10  A11 A12 A13  A14  A15 class  \\\n",
      "0    b  30.83   0.000  g   w   v  1.25  t   t    1   f   g  202    0     +   \n",
      "1    a  58.67   4.460  g   q   h  3.04  t   t    6   f   g   43  560     +   \n",
      "2    a   24.5   0.500  g   q   h  1.50  t   f    0   f   g  280  824     +   \n",
      "3    b  27.83   1.540  g   w   v  3.75  t   t    5   t   g  100    3     +   \n",
      "4    b  20.17   5.625  g   w   v  1.71  t   f    0   f   s  120    0     +   \n",
      "..  ..    ...     ... ..  ..  ..   ... ..  ..  ...  ..  ..  ...  ...   ...   \n",
      "685  b  21.08  10.085  p   e   h  1.25  f   f    0   f   g  260    0     -   \n",
      "686  a  22.67   0.750  g   c   v  2.00  f   t    2   t   g  200  394     -   \n",
      "687  a  25.25  13.500  p  ff  ff  2.00  f   t    1   t   g  200    1     -   \n",
      "688  b  17.92   0.205  g  aa   v  0.04  f   f    0   f   g  280  750     -   \n",
      "689  b     35   3.375  g   c   h  8.29  f   f    0   t   g    0    0     -   \n",
      "\n",
      "     A4x_?  A4x_l  A4x_u  A4x_y  \n",
      "0        0      0      1      0  \n",
      "1        0      0      1      0  \n",
      "2        0      0      1      0  \n",
      "3        0      0      1      0  \n",
      "4        0      0      1      0  \n",
      "..     ...    ...    ...    ...  \n",
      "685      0      0      0      1  \n",
      "686      0      0      1      0  \n",
      "687      0      0      0      1  \n",
      "688      0      0      1      0  \n",
      "689      0      0      1      0  \n",
      "\n",
      "[690 rows x 19 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#from google.colab import data_table\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/MauricioRR-Tec/Machine-Learning/master/Notebooks/Module_2/Datasets/creditApproval_dataset.csv'\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "# With Pandas is too easy\n",
    "cat_columns = [\"A4\"]\n",
    "df_processed = pd.get_dummies(df, prefix_sep=\"_\",\n",
    "                             columns=cat_columns)\n",
    "\n",
    "print(df_processed)\n",
    "#data_table.DataTable(df_processed, include_index=True, num_rows_per_page=10)\n",
    "\n",
    "#With pandas but step by step\n",
    "print(df.A4.dtype)\n",
    "df['A4'] = pd.Categorical(df['A4']) # cast our categorical variable into the built-in pandas Categorical data type\n",
    "print(df.A4.dtype)\n",
    "dfDummies = pd.get_dummies(df['A4'], prefix = 'A4x')\n",
    "df = pd.concat([df, dfDummies], axis=1)\n",
    "df = df.drop(['A4'], axis=1)\n",
    "\n",
    "print(df)\n",
    "#data_table.DataTable(df, include_index=True, num_rows_per_page=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K9oNyPnM15Q_"
   },
   "source": [
    "---\n",
    "### a.4. Imputación de datos faltantes.\n",
    "\n",
    "El propósito es llenar las variables que contienen **valores faltantes** (missing values en inglés) con algunos datos intuitivos. \n",
    "\n",
    "En la mayoría de los casos, es mejor agregar una estimación razonable de un valor de datos adecuado que dejarlo en blanco.\n",
    "\n",
    "<img src=\"https://stefvanbuuren.name/fimd/fig/ch04-patterns-1.png\" width=\"700\">\n",
    "\n",
    "Tomado de: https://stefvanbuuren.name/fimd/missing-data-pattern.html\n",
    "\n",
    "---\n",
    "### a.5. Integración de Datos.\n",
    "\n",
    "Comprende la fusión de datos de múltiples fuentes de datos. \n",
    "\n",
    "Este proceso debe realizarse con cuidado para evitar redundancias e inconsistencias en el conjunto de datos resultante. \n",
    "\n",
    "Las operaciones típicas logradas dentro de la integración de datos son la \n",
    "* **identificación** y **unificación** de variables y dominios\n",
    "* **análisis de correlación** entre atributos\n",
    "* **identificación de duplicación** y **detección de conflictos** en los valores de datos de diferentes fuentes.\n",
    "\n",
    "![](https://pandas.pydata.org/pandas-docs/stable/_images/merging_append2.png)\n",
    "\n",
    "<!-- <img src=\"https://pandas.pydata.org/pandas-docs/stable/_images/merging_append2.png\" width=\"700\"> -->\n",
    "\n",
    "#### Ejercicio:\n",
    "\n",
    "Dada la pandemia desencadenada por el COVID19, deseamos entender mejor la  propagación del virus. Los datos por país se pueden consultar en el repositorio de github https://datahub.io/core/covid-19#data. Sin embargo, para nuestro análisis requerimos enfocarnos en los **casos confirmados acumulados por día y país**.\n",
    "\n",
    "Para esto necesitamos en el eje $y$ (renglones) las fechas, y en el eje $x$ (columnas) los paises. **Realice las acciones de pivoteo necesarias para obtener el DataFrame requerido. La variable ``Date`` debe de ser de tipo fecha. Aseguresé de guardar los resultados en un archivo CSV para su posterior análisis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>small</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>large</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>large</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>small</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>small</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>large</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>small</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>small</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>large</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B      C  D  E\n",
       "0  foo  one  small  1  2\n",
       "1  foo  one  large  2  4\n",
       "2  foo  one  large  2  5\n",
       "3  foo  two  small  3  5\n",
       "4  foo  two  small  3  6\n",
       "5  bar  one  large  4  6\n",
       "6  bar  one  small  5  8\n",
       "7  bar  two  small  6  9\n",
       "8  bar  two  large  7  9"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Qué es lo que hace pivot_table?\n",
    "df = pd.DataFrame({\"A\": [\"foo\", \"foo\", \"foo\", \"foo\", \"foo\",\n",
    "                         \"bar\", \"bar\", \"bar\", \"bar\"],\n",
    "                   \"B\": [\"one\", \"one\", \"one\", \"two\", \"two\",\n",
    "                         \"one\", \"one\", \"two\", \"two\"],\n",
    "                   \"C\": [\"small\", \"large\", \"large\", \"small\",\n",
    "                         \"small\", \"large\", \"small\", \"small\",\n",
    "                         \"large\"],\n",
    "                   \"D\": [1, 2, 2, 3, 3, 4, 5, 6, 7],\n",
    "                   \"E\": [2, 4, 5, 5, 6, 6, 8, 9, 9]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "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></th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>large</th>\n",
       "      <td>11</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small</th>\n",
       "      <td>11</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>large</th>\n",
       "      <td>4</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small</th>\n",
       "      <td>7</td>\n",
       "      <td>4.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            D         E\n",
       "A   C                  \n",
       "bar large  11  7.500000\n",
       "    small  11  8.500000\n",
       "foo large   4  4.500000\n",
       "    small   7  4.333333"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],\n",
    "                    aggfunc={'D': np.sum,\n",
    "                             'E': np.mean})\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312,
     "output_embedded_package_id": "1QexFzsfdbkVMl13mZdIeCmgvYqp2SYTa"
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4868,
     "status": "ok",
     "timestamp": 1585163269153,
     "user": {
      "displayName": "Guillermo Santamaria",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhFOdraf7be0jgGxdadpJk6jB59MMNn-k2Hl7rJ=s64",
      "userId": "14757125121333916384"
     },
     "user_tz": 360
    },
    "id": "7dHlN0_PD08L",
    "outputId": "bcaaedda-1e95-42e9-f80f-beb38b5f8ce1"
   },
   "outputs": [
    {
     "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/23/2020</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/24/2020</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/25/2020</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/26/2020</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33103</th>\n",
       "      <td>5/20/2020</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33104</th>\n",
       "      <td>5/21/2020</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33105</th>\n",
       "      <td>5/22/2020</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33106</th>\n",
       "      <td>5/23/2020</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33107</th>\n",
       "      <td>5/24/2020</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33108 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Country/Region Province/State   Lat  Long  Confirmed  \\\n",
       "0      1/22/2020    Afghanistan            NaN  33.0  65.0        0.0   \n",
       "1      1/23/2020    Afghanistan            NaN  33.0  65.0        0.0   \n",
       "2      1/24/2020    Afghanistan            NaN  33.0  65.0        0.0   \n",
       "3      1/25/2020    Afghanistan            NaN  33.0  65.0        0.0   \n",
       "4      1/26/2020    Afghanistan            NaN  33.0  65.0        0.0   \n",
       "...          ...            ...            ...   ...   ...        ...   \n",
       "33103  5/20/2020       Zimbabwe            NaN -20.0  30.0       48.0   \n",
       "33104  5/21/2020       Zimbabwe            NaN -20.0  30.0       51.0   \n",
       "33105  5/22/2020       Zimbabwe            NaN -20.0  30.0       51.0   \n",
       "33106  5/23/2020       Zimbabwe            NaN -20.0  30.0       56.0   \n",
       "33107  5/24/2020       Zimbabwe            NaN -20.0  30.0       56.0   \n",
       "\n",
       "       Recovered  Deaths  \n",
       "0            0.0     0.0  \n",
       "1            0.0     0.0  \n",
       "2            0.0     0.0  \n",
       "3            0.0     0.0  \n",
       "4            0.0     0.0  \n",
       "...          ...     ...  \n",
       "33103       18.0     4.0  \n",
       "33104       18.0     4.0  \n",
       "33105       18.0     4.0  \n",
       "33106       18.0     4.0  \n",
       "33107       25.0     4.0  \n",
       "\n",
       "[33108 rows x 8 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#from google.colab import data_table\n",
    "url = 'https://raw.githubusercontent.com/MauricioRR-Tec/Machine-Learning/master/Notebooks/Module_2/Datasets/covid19WW.csv'\n",
    "df = pd.read_csv(url)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ve7OMKqe15-r"
   },
   "outputs": [
    {
     "ename": "DataError",
     "evalue": "No numeric types to aggregate",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mDataError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-65-9d79f7d6ed67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m df1 = pd.pivot_table(df, values=['Country/Region', 'Confirmed'],\n\u001b[0m\u001b[1;32m      2\u001b[0m                      \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                      aggfunc={'Country/Region': np.cumsum,\n\u001b[1;32m      4\u001b[0m                              'Confirmed': np.sum})\n\u001b[1;32m      5\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/reshape/pivot.py\u001b[0m in \u001b[0;36mpivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)\u001b[0m\n\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m     \u001b[0mgrouped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m     \u001b[0magged\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdropna\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magged\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0magged\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"all\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    943\u001b[0m             )\n\u001b[1;32m    944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 945\u001b[0;31m         \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    946\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_aggregate\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m    414\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_agg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_agg_1dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mSpecificationError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_agg\u001b[0;34m(arg, func)\u001b[0m\n\u001b[1;32m    381\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magg_how\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m                     \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magg_how\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    384\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_agg_1dim\u001b[0;34m(name, how, subset)\u001b[0m\n\u001b[1;32m    365\u001b[0m                         \u001b[0;34m\"nested dictionary is ambiguous in aggregation\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m                     )\n\u001b[0;32m--> 367\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mcolg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0m_agg_2dim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    250\u001b[0m             \u001b[0mcyfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_cython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcyfunc\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcyfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnkeys\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mcumsum\u001b[0;34m(self, axis, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2335\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2337\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cython_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cumsum\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2339\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mSubstitution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"groupby\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_cython_transform\u001b[0;34m(self, how, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m    970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 972\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mDataError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No numeric types to aggregate\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_transformed_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDataError\u001b[0m: No numeric types to aggregate"
     ]
    }
   ],
   "source": [
    "df1 = pd.pivot_table(df, values=['Country/Region', 'Confirmed'],\n",
    "                     index=['Date'], \n",
    "                     aggfunc={'Country/Region': np.cumsum,\n",
    "                             'Confirmed': np.sum})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Mexico'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2888\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2889\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2890\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Mexico'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-56-4128602229fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Mexico'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2900\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/ml/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2889\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2890\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2893\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Mexico'"
     ]
    }
   ],
   "source": [
    "df1['Mexico'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Html paths for csv files from John Hopkins - Confirmed / Death cases\n",
    "confirmedPath = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'\n",
    "deathsPath = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'\n",
    "\n",
    "df_Confirmed = pd.read_csv(confirmedPath, header=0, index_col=0, parse_dates=True, squeeze=True)\n",
    "df_Confirmed.rename(columns={'Country/Region':'Country'}, inplace=True)\n",
    "df_Confirmed = df_Confirmed.drop([\"Lat\",\"Long\"],axis=\"columns\")\n",
    "\n",
    "df_Confirmed = df_Confirmed.set_index('Country')\n",
    "df_Confirmed = df_Confirmed.stack()\n",
    "df_Confirmed.index = df_Confirmed.index.rename('Date', level=1)\n",
    "df_Confirmed.name = 'Confirmed'\n",
    "df_Confirmed = df_Confirmed.reset_index()\n",
    "\n",
    "df_Deaths = pd.read_csv(deathsPath, header=0, index_col=0, parse_dates=True, squeeze=True)\n",
    "df_Deaths.rename(columns={'Country/Region':'Country'}, inplace=True)\n",
    "df_Deaths = df_Deaths.drop([\"Lat\",\"Long\"],axis=\"columns\")\n",
    "\n",
    "df_Deaths = df_Deaths.set_index('Country')\n",
    "df_Deaths = df_Deaths.stack()\n",
    "df_Deaths.index = df_Deaths.index.rename('Date', level=1)\n",
    "df_Deaths.name = 'Deaths'\n",
    "df_Deaths = df_Deaths.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Your answer HeRe\n",
    "#Concatenar ambos dataframes, pivotear y plotear algo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Mexico'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cBYtiaM7SE7I"
   },
   "source": [
    "---\n",
    "### a.6. Identificación de Datos Anómalos/Outliers.\n",
    "\n",
    "El objetivo principal es detectar errores aleatorios o variaciones en una variable medida. Tenga en cuenta que nos referimos a este proceso como  **identificación** de ruido en lugar de la **eliminación** de ruido. \n",
    "\n",
    "<img src=\"https://www.mdpi.com/energies/energies-12-03310/article_deploy/html/images/energies-12-03310-g002.png\" width=\"600\">\n",
    "\n",
    "Una vez que se detecta un ejemplo ruidoso, podemos aplicar un proceso basado en la corrección que podría involucrar algún tipo de operación subyacente.\n",
    "\n",
    "<img src=\"https://anomaly.io/wp-content/uploads/2017/01/correlation-anomaly-detected.png\" width=\"800\" align=\"middle\">\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LiceVfdBCTdC"
   },
   "source": [
    "### Otras actividades asociadas al PP: Reducción de Datos\n",
    "\n",
    "La **reducción de datos** comprende el conjunto de técnicas que, de una forma u otra, obtienen una representación reducida de los datos originales.\n",
    "\n",
    "El **objetivo** de la redución de los datos es ***encontrar un sub-conjunto $C'$ que se aproxime al conjunto original $C$ tal que la información contenida en $C' \\approx C$***. Esto típicamente significa que se mantienen la estructura esencial de los datos originales ($C$), pero la cantidad de datos se reduce. \n",
    "\n",
    "<img src=\"https://docs.google.com/uc?export=download&id=1kJvqifJpTjT-3NjyUzdD-6EvYGPSHE3v\" width=\"400\" align=\"middle\">\n",
    "\n",
    "Algo interesante, es que mientras el PP es fundamental, la reducción de datos puede considerarse como *opcional*. Sin embargo, existen muchas razones por las cuales este proceso es más que una sugerencia:\n",
    "\n",
    "* Para su ejecución y de acuerdo a formulación, todos los algoritmos de ML estan sujetos a un \n",
    "  * **tiempo de computo específico** \n",
    "  * **memoria** específica.\n",
    "  * **tamaño del conjunto de datos** \n",
    "  * etc.\n",
    "\n",
    "  Entonces, si el tamaño del conjunto de datos es mayor al limite de un algoritmo, el tiempo de computo se volverá **prohibitivo**.  \n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/0/09/Binary_search_vs_Linear_search_example_svg.svg\" width=\"300\" style=\"background-color:white;\">\n",
    "\n",
    "* Respecto a la **memoria** y **tamaño del conjunto de datos**, estos se encuentra relacionados a la **Maldición de la Dimensionalidad** (Curse of Dimensionality en inglés).\n",
    "La maldición de la dimensionalidad es una familia de fenómenos relacionados a aquellos problemas descritos por muchas dimensiones, lo que no sucede en aquellos sistemas de pocas dimensiones. \n",
    "\n",
    "    * Alta Dimensionalidad\n",
    "        * Definición: ***Espacio de Características*** (Feature Space en inglés); determina todo el posible conjunto de combinaciones (realistas o no) de los valores que tendrán las variables del problema.\n",
    "\n",
    "        Ahora supongamos que queremos realizar una predicción usando solo una dimensión que esta acotada entre 0 y 1, $X^D \\mid D=1,\\ 0\\leq x_1 \\leq 1$. En este caso, podemos conocer $1/3$ de todos los posibles resultados conociendo una tercera parte de la variable $x_1$, como se observa en la siguiente figura  <img src=\"https://docs.google.com/uc?export=download&id=1DCqkNuAH2Dc32HOz04aBBekn8nAk1zHH\" width=\"600\" style=\"background-color:white;\">\n",
    "        \n",
    "        Si ahora utilizamos dos variables, $X^D \\mid D=2, 0\\leq x_1,\\ x_2 \\leq 1$, y dividimos ambas en 3 partes, podemos dibujar un cuadrado que cubra $1/3$ de los valores de cada variable en 3 partes. Sin embargo, este cuadrado, no cubre $1/3$ parte de todas las combinaciones de $x_1 \\text{ y } x_2$, en cambio, ¡solo cubre $1/9 \\sim 11 \\text{\\%}$ ! <img src=\"https://docs.google.com/uc?export=download&id=18wZ8GWiQNYnZMGvIKyAvSqyT1dfDE_lK\" width=\"600\" style=\"background-color:white;\">\n",
    "\n",
    "        Si añadimos una tercer variable y repetimos el mismo proceso, el cubo resultante solo cubrirá $1/27 \\approx 4\\text{\\%}$ de todas las posibles combinaciones. <img src=\"https://docs.google.com/uc?export=download&id=1rBP3TXW2SjowCslJOEa9SN0RlOdwir90\" width=\"600\" style=\"background-color:white;\">\n",
    "\n",
    "        De hecho\n",
    "        <img src=\"https://docs.google.com/uc?export=download&id=18hw5NcUSEKAZw9VJkZzH5-AjqcYWgl2L\" width=\"600\" style=\"background-color:white;\">\n",
    "\n",
    "<!--Por ejemplo, si el objetivo es reducir el número de dimensiones el objetivo estaría dado por  \n",
    "$C'^D \\approx C^F \\mid F>D $. -->\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "01Intro2PPwoAns.ipynb",
   "provenance": [
    {
     "file_id": "1stxC6GZiDP_O7tqbaXBF6ZNGpUFxndPF",
     "timestamp": 1584993368120
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}