데이터셋_구축.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HASpgDxzaqgW"
      },
      "source": [
        "# 목차\n",
        "\n",
        "1. 데이터셋 구축  \n",
        "  a. 삼성전자 주가 데이터  \n",
        "  b. 보조지표  \n",
        "  c. 투자자별 매매동향  \n",
        "  d. 뉴스기사 감성분석\n",
        "2. 시각화  \n",
        "3. 모델 훈련  \n",
        "4. 성능 비교  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hMQRJLwP8_Mv"
      },
      "source": [
        "# 데이터셋 구축"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "byGC5Zt-aqgd"
      },
      "source": [
        "## 1. 삼성전자 주가 데이터\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "id": "JYpWO1fmbGmo",
        "jupyter": {
          "outputs_hidden": true
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90950044-3c47-4fe9-8648-984e9967bed7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: finance-datareader in /usr/local/lib/python3.10/dist-packages (0.9.93)\n",
            "Requirement already satisfied: pandas>=0.19.2 in /usr/local/lib/python3.10/dist-packages (from finance-datareader) (2.1.4)\n",
            "Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from finance-datareader) (2.32.3)\n",
            "Requirement already satisfied: requests-file in /usr/local/lib/python3.10/dist-packages (from finance-datareader) (2.1.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from finance-datareader) (4.9.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from finance-datareader) (4.66.5)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19.2->finance-datareader) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19.2->finance-datareader) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19.2->finance-datareader) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19.2->finance-datareader) (2024.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->finance-datareader) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->finance-datareader) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->finance-datareader) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->finance-datareader) (2024.7.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=0.19.2->finance-datareader) (1.16.0)\n",
            "Requirement already satisfied: ta in /usr/local/lib/python3.10/dist-packages (0.11.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from ta) (1.26.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from ta) (2.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->ta) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->ta) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->ta) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->ta) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install finance-datareader\n",
        "!pip install ta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "yEJ8yDGsaqge"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import FinanceDataReader as fdr\n",
        "import ta\n",
        "import tensorflow as tf\n",
        "\n",
        "# 삼성전자 주가 데이터 불러오기\n",
        "df = fdr.DataReader('005930','2014-10-01', '2024-08-28')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "nndLTHbfaqgk"
      },
      "outputs": [],
      "source": [
        "# 변동성\n",
        "df['Volatility'] = df['Close'].rolling(window=20).std()\n",
        "\n",
        "# CHANGE 변수 삭제\n",
        "df = df.drop('Change', axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0_rDzmOaqgh"
      },
      "source": [
        "## 2. 보조지표"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfOuFDgrvh2H"
      },
      "source": [
        "### 1. 볼린저밴드  \n",
        "\n",
        "**사용하는 기술 지표: %B, 밴드폭**\n",
        "- %B: 볼린저 밴드 상에서 주가가 어느 위치에 있는지를 나타낸 지표\n",
        "- 밴드폭: 볼린저밴드의 폭"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "w8yhTDkKvh2H"
      },
      "outputs": [],
      "source": [
        "# 2번째 데이터셋 정의\n",
        "df2 = df.copy().drop(columns=['Open','High','Low','Close','Volume','Volatility'])\n",
        "\n",
        "# 볼린저밴드\n",
        "bollinger_band = ta.volatility.BollingerBands(close=df['Close'], window=20, fillna=False)\n",
        "\n",
        "df2['PB'] = bollinger_band.bollinger_pband()        # %B\n",
        "df2['BandWidth'] = bollinger_band.bollinger_wband() # 밴드폭"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QE5MXBIMvh2I"
      },
      "source": [
        "### 2. DMI, ADX  \n",
        "\n",
        "주가가 어떤 방향의 추세로 움직이고 있는지를 판단하고 그 추세의 강도를 측정하기 위해 사용되는 지표  \n",
        "**사용하는 기술 지표: +DI, -DI, ADX**\n",
        "- +DI: 주가의 상승 강도\n",
        "- -DI: 주가의 하락 강도\n",
        "- ADX: 주가의 변동 추세의 강도\n",
        "\n",
        "#### 계산식  \n",
        "- +DM: (당일 고가>전일 고가)이면서 (고가의 변동폭>저가의 변동폭)일 경우, (당일 고가-전일 고가)의 절댓값, 이외에는 0\n",
        "- -DM: (당일 저가<전일 저가)이면서 (고가의 변동폭<저가의 변동폭)일 경우, (당일 저가-전일 저가)의 절댓값, 이외에는 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "FaeC8dTvvh2J"
      },
      "outputs": [],
      "source": [
        "# pdiff = 당일 고가-전일 고가, mdiff = 당일 저가-전일 저가\n",
        "high_diff, low_diff = df['High'].diff(1), df['Low'].diff(1)\n",
        "\n",
        "plus_dm = pd.Series(np.where((high_diff > 0) & (high_diff > -low_diff), high_diff, 0), index=df.index)   # +DM\n",
        "minus_dm = pd.Series(np.where((-low_diff > 0) & (-low_diff > high_diff), -low_diff, 0), index=df.index)   # -DM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAE8485-vh2J"
      },
      "source": [
        "- TR = max{(당일 고가-당일 저가), (당일 고가-전일 종가)의 절댓값, (당일 저가-전일 종가)의 절댓값}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "id": "Rs3vXUSlvh2J"
      },
      "outputs": [],
      "source": [
        "# 최댓값을 비교하기 위해 각각의 값을 계산하여 입력하기 위한 데이터프레임 정의\n",
        "tr_df = pd.DataFrame()\n",
        "\n",
        "# 각 조건에 해당하는 값을 계산하여 입력\n",
        "tr_df['Condition 1'] = df['High'] - df['Low']\n",
        "tr_df['Condition 2'] = (df['High'] - df['Close'].shift(-1)).abs()\n",
        "tr_df['Condition 3'] = (df['Low'] - df['Close'].shift(-1)).abs()\n",
        "\n",
        "# 각 행으로부터 최댓값 산출하여 TR을 series로 반환\n",
        "tr = tr_df.max(axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "![dmi.PNG](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "w-8N_FjGz0Zn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "R056tog8vh2K"
      },
      "outputs": [],
      "source": [
        "# +DM_sum, -DM_sum, TR_sum을 계산하는 함수 정의\n",
        "def sum_index(df,series):\n",
        "    # 빈 시리즈 정의\n",
        "    sum = pd.Series(np.nan, index=df.index)\n",
        "    # 14일 동안의 DM 또는 TR 합을 계산하여 초항으로 입력\n",
        "    sum.iloc[13] = series[0:14].sum()\n",
        "    for i in range(14,len(df)):\n",
        "        # 위의 계산식에 따라 DM_sum 또는 TR_sum을 계산하여 입력\n",
        "        sum.iloc[i] = sum.iloc[i-1] - sum.iloc[i-1]/14 + series.iloc[i]\n",
        "    return sum\n",
        "\n",
        "# +DM_sum, -DM_sum, TR_sum\n",
        "plus_dm_sum, minus_dm_sum, tr_sum = sum_index(df,plus_dm), sum_index(df,minus_dm), sum_index(df,tr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8q-RaiAxvh2L"
      },
      "source": [
        "- +DI = +DMN/TRN*100\n",
        "- -DI = -DMN/TRN*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "vKrKRGNkvh2L"
      },
      "outputs": [],
      "source": [
        "# +DI, -DI를 데이터프레임에 추가\n",
        "df2['PDI'], df2['MDI'] = plus_dm_sum/tr_sum*100, minus_dm_sum/tr_sum*100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6KnMO2jlvh2L"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "-oDaH669vh2L"
      },
      "outputs": [],
      "source": [
        "# 위의 계산식에 따라 DX 계산\n",
        "dx = ((df2['PDI']-df2['MDI']).abs()) / (df2['PDI']+df2['MDI']) * 100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z7UGJxo1vh2M"
      },
      "source": [
        "![adx.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "id": "jKjYrx4_aqgi"
      },
      "outputs": [],
      "source": [
        "# ADX를 입력하기 위한 빈 series 선언\n",
        "adx = pd.Series(np.nan, index=df.index)\n",
        "\n",
        "# 14일 동안의 DX의 평균을 계산하여 ADX의 초항으로 입력\n",
        "adx.iloc[26] = dx[13:27].mean()\n",
        "\n",
        "# 위의 계산식에 따라 ADX 계산\n",
        "for i in range(27,len(df2)):\n",
        "    adx.iloc[i] = (adx.iloc[i-1]*13 + dx.iloc[i])/14\n",
        "\n",
        "# ADX를 데이터프레임에 추가\n",
        "df2['ADX'] = adx"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaevfF7Kvh2M"
      },
      "source": [
        "### 3. WMA (이동평균선)  \n",
        "\n",
        "- WMA_short: 단기 이동평균선 (5일)\n",
        "- WMA_long: 장기 이동평균선 (20일)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "id": "uNSntZFFvh2M"
      },
      "outputs": [],
      "source": [
        "df2['WMA_short'] = ta.trend.WMAIndicator(close=df['Close'], window=5, fillna=False).wma()\n",
        "df2['WMA_long'] = ta.trend.WMAIndicator(close=df['Close'], window=20, fillna=False).wma()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FxPaT14uvh2N"
      },
      "source": [
        "### 4. MACD  \n",
        "\n",
        "- MACD: 주가 흐름의 추세를 확인하고 추세의 강도와 지속성을 파악하는데 활용되는 지표\n",
        "- MACD_sig: MACD의 지수이동평균 (9일)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "d0ajwBI_vh2N"
      },
      "outputs": [],
      "source": [
        "df2['MACD'] = ta.trend.macd(close=df['Close'], window_slow=26, window_fast=12, fillna=False)\n",
        "df2['MACD_sig'] = ta.trend.macd_signal(close=df['Close'], window_slow=26, window_fast=12, window_sign=9, fillna=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyofxyEMvh2N"
      },
      "source": [
        "### 5. RSI  \n",
        "\n",
        "- RSI: 주가의 평균 상승폭과 하락폭을 비교하여 가격의 상승 압력과 하락 압력 간 상대적인 강도를 나타내는 기술적 지표\n",
        "- RSI_sig: RSI의 단순이동평균(9일)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "4xSYm97evh2N"
      },
      "outputs": [],
      "source": [
        "df2['RSI'] = ta.momentum.rsi(close=df['Close'], window=14, fillna=False)\n",
        "df2['RSI_sig'] = df2['RSI'].rolling(9).mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzuuKwatvh2N"
      },
      "source": [
        "### 6. Stochastic Fast  \n",
        "\n",
        "일정 기간 동안 최고가와 최저가의 범위 중 현재 가격의 위치를 백분율로 나타내는 지표  \n",
        "- %K: 최고가와 최저가의 범위 중 현재 가격의 위치를 백분율로 나타낸 값\n",
        "- %D: %K의 이동평균"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "R89AsG6dvh2N"
      },
      "outputs": [],
      "source": [
        "df2['%K'] = ta.momentum.stoch(high=df['High'], low=df['Low'], close=df['Close'], window=14, smooth_window=3, fillna=False)\n",
        "df2['%D'] = ta.momentum.stoch_signal(high=df['High'], low=df['Low'], close=df['Close'], window=14, smooth_window=3, fillna=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2015년 1월 2일부터 시작하도록 데이터프레임 자르기\n",
        "df = df[62:]\n",
        "\n",
        "# 데이터 확인\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
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        "id": "okM5EPd04P9y",
        "outputId": "01710b52-f8ca-4083-cb59-30c40559881b"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Open   High    Low  Close    Volume   Volatility\n",
              "Date                                                         \n",
              "2024-08-22  78700  78900  77800  78300   8149101  3478.898797\n",
              "2024-08-23  77700  78400  77500  77700   9420306  3432.119953\n",
              "2024-08-26  78100  78200  76000  76100  15655938  3374.591593\n",
              "2024-08-27  75700  76500  75600  75800  11130145  3286.699672\n",
              "2024-08-28  75800  76400  75400  76400   9671798  3175.809056"
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              "      <th>2024-08-22</th>\n",
              "      <td>78700</td>\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-08-22 00:00:00\",\n        \"max\": \"2024-08-28 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-08-23 00:00:00\",\n          \"2024-08-28 00:00:00\",\n          \"2024-08-26 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1371,\n        \"min\": 75700,\n        \"max\": 78700,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          77700,\n          75800,\n          78100\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1151,\n        \"min\": 76400,\n        \"max\": 78900,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          78400,\n          76400,\n          78200\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1112,\n        \"min\": 75400,\n        \"max\": 77800,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          77500,\n          75400,\n          76000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1083,\n        \"min\": 75800,\n        \"max\": 78300,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          77700,\n          76400,\n          76100\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2910869,\n        \"min\": 8149101,\n        \"max\": 15655938,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9420306,\n          9671798,\n          15655938\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volatility\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120.73943224853227,\n        \"min\": 3175.809055641512,\n        \"max\": 3478.8987973358976,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3432.119952510539,\n          3175.809055641512,\n          3374.5915932231746\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2015년 1월 2일부터 시작하도록 데이터프레임 자르기\n",
        "df2 = df2[62:]\n",
        "\n",
        "# 데이터 확인\n",
        "df2.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "4SkV63sB43mm",
        "outputId": "3bab5534-dec4-43ed-cb62-0504b3d5e81c"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  PB  BandWidth        PDI        MDI        ADX  \\\n",
              "Date                                                               \n",
              "2024-08-22  0.523593  17.393235  23.690890  26.885275  17.296170   \n",
              "2024-08-23  0.489164  17.189115  22.105095  25.958748  16.633428   \n",
              "2024-08-26  0.385609  16.953263  20.558670  28.515095  16.603411   \n",
              "2024-08-27  0.380208  16.569360  19.993801  28.952769  16.724851   \n",
              "2024-08-28  0.443061  16.058081  19.357385  28.667796  16.914970   \n",
              "\n",
              "               WMA_short      WMA_long         MACD     MACD_sig        RSI  \\\n",
              "Date                                                                          \n",
              "2024-08-22  78546.666667  77393.809524 -1000.619600 -1378.437514  48.042565   \n",
              "2024-08-23  78180.000000  77367.142857  -946.960787 -1292.142169  46.517586   \n",
              "2024-08-26  77446.666667  77200.952381 -1021.764158 -1238.066567  42.631423   \n",
              "2024-08-27  76760.000000  77029.047619 -1092.658375 -1208.984928  41.924203   \n",
              "2024-08-28  76480.000000  76940.000000 -1087.887083 -1184.765359  43.927696   \n",
              "\n",
              "              RSI_sig         %K         %D  \n",
              "Date                                         \n",
              "2024-08-22  45.810153  72.321429  59.327269  \n",
              "2024-08-23  46.705418  75.000000  66.088275  \n",
              "2024-08-26  46.931899  48.101266  65.140898  \n",
              "2024-08-27  46.900053  42.857143  55.319470  \n",
              "2024-08-28  46.763210  50.649351  47.202586  "
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              "\n",
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              "      <th>2024-08-22</th>\n",
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              "      <td>66.088275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-26</th>\n",
              "      <td>0.385609</td>\n",
              "      <td>16.953263</td>\n",
              "      <td>20.558670</td>\n",
              "      <td>28.515095</td>\n",
              "      <td>16.603411</td>\n",
              "      <td>77446.666667</td>\n",
              "      <td>77200.952381</td>\n",
              "      <td>-1021.764158</td>\n",
              "      <td>-1238.066567</td>\n",
              "      <td>42.631423</td>\n",
              "      <td>46.931899</td>\n",
              "      <td>48.101266</td>\n",
              "      <td>65.140898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-27</th>\n",
              "      <td>0.380208</td>\n",
              "      <td>16.569360</td>\n",
              "      <td>19.993801</td>\n",
              "      <td>28.952769</td>\n",
              "      <td>16.724851</td>\n",
              "      <td>76760.000000</td>\n",
              "      <td>77029.047619</td>\n",
              "      <td>-1092.658375</td>\n",
              "      <td>-1208.984928</td>\n",
              "      <td>41.924203</td>\n",
              "      <td>46.900053</td>\n",
              "      <td>42.857143</td>\n",
              "      <td>55.319470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-28</th>\n",
              "      <td>0.443061</td>\n",
              "      <td>16.058081</td>\n",
              "      <td>19.357385</td>\n",
              "      <td>28.667796</td>\n",
              "      <td>16.914970</td>\n",
              "      <td>76480.000000</td>\n",
              "      <td>76940.000000</td>\n",
              "      <td>-1087.887083</td>\n",
              "      <td>-1184.765359</td>\n",
              "      <td>43.927696</td>\n",
              "      <td>46.763210</td>\n",
              "      <td>50.649351</td>\n",
              "      <td>47.202586</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a9c5c50-d410-4ed4-9316-8cabd2c55065')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-9a9c5c50-d410-4ed4-9316-8cabd2c55065 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-9a9c5c50-d410-4ed4-9316-8cabd2c55065');\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",
              "\n",
              "\n",
              "<div id=\"df-723dcacb-0202-4eda-9688-66c4cd82c473\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-723dcacb-0202-4eda-9688-66c4cd82c473')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-723dcacb-0202-4eda-9688-66c4cd82c473 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-08-22 00:00:00\",\n        \"max\": \"2024-08-28 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-08-23 00:00:00\",\n          \"2024-08-28 00:00:00\",\n          \"2024-08-26 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PB\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06295655179369866,\n        \"min\": 0.38020834519390867,\n        \"max\": 0.5235931750097665,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.4891636318725161,\n          0.4430605929428333,\n          0.38560857090166123\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BandWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5303654216094714,\n        \"min\": 16.058080665959302,\n        \"max\": 17.39323473918247,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          17.189115469887984,\n          16.058080665959302,\n          16.953263453059744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PDI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.751276052635512,\n        \"min\": 19.35738486122563,\n        \"max\": 23.690889648685282,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          22.105094877961196,\n          19.35738486122563,\n          20.558670369680566\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MDI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3057712251681688,\n        \"min\": 25.95874807122685,\n        \"max\": 28.95276876509002,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          25.95874807122685,\n          28.66779590925112,\n          28.51509482557852\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ADX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2852497308392185,\n        \"min\": 16.603411215394136,\n        \"max\": 17.296170121680127,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          16.633427971031278,\n          16.914969968450592,\n          16.603411215394136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WMA_short\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 887.0249902530007,\n        \"min\": 76480.0,\n        \"max\": 78546.66666666667,\n        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\"\"\n      }\n    },\n    {\n      \"column\": \"RSI_sig\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.46350437777637044,\n        \"min\": 45.81015320084951,\n        \"max\": 46.93189926834938,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          46.70541795424831,\n          46.763210113201886,\n          46.93189926834938\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"%K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.791865122868055,\n        \"min\": 42.857142857142854,\n        \"max\": 75.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          75.0,\n          50.64935064935065,\n          48.10126582278481\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"%D\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.7467446854850035,\n        \"min\": 47.20258644309277,\n        \"max\": 66.08827493261455,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          66.08827493261455,\n          47.20258644309277,\n          65.14089813140446\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0doo3IWXk9S"
      },
      "source": [
        "## 3. 투자자별 매매동향"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "id": "C_ap9YqVSxkt"
      },
      "outputs": [],
      "source": [
        "# 3번째 데이터셋 정의\n",
        "df3 = pd.read_csv('/content/sample_data/투자자별 매매동향(삼성전자).csv')\n",
        "\n",
        "# 과거부터 현재 순서로 정렬해주기\n",
        "df3 = df3.iloc[::-1].reset_index(drop=True)\n",
        "\n",
        "# '기관 순매매량', '외국인 순매매량', '외국인 보유주수', '외국인 보유율' 열만 가져오기\n",
        "df3 = pd.concat([df3.iloc[:len(df3)-1,0],df3.iloc[:len(df3)-1,5:]],axis=1)\n",
        "df3.columns = ['날짜','기관 순매매량', '외국인 순매매량', '외국인 보유주수', '외국인 보유율']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2015년 1월 2일의 인덱스 확인\n",
        "df3[df3['날짜'] == '2015.01.02']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "zEFUyYBl0f4j",
        "outputId": "3cfaf71a-d7f3-4f77-8c6d-e16dcf6dce91"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              날짜  기관 순매매량 외국인 순매매량    외국인 보유주수 외국인 보유율\n",
              "2482  2015.01.02  -20,583   21,328  76,336,351  51.82%"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-798f9615-ceb8-409e-98e0-2ff137b2658e\" 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>날짜</th>\n",
              "      <th>기관 순매매량</th>\n",
              "      <th>외국인 순매매량</th>\n",
              "      <th>외국인 보유주수</th>\n",
              "      <th>외국인 보유율</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2482</th>\n",
              "      <td>2015.01.02</td>\n",
              "      <td>-20,583</td>\n",
              "      <td>21,328</td>\n",
              "      <td>76,336,351</td>\n",
              "      <td>51.82%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-798f9615-ceb8-409e-98e0-2ff137b2658e')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-798f9615-ceb8-409e-98e0-2ff137b2658e 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-798f9615-ceb8-409e-98e0-2ff137b2658e');\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",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2015년 1월 2일부터 시작하도록 데이터프레임 자르기\n",
        "df3 = df3[2482:].drop(columns=['날짜'])\n",
        "\n",
        "# 날짜 인덱스 가져오기\n",
        "df3.index = fdr.DataReader('005930','2015-01-02', '2024-08-28').index"
      ],
      "metadata": {
        "id": "4X8KEC221u5S"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 각 셀의 데이터 타입을 숫자로 변환\n",
        "df3['기관 순매매량'] = df3['기관 순매매량'].apply(lambda x: int(x.replace(\",\", \"\")))\n",
        "df3['외국인 순매매량'] = df3['외국인 순매매량'].apply(lambda x: int(x.replace(\",\", \"\")))\n",
        "df3['외국인 보유주수'] = df3['외국인 보유주수'].apply(lambda x: int(x.replace(\",\", \"\")))\n",
        "df3['외국인 보유율'] = df3['외국인 보유율'].str.rstrip('%').astype(float) / 100.0"
      ],
      "metadata": {
        "id": "2CPWDkwA0dK9"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 확인\n",
        "df3.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "PtsbdMnu48_m",
        "outputId": "7e451f28-ae17-4937-88d9-5800df089c3b"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            기관 순매매량  외국인 순매매량    외국인 보유주수  외국인 보유율\n",
              "Date                                              \n",
              "2024-08-22  -452139    310180  3358830711   0.5626\n",
              "2024-08-23   748590  -1392121  3357636890   0.5624\n",
              "2024-08-26   584334  -3737763  3354085777   0.5618\n",
              "2024-08-27   304677  -1446782  3352910295   0.5616\n",
              "2024-08-28   533263  -1470392  3351439903   0.5614"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c3b20892-1453-41c6-8d6b-191a13b0a2c1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>기관 순매매량</th>\n",
              "      <th>외국인 순매매량</th>\n",
              "      <th>외국인 보유주수</th>\n",
              "      <th>외국인 보유율</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-08-22</th>\n",
              "      <td>-452139</td>\n",
              "      <td>310180</td>\n",
              "      <td>3358830711</td>\n",
              "      <td>0.5626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-23</th>\n",
              "      <td>748590</td>\n",
              "      <td>-1392121</td>\n",
              "      <td>3357636890</td>\n",
              "      <td>0.5624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-26</th>\n",
              "      <td>584334</td>\n",
              "      <td>-3737763</td>\n",
              "      <td>3354085777</td>\n",
              "      <td>0.5618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-27</th>\n",
              "      <td>304677</td>\n",
              "      <td>-1446782</td>\n",
              "      <td>3352910295</td>\n",
              "      <td>0.5616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-28</th>\n",
              "      <td>533263</td>\n",
              "      <td>-1470392</td>\n",
              "      <td>3351439903</td>\n",
              "      <td>0.5614</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3b20892-1453-41c6-8d6b-191a13b0a2c1')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c3b20892-1453-41c6-8d6b-191a13b0a2c1 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-c3b20892-1453-41c6-8d6b-191a13b0a2c1');\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",
              "\n",
              "\n",
              "<div id=\"df-08d1728e-80b6-4b18-a64b-1928e71ac7b9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-08d1728e-80b6-4b18-a64b-1928e71ac7b9')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-08d1728e-80b6-4b18-a64b-1928e71ac7b9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df3\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-08-22 00:00:00\",\n        \"max\": \"2024-08-28 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-08-23 00:00:00\",\n          \"2024-08-28 00:00:00\",\n          \"2024-08-26 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uae30\\uad00 \\uc21c\\ub9e4\\ub9e4\\ub7c9\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 472402,\n        \"min\": -452139,\n        \"max\": 748590,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          748590,\n          533263,\n          584334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc678\\uad6d\\uc778 \\uc21c\\ub9e4\\ub9e4\\ub7c9\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1439484,\n        \"min\": -3737763,\n        \"max\": 310180,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -1392121,\n          -1470392,\n          -3737763\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc678\\uad6d\\uc778 \\ubcf4\\uc720\\uc8fc\\uc218\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3142556,\n        \"min\": 3351439903,\n        \"max\": 3358830711,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3357636890,\n          3351439903,\n          3354085777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc678\\uad6d\\uc778 \\ubcf4\\uc720\\uc728\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005176871642217923,\n        \"min\": 0.5614,\n        \"max\": 0.5626,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5624,\n          0.5614,\n          0.5618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3wyEU0eiXpdt"
      },
      "source": [
        "## 4. 뉴스기사 감성분석\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "xITpk68Laqgt"
      },
      "outputs": [],
      "source": [
        "# 4번째 데이터셋 정의\n",
        "df4 = pd.read_csv('/content/sample_data/new_sentiment_data.csv').drop(columns=['adjusted_date'])\n",
        "\n",
        "# 날짜 인덱스 가져오기\n",
        "df4.index = fdr.DataReader('005930','2015-01-02', '2024-08-28').index"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 확인\n",
        "df4.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "9OGKL6-P4_Hy",
        "outputId": "ffbb9233-b5d6-4484-c58e-2115dab80a1d"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             neutral  positive  negative\n",
              "Date                                    \n",
              "2024-08-22  0.250000  0.750000  0.000000\n",
              "2024-08-23  0.000000  0.833333  0.166667\n",
              "2024-08-26  0.000000  0.833333  0.166667\n",
              "2024-08-27  0.375000  0.500000  0.125000\n",
              "2024-08-28  0.166667  0.333333  0.500000"
            ],
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              "      <th>neutral</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-08-22</th>\n",
              "      <td>0.250000</td>\n",
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              "      <td>0.000000</td>\n",
              "    </tr>\n",
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              "      <th>2024-08-23</th>\n",
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              "      <td>0.166667</td>\n",
              "    </tr>\n",
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              "      <th>2024-08-27</th>\n",
              "      <td>0.375000</td>\n",
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              "      <td>0.125000</td>\n",
              "    </tr>\n",
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              "      <th>2024-08-28</th>\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-08-22 00:00:00\",\n        \"max\": \"2024-08-28 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-08-23 00:00:00\",\n          \"2024-08-28 00:00:00\",\n          \"2024-08-26 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"neutral\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16244657241348273,\n        \"min\": 0.0,\n        \"max\": 0.375,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.1666666666666666,\n          0.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"positive\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22360679774997902,\n        \"min\": 0.3333333333333333,\n        \"max\": 0.8333333333333334,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8333333333333334,\n          0.3333333333333333,\n          0.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"negative\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18540496217739158,\n        \"min\": 0.0,\n        \"max\": 0.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.1666666666666666,\n          0.5,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
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    "kernelspec": {
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