2.6 Actividad Iris Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.6  Actividad: Iris Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis de estadística descriptiva\n",
    "El análisis de estadística descriptiva es el primer \n",
    "paso que hay que dar para conocer los datos \n",
    "\n",
    "### Metodología\n",
    "Paso 0. Detectar el tipo de características con las que \n",
    "vamos a trabajar: nominales, ordinales, de intervalo o \n",
    "de ratio\n",
    "\n",
    "-  Características nominales: no son medibles y no pueden ser objeto de\n",
    "ninguna operación, por ejemplo, ser ordenadas. Ej emplo: Sexo,\n",
    "Estado Civil.\n",
    "-  Características ordinales: no son medibles, pero pueden ser ordenadas,\n",
    "por ejemplo: Relativos a una opinión: Bueno, Regular, Malo. \n",
    "-  Características de intervalo: son medibles, pero sólo tiene sentido\n",
    "establecer diferencias o intervalos, por ejemplo: Fechas o las\n",
    "temperaturas en grados centígrados. \n",
    "-  Características de ratio: son medibles y tiene sentido establecer\n",
    "divisiones entre ellas, por ejemplo: temperatura en grados Kelvin,\n",
    "cantidades monetarias, pesos, longitudes. \n",
    "\n",
    "Paso 1: ¿Cómo se centran los datos? \n",
    "\n",
    "    • Mediana \n",
    "    • Moda \n",
    "    • Media\n",
    "    \n",
    "\n",
    "Paso 2: ¿Cómo se dispersan los datos? \n",
    "\n",
    "    • Varianza y desviación típica\n",
    "    • Coeficiente de variación \n",
    "    • Cuartiles y percentiles\n",
    "    • Máximo, minimos y Rango\n",
    "    \n",
    "Paso 3: ¿Qué forma tienen los datos?\n",
    "\n",
    "    • Histogramas\n",
    "    • Diagrana de cajas\n",
    "    \n",
    "Paso 4: (Más de una variable). ¿Cómo se relacionan los datos? \n",
    "\n",
    "Miden en la grado de relación de una variable con otra. Una alta relación puede significar redundancia de la información.\n",
    "\n",
    "    • Coeficiente de correlación de Pearson.\n",
    "    • Matriz de correlación.\n",
    "\n",
    "Éstas son siempre las primeras preguntas que se deben hacer a los datos, porque\n",
    "son también las más sencillas de responder. Si no las respondemos, no\n",
    "deberíamos seguir adelante haciéndonos más, y si lo hacemos, tenemos que\n",
    "pararnos a pensar sobre la lógica y el conocimiento que nos aportan. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as ss\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv('iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://i0.wp.com/farm8.staticflickr.com/7866/47075467621_85ab810139_b.jpg?resize=900%2C300&ssl=1\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "Image(url= \"https://i0.wp.com/farm8.staticflickr.com/7866/47075467621_85ab810139_b.jpg?resize=900%2C300&ssl=1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://staticserver2.com/edu/static/it/800/petalo.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "Image(url= \"https://staticserver2.com/edu/static/it/800/petalo.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tipos de Iris\n",
    "#![tipos_iris.jpg](attachment:tipos_iris.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#![iris-sepalo-petalo.jpg](attachment:iris-sepalo-petalo.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paso 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa\n",
       "5   6            5.4           3.9            1.7           0.4  Iris-setosa\n",
       "6   7            4.6           3.4            1.4           0.3  Iris-setosa\n",
       "7   8            5.0           3.4            1.5           0.2  Iris-setosa\n",
       "8   9            4.4           2.9            1.4           0.2  Iris-setosa\n",
       "9  10            4.9           3.1            1.5           0.1  Iris-setosa"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>142</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "140  141            6.7           3.1            5.6           2.4   \n",
       "141  142            6.9           3.1            5.1           2.3   \n",
       "142  143            5.8           2.7            5.1           1.9   \n",
       "143  144            6.8           3.2            5.9           2.3   \n",
       "144  145            6.7           3.3            5.7           2.5   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "140  Iris-virginica  \n",
       "141  Iris-virginica  \n",
       "142  Iris-virginica  \n",
       "143  Iris-virginica  \n",
       "144  Iris-virginica  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm',\n",
       "       'PetalWidthCm', 'Species'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusión: \n",
    "#### La data esta conpuesta por un DataFrame de 150 filas y 6 columnas, estos datos son de características de ratio por que son medibles y tiene sentido establecer medidas entre ellas ya que tienen valores de longitud."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paso 1\n",
    "\n",
    "    • Mediana = Si ordenamos los datos de una muestra de mayor a menor, es el valor que divide a la muestra en dos partes\n",
    "                iguales.\n",
    "    • Moda    = Es el valor más frecuente de una muestra.\n",
    "    • Media   = Es la suma de N elementos dividida entre N."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LONGITUD SEPALO\n",
      "    Media   : 5.843333\n",
      "    Mediana : 5.800000\n",
      "    Moda    : 5.000000\n",
      "\n",
      "\n",
      "ANCHO SEPALO\n",
      "    Media   : 3.054000\n",
      "    Mediana : 3.000000\n",
      "    Moda    : 3.000000\n",
      "\n",
      "\n",
      "LONGITUD PETALO\n",
      "    Media   : 3.758667\n",
      "    Mediana : 4.350000\n",
      "    Moda    : 1.500000\n",
      "\n",
      "\n",
      "ANCHO PETALO\n",
      "    Media   : 1.198667\n",
      "    Mediana : 1.300000\n",
      "    Moda    : 0.200000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "media = iris[\"SepalLengthCm\"].mean()\n",
    "mediana = iris[\"SepalLengthCm\"].median()\n",
    "moda = iris[\"SepalLengthCm\"].mode()\n",
    "print(\"\"\"\n",
    "LONGITUD SEPALO\n",
    "    Media   : %f\n",
    "    Mediana : %f\n",
    "    Moda    : %f\n",
    "\"\"\" % (media,mediana,moda))\n",
    "\n",
    "media = iris[\"SepalWidthCm\"].mean()\n",
    "mediana = iris[\"SepalWidthCm\"].median()\n",
    "moda = iris[\"SepalWidthCm\"].mode()\n",
    "print(\"\"\"\n",
    "ANCHO SEPALO\n",
    "    Media   : %f\n",
    "    Mediana : %f\n",
    "    Moda    : %f\n",
    "\"\"\" % (media,mediana,moda))\n",
    "\n",
    "media = iris[\"PetalLengthCm\"].mean()\n",
    "mediana = iris[\"PetalLengthCm\"].median()\n",
    "moda = iris[\"PetalLengthCm\"].mode()\n",
    "print(\"\"\"\n",
    "LONGITUD PETALO\n",
    "    Media   : %f\n",
    "    Mediana : %f\n",
    "    Moda    : %f\n",
    "\"\"\" % (media,mediana,moda))\n",
    "\n",
    "media = iris[\"PetalWidthCm\"].mean()\n",
    "mediana = iris[\"PetalWidthCm\"].median()\n",
    "moda = iris[\"PetalWidthCm\"].mode()\n",
    "print(\"\"\"\n",
    "ANCHO PETALO\n",
    "    Media   : %f\n",
    "    Mediana : %f\n",
    "    Moda    : %f\n",
    "\"\"\" % (media,mediana,moda))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusión: \n",
    "#### La longitud y el ancho de los Sepalos son casi iguales en los tres tipos de Iris, sin embargo la longitud y ancho de los petalos son variados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paso 2\n",
    "    • Desviación Típica     = Mide la dispersión de los datos respecto a la media. Se trata de la raíz cuadrada de \n",
    "                              la varianza.\n",
    "    • Varianza              = Nos permite identificar la diferencia promedio que hay entre cada uno de los valores respecto a\n",
    "                              la Media.\n",
    "    • Coeficiente Variación = Se utiliza para tener una idea de las veces que la Desviación Típica supera a la Media y en \n",
    "                              donde estan los datos más dispersos.\n",
    "    • Percentil al 90%      = El concepto es igual al de la mediana, salvo que aquí la división ya no es en el 50%.\n",
    "    • Máximo                = Es el máximo valor que contiene el DataSet.\n",
    "    • Mínimo                = Es el mínimo valor que contiene el DataSet.\n",
    "    • Rango                 = Es la diferencia entre el máximo y el mínimo del DataSet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LONGITUD SEPALO\n",
      "    Desviación Típica     : 0.825301\n",
      "    Varianza              : 0.681122\n",
      "    Coeficiente Variación : 0.141238\n",
      "    Percentil al 90       : 6.900000\n",
      "    Máximo                : 7.900000\n",
      "    Mínimo                : 4.300000\n",
      "    Rango                 : 3.600000\n",
      "\n",
      "\n",
      "ANCHO SEPALO\n",
      "    Desviación Típica     : 0.432147\n",
      "    Varianza              : 0.186751\n",
      "    Coeficiente Variación : 0.141502\n",
      "    Percentil al 90       : 3.610000\n",
      "    Máximo                : 4.400000\n",
      "    Mínimo                : 2.000000\n",
      "    Rango                 : 2.400000\n",
      "\n",
      "\n",
      "LARGO PETALO\n",
      "    Desviación Típica     : 1.758529\n",
      "    Varianza              : 3.092425\n",
      "    Coeficiente Variación : 0.467860\n",
      "    Percentil al 90       : 5.800000\n",
      "    Máximo                : 6.900000\n",
      "    Mínimo                : 1.000000\n",
      "    Rango                 : 5.900000\n",
      "\n",
      "\n",
      "ANCHO SEPALO\n",
      "    Desviación Típica     : 0.760613\n",
      "    Varianza              : 0.578532\n",
      "    Coeficiente Variación : 0.634549\n",
      "    Percentil al 90       : 2.200000\n",
      "    Máximo                : 2.500000\n",
      "    Mínimo                : 0.100000\n",
      "    Rango                 : 2.400000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "desviacion_tipica = iris[\"SepalLengthCm\"].std(ddof=0)\n",
    "varianza = iris[\"SepalLengthCm\"].var(ddof=0)\n",
    "coeficiente_variacion = iris[\"SepalLengthCm\"].std(ddof=0) / iris[\"SepalLengthCm\"].mean()\n",
    "percentil = iris[\"SepalLengthCm\"].quantile(0.9)\n",
    "maximo = iris[\"SepalLengthCm\"].max()\n",
    "minimo = iris[\"SepalLengthCm\"].min()\n",
    "rango = iris[\"SepalLengthCm\"].max() - iris[\"SepalLengthCm\"].min()\n",
    "#coeficiente_variacion2 = ss.variation(iris[\"SepalLengthCm\"])\n",
    "print(\"\"\"\n",
    "LONGITUD SEPALO\n",
    "    Desviación Típica     : %f\n",
    "    Varianza              : %f\n",
    "    Coeficiente Variación : %f\n",
    "    Percentil al 90       : %f\n",
    "    Máximo                : %f\n",
    "    Mínimo                : %f\n",
    "    Rango                 : %f\n",
    "\"\"\" % (desviacion_tipica,varianza,coeficiente_variacion, percentil, maximo, minimo, rango))\n",
    "\n",
    "desviacion_tipica = iris[\"SepalWidthCm\"].std(ddof=0)\n",
    "varianza = iris[\"SepalWidthCm\"].var(ddof=0)\n",
    "coeficiente_variacion = iris[\"SepalWidthCm\"].std(ddof=0) / iris[\"SepalWidthCm\"].mean()\n",
    "percentil = iris[\"SepalWidthCm\"].quantile(0.9)\n",
    "maximo = iris[\"SepalWidthCm\"].max()\n",
    "minimo = iris[\"SepalWidthCm\"].min()\n",
    "rango = iris[\"SepalWidthCm\"].max() - iris[\"SepalWidthCm\"].min()\n",
    "#coeficiente_variacion2 = ss.variation(iris[\"SepalLengthCm\"])\n",
    "print(\"\"\"\n",
    "ANCHO SEPALO\n",
    "    Desviación Típica     : %f\n",
    "    Varianza              : %f\n",
    "    Coeficiente Variación : %f\n",
    "    Percentil al 90       : %f\n",
    "    Máximo                : %f\n",
    "    Mínimo                : %f\n",
    "    Rango                 : %f\n",
    "\"\"\" % (desviacion_tipica,varianza,coeficiente_variacion, percentil, maximo, minimo, rango))\n",
    "\n",
    "desviacion_tipica = iris[\"PetalLengthCm\"].std(ddof=0)\n",
    "varianza = iris[\"PetalLengthCm\"].var(ddof=0)\n",
    "coeficiente_variacion = iris[\"PetalLengthCm\"].std(ddof=0) / iris[\"PetalLengthCm\"].mean()\n",
    "percentil = iris[\"PetalLengthCm\"].quantile(0.9)\n",
    "maximo = iris[\"PetalLengthCm\"].max()\n",
    "minimo = iris[\"PetalLengthCm\"].min()\n",
    "rango = iris[\"PetalLengthCm\"].max() - iris[\"PetalLengthCm\"].min()\n",
    "#coeficiente_variacion2 = ss.variation(iris[\"SepalLengthCm\"])\n",
    "print(\"\"\"\n",
    "LARGO PETALO\n",
    "    Desviación Típica     : %f\n",
    "    Varianza              : %f\n",
    "    Coeficiente Variación : %f\n",
    "    Percentil al 90       : %f\n",
    "    Máximo                : %f\n",
    "    Mínimo                : %f\n",
    "    Rango                 : %f\n",
    "\"\"\" % (desviacion_tipica,varianza,coeficiente_variacion, percentil, maximo, minimo, rango))\n",
    "\n",
    "desviacion_tipica = iris[\"PetalWidthCm\"].std(ddof=0)\n",
    "varianza = iris[\"PetalWidthCm\"].var(ddof=0)\n",
    "coeficiente_variacion = iris[\"PetalWidthCm\"].std(ddof=0) / iris[\"PetalWidthCm\"].mean()\n",
    "percentil = iris[\"PetalWidthCm\"].quantile(0.9)\n",
    "maximo = iris[\"PetalWidthCm\"].max()\n",
    "minimo = iris[\"PetalWidthCm\"].min()\n",
    "rango = iris[\"PetalWidthCm\"].max() - iris[\"PetalWidthCm\"].min()\n",
    "#coeficiente_variacion2 = ss.variation(iris[\"SepalLengthCm\"])\n",
    "print(\"\"\"\n",
    "ANCHO SEPALO\n",
    "    Desviación Típica     : %f\n",
    "    Varianza              : %f\n",
    "    Coeficiente Variación : %f\n",
    "    Percentil al 90       : %f\n",
    "    Máximo                : %f\n",
    "    Mínimo                : %f\n",
    "    Rango                 : %f\n",
    "\"\"\" % (desviacion_tipica,varianza,coeficiente_variacion, percentil, maximo, minimo, rango))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusión: \n",
    "#### Los sépalos tienen poca disperción y su valores son parecidos al valor de la  media, mientras que los pétalos tienen una disperción más amplia con respecto a la media."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paso 3\n",
    "    • Histogramas\n",
    "    • Diagrana de cajas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000027AAC9CF518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000027AACC62160>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000027AACC7D3C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000027AACC98630>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.hist(\n",
    "    column=[\"SepalLengthCm\", \"SepalWidthCm\", \"PetalLengthCm\", \"PetalWidthCm\", \"Species\"],\n",
    "    figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PetalWidthCm')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2)\n",
    "\n",
    "# basic plot\n",
    "axs[0, 0].boxplot(iris[\"SepalLengthCm\"])\n",
    "axs[0, 0].set_title('SepalLengthCm')\n",
    "\n",
    "axs[0, 1].boxplot(iris[\"SepalWidthCm\"])\n",
    "axs[0, 1].set_title('SepalWidthCm')\n",
    "\n",
    "axs[1, 0].boxplot(iris[\"PetalLengthCm\"])\n",
    "axs[1, 0].set_title('PetalLengthCm')\n",
    "\n",
    "axs[1, 1].boxplot(iris[\"PetalWidthCm\"])\n",
    "axs[1, 1].set_title('PetalWidthCm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusión: \n",
    "#### Los histogramas y diagramas de cajas confirman lo concluído en el paso 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paso 4\n",
    "    • Coeficiente de correlación de Pearson.\n",
    "    • Matriz de correlación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "del iris['Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "SepalLengthCm       1.000000     -0.109369       0.871754      0.817954\n",
       "SepalWidthCm       -0.109369      1.000000      -0.420516     -0.356544\n",
       "PetalLengthCm       0.871754     -0.420516       1.000000      0.962757\n",
       "PetalWidthCm        0.817954     -0.356544       0.962757      1.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.corr(method=\"pearson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27aad2b99b0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(iris.corr(), square=True, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'PetalLengthCm')"
      ]
     },
     "execution_count": 37,
     "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": [
    "plt.plot(iris[\"PetalLengthCm\"],iris[\"SepalLengthCm\"], \"go\")\n",
    "plt.ylabel(\"SepalLengthCm\")\n",
    "plt.xlabel(\"PetalLengthCm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'PetalWidthCm')"
      ]
     },
     "execution_count": 39,
     "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": [
    "plt.plot(iris[\"PetalWidthCm\"],iris[\"SepalLengthCm\"], \"ro\")\n",
    "plt.ylabel(\"SepalLengthCm\")\n",
    "plt.xlabel(\"PetalWidthCm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'PetalWidthCm')"
      ]
     },
     "execution_count": 40,
     "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": [
    "plt.plot(iris[\"PetalWidthCm\"],iris[\"PetalLengthCm\"], \"bo\")\n",
    "plt.ylabel(\"SepalLengthCm\")\n",
    "plt.xlabel(\"PetalWidthCm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusión: \n",
    "#### Existe una fuerte relacion entre las variables de PetalLengthCm y SepalLengthCm\n",
    "#### Existe una fuerte relacion entre las variables de PetalWidthCm y SepalLengthCm\n",
    "#### Existe una fuerte relacion entre las variables de PetalWidthCm y PetalLengthCm"
   ]
  }
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