Time series EDA.ipynb

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      "source": [
        "import pandas_datareader as pdr\n",
        "import os\n",
        "import datetime\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas-datareader"
      ],
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            "Requirement already satisfied: pandas-datareader in /usr/local/lib/python3.10/dist-packages (0.10.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (4.9.4)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (2.31.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (2023.4)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (1.25.2)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.23->pandas-datareader) (1.16.0)\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        " from pandas_datareader import data as pdr\n",
        "import yfinance as yf\n",
        "yf.pdr_override()# Correct: passing a string\n"
      ],
      "metadata": {
        "id": "P6EaPFOP4UdF"
      },
      "execution_count": 164,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df_tesla=pdr.get_data_yahoo(\"TSLA\")"
      ],
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            "\r[*********************100%%**********************]  1 of 1 completed\n"
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            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index.\n",
              "\n",
              "    .. versionchanged:: 0.25.0\n",
              "       If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3</pre>\n",
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        "df_tesla.head()"
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              "                Open      High       Low     Close  Adj Close     Volume\n",
              "Date                                                                    \n",
              "2010-06-29  1.266667  1.666667  1.169333  1.592667   1.592667  281494500\n",
              "2010-06-30  1.719333  2.028000  1.553333  1.588667   1.588667  257806500\n",
              "2010-07-01  1.666667  1.728000  1.351333  1.464000   1.464000  123282000\n",
              "2010-07-02  1.533333  1.540000  1.247333  1.280000   1.280000   77097000\n",
              "2010-07-06  1.333333  1.333333  1.055333  1.074000   1.074000  103003500"
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              "      <th>Open</th>\n",
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              "      <th>2010-06-29</th>\n",
              "      <td>1.266667</td>\n",
              "      <td>1.666667</td>\n",
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              "      <th>2010-07-02</th>\n",
              "      <td>1.533333</td>\n",
              "      <td>1.540000</td>\n",
              "      <td>1.247333</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>77097000</td>\n",
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              "      <th>2010-07-06</th>\n",
              "      <td>1.333333</td>\n",
              "      <td>1.333333</td>\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\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-20d17683-9d43-48f7-ba3a-77d28e48ca03 button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "\n",
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              "type": "dataframe",
              "variable_name": "df_tesla",
              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 3445,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.04437828241103,\n        \"min\": 1.0759999752044678,\n        \"max\": 411.4700012207031,\n        \"num_unique_values\": 3143,\n        \"samples\": [\n          268.30999755859375,\n          250.07000732421875,\n          16.72333335876465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.29817067760874,\n        \"min\": 1.108667016029358,\n        \"max\": 414.4966735839844,\n        \"num_unique_values\": 3135,\n        \"samples\": [\n          15.652000427246094,\n          2.629333019256592,\n          1.9713330268859863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.55555106142745,\n        \"min\": 0.9986670017242432,\n        \"max\": 405.6666564941406,\n        \"num_unique_values\": 3152,\n        \"samples\": [\n          191.9499969482422,\n          18.511333465576172,\n          207.52333068847656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79563803,\n        \"min\": 1777500,\n        \"max\": 914082000,\n        \"num_unique_values\": 3414,\n        \"samples\": [\n          85110000,\n          57526500,\n          9949500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "uQ0wLQu-8JKC",
        "outputId": "93257871-cc83-4b27-98b9-c4b16eb38b8b"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2024-02-29  204.179993  205.279999  198.449997  201.880005  201.880005   \n",
              "2024-03-01  200.520004  204.520004  198.500000  202.639999  202.639999   \n",
              "2024-03-04  198.729996  199.750000  186.720001  188.139999  188.139999   \n",
              "2024-03-05  183.050003  184.589996  177.570007  180.740005  180.740005   \n",
              "2024-03-06  179.990005  181.580002  173.699997  176.539993  176.539993   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2024-02-29   85907000  \n",
              "2024-03-01   82099200  \n",
              "2024-03-04  134334900  \n",
              "2024-03-05  119660800  \n",
              "2024-03-06  107436900  "
            ],
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-b129a372-9a08-4373-8ed1-96a84b55d0b6 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-b129a372-9a08-4373-8ed1-96a84b55d0b6');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --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",
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              "    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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-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",
              "      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-1857a8a1-9348-43b2-b5ec-03857aed052b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.97959205920381,\n        \"min\": 179.99000549316406,\n        \"max\": 204.17999267578125,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          200.52000427246094,\n          179.99000549316406,\n          198.72999572753906\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.260908078370978,\n        \"min\": 181.5800018310547,\n        \"max\": 205.27999877929688,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          204.52000427246094,\n          181.5800018310547,\n          199.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.502662219371336,\n        \"min\": 173.6999969482422,\n        \"max\": 198.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          198.5,\n          173.6999969482422,\n          186.72000122070312\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.950755853909868,\n        \"min\": 176.5399932861328,\n        \"max\": 202.63999938964844,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          202.63999938964844,\n          176.5399932861328,\n          188.13999938964844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.950755853909868,\n        \"min\": 176.5399932861328,\n        \"max\": 202.63999938964844,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          202.63999938964844,\n          176.5399932861328,\n          188.13999938964844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22172407,\n        \"min\": 82099200,\n        \"max\": 134334900,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          82099200,\n          107436900,\n          134334900\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla[\"High\"].plot(figsize=(12,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "7RWabhpG8KHU",
        "outputId": "52c28a9e-b2c3-4b2b-cc07-e9caa7039c6a"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla[\"High\"].plot(xlim=[\"2020-01-01\",\"2024-01-01\"],ylim=[100,400],figsize=(12,5),c=\"green\",ls=\"--\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "-ZupJtnO8i3I",
        "outputId": "db4f2fb0-2354-4381-e8e4-615024401cd1"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "ZrGWOBfX9IxU",
        "outputId": "12471c13-d13a-42d4-ec39-a6d0c39be3bc"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2010-06-29    1.266667    1.666667    1.169333    1.592667    1.592667   \n",
              "2010-06-30    1.719333    2.028000    1.553333    1.588667    1.588667   \n",
              "2010-07-01    1.666667    1.728000    1.351333    1.464000    1.464000   \n",
              "2010-07-02    1.533333    1.540000    1.247333    1.280000    1.280000   \n",
              "2010-07-06    1.333333    1.333333    1.055333    1.074000    1.074000   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2024-02-29  204.179993  205.279999  198.449997  201.880005  201.880005   \n",
              "2024-03-01  200.520004  204.520004  198.500000  202.639999  202.639999   \n",
              "2024-03-04  198.729996  199.750000  186.720001  188.139999  188.139999   \n",
              "2024-03-05  183.050003  184.589996  177.570007  180.740005  180.740005   \n",
              "2024-03-06  179.990005  181.580002  173.699997  176.539993  176.539993   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2010-06-29  281494500  \n",
              "2010-06-30  257806500  \n",
              "2010-07-01  123282000  \n",
              "2010-07-02   77097000  \n",
              "2010-07-06  103003500  \n",
              "...               ...  \n",
              "2024-02-29   85907000  \n",
              "2024-03-01   82099200  \n",
              "2024-03-04  134334900  \n",
              "2024-03-05  119660800  \n",
              "2024-03-06  107436900  \n",
              "\n",
              "[3445 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9396c879-67f3-4cd6-b20a-33b63486ad69\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-06-29</th>\n",
              "      <td>1.266667</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>1.169333</td>\n",
              "      <td>1.592667</td>\n",
              "      <td>1.592667</td>\n",
              "      <td>281494500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-06-30</th>\n",
              "      <td>1.719333</td>\n",
              "      <td>2.028000</td>\n",
              "      <td>1.553333</td>\n",
              "      <td>1.588667</td>\n",
              "      <td>1.588667</td>\n",
              "      <td>257806500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-01</th>\n",
              "      <td>1.666667</td>\n",
              "      <td>1.728000</td>\n",
              "      <td>1.351333</td>\n",
              "      <td>1.464000</td>\n",
              "      <td>1.464000</td>\n",
              "      <td>123282000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-02</th>\n",
              "      <td>1.533333</td>\n",
              "      <td>1.540000</td>\n",
              "      <td>1.247333</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>77097000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-06</th>\n",
              "      <td>1.333333</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>1.055333</td>\n",
              "      <td>1.074000</td>\n",
              "      <td>1.074000</td>\n",
              "      <td>103003500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-02-29</th>\n",
              "      <td>204.179993</td>\n",
              "      <td>205.279999</td>\n",
              "      <td>198.449997</td>\n",
              "      <td>201.880005</td>\n",
              "      <td>201.880005</td>\n",
              "      <td>85907000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-01</th>\n",
              "      <td>200.520004</td>\n",
              "      <td>204.520004</td>\n",
              "      <td>198.500000</td>\n",
              "      <td>202.639999</td>\n",
              "      <td>202.639999</td>\n",
              "      <td>82099200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-04</th>\n",
              "      <td>198.729996</td>\n",
              "      <td>199.750000</td>\n",
              "      <td>186.720001</td>\n",
              "      <td>188.139999</td>\n",
              "      <td>188.139999</td>\n",
              "      <td>134334900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-05</th>\n",
              "      <td>183.050003</td>\n",
              "      <td>184.589996</td>\n",
              "      <td>177.570007</td>\n",
              "      <td>180.740005</td>\n",
              "      <td>180.740005</td>\n",
              "      <td>119660800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-06</th>\n",
              "      <td>179.990005</td>\n",
              "      <td>181.580002</td>\n",
              "      <td>173.699997</td>\n",
              "      <td>176.539993</td>\n",
              "      <td>176.539993</td>\n",
              "      <td>107436900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3445 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "      display:flex;\n",
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              "    .colab-df-convert:hover {\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\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",
              "      } 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",
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              "        document.querySelector('#df-40e6f0e7-1c2e-436b-bad6-286611eb1b03 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_806353c4-9507-4f34-a185-c697da592a9b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tesla')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
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              "      (() => {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_tesla');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_tesla",
              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 3445,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.04437828241103,\n        \"min\": 1.0759999752044678,\n        \"max\": 411.4700012207031,\n        \"num_unique_values\": 3143,\n        \"samples\": [\n          268.30999755859375,\n          250.07000732421875,\n          16.72333335876465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.29817067760874,\n        \"min\": 1.108667016029358,\n        \"max\": 414.4966735839844,\n        \"num_unique_values\": 3135,\n        \"samples\": [\n          15.652000427246094,\n          2.629333019256592,\n          1.9713330268859863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.55555106142745,\n        \"min\": 0.9986670017242432,\n        \"max\": 405.6666564941406,\n        \"num_unique_values\": 3152,\n        \"samples\": [\n          191.9499969482422,\n          18.511333465576172,\n          207.52333068847656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79563803,\n        \"min\": 1777500,\n        \"max\": 914082000,\n        \"num_unique_values\": 3414,\n        \"samples\": [\n          85110000,\n          57526500,\n          9949500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.loc[\"2020-01-01\":\"2021-09-01\"].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RnQNmXn5-GG6",
        "outputId": "d7bed972-9884-4d62-c93f-26733ab14fb6"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-06', '2020-01-07',\n",
              "               '2020-01-08', '2020-01-09', '2020-01-10', '2020-01-13',\n",
              "               '2020-01-14', '2020-01-15',\n",
              "               ...\n",
              "               '2021-08-19', '2021-08-20', '2021-08-23', '2021-08-24',\n",
              "               '2021-08-25', '2021-08-26', '2021-08-27', '2021-08-30',\n",
              "               '2021-08-31', '2021-09-01'],\n",
              "              dtype='datetime64[ns]', name='Date', length=421, freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla_open=df_tesla.loc[\"2020-01-01\":\"2021-09-01\"][\"Open\"]"
      ],
      "metadata": {
        "id": "Qxt7qsbL-H76"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla_open.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EIvL8yAR-nJ6",
        "outputId": "6c594eab-053c-469d-e0dd-2b2dfd61877f"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-06', '2020-01-07',\n",
              "               '2020-01-08', '2020-01-09', '2020-01-10', '2020-01-13',\n",
              "               '2020-01-14', '2020-01-15',\n",
              "               ...\n",
              "               '2021-08-19', '2021-08-20', '2021-08-23', '2021-08-24',\n",
              "               '2021-08-25', '2021-08-26', '2021-08-27', '2021-08-30',\n",
              "               '2021-08-31', '2021-09-01'],\n",
              "              dtype='datetime64[ns]', name='Date', length=421, freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "kpX9u8Sx-slc"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "figure,axis=plt.subplots()\n",
        "plt.plot(df_tesla_open.index,df_tesla_open)\n",
        "figure.autofmt_xdate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "SKD_oQ2Y-8V2",
        "outputId": "3c3363f2-67ea-4b57-bc8b-71a0de466619"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VxJHI0K_paM",
        "outputId": "474187b4-1a92-4352-ee24-966dd2fcbe49"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 3445 entries, 2010-06-29 to 2024-03-06\n",
            "Data columns (total 6 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Open       3445 non-null   float64\n",
            " 1   High       3445 non-null   float64\n",
            " 2   Low        3445 non-null   float64\n",
            " 3   Close      3445 non-null   float64\n",
            " 4   Adj Close  3445 non-null   float64\n",
            " 5   Volume     3445 non-null   int64  \n",
            "dtypes: float64(5), int64(1)\n",
            "memory usage: 317.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla=df_tesla.reset_index()"
      ],
      "metadata": {
        "id": "U09ArURBAoZs"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5J7NXP3LArCS",
        "outputId": "3da3ddb5-ea57-4b15-f5b0-83a10c72e07f"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3445 entries, 0 to 3444\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype         \n",
            "---  ------     --------------  -----         \n",
            " 0   Date       3445 non-null   datetime64[ns]\n",
            " 1   Open       3445 non-null   float64       \n",
            " 2   High       3445 non-null   float64       \n",
            " 3   Low        3445 non-null   float64       \n",
            " 4   Close      3445 non-null   float64       \n",
            " 5   Adj Close  3445 non-null   float64       \n",
            " 6   Volume     3445 non-null   int64         \n",
            "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
            "memory usage: 188.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla[\"Date\"]=pd.to_datetime(df_tesla[\"Date\"])"
      ],
      "metadata": {
        "id": "PkRjc0juAy0m"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "G5Eq2duPBBm2",
        "outputId": "e3af3a57-04f8-4089-e2da-731f35c93901"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date      Open      High       Low     Close  Adj Close     Volume\n",
              "0 2010-06-29  1.266667  1.666667  1.169333  1.592667   1.592667  281494500\n",
              "1 2010-06-30  1.719333  2.028000  1.553333  1.588667   1.588667  257806500\n",
              "2 2010-07-01  1.666667  1.728000  1.351333  1.464000   1.464000  123282000\n",
              "3 2010-07-02  1.533333  1.540000  1.247333  1.280000   1.280000   77097000\n",
              "4 2010-07-06  1.333333  1.333333  1.055333  1.074000   1.074000  103003500"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-11d6408a-21ad-457e-944d-9699b30e746e\" 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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              "      <td>257806500</td>\n",
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              "      <td>1.533333</td>\n",
              "      <td>1.540000</td>\n",
              "      <td>1.247333</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>77097000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-07-06</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>1.055333</td>\n",
              "      <td>1.074000</td>\n",
              "      <td>1.074000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_tesla",
              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 3445,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-06-29 00:00:00\",\n        \"max\": \"2024-03-06 00:00:00\",\n        \"num_unique_values\": 3445,\n        \"samples\": [\n          \"2012-03-30 00:00:00\",\n          \"2010-09-10 00:00:00\",\n          \"2013-08-23 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.04437828241103,\n        \"min\": 1.0759999752044678,\n        \"max\": 411.4700012207031,\n        \"num_unique_values\": 3143,\n        \"samples\": [\n          268.30999755859375,\n          250.07000732421875,\n          16.72333335876465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.29817067760874,\n        \"min\": 1.108667016029358,\n        \"max\": 414.4966735839844,\n        \"num_unique_values\": 3135,\n        \"samples\": [\n          15.652000427246094,\n          2.629333019256592,\n          1.9713330268859863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.55555106142745,\n        \"min\": 0.9986670017242432,\n        \"max\": 405.6666564941406,\n        \"num_unique_values\": 3152,\n        \"samples\": [\n          191.9499969482422,\n          18.511333465576172,\n          207.52333068847656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79563803,\n        \"min\": 1777500,\n        \"max\": 914082000,\n        \"num_unique_values\": 3414,\n        \"samples\": [\n          85110000,\n          57526500,\n          9949500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.set_index(df_tesla[\"Date\"],drop=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "TC5HvwVUBRcJ",
        "outputId": "df79dfdd-8239-4d7e-ee8b-674b7b6e645c"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Date        Open        High         Low       Close  \\\n",
              "Date                                                                    \n",
              "2010-06-29 2010-06-29    1.266667    1.666667    1.169333    1.592667   \n",
              "2010-06-30 2010-06-30    1.719333    2.028000    1.553333    1.588667   \n",
              "2010-07-01 2010-07-01    1.666667    1.728000    1.351333    1.464000   \n",
              "2010-07-02 2010-07-02    1.533333    1.540000    1.247333    1.280000   \n",
              "2010-07-06 2010-07-06    1.333333    1.333333    1.055333    1.074000   \n",
              "...               ...         ...         ...         ...         ...   \n",
              "2024-02-29 2024-02-29  204.179993  205.279999  198.449997  201.880005   \n",
              "2024-03-01 2024-03-01  200.520004  204.520004  198.500000  202.639999   \n",
              "2024-03-04 2024-03-04  198.729996  199.750000  186.720001  188.139999   \n",
              "2024-03-05 2024-03-05  183.050003  184.589996  177.570007  180.740005   \n",
              "2024-03-06 2024-03-06  179.990005  181.580002  173.699997  176.539993   \n",
              "\n",
              "             Adj Close     Volume  \n",
              "Date                               \n",
              "2010-06-29    1.592667  281494500  \n",
              "2010-06-30    1.588667  257806500  \n",
              "2010-07-01    1.464000  123282000  \n",
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              "2010-07-06    1.074000  103003500  \n",
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              "2024-02-29  201.880005   85907000  \n",
              "2024-03-01  202.639999   82099200  \n",
              "2024-03-04  188.139999  134334900  \n",
              "2024-03-05  180.740005  119660800  \n",
              "2024-03-06  176.539993  107436900  \n",
              "\n",
              "[3445 rows x 7 columns]"
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              "      <th>2024-03-01</th>\n",
              "      <td>2024-03-01</td>\n",
              "      <td>200.520004</td>\n",
              "      <td>204.520004</td>\n",
              "      <td>198.500000</td>\n",
              "      <td>202.639999</td>\n",
              "      <td>202.639999</td>\n",
              "      <td>82099200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-04</th>\n",
              "      <td>2024-03-04</td>\n",
              "      <td>198.729996</td>\n",
              "      <td>199.750000</td>\n",
              "      <td>186.720001</td>\n",
              "      <td>188.139999</td>\n",
              "      <td>188.139999</td>\n",
              "      <td>134334900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-05</th>\n",
              "      <td>2024-03-05</td>\n",
              "      <td>183.050003</td>\n",
              "      <td>184.589996</td>\n",
              "      <td>177.570007</td>\n",
              "      <td>180.740005</td>\n",
              "      <td>180.740005</td>\n",
              "      <td>119660800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-06</th>\n",
              "      <td>2024-03-06</td>\n",
              "      <td>179.990005</td>\n",
              "      <td>181.580002</td>\n",
              "      <td>173.699997</td>\n",
              "      <td>176.539993</td>\n",
              "      <td>176.539993</td>\n",
              "      <td>107436900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3445 rows × 7 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "      border-bottom-color: var(--fill-color);\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",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 3445,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-06-29 00:00:00\",\n        \"max\": \"2024-03-06 00:00:00\",\n        \"num_unique_values\": 3445,\n        \"samples\": [\n          \"2012-03-30 00:00:00\",\n          \"2010-09-10 00:00:00\",\n          \"2013-08-23 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.04437828241103,\n        \"min\": 1.0759999752044678,\n        \"max\": 411.4700012207031,\n        \"num_unique_values\": 3143,\n        \"samples\": [\n          268.30999755859375,\n          250.07000732421875,\n          16.72333335876465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.29817067760874,\n        \"min\": 1.108667016029358,\n        \"max\": 414.4966735839844,\n        \"num_unique_values\": 3135,\n        \"samples\": [\n          15.652000427246094,\n          2.629333019256592,\n          1.9713330268859863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.55555106142745,\n        \"min\": 0.9986670017242432,\n        \"max\": 405.6666564941406,\n        \"num_unique_values\": 3152,\n        \"samples\": [\n          191.9499969482422,\n          18.511333465576172,\n          207.52333068847656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79563803,\n        \"min\": 1777500,\n        \"max\": 914082000,\n        \"num_unique_values\": 3414,\n        \"samples\": [\n          85110000,\n          57526500,\n          9949500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla=df_tesla.set_index(\"Date\",drop=True)"
      ],
      "metadata": {
        "id": "ud7-9SX_BUOA"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Ci_ldQzqBq2U",
        "outputId": "637bd7f9-ccfe-4a72-aa2c-d0467f02f44d"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close     Volume\n",
              "Date                                                                    \n",
              "2010-06-29  1.266667  1.666667  1.169333  1.592667   1.592667  281494500\n",
              "2010-06-30  1.719333  2.028000  1.553333  1.588667   1.588667  257806500\n",
              "2010-07-01  1.666667  1.728000  1.351333  1.464000   1.464000  123282000\n",
              "2010-07-02  1.533333  1.540000  1.247333  1.280000   1.280000   77097000\n",
              "2010-07-06  1.333333  1.333333  1.055333  1.074000   1.074000  103003500"
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              "\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",
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              "      fill: #FFFFFF;\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-2897d209-19bf-4cfa-81a9-b434dea1dd68');\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",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </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-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-49a2a91f-0123-4bb4-b868-1a5601881ab7 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_tesla",
              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 3445,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.04437828241103,\n        \"min\": 1.0759999752044678,\n        \"max\": 411.4700012207031,\n        \"num_unique_values\": 3143,\n        \"samples\": [\n          268.30999755859375,\n          250.07000732421875,\n          16.72333335876465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.29817067760874,\n        \"min\": 1.108667016029358,\n        \"max\": 414.4966735839844,\n        \"num_unique_values\": 3135,\n        \"samples\": [\n          15.652000427246094,\n          2.629333019256592,\n          1.9713330268859863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.55555106142745,\n        \"min\": 0.9986670017242432,\n        \"max\": 405.6666564941406,\n        \"num_unique_values\": 3152,\n        \"samples\": [\n          191.9499969482422,\n          18.511333465576172,\n          207.52333068847656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79563803,\n        \"min\": 1777500,\n        \"max\": 914082000,\n        \"num_unique_values\": 3414,\n        \"samples\": [\n          85110000,\n          57526500,\n          9949500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime"
      ],
      "metadata": {
        "id": "-uUIz2SkByhG"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a=datetime.now()"
      ],
      "metadata": {
        "id": "xf7X6vp2B6t-"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datetime.now()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooThPtd8CJwQ",
        "outputId": "3d5a29c1-0a87-4b5a-ca63-bcdd19423bc2"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2024, 3, 7, 13, 14, 27, 342667)"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.day"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUEIXCHDCLto",
        "outputId": "3bb41587-9a1e-4ba6-86fe-8b47f13c7c6b"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.month"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AmMR6JNkC5N-",
        "outputId": "ca1ce9a5-02fe-4d53-debf-970bf9c76bfd"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.year"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiVohJiDC6YG",
        "outputId": "b272c4d4-2bbb-46f5-a476-c556b495c5fb"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2024"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.date()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Xc6a2lwC7Js",
        "outputId": "b707b798-c5cc-42c5-b530-117abaf4cfb9"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.date(2024, 3, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.time()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jw_gygIbDH9f",
        "outputId": "980b0673-a4a2-4e8b-b1bf-69dc581cec8b"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.time(13, 14, 27, 333745)"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.weekday()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bja9Y8JODIXn",
        "outputId": "09b8d359-fbe3-4e30-afdf-93d2f943802a"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.resample(rule=\"A\").min() #Rule A=year end frequency"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "VyP17u5sDLRm",
        "outputId": "b80b6a59-85d7-478b-a734-9e95467bd36c"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2010-12-31    1.076000    1.108667    0.998667    1.053333    1.053333   \n",
              "2011-12-31    1.452000    1.484667    1.407333    1.455333    1.455333   \n",
              "2012-12-31    1.774667    1.790000    1.509333    1.519333    1.519333   \n",
              "2013-12-31    2.205333    2.225333    2.140667    2.194000    2.194000   \n",
              "2014-12-31    9.366667    9.800000    9.111333    9.289333    9.289333   \n",
              "2015-12-31   12.388667   12.619333   12.093333   12.333333   12.333333   \n",
              "2016-12-31    9.488000   10.331333    9.403333    9.578000    9.578000   \n",
              "2017-12-31   14.316667   14.688667   14.064000   14.466000   14.466000   \n",
              "2018-12-31   16.851999   17.355333   16.306000   16.704000   16.704000   \n",
              "2019-12-31   12.073333   12.445333   11.799333   11.931333   11.931333   \n",
              "2020-12-31   24.980000   26.990667   23.367332   24.081333   24.081333   \n",
              "2021-12-31  184.183334  188.736664  179.830002  187.666672  187.666672   \n",
              "2022-12-31  110.349998  116.269997  108.239998  109.099998  109.099998   \n",
              "2023-12-31  103.000000  111.750000  101.809998  108.099998  108.099998   \n",
              "2024-12-31  177.210007  181.580002  173.699997  176.539993  176.539993   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2010-12-31   1777500  \n",
              "2011-12-31   3594000  \n",
              "2012-12-31   5473500  \n",
              "2013-12-31   6603000  \n",
              "2014-12-31  19983000  \n",
              "2015-12-31  10620000  \n",
              "2016-12-31  24892500  \n",
              "2017-12-31  32800500  \n",
              "2018-12-31  46210500  \n",
              "2019-12-31  36984000  \n",
              "2020-12-31  52073100  \n",
              "2021-12-31  29401800  \n",
              "2022-12-31  41864700  \n",
              "2023-12-31  65125200  \n",
              "2024-12-31  78841900  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-71cffa3e-0728-439c-bc32-a70a3108e901\" 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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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-12-31</th>\n",
              "      <td>1.076000</td>\n",
              "      <td>1.108667</td>\n",
              "      <td>0.998667</td>\n",
              "      <td>1.053333</td>\n",
              "      <td>1.053333</td>\n",
              "      <td>1777500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-31</th>\n",
              "      <td>1.452000</td>\n",
              "      <td>1.484667</td>\n",
              "      <td>1.407333</td>\n",
              "      <td>1.455333</td>\n",
              "      <td>1.455333</td>\n",
              "      <td>3594000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-31</th>\n",
              "      <td>1.774667</td>\n",
              "      <td>1.790000</td>\n",
              "      <td>1.509333</td>\n",
              "      <td>1.519333</td>\n",
              "      <td>1.519333</td>\n",
              "      <td>5473500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-12-31</th>\n",
              "      <td>2.205333</td>\n",
              "      <td>2.225333</td>\n",
              "      <td>2.140667</td>\n",
              "      <td>2.194000</td>\n",
              "      <td>2.194000</td>\n",
              "      <td>6603000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-31</th>\n",
              "      <td>9.366667</td>\n",
              "      <td>9.800000</td>\n",
              "      <td>9.111333</td>\n",
              "      <td>9.289333</td>\n",
              "      <td>9.289333</td>\n",
              "      <td>19983000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-31</th>\n",
              "      <td>12.388667</td>\n",
              "      <td>12.619333</td>\n",
              "      <td>12.093333</td>\n",
              "      <td>12.333333</td>\n",
              "      <td>12.333333</td>\n",
              "      <td>10620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31</th>\n",
              "      <td>9.488000</td>\n",
              "      <td>10.331333</td>\n",
              "      <td>9.403333</td>\n",
              "      <td>9.578000</td>\n",
              "      <td>9.578000</td>\n",
              "      <td>24892500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-31</th>\n",
              "      <td>14.316667</td>\n",
              "      <td>14.688667</td>\n",
              "      <td>14.064000</td>\n",
              "      <td>14.466000</td>\n",
              "      <td>14.466000</td>\n",
              "      <td>32800500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>16.851999</td>\n",
              "      <td>17.355333</td>\n",
              "      <td>16.306000</td>\n",
              "      <td>16.704000</td>\n",
              "      <td>16.704000</td>\n",
              "      <td>46210500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>12.073333</td>\n",
              "      <td>12.445333</td>\n",
              "      <td>11.799333</td>\n",
              "      <td>11.931333</td>\n",
              "      <td>11.931333</td>\n",
              "      <td>36984000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>24.980000</td>\n",
              "      <td>26.990667</td>\n",
              "      <td>23.367332</td>\n",
              "      <td>24.081333</td>\n",
              "      <td>24.081333</td>\n",
              "      <td>52073100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-31</th>\n",
              "      <td>184.183334</td>\n",
              "      <td>188.736664</td>\n",
              "      <td>179.830002</td>\n",
              "      <td>187.666672</td>\n",
              "      <td>187.666672</td>\n",
              "      <td>29401800</td>\n",
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              "    <tr>\n",
              "      <th>2022-12-31</th>\n",
              "      <td>110.349998</td>\n",
              "      <td>116.269997</td>\n",
              "      <td>108.239998</td>\n",
              "      <td>109.099998</td>\n",
              "      <td>109.099998</td>\n",
              "      <td>41864700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-12-31</th>\n",
              "      <td>103.000000</td>\n",
              "      <td>111.750000</td>\n",
              "      <td>101.809998</td>\n",
              "      <td>108.099998</td>\n",
              "      <td>108.099998</td>\n",
              "      <td>65125200</td>\n",
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              "    <tr>\n",
              "      <th>2024-12-31</th>\n",
              "      <td>177.210007</td>\n",
              "      <td>181.580002</td>\n",
              "      <td>173.699997</td>\n",
              "      <td>176.539993</td>\n",
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              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64.81984231387281,\n        \"min\": 1.0759999752044678,\n        \"max\": 184.18333435058594,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          12.073332786560059,\n          184.18333435058594,\n          1.0759999752044678\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 66.99938927116023,\n        \"min\": 1.108667016029358,\n        \"max\": 188.73666381835938,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          12.445332527160645,\n          188.73666381835938,\n          1.108667016029358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.53695776580975,\n        \"min\": 0.9986670017242432,\n        \"max\": 179.8300018310547,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          11.799332618713379,\n          179.8300018310547,\n          0.9986670017242432\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.54739347953054,\n        \"min\": 1.053333044052124,\n        \"max\": 187.6666717529297,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          11.9313325881958,\n          187.6666717529297,\n          1.053333044052124\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.54739347953054,\n        \"min\": 1.053333044052124,\n        \"max\": 187.6666717529297,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          11.9313325881958,\n          187.6666717529297,\n          1.053333044052124\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23466126,\n        \"min\": 1777500,\n        \"max\": 78841900,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          36984000,\n          29401800,\n          1777500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.resample(rule=\"A\").max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "nNlr6AYED-h4",
        "outputId": "c8f56edf-d0dc-479d-98b3-63d1c75b66c2"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2010-12-31    2.391333    2.428000    2.316667    2.364667    2.364667   \n",
              "2011-12-31    2.308667    2.333333    2.268667    2.329333    2.329333   \n",
              "2012-12-31    2.546000    2.663333    2.474000    2.534000    2.534000   \n",
              "2013-12-31   12.930667   12.966667   12.558000   12.891333   12.891333   \n",
              "2014-12-31   19.177999   19.427999   18.693333   19.069332   19.069332   \n",
              "2015-12-31   18.680000   19.110001   18.420000   18.817333   18.817333   \n",
              "2016-12-31   17.763332   17.955999   16.967333   17.694668   17.694668   \n",
              "2017-12-31   25.779333   25.974001   25.290001   25.666668   25.666668   \n",
              "2018-12-31   25.000000   25.830667   24.474667   25.304667   25.304667   \n",
              "2019-12-31   29.000000   29.020666   28.423332   28.729334   28.729334   \n",
              "2020-12-31  233.330002  239.573334  230.373337  235.223328  235.223328   \n",
              "2021-12-31  411.470001  414.496674  405.666656  409.970001  409.970001   \n",
              "2022-12-31  396.516663  402.666656  378.679993  399.926666  399.926666   \n",
              "2023-12-31  296.040009  299.290009  289.519989  293.339996  293.339996   \n",
              "2024-12-31  250.080002  251.250000  244.410004  248.419998  248.419998   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2010-12-31  281494500  \n",
              "2011-12-31  172767000  \n",
              "2012-12-31   85206000  \n",
              "2013-12-31  557458500  \n",
              "2014-12-31  490225500  \n",
              "2015-12-31  234744000  \n",
              "2016-12-31  356136000  \n",
              "2017-12-31  296871000  \n",
              "2018-12-31  504745500  \n",
              "2019-12-31  450091500  \n",
              "2020-12-31  914082000  \n",
              "2021-12-31  268189500  \n",
              "2022-12-31  221923300  \n",
              "2023-12-31  306590600  \n",
              "2024-12-31  198076800  "
            ],
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              "      <td>17.694668</td>\n",
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              "      <td>25.779333</td>\n",
              "      <td>25.974001</td>\n",
              "      <td>25.290001</td>\n",
              "      <td>25.666668</td>\n",
              "      <td>25.666668</td>\n",
              "      <td>296871000</td>\n",
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              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>25.830667</td>\n",
              "      <td>24.474667</td>\n",
              "      <td>25.304667</td>\n",
              "      <td>25.304667</td>\n",
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              "      <th>2019-12-31</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>29.020666</td>\n",
              "      <td>28.423332</td>\n",
              "      <td>28.729334</td>\n",
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              "      <th>2020-12-31</th>\n",
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              "    <tr>\n",
              "      <th>2021-12-31</th>\n",
              "      <td>411.470001</td>\n",
              "      <td>414.496674</td>\n",
              "      <td>405.666656</td>\n",
              "      <td>409.970001</td>\n",
              "      <td>409.970001</td>\n",
              "      <td>268189500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-31</th>\n",
              "      <td>396.516663</td>\n",
              "      <td>402.666656</td>\n",
              "      <td>378.679993</td>\n",
              "      <td>399.926666</td>\n",
              "      <td>399.926666</td>\n",
              "      <td>221923300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-12-31</th>\n",
              "      <td>296.040009</td>\n",
              "      <td>299.290009</td>\n",
              "      <td>289.519989</td>\n",
              "      <td>293.339996</td>\n",
              "      <td>293.339996</td>\n",
              "      <td>306590600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-12-31</th>\n",
              "      <td>250.080002</td>\n",
              "      <td>251.250000</td>\n",
              "      <td>244.410004</td>\n",
              "      <td>248.419998</td>\n",
              "      <td>248.419998</td>\n",
              "      <td>198076800</td>\n",
              "    </tr>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 153.98593037825864,\n        \"min\": 2.308666944503784,\n        \"max\": 411.4700012207031,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          29.0,\n          411.4700012207031,\n          2.3913331031799316\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 155.7932217436263,\n        \"min\": 2.3333330154418945,\n        \"max\": 414.4966735839844,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          29.020666122436523,\n          414.4966735839844,\n          2.427999973297119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 150.00075994270165,\n        \"min\": 2.268666982650757,\n        \"max\": 405.6666564941406,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          28.42333221435547,\n          405.6666564941406,\n          2.316667079925537\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.01227349870177,\n        \"min\": 2.3293330669403076,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          28.729333877563477,\n          409.9700012207031,\n          2.3646669387817383\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.01227349870177,\n        \"min\": 2.3293330669403076,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          28.729333877563477,\n          409.9700012207031,\n          2.3646669387817383\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 204373288,\n        \"min\": 85206000,\n        \"max\": 914082000,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          450091500,\n          268189500,\n          281494500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.resample(rule=\"A\").min()[\"Open\"].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "kD0nP4L5EB9i",
        "outputId": "55306315-fdcd-46c5-ba9e-59d79970e288"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 128
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.resample(rule=\"QS\").min() #Rule A=year end frequency"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "r6CsQJ1tEPl3",
        "outputId": "c9b8922f-5e31-47e6-9268-abb51f4683c9"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2010-04-01    1.266667    1.666667    1.169333    1.588667    1.588667   \n",
              "2010-07-01    1.076000    1.108667    0.998667    1.053333    1.053333   \n",
              "2010-10-01    1.344000    1.352000    1.333333    1.336667    1.336667   \n",
              "2011-01-01    1.452000    1.484667    1.407333    1.455333    1.455333   \n",
              "2011-04-01    1.658000    1.680667    1.613333    1.643333    1.643333   \n",
              "2011-07-01    1.462000    1.540667    1.433333    1.463333    1.463333   \n",
              "2011-10-01    1.552667    1.621333    1.528667    1.577333    1.577333   \n",
              "2012-01-01    1.774667    1.792000    1.509333    1.519333    1.519333   \n",
              "2012-04-01    1.838667    1.892667    1.788667    1.837333    1.837333   \n",
              "2012-07-01    1.789333    1.790000    1.701333    1.740000    1.740000   \n",
              "2012-10-01    1.825333    1.853333    1.790667    1.822000    1.822000   \n",
              "2013-01-01    2.205333    2.225333    2.140667    2.194000    2.194000   \n",
              "2013-04-01    2.713333    2.788667    2.680667    2.700000    2.700000   \n",
              "2013-07-01    7.101333    7.851333    6.966667    7.270000    7.270000   \n",
              "2013-10-01    7.958667    8.181333    7.740000    8.033333    8.033333   \n",
              "2014-01-01    9.366667    9.800000    9.111333    9.289333    9.289333   \n",
              "2014-04-01   11.990667   12.226667   11.814667   11.906000   11.906000   \n",
              "2014-07-01   14.396667   14.703333   14.240000   14.360000   14.360000   \n",
              "2014-10-01   12.870667   13.578667   12.843333   13.187333   13.187333   \n",
              "2015-01-01   12.388667   12.619333   12.093333   12.333333   12.333333   \n",
              "2015-04-01   12.580000   12.820000   12.403333   12.506000   12.506000   \n",
              "2015-07-01   13.519333   15.200000   13.000000   14.591333   14.591333   \n",
              "2015-10-01   13.739333   14.108667   13.466667   13.795333   13.795333   \n",
              "2016-01-01    9.488000   10.331333    9.403333    9.578000    9.578000   \n",
              "2016-04-01   12.670000   13.008000   12.524667   12.876667   12.876667   \n",
              "2016-07-01   13.000000   13.194667   12.896667   12.964667   12.964667   \n",
              "2016-10-01   12.167333   12.315333   11.879333   12.096667   12.096667   \n",
              "2017-01-01   14.316667   14.688667   14.064000   14.466000   14.466000   \n",
              "2017-04-01   19.126667   19.933332   18.972000   19.666668   19.666668   \n",
              "2017-07-01   20.860001   21.133333   20.208668   20.588667   20.588667   \n",
              "2017-10-01   19.966667   20.297333   19.508667   19.950666   19.950666   \n",
              "2018-01-01   17.099333   17.912001   16.547333   17.185333   17.185333   \n",
              "2018-04-01   16.851999   17.355333   16.306000   16.832001   16.832001   \n",
              "2018-07-01   17.340000   17.889999   16.816668   17.549334   17.549334   \n",
              "2018-10-01   17.016666   17.457333   16.518000   16.704000   16.704000   \n",
              "2019-01-01   17.313999   17.545334   16.964001   17.361334   17.361334   \n",
              "2019-04-01   12.073333   12.445333   11.799333   11.931333   11.931333   \n",
              "2019-07-01   14.240000   14.334667   14.066667   14.093333   14.093333   \n",
              "2019-10-01   15.320000   15.632000   14.952000   15.428667   15.428667   \n",
              "2020-01-01   24.980000   26.990667   23.367332   24.081333   24.081333   \n",
              "2020-04-01   32.068668   32.950668   29.760000   30.298000   30.298000   \n",
              "2020-07-01   72.199997   75.688667   72.033333   74.641998   74.641998   \n",
              "2020-10-01  131.333328  135.660004  126.370003  129.346664  129.346664   \n",
              "2021-01-01  200.183334  205.493332  179.830002  187.666672  187.666672   \n",
              "2021-04-01  184.183334  188.736664  182.326660  187.820007  187.820007   \n",
              "2021-07-01  209.456665  215.733337  206.820007  214.460007  214.460007   \n",
              "2021-10-01  258.733337  260.260010  254.529999  258.406677  258.406677   \n",
              "2022-01-01  233.463333  266.899994  233.333328  254.679993  254.679993   \n",
              "2022-04-01  207.949997  217.973328  206.856674  209.386673  209.386673   \n",
              "2022-07-01  223.000000  230.229996  216.166672  227.263336  227.263336   \n",
              "2022-10-01  110.349998  116.269997  108.239998  109.099998  109.099998   \n",
              "2023-01-01  103.000000  111.750000  101.809998  108.099998  108.099998   \n",
              "2023-04-01  152.639999  160.479996  152.369995  153.750000  153.750000   \n",
              "2023-07-01  214.119995  217.580002  212.360001  215.490005  215.490005   \n",
              "2023-10-01  196.119995  202.800003  194.070007  197.360001  197.360001   \n",
              "2024-01-01  177.210007  181.580002  173.699997  176.539993  176.539993   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2010-04-01  257806500  \n",
              "2010-07-01    3016500  \n",
              "2010-10-01    1777500  \n",
              "2011-01-01    6175500  \n",
              "2011-04-01    8230500  \n",
              "2011-07-01    5493000  \n",
              "2011-10-01    3594000  \n",
              "2012-01-01    5473500  \n",
              "2012-04-01    6208500  \n",
              "2012-07-01    7623000  \n",
              "2012-10-01    5637000  \n",
              "2013-01-01    6603000  \n",
              "2013-04-01   23286000  \n",
              "2013-07-01   72100500  \n",
              "2013-10-01   63936000  \n",
              "2014-01-01   57268500  \n",
              "2014-04-01   42085500  \n",
              "2014-07-01   40950000  \n",
              "2014-10-01   19983000  \n",
              "2015-01-01   39474000  \n",
              "2015-04-01   21334500  \n",
              "2015-07-01   24240000  \n",
              "2015-10-01   10620000  \n",
              "2016-01-01   40480500  \n",
              "2016-04-01   30492000  \n",
              "2016-07-01   24892500  \n",
              "2016-10-01   28162500  \n",
              "2017-01-01   32800500  \n",
              "2017-04-01   45535500  \n",
              "2017-07-01   45742500  \n",
              "2017-10-01   48661500  \n",
              "2018-01-01   48294000  \n",
              "2018-04-01   58126500  \n",
              "2018-07-01   54039000  \n",
              "2018-10-01   46210500  \n",
              "2019-01-01   55150500  \n",
              "2019-04-01   76897500  \n",
              "2019-07-01   48382500  \n",
              "2019-10-01   36984000  \n",
              "2020-01-01  142981500  \n",
              "2020-04-01   95436000  \n",
              "2020-07-01   74217000  \n",
              "2020-10-01   52073100  \n",
              "2021-01-01   45473100  \n",
              "2021-04-01   48615900  \n",
              "2021-07-01   29401800  \n",
              "2021-10-01   35042700  \n",
              "2022-01-01   48992700  \n",
              "2022-04-01   49847700  \n",
              "2022-07-01   41864700  \n",
              "2022-10-01   50672700  \n",
              "2023-01-01   98654600  \n",
              "2023-04-01   88965000  \n",
              "2023-07-01   83166000  \n",
              "2023-10-01   65125200  \n",
              "2024-01-01   78841900  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d863cdf-60d5-467c-a35c-4cfd00d4c0d1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "\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>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",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-04-01</th>\n",
              "      <td>1.266667</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>1.169333</td>\n",
              "      <td>1.588667</td>\n",
              "      <td>1.588667</td>\n",
              "      <td>257806500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-01</th>\n",
              "      <td>1.076000</td>\n",
              "      <td>1.108667</td>\n",
              "      <td>0.998667</td>\n",
              "      <td>1.053333</td>\n",
              "      <td>1.053333</td>\n",
              "      <td>3016500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-10-01</th>\n",
              "      <td>1.344000</td>\n",
              "      <td>1.352000</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>1.336667</td>\n",
              "      <td>1.336667</td>\n",
              "      <td>1777500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-01</th>\n",
              "      <td>1.452000</td>\n",
              "      <td>1.484667</td>\n",
              "      <td>1.407333</td>\n",
              "      <td>1.455333</td>\n",
              "      <td>1.455333</td>\n",
              "      <td>6175500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-04-01</th>\n",
              "      <td>1.658000</td>\n",
              "      <td>1.680667</td>\n",
              "      <td>1.613333</td>\n",
              "      <td>1.643333</td>\n",
              "      <td>1.643333</td>\n",
              "      <td>8230500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-07-01</th>\n",
              "      <td>1.462000</td>\n",
              "      <td>1.540667</td>\n",
              "      <td>1.433333</td>\n",
              "      <td>1.463333</td>\n",
              "      <td>1.463333</td>\n",
              "      <td>5493000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-10-01</th>\n",
              "      <td>1.552667</td>\n",
              "      <td>1.621333</td>\n",
              "      <td>1.528667</td>\n",
              "      <td>1.577333</td>\n",
              "      <td>1.577333</td>\n",
              "      <td>3594000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-01</th>\n",
              "      <td>1.774667</td>\n",
              "      <td>1.792000</td>\n",
              "      <td>1.509333</td>\n",
              "      <td>1.519333</td>\n",
              "      <td>1.519333</td>\n",
              "      <td>5473500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-04-01</th>\n",
              "      <td>1.838667</td>\n",
              "      <td>1.892667</td>\n",
              "      <td>1.788667</td>\n",
              "      <td>1.837333</td>\n",
              "      <td>1.837333</td>\n",
              "      <td>6208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-07-01</th>\n",
              "      <td>1.789333</td>\n",
              "      <td>1.790000</td>\n",
              "      <td>1.701333</td>\n",
              "      <td>1.740000</td>\n",
              "      <td>1.740000</td>\n",
              "      <td>7623000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-01</th>\n",
              "      <td>1.825333</td>\n",
              "      <td>1.853333</td>\n",
              "      <td>1.790667</td>\n",
              "      <td>1.822000</td>\n",
              "      <td>1.822000</td>\n",
              "      <td>5637000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-01</th>\n",
              "      <td>2.205333</td>\n",
              "      <td>2.225333</td>\n",
              "      <td>2.140667</td>\n",
              "      <td>2.194000</td>\n",
              "      <td>2.194000</td>\n",
              "      <td>6603000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-01</th>\n",
              "      <td>2.713333</td>\n",
              "      <td>2.788667</td>\n",
              "      <td>2.680667</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>23286000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-07-01</th>\n",
              "      <td>7.101333</td>\n",
              "      <td>7.851333</td>\n",
              "      <td>6.966667</td>\n",
              "      <td>7.270000</td>\n",
              "      <td>7.270000</td>\n",
              "      <td>72100500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-01</th>\n",
              "      <td>7.958667</td>\n",
              "      <td>8.181333</td>\n",
              "      <td>7.740000</td>\n",
              "      <td>8.033333</td>\n",
              "      <td>8.033333</td>\n",
              "      <td>63936000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>9.366667</td>\n",
              "      <td>9.800000</td>\n",
              "      <td>9.111333</td>\n",
              "      <td>9.289333</td>\n",
              "      <td>9.289333</td>\n",
              "      <td>57268500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-01</th>\n",
              "      <td>11.990667</td>\n",
              "      <td>12.226667</td>\n",
              "      <td>11.814667</td>\n",
              "      <td>11.906000</td>\n",
              "      <td>11.906000</td>\n",
              "      <td>42085500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-07-01</th>\n",
              "      <td>14.396667</td>\n",
              "      <td>14.703333</td>\n",
              "      <td>14.240000</td>\n",
              "      <td>14.360000</td>\n",
              "      <td>14.360000</td>\n",
              "      <td>40950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-10-01</th>\n",
              "      <td>12.870667</td>\n",
              "      <td>13.578667</td>\n",
              "      <td>12.843333</td>\n",
              "      <td>13.187333</td>\n",
              "      <td>13.187333</td>\n",
              "      <td>19983000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-01</th>\n",
              "      <td>12.388667</td>\n",
              "      <td>12.619333</td>\n",
              "      <td>12.093333</td>\n",
              "      <td>12.333333</td>\n",
              "      <td>12.333333</td>\n",
              "      <td>39474000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-04-01</th>\n",
              "      <td>12.580000</td>\n",
              "      <td>12.820000</td>\n",
              "      <td>12.403333</td>\n",
              "      <td>12.506000</td>\n",
              "      <td>12.506000</td>\n",
              "      <td>21334500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-01</th>\n",
              "      <td>13.519333</td>\n",
              "      <td>15.200000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>14.591333</td>\n",
              "      <td>14.591333</td>\n",
              "      <td>24240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-10-01</th>\n",
              "      <td>13.739333</td>\n",
              "      <td>14.108667</td>\n",
              "      <td>13.466667</td>\n",
              "      <td>13.795333</td>\n",
              "      <td>13.795333</td>\n",
              "      <td>10620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01</th>\n",
              "      <td>9.488000</td>\n",
              "      <td>10.331333</td>\n",
              "      <td>9.403333</td>\n",
              "      <td>9.578000</td>\n",
              "      <td>9.578000</td>\n",
              "      <td>40480500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01</th>\n",
              "      <td>12.670000</td>\n",
              "      <td>13.008000</td>\n",
              "      <td>12.524667</td>\n",
              "      <td>12.876667</td>\n",
              "      <td>12.876667</td>\n",
              "      <td>30492000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-07-01</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>13.194667</td>\n",
              "      <td>12.896667</td>\n",
              "      <td>12.964667</td>\n",
              "      <td>12.964667</td>\n",
              "      <td>24892500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-10-01</th>\n",
              "      <td>12.167333</td>\n",
              "      <td>12.315333</td>\n",
              "      <td>11.879333</td>\n",
              "      <td>12.096667</td>\n",
              "      <td>12.096667</td>\n",
              "      <td>28162500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>14.316667</td>\n",
              "      <td>14.688667</td>\n",
              "      <td>14.064000</td>\n",
              "      <td>14.466000</td>\n",
              "      <td>14.466000</td>\n",
              "      <td>32800500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-04-01</th>\n",
              "      <td>19.126667</td>\n",
              "      <td>19.933332</td>\n",
              "      <td>18.972000</td>\n",
              "      <td>19.666668</td>\n",
              "      <td>19.666668</td>\n",
              "      <td>45535500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-01</th>\n",
              "      <td>20.860001</td>\n",
              "      <td>21.133333</td>\n",
              "      <td>20.208668</td>\n",
              "      <td>20.588667</td>\n",
              "      <td>20.588667</td>\n",
              "      <td>45742500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-10-01</th>\n",
              "      <td>19.966667</td>\n",
              "      <td>20.297333</td>\n",
              "      <td>19.508667</td>\n",
              "      <td>19.950666</td>\n",
              "      <td>19.950666</td>\n",
              "      <td>48661500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>17.099333</td>\n",
              "      <td>17.912001</td>\n",
              "      <td>16.547333</td>\n",
              "      <td>17.185333</td>\n",
              "      <td>17.185333</td>\n",
              "      <td>48294000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-01</th>\n",
              "      <td>16.851999</td>\n",
              "      <td>17.355333</td>\n",
              "      <td>16.306000</td>\n",
              "      <td>16.832001</td>\n",
              "      <td>16.832001</td>\n",
              "      <td>58126500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-07-01</th>\n",
              "      <td>17.340000</td>\n",
              "      <td>17.889999</td>\n",
              "      <td>16.816668</td>\n",
              "      <td>17.549334</td>\n",
              "      <td>17.549334</td>\n",
              "      <td>54039000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-01</th>\n",
              "      <td>17.016666</td>\n",
              "      <td>17.457333</td>\n",
              "      <td>16.518000</td>\n",
              "      <td>16.704000</td>\n",
              "      <td>16.704000</td>\n",
              "      <td>46210500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-01</th>\n",
              "      <td>17.313999</td>\n",
              "      <td>17.545334</td>\n",
              "      <td>16.964001</td>\n",
              "      <td>17.361334</td>\n",
              "      <td>17.361334</td>\n",
              "      <td>55150500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-01</th>\n",
              "      <td>12.073333</td>\n",
              "      <td>12.445333</td>\n",
              "      <td>11.799333</td>\n",
              "      <td>11.931333</td>\n",
              "      <td>11.931333</td>\n",
              "      <td>76897500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-07-01</th>\n",
              "      <td>14.240000</td>\n",
              "      <td>14.334667</td>\n",
              "      <td>14.066667</td>\n",
              "      <td>14.093333</td>\n",
              "      <td>14.093333</td>\n",
              "      <td>48382500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-01</th>\n",
              "      <td>15.320000</td>\n",
              "      <td>15.632000</td>\n",
              "      <td>14.952000</td>\n",
              "      <td>15.428667</td>\n",
              "      <td>15.428667</td>\n",
              "      <td>36984000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>24.980000</td>\n",
              "      <td>26.990667</td>\n",
              "      <td>23.367332</td>\n",
              "      <td>24.081333</td>\n",
              "      <td>24.081333</td>\n",
              "      <td>142981500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>32.068668</td>\n",
              "      <td>32.950668</td>\n",
              "      <td>29.760000</td>\n",
              "      <td>30.298000</td>\n",
              "      <td>30.298000</td>\n",
              "      <td>95436000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>72.199997</td>\n",
              "      <td>75.688667</td>\n",
              "      <td>72.033333</td>\n",
              "      <td>74.641998</td>\n",
              "      <td>74.641998</td>\n",
              "      <td>74217000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-01</th>\n",
              "      <td>131.333328</td>\n",
              "      <td>135.660004</td>\n",
              "      <td>126.370003</td>\n",
              "      <td>129.346664</td>\n",
              "      <td>129.346664</td>\n",
              "      <td>52073100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-01</th>\n",
              "      <td>200.183334</td>\n",
              "      <td>205.493332</td>\n",
              "      <td>179.830002</td>\n",
              "      <td>187.666672</td>\n",
              "      <td>187.666672</td>\n",
              "      <td>45473100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-01</th>\n",
              "      <td>184.183334</td>\n",
              "      <td>188.736664</td>\n",
              "      <td>182.326660</td>\n",
              "      <td>187.820007</td>\n",
              "      <td>187.820007</td>\n",
              "      <td>48615900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-01</th>\n",
              "      <td>209.456665</td>\n",
              "      <td>215.733337</td>\n",
              "      <td>206.820007</td>\n",
              "      <td>214.460007</td>\n",
              "      <td>214.460007</td>\n",
              "      <td>29401800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-01</th>\n",
              "      <td>258.733337</td>\n",
              "      <td>260.260010</td>\n",
              "      <td>254.529999</td>\n",
              "      <td>258.406677</td>\n",
              "      <td>258.406677</td>\n",
              "      <td>35042700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-01</th>\n",
              "      <td>233.463333</td>\n",
              "      <td>266.899994</td>\n",
              "      <td>233.333328</td>\n",
              "      <td>254.679993</td>\n",
              "      <td>254.679993</td>\n",
              "      <td>48992700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-01</th>\n",
              "      <td>207.949997</td>\n",
              "      <td>217.973328</td>\n",
              "      <td>206.856674</td>\n",
              "      <td>209.386673</td>\n",
              "      <td>209.386673</td>\n",
              "      <td>49847700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-07-01</th>\n",
              "      <td>223.000000</td>\n",
              "      <td>230.229996</td>\n",
              "      <td>216.166672</td>\n",
              "      <td>227.263336</td>\n",
              "      <td>227.263336</td>\n",
              "      <td>41864700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-01</th>\n",
              "      <td>110.349998</td>\n",
              "      <td>116.269997</td>\n",
              "      <td>108.239998</td>\n",
              "      <td>109.099998</td>\n",
              "      <td>109.099998</td>\n",
              "      <td>50672700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-01</th>\n",
              "      <td>103.000000</td>\n",
              "      <td>111.750000</td>\n",
              "      <td>101.809998</td>\n",
              "      <td>108.099998</td>\n",
              "      <td>108.099998</td>\n",
              "      <td>98654600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-04-01</th>\n",
              "      <td>152.639999</td>\n",
              "      <td>160.479996</td>\n",
              "      <td>152.369995</td>\n",
              "      <td>153.750000</td>\n",
              "      <td>153.750000</td>\n",
              "      <td>88965000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-01</th>\n",
              "      <td>214.119995</td>\n",
              "      <td>217.580002</td>\n",
              "      <td>212.360001</td>\n",
              "      <td>215.490005</td>\n",
              "      <td>215.490005</td>\n",
              "      <td>83166000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-10-01</th>\n",
              "      <td>196.119995</td>\n",
              "      <td>202.800003</td>\n",
              "      <td>194.070007</td>\n",
              "      <td>197.360001</td>\n",
              "      <td>197.360001</td>\n",
              "      <td>65125200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-01-01</th>\n",
              "      <td>177.210007</td>\n",
              "      <td>181.580002</td>\n",
              "      <td>173.699997</td>\n",
              "      <td>176.539993</td>\n",
              "      <td>176.539993</td>\n",
              "      <td>78841900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 56,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79.78272819669496,\n        \"min\": 1.0759999752044678,\n        \"max\": 258.73333740234375,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          1.2666670083999634,\n          1.4620000123977661,\n          17.34000015258789\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 83.18948772295246,\n        \"min\": 1.108667016029358,\n        \"max\": 266.8999938964844,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          1.6666669845581055,\n          1.5406670570373535,\n          17.889999389648438\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.25393438044017,\n        \"min\": 0.9986670017242432,\n        \"max\": 254.52999877929688,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          1.1693329811096191,\n          1.4333330392837524,\n          16.816667556762695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.88973589081395,\n        \"min\": 1.053333044052124,\n        \"max\": 258.40667724609375,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          1.5886670351028442,\n          1.463333010673523,\n          17.549333572387695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.88973589081395,\n        \"min\": 1.053333044052124,\n        \"max\": 258.40667724609375,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          1.5886670351028442,\n          1.463333010673523,\n          17.549333572387695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40650636,\n        \"min\": 1777500,\n        \"max\": 257806500,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          257806500,\n          5493000,\n          54039000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.resample(rule=\"QS\").min()[\"Open\"].plot() #QS=Quater"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "4fWb6AaXEkha",
        "outputId": "88b8b8d8-47eb-45e5-9161-705d352b7141"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.resample(rule=\"BA\").min()[\"Open\"].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "kwHCn9FwEpA9",
        "outputId": "779fb24a-e1c1-4b25-eeb3-eed6f84f9da7"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.resample(rule=\"BA\").min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "f2F9IYlTEz2M",
        "outputId": "5d173b5c-b1de-45e9-f685-c71e62c53a4f"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2010-12-31    1.076000    1.108667    0.998667    1.053333    1.053333   \n",
              "2011-12-30    1.452000    1.484667    1.407333    1.455333    1.455333   \n",
              "2012-12-31    1.774667    1.790000    1.509333    1.519333    1.519333   \n",
              "2013-12-31    2.205333    2.225333    2.140667    2.194000    2.194000   \n",
              "2014-12-31    9.366667    9.800000    9.111333    9.289333    9.289333   \n",
              "2015-12-31   12.388667   12.619333   12.093333   12.333333   12.333333   \n",
              "2016-12-30    9.488000   10.331333    9.403333    9.578000    9.578000   \n",
              "2017-12-29   14.316667   14.688667   14.064000   14.466000   14.466000   \n",
              "2018-12-31   16.851999   17.355333   16.306000   16.704000   16.704000   \n",
              "2019-12-31   12.073333   12.445333   11.799333   11.931333   11.931333   \n",
              "2020-12-31   24.980000   26.990667   23.367332   24.081333   24.081333   \n",
              "2021-12-31  184.183334  188.736664  179.830002  187.666672  187.666672   \n",
              "2022-12-30  110.349998  116.269997  108.239998  109.099998  109.099998   \n",
              "2023-12-29  103.000000  111.750000  101.809998  108.099998  108.099998   \n",
              "2024-12-31  177.210007  181.580002  173.699997  176.539993  176.539993   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2010-12-31   1777500  \n",
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              "2020-12-31  52073100  \n",
              "2021-12-31  29401800  \n",
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              "      <th>2017-12-29</th>\n",
              "      <td>14.316667</td>\n",
              "      <td>14.688667</td>\n",
              "      <td>14.064000</td>\n",
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              "      <th>2018-12-31</th>\n",
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              "      <td>17.355333</td>\n",
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          },
          "metadata": {},
          "execution_count": 136
        }
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    {
      "cell_type": "code",
      "source": [
        "df_tesla.resample(rule=\"BQS\").min()"
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          "height": 1000
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        "id": "8FkmYR3XE9oQ",
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      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2010-04-01    1.266667    1.666667    1.169333    1.588667    1.588667   \n",
              "2010-07-01    1.076000    1.108667    0.998667    1.053333    1.053333   \n",
              "2010-10-01    1.344000    1.352000    1.333333    1.336667    1.336667   \n",
              "2011-01-03    1.452000    1.484667    1.407333    1.455333    1.455333   \n",
              "2011-04-01    1.658000    1.680667    1.613333    1.643333    1.643333   \n",
              "2011-07-01    1.462000    1.540667    1.433333    1.463333    1.463333   \n",
              "2011-10-03    1.552667    1.621333    1.528667    1.577333    1.577333   \n",
              "2012-01-02    1.774667    1.792000    1.509333    1.519333    1.519333   \n",
              "2012-04-02    1.838667    1.892667    1.788667    1.837333    1.837333   \n",
              "2012-07-02    1.789333    1.790000    1.701333    1.740000    1.740000   \n",
              "2012-10-01    1.825333    1.853333    1.790667    1.822000    1.822000   \n",
              "2013-01-01    2.205333    2.225333    2.140667    2.194000    2.194000   \n",
              "2013-04-01    2.713333    2.788667    2.680667    2.700000    2.700000   \n",
              "2013-07-01    7.101333    7.851333    6.966667    7.270000    7.270000   \n",
              "2013-10-01    7.958667    8.181333    7.740000    8.033333    8.033333   \n",
              "2014-01-01    9.366667    9.800000    9.111333    9.289333    9.289333   \n",
              "2014-04-01   11.990667   12.226667   11.814667   11.906000   11.906000   \n",
              "2014-07-01   14.396667   14.703333   14.240000   14.360000   14.360000   \n",
              "2014-10-01   12.870667   13.578667   12.843333   13.187333   13.187333   \n",
              "2015-01-01   12.388667   12.619333   12.093333   12.333333   12.333333   \n",
              "2015-04-01   12.580000   12.820000   12.403333   12.506000   12.506000   \n",
              "2015-07-01   13.519333   15.200000   13.000000   14.591333   14.591333   \n",
              "2015-10-01   13.739333   14.108667   13.466667   13.795333   13.795333   \n",
              "2016-01-01    9.488000   10.331333    9.403333    9.578000    9.578000   \n",
              "2016-04-01   12.670000   13.008000   12.524667   12.876667   12.876667   \n",
              "2016-07-01   13.000000   13.194667   12.896667   12.964667   12.964667   \n",
              "2016-10-03   12.167333   12.315333   11.879333   12.096667   12.096667   \n",
              "2017-01-02   14.316667   14.688667   14.064000   14.466000   14.466000   \n",
              "2017-04-03   19.126667   19.933332   18.972000   19.666668   19.666668   \n",
              "2017-07-03   20.860001   21.133333   20.208668   20.588667   20.588667   \n",
              "2017-10-02   19.966667   20.297333   19.508667   19.950666   19.950666   \n",
              "2018-01-01   17.099333   17.912001   16.547333   17.185333   17.185333   \n",
              "2018-04-02   16.851999   17.355333   16.306000   16.832001   16.832001   \n",
              "2018-07-02   17.340000   17.889999   16.816668   17.549334   17.549334   \n",
              "2018-10-01   17.016666   17.457333   16.518000   16.704000   16.704000   \n",
              "2019-01-01   17.313999   17.545334   16.964001   17.361334   17.361334   \n",
              "2019-04-01   12.073333   12.445333   11.799333   11.931333   11.931333   \n",
              "2019-07-01   14.240000   14.334667   14.066667   14.093333   14.093333   \n",
              "2019-10-01   15.320000   15.632000   14.952000   15.428667   15.428667   \n",
              "2020-01-01   24.980000   26.990667   23.367332   24.081333   24.081333   \n",
              "2020-04-01   32.068668   32.950668   29.760000   30.298000   30.298000   \n",
              "2020-07-01   72.199997   75.688667   72.033333   74.641998   74.641998   \n",
              "2020-10-01  131.333328  135.660004  126.370003  129.346664  129.346664   \n",
              "2021-01-01  200.183334  205.493332  179.830002  187.666672  187.666672   \n",
              "2021-04-01  184.183334  188.736664  182.326660  187.820007  187.820007   \n",
              "2021-07-01  209.456665  215.733337  206.820007  214.460007  214.460007   \n",
              "2021-10-01  258.733337  260.260010  254.529999  258.406677  258.406677   \n",
              "2022-01-03  233.463333  266.899994  233.333328  254.679993  254.679993   \n",
              "2022-04-01  207.949997  217.973328  206.856674  209.386673  209.386673   \n",
              "2022-07-01  223.000000  230.229996  216.166672  227.263336  227.263336   \n",
              "2022-10-03  110.349998  116.269997  108.239998  109.099998  109.099998   \n",
              "2023-01-02  103.000000  111.750000  101.809998  108.099998  108.099998   \n",
              "2023-04-03  152.639999  160.479996  152.369995  153.750000  153.750000   \n",
              "2023-07-03  214.119995  217.580002  212.360001  215.490005  215.490005   \n",
              "2023-10-02  196.119995  202.800003  194.070007  197.360001  197.360001   \n",
              "2024-01-01  177.210007  181.580002  173.699997  176.539993  176.539993   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2010-04-01  257806500  \n",
              "2010-07-01    3016500  \n",
              "2010-10-01    1777500  \n",
              "2011-01-03    6175500  \n",
              "2011-04-01    8230500  \n",
              "2011-07-01    5493000  \n",
              "2011-10-03    3594000  \n",
              "2012-01-02    5473500  \n",
              "2012-04-02    6208500  \n",
              "2012-07-02    7623000  \n",
              "2012-10-01    5637000  \n",
              "2013-01-01    6603000  \n",
              "2013-04-01   23286000  \n",
              "2013-07-01   72100500  \n",
              "2013-10-01   63936000  \n",
              "2014-01-01   57268500  \n",
              "2014-04-01   42085500  \n",
              "2014-07-01   40950000  \n",
              "2014-10-01   19983000  \n",
              "2015-01-01   39474000  \n",
              "2015-04-01   21334500  \n",
              "2015-07-01   24240000  \n",
              "2015-10-01   10620000  \n",
              "2016-01-01   40480500  \n",
              "2016-04-01   30492000  \n",
              "2016-07-01   24892500  \n",
              "2016-10-03   28162500  \n",
              "2017-01-02   32800500  \n",
              "2017-04-03   45535500  \n",
              "2017-07-03   45742500  \n",
              "2017-10-02   48661500  \n",
              "2018-01-01   48294000  \n",
              "2018-04-02   58126500  \n",
              "2018-07-02   54039000  \n",
              "2018-10-01   46210500  \n",
              "2019-01-01   55150500  \n",
              "2019-04-01   76897500  \n",
              "2019-07-01   48382500  \n",
              "2019-10-01   36984000  \n",
              "2020-01-01  142981500  \n",
              "2020-04-01   95436000  \n",
              "2020-07-01   74217000  \n",
              "2020-10-01   52073100  \n",
              "2021-01-01   45473100  \n",
              "2021-04-01   48615900  \n",
              "2021-07-01   29401800  \n",
              "2021-10-01   35042700  \n",
              "2022-01-03   48992700  \n",
              "2022-04-01   49847700  \n",
              "2022-07-01   41864700  \n",
              "2022-10-03   50672700  \n",
              "2023-01-02   98654600  \n",
              "2023-04-03   88965000  \n",
              "2023-07-03   83166000  \n",
              "2023-10-02   65125200  \n",
              "2024-01-01   78841900  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fced2428-d5db-4acd-9193-ba4b7cfad689\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-04-01</th>\n",
              "      <td>1.266667</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>1.169333</td>\n",
              "      <td>1.588667</td>\n",
              "      <td>1.588667</td>\n",
              "      <td>257806500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-01</th>\n",
              "      <td>1.076000</td>\n",
              "      <td>1.108667</td>\n",
              "      <td>0.998667</td>\n",
              "      <td>1.053333</td>\n",
              "      <td>1.053333</td>\n",
              "      <td>3016500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-10-01</th>\n",
              "      <td>1.344000</td>\n",
              "      <td>1.352000</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>1.336667</td>\n",
              "      <td>1.336667</td>\n",
              "      <td>1777500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-03</th>\n",
              "      <td>1.452000</td>\n",
              "      <td>1.484667</td>\n",
              "      <td>1.407333</td>\n",
              "      <td>1.455333</td>\n",
              "      <td>1.455333</td>\n",
              "      <td>6175500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-04-01</th>\n",
              "      <td>1.658000</td>\n",
              "      <td>1.680667</td>\n",
              "      <td>1.613333</td>\n",
              "      <td>1.643333</td>\n",
              "      <td>1.643333</td>\n",
              "      <td>8230500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-07-01</th>\n",
              "      <td>1.462000</td>\n",
              "      <td>1.540667</td>\n",
              "      <td>1.433333</td>\n",
              "      <td>1.463333</td>\n",
              "      <td>1.463333</td>\n",
              "      <td>5493000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-10-03</th>\n",
              "      <td>1.552667</td>\n",
              "      <td>1.621333</td>\n",
              "      <td>1.528667</td>\n",
              "      <td>1.577333</td>\n",
              "      <td>1.577333</td>\n",
              "      <td>3594000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-02</th>\n",
              "      <td>1.774667</td>\n",
              "      <td>1.792000</td>\n",
              "      <td>1.509333</td>\n",
              "      <td>1.519333</td>\n",
              "      <td>1.519333</td>\n",
              "      <td>5473500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-04-02</th>\n",
              "      <td>1.838667</td>\n",
              "      <td>1.892667</td>\n",
              "      <td>1.788667</td>\n",
              "      <td>1.837333</td>\n",
              "      <td>1.837333</td>\n",
              "      <td>6208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-07-02</th>\n",
              "      <td>1.789333</td>\n",
              "      <td>1.790000</td>\n",
              "      <td>1.701333</td>\n",
              "      <td>1.740000</td>\n",
              "      <td>1.740000</td>\n",
              "      <td>7623000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-01</th>\n",
              "      <td>1.825333</td>\n",
              "      <td>1.853333</td>\n",
              "      <td>1.790667</td>\n",
              "      <td>1.822000</td>\n",
              "      <td>1.822000</td>\n",
              "      <td>5637000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-01</th>\n",
              "      <td>2.205333</td>\n",
              "      <td>2.225333</td>\n",
              "      <td>2.140667</td>\n",
              "      <td>2.194000</td>\n",
              "      <td>2.194000</td>\n",
              "      <td>6603000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-01</th>\n",
              "      <td>2.713333</td>\n",
              "      <td>2.788667</td>\n",
              "      <td>2.680667</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>23286000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-07-01</th>\n",
              "      <td>7.101333</td>\n",
              "      <td>7.851333</td>\n",
              "      <td>6.966667</td>\n",
              "      <td>7.270000</td>\n",
              "      <td>7.270000</td>\n",
              "      <td>72100500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-01</th>\n",
              "      <td>7.958667</td>\n",
              "      <td>8.181333</td>\n",
              "      <td>7.740000</td>\n",
              "      <td>8.033333</td>\n",
              "      <td>8.033333</td>\n",
              "      <td>63936000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>9.366667</td>\n",
              "      <td>9.800000</td>\n",
              "      <td>9.111333</td>\n",
              "      <td>9.289333</td>\n",
              "      <td>9.289333</td>\n",
              "      <td>57268500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-01</th>\n",
              "      <td>11.990667</td>\n",
              "      <td>12.226667</td>\n",
              "      <td>11.814667</td>\n",
              "      <td>11.906000</td>\n",
              "      <td>11.906000</td>\n",
              "      <td>42085500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-07-01</th>\n",
              "      <td>14.396667</td>\n",
              "      <td>14.703333</td>\n",
              "      <td>14.240000</td>\n",
              "      <td>14.360000</td>\n",
              "      <td>14.360000</td>\n",
              "      <td>40950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-10-01</th>\n",
              "      <td>12.870667</td>\n",
              "      <td>13.578667</td>\n",
              "      <td>12.843333</td>\n",
              "      <td>13.187333</td>\n",
              "      <td>13.187333</td>\n",
              "      <td>19983000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-01</th>\n",
              "      <td>12.388667</td>\n",
              "      <td>12.619333</td>\n",
              "      <td>12.093333</td>\n",
              "      <td>12.333333</td>\n",
              "      <td>12.333333</td>\n",
              "      <td>39474000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-04-01</th>\n",
              "      <td>12.580000</td>\n",
              "      <td>12.820000</td>\n",
              "      <td>12.403333</td>\n",
              "      <td>12.506000</td>\n",
              "      <td>12.506000</td>\n",
              "      <td>21334500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-01</th>\n",
              "      <td>13.519333</td>\n",
              "      <td>15.200000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>14.591333</td>\n",
              "      <td>14.591333</td>\n",
              "      <td>24240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-10-01</th>\n",
              "      <td>13.739333</td>\n",
              "      <td>14.108667</td>\n",
              "      <td>13.466667</td>\n",
              "      <td>13.795333</td>\n",
              "      <td>13.795333</td>\n",
              "      <td>10620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01</th>\n",
              "      <td>9.488000</td>\n",
              "      <td>10.331333</td>\n",
              "      <td>9.403333</td>\n",
              "      <td>9.578000</td>\n",
              "      <td>9.578000</td>\n",
              "      <td>40480500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01</th>\n",
              "      <td>12.670000</td>\n",
              "      <td>13.008000</td>\n",
              "      <td>12.524667</td>\n",
              "      <td>12.876667</td>\n",
              "      <td>12.876667</td>\n",
              "      <td>30492000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-07-01</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>13.194667</td>\n",
              "      <td>12.896667</td>\n",
              "      <td>12.964667</td>\n",
              "      <td>12.964667</td>\n",
              "      <td>24892500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-10-03</th>\n",
              "      <td>12.167333</td>\n",
              "      <td>12.315333</td>\n",
              "      <td>11.879333</td>\n",
              "      <td>12.096667</td>\n",
              "      <td>12.096667</td>\n",
              "      <td>28162500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-02</th>\n",
              "      <td>14.316667</td>\n",
              "      <td>14.688667</td>\n",
              "      <td>14.064000</td>\n",
              "      <td>14.466000</td>\n",
              "      <td>14.466000</td>\n",
              "      <td>32800500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-04-03</th>\n",
              "      <td>19.126667</td>\n",
              "      <td>19.933332</td>\n",
              "      <td>18.972000</td>\n",
              "      <td>19.666668</td>\n",
              "      <td>19.666668</td>\n",
              "      <td>45535500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-03</th>\n",
              "      <td>20.860001</td>\n",
              "      <td>21.133333</td>\n",
              "      <td>20.208668</td>\n",
              "      <td>20.588667</td>\n",
              "      <td>20.588667</td>\n",
              "      <td>45742500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-10-02</th>\n",
              "      <td>19.966667</td>\n",
              "      <td>20.297333</td>\n",
              "      <td>19.508667</td>\n",
              "      <td>19.950666</td>\n",
              "      <td>19.950666</td>\n",
              "      <td>48661500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>17.099333</td>\n",
              "      <td>17.912001</td>\n",
              "      <td>16.547333</td>\n",
              "      <td>17.185333</td>\n",
              "      <td>17.185333</td>\n",
              "      <td>48294000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-04-02</th>\n",
              "      <td>16.851999</td>\n",
              "      <td>17.355333</td>\n",
              "      <td>16.306000</td>\n",
              "      <td>16.832001</td>\n",
              "      <td>16.832001</td>\n",
              "      <td>58126500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-07-02</th>\n",
              "      <td>17.340000</td>\n",
              "      <td>17.889999</td>\n",
              "      <td>16.816668</td>\n",
              "      <td>17.549334</td>\n",
              "      <td>17.549334</td>\n",
              "      <td>54039000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-01</th>\n",
              "      <td>17.016666</td>\n",
              "      <td>17.457333</td>\n",
              "      <td>16.518000</td>\n",
              "      <td>16.704000</td>\n",
              "      <td>16.704000</td>\n",
              "      <td>46210500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-01</th>\n",
              "      <td>17.313999</td>\n",
              "      <td>17.545334</td>\n",
              "      <td>16.964001</td>\n",
              "      <td>17.361334</td>\n",
              "      <td>17.361334</td>\n",
              "      <td>55150500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-01</th>\n",
              "      <td>12.073333</td>\n",
              "      <td>12.445333</td>\n",
              "      <td>11.799333</td>\n",
              "      <td>11.931333</td>\n",
              "      <td>11.931333</td>\n",
              "      <td>76897500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-07-01</th>\n",
              "      <td>14.240000</td>\n",
              "      <td>14.334667</td>\n",
              "      <td>14.066667</td>\n",
              "      <td>14.093333</td>\n",
              "      <td>14.093333</td>\n",
              "      <td>48382500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-01</th>\n",
              "      <td>15.320000</td>\n",
              "      <td>15.632000</td>\n",
              "      <td>14.952000</td>\n",
              "      <td>15.428667</td>\n",
              "      <td>15.428667</td>\n",
              "      <td>36984000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>24.980000</td>\n",
              "      <td>26.990667</td>\n",
              "      <td>23.367332</td>\n",
              "      <td>24.081333</td>\n",
              "      <td>24.081333</td>\n",
              "      <td>142981500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>32.068668</td>\n",
              "      <td>32.950668</td>\n",
              "      <td>29.760000</td>\n",
              "      <td>30.298000</td>\n",
              "      <td>30.298000</td>\n",
              "      <td>95436000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>72.199997</td>\n",
              "      <td>75.688667</td>\n",
              "      <td>72.033333</td>\n",
              "      <td>74.641998</td>\n",
              "      <td>74.641998</td>\n",
              "      <td>74217000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-01</th>\n",
              "      <td>131.333328</td>\n",
              "      <td>135.660004</td>\n",
              "      <td>126.370003</td>\n",
              "      <td>129.346664</td>\n",
              "      <td>129.346664</td>\n",
              "      <td>52073100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-01</th>\n",
              "      <td>200.183334</td>\n",
              "      <td>205.493332</td>\n",
              "      <td>179.830002</td>\n",
              "      <td>187.666672</td>\n",
              "      <td>187.666672</td>\n",
              "      <td>45473100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-01</th>\n",
              "      <td>184.183334</td>\n",
              "      <td>188.736664</td>\n",
              "      <td>182.326660</td>\n",
              "      <td>187.820007</td>\n",
              "      <td>187.820007</td>\n",
              "      <td>48615900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-01</th>\n",
              "      <td>209.456665</td>\n",
              "      <td>215.733337</td>\n",
              "      <td>206.820007</td>\n",
              "      <td>214.460007</td>\n",
              "      <td>214.460007</td>\n",
              "      <td>29401800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-01</th>\n",
              "      <td>258.733337</td>\n",
              "      <td>260.260010</td>\n",
              "      <td>254.529999</td>\n",
              "      <td>258.406677</td>\n",
              "      <td>258.406677</td>\n",
              "      <td>35042700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-03</th>\n",
              "      <td>233.463333</td>\n",
              "      <td>266.899994</td>\n",
              "      <td>233.333328</td>\n",
              "      <td>254.679993</td>\n",
              "      <td>254.679993</td>\n",
              "      <td>48992700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-01</th>\n",
              "      <td>207.949997</td>\n",
              "      <td>217.973328</td>\n",
              "      <td>206.856674</td>\n",
              "      <td>209.386673</td>\n",
              "      <td>209.386673</td>\n",
              "      <td>49847700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-07-01</th>\n",
              "      <td>223.000000</td>\n",
              "      <td>230.229996</td>\n",
              "      <td>216.166672</td>\n",
              "      <td>227.263336</td>\n",
              "      <td>227.263336</td>\n",
              "      <td>41864700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-03</th>\n",
              "      <td>110.349998</td>\n",
              "      <td>116.269997</td>\n",
              "      <td>108.239998</td>\n",
              "      <td>109.099998</td>\n",
              "      <td>109.099998</td>\n",
              "      <td>50672700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-02</th>\n",
              "      <td>103.000000</td>\n",
              "      <td>111.750000</td>\n",
              "      <td>101.809998</td>\n",
              "      <td>108.099998</td>\n",
              "      <td>108.099998</td>\n",
              "      <td>98654600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-04-03</th>\n",
              "      <td>152.639999</td>\n",
              "      <td>160.479996</td>\n",
              "      <td>152.369995</td>\n",
              "      <td>153.750000</td>\n",
              "      <td>153.750000</td>\n",
              "      <td>88965000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-07-03</th>\n",
              "      <td>214.119995</td>\n",
              "      <td>217.580002</td>\n",
              "      <td>212.360001</td>\n",
              "      <td>215.490005</td>\n",
              "      <td>215.490005</td>\n",
              "      <td>83166000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-10-02</th>\n",
              "      <td>196.119995</td>\n",
              "      <td>202.800003</td>\n",
              "      <td>194.070007</td>\n",
              "      <td>197.360001</td>\n",
              "      <td>197.360001</td>\n",
              "      <td>65125200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-01-01</th>\n",
              "      <td>177.210007</td>\n",
              "      <td>181.580002</td>\n",
              "      <td>173.699997</td>\n",
              "      <td>176.539993</td>\n",
              "      <td>176.539993</td>\n",
              "      <td>78841900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 56,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79.78272819669496,\n        \"min\": 1.0759999752044678,\n        \"max\": 258.73333740234375,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          1.2666670083999634,\n          1.4620000123977661,\n          17.34000015258789\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 83.18948772295246,\n        \"min\": 1.108667016029358,\n        \"max\": 266.8999938964844,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          1.6666669845581055,\n          1.5406670570373535,\n          17.889999389648438\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.25393438044017,\n        \"min\": 0.9986670017242432,\n        \"max\": 254.52999877929688,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          1.1693329811096191,\n          1.4333330392837524,\n          16.816667556762695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.88973589081395,\n        \"min\": 1.053333044052124,\n        \"max\": 258.40667724609375,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          1.5886670351028442,\n          1.463333010673523,\n          17.549333572387695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.88973589081395,\n        \"min\": 1.053333044052124,\n        \"max\": 258.40667724609375,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          1.5886670351028442,\n          1.463333010673523,\n          17.549333572387695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40650636,\n        \"min\": 1777500,\n        \"max\": 257806500,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          257806500,\n          5493000,\n          54039000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla.resample(rule=\"BQS\").min()[\"Open\"].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "3kA8cNPYFGpz",
        "outputId": "74185f6f-fff8-4b69-d7fb-3527fbfc9cb1"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 139
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla[\"Open\"].resample(rule=\"A\").min().plot(kind=\"bar\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "S4HeSsUyFI8b",
        "outputId": "d507babd-8a23-4a5a-d4c8-8c1b2ac2b7de"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 144
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla[\"Open\"].resample(rule=\"M\").max().plot(kind=\"bar\",figsize=(15,6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "OYxCxRVcFRg1",
        "outputId": "bf2f9302-139a-4807-9914-7c91e1400edb"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla[\"High\"].rolling(10).mean().plot(figsize=(15,6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "g5UgRqFNFjtV",
        "outputId": "10a33a76-6d9d-4a3d-9ca1-3cb13a976609"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 153
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla[\"Open 30 Days rolling\"]=df_tesla[\"Open\"].rolling(30).mean()"
      ],
      "metadata": {
        "id": "ZQjJo-oMGsaL"
      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "AM_drMkSHlzC",
        "outputId": "b091cdfc-cf89-422d-f45d-9d614e15560f"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2010-06-29    1.266667    1.666667    1.169333    1.592667    1.592667   \n",
              "2010-06-30    1.719333    2.028000    1.553333    1.588667    1.588667   \n",
              "2010-07-01    1.666667    1.728000    1.351333    1.464000    1.464000   \n",
              "2010-07-02    1.533333    1.540000    1.247333    1.280000    1.280000   \n",
              "2010-07-06    1.333333    1.333333    1.055333    1.074000    1.074000   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2024-02-29  204.179993  205.279999  198.449997  201.880005  201.880005   \n",
              "2024-03-01  200.520004  204.520004  198.500000  202.639999  202.639999   \n",
              "2024-03-04  198.729996  199.750000  186.720001  188.139999  188.139999   \n",
              "2024-03-05  183.050003  184.589996  177.570007  180.740005  180.740005   \n",
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              "\n",
              "               Volume  Open 30 Days rolling  \n",
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              "type": "dataframe",
              "variable_name": "df_tesla",
              "summary": "{\n  \"name\": \"df_tesla\",\n  \"rows\": 3445,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.04437828241103,\n        \"min\": 1.0759999752044678,\n        \"max\": 411.4700012207031,\n        \"num_unique_values\": 3143,\n        \"samples\": [\n          268.30999755859375,\n          250.07000732421875,\n          16.72333335876465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.29817067760874,\n        \"min\": 1.108667016029358,\n        \"max\": 414.4966735839844,\n        \"num_unique_values\": 3135,\n        \"samples\": [\n          15.652000427246094,\n          2.629333019256592,\n          1.9713330268859863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.55555106142745,\n        \"min\": 0.9986670017242432,\n        \"max\": 405.6666564941406,\n        \"num_unique_values\": 3152,\n        \"samples\": [\n          191.9499969482422,\n          18.511333465576172,\n          207.52333068847656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.95875837561229,\n        \"min\": 1.053333044052124,\n        \"max\": 409.9700012207031,\n        \"num_unique_values\": 3245,\n        \"samples\": [\n          228.56666564941406,\n          15.65999984741211,\n          18.667999267578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79563803,\n        \"min\": 1777500,\n        \"max\": 914082000,\n        \"num_unique_values\": 3414,\n        \"samples\": [\n          85110000,\n          57526500,\n          9949500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open 30 Days rolling\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.4779293038962,\n        \"min\": 1.3116889397303264,\n        \"max\": 368.0764455159505,\n        \"num_unique_values\": 3415,\n        \"samples\": [\n          19.26671085357666,\n          23.842755444844563,\n          1.6285333037376404\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tesla[[\"Open\",\"Open 30 Days rolling\"]].plot(figsize=(15,6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "KK0gQtJZHnm7",
        "outputId": "87ab1407-17b5-4b67-bede-2c03bcaed7ea"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 159
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msft_data=pdr.get_data_yahoo(\"MSFT\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NT5dvYZvHslF",
        "outputId": "0a45e45b-d117-47b4-9d46-b01aa0d38233"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msft_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "0_sbcqtHJIUP",
        "outputId": "2bff8466-08f6-4a56-c33d-f1d1a50b4789"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close      Volume\n",
              "Date                                                                     \n",
              "1986-03-13  0.088542  0.101563  0.088542  0.097222   0.060163  1031788800\n",
              "1986-03-14  0.097222  0.102431  0.097222  0.100694   0.062311   308160000\n",
              "1986-03-17  0.100694  0.103299  0.100694  0.102431   0.063386   133171200\n",
              "1986-03-18  0.102431  0.103299  0.098958  0.099826   0.061774    67766400\n",
              "1986-03-19  0.099826  0.100694  0.097222  0.098090   0.060700    47894400"
            ],
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              "      <th>1986-03-13</th>\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cfc05810-8bfb-40ac-9071-72c8d325ba60 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-cfc05810-8bfb-40ac-9071-72c8d325ba60');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b3044ffb-ec37-41c1-997d-cfac5fea848c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b3044ffb-ec37-41c1-997d-cfac5fea848c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b3044ffb-ec37-41c1-997d-cfac5fea848c 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": "msft_data",
              "summary": "{\n  \"name\": \"msft_data\",\n  \"rows\": 9572,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82.28149040194798,\n        \"min\": 0.08854199945926666,\n        \"max\": 420.55999755859375,\n        \"num_unique_values\": 5692,\n        \"samples\": [\n          23.03125,\n          2.589843988418579,\n          43.310001373291016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 83.086033088438,\n        \"min\": 0.09201399981975555,\n        \"max\": 420.82000732421875,\n        \"num_unique_values\": 5626,\n        \"samples\": [\n          138.6699981689453,\n          38.53125,\n          7.546875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81.4465545728261,\n        \"min\": 0.08854199945926666,\n        \"max\": 415.0899963378906,\n        \"num_unique_values\": 5648,\n        \"samples\": [\n          267.9800109863281,\n          108.20999908447266,\n          30.760000228881836\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82.3101885542487,\n        \"min\": 0.09027799963951111,\n        \"max\": 420.54998779296875,\n        \"num_unique_values\": 5841,\n        \"samples\": [\n          324.0400085449219,\n          0.32552099227905273,\n          0.3637149930000305\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82.29879273349894,\n        \"min\": 0.05586576089262962,\n        \"max\": 419.7737121582031,\n        \"num_unique_values\": 7469,\n        \"samples\": [\n          208.10545349121094,\n          302.4842529296875,\n          8.557112693786621\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38140836,\n        \"min\": 2304000,\n        \"max\": 1031788800,\n        \"num_unique_values\": 9380,\n        \"samples\": [\n          43913600,\n          55496400,\n          89391000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msft_data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "k6axDFksIeGR",
        "outputId": "029275a1-f671-4de7-8032-83f538e53fc5"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2024-02-29  408.640015  414.200012  405.920013  413.640015  413.640015   \n",
              "2024-03-01  411.269989  415.869995  410.880005  415.500000  415.500000   \n",
              "2024-03-04  413.440002  417.350006  412.320007  414.920013  414.920013   \n",
              "2024-03-05  413.959991  414.250000  400.640015  402.649994  402.649994   \n",
              "2024-03-06  402.970001  405.160004  398.390015  402.089996  402.089996   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2024-02-29  31947300  \n",
              "2024-03-01  17800300  \n",
              "2024-03-04  17596000  \n",
              "2024-03-05  26919200  \n",
              "2024-03-06  22315200  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c53c5782-fabd-4dbe-980d-96ca2960ccf3\" 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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-02-29</th>\n",
              "      <td>408.640015</td>\n",
              "      <td>414.200012</td>\n",
              "      <td>405.920013</td>\n",
              "      <td>413.640015</td>\n",
              "      <td>413.640015</td>\n",
              "      <td>31947300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-01</th>\n",
              "      <td>411.269989</td>\n",
              "      <td>415.869995</td>\n",
              "      <td>410.880005</td>\n",
              "      <td>415.500000</td>\n",
              "      <td>415.500000</td>\n",
              "      <td>17800300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-04</th>\n",
              "      <td>413.440002</td>\n",
              "      <td>417.350006</td>\n",
              "      <td>412.320007</td>\n",
              "      <td>414.920013</td>\n",
              "      <td>414.920013</td>\n",
              "      <td>17596000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-05</th>\n",
              "      <td>413.959991</td>\n",
              "      <td>414.250000</td>\n",
              "      <td>400.640015</td>\n",
              "      <td>402.649994</td>\n",
              "      <td>402.649994</td>\n",
              "      <td>26919200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-03-06</th>\n",
              "      <td>402.970001</td>\n",
              "      <td>405.160004</td>\n",
              "      <td>398.390015</td>\n",
              "      <td>402.089996</td>\n",
              "      <td>402.089996</td>\n",
              "      <td>22315200</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-c53c5782-fabd-4dbe-980d-96ca2960ccf3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c53c5782-fabd-4dbe-980d-96ca2960ccf3 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-c53c5782-fabd-4dbe-980d-96ca2960ccf3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-05007eeb-91cc-454a-9613-ef820924abaf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-05007eeb-91cc-454a-9613-ef820924abaf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-05007eeb-91cc-454a-9613-ef820924abaf 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\": \"msft_data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.482822334928035,\n        \"min\": 402.9700012207031,\n        \"max\": 413.9599914550781,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          411.2699890136719,\n          402.9700012207031,\n          413.44000244140625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.768587433366449,\n        \"min\": 405.1600036621094,\n        \"max\": 417.3500061035156,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          415.8699951171875,\n          405.1600036621094,\n          417.3500061035156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.11801027203629,\n        \"min\": 398.3900146484375,\n        \"max\": 412.32000732421875,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          410.8800048828125,\n          398.3900146484375,\n          412.32000732421875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.782495765115196,\n        \"min\": 402.0899963378906,\n        \"max\": 415.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          415.5,\n          402.0899963378906,\n          414.9200134277344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.782495765115196,\n        \"min\": 402.0899963378906,\n        \"max\": 415.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          415.5,\n          402.0899963378906,\n          414.9200134277344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6156812,\n        \"min\": 17596000,\n        \"max\": 31947300,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          17800300,\n          22315200,\n          17596000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msft_data.resample(rule=\"A\").max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "47pWfwhkJKcK",
        "outputId": "6fb9ded5-f4d0-499c-db5f-d3d950139959"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "1986-12-31    0.177083    0.177951    0.175347    0.177083    0.109582   \n",
              "1987-12-31    0.548611    0.550347    0.510417    0.548611    0.339491   \n",
              "1988-12-31    0.484375    0.489583    0.475694    0.483507    0.299203   \n",
              "1989-12-31    0.618056    0.619792    0.607639    0.614583    0.380316   \n",
              "1990-12-31    1.102431    1.121528    1.090278    1.100694    0.681131   \n",
              "1991-12-31    2.307292    2.333333    2.281250    2.317708    1.434243   \n",
              "1992-12-31    2.937500    2.968750    2.929688    2.968750    1.837120   \n",
              "1993-12-31    2.992188    3.062500    2.976563    3.007813    1.861293   \n",
              "1994-12-31    4.046875    4.070313    4.031250    4.039063    2.499450   \n",
              "1995-12-31    6.734375    6.828125    6.656250    6.812500    4.215705   \n",
              "1996-12-31   10.687500   10.765625   10.640625   10.687500    6.613636   \n",
              "1997-12-31   18.812500   18.843750   18.312500   18.687500   11.564189   \n",
              "1998-12-31   35.906250   36.000000   35.375000   35.890625   22.209812   \n",
              "1999-12-31   59.375000   59.968750   58.562500   59.562500   36.858418   \n",
              "2000-12-31   58.687500   59.312500   56.125000   58.281250   36.065571   \n",
              "2001-12-31   36.849998   38.075001   36.040001   36.840000   22.797306   \n",
              "2002-12-31   34.875000   35.310001   34.334999   34.930000   21.615362   \n",
              "2003-12-31   29.760000   30.000000   29.520000   29.959999   18.599442   \n",
              "2004-12-31   30.160000   30.200001   29.820000   29.980000   19.114153   \n",
              "2005-12-31   28.120001   28.250000   27.900000   28.160000   19.891449   \n",
              "2006-12-31   30.190001   30.260000   30.030001   30.190001   21.633902   \n",
              "2007-12-31   37.220001   37.500000   36.630001   37.060001   26.829006   \n",
              "2008-12-31   35.790001   35.959999   35.000000   35.369999   25.690214   \n",
              "2009-12-31   31.350000   31.500000   31.230000   31.389999   23.764702   \n",
              "2010-12-31   31.330000   31.580000   31.230000   31.389999   23.875832   \n",
              "2011-12-31   28.900000   29.459999   28.500000   28.870001   22.312088   \n",
              "2012-12-31   32.910000   32.950001   32.580002   32.849998   26.234070   \n",
              "2013-12-31   38.849998   38.980000   38.369999   38.939999   32.729156   \n",
              "2014-12-31   49.740002   50.049999   49.389999   49.610001   42.570595   \n",
              "2015-12-31   56.470001   56.849998   56.290001   56.549999   50.161293   \n",
              "2016-12-31   63.840000   64.099998   63.410000   63.619999   57.981079   \n",
              "2017-12-31   87.120003   87.500000   86.230003   86.849998   80.919456   \n",
              "2018-12-31  115.419998  116.180000  114.930000  115.610001  109.108940   \n",
              "2019-12-31  159.449997  159.550003  158.220001  158.960007  152.897171   \n",
              "2020-12-31  229.270004  232.860001  227.350006  231.649994  224.587143   \n",
              "2021-12-31  344.619995  349.670013  342.200012  343.109985  336.315277   \n",
              "2022-12-31  335.350006  338.000000  329.779999  334.750000  328.120850   \n",
              "2023-12-31  383.760010  384.299988  378.160004  382.700012  381.993622   \n",
              "2024-12-31  420.559998  420.820007  415.089996  420.549988  419.773712   \n",
              "\n",
              "                Volume  \n",
              "Date                    \n",
              "1986-12-31  1031788800  \n",
              "1987-12-31   324000000  \n",
              "1988-12-31   228960000  \n",
              "1989-12-31   788688000  \n",
              "1990-12-31   303321600  \n",
              "1991-12-31   191630400  \n",
              "1992-12-31   309884800  \n",
              "1993-12-31   287936000  \n",
              "1994-12-31   764504000  \n",
              "1995-12-31   305092800  \n",
              "1996-12-31   257678400  \n",
              "1997-12-31   231764000  \n",
              "1998-12-31   159139200  \n",
              "1999-12-31   243819200  \n",
              "2000-12-31   313645800  \n",
              "2001-12-31   209348800  \n",
              "2002-12-31   202307800  \n",
              "2003-12-31   210558300  \n",
              "2004-12-31   258269000  \n",
              "2005-12-31   187384300  \n",
              "2006-12-31   591052200  \n",
              "2007-12-31   288121200  \n",
              "2008-12-31   291138900  \n",
              "2009-12-31   281761000  \n",
              "2010-12-31   193888500  \n",
              "2011-12-31   319317900  \n",
              "2012-12-31   165902900  \n",
              "2013-12-31   248428500  \n",
              "2014-12-31   202522400  \n",
              "2015-12-31   169164000  \n",
              "2016-12-31   133503000  \n",
              "2017-12-31    71066700  \n",
              "2018-12-31   111242100  \n",
              "2019-12-31    55636400  \n",
              "2020-12-31    97012700  \n",
              "2021-12-31    69870600  \n",
              "2022-12-31    90428900  \n",
              "2023-12-31    78478200  \n",
              "2024-12-31    47871100  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64276b1f-3873-4ea3-876e-a5a7e9e9a287\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1986-12-31</th>\n",
              "      <td>0.177083</td>\n",
              "      <td>0.177951</td>\n",
              "      <td>0.175347</td>\n",
              "      <td>0.177083</td>\n",
              "      <td>0.109582</td>\n",
              "      <td>1031788800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1987-12-31</th>\n",
              "      <td>0.548611</td>\n",
              "      <td>0.550347</td>\n",
              "      <td>0.510417</td>\n",
              "      <td>0.548611</td>\n",
              "      <td>0.339491</td>\n",
              "      <td>324000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1988-12-31</th>\n",
              "      <td>0.484375</td>\n",
              "      <td>0.489583</td>\n",
              "      <td>0.475694</td>\n",
              "      <td>0.483507</td>\n",
              "      <td>0.299203</td>\n",
              "      <td>228960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1989-12-31</th>\n",
              "      <td>0.618056</td>\n",
              "      <td>0.619792</td>\n",
              "      <td>0.607639</td>\n",
              "      <td>0.614583</td>\n",
              "      <td>0.380316</td>\n",
              "      <td>788688000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990-12-31</th>\n",
              "      <td>1.102431</td>\n",
              "      <td>1.121528</td>\n",
              "      <td>1.090278</td>\n",
              "      <td>1.100694</td>\n",
              "      <td>0.681131</td>\n",
              "      <td>303321600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991-12-31</th>\n",
              "      <td>2.307292</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>2.281250</td>\n",
              "      <td>2.317708</td>\n",
              "      <td>1.434243</td>\n",
              "      <td>191630400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-12-31</th>\n",
              "      <td>2.937500</td>\n",
              "      <td>2.968750</td>\n",
              "      <td>2.929688</td>\n",
              "      <td>2.968750</td>\n",
              "      <td>1.837120</td>\n",
              "      <td>309884800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-12-31</th>\n",
              "      <td>2.992188</td>\n",
              "      <td>3.062500</td>\n",
              "      <td>2.976563</td>\n",
              "      <td>3.007813</td>\n",
              "      <td>1.861293</td>\n",
              "      <td>287936000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-12-31</th>\n",
              "      <td>4.046875</td>\n",
              "      <td>4.070313</td>\n",
              "      <td>4.031250</td>\n",
              "      <td>4.039063</td>\n",
              "      <td>2.499450</td>\n",
              "      <td>764504000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-12-31</th>\n",
              "      <td>6.734375</td>\n",
              "      <td>6.828125</td>\n",
              "      <td>6.656250</td>\n",
              "      <td>6.812500</td>\n",
              "      <td>4.215705</td>\n",
              "      <td>305092800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996-12-31</th>\n",
              "      <td>10.687500</td>\n",
              "      <td>10.765625</td>\n",
              "      <td>10.640625</td>\n",
              "      <td>10.687500</td>\n",
              "      <td>6.613636</td>\n",
              "      <td>257678400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997-12-31</th>\n",
              "      <td>18.812500</td>\n",
              "      <td>18.843750</td>\n",
              "      <td>18.312500</td>\n",
              "      <td>18.687500</td>\n",
              "      <td>11.564189</td>\n",
              "      <td>231764000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998-12-31</th>\n",
              "      <td>35.906250</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>35.375000</td>\n",
              "      <td>35.890625</td>\n",
              "      <td>22.209812</td>\n",
              "      <td>159139200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999-12-31</th>\n",
              "      <td>59.375000</td>\n",
              "      <td>59.968750</td>\n",
              "      <td>58.562500</td>\n",
              "      <td>59.562500</td>\n",
              "      <td>36.858418</td>\n",
              "      <td>243819200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-12-31</th>\n",
              "      <td>58.687500</td>\n",
              "      <td>59.312500</td>\n",
              "      <td>56.125000</td>\n",
              "      <td>58.281250</td>\n",
              "      <td>36.065571</td>\n",
              "      <td>313645800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001-12-31</th>\n",
              "      <td>36.849998</td>\n",
              "      <td>38.075001</td>\n",
              "      <td>36.040001</td>\n",
              "      <td>36.840000</td>\n",
              "      <td>22.797306</td>\n",
              "      <td>209348800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-12-31</th>\n",
              "      <td>34.875000</td>\n",
              "      <td>35.310001</td>\n",
              "      <td>34.334999</td>\n",
              "      <td>34.930000</td>\n",
              "      <td>21.615362</td>\n",
              "      <td>202307800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-12-31</th>\n",
              "      <td>29.760000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>29.520000</td>\n",
              "      <td>29.959999</td>\n",
              "      <td>18.599442</td>\n",
              "      <td>210558300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-12-31</th>\n",
              "      <td>30.160000</td>\n",
              "      <td>30.200001</td>\n",
              "      <td>29.820000</td>\n",
              "      <td>29.980000</td>\n",
              "      <td>19.114153</td>\n",
              "      <td>258269000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-31</th>\n",
              "      <td>28.120001</td>\n",
              "      <td>28.250000</td>\n",
              "      <td>27.900000</td>\n",
              "      <td>28.160000</td>\n",
              "      <td>19.891449</td>\n",
              "      <td>187384300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-31</th>\n",
              "      <td>30.190001</td>\n",
              "      <td>30.260000</td>\n",
              "      <td>30.030001</td>\n",
              "      <td>30.190001</td>\n",
              "      <td>21.633902</td>\n",
              "      <td>591052200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-12-31</th>\n",
              "      <td>37.220001</td>\n",
              "      <td>37.500000</td>\n",
              "      <td>36.630001</td>\n",
              "      <td>37.060001</td>\n",
              "      <td>26.829006</td>\n",
              "      <td>288121200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-12-31</th>\n",
              "      <td>35.790001</td>\n",
              "      <td>35.959999</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.369999</td>\n",
              "      <td>25.690214</td>\n",
              "      <td>291138900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-12-31</th>\n",
              "      <td>31.350000</td>\n",
              "      <td>31.500000</td>\n",
              "      <td>31.230000</td>\n",
              "      <td>31.389999</td>\n",
              "      <td>23.764702</td>\n",
              "      <td>281761000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-31</th>\n",
              "      <td>31.330000</td>\n",
              "      <td>31.580000</td>\n",
              "      <td>31.230000</td>\n",
              "      <td>31.389999</td>\n",
              "      <td>23.875832</td>\n",
              "      <td>193888500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-31</th>\n",
              "      <td>28.900000</td>\n",
              "      <td>29.459999</td>\n",
              "      <td>28.500000</td>\n",
              "      <td>28.870001</td>\n",
              "      <td>22.312088</td>\n",
              "      <td>319317900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-31</th>\n",
              "      <td>32.910000</td>\n",
              "      <td>32.950001</td>\n",
              "      <td>32.580002</td>\n",
              "      <td>32.849998</td>\n",
              "      <td>26.234070</td>\n",
              "      <td>165902900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-12-31</th>\n",
              "      <td>38.849998</td>\n",
              "      <td>38.980000</td>\n",
              "      <td>38.369999</td>\n",
              "      <td>38.939999</td>\n",
              "      <td>32.729156</td>\n",
              "      <td>248428500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-31</th>\n",
              "      <td>49.740002</td>\n",
              "      <td>50.049999</td>\n",
              "      <td>49.389999</td>\n",
              "      <td>49.610001</td>\n",
              "      <td>42.570595</td>\n",
              "      <td>202522400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-31</th>\n",
              "      <td>56.470001</td>\n",
              "      <td>56.849998</td>\n",
              "      <td>56.290001</td>\n",
              "      <td>56.549999</td>\n",
              "      <td>50.161293</td>\n",
              "      <td>169164000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31</th>\n",
              "      <td>63.840000</td>\n",
              "      <td>64.099998</td>\n",
              "      <td>63.410000</td>\n",
              "      <td>63.619999</td>\n",
              "      <td>57.981079</td>\n",
              "      <td>133503000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-31</th>\n",
              "      <td>87.120003</td>\n",
              "      <td>87.500000</td>\n",
              "      <td>86.230003</td>\n",
              "      <td>86.849998</td>\n",
              "      <td>80.919456</td>\n",
              "      <td>71066700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>115.419998</td>\n",
              "      <td>116.180000</td>\n",
              "      <td>114.930000</td>\n",
              "      <td>115.610001</td>\n",
              "      <td>109.108940</td>\n",
              "      <td>111242100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>159.449997</td>\n",
              "      <td>159.550003</td>\n",
              "      <td>158.220001</td>\n",
              "      <td>158.960007</td>\n",
              "      <td>152.897171</td>\n",
              "      <td>55636400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>229.270004</td>\n",
              "      <td>232.860001</td>\n",
              "      <td>227.350006</td>\n",
              "      <td>231.649994</td>\n",
              "      <td>224.587143</td>\n",
              "      <td>97012700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-31</th>\n",
              "      <td>344.619995</td>\n",
              "      <td>349.670013</td>\n",
              "      <td>342.200012</td>\n",
              "      <td>343.109985</td>\n",
              "      <td>336.315277</td>\n",
              "      <td>69870600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-31</th>\n",
              "      <td>335.350006</td>\n",
              "      <td>338.000000</td>\n",
              "      <td>329.779999</td>\n",
              "      <td>334.750000</td>\n",
              "      <td>328.120850</td>\n",
              "      <td>90428900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-12-31</th>\n",
              "      <td>383.760010</td>\n",
              "      <td>384.299988</td>\n",
              "      <td>378.160004</td>\n",
              "      <td>382.700012</td>\n",
              "      <td>381.993622</td>\n",
              "      <td>78478200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-12-31</th>\n",
              "      <td>420.559998</td>\n",
              "      <td>420.820007</td>\n",
              "      <td>415.089996</td>\n",
              "      <td>420.549988</td>\n",
              "      <td>419.773712</td>\n",
              "      <td>47871100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"msft_data\",\n  \"rows\": 39,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111.76411108441869,\n        \"min\": 0.17708300054073334,\n        \"max\": 420.55999755859375,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          159.4499969482422,\n          335.3500061035156,\n          1.102431058883667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112.40499611696941,\n        \"min\": 0.17795099318027496,\n        \"max\": 420.82000732421875,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          159.5500030517578,\n          338.0,\n          1.1215280294418335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 110.38888857149514,\n        \"min\": 0.1753470003604889,\n        \"max\": 415.0899963378906,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          227.35000610351562,\n          378.1600036621094,\n          1.0902780294418335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111.63682540366086,\n        \"min\": 0.17708300054073334,\n        \"max\": 420.54998779296875,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          231.64999389648438,\n          382.70001220703125,\n          1.100693941116333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112.15818899551888,\n        \"min\": 0.10958241671323776,\n        \"max\": 419.7737121582031,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          152.8971710205078,\n          328.120849609375,\n          0.6811307072639465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 205119365,\n        \"min\": 47871100,\n        \"max\": 1031788800,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          55636400,\n          90428900,\n          303321600\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msft_data[\"Open\"].resample(rule=\"A\").max().plot(xlim=[\"2017-01-01\",\"2022-12-31\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "TEXI-HvyJVBi",
        "outputId": "6266d12a-a0a8-45c6-a631-8406b67979a8"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 178
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msft_data[msft_data[\"Open\"]==msft_data[\"Open\"].max()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "jCh0zPLJJY_r",
        "outputId": "a36d8ab1-20cf-4345-fd66-1d022f92b458"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open       High     Low      Close  Adj Close    Volume\n",
              "Date                                                                     \n",
              "2024-02-12  420.559998  420.73999  414.75  415.26001   414.4935  21202900"
            ],
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              "      const buttonEl =\n",
              "        document.querySelector('#df-c94e910c-e296-4cff-9eb6-5c0a8a9ea80b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-c94e910c-e296-4cff-9eb6-5c0a8a9ea80b');\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",
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          "metadata": {},
          "execution_count": 184
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        "msft_data[msft_data[\"Open\"]==msft_data[\"Open\"].min()]"
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      "execution_count": 185,
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          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close      Volume\n",
              "Date                                                                     \n",
              "1986-03-13  0.088542  0.101563  0.088542  0.097222   0.060163  1031788800"
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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}