{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/financieras/pyCourse/blob/main/jupyter/calisto3/0230_libreria_pyliferisk.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c15ecb78",
      "metadata": {
        "id": "c15ecb78"
      },
      "source": [
        "# Librería actuarial pyliferisk\n",
        "[pyliferisk](https://pypi.org/project/pyliferisk)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "4071948c",
      "metadata": {
        "id": "4071948c",
        "outputId": "61633ed1-b929-4a3e-b6a0-52575b66a5ea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyliferisk\n",
            "  Downloading pyliferisk-1.12.0-py3-none-any.whl (39 kB)\n",
            "Installing collected packages: pyliferisk\n",
            "Successfully installed pyliferisk-1.12.0\n"
          ]
        }
      ],
      "source": [
        "!pip install pyliferisk"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0cd40427",
      "metadata": {
        "id": "0cd40427"
      },
      "source": [
        "### Example 1\n",
        "Print the omega (limiting age) of the both mortality tables and the qx at 50 years-old."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "53887354",
      "metadata": {
        "id": "53887354",
        "outputId": "a445e67e-34f4-44c6-9b17-204cc676bb15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "101\n",
            "121\n",
            "0.003113\n",
            "0.003662395\n"
          ]
        }
      ],
      "source": [
        "from pyliferisk import MortalityTable\n",
        "from pyliferisk.mortalitytables import SPAININE2004, GKM95\n",
        "\n",
        "tariff = MortalityTable(nt=SPAININE2004)\n",
        "experience = MortalityTable(nt=GKM95, perc=85)\n",
        "\n",
        "# Print the omega (limiting age) of the both tables:\n",
        "print(tariff.w)\n",
        "print(experience.w)\n",
        "\n",
        "# Print the qx at 50 years old:\n",
        "print(tariff.qx[50] / 1000)\n",
        "print(experience.qx[50] / 1000)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1076f4a",
      "metadata": {
        "id": "b1076f4a"
      },
      "source": [
        "### Example 2\n",
        "Plotting a surviving graph."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2986bdfb",
      "metadata": {
        "id": "2986bdfb",
        "outputId": "3041327d-92f7-45d0-a4cc-8f8d8bbb4c66"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'age')"
            ]
          },
          "execution_count": 6,
          "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": [
        "import matplotlib.pyplot as plt\n",
        "from pyliferisk import *\n",
        "from pyliferisk.mortalitytables import SPAININE2004, GKM95\n",
        "\n",
        "tariff = MortalityTable(nt=SPAININE2004)\n",
        "experience = MortalityTable(nt=GKM95, perc=75)\n",
        "x = range(0, tariff.w)\n",
        "y = tariff.lx[:tariff.w]\n",
        "z = experience.lx[:tariff.w]\n",
        "\n",
        "plt.plot(x,y, color = 'blue')\n",
        "plt.plot(x,z, color = 'red')\n",
        "plt.ylabel('lx')\n",
        "plt.xlabel('age')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a7985487",
      "metadata": {
        "id": "a7985487"
      },
      "source": [
        "### Example 3\n",
        "A Life Temporal insurance for a male, 30 years-old and a horizon for 10 years, fixed annual premium (GKM95, interest 6%):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b644d2d",
      "metadata": {
        "id": "3b644d2d",
        "outputId": "339554a3-1a48-435e-e04c-9da13f56042f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.3581166922356989\n"
          ]
        }
      ],
      "source": [
        "from pyliferisk import *\n",
        "from pyliferisk.mortalitytables import GKM95\n",
        "\n",
        "nt = Actuarial(nt=GKM95, i=0.06)\n",
        "x = 30\n",
        "n = 10\n",
        "C = 1000\n",
        "\n",
        "print(C * (Axn(nt, x, n) / annuity(nt, x, n, 0)))"
      ]
    }
  ],
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