netflix_stock_price_prediction.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn import metrics\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ]
    },
    {
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      "source": [
        "df = pd.read_csv('/content/NFLX.csv')\n",
        "df.head()\n"
      ],
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              "         Date        Open        High         Low       Close   Adj Close  \\\n",
              "0  2018-02-05  262.000000  267.899994  250.029999  254.259995  254.259995   \n",
              "1  2018-02-06  247.699997  266.700012  245.000000  265.720001  265.720001   \n",
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              "3  2018-02-08  267.079987  267.619995  250.000000  250.100006  250.100006   \n",
              "4  2018-02-09  253.850006  255.800003  236.110001  249.470001  249.470001   \n",
              "\n",
              "     Volume  \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1009,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1009,\n        \"samples\": [\n          \"2020-08-04\",\n          \"2020-08-07\",\n          \"2021-01-14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.53753170401454,\n        \"min\": 233.919998,\n        \"max\": 692.349976,\n        \"num_unique_values\": 976,\n        \"samples\": [\n          254.630005,\n          427.559998,\n          339.570007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109.26295957119457,\n        \"min\": 250.649994,\n        \"max\": 700.98999,\n        \"num_unique_values\": 983,\n        \"samples\": [\n          511.76001,\n          494.850006,\n          496.660004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 107.55586739006051,\n        \"min\": 231.229996,\n        \"max\": 686.090027,\n        \"num_unique_values\": 989,\n        \"samples\": [\n          266.480011,\n          349.290009,\n          384.899994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.28999877034998,\n        \"min\": 233.880005,\n        \"max\": 691.690002,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          265.320007,\n          345.559998,\n          380.070007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.28999877034998,\n        \"min\": 233.880005,\n        \"max\": 691.690002,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          265.320007,\n          345.559998,\n          380.070007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5465535,\n        \"min\": 1144000,\n        \"max\": 58904300,\n        \"num_unique_values\": 1005,\n        \"samples\": [\n          2671700,\n          3725900,\n          4927900\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "cell_type": "code",
      "source": [
        "\n",
        "#This line of code converts the 'Date' column in the tesla dataframe to datetime objects. This is useful for time series analysis and plotting.\n",
        "df['Date'] = pd.to_datetime(df['Date'])"
      ],
      "metadata": {
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      },
      "execution_count": 6,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "print(f'the first date in the dataset is {df[\"Date\"].min()}')\n",
        "print(f'the last date in the dataset is {df[\"Date\"].max()}')"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "BIC9Z7oFmbYB",
        "outputId": "29208d6e-d6c7-467b-c36e-d218cd1dd833"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the first date in the dataset is 2018-02-05 00:00:00\n",
            "the last date in the dataset is 2022-02-04 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"the total number of days in the dataset is {df['Date'].max() - df['Date'].min()}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T389OvbUmuAy",
        "outputId": "340c613b-9721-4c99-9dca-10389488db91"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the total number of days in the dataset is 1460 days 00:00:00\n"
          ]
        }
      ]
    },
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        "df.describe()"
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              "                                Date         Open         High          Low  \\\n",
              "count                           1009  1009.000000  1009.000000  1009.000000   \n",
              "mean   2020-02-05 07:30:58.870168320   419.059673   425.320703   412.374044   \n",
              "min              2018-02-05 00:00:00   233.919998   250.649994   231.229996   \n",
              "25%              2019-02-06 00:00:00   331.489990   336.299988   326.000000   \n",
              "50%              2020-02-06 00:00:00   377.769989   383.010010   370.880005   \n",
              "75%              2021-02-05 00:00:00   509.130005   515.630005   502.529999   \n",
              "max              2022-02-04 00:00:00   692.349976   700.989990   686.090027   \n",
              "std                              NaN   108.537532   109.262960   107.555867   \n",
              "\n",
              "             Close    Adj Close        Volume  \n",
              "count  1009.000000  1009.000000  1.009000e+03  \n",
              "mean    419.000733   419.000733  7.570685e+06  \n",
              "min     233.880005   233.880005  1.144000e+06  \n",
              "25%     331.619995   331.619995  4.091900e+06  \n",
              "50%     378.670013   378.670013  5.934500e+06  \n",
              "75%     509.079987   509.079987  9.322400e+06  \n",
              "max     691.690002   691.690002  5.890430e+07  \n",
              "std     108.289999   108.289999  5.465535e+06  "
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              "  <div id=\"df-bd29b995-ed32-4181-b4ba-df663422d96a\" class=\"colab-df-container\">\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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1009</td>\n",
              "      <td>1009.000000</td>\n",
              "      <td>1009.000000</td>\n",
              "      <td>1009.000000</td>\n",
              "      <td>1009.000000</td>\n",
              "      <td>1009.000000</td>\n",
              "      <td>1.009000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2020-02-05 07:30:58.870168320</td>\n",
              "      <td>419.059673</td>\n",
              "      <td>425.320703</td>\n",
              "      <td>412.374044</td>\n",
              "      <td>419.000733</td>\n",
              "      <td>419.000733</td>\n",
              "      <td>7.570685e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2018-02-05 00:00:00</td>\n",
              "      <td>233.919998</td>\n",
              "      <td>250.649994</td>\n",
              "      <td>231.229996</td>\n",
              "      <td>233.880005</td>\n",
              "      <td>233.880005</td>\n",
              "      <td>1.144000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2019-02-06 00:00:00</td>\n",
              "      <td>331.489990</td>\n",
              "      <td>336.299988</td>\n",
              "      <td>326.000000</td>\n",
              "      <td>331.619995</td>\n",
              "      <td>331.619995</td>\n",
              "      <td>4.091900e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2020-02-06 00:00:00</td>\n",
              "      <td>377.769989</td>\n",
              "      <td>383.010010</td>\n",
              "      <td>370.880005</td>\n",
              "      <td>378.670013</td>\n",
              "      <td>378.670013</td>\n",
              "      <td>5.934500e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2021-02-05 00:00:00</td>\n",
              "      <td>509.130005</td>\n",
              "      <td>515.630005</td>\n",
              "      <td>502.529999</td>\n",
              "      <td>509.079987</td>\n",
              "      <td>509.079987</td>\n",
              "      <td>9.322400e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2022-02-04 00:00:00</td>\n",
              "      <td>692.349976</td>\n",
              "      <td>700.989990</td>\n",
              "      <td>686.090027</td>\n",
              "      <td>691.690002</td>\n",
              "      <td>691.690002</td>\n",
              "      <td>5.890430e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>108.537532</td>\n",
              "      <td>109.262960</td>\n",
              "      <td>107.555867</td>\n",
              "      <td>108.289999</td>\n",
              "      <td>108.289999</td>\n",
              "      <td>5.465535e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "        document.querySelector('#df-bd29b995-ed32-4181-b4ba-df663422d96a button.colab-df-convert');\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "  }\n",
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              "\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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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000001009\",\n        \"max\": \"2022-02-04 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"1009\",\n          \"2020-02-05 07:30:58.870168320\",\n          \"2021-02-05 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 282.1832770360729,\n        \"min\": 108.53753170401454,\n        \"max\": 1009.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          419.05967286223984,\n          509.130005,\n          1009.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 280.6963345154866,\n        \"min\": 109.26295957119457,\n        \"max\": 1009.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          425.3207030802775,\n          515.630005,\n          1009.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 282.56434986365855,\n        \"min\": 107.55586739006051,\n        \"max\": 1009.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          412.37404380178396,\n          502.529999,\n          1009.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 282.10864732949005,\n        \"min\": 108.28999877034998,\n        \"max\": 1009.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          419.0007329207136,\n          509.079987,\n          1009.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 282.10864732949005,\n        \"min\": 108.28999877034998,\n        \"max\": 1009.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          419.0007329207136,\n          509.079987,\n          1009.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19379918.05636072,\n        \"min\": 1009.0,\n        \"max\": 58904300.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7570685.03468781,\n          9322400.0,\n          1009.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMo1SxQ-ngor",
        "outputId": "28cd83c9-17dd-428a-8c41-647b16a05aa6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1009 entries, 0 to 1008\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype         \n",
            "---  ------     --------------  -----         \n",
            " 0   Date       1009 non-null   datetime64[ns]\n",
            " 1   Open       1009 non-null   float64       \n",
            " 2   High       1009 non-null   float64       \n",
            " 3   Low        1009 non-null   float64       \n",
            " 4   Close      1009 non-null   float64       \n",
            " 5   Adj Close  1009 non-null   float64       \n",
            " 6   Volume     1009 non-null   int64         \n",
            "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
            "memory usage: 55.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-q3QPXADnkBX",
        "outputId": "f0d09f9a-2677-4261-90c2-d4e0502e58fe"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1009, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.plot(df['Close'])\n",
        "plt.title('netflix Close price.', fontsize=15)\n",
        "plt.ylabel('Price in dollars.')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "vL5UaurJnqx7",
        "outputId": "e978500c-a6d5-4d52-e672-1c9eda070723"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for null values\n",
        "df.isnull().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64X7GvGToZuF",
        "outputId": "a7acfb73-d4cb-42b3-ffba-729f2721dcce"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date         0\n",
              "Open         0\n",
              "High         0\n",
              "Low          0\n",
              "Close        0\n",
              "Adj Close    0\n",
              "Volume       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = ['Open', 'High', 'Low', 'Adj Close', 'Volume']\n",
        "plt.subplots(figsize=(20,10))\n",
        "for i, col in enumerate(features):\n",
        "    plt.subplot(2,3,i+1)\n",
        "    sb.distplot(df[col])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "zq_Gjjzkpa2Q",
        "outputId": "eaeeac7e-5444-450a-b80d-89a7e8397d82"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(20,10))\n",
        "for i, col in enumerate(features):\n",
        "  plt.subplot(2,3,i+1)\n",
        "  sb.boxplot(df[col])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "0dVnn9efr_Nv",
        "outputId": "cb2de329-ae25-409d-cd28-2a9750275fad"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "XQ8BAAVFt66y",
        "outputId": "39aabeba-46c6-4ca1-b328-65feabbfd724"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date        Open        High         Low       Close   Adj Close  \\\n",
              "1004 2022-01-31  401.970001  427.700012  398.200012  427.140015  427.140015   \n",
              "1005 2022-02-01  432.959991  458.480011  425.540009  457.130005  457.130005   \n",
              "1006 2022-02-02  448.250000  451.980011  426.480011  429.480011  429.480011   \n",
              "1007 2022-02-03  421.440002  429.260010  404.279999  405.600006  405.600006   \n",
              "1008 2022-02-04  407.309998  412.769989  396.640015  410.170013  410.170013   \n",
              "\n",
              "        Volume  \n",
              "1004  20047500  \n",
              "1005  22542300  \n",
              "1006  14346000  \n",
              "1007   9905200  \n",
              "1008   7782400  "
            ],
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              "      <td>2022-02-04</td>\n",
              "      <td>407.309998</td>\n",
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              "    .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",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-976e42a0-66ed-4e53-b8bc-ee2b75188c7a 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-976e42a0-66ed-4e53-b8bc-ee2b75188c7a');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dffc2419-fef1-455a-b897-bcdcfa8472e0')\"\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",
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              "    </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",
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              "    20% {\n",
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              "      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-dffc2419-fef1-455a-b897-bcdcfa8472e0 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\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2022-01-31 00:00:00\",\n        \"max\": \"2022-02-04 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2022-02-01 00:00:00\",\n          \"2022-02-04 00:00:00\",\n          \"2022-02-02 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.879549420974513,\n        \"min\": 401.970001,\n        \"max\": 448.25,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          432.959991,\n          407.309998,\n          448.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.80595052551444,\n        \"min\": 412.769989,\n        \"max\": 458.480011,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          458.480011,\n          412.769989,\n          451.980011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.690728613177669,\n        \"min\": 396.640015,\n        \"max\": 426.480011,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          425.540009,\n          396.640015,\n          426.480011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.30510708713497,\n        \"min\": 405.600006,\n        \"max\": 457.130005,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          457.130005,\n          410.170013,\n          429.480011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.30510708713497,\n        \"min\": 405.600006,\n        \"max\": 457.130005,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          457.130005,\n          410.170013,\n          429.480011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6340615,\n        \"min\": 7782400,\n        \"max\": 22542300,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          22542300,\n          7782400,\n          14346000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#feature engineering\n",
        "df['Date'] = df['Date'].astype(str) # Convert 'Date' column to string type\n",
        "splitted = df['Date'].str.split('-', expand=True)\n",
        "df['Year'] = splitted[0].astype(int)\n",
        "df['Month'] = splitted[1].astype(int)\n",
        "df['Day'] = splitted[2].astype(int)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "jxgpJQ4FsSD-",
        "outputId": "b694bbb3-9c59-4e31-f80d-22f82e57af1f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date        Open        High         Low       Close   Adj Close  \\\n",
              "0  2018-02-05  262.000000  267.899994  250.029999  254.259995  254.259995   \n",
              "1  2018-02-06  247.699997  266.700012  245.000000  265.720001  265.720001   \n",
              "2  2018-02-07  266.579987  272.450012  264.329987  264.559998  264.559998   \n",
              "3  2018-02-08  267.079987  267.619995  250.000000  250.100006  250.100006   \n",
              "4  2018-02-09  253.850006  255.800003  236.110001  249.470001  249.470001   \n",
              "\n",
              "     Volume  Year  Month  Day  \n",
              "0  11896100  2018      2    5  \n",
              "1  12595800  2018      2    6  \n",
              "2   8981500  2018      2    7  \n",
              "3   9306700  2018      2    8  \n",
              "4  16906900  2018      2    9  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Year</th>\n",
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              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-02-05</td>\n",
              "      <td>262.000000</td>\n",
              "      <td>267.899994</td>\n",
              "      <td>250.029999</td>\n",
              "      <td>254.259995</td>\n",
              "      <td>254.259995</td>\n",
              "      <td>11896100</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-02-06</td>\n",
              "      <td>247.699997</td>\n",
              "      <td>266.700012</td>\n",
              "      <td>245.000000</td>\n",
              "      <td>265.720001</td>\n",
              "      <td>265.720001</td>\n",
              "      <td>12595800</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-02-07</td>\n",
              "      <td>266.579987</td>\n",
              "      <td>272.450012</td>\n",
              "      <td>264.329987</td>\n",
              "      <td>264.559998</td>\n",
              "      <td>264.559998</td>\n",
              "      <td>8981500</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-02-08</td>\n",
              "      <td>267.079987</td>\n",
              "      <td>267.619995</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>250.100006</td>\n",
              "      <td>250.100006</td>\n",
              "      <td>9306700</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-02-09</td>\n",
              "      <td>253.850006</td>\n",
              "      <td>255.800003</td>\n",
              "      <td>236.110001</td>\n",
              "      <td>249.470001</td>\n",
              "      <td>249.470001</td>\n",
              "      <td>16906900</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      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",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-b81a72e9-1a9a-4d10-94a0-a929e4be9744 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-b81a72e9-1a9a-4d10-94a0-a929e4be9744');\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",
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              "            title=\"Suggest charts\"\n",
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              "    </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-e42959c9-69b1-47cf-92b9-fb9e2032ed39 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",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1009,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1009,\n        \"samples\": [\n          \"2020-08-04\",\n          \"2020-08-07\",\n          \"2021-01-14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.53753170401454,\n        \"min\": 233.919998,\n        \"max\": 692.349976,\n        \"num_unique_values\": 976,\n        \"samples\": [\n          254.630005,\n          427.559998,\n          339.570007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109.26295957119457,\n        \"min\": 250.649994,\n        \"max\": 700.98999,\n        \"num_unique_values\": 983,\n        \"samples\": [\n          511.76001,\n          494.850006,\n          496.660004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 107.55586739006051,\n        \"min\": 231.229996,\n        \"max\": 686.090027,\n        \"num_unique_values\": 989,\n        \"samples\": [\n          266.480011,\n          349.290009,\n          384.899994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.28999877034998,\n        \"min\": 233.880005,\n        \"max\": 691.690002,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          265.320007,\n          345.559998,\n          380.070007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.28999877034998,\n        \"min\": 233.880005,\n        \"max\": 691.690002,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          265.320007,\n          345.559998,\n          380.070007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5465535,\n        \"min\": 1144000,\n        \"max\": 58904300,\n        \"num_unique_values\": 1005,\n        \"samples\": [\n          2671700,\n          3725900,\n          4927900\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2018,\n        \"max\": 2022,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2019,\n          2022,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          12,\n          11,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          24,\n          27,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TKnWnRO-xYeZ"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['is_quarter_end'] = np.where(df['Month']%3==0,1,0)\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "NQEz6FLoxBlF",
        "outputId": "aaacf2e7-804e-4532-e5c1-cf3d4c6b54b8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date        Open        High         Low       Close   Adj Close  \\\n",
              "0  2018-02-05  262.000000  267.899994  250.029999  254.259995  254.259995   \n",
              "1  2018-02-06  247.699997  266.700012  245.000000  265.720001  265.720001   \n",
              "2  2018-02-07  266.579987  272.450012  264.329987  264.559998  264.559998   \n",
              "3  2018-02-08  267.079987  267.619995  250.000000  250.100006  250.100006   \n",
              "4  2018-02-09  253.850006  255.800003  236.110001  249.470001  249.470001   \n",
              "\n",
              "     Volume  Year  Month  Day  is_quarter_end  \n",
              "0  11896100  2018      2    5               0  \n",
              "1  12595800  2018      2    6               0  \n",
              "2   8981500  2018      2    7               0  \n",
              "3   9306700  2018      2    8               0  \n",
              "4  16906900  2018      2    9               0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e4e937b6-f856-47b9-a001-480d25396b74\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>is_quarter_end</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-02-05</td>\n",
              "      <td>262.000000</td>\n",
              "      <td>267.899994</td>\n",
              "      <td>250.029999</td>\n",
              "      <td>254.259995</td>\n",
              "      <td>254.259995</td>\n",
              "      <td>11896100</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-02-06</td>\n",
              "      <td>247.699997</td>\n",
              "      <td>266.700012</td>\n",
              "      <td>245.000000</td>\n",
              "      <td>265.720001</td>\n",
              "      <td>265.720001</td>\n",
              "      <td>12595800</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-02-07</td>\n",
              "      <td>266.579987</td>\n",
              "      <td>272.450012</td>\n",
              "      <td>264.329987</td>\n",
              "      <td>264.559998</td>\n",
              "      <td>264.559998</td>\n",
              "      <td>8981500</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-02-08</td>\n",
              "      <td>267.079987</td>\n",
              "      <td>267.619995</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>250.100006</td>\n",
              "      <td>250.100006</td>\n",
              "      <td>9306700</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-02-09</td>\n",
              "      <td>253.850006</td>\n",
              "      <td>255.800003</td>\n",
              "      <td>236.110001</td>\n",
              "      <td>249.470001</td>\n",
              "      <td>249.470001</td>\n",
              "      <td>16906900</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>0</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-e4e937b6-f856-47b9-a001-480d25396b74')\"\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",
              "      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-e4e937b6-f856-47b9-a001-480d25396b74 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-e4e937b6-f856-47b9-a001-480d25396b74');\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",
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              "\n",
              "\n",
              "<div id=\"df-8732ef09-1d25-4ea8-a642-b50c76c5a62f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8732ef09-1d25-4ea8-a642-b50c76c5a62f')\"\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",
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              "    </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",
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              "    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-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      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-8732ef09-1d25-4ea8-a642-b50c76c5a62f 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",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1009,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1009,\n        \"samples\": [\n          \"2020-08-04\",\n          \"2020-08-07\",\n          \"2021-01-14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.53753170401454,\n        \"min\": 233.919998,\n        \"max\": 692.349976,\n        \"num_unique_values\": 976,\n        \"samples\": [\n          254.630005,\n          427.559998,\n          339.570007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109.26295957119457,\n        \"min\": 250.649994,\n        \"max\": 700.98999,\n        \"num_unique_values\": 983,\n        \"samples\": [\n          511.76001,\n          494.850006,\n          496.660004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 107.55586739006051,\n        \"min\": 231.229996,\n        \"max\": 686.090027,\n        \"num_unique_values\": 989,\n        \"samples\": [\n          266.480011,\n          349.290009,\n          384.899994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.28999877034998,\n        \"min\": 233.880005,\n        \"max\": 691.690002,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          265.320007,\n          345.559998,\n          380.070007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.28999877034998,\n        \"min\": 233.880005,\n        \"max\": 691.690002,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          265.320007,\n          345.559998,\n          380.070007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5465535,\n        \"min\": 1144000,\n        \"max\": 58904300,\n        \"num_unique_values\": 1005,\n        \"samples\": [\n          2671700,\n          3725900,\n          4927900\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2018,\n        \"max\": 2022,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2019,\n          2022,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          12,\n          11,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          24,\n          27,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_quarter_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert relevant columns to numeric before calculating the mean\n",
        "df['Open'] = pd.to_numeric(df['Open'], errors='coerce')\n",
        "df['High'] = pd.to_numeric(df['High'], errors='coerce')\n",
        "df['Low'] = pd.to_numeric(df['Low'], errors='coerce')\n",
        "df['Close'] = pd.to_numeric(df['Close'], errors='coerce')\n",
        "\n",
        "data_grouped = df.groupby('Year').mean()\n",
        "plt.subplots(figsize=(20,10))\n",
        "\n",
        "for i, col in enumerate(['Open', 'High', 'Low', 'Close']):\n",
        "  plt.subplot(2,2,i+1)\n",
        "  data_grouped[col].plot.bar()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qt8Q06R-xeQf",
        "outputId": "cdbc349a-cd71-415b-8be0-9485c3b9e9fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "Could not convert 2018-02-052018-02-062018-02-072018-02-082018-02-092018-02-122018-02-132018-02-142018-02-152018-02-162018-02-202018-02-212018-02-222018-02-232018-02-262018-02-272018-02-282018-03-012018-03-022018-03-052018-03-062018-03-072018-03-082018-03-092018-03-122018-03-132018-03-142018-03-152018-03-162018-03-192018-03-202018-03-212018-03-222018-03-232018-03-262018-03-272018-03-282018-03-292018-04-022018-04-032018-04-042018-04-052018-04-062018-04-092018-04-102018-04-112018-04-122018-04-132018-04-162018-04-172018-04-182018-04-192018-04-202018-04-232018-04-242018-04-252018-04-262018-04-272018-04-302018-05-012018-05-022018-05-032018-05-042018-05-072018-05-082018-05-092018-05-102018-05-112018-05-142018-05-152018-05-162018-05-172018-05-182018-05-212018-05-222018-05-232018-05-242018-05-252018-05-292018-05-302018-05-312018-06-012018-06-042018-06-052018-06-062018-06-072018-06-082018-06-112018-06-122018-06-132018-06-142018-06-152018-06-182018-06-192018-06-202018-06-212018-06-222018-06-252018-06-262018-06-272018-06-282018-06-292018-07-022018-07-032018-07-052018-07-062018-07-092018-07-102018-07-112018-07-122018-07-132018-07-162018-07-172018-07-182018-07-192018-07-202018-07-232018-07-242018-07-252018-07-262018-07-272018-07-302018-07-312018-08-012018-08-022018-08-032018-08-062018-08-072018-08-082018-08-092018-08-102018-08-132018-08-142018-08-152018-08-162018-08-172018-08-202018-08-212018-08-222018-08-232018-08-242018-08-272018-08-282018-08-292018-08-302018-08-312018-09-042018-09-052018-09-062018-09-072018-09-102018-09-112018-09-122018-09-132018-09-142018-09-172018-09-182018-09-192018-09-202018-09-212018-09-242018-09-252018-09-262018-09-272018-09-282018-10-012018-10-022018-10-032018-10-042018-10-052018-10-082018-10-092018-10-102018-10-112018-10-122018-10-152018-10-162018-10-172018-10-182018-10-192018-10-222018-10-232018-10-242018-10-252018-10-262018-10-292018-10-302018-10-312018-11-012018-11-022018-11-052018-11-062018-11-072018-11-082018-11-092018-11-122018-11-132018-11-142018-11-152018-11-162018-11-192018-11-202018-11-212018-11-232018-11-262018-11-272018-11-282018-11-292018-11-302018-12-032018-12-042018-12-062018-12-072018-12-102018-12-112018-12-122018-12-132018-12-142018-12-172018-12-182018-12-192018-12-202018-12-212018-12-242018-12-262018-12-272018-12-282018-12-31 to numeric",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36marray_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m   1489\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1490\u001b[0;31m                 result = self.grouper._cython_operation(\n\u001b[0m\u001b[1;32m   1491\u001b[0m                     \u001b[0;34m\"aggregate\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_cython_operation\u001b[0;34m(self, kind, values, how, axis, min_count, **kwargs)\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0mngroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mngroups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 959\u001b[0;31m         return cy_op.cython_operation(\n\u001b[0m\u001b[1;32m    960\u001b[0m             \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mcython_operation\u001b[0;34m(self, values, axis, min_count, comp_ids, ngroups, **kwargs)\u001b[0m\n\u001b[1;32m    656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 657\u001b[0;31m         return self._cython_op_ndim_compat(\n\u001b[0m\u001b[1;32m    658\u001b[0m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_cython_op_ndim_compat\u001b[0;34m(self, values, min_count, ngroups, comp_ids, mask, result_mask, **kwargs)\u001b[0m\n\u001b[1;32m    496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m         return self._call_cython_op(\n\u001b[0m\u001b[1;32m    498\u001b[0m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_call_cython_op\u001b[0;34m(self, values, min_count, ngroups, comp_ids, mask, result_mask, **kwargs)\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0mout_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mngroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m         \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_cython_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_numeric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_cython_vals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_get_cython_function\u001b[0;34m(cls, kind, how, dtype, is_numeric)\u001b[0m\n\u001b[1;32m    172\u001b[0m                 \u001b[0;31m# raise NotImplementedError here rather than TypeError later\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                 raise NotImplementedError(\n\u001b[0m\u001b[1;32m    174\u001b[0m                     \u001b[0;34mf\"function is not implemented for this dtype: \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNotImplementedError\u001b[0m: function is not implemented for this dtype: [how->mean,dtype->object]",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1691\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1692\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1693\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '2018-02-052018-02-062018-02-072018-02-082018-02-092018-02-122018-02-132018-02-142018-02-152018-02-162018-02-202018-02-212018-02-222018-02-232018-02-262018-02-272018-02-282018-03-012018-03-022018-03-052018-03-062018-03-072018-03-082018-03-092018-03-122018-03-132018-03-142018-03-152018-03-162018-03-192018-03-202018-03-212018-03-222018-03-232018-03-262018-03-272018-03-282018-03-292018-04-022018-04-032018-04-042018-04-052018-04-062018-04-092018-04-102018-04-112018-04-122018-04-132018-04-162018-04-172018-04-182018-04-192018-04-202018-04-232018-04-242018-04-252018-04-262018-04-272018-04-302018-05-012018-05-022018-05-032018-05-042018-05-072018-05-082018-05-092018-05-102018-05-112018-05-142018-05-152018-05-162018-05-172018-05-182018-05-212018-05-222018-05-232018-05-242018-05-252018-05-292018-05-302018-05-312018-06-012018-06-042018-06-052018-06-062018-06-072018-06-082018-06-112018-06-122018-06-132018-06-142018-06-152018-06-182018-06-192018-06-202018-06-212018-06-222018-06-252018-06-262018-06-272018-06-282018-06-292018-07-022018-07-032018-07-052018-07-062018-07-092018-07-102018-07-112018-07-122018-07-132018-07-162018-07-172018-07-182018-07-192018-07-202018-07-232018-07-242018-07-252018-07-262018-07-272018-07-302018-07-312018-08-012018-08-022018-08-032018-08-062018-08-072018-08-082018-08-092018-08-102018-08-132018-08-142018-08-152018-08-162018-08-172018-08-202018-08-212018-08-222018-08-232018-08-242018-08-272018-08-282018-08-...",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1695\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1696\u001b[0;31m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1697\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: complex() arg is a malformed string",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-d63e4fbe71a6>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Close'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'coerce'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdata_grouped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(self, numeric_only, engine, engine_kwargs)\u001b[0m\n\u001b[1;32m   1853\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numba_agg_general\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msliding_mean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1855\u001b[0;31m             result = self._cython_agg_general(\n\u001b[0m\u001b[1;32m   1856\u001b[0m                 \u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1857\u001b[0m                 \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m   1505\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1507\u001b[0;31m         \u001b[0mnew_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouped_reduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1508\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_agged_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_mgr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1509\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_aggregated_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mgrouped_reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m   1501\u001b[0m                 \u001b[0;31m#  while others do not.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1502\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0msb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1503\u001b[0;31m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1504\u001b[0m                     \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1505\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m    327\u001b[0m         \u001b[0mone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m         \"\"\"\n\u001b[0;32m--> 329\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split_op_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36marray_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m   1501\u001b[0m                 \u001b[0;31m# try to python agg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1502\u001b[0m                 \u001b[0;31m# TODO: shouldn't min_count matter?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1503\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_agg_py_fallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1505\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_agg_py_fallback\u001b[0;34m(self, values, ndim, alt)\u001b[0m\n\u001b[1;32m   1455\u001b[0m         \u001b[0;31m#  should always be preserved by the implemented aggregations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1456\u001b[0m         \u001b[0;31m# TODO: Is this exactly right; see WrappedCythonOp get_result_dtype?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1457\u001b[0;31m         \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreserve_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1459\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCategorical\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36magg_series\u001b[0;34m(self, obj, func, preserve_dtype)\u001b[0m\n\u001b[1;32m    992\u001b[0m             \u001b[0mpreserve_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    993\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 994\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aggregate_series_pure_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    995\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m         \u001b[0mnpvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_convert_objects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtry_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_aggregate_series_pure_python\u001b[0;34m(self, obj, func)\u001b[0m\n\u001b[1;32m   1013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplitter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1015\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1016\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibreduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1855\u001b[0m             result = self._cython_agg_general(\n\u001b[1;32m   1856\u001b[0m                 \u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1857\u001b[0;31m                 \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1858\u001b[0m                 \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1859\u001b[0m             )\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m  11199\u001b[0m         \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11200\u001b[0m     ) -> Series | float:\n\u001b[0;32m> 11201\u001b[0;31m         return self._stat_function(\n\u001b[0m\u001b[1;32m  11202\u001b[0m             \u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnanops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11203\u001b[0m         )\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m    419\u001b[0m             \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdatetimelike\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnanmean\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m    725\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m     \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m     \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_sum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_sum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ndim\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1697\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1698\u001b[0m                 \u001b[0;31m# e.g. \"foo\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1699\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Could not convert {x} to numeric\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1700\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Could not convert 2018-02-052018-02-062018-02-072018-02-082018-02-092018-02-122018-02-132018-02-142018-02-152018-02-162018-02-202018-02-212018-02-222018-02-232018-02-262018-02-272018-02-282018-03-012018-03-022018-03-052018-03-062018-03-072018-03-082018-03-092018-03-122018-03-132018-03-142018-03-152018-03-162018-03-192018-03-202018-03-212018-03-222018-03-232018-03-262018-03-272018-03-282018-03-292018-04-022018-04-032018-04-042018-04-052018-04-062018-04-092018-04-102018-04-112018-04-122018-04-132018-04-162018-04-172018-04-182018-04-192018-04-202018-04-232018-04-242018-04-252018-04-262018-04-272018-04-302018-05-012018-05-022018-05-032018-05-042018-05-072018-05-082018-05-092018-05-102018-05-112018-05-142018-05-152018-05-162018-05-172018-05-182018-05-212018-05-222018-05-232018-05-242018-05-252018-05-292018-05-302018-05-312018-06-012018-06-042018-06-052018-06-062018-06-072018-06-082018-06-112018-06-122018-06-132018-06-142018-06-152018-06-182018-06-192018-06-202018-06-212018-06-222018-06-252018-06-262018-06-272018-06-282018-06-292018-07-022018-07-032018-07-052018-07-062018-07-092018-07-102018-07-112018-07-122018-07-132018-07-162018-07-172018-07-182018-07-192018-07-202018-07-232018-07-242018-07-252018-07-262018-07-272018-07-302018-07-312018-08-012018-08-022018-08-032018-08-062018-08-072018-08-082018-08-092018-08-102018-08-132018-08-142018-08-152018-08-162018-08-172018-08-202018-08-212018-08-222018-08-232018-08-242018-08-272018-08-282018-08-292018-08-302018-08..."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['open-close']  = df['Open'] - df['Close']\n",
        "df['low-high']  = df['Low'] - df['High']\n",
        "df['target'] = np.where(df['Close'].shift(-1) > df['Close'], 1, 0)"
      ],
      "metadata": {
        "id": "gKPdxNQtQHI7"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#data spliting and normalization\n",
        "features = df[['open-close', 'low-high', 'is_quarter_end']]\n",
        "target = df['target']"
      ],
      "metadata": {
        "id": "nsvtoQpnKifu"
      },
      "execution_count": 24,
      "outputs": []
    }
  ]
}