3_numpy/3_5_funciones.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Funciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- En numpy existen distintos tipos de funciones de funciones, aquí vamos a ver las que consideramos más relevantes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratamiento de arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Función|Descripcción|\n",
    "|----|---|\n",
    "|`unique`| Calcula el conjunto único de elementos sin duplicados.|\n",
    "|`intersect1d`| Calcula la intersección de los elementos de dos arrays.|\n",
    "|`union1d`| Calcula la unión de los elementos de dos arrays.|\n",
    "|`in1d`| Calcula un array booleano que indica si cada elemento del primer array está contenido en el segundo.|\n",
    "|`setdiff1d`| Calcula la diferencia entre ambos conjuntos.|\n",
    "|`setxor1d`| Calcula la diferencia simétrica entre ambos conjuntos.|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- La función unique es de las más usadas a la hora de hacer análisis sobre conjuntos de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BMW', 'Ford', 'Nissan', 'Seat', 'Volvo'], dtype='<U6')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coches_array = np.array(['Ford', 'Volvo', 'BMW','Ford','Nissan','BMW','Seat'])\n",
    "np.unique(coches_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['BMW', 'Ford', 'Nissan', 'Seat', 'Volvo'], dtype='<U6'),\n",
       " array([2, 2, 1, 1, 1], dtype=int64))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(coches_array, return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También es válida para números, en general para cualquier dato compatible con arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 3, 4, 5, 7, 9]), array([1, 1, 1, 2, 2, 1, 1], dtype=int64))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeros_array = np.array([1,3,5,4,5,7,4,9,0])\n",
    "np.unique(numeros_array, return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Guardado de arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Función|Descripcción|\n",
    "|----|---|\n",
    "|`save`| Guarda un array en un archivo binario de numpy con extensión .npy|\n",
    "|`savez`| Guarda multiples arrays en un archivo binario de numpy con extensión .npz|\n",
    "|`savetxt`| Guarda un array en un archivo .txt|\n",
    "|`load`| Carga los archivos guardados en .npy .npz o pickle|\n",
    "|`loadtxt`| Carga los archivos guardados en .txt|\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(10)\n",
    "y = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('out.npy', x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez('out.npz', x=x, y=y) # Guarda los datos en un fichero .npz si indicamos x=x y=y, se guardan con el nombre x e y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('out.txt', x, delimiter=',') # Guarda los datos en un fichero .txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora los cargamos para ver los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load('out.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "[ 0.          0.84147098  0.90929743  0.14112001 -0.7568025  -0.95892427\n",
      " -0.2794155   0.6569866   0.98935825  0.41211849]\n"
     ]
    }
   ],
   "source": [
    "print(np.load('out.npz')['x'])\n",
    "print(np.load('out.npz')['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.loadtxt('out.txt', delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones financieras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Una descripción detallada de las funciones se puede ver [aquí](https://numpy.org/doc/1.17/reference/routines.financial.html)\n",
    "\n",
    "|Función|Descripcción|\n",
    "|----|---|\n",
    "|`fv(rate, nper, pmt, pv[, when])`|Calcula el valor futuro.|\n",
    "|`pv(rate, nper, pmt,[fv, when])`|Calcula el valor presente.|\n",
    "|`npv(rate, values)`|VAN NPV (Net Present Value) de una serie de flujo de cajas.|\n",
    "|`pmt(rate, nper, pv[, fv, when])`|Calcula el pago total, principal y intéres.|\n",
    "|`ppmt(rate, per, nper, pv[, fv, when])`|Calcula el pago contra el principal.|\n",
    "|`ipmt(rate, per, nper, pv[, fv, when])`|Calcula la proporción del interés del pago.|\n",
    "|`irr(values)`|TIR: Internal Rate of Return (IRR).|\n",
    "|`mirr(values, finance_rate, reinvest_rate)`| Internal Rate of Return (IRR) Modificada.|\n",
    "|`nper(rate, pmt, pv[, fv, when])`|Calcula el número de pagos periodicos|\n",
    "|`rate(nper, pmt, pv, fv[, when, guess, tol, …])`|Calcula la tasa de interes por periodo.|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Vamos a hacer un ejemplo:\n",
    "    - Teniendo que pagar cuotas de 25.000 € anuales\n",
    "    - A 20 años\n",
    "    - Y un tipo de interés del 2% anual\n",
    "    - ¿Cual será su valor futuro?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adriy\\AppData\\Local\\Temp\\ipykernel_36572\\1813925726.py:1: DeprecationWarning: In accordance with NEP 32, the function fv was removed from NumPy version 1.20.  A replacement for this function is available in the numpy_financial library: https://pypi.org/project/numpy-financial\n",
      "  np.fv(rate = 0.02, nper = 20, pmt = 25000, pv = 0)\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "In accordance with NEP 32, the function fv was removed from NumPy version 1.20.  A replacement for this function is available in the numpy_financial library: https://pypi.org/project/numpy-financial",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\adriy\\Desktop\\Curso_uam\\python_finance\\3_numpy\\3_5_funciones.ipynb Celda 24\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/adriy/Desktop/Curso_uam/python_finance/3_numpy/3_5_funciones.ipynb#X36sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m np\u001b[39m.\u001b[39;49mfv(rate \u001b[39m=\u001b[39;49m \u001b[39m0.02\u001b[39;49m, nper \u001b[39m=\u001b[39;49m \u001b[39m20\u001b[39;49m, pmt \u001b[39m=\u001b[39;49m \u001b[39m25000\u001b[39;49m, pv \u001b[39m=\u001b[39;49m \u001b[39m0\u001b[39;49m)\n",
      "File \u001b[1;32mc:\\Users\\adriy\\anaconda3\\lib\\site-packages\\numpy\\__init__.py:287\u001b[0m, in \u001b[0;36m__getattr__.<locals>._expired\u001b[1;34m(*args, **kwds)\u001b[0m\n\u001b[0;32m    286\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_expired\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds):\n\u001b[1;32m--> 287\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(msg)\n",
      "\u001b[1;31mRuntimeError\u001b[0m: In accordance with NEP 32, the function fv was removed from NumPy version 1.20.  A replacement for this function is available in the numpy_financial library: https://pypi.org/project/numpy-financial"
     ]
    }
   ],
   "source": [
    "np.fv(rate = 0.02, nper = 20, pmt = 25000, pv = 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Como vemos estas funciones han sido movidas de numpy a numpy_financial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting numpy_financial\n",
      "  Downloading numpy_financial-1.0.0-py3-none-any.whl (14 kB)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\adriy\\anaconda3\\lib\\site-packages (from numpy_financial) (1.21.5)\n",
      "Installing collected packages: numpy-financial\n",
      "Successfully installed numpy-financial-1.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy_financial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy_financial as npf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-607434.2449729437"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npf.fv(rate = 0.02, nper = 20, pmt = 25000, pv = 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Otros ejemplos**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Cuál es el valor futuro después de 10 años de :\n",
    "    - ahorrar 100€ ahora\n",
    "    - más 100€ al mes \n",
    "    - asumiendo un retorno anual del 5%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15692.928894335748"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npf.fv(0.05/12, 10*12, -100, -100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12100"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*10*12+100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "podemos hacer lo mismos para distintos retornos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([0.02, 0.05, 0.07])/12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13394.08597514, 15692.92889434, 17509.44688102])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npf.fv(a, 10*12, -100, -100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hipoteca a 20 años:\n",
    "    - 2% anual\n",
    "    - 10000 de entrada\n",
    "    - pagos mensuales de 450€"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-147571.8560345149"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npf.fv(0.02/12, 20*12, 450, 10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Ejercicios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.-** Crea un array y guardalo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.-** Lee el array y obten sus elementos únicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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