5_2주차.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "5-2주차.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOmk8FFRnW/SZ0+WQTnGsXc",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yujungha/python-study/blob/main/5_2%EC%A3%BC%EC%B0%A8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzByynPmIipJ",
        "outputId": "67696e89-9fb4-48d5-c65e-f897677fae0e"
      },
      "source": [
        "arr = [4,2,1,5,9,1,2]\r\n",
        "# arr.count(min(arr))\r\n",
        "arr.sort()\r\n",
        "arr.reverse()\r\n",
        "arr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9, 5, 4, 2, 2, 1, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "laFZWm5oImc0",
        "outputId": "c1b1336d-8bd6-4f53-90f0-c01bb073845d"
      },
      "source": [
        "arr = [4,2,1,5,9,1,2]\r\n",
        "a = arr[0]\r\n",
        "arr[0] = arr[len(arr)-1]\r\n",
        "arr[len(arr)-1] = a\r\n",
        "arr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 2, 1, 5, 9, 1, 4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aHVNUZzUIphG",
        "outputId": "a10dc775-1d0e-4397-934c-2a70a97aa0ea"
      },
      "source": [
        "import random\r\n",
        "l = []\r\n",
        "for i in range(20):\r\n",
        "  l.append(random.randint(1, 10))\r\n",
        "print(l)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 4, 10, 8, 10, 6, 7, 5, 10, 6, 7, 9, 7, 4, 5, 4, 8, 8, 5, 10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKPXGRNFIrc9",
        "outputId": "ce0af077-7a67-4dc5-9810-b998cdb4759a"
      },
      "source": [
        "'''\r\n",
        "내가 가진 돈 = 100\r\n",
        "딱지 가격이 5원 이하면 산다\r\n",
        "가진 딱지의 개수, 내가 가진 돈 출력\r\n",
        "'''\r\n",
        "money = 20\r\n",
        "딱지_개수 = 0\r\n",
        "전설_딱지 = 0\r\n",
        "z = []\r\n",
        "m = []\r\n",
        "for i in range(20):\r\n",
        "  if l[i] <= 5:\r\n",
        "    if money >= l[i]:\r\n",
        "      rand2 = random.random()\r\n",
        "      if rand2 < 0.1:\r\n",
        "        전설_딱지 += 1\r\n",
        "      else:\r\n",
        "        딱지_개수 += 1\r\n",
        "      money -= l[i]\r\n",
        "  if i % 2 == 0:\r\n",
        "    money += l[i] * 2\r\n",
        "  rand = random.random()\r\n",
        "  if rand < 0.7:\r\n",
        "    딱지_개수 *= 2\r\n",
        "  elif 0.7 <= rand and rand < 0.8:\r\n",
        "    딱지_개수 = 0\r\n",
        "  z.append(딱지_개수)\r\n",
        "  m.append(money)\r\n",
        "\r\n",
        "print(딱지_개수, money)\r\n",
        "print(z)\r\n",
        "print(m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4 132\n",
            "[0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 4, 10, 0, 0, 2, 4]\n",
            "[21, 17, 37, 37, 57, 57, 71, 66, 86, 86, 100, 100, 114, 110, 115, 111, 127, 127, 132, 132]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rA-YrSwItIL"
      },
      "source": [
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "C97WPpoPIvB_",
        "outputId": "3b6fbb89-1e18-4f4e-c3f7-ec6204d014ef"
      },
      "source": [
        "plt.plot(range(20), z)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "GR8CAYY8Iwa_",
        "outputId": "84e4720d-0751-4361-f14a-1c6da04df659"
      },
      "source": [
        "plt.plot(range(20), m)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}