3_1주차.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "3-1주차.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPaMGFzyKPTfcpc8sqCE2Wx",
      "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/3_1%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": "KTtamCbn7x4b",
        "outputId": "35831404-109f-420b-e651-3aae8762f4da"
      },
      "source": [
        "n = int(input())\r\n",
        "res = []\r\n",
        "for i in range(n):\r\n",
        "  t = input()\r\n",
        "  t = t.split(' ')\r\n",
        "  l = list(map(int, t))\r\n",
        "\r\n",
        "  a = 0\r\n",
        "  for i in range(1, len(l)):\r\n",
        "    a += l[i]\r\n",
        "\r\n",
        "  b = a / l[0]\r\n",
        "  c = 0\r\n",
        "  for i in range(1, len(l)):\r\n",
        "    if b < l[i]:\r\n",
        "      c += 1  \r\n",
        "  res.append(c / l[0] * 100)\r\n",
        "\r\n",
        "for i in range(len(res)):\r\n",
        "  print('%.3f%%'%(res[i]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n",
            "5 50 50 70 80 100\n",
            "7 100 95 90 80 70 60 50\n",
            "3 70 90 80\n",
            "3 70 90 81\n",
            "9 100 99 98 97 96 95 94 93 91\n",
            "40.000%\n",
            "57.143%\n",
            "33.333%\n",
            "66.667%\n",
            "55.556%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5pEX_fDO8B1L",
        "outputId": "77d683ef-cf8c-4644-c1ca-f11783e83412"
      },
      "source": [
        "import numpy as np\r\n",
        "np.sum([[1, 2],\r\n",
        "           [3, 4]], axis=0)\r\n",
        "\r\n",
        "#[1, 2] + [3, 4] = [4, 6]\r\n",
        "\r\n",
        "np.sum([[1, 2],\r\n",
        "          [3, 4]], axis=1)\r\n",
        "# 1 + 2\r\n",
        "# 3 + 4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "dZVn0pHC8W_z",
        "outputId": "399797d6-c436-4502-f959-c65d73a6c65d"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "x = np.array([1,2,4,1,3])\r\n",
        "y = np.array([2,5,2,6,3])\r\n",
        "plt.plot(x,y)\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/"
        },
        "id": "02pT93zy8av3",
        "outputId": "eef81393-9d26-46e6-c9e5-4fe28f6162f2"
      },
      "source": [
        "10**3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Br81E8OWCm6g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfc798ed-51c6-4755-e59f-f91e54ea7398"
      },
      "source": [
        "a = list(map(int, '72036854775807'))\r\n",
        "b = list(map(int, '72036854775808'))\r\n",
        "e = 0\r\n",
        "for i in range(len(a)):\r\n",
        "  c = a[-(i+1)] + b[-(i+1)]\r\n",
        "  d = 10**i\r\n",
        "  print(c * d)\r\n",
        "  e += c * d\r\n",
        "print(e)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "15\n",
            "0\n",
            "1600\n",
            "10000\n",
            "140000\n",
            "1400000\n",
            "8000000\n",
            "100000000\n",
            "1600000000\n",
            "12000000000\n",
            "60000000000\n",
            "0\n",
            "4000000000000\n",
            "140000000000000\n",
            "144073709551615\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}