Stage_4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Stage 4"
      ],
      "metadata": {
        "id": "ee6Je6-u_TGm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "<font color=red>**최종 학습 목표**</font>\n",
        "\n",
        "* 신용카드 사기거래 탐지 프로젝트를 통해 여러분은 이상치 감지를 훌륭히 수행할 수 있는 데이커가 되는 것에 한 발짝 더 다가가게 될 것입니다.\n",
        "\n",
        "* 이 뿐만 아니라 이번 교육을 통해 키운 역량은 다른 곳에서도 큰 도움이 될 것이라 기대합니다.<br>\n",
        "\n",
        "<br><font color=red>**Stage 4 학습 목표**</font>\n",
        "\n",
        "* 데이터 시각화가 무엇인지 이해한다.\n",
        "\n",
        "* 다양한 그래프를 그려보고 데이터 분포를 확인하여 본다.\n",
        "\n",
        "* 그래프를 이미지로 저장하여 본다.\n",
        "\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "to2RU03l_ViL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Intro"
      ],
      "metadata": {
        "id": "PnMp77IU_WhR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "**데이커 여러분 환영합니다.** 🤗 \n",
        "\n",
        "지난 Stage 3에서는 모델이 학습하는 방법과 성능이 좋은 모델이 무엇인지에 대해 배워보았습니다.\n",
        "<br><br>\n",
        "이번 Stage 4에서는 데이터 분석 결과를 쉽게 이해할 수 있도록 시각적 수단을 통해 정보를 효과적으로 전달하는 **데이터 시각화**에 대해 알아볼 것입니다.\n",
        "<br><br>\n",
        "오늘 우리는 다양한 그래프를 통해 <font color=blue>**각 그래프의 의미**</font>, <font color=red>**그래프 꾸미기**</font>, <font color=green>**데이터 분포 확인**</font>,  <font color=#A52A2A>**그래프를 이미지로 저장하기**</font>에 관하여 실습과 퀴즈를 통해 공부하게 될 것입니다.\n",
        "\n",
        "퀴즈가 있다고 당황하지 마세요. <font color=red>**Hint**</font>가 여러분을 언제나 곁에서 도와줄 것입니다. \n",
        "<br><br>\n",
        "그럼 이제 고고씽 ~ ! 🚅<br><br>"
      ],
      "metadata": {
        "id": "59Rb4fZY_ZXS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Stage 4 배경\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "DVydks8m_f5-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
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)"
      ],
      "metadata": {
        "id": "JUZ_BfXg_gfF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "\n",
        "데이터를 이해하는 것은 여간 쉬운 일은 아닙니다. 그리고 데이터를 어떻게 활용해야 할 것인지 감을 잡는 것 또한 쉽지 않습니다.\n",
        "\n",
        "이를 도와줄 방법이 바로 시각화입니다.\n",
        "\n",
        "데이터 시각화는 여러분이 만들고 하는 모델의 <font color=blue>**비즈니스 인사이트를 도출하기 위해서는 필수적**</font>입니다. ⭐️\n",
        "\n",
        "\n",
        "본격적으로 시작하기 전에 데이터 시각화란 무엇이며, 왜 중요한지 알아봅시다.\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "p6H9OHvW_isG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "# 3. Let's Start"
      ],
      "metadata": {
        "id": "bO0m4Ov3_jqt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "여러분은 다시 중대한 임무를 맡게되었습니다. ⭐️\n",
        "\n",
        "비즈니스 인사이트를 도출하는데 가장 중요한 <font color=blue>**데이터 시각화**</font>를 잘하기 위해 차근차근 배워봅시다."
      ],
      "metadata": {
        "id": "ocLL1HTW_nWS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "## 3 - 1 데이터 시각화란 무엇인가요?"
      ],
      "metadata": {
        "id": "043TMf2I_pl-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "**데이터 시각화**\n",
        "\n",
        "  * 지금 시대는 다양한 정보로 넘쳐나고 있습니다. 하지만 중요한 것과 그렇지 않은 것을 이해하기 위해 정보를 살펴 보는 것이 점점 더 어려워지고 있습니다.\n",
        "\n",
        "  * 이럴 때 **데이터 분석 과정에서 더 좋은 인사이트를 발견하기 위해서** 혹은 **생성된 모든 정보로 데이터를 이해하기** 위하여 적합한 형태의 데이터로 시각화하는 것입니다.\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "whC8RgAY_oi2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "**데이터 시각화의 필요성**<br><br>\n",
        "\n",
        "  * <font color=darkblue>**많은 양의 데이터를 한눈에 볼 수 있습니다.**</font> 모니터에 띄워진 엑셀 화면 영역을 벗어날 만큼 많은 양의 데이터를 상상해보세요. 어떻게 데이터를 보고, 데이터의 변화를 파악할 수 있을까요?<br><br>\n",
        "\n",
        "  * <font color=darkblue>**데이터 분석에 대한 전문 지식이 없어도, 누구나 쉽게 데이터 인사이트를 찾을 수 있습니다.**</font> 차트의 시각적 패턴을 근거로 데이터의 추세는 어떤지, 다른 값들과 구별되는 이상값을 빠르게 찾을 수 있습니다. <br><br>\n",
        "\n",
        "  * <font color=darkblue>**요약 통계보다 정확한 데이터 분석 결과를 도출할 수 있습니다.** </font> Anscomes’ Quarter는 동일한 요약 통계(평균, 표준편차, 상관관계)를 가진 4개의 데이터 셋(set)을 산점도로 시각화하였을 때, 시각적 패턴이 명확히 다르다는 것을 입증합니다.<br><br>\n",
        "\n",
        "  * <font color=darkblue>**효과적인 데이터 인사이트 공유로 데이터 기반의 의사결정을 할 수 있습니다.**</font> 시각화 자료는 사람들의 머릿속에 빠르게 인지되고 기억될 뿐만 아니라, 메시지의 스토리텔링에 대한 공감을 더 합니다.<br><br>\n"
      ],
      "metadata": {
        "id": "Exinb93-_rCD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3 - 2 시각화에 사용할 데이터 불러오기\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "gIfN_UPN_vG_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "MgLAW3se-m_U"
      },
      "outputs": [],
      "source": [
        "# 데이터 불러오는 방법은 Stage 1 참조\n",
        "import pandas as pd\n",
        "\n",
        "train = pd.read_csv('train.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "## 3 - 3 그래프를 그리기위한 라이브러리"
      ],
      "metadata": {
        "id": "MxgU8RyB_z8C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "<font color=blue>**Matplotlib**</font>는 다양한 데이터를 많은 방법으로 도식화 할 수 있도록 하는 파이썬 라이브러리입니다.\n",
        "\n",
        "<br> "
      ],
      "metadata": {
        "id": "dTN7cX9i_6t0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# matplotlib 친구의 pyplot를 plt라고 부릅니다.\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 데이터를 도식화한 결과가 Jupyter 내부에 도출됩니다.\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "ycyB7XBl_5I0"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈 도화지를 그려냅니다.\n",
        "plt.plot()\n",
        "\n",
        "# X축과 Y축 이름을 설정합니다.\n",
        "plt.xlabel('X axis')\n",
        "plt.ylabel('Y axis')\n",
        "\n",
        "# 그래프을 제목을 설정합니다.\n",
        "plt.title('matplotlib sample')\n",
        "\n",
        "# 완성된 그래프를 도출합니다.\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "iPV2_BCUCn3S",
        "outputId": "c61568be-2baf-4ee4-ac33-98f39964e534"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "**여기까지 잘 따라오셨나요? 그렇다면 이제 본격적으로 데이터 시각화를 위한 다양한 그래프들에 대해 배워봅시다.**\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "41Ic8yjOAFdB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3 - 5 사용할 그래프 선택하기\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "sd_N2VekAIe7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2009년 Dr. Andrew Abela는 사용자의 목적과 데이터의 특징에 따른 적절한 그래프 선택 방안을 고안했습니다.\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "oSiDTudOALJ6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![chart 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      ],
      "metadata": {
        "id": "cqk8eW_EAL0w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color=darkblue>**비교** (*Comparision*)</font><br><br>\n",
        "  * 데이터의 속성과 값을 비교할 때 사용됩니다.<br>\n",
        "  * Bar Chart, Line Chart<br><br>\n",
        "\n",
        "<font color=darkblue>**분포** (*Distribution*)</font><br><br>\n",
        "  * 데이터의 분포를 확인할 때 사용됩니다.<br>\n",
        "  * Histogram, Scatter Chart<br><br>\n",
        "\n",
        "<font color=darkblue>**구성** (*Composition*)</font><br><br>\n",
        "  * 각각의 데이터가 전체 데이터 셋에 어떻게 분산되어있는 확인할 때 사용됩니다.<br>\n",
        "  * Pie Chart, Stacked Bar Chart<br><br>\n",
        "\n",
        "<font color=darkblue>**관계** (*Relatrionship*)</font><br><br>\n",
        "  * 한 데이터 값이 다른 데이터 값과 어떤 관련이 있는 지 확인할 대 사용됩니다.<br>\n",
        "  * Scatter Chart, Bubble Chart<br><br>\n"
      ],
      "metadata": {
        "id": "7m9qEHZkAND-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 비교 (Comparision)"
      ],
      "metadata": {
        "id": "hAu9vKdWCATI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "**<font color=red>막대차트 (barplot)**</font><br><br>\n",
        "  * 범주가 있는 데이터 값을 직사각형의 막대로 표현하는 그래프입니다.<br>\n",
        "  * 각각의 데이터 셋의 Feature 별 데이터를 비교하기 좋습니다.<br><br>"
      ],
      "metadata": {
        "id": "tEXHwbctCVdT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 각각의 Feature 별 최대값을 구합니다.\n",
        "max_train = pd.DataFrame(train.drop(columns=['ID']).max())\n",
        "max_train.columns = ['MAX Value']\n",
        "\n",
        "# 데이터의 일부만 사용합니다.\n",
        "max_train = max_train.iloc[:5]\n",
        "display(max_train.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KgLu_LnZEBuQ",
        "outputId": "601b36bc-0294-42f2-c6e7-cc719a868b1b"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    MAX Value\n",
              "V1   2.454930\n",
              "V2  21.467203\n",
              "V3   4.187811\n",
              "V4  16.491217\n",
              "V5  34.801666"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAX Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>V1</th>\n",
              "      <td>2.454930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V2</th>\n",
              "      <td>21.467203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V3</th>\n",
              "      <td>4.187811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V4</th>\n",
              "      <td>16.491217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V5</th>\n",
              "      <td>34.801666</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          document.querySelector('#df-a4cfb666-6941-44a5-ae77-8dfb078ca0da button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a4cfb666-6941-44a5-ae77-8dfb078ca0da');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X축과 Y축에 들어갈 각각의 값을 지정합니다.\n",
        "x = list(max_train.index)\n",
        "height = list(max_train['MAX Value'])\n",
        "\n",
        "# 막대 차트 생성\n",
        "plt.bar(\n",
        "    x=x, # X축\n",
        "    height=height, # Y축\n",
        "    color=['b', 'g', 'b', 'g', 'r'] # 막대의 색상을 지정\n",
        "    )\n",
        "\n",
        "# X축과 Y축 이름을 설정합니다.\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Max Value')\n",
        "\n",
        "# 그래프을 제목을 설정합니다.\n",
        "plt.title('Bar Chart')\n",
        "\n",
        "# 완성된 그래프를 도출합니다.\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "RatlyVTjAFoA",
        "outputId": "9d2094bb-15b7-4ba0-a61a-f48d7f7e6770"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Quiz 1\n",
        "\n",
        "  * 'V1'에서 'V30'까지의 칼럼 중 표준편차가 어디가 가장 큰 지 막대 그래프로 확인해보세요."
      ],
      "metadata": {
        "id": "Aneg6hX8j_8c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hint**\n",
        "  * 최대값 대신 표준편차를 계산하면 됩니다.\n",
        "  * plt.figure(figsize=(12,6))를 통해 그래프 사이즈를 조절할 수 있습니다.\n",
        "  * sort_values()를 활용해서 정렬해보세요.\n",
        "\n",
        "\n",
        "```\n",
        "max_train = pd.DataFrame(train.drop(columns=['ID']).max())\n",
        "max_train.columns = ['MAX Value']\n",
        "\n",
        "# 데이터의 일부만 사용합니다.\n",
        "max_train = max_train.iloc[:5]\n",
        "display(max_train.head())\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "XTACj1lokUMV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 실습\n",
        "\n",
        "train = pd.read_csv('train.csv')"
      ],
      "metadata": {
        "id": "szftETAVkNcO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Solution**\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "# 데이터 전처리\n",
        "\n",
        "# 각각의 Feature 별 최대값을 구합니다.\n",
        "std_train = pd.DataFrame(train.drop(columns=['ID']).std())\n",
        "std_train.columns = ['std Value']\n",
        "\n",
        "# 내림차순으로 정렬\n",
        "std_train.sort_values('std Value', \n",
        "                      ascending=False,\n",
        "                      inplace=True)\n",
        "\n",
        "# 데이터의 일부만 사용합니다.\n",
        "display(std_train.head())\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "# 시각화\n",
        "# X축과 Y축에 들어갈 각각의 값을 지정합니다.\n",
        "x = list(std_train.index)\n",
        "height = list(std_train['std Value'])\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "# 막대 차트 생성\n",
        "plt.bar(\n",
        "    x=x, # X축\n",
        "    height=height, # Y축\n",
        "    )\n",
        "\n",
        "# X축과 Y축 이름을 설정합니다.\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Max Value')\n",
        "\n",
        "# 그래프을 제목을 설정합니다.\n",
        "plt.title('Bar Chart')\n",
        "\n",
        "# 완성된 그래프를 도출합니다.\n",
        "plt.show()\n",
        "```\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "J6FkEwyUkp1J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "**<font color=red>선 차트 (lineplot)**</font><br><br>\n",
        "  * 연속형 데이터 값을 하나의 선으로 표현하는 그래프입니다.<br>\n",
        "  * 시간 경과에 따른 데이터 변화를 비교하기 좋습니다.<br><br>"
      ],
      "metadata": {
        "id": "snNSv7Y5HXQx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 'V1' 칼럼만 사용합니다.\n",
        "v1_train = train[['V1']]\n",
        "\n",
        "# 데이터 일부만 사용합니다.\n",
        "v1_train = v1_train.iloc[:7]\n",
        "\n",
        "display(v1_train.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "S6WYHgPaEoTO",
        "outputId": "30cff025-92c1-49d0-b519-af34b9ce8539"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         V1\n",
              "0 -1.358354\n",
              "1 -0.966272\n",
              "2 -0.425966\n",
              "3 -0.644269\n",
              "4 -0.894286"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13d416ed-2326-412e-aba8-7c4dcd1cb9d6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>V1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.358354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.966272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.425966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.644269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.894286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13d416ed-2326-412e-aba8-7c4dcd1cb9d6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-13d416ed-2326-412e-aba8-7c4dcd1cb9d6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-13d416ed-2326-412e-aba8-7c4dcd1cb9d6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X축과 Y축에 들어갈 각각의 값을 지정합니다.\n",
        "x = list(v1_train.index)\n",
        "Y = list(v1_train['V1'])\n",
        "\n",
        "# 막대 차트 생성\n",
        "plt.plot(\n",
        "    x, # X축\n",
        "    Y, # Y축\n",
        "    c='r', # 선 색상 설정\n",
        "    marker='o', # 마커 모양 설정\n",
        "    mfc='b' # 마커 색상 설정 \n",
        "    )\n",
        "\n",
        "# X축과 Y축 이름을 설정합니다.\n",
        "plt.xlabel('V1 Feature')\n",
        "plt.ylabel('V1 Value')\n",
        "\n",
        "# 그래프을 제목을 설정합니다.\n",
        "plt.title('Line Chart')\n",
        "\n",
        "# 완성된 그래프를 도출합니다.\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "CPNvPgQVIGa6",
        "outputId": "b709146a-36f1-4437-dac9-4566a6a12119"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "### 분포 (Distribution)\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "LO4Jy4AnLBjf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<font color=red>히스토그램 (Histogram)**</font><br><br>\n",
        "  * 연속형 데이터 값의 분포를 막대로 표현하는 그래프입니다.<br>\n",
        "  * 특정 데이터 셋의 Feature의 데이터 분포를 확인하기 좋습니다.<br><br>"
      ],
      "metadata": {
        "id": "4GjdO7EILLGy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 'V1' 칼럼만 사용합니다.\n",
        "v1_train = train[['V1']]\n",
        "\n",
        "display(v1_train.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XgWc9LgPIsli",
        "outputId": "1f6de0be-3984-49bf-a5d7-ff464942225b"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         V1\n",
              "0 -1.358354\n",
              "1 -0.966272\n",
              "2 -0.425966\n",
              "3 -0.644269\n",
              "4 -0.894286"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <th>V1</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>-1.358354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.966272</td>\n",
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              "    <tr>\n",
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              "      <td>-0.425966</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.644269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.894286</td>\n",
              "    </tr>\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0ceba945-8611-43cd-8483-2540837d8523 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0ceba945-8611-43cd-8483-2540837d8523');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = v1_train['V1']\n",
        "\n",
        "plt.hist(\n",
        "    x=x, # X축\n",
        "    bins=100, # 구간 개수 설정\n",
        "    color='green' # 색상 설정\n",
        "    )\n",
        "\n",
        "plt.xlim([-15, 3]) # X축 범위\n",
        "plt.ylim([0, 20000]) # y축 범위\n",
        "\n",
        "# X축과 Y축 이름을 설정합니다.\n",
        "plt.xlabel('V1 Value')\n",
        "plt.ylabel('V1 Value Count')\n",
        "\n",
        "# 그래프을 제목을 설정합니다.\n",
        "plt.title('Histogram')\n",
        "\n",
        "# 완성된 그래프를 도출합니다.\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "yMHRANtlLvBA",
        "outputId": "349e893b-6031-4d54-8120-ce5fd64e93aa"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Quiz 2\n",
        "\n",
        "  * 'V29' 칼럼을 히스토그램으로 시각해보세요.\n",
        "  * 이상치가 존재하는 지 확인해보세요."
      ],
      "metadata": {
        "id": "R50Ua5UomeTw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hint**\n",
        "\n",
        "  * 구간 개수와 그래프 범위를 조절함에 따라 그래프가 다르게 그려집니다.\n",
        "  * 즉, 이상치에 대한 주관적인 해석이 달라질 수 있습니다."
      ],
      "metadata": {
        "id": "MMylZg5SmojN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 실습\n",
        "\n",
        "train = pd.read_csv('train.csv')"
      ],
      "metadata": {
        "id": "yep83VE_m1bS"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Solution**\n",
        "\n",
        "  * 'V29' 칼럼의 값 중에서 6이상의 데이터는 극 소수일 것으로 추정됩니다.\n",
        "\n",
        "\n",
        "```\n",
        "# 전처리\n",
        "v29_train = train[['V29']]\n",
        "\n",
        "display(v29_train.head())\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "# 시각화\n",
        "x = v29_train['V29']\n",
        "\n",
        "plt.hist(\n",
        "    x=x, # X축\n",
        "    bins=500, # 구간 개수 설정\n",
        "    color='blue' # 색상 설정\n",
        "    )\n",
        "\n",
        "plt.xlim([-1, 15]) # X축 범위\n",
        "plt.ylim([0, 40000]) # y축 범위\n",
        "\n",
        "# X축과 Y축 이름을 설정합니다.\n",
        "plt.xlabel('V29 Value')\n",
        "plt.ylabel('V29 Value Count')\n",
        "\n",
        "# 그래프을 제목을 설정합니다.\n",
        "plt.title('Histogram')\n",
        "\n",
        "# 완성된 그래프를 도출합니다.\n",
        "plt.show()\n",
        "```\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "attKBiQ9m5zf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "**<font color=red>산점도 (Scatter)**</font><br><br>\n",
        "  * 연속형 데이터 값의 Feature 간 상관관계를 표현하는 그래프입니다.<br>\n",
        "  * 특정 데이터 특성 간의 데이터 분포 및 상관관계를 확인하기 좋습니다.<br><br>"
      ],
      "metadata": {
        "id": "o4bo6wnBNlzi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 'V5', 'V7' 칼럼만 사용합니다.\n",
        "v57_train = train[['V5', 'V7']]\n",
        "\n",
        "display(v57_train.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "p319bsNPL3p8",
        "outputId": "79f3b5bb-adf5-41e8-b6e2-57dcc20db654"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         V5        V7\n",
              "0 -0.503198  0.791461\n",
              "1 -0.010309  0.237609\n",
              "2  0.420987  0.476201\n",
              "3  0.948934  1.120631\n",
              "4  2.669599  0.370145"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d90e31ae-957e-4597-b08c-00e5c9e422f9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>V5</th>\n",
              "      <th>V7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.503198</td>\n",
              "      <td>0.791461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.010309</td>\n",
              "      <td>0.237609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.420987</td>\n",
              "      <td>0.476201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.948934</td>\n",
              "      <td>1.120631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.669599</td>\n",
              "      <td>0.370145</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d90e31ae-957e-4597-b08c-00e5c9e422f9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d90e31ae-957e-4597-b08c-00e5c9e422f9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d90e31ae-957e-4597-b08c-00e5c9e422f9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = v57_train['V5']\n",
        "y = v57_train['V7']\n",
        "\n",
        "plt.scatter(\n",
        "     x=x, # X축\n",
        "     y=y, # Y축\n",
        "     color='red', # 마커 색상\n",
        "     s=5 # 마커 크기\n",
        "     )\n",
        "\n",
        "# X축과 Y축 이름을 설정합니다.\n",
        "plt.xlabel('V5 Feature')\n",
        "plt.ylabel('V7 Feature')\n",
        "\n",
        "# 그래프을 제목을 설정합니다.\n",
        "plt.title('Scatter')\n",
        "\n",
        "# 완성된 그래프를 도출합니다.\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "XGuTcKK1N_Ut",
        "outputId": "98a85010-18cf-47ea-c57f-69c64ae50870"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "### 구성 (Composition)\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "iP6q0DQVPqgy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<font color=red>파이 차트 (Pie Chart)**</font><br><br>\n",
        "  * 전체에 대한 각 부분의 비율을 부채꼴 모양으로 백분율로 나타내는 그래프입니다.<br>\n",
        "  * 각 부채꼴의 중심각이 전체에서 차지하는 비율을 한눈에 볼 수 있다는 장점이 있습니다.<br><br>"
      ],
      "metadata": {
        "id": "Gbpt9caPUAn4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# V1 칼럼만 사용합니다.\n",
        "v1_train = train[['V1']]\n",
        "\n",
        "# sklearn 라이브러리의 cluster 중에서 KMeans를 불러옵니다.\n",
        "# 자세한 내용은 Stage 2를 참조해주세요.\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "cluster = KMeans(n_clusters=3)\n",
        "\n",
        "array_clusted = cluster.fit_predict(v1_train)\n",
        "\n",
        "v1_clusted = pd.DataFrame(array_clusted)\n",
        "\n",
        "# 군집에 모인 데이터의 수로 데이터프레임을 만듭니다.\n",
        "v1_value_counts = pd.DataFrame(v1_clusted[0].value_counts())\n",
        "v1_value_counts.columns = ['V1_Value_Counts']\n",
        "\n",
        "display(v1_value_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "WK04J1mMOPSR",
        "outputId": "0ba2f34d-1f52-472b-aee9-1b331ddee50e"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   V1_Value_Counts\n",
              "1            57599\n",
              "0            52758\n",
              "2             3485"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5bbf63ac-04b5-42f2-b4c1-108b9d57637a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>V1_Value_Counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>52758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3485</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5bbf63ac-04b5-42f2-b4c1-108b9d57637a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5bbf63ac-04b5-42f2-b4c1-108b9d57637a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5bbf63ac-04b5-42f2-b4c1-108b9d57637a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = v1_value_counts['V1_Value_Counts']\n",
        "labels = v1_value_counts.index\n",
        "\n",
        "plt.pie(\n",
        "    x=x,# 사용할 데이터\n",
        "    labels=labels,# 데이터의 라벨\n",
        "    startangle=70,# 시계방향으로 각도를 돌리도록 설정\n",
        "    explode=[0.1, 0.1, 0.1], # 각각의 파이가 떨어지도록 설정\n",
        "    autopct = '%.1f%%' # 파이 차트안에 퍼센트 수치 삽입\n",
        "    )\n",
        "\n",
        "# 범례 생성\n",
        "plt.legend(labels)\n",
        "\n",
        "# X축 이름을 설정합니다.\n",
        "plt.xlabel('V1 Clustering')\n",
        "\n",
        "# 그래프을 제목을 설정합니다.\n",
        "plt.title('Pie Chart')\n",
        "\n",
        "# 완성된 그래프를 도출합니다.\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "jPKJ7ZsKVDb9",
        "outputId": "274860e3-49f4-4953-81bf-4c592c0d24d7"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "<font color=red>**누적 막대 그래프 (Stacked Bar Chart)**</font><br><br>\n",
        "  * 데이터의 Feature 데이터의 비충을 나타내는 그래프입니다.<br>\n",
        "  * 다양한 데이터 Feature의 구성 비율을 한눈에 볼 수 있다는 장점이 있습니다.<br><br>"
      ],
      "metadata": {
        "id": "KppTs5lmYvK7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# V1 칼럼만 사용합니다.\n",
        "v1_train = train[['V1']]\n",
        "v2_train = train[['V2']]\n",
        "\n",
        "# sklearn 라이브러리의 cluster 중에서 KMeans를 불러옵니다.\n",
        "# 자세한 내용은 Stage 2를 참조해주세요.\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "cluster = KMeans(n_clusters=3)\n",
        "\n",
        "array_clusted_1 = cluster.fit_predict(v1_train)\n",
        "array_clusted_2 = cluster.fit_predict(v1_train)\n",
        "\n",
        "v1_clusted = pd.DataFrame(array_clusted_1)\n",
        "v2_clusted = pd.DataFrame(array_clusted_2)\n",
        "\n",
        "# 군집에 모인 데이터의 수로 데이터프레임을 만듭니다.\n",
        "v1_value_counts = pd.DataFrame(v1_clusted[0].value_counts())\n",
        "v1_value_counts.columns = ['V1_Value_Counts']\n",
        "\n",
        "v2_value_counts = pd.DataFrame(v2_clusted[0].value_counts())\n",
        "v2_value_counts.columns = ['V2_Value_Counts']\n",
        "\n",
        "\n",
        "clusted_value_counts = pd.concat([v1_value_counts,\n",
        "                                  v2_value_counts],\n",
        "                                  axis=1)\n",
        "\n",
        "display(clusted_value_counts.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "pmzGhhyOWEbI",
        "outputId": "eb5f774b-3bb0-4fe3-9577-a92fa2dca162"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   V1_Value_Counts  V2_Value_Counts\n",
              "0            57607            57599\n",
              "1            52749            52758\n",
              "2             3486             3485"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b5e92e22-353b-480a-b39a-b91b983bf8c3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>V1_Value_Counts</th>\n",
              "      <th>V2_Value_Counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>57607</td>\n",
              "      <td>57599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>52749</td>\n",
              "      <td>52758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3486</td>\n",
              "      <td>3485</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5e92e22-353b-480a-b39a-b91b983bf8c3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b5e92e22-353b-480a-b39a-b91b983bf8c3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b5e92e22-353b-480a-b39a-b91b983bf8c3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈 도화지를 생성합니다.\n",
        "# fig : 그래프가 담기게 되는 프레임 (액자)\n",
        "# ax : 실제 데이터가 도식화되는 캔퍼스 (도화지)\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# 동일한 도화지에 'V1'에 관한 막대 그래프를 그린다.\n",
        "ax.bar(clusted_value_counts.index,\n",
        "        clusted_value_counts['V1_Value_Counts'],\n",
        "        label='V1')\n",
        "\n",
        "# 동일한 도화지에 '21'에 관한 막대 그래프를 그린다.\n",
        "# 아래에 'V1' 그림이 들어가도록 한다.\n",
        "ax.bar(clusted_value_counts.index,\n",
        "       clusted_value_counts['V2_Value_Counts'],\n",
        "       bottom=clusted_value_counts['V1_Value_Counts'],\n",
        "       label='V2')\n",
        "\n",
        "# 범례 생성\n",
        "plt.legend()\n",
        "\n",
        "# X축과 Y축 이름을 설정합니다.\n",
        "plt.xlabel('V1 & V2 Clustering')\n",
        "plt.ylabel('V1 & V2 Value Count')\n",
        "\n",
        "# 그래프을 제목을 설정합니다.\n",
        "plt.title('Stacked Bar Chart')\n",
        "\n",
        "# 완성된 그래프를 도출합니다.\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "n8D3paLaZ25T",
        "outputId": "f1c26e29-4e4c-48fd-9fd2-a29c38a5a910"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "### 관계 (Relatrionship)\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "l5NviTprccfE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color=red>**버블 차트 (Bubble Chart)**</font><br><br>\n",
        "  * X축과 Y축에 버블의 크기를 더하여 3차원으로 데이터의 관계를 나타내는 그래프입니다.<br>\n",
        "  * 산점도에 비해 더 많은 정보를 한 눈에 볼 수 있다는 장점이 있습니다.<br><br>"
      ],
      "metadata": {
        "id": "nxzC5mUHdqz9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 'V1', 'V2', 'V29' 칼럼만 사용합니다.\n",
        "V1229 = train[['V1', 'V2', 'V29']]"
      ],
      "metadata": {
        "id": "rkdwCGCfbRCx"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = V1229['V1']\n",
        "y = V1229['V2']\n",
        "s = V1229['V29']\n",
        "\n",
        "plt.scatter(\n",
        "     x=x, # X축\n",
        "     y=y, # Y축\n",
        "     s=s, # 버블 / 마커 크기\n",
        "     color='green', # 마커 색상\n",
        "     alpha=0.3 # 마커 투몀도\n",
        "     )\n",
        "\n",
        "plt.xlim([-40, 0]) # X축 범위\n",
        "plt.ylim([-60, 15]) # y축 범위\n",
        "\n",
        "\n",
        "# X축과 Y축 이름을 설정합니다.\n",
        "plt.xlabel('V1 Feature')\n",
        "plt.ylabel('V2 Feature')\n",
        "\n",
        "# 그래프을 제목을 설정합니다.\n",
        "plt.title('Bubble Chart')\n",
        "\n",
        "# 완성된 그래프를 도출합니다.\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "F2OXlAGEeejG",
        "outputId": "ac92649f-6e16-40e0-8c93-c69660595ab8"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3 - 6 여러 개 그래프 그리기 & 이미지 저장하기\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "27CFoffSgFkx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# 임의의 무작위 값을 생성합니다\n",
        "x1 = np.linspace(0.0, 7.0)\n",
        "x2 = np.linspace(0.0, 3.0)\n",
        "\n",
        "y1 = np.cos(2 * np.pi * x1)\n",
        "y2 = np.cos(3 * np.pi * x2)\n",
        "\n",
        "# 한 개의 프레임에 두 개의 도화지를 그립니다.\n",
        "# 행이 두 개, 열이 1개\n",
        "# 위치는 첫 번째\n",
        "plt.subplot(2, 1, 1) # 순서대로 행 수, 열 수, 위치\n",
        "plt.plot(x1, y1, 'o-')\n",
        "plt.title('1st Graph')\n",
        "plt.ylabel('Damped oscillation')\n",
        "\n",
        "# 위치는 두 번째\n",
        "plt.subplot(2, 1, 2) # 순서대로 행 수, 열 수, 위치\n",
        "plt.plot(x2, y2, '.-')\n",
        "plt.title('2nd Graph')\n",
        "plt.xlabel('time (s)')\n",
        "plt.ylabel('Undamped')\n",
        "\n",
        "# 두 그래프 간의 여백을 설정합니다.\n",
        "plt.tight_layout(pad=5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "6B-DVf_pfJ1I",
        "outputId": "86ab55bf-2eb3-4ea2-b09b-3bc4716b4782"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "matplotlib.pyplot 모듈의 <font color=blue>**savefig() 함수**</font>를 사용해서 그래프를 이미지 파일 등으로 저장하는 방법에 대해 알아봅시다.\n",
        "\n",
        "<br>\n",
        "\n",
        "```\n",
        "# 기본사용\n",
        "plt.savefig('filename.png')\n",
        "\n",
        "# dpi (이미지 해상도) 설정하기\n",
        "plt.savefig('filename.png', dpi=100)\n",
        "\n",
        "# facecolor 설정하기 (배경색)\n",
        "plt.savefig('facecolor.png', facecolor='#eeeeee')\n",
        "\n",
        "# edgecolor 설정하기 (테두리 색)\n",
        "plt.savefig('edgecolor.png', edgecolor='#eeeeee')\n",
        "```\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "HTyRlILWiPQ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.savefig('sample_img.png', dpi=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "sRp7w5ebg-cn",
        "outputId": "a5409ed2-09a6-4d5d-def0-db0ecdb301e2"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Quiz 3 \n",
        "\n",
        "  * 원하는 그래프 두 개를 하나의 프레임에 그려보세요."
      ],
      "metadata": {
        "id": "C-mw9Pu-oN26"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hint**\n",
        "    \n",
        "\n",
        "```\n",
        "# 다음과 같이 설정한 경우 행이 하나 열이 두 개인 그래프에서 첫번째에 그래프가 그려집니다.\n",
        "plt.subplot(1, 2, 1)\n",
        "```"
      ],
      "metadata": {
        "id": "2iUlg07GoSb-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 실습\n",
        "\n",
        "train = pd.read_csv('train.csv')"
      ],
      "metadata": {
        "id": "oewKYIFBoqMB"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Solution**\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "# 전처리\n",
        "# 'V1' 칼럼만 사용합니다.\n",
        "v12_train = train[['V1', 'V2']]\n",
        "\n",
        "# 데이터 일부만 사용합니다.\n",
        "v12_train = v12_train.iloc[:7]\n",
        "\n",
        "display(v12_train.head())\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "# 시각화\n",
        "# X축과 Y축에 들어갈 각각의 값을 지정합니다.\n",
        "x1 = list(v12_train.index)\n",
        "Y1 = list(v12_train['V1'])\n",
        "\n",
        "x2 = list(v12_train.index)\n",
        "Y2 = list(v12_train['V2'])\n",
        "\n",
        "plt.subplot(1, 2, 1) # 순서대로 행 수, 열 수, 위치\n",
        "plt.plot(\n",
        "    x1, # X축\n",
        "    Y1, # Y축\n",
        "    c='r', # 선 색상 설정\n",
        "    marker='o', # 마커 모양 설정\n",
        "    mfc='b' # 마커 색상 설정 \n",
        "    )\n",
        "plt.title('1st Graph')\n",
        "plt.ylabel('Damped oscillation')\n",
        "\n",
        "# 위치는 두 번째\n",
        "plt.subplot(1, 2, 2) # 순서대로 행 수, 열 수, 위치\n",
        "plt.plot(\n",
        "    x2, # X축\n",
        "    Y2, # Y축\n",
        "    c='b', # 선 색상 설정\n",
        "    marker='o', # 마커 모양 설정\n",
        "    mfc='r' # 마커 색상 설정 \n",
        "    )\n",
        "plt.title('2nd Graph')\n",
        "plt.xlabel('time (s)')\n",
        "plt.ylabel('Undamped')\n",
        "\n",
        "# 두 그래프 간의 여백을 설정합니다.\n",
        "plt.tight_layout(pad=5)\n",
        "plt.show()\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "80dXswM8ow8s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "w0MWciW3jN7-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# END\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "CB8AnCXCjQko"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Stage 4 과정을 마무리하였습니다.\n",
        "\n",
        "이번 Stage에서 배운 <font color=darkblue>**시각화 방법으로 이상치와 데이터 패턴을 확인**</font>할 수 있을 것입니다.\n",
        "\n",
        "그리고 이를 바탕으로 적절한 전처리와 특성 공학 기법을 적용시킬 수 있을 것입니다.\n",
        "\n",
        "데이커 여러분 잘 따라오셨나요. **우리는 다음 여행지로 이동합니다.** 🏝️"
      ],
      "metadata": {
        "id": "Xjl5PzT1jUT6"
      }
    }
  ]
}