{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar bibliotecas clásicas\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]\n"
     ]
    }
   ],
   "source": [
    "# Crear arreglo de 0 a 15 (vector fila)\n",
    "a = np.arange(16)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arreglo creado:\n",
      " [[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]]\n"
     ]
    }
   ],
   "source": [
    "# Llevar arreglo a formato matriz\n",
    "A = a.reshape(4,4)\n",
    "print('Arreglo creado:\\n', A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11, 12, 13, 14, 15]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reshape(2,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 16 into shape (2,4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m a\u001b[39m.\u001b[39;49mreshape(\u001b[39m2\u001b[39;49m,\u001b[39m4\u001b[39;49m)\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 16 into shape (2,4)"
     ]
    }
   ],
   "source": [
    "a.reshape(2,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**Acceso a elementos de un arreglo:**_\n",
    "\n",
    "El acceso **en cada dimensión** sigue el esquema:\n",
    "\n",
    "`[desde(incluido) : hasta(excluido) : paso]`   \n",
    "* Si no se especifica 'desde' entonces equivale a 'desde el índice 0'  \n",
    "* Si no se especifica 'hasta' entonces equivale a 'hasta el último (en el sentido que dictamine 'paso')'  \n",
    "* El 'paso' por defecto es 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primer elemento: 0 \n",
      "\n",
      "Primeros dos elementos: [0 1] \n",
      "\n",
      "Último elemento 15 \n",
      "\n",
      "Saltear de a dos elementos: [ 0  2  4  6  8 10 12 14] \n",
      "\n",
      "Invertir orden del arreglo [15 14 13 12 11 10  9  8  7  6  5  4  3  2  1  0] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Acceso a elementos con índices array 1D\n",
    "print('Primer elemento:', a[0],'\\n')\n",
    "\n",
    "print('Primeros dos elementos:',a[:2],'\\n')\n",
    "\n",
    "print('Último elemento',a[-1],'\\n')\n",
    "\n",
    "print('Saltear de a dos elementos:',a[::2],'\\n')\n",
    "\n",
    "print('Invertir orden del arreglo', a[::-1],'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna 0 del arreglo:\n",
      " [ 0  4  8 12] \n",
      "\n",
      "Elementos con salto de 2 (filas y columnas):\n",
      " [[ 0  2]\n",
      " [ 8 10]] \n",
      "\n",
      "Elementos del medio del arreglo:\n",
      " [[ 5  6]\n",
      " [ 9 10]] \n",
      "\n",
      "Recorrer elementos en sentido \"inverso\":\n",
      " [[15 13]\n",
      " [11  9]\n",
      " [ 7  5]\n",
      " [ 3  1]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Acceso a elementos con índices array 2D\n",
    "\n",
    "A_col0 = A[:,0]\n",
    "print('Columna 0 del arreglo:\\n', A_col0,'\\n')\n",
    "\n",
    "A_step2 = A[::2,::2]\n",
    "print('Elementos con salto de 2 (filas y columnas):\\n', A_step2,'\\n')\n",
    "\n",
    "A_center = A[1:3,1:3]\n",
    "print('Elementos del medio del arreglo:\\n', A_center,'\\n')\n",
    "\n",
    "A_invert = A[::-1,::-2]\n",
    "print('Recorrer elementos en sentido \"inverso\":\\n', A_invert,'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acceso con lista booleana:\n",
      " [ 0  1 14 15] \n",
      "\n",
      "Elementos que cumplen ser mayores a 4 y menores a 11:\n",
      " [ 5  6  7  8  9 10]\n"
     ]
    }
   ],
   "source": [
    "# Acceso a elementos con array booleano (arreglos de True/False)\n",
    "\n",
    "bool_arr = [True]*2 + [False]*12 + [True]*2 # sólo los dos primeros y últimos indices\n",
    "a_bool = a[bool_arr]\n",
    "print('Acceso con lista booleana:\\n',a_bool, '\\n')\n",
    "\n",
    "a_bool = a[a > 4]\n",
    "a_bool = a_bool[a_bool < 11]\n",
    "print('Elementos que cumplen ser mayores a 4 y menores a 11:\\n', a_bool)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suma de b y c:\n",
      " [[11 22]\n",
      " [33 44]] \n",
      "\n",
      "Multiplicación de b y c:\n",
      " [[ 10  40]\n",
      " [ 90 160]] \n",
      "\n",
      "División de b y c:\n",
      " [[10. 10.]\n",
      " [10. 10.]] \n",
      "\n",
      "Aplicar función exponencial e^x a cada elemento x de c:\n",
      " [[ 2.71828183  7.3890561 ]\n",
      " [20.08553692 54.59815003]]\n"
     ]
    }
   ],
   "source": [
    "# Operaciones a elemento a elemento\n",
    "b = np.array([[10,20],[30,40]])\n",
    "c = np.array([[1,2],[3,4]])\n",
    "\n",
    "print('Suma de b y c:\\n', b + c, '\\n')\n",
    "\n",
    "print('Multiplicación de b y c:\\n', b * c, '\\n')\n",
    "\n",
    "print('División de b y c:\\n', b / c, '\\n')\n",
    "\n",
    "print('Aplicar función exponencial e^x a cada elemento x de c:\\n', np.exp(c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------\n",
      "Arreglo p: \n",
      " [[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "-------------------------------------------------------------\n",
      "Promedio de p:\n",
      " 5.5\n",
      "-------------------------------------------------------------\n",
      "Promedio por columnas de p:\n",
      " [4. 5. 6. 7.]\n",
      "-------------------------------------------------------------\n",
      "Promedio por filas de p:\n",
      " [1.5 5.5 9.5]\n"
     ]
    }
   ],
   "source": [
    "# Operaciones globales y por ejes\n",
    "\n",
    "p = np.arange(12).reshape(3,4)\n",
    "\n",
    "print('-------------------------------------------------------------')\n",
    "print('Arreglo p: \\n', p)\n",
    "\n",
    "print('-------------------------------------------------------------')\n",
    "print('Promedio de p:\\n',np.mean(p))\n",
    "\n",
    "\n",
    "print('-------------------------------------------------------------')\n",
    "print('Promedio por columnas de p:\\n', np.mean(p,axis=0))\n",
    "\n",
    "\n",
    "print('-------------------------------------------------------------')\n",
    "print('Promedio por filas de p:\\n', np.mean(p,axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráficas Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot sencillo con titulos en axis\n",
    "\n",
    "N = 100\n",
    "f = 2\n",
    "# Array de \"tiempo\" con N números equiespaciados entre 0 y 1\n",
    "t = np.linspace(0,1,N)\n",
    "\n",
    "# Obtener los valores de seno y coseno para esos tiempos\n",
    "sine = np.sin(2 * np.pi * f * t)\n",
    "cosine = np.cos(2 * np.pi * f * t)\n",
    "\n",
    "# Graficar la función seno:\n",
    "\n",
    "# Iniciar nueva figura e indicar parámetro de tamaño (figsize)\n",
    "plt.figure(figsize=(8,2)) # Variar los valores de figsize (p.ej. (15,4), (10,10), (5,20))\n",
    "\n",
    "plt.title(\"Función $sin (2 \\pi ft)$\") # Título de la gráfica\n",
    "plt.plot(t,sine, 'r') # Graficar seno en color rojo\n",
    "plt.plot(t,cosine, 'g') # Graficar coseno en color verde\n",
    "\n",
    "plt.xlabel('Tiempo (s)')\n",
    "plt.ylabel('Valor')\n",
    "\n",
    "plt.grid() # Agregar grilla\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Subplots\n",
    "\n",
    "# Cantidad de muestras\n",
    "N = 100\n",
    "\n",
    "f = 2\n",
    "t = np.linspace(0,1,N)\n",
    "sine = np.sin(2*np.pi*f*t)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2,ncols=2,figsize=(10,4))\n",
    "\n",
    "ax[0,0].plot(sine), ax[0,0].set_title('Gráfica contínua')\n",
    "ax[0,1].plot(sine,'--'), ax[0,1].set_title('Gráfica discontinua')\n",
    "ax[1,0].plot(sine,'*'), ax[1,0].set_title('Gráfica punteada')\n",
    "ax[1,1].plot(sine,'-*'), ax[1,1].set_title('Gráfica punteada+continua')\n",
    "\n",
    "plt.tight_layout() # ajustar posición de las imágenes\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Levantar un csv como un dataframe\n",
    "csv_path = '../iris_dataset.csv'\n",
    "iris_df = pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  150 non-null    float64\n",
      " 1   sepal width (cm)   150 non-null    float64\n",
      " 2   petal length (cm)  150 non-null    float64\n",
      " 3   petal width (cm)   150 non-null    float64\n",
      " 4   target             150 non-null    int64  \n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 6.0 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primeros 5 datos\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</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>0</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>0</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>0</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>0</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Últimos 5 datos\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Descripción cuantificada de datos\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)      target  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.199333    1.000000  \n",
       "std            0.762238    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Propiedades y estadisticas\n",
    "print('Info:')\n",
    "display(iris_df.info()) # se usa display() para desplegar con el formato de pandas\n",
    "\n",
    "print('Primeros 5 datos')\n",
    "display(iris_df.head(n=5))\n",
    "\n",
    "print('Últimos 5 datos')\n",
    "display(iris_df.tail(n=5))\n",
    "\n",
    "print('Descripción cuantificada de datos')\n",
    "display(iris_df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 2 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal width (cm)   150 non-null    float64\n",
      " 1   petal length (cm)  150 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 2.5 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hay 100 flores con largo de pétalo mayor a 2.9 cm\n"
     ]
    }
   ],
   "source": [
    "# Acceso a datos por columnas\n",
    "iris_petal_length_df = iris_df[ ['sepal width (cm)' , 'petal length (cm)'] ] # quedarse sólo con sepal with y petal lenght\n",
    "\n",
    "print('Info:')\n",
    "display(iris_petal_length_df.info())\n",
    "\n",
    "# Flores hay con el largo del pétalo mayor a 2.9 cm \n",
    "cantidad_flores = len(iris_df[iris_df['petal length (cm)'] > 2.9])\n",
    "print(f'Hay {cantidad_flores} flores con largo de pétalo mayor a 2.9 cm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quitar los datos en indices [0,3,4]:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "5                5.4               3.9                1.7               0.4   \n",
       "6                4.6               3.4                1.4               0.3   \n",
       "7                5.0               3.4                1.5               0.2   \n",
       "\n",
       "   target  \n",
       "1       0  \n",
       "2       0  \n",
       "5       0  \n",
       "6       0  \n",
       "7       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quitar la columna \"target\":\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "5                5.4               3.9                1.7               0.4\n",
       "6                4.6               3.4                1.4               0.3\n",
       "7                5.0               3.4                1.5               0.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quitar fila/columna\n",
    "\n",
    "iris_incomplete_df = iris_df.drop( index=[0,3,4] )\n",
    "print('Quitar los datos en indices [0,3,4]:')\n",
    "display(iris_incomplete_df.head())\n",
    "\n",
    "\n",
    "iris_features_df = iris_incomplete_df.drop(['target'], axis=1) \n",
    "print('Quitar la columna \"target\":')\n",
    "display(iris_features_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots(1,len(iris_df.columns),figsize=(12,3))\n",
    "iris_df.hist(bins=25, ax=axis) # método del dataframe\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sci-kit Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### División de datos en conjuntos _**train-test**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Cargar base\n",
    "iris = load_iris()\n",
    "# Extraer datos y etiquetas objetivo\n",
    "X, y = iris.data, iris.target\n",
    "\n",
    "# Usar las primeras dos características (opcional)\n",
    "X = X[:, :2]\n",
    "\n",
    "# Separar en train y test \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=42) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Métodos de un clasificador"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Llamado de clasificador\n",
    "clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instancia de ajuste a datos de entrenamiento\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verdadero: 1, Predicción: 1\n",
      "Verdadero: 0, Predicción: 0\n",
      "Verdadero: 2, Predicción: 2\n",
      "Verdadero: 1, Predicción: 2\n",
      "Verdadero: 1, Predicción: 2\n",
      "Verdadero: 0, Predicción: 0\n",
      "Verdadero: 1, Predicción: 1\n",
      "Verdadero: 2, Predicción: 1\n",
      "Verdadero: 1, Predicción: 1\n",
      "Verdadero: 1, Predicción: 2\n"
     ]
    }
   ],
   "source": [
    "# Predicción de datos test\n",
    "y_test_pred = clf.predict(X_test)\n",
    "for y1, y2 in zip(y_test[:10], y_test_pred[:10]):\n",
    "    print(f'Verdadero: {y1}, Predicción: {y2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score del clasificador: 0.6333333333333333\n"
     ]
    }
   ],
   "source": [
    "# Cálculo de score (accuracy)\n",
    "clf_acc = clf.score(X_test,y_test)  # COMPLETAR: usar método score del clasificador\n",
    "\n",
    "# Verificar que den lo mismo\n",
    "print('Score del clasificador:',clf_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilidad de confianza por clase y predicción:\n",
      "\n",
      "Probs: [0. 1. 0.], \t Predicción: 1, \t Target: 1\n",
      "Probs: [1. 0. 0.], \t Predicción: 0, \t Target: 0\n",
      "Probs: [0. 0. 1.], \t Predicción: 2, \t Target: 2\n",
      "Probs: [0. 0. 1.], \t Predicción: 2, \t Target: 1\n",
      "Probs: [0. 0. 1.], \t Predicción: 2, \t Target: 1\n",
      "Probs: [1. 0. 0.], \t Predicción: 0, \t Target: 0\n",
      "Probs: [0. 1. 0.], \t Predicción: 1, \t Target: 1\n",
      "Probs: [0.  0.5 0.5], \t Predicción: 1, \t Target: 2\n",
      "Probs: [0. 1. 0.], \t Predicción: 1, \t Target: 1\n",
      "Probs: [0.         0.33333333 0.66666667], \t Predicción: 2, \t Target: 1\n"
     ]
    }
   ],
   "source": [
    "# Predicción de probabilidad\n",
    "y_test_proba = clf.predict_proba(X_test) \n",
    "print('Probabilidad de confianza por clase y predicción:\\n')\n",
    "\n",
    "for y_proba, y_pred, y_gt in zip(y_test_proba[:10], y_test_pred[:10], y_test[:10]):\n",
    "    print(f'Probs: {y_proba}, \\t Predicción: {y_pred}, \\t Target: {y_gt}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Volver a hacer esta parte pero cambiando el clasificador"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matriz de confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------\n",
      "CM:\n",
      "[[9 1 0]\n",
      " [0 4 5]\n",
      " [0 5 6]]\n",
      "-----------------------\n",
      "CM normalizada:\n",
      "[[0.9        0.1        0.        ]\n",
      " [0.         0.44444444 0.55555556]\n",
      " [0.         0.45454545 0.54545455]]\n",
      "-----------------------\n"
     ]
    }
   ],
   "source": [
    "# Matriz con y sin normalización\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "mat_conf = confusion_matrix(y_test, y_test_pred, normalize = None) # COMPLETAR\n",
    "mat_conf_norm = confusion_matrix(y_test, y_test_pred, normalize = 'true') # COMPLETAR\n",
    "\n",
    "print('-----------------------')\n",
    "print('CM:')\n",
    "print( mat_conf )\n",
    "print('-----------------------')\n",
    "print('CM normalizada:')\n",
    "print( mat_conf_norm )\n",
    "print('-----------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "# Matriz graficada\n",
    "disp = ConfusionMatrixDisplay.from_estimator(\n",
    "            clf,    \n",
    "            X_test, \n",
    "            y_test,\n",
    "            display_labels=iris.target_names, # nombres etiquetas\n",
    "            cmap=plt.cm.Blues, # estética\n",
    "            normalize=None,\n",
    "            xticks_rotation=45\n",
    "        )\n",
    "disp.ax_.set_title('Clasificador en test')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "telefonicaAD",
   "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": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}