20509류동하0914.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPwBYZfMOUtol7lxtvmp7QY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dongdongdongha/cu/blob/main/20509%EB%A5%98%EB%8F%99%ED%95%980914.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "M-Mvasyf_j1F"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdFbULFj-Ln4",
        "outputId": "153fe4bd-8318-417f-ce5f-dac032c9a344"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "fonts-nanum is already the newest version (20200506-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.\n",
            "/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
            "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 3 dirs\n",
            "/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/nanum: caching, new cache contents: 12 fonts, 0 dirs\n",
            "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
            "/root/.local/share/fonts: skipping, no such directory\n",
            "/root/.fonts: skipping, no such directory\n",
            "/usr/share/fonts/truetype: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/humor-sans: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/liberation: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/nanum: skipping, looped directory detected\n",
            "/var/cache/fontconfig: cleaning cache directory\n",
            "/root/.cache/fontconfig: not cleaning non-existent cache directory\n",
            "/root/.fontconfig: not cleaning non-existent cache directory\n",
            "fc-cache: succeeded\n"
          ]
        }
      ],
      "source": [
        "\n",
        "!sudo apt-get install -y fonts-nanum\n",
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xP_G_QXNDEcT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "from google.colab import files\n",
        "uploded=files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "id": "A7rV9w60-UpM",
        "outputId": "197abc05-3ec9-4685-8e7c-6f143a104d07"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7612cd8e-3844-4138-98b8-b6973205b8cc\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7612cd8e-3844-4138-98b8-b6973205b8cc\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving weather.csv to weather (2).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f=open('weather (2).csv', encoding = 'cp949')\n",
        "data = csv.reader(f)\n",
        "header=next(data)\n",
        "data=list(data)\n",
        "f.close()"
      ],
      "metadata": {
        "id": "uTO_Keh6BGw_"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T3s08WZZCBk3",
        "outputId": "de1de4df-c69d-448f-fa2d-9137f5545427"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['1921-01-01', '2.3', '5.2', '-0.4'],\n",
              " ['1921-01-02', '-3.4', '1.5', '-5.8'],\n",
              " ['1921-01-03', '-6.5', '-2.7', '-9.5'],\n",
              " ['1921-01-04', '-7.8', '-3.4', '-12.5'],\n",
              " ['1921-01-05', '-4.4', '-1.1', '-11.9'],\n",
              " ['1921-01-06', '-3.5', '0.7', '-7.9'],\n",
              " ['1921-01-07', '-4.2', '0.2', '-10.6'],\n",
              " ['1921-01-08', '-7.9', '-3', '-11.5'],\n",
              " ['1921-01-09', '-8.8', '-1.5', '-15.3'],\n",
              " ['1921-01-10', '-5.2', '-2.4', '-11.6'],\n",
              " ['1921-01-11', '-5.8', '-1.1', '-10.3'],\n",
              " ['1921-01-12', '-8.5', '-4.1', '-13.6'],\n",
              " ['1921-01-13', '-5.4', '-2.3', '-9.6'],\n",
              " ['1921-01-14', '-5.2', '-1.5', '-9.1'],\n",
              " ['1921-01-15', '-4.6', '1', '-14.6'],\n",
              " ['1921-01-16', '0.2', '4.7', '-3.1'],\n",
              " ['1921-01-17', '-0.8', '5.2', '-5.1'],\n",
              " ['1921-01-18', '0', '6.6', '-6.3'],\n",
              " ['1921-01-19', '-0.1', '2.1', '-2.9'],\n",
              " ['1921-01-20', '1.2', '6', '-2.7'],\n",
              " ['1921-01-21', '-2.4', '0.7', '-7.6'],\n",
              " ['1921-01-22', '-5.3', '2.4', '-12.1'],\n",
              " ['1921-01-23', '-2.2', '4.8', '-9.8'],\n",
              " ['1921-01-24', '0.7', '8.5', '-5.8'],\n",
              " ['1921-01-25', '2.3', '7.6', '-3.5'],\n",
              " ['1921-01-26', '1.1', '4.5', '-0.8'],\n",
              " ['1921-01-27', '0.8', '3.5', '-2.6'],\n",
              " ['1921-01-28', '0', '1.2', '-0.9'],\n",
              " ['1921-01-29', '-0.4', '2.5', '-4.3'],\n",
              " ['1921-01-30', '-2.3', '2.2', '-6'],\n",
              " ['1921-02-01', '-0.3', '5.3', '-5.3'],\n",
              " ['1921-02-02', '-3.2', '-0.5', '-5.8'],\n",
              " ['1921-02-03', '-2', '2', '-5.1'],\n",
              " ['1921-02-04', '-4.5', '0', '-8.6'],\n",
              " ['1921-02-05', '-5.4', '-0.8', '-9.3'],\n",
              " ['1921-02-06', '-4.8', '1.5', '-10.8'],\n",
              " ['1921-02-07', '-2.7', '3.7', '-9.5'],\n",
              " ['1921-02-08', '-2', '4.2', '-8.6'],\n",
              " ['1921-02-09', '-1.3', '6.4', '-8.5'],\n",
              " ['1921-02-10', '0.2', '8.6', '-7.4'],\n",
              " ['1921-02-11', '2.4', '7.6', '-3.8'],\n",
              " ['1921-02-12', '2.7', '9.3', '-2.8'],\n",
              " ['1921-02-13', '1.2', '3.3', '-1'],\n",
              " ['1921-02-14', '1.5', '5.1', '-1.1'],\n",
              " ['1921-02-15', '-0.5', '6.3', '-6.6'],\n",
              " ['1921-02-16', '-1.4', '2.4', '-4'],\n",
              " ['1921-02-17', '-5.9', '-1.8', '-8.7'],\n",
              " ['1921-02-18', '-5.8', '-0.9', '-10.3'],\n",
              " ['1921-02-19', '-2.6', '3.3', '-6.8'],\n",
              " ['1921-02-20', '-3.3', '2.1', '-10.6'],\n",
              " ['1921-02-21', '-5', '-0.5', '-7.5'],\n",
              " ['1921-02-22', '-4.9', '0.8', '-11.6'],\n",
              " ['1921-02-23', '1.4', '6.3', '-5.6'],\n",
              " ['1921-02-24', '0', '3.5', '-2.7'],\n",
              " ['1921-02-25', '-0.6', '7', '-7.5'],\n",
              " ['1921-02-26', '4.4', '9.5', '-2.2'],\n",
              " ['1921-02-27', '4', '8.6', '0'],\n",
              " ['1921-02-28', '2.8', '5.7', '-3.1'],\n",
              " ['1921-03-01', '5.5', '9.2', '3.3'],\n",
              " ['1921-03-02', '2.5', '7.5', '-0.8'],\n",
              " ['1921-03-03', '-1', '3.9', '-3.8'],\n",
              " ['1921-03-04', '-0.8', '3.6', '-5.9'],\n",
              " ['1921-03-05', '2', '7.5', '-3.7'],\n",
              " ['1921-03-06', '2.5', '8.5', '-2.4'],\n",
              " ['1921-03-07', '0.7', '7.3', '-5.1'],\n",
              " ['1921-03-08', '3.2', '10.5', '-4.5'],\n",
              " ['1921-03-09', '6', '12.7', '0.2'],\n",
              " ['1921-03-10', '6.8', '12.2', '2.3'],\n",
              " ['1921-03-11', '8.2', '12.5', '3.9'],\n",
              " ['1921-03-12', '3.8', '8.2', '-0.8'],\n",
              " ['1921-03-13', '-1', '2.4', '-2.9'],\n",
              " ['1921-03-14', '-3.3', '-0.8', '-5'],\n",
              " ['1921-03-15', '-1.6', '3.5', '-6.6'],\n",
              " ['1921-03-16', '-0.5', '5', '-5'],\n",
              " ['1921-03-17', '2.8', '9.1', '-1.5'],\n",
              " ['1921-03-18', '4.5', '10.2', '0.5'],\n",
              " ['1921-03-19', '9.2', '11.9', '3.3'],\n",
              " ['1921-03-20', '7.4', '12.7', '3.3'],\n",
              " ['1921-03-21', '2.3', '7.1', '-0.4'],\n",
              " ['1921-03-22', '1.9', '7.2', '-3'],\n",
              " ['1921-03-23', '2', '9', '-4.7'],\n",
              " ['1921-03-24', '5.1', '9.2', '0.2'],\n",
              " ['1921-03-25', '6', '10', '2.2'],\n",
              " ['1921-03-26', '5.1', '12', '0'],\n",
              " ['1921-03-27', '4.2', '9.6', '-2.2'],\n",
              " ['1921-03-28', '3.1', '9.2', '-0.3'],\n",
              " ['1921-03-29', '3.2', '9.2', '-3.9'],\n",
              " ['1921-03-30', '5.5', '13.2', '-1.4'],\n",
              " ['1921-04-01', '11.8', '20.5', '1.7'],\n",
              " ['1921-04-02', '11.5', '13.4', '9.4'],\n",
              " ['1921-04-03', '10.2', '14.5', '7.7'],\n",
              " ['1921-04-04', '4.9', '11.9', '1.2'],\n",
              " ['1921-04-05', '6', '11', '2.6'],\n",
              " ['1921-04-06', '6.5', '11.9', '-0.7'],\n",
              " ['1921-04-07', '5.3', '10.7', '1.2'],\n",
              " ['1921-04-08', '4.7', '10.3', '-0.5'],\n",
              " ['1921-04-09', '7', '13.5', '1.1'],\n",
              " ['1921-04-10', '7.5', '15', '0.3'],\n",
              " ['1921-04-11', '10.1', '15.5', '6'],\n",
              " ['1921-04-12', '12.1', '19.7', '5'],\n",
              " ['1921-04-13', '10.9', '16.9', '6.5'],\n",
              " ['1921-04-14', '10.4', '12', '8'],\n",
              " ['1921-04-15', '10.7', '15.7', '6.8'],\n",
              " ['1921-04-16', '11', '15.5', '7.4'],\n",
              " ['1921-04-17', '11', '16.2', '7.2'],\n",
              " ['1921-04-18', '10.1', '20.1', '0.6'],\n",
              " ['1921-04-19', '11.6', '20.1', '3.3'],\n",
              " ['1921-04-20', '10', '17.7', '3.6'],\n",
              " ['1921-04-21', '13', '21.1', '5.6'],\n",
              " ['1921-04-22', '12.2', '20.5', '6.1'],\n",
              " ['1921-04-23', '14.5', '23.5', '4.6'],\n",
              " ['1921-04-24', '15.5', '21.2', '10.8'],\n",
              " ['1921-04-25', '16.7', '26.9', '9.6'],\n",
              " ['1921-04-26', '19', '28.3', '11.7'],\n",
              " ['1921-04-27', '13.1', '18.7', '10.7'],\n",
              " ['1921-04-28', '12.2', '19.5', '8.1'],\n",
              " ['1921-04-29', '12.9', '20.6', '5.2'],\n",
              " ['1921-04-30', '15.6', '23.8', '8.9'],\n",
              " ['1921-05-01', '17.3', '26.5', '10.3'],\n",
              " ['1921-05-02', '17.1', '22.7', '13'],\n",
              " ['1921-05-03', '17.2', '24.4', '10.9'],\n",
              " ['1921-05-04', '17.6', '24.3', '12.2'],\n",
              " ['1921-05-05', '17.3', '20.6', '14.5'],\n",
              " ['1921-05-06', '16.5', '23.8', '11.1'],\n",
              " ['1921-05-07', '14.5', '23.3', '5.9'],\n",
              " ['1921-05-08', '13.4', '19.2', '8.2'],\n",
              " ['1921-05-09', '16.9', '24.9', '9.6'],\n",
              " ['1921-05-10', '18.3', '25.9', '12.4'],\n",
              " ['1921-05-11', '15.1', '21.2', '12.1'],\n",
              " ['1921-05-12', '13.4', '20.4', '8.5'],\n",
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              " ['1923-09-09', '20.2', '24.6', '15.5'],\n",
              " ['1923-09-10', '18.3', '20.4', '16.5'],\n",
              " ['1923-09-11', '19.2', '20.9', '16.6'],\n",
              " ['1923-09-12', '20.9', '23.8', '18.2'],\n",
              " ['1923-09-13', '22.7', '28.4', '19.8'],\n",
              " ['1923-09-14', '22.8', '29', '16.8'],\n",
              " ['1923-09-15', '19.8', '25', '16.2'],\n",
              " ['1923-09-16', '18.2', '24.7', '13.6'],\n",
              " ['1923-09-17', '20', '27.5', '12.2'],\n",
              " ['1923-09-18', '22.2', '28.1', '17.5'],\n",
              " ['1923-09-19', '18.6', '21.5', '17'],\n",
              " ['1923-09-20', '19.1', '23', '16'],\n",
              " ['1923-09-21', '16.1', '22.7', '11'],\n",
              " ['1923-09-22', '15.9', '21', '10.6'],\n",
              " ['1923-09-23', '17.3', '21.1', '13.5'],\n",
              " ['1923-09-24', '18.1', '25.1', '12.4'],\n",
              " ['1923-09-25', '18.1', '24.8', '11.5'],\n",
              " ['1923-09-26', '17.5', '23.9', '10.9'],\n",
              " ['1923-09-27', '15.5', '21.3', '11.2'],\n",
              " ['1923-09-28', '13.3', '20.7', '5.6'],\n",
              " ['1923-09-29', '16.7', '22.6', '11.7'],\n",
              " ['1923-09-30', '14.5', '22.5', '8'],\n",
              " ['1923-10-01', '16.5', '20.5', '11.6'],\n",
              " ['1923-10-02', '14.4', '16.9', '10.5'],\n",
              " ['1923-10-03', '11.9', '18.6', '5.4'],\n",
              " ['1923-10-04', '12.9', '19', '6.6'],\n",
              " ['1923-10-05', '12.8', '16.3', '10.2'],\n",
              " ['1923-10-06', '13.4', '19.5', '9.2'],\n",
              " ['1923-10-07', '13.6', '21.9', '5.4'],\n",
              " ['1923-10-08', '13.9', '18.8', '8.8'],\n",
              " ['1923-10-09', '12.6', '20', '4.8'],\n",
              " ['1923-10-10', '13.2', '19.5', '9.9'],\n",
              " ['1923-10-11', '11.7', '18.4', '4'],\n",
              " ['1923-10-12', '10.4', '19.1', '3.2'],\n",
              " ['1923-10-13', '12.5', '18.1', '5.8'],\n",
              " ['1923-10-14', '12.5', '17.1', '10.2'],\n",
              " ['1923-10-15', '11.2', '16.3', '8.6'],\n",
              " ['1923-10-16', '11.7', '20.2', '3.9'],\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(header)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uzWLphpvEhdo",
        "outputId": "63c92093-9c7f-4521-eb57-6f24c6c3017e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['일시', '평균기온(℃)', '최고기온(℃)', '최저기온(℃)']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KkuDmPnEoE4",
        "outputId": "7933b255-917e-40b3-fdcb-77d2754a372a"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1921-01-01', '2.3', '5.2', '-0.4']"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ZcQUcJXEqGR",
        "outputId": "0a70491d-59d2-45dd-e7d3-41fae2044303"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[0][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "6P080SxDFIOY",
        "outputId": "5519b147-2819-4bb1-fe1e-899793774788"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1921-01-01'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[0][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MLTM7njlFKbg",
        "outputId": "82f60723-5e9c-4f9d-fc0e-d88253e762c6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.3'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8tMLCpdFdK5",
        "outputId": "d1e23c25-a519-4e5e-849d-00097fb78cc0"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35651\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for row in data[:5]:\n",
        "  print(row)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30PAa7TjFhYp",
        "outputId": "b910a513-5452-4a77-8229-b35721a338c1"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['1921-01-01', '2.3', '5.2', '-0.4']\n",
            "['1921-01-02', '-3.4', '1.5', '-5.8']\n",
            "['1921-01-03', '-6.5', '-2.7', '-9.5']\n",
            "['1921-01-04', '-7.8', '-3.4', '-12.5']\n",
            "['1921-01-05', '-4.4', '-1.1', '-11.9']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for row in data[:5]:\n",
        "  print(row[-0],'의 최저기온은',row[3],'도 이다.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7n5QOgJxFpos",
        "outputId": "7fd20b27-4e07-4a0c-b81c-0e3094ff7c40"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1921-01-01 의 최저기온은 -0.4 도 이다.\n",
            "1921-01-02 의 최저기온은 -5.8 도 이다.\n",
            "1921-01-03 의 최저기온은 -9.5 도 이다.\n",
            "1921-01-04 의 최저기온은 -12.5 도 이다.\n",
            "1921-01-05 의 최저기온은 -11.9 도 이다.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for row in data:\n",
        "  row[2] = float(row[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "94p7vmYmGOJf",
        "outputId": "3e1f48ec-21ae-474e-fd1b-64a9f34c7ec1"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-f6fd4f88713c>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: ''"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "fRRVWvq2MLeP",
        "outputId": "56f896f5-0a2c-4760-839b-5a6feae52ffd"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-43-9f69946bb28a>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    \u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m incomplete input\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_temp=-999\n",
        "max_data=''\n",
        "\n",
        "for row in data:\n",
        "  if row[-2] =='':\n",
        "    row[-2] = -999\n",
        "  row[-2] = float(row[-2])\n",
        "  if max_temp < row[-2]:\n",
        "    max_temp=row[-2]\n",
        "    max_data=row[0]\n",
        "print('기상 관측 아래 서울의 최고기온이 가장 높았던날은', max_data,'로', max_temp,',도이다')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TtTOlrxxM54P",
        "outputId": "781d2244-1ffa-40f2-a7c9-aa49a851e40f"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "기상 관측 아래 서울의 최고기온이 가장 높았던날은 2018-08-01 로 39.6 ,도이다\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s='hello python'\n",
        "print(s.split())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gfLYzrttNxwM",
        "outputId": "7ddab7e8-9c54-4c1e-9342-f75cd73ba97a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['hello', 'python']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(s[0])\n",
        "print(s[6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "coGgRcrpP3ve",
        "outputId": "f3f64263-ccf0-4584-85e3-69c96595e2d4"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "h\n",
            "p\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "birth = '2006-02-09'\n",
        "print(birth.split('-')[0])\n",
        "print(birth.split('-')[1])\n",
        "print(birth.split('-')[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZAxMMUIwP5aO",
        "outputId": "fe35178d-71fd-4abc-bf5f-58eaf6d94379"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2006\n",
            "02\n",
            "09\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = []\n",
        "for row in data:\n",
        "  if row[0].split('-')[1] == '02' and row[0].split('-')[2] == '09':\n",
        "    if row[1] != '':\n",
        "      result.append(float(row[1]))\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ho0ERL12QozK",
        "outputId": "501aa769-69e4-4afd-ab9c-ec9cda009420"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1.3, 0.7, -2.8, 4.7, -1.0, 2.1, -8.7, -6.0, -10.1, 0.1, -6.5, 2.8, -4.2, -2.7, 2.3, -4.6, 2.3, -9.7, -3.1, -1.7, -3.9, -8.2, -5.2, -0.2, -7.5, -7.3, -5.4, -6.3, 0.7, 4.2, 1.5, 1.8, -8.8, -10.1, 0.0, 0.5, 2.9, -1.5, 3.8, -4.0, 0.3, 2.5, 4.9, -0.8, -6.8, 0.5, -8.6, -4.0, 3.0, -4.3, -6.5, -4.5, 1.5, -0.4, 2.5, 2.1, -8.2, -0.7, -1.9, -3.5, -8.2, 4.3, -4.6, 7.1, -5.3, 1.1, 3.2, 2.8, -1.8, -1.8, -5.7, 4.1, -5.9, 2.3, -4.7, 2.1, -5.2, -0.9, -1.9, 4.6, -2.2, -1.4, -4.5, 4.6, -1.9, 5.0, 3.7, -1.2, -4.5, -7.2, -1.6, -5.7, 0.3, -5.3, 0.4, -5.4, -0.5, -0.9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum=0\n",
        "for i in result:\n",
        "  sum += i\n",
        "print('기상이 관측된', len(result), \"해 동안, 생일의 평균기온은\", sum/len(result),'이다')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jg9fjjI8Y0V7",
        "outputId": "f28d4b7c-61a7-455a-99a1-8f70072290d3"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "기상이 관측된 98 해 동안, 생일의 평균기온은 -1.7959183673469385 이다\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.title('생일 편균 기온 시각화')\n",
        "plt.plot(result, color='orange', label='편균기온')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "gYumyQPmXwVE",
        "outputId": "61c6fef0-1284-4774-e516-9a27867c8ad2"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "christmas=[]\n",
        "max_temp=-999\n",
        "max_data=''\n",
        "\n",
        "for row in data:\n",
        "  if row[0].split('-')[1]=='12' and row[0].split('-')[2] == '25':\n",
        "    if row[1] != '':\n",
        "      christmas.append(float(row[1]))"
      ],
      "metadata": {
        "id": "h5cbZ_uoVBTy"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rphQeJKAbKvH"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "avfN2knMcy8W"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}