dmi_00_pandas_00.ipynb

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        "<a href=\"https://colab.research.google.com/github/sbogde/pandamonium/blob/main/dmi_00_pandas_00.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ThSADq7Jck3"
      },
      "source": [
        "## Loading in Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Bn9P-nwHAb5"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTpwRFDxIjfx",
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        "outputId": "ec950d66-5bc4-4822-e51f-b8e538005aa1"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/sbux.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-03-05 19:53:58--  https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/sbux.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 61896 (60K) [text/plain]\n",
            "Saving to: ‘sbux.csv’\n",
            "\n",
            "sbux.csv            100%[===================>]  60.45K  --.-KB/s    in 0.05s   \n",
            "\n",
            "2025-03-05 19:53:59 (1.18 MB/s) - ‘sbux.csv’ saved [61896/61896]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bv6JUKeNIm7j"
      },
      "source": [
        "df = pd.read_csv('sbux.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3Iz3Xa3IqFQ"
      },
      "source": [
        "# using the URL directly works too!\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/sbux.csv')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
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        "outputId": "e7a8b95d-fad9-431c-e916-0cece94c13e6"
      },
      "source": [
        "type(df)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
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              "    <pre style=\"white-space: initial; background:\n",
              "         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/python3.11/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. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\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\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              "&gt;&gt;&gt; ser = pd.Series([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=ser, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              "&gt;&gt;&gt; df1 = pd.DataFrame([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], columns=[&quot;x&quot;])\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(data=df1, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df2\n",
              "   x\n",
              "a  1\n",
              "c  3</pre>\n",
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        "id": "Yow8DbJlIvjd",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f21e4c0-8c84-40e8-dc19-941bd92139ac"
      },
      "source": [
        "!head sbux.csv"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "date,open,high,low,close,volume,Name\n",
            "2013-02-08,27.92,28.325,27.92,28.185,7146296,SBUX\n",
            "2013-02-11,28.26,28.26,27.93,28.07,5457354,SBUX\n",
            "2013-02-12,28.0,28.275,27.975,28.13,8665592,SBUX\n",
            "2013-02-13,28.23,28.23,27.75,27.915,7022056,SBUX\n",
            "2013-02-14,27.765,27.905,27.675,27.775,8899188,SBUX\n",
            "2013-02-15,27.805,27.85,27.085,27.17,18195730,SBUX\n",
            "2013-02-19,27.18,27.305,27.01,27.225,11760912,SBUX\n",
            "2013-02-20,27.3,27.42,26.59,26.655,12472506,SBUX\n",
            "2013-02-21,26.535,26.82,26.26,26.675,13896450,SBUX\n"
          ]
        }
      ]
    },
    {
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          "height": 206
        },
        "outputId": "33ec17c3-f6fa-4d36-aeec-4652382d007e"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date    open    high     low   close   volume  Name\n",
              "0  2013-02-08  27.920  28.325  27.920  28.185  7146296  SBUX\n",
              "1  2013-02-11  28.260  28.260  27.930  28.070  5457354  SBUX\n",
              "2  2013-02-12  28.000  28.275  27.975  28.130  8665592  SBUX\n",
              "3  2013-02-13  28.230  28.230  27.750  27.915  7022056  SBUX\n",
              "4  2013-02-14  27.765  27.905  27.675  27.775  8899188  SBUX"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
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              "      <th>0</th>\n",
              "      <td>2013-02-08</td>\n",
              "      <td>27.920</td>\n",
              "      <td>28.325</td>\n",
              "      <td>27.920</td>\n",
              "      <td>28.185</td>\n",
              "      <td>7146296</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013-02-11</td>\n",
              "      <td>28.260</td>\n",
              "      <td>28.260</td>\n",
              "      <td>27.930</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-02-12</td>\n",
              "      <td>28.000</td>\n",
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              "      <td>27.975</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013-02-13</td>\n",
              "      <td>28.230</td>\n",
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              "      <td>7022056</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013-02-14</td>\n",
              "      <td>27.765</td>\n",
              "      <td>27.905</td>\n",
              "      <td>27.675</td>\n",
              "      <td>27.775</td>\n",
              "      <td>8899188</td>\n",
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              "\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-d577ae02-b6ae-43dd-9a90-4a734064bd5e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1259,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1259,\n        \"samples\": [\n          \"2013-05-30\",\n          \"2017-03-08\",\n          \"2013-04-12\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.389108044534797,\n        \"min\": 26.535,\n        \"max\": 64.85,\n        \"num_unique_values\": 992,\n        \"samples\": [\n          39.05,\n          39.32,\n          49.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.458484718588727,\n        \"min\": 26.82,\n        \"max\": 64.87,\n        \"num_unique_values\": 1032,\n        \"samples\": [\n          34.15,\n          31.5275,\n          37.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.304081842518338,\n        \"min\": 26.26,\n        \"max\": 64.18,\n        \"num_unique_values\": 1043,\n        \"samples\": [\n          60.95,\n          35.085,\n          51.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.391866673432563,\n        \"min\": 26.605,\n        \"max\": 64.57,\n        \"num_unique_values\": 1023,\n        \"samples\": [\n          51.96,\n          38.985,\n          43.995\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4382209,\n        \"min\": 2215418,\n        \"max\": 53454789,\n        \"num_unique_values\": 1259,\n        \"samples\": [\n          8798456,\n          13061632,\n          10418310\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"SBUX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8mTO3DmJVgQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "409aa724-1d27-4067-a395-ec3a7be4a987"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date    open    high     low   close    volume  Name\n",
              "0  2013-02-08  27.920  28.325  27.920  28.185   7146296  SBUX\n",
              "1  2013-02-11  28.260  28.260  27.930  28.070   5457354  SBUX\n",
              "2  2013-02-12  28.000  28.275  27.975  28.130   8665592  SBUX\n",
              "3  2013-02-13  28.230  28.230  27.750  27.915   7022056  SBUX\n",
              "4  2013-02-14  27.765  27.905  27.675  27.775   8899188  SBUX\n",
              "5  2013-02-15  27.805  27.850  27.085  27.170  18195730  SBUX\n",
              "6  2013-02-19  27.180  27.305  27.010  27.225  11760912  SBUX\n",
              "7  2013-02-20  27.300  27.420  26.590  26.655  12472506  SBUX\n",
              "8  2013-02-21  26.535  26.820  26.260  26.675  13896450  SBUX\n",
              "9  2013-02-22  26.850  27.105  26.640  27.085  11487316  SBUX"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-74f4bbef-48f9-452e-b7ad-f638dca95030\" 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>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2013-02-08</td>\n",
              "      <td>27.920</td>\n",
              "      <td>28.325</td>\n",
              "      <td>27.920</td>\n",
              "      <td>28.185</td>\n",
              "      <td>7146296</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013-02-11</td>\n",
              "      <td>28.260</td>\n",
              "      <td>28.260</td>\n",
              "      <td>27.930</td>\n",
              "      <td>28.070</td>\n",
              "      <td>5457354</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-02-12</td>\n",
              "      <td>28.000</td>\n",
              "      <td>28.275</td>\n",
              "      <td>27.975</td>\n",
              "      <td>28.130</td>\n",
              "      <td>8665592</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013-02-13</td>\n",
              "      <td>28.230</td>\n",
              "      <td>28.230</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.915</td>\n",
              "      <td>7022056</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013-02-14</td>\n",
              "      <td>27.765</td>\n",
              "      <td>27.905</td>\n",
              "      <td>27.675</td>\n",
              "      <td>27.775</td>\n",
              "      <td>8899188</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2013-02-15</td>\n",
              "      <td>27.805</td>\n",
              "      <td>27.850</td>\n",
              "      <td>27.085</td>\n",
              "      <td>27.170</td>\n",
              "      <td>18195730</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2013-02-19</td>\n",
              "      <td>27.180</td>\n",
              "      <td>27.305</td>\n",
              "      <td>27.010</td>\n",
              "      <td>27.225</td>\n",
              "      <td>11760912</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2013-02-20</td>\n",
              "      <td>27.300</td>\n",
              "      <td>27.420</td>\n",
              "      <td>26.590</td>\n",
              "      <td>26.655</td>\n",
              "      <td>12472506</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2013-02-21</td>\n",
              "      <td>26.535</td>\n",
              "      <td>26.820</td>\n",
              "      <td>26.260</td>\n",
              "      <td>26.675</td>\n",
              "      <td>13896450</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2013-02-22</td>\n",
              "      <td>26.850</td>\n",
              "      <td>27.105</td>\n",
              "      <td>26.640</td>\n",
              "      <td>27.085</td>\n",
              "      <td>11487316</td>\n",
              "      <td>SBUX</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-74f4bbef-48f9-452e-b7ad-f638dca95030')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-74f4bbef-48f9-452e-b7ad-f638dca95030 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-74f4bbef-48f9-452e-b7ad-f638dca95030');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-33275efb-990c-4351-8f60-75323ad17418\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-33275efb-990c-4351-8f60-75323ad17418')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
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              "  }\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-33275efb-990c-4351-8f60-75323ad17418 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1259,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1259,\n        \"samples\": [\n          \"2013-05-30\",\n          \"2017-03-08\",\n          \"2013-04-12\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.389108044534797,\n        \"min\": 26.535,\n        \"max\": 64.85,\n        \"num_unique_values\": 992,\n        \"samples\": [\n          39.05,\n          39.32,\n          49.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.458484718588727,\n        \"min\": 26.82,\n        \"max\": 64.87,\n        \"num_unique_values\": 1032,\n        \"samples\": [\n          34.15,\n          31.5275,\n          37.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.304081842518338,\n        \"min\": 26.26,\n        \"max\": 64.18,\n        \"num_unique_values\": 1043,\n        \"samples\": [\n          60.95,\n          35.085,\n          51.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.391866673432563,\n        \"min\": 26.605,\n        \"max\": 64.57,\n        \"num_unique_values\": 1023,\n        \"samples\": [\n          51.96,\n          38.985,\n          43.995\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4382209,\n        \"min\": 2215418,\n        \"max\": 53454789,\n        \"num_unique_values\": 1259,\n        \"samples\": [\n          8798456,\n          13061632,\n          10418310\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"SBUX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7YHHXXlIzWk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6edfde84-4303-44f5-97b2-bc428dd0d51c"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            date    open   high    low  close    volume  Name\n",
              "1254  2018-02-01  56.280  56.42  55.89  56.00  14690146  SBUX\n",
              "1255  2018-02-02  55.900  56.32  55.70  55.77  15358909  SBUX\n",
              "1256  2018-02-05  55.530  56.26  54.57  54.69  16059955  SBUX\n",
              "1257  2018-02-06  53.685  56.06  53.56  55.61  17415065  SBUX\n",
              "1258  2018-02-07  55.080  55.43  54.44  54.46  13927022  SBUX"
            ],
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              "      <th>1254</th>\n",
              "      <td>2018-02-01</td>\n",
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              "      <td>56.42</td>\n",
              "      <td>55.89</td>\n",
              "      <td>56.00</td>\n",
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              "      <td>SBUX</td>\n",
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              "      <th>1255</th>\n",
              "      <td>2018-02-02</td>\n",
              "      <td>55.900</td>\n",
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              "      <td>55.70</td>\n",
              "      <td>55.77</td>\n",
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              "      <th>1256</th>\n",
              "      <td>2018-02-05</td>\n",
              "      <td>55.530</td>\n",
              "      <td>56.26</td>\n",
              "      <td>54.57</td>\n",
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              "      <td>SBUX</td>\n",
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              "    <tr>\n",
              "      <th>1257</th>\n",
              "      <td>2018-02-06</td>\n",
              "      <td>53.685</td>\n",
              "      <td>56.06</td>\n",
              "      <td>53.56</td>\n",
              "      <td>55.61</td>\n",
              "      <td>17415065</td>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1258</th>\n",
              "      <td>2018-02-07</td>\n",
              "      <td>55.080</td>\n",
              "      <td>55.43</td>\n",
              "      <td>54.44</td>\n",
              "      <td>54.46</td>\n",
              "      <td>13927022</td>\n",
              "      <td>SBUX</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d17350de-55f2-4b82-ab9b-f579e1533203 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-d17350de-55f2-4b82-ab9b-f579e1533203');\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-dc91575e-58a7-4cd2-9d10-5dea92fde1f4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc91575e-58a7-4cd2-9d10-5dea92fde1f4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .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-dc91575e-58a7-4cd2-9d10-5dea92fde1f4 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",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dwLJEb4I0sl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84199005-f173-41e9-9e2b-dfb08294902d"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1259 entries, 0 to 1258\n",
            "Data columns (total 7 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    1259 non-null   object \n",
            " 1   open    1259 non-null   float64\n",
            " 2   high    1259 non-null   float64\n",
            " 3   low     1259 non-null   float64\n",
            " 4   close   1259 non-null   float64\n",
            " 5   volume  1259 non-null   int64  \n",
            " 6   Name    1259 non-null   object \n",
            "dtypes: float64(4), int64(1), object(2)\n",
            "memory usage: 69.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95921SlgJoz7"
      },
      "source": [
        "## Selecting Rows and Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yfo8TUtmSvQX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "1332c862-f192-4263-f041-3adcc81f7f84"
      },
      "source": [
        "df[0,0] # doesn't work"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "(0, 0)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: (0, 0)",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-0714b26a0933>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# doesn't work\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4103\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4104\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3810\u001b[0m             ):\n\u001b[1;32m   3811\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3813\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3814\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: (0, 0)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4imWW4tHS6Ma",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de041746-5879-49ba-dac2-47585e9fc3f7"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'open', 'high', 'low', 'close', 'volume', 'Name'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luEwbCEGWJNb"
      },
      "source": [
        "# columns can be assigned\n",
        "df.columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'name']"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6sQLWwTS_lv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "8feffe7c-97f4-4096-faf9-338b4f10af29"
      },
      "source": [
        "df['open']"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       27.920\n",
              "1       28.260\n",
              "2       28.000\n",
              "3       28.230\n",
              "4       27.765\n",
              "         ...  \n",
              "1254    56.280\n",
              "1255    55.900\n",
              "1256    55.530\n",
              "1257    53.685\n",
              "1258    55.080\n",
              "Name: open, Length: 1259, dtype: float64"
            ],
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              "    <tr>\n",
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              "      <th>1254</th>\n",
              "      <td>56.280</td>\n",
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              "      <td>55.900</td>\n",
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              "      <td>53.685</td>\n",
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              "      <th>1258</th>\n",
              "      <td>55.080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1259 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "989xFodRTBUK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "bd7cfa59-3e6c-410d-9afc-ca7c26d0913b"
      },
      "source": [
        "df[['open', 'close']]"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        open   close\n",
              "0     27.920  28.185\n",
              "1     28.260  28.070\n",
              "2     28.000  28.130\n",
              "3     28.230  27.915\n",
              "4     27.765  27.775\n",
              "...      ...     ...\n",
              "1254  56.280  56.000\n",
              "1255  55.900  55.770\n",
              "1256  55.530  54.690\n",
              "1257  53.685  55.610\n",
              "1258  55.080  54.460\n",
              "\n",
              "[1259 rows x 2 columns]"
            ],
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              "<p>1259 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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-49fc584f-4365-41e2-a6f2-c486cc34762b 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-49fc584f-4365-41e2-a6f2-c486cc34762b');\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-2b4ff4a1-4299-44ff-9fa4-084fd7c964d6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2b4ff4a1-4299-44ff-9fa4-084fd7c964d6')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2b4ff4a1-4299-44ff-9fa4-084fd7c964d6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['open', 'close']]\",\n  \"rows\": 1259,\n  \"fields\": [\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.389108044534797,\n        \"min\": 26.535,\n        \"max\": 64.85,\n        \"num_unique_values\": 992,\n        \"samples\": [\n          39.05,\n          39.32,\n          49.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.391866673432563,\n        \"min\": 26.605,\n        \"max\": 64.57,\n        \"num_unique_values\": 1023,\n        \"samples\": [\n          51.96,\n          38.985,\n          43.995\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 204
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        "outputId": "7dee7d67-6300-4756-a8a1-b114d7148cfe"
      },
      "source": [
        "type(df['open'])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ],
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
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              "        cursor: pointer;\n",
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              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.series.Series</b><br/>def __init__(data=None, index=None, dtype: Dtype | None=None, name=None, copy: bool | None=None, fastpath: bool | lib.NoDefault=lib.no_default) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.11/dist-packages/pandas/core/series.py</a>One-dimensional ndarray with axis labels (including time series).\n",
              "\n",
              "Labels need not be unique but must be a hashable type. The object\n",
              "supports both integer- and label-based indexing and provides a host of\n",
              "methods for performing operations involving the index. Statistical\n",
              "methods from ndarray have been overridden to automatically exclude\n",
              "missing data (currently represented as NaN).\n",
              "\n",
              "Operations between Series (+, -, /, \\*, \\*\\*) align values based on their\n",
              "associated index values-- they need not be the same length. The result\n",
              "index will be the sorted union of the two indexes.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : array-like, Iterable, dict, or scalar value\n",
              "    Contains data stored in Series. If data is a dict, argument order is\n",
              "    maintained.\n",
              "index : array-like or Index (1d)\n",
              "    Values must be hashable and have the same length as `data`.\n",
              "    Non-unique index values are allowed. Will default to\n",
              "    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like\n",
              "    and index is None, then the keys in the data are used as the index. If the\n",
              "    index is not None, the resulting Series is reindexed with the index values.\n",
              "dtype : str, numpy.dtype, or ExtensionDtype, optional\n",
              "    Data type for the output Series. If not specified, this will be\n",
              "    inferred from `data`.\n",
              "    See the :ref:`user guide &lt;basics.dtypes&gt;` for more usages.\n",
              "name : Hashable, default None\n",
              "    The name to give to the Series.\n",
              "copy : bool, default False\n",
              "    Copy input data. Only affects Series or 1d ndarray input. See examples.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.series&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing Series from a dictionary with an Index specified\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "a   1\n",
              "b   2\n",
              "c   3\n",
              "dtype: int64\n",
              "\n",
              "The keys of the dictionary match with the Index values, hence the Index\n",
              "values have no effect.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "x   NaN\n",
              "y   NaN\n",
              "z   NaN\n",
              "dtype: float64\n",
              "\n",
              "Note that the Index is first build with the keys from the dictionary.\n",
              "After this the Series is reindexed with the given Index values, hence we\n",
              "get all NaN as a result.\n",
              "\n",
              "Constructing Series from a list with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = [1, 2]\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "[1, 2]\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `copy` of\n",
              "the original data even though `copy=False`, so\n",
              "the data is unchanged.\n",
              "\n",
              "Constructing Series from a 1d ndarray with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = np.array([1, 2])\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "array([999,   2])\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `view` on\n",
              "the original data, so\n",
              "the data is changed as well.</pre>\n",
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      "source": [
        "type(df[['open', 'close']])"
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      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
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              "    <pre style=\"white-space: initial; background:\n",
              "         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/python3.11/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. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\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\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              "&gt;&gt;&gt; ser = pd.Series([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=ser, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              "&gt;&gt;&gt; df1 = pd.DataFrame([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], columns=[&quot;x&quot;])\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(data=df1, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df2\n",
              "   x\n",
              "a  1\n",
              "c  3</pre>\n",
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              "      <th>volume</th>\n",
              "      <td>7146296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <td>SBUX</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a23A0OwrTY9o"
      },
      "source": [
        "df2 = pd.read_csv('sbux.csv', index_col='date')"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1T2BhoCbUBta",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "bc28efd1-d8fc-4e1f-dc0e-d15e266e25e2"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              open    high     low   close   volume  Name\n",
              "date                                                     \n",
              "2013-02-08  27.920  28.325  27.920  28.185  7146296  SBUX\n",
              "2013-02-11  28.260  28.260  27.930  28.070  5457354  SBUX\n",
              "2013-02-12  28.000  28.275  27.975  28.130  8665592  SBUX\n",
              "2013-02-13  28.230  28.230  27.750  27.915  7022056  SBUX\n",
              "2013-02-14  27.765  27.905  27.675  27.775  8899188  SBUX"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
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            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
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    {
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          "height": 272
        },
        "outputId": "9996a140-aea8-4ee5-e66e-b71b9c139223"
      },
      "source": [
        "df2.loc['2013-02-08']"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "open        27.92\n",
              "high       28.325\n",
              "low         27.92\n",
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    {
      "cell_type": "code",
      "metadata": {
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          "height": 204
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        "outputId": "011357f3-85da-48d1-b02e-ab03c5bb78b0"
      },
      "source": [
        "type(df2.loc['2013-02-08'])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.series.Series</b><br/>def __init__(data=None, index=None, dtype: Dtype | None=None, name=None, copy: bool | None=None, fastpath: bool | lib.NoDefault=lib.no_default) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.11/dist-packages/pandas/core/series.py</a>One-dimensional ndarray with axis labels (including time series).\n",
              "\n",
              "Labels need not be unique but must be a hashable type. The object\n",
              "supports both integer- and label-based indexing and provides a host of\n",
              "methods for performing operations involving the index. Statistical\n",
              "methods from ndarray have been overridden to automatically exclude\n",
              "missing data (currently represented as NaN).\n",
              "\n",
              "Operations between Series (+, -, /, \\*, \\*\\*) align values based on their\n",
              "associated index values-- they need not be the same length. The result\n",
              "index will be the sorted union of the two indexes.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : array-like, Iterable, dict, or scalar value\n",
              "    Contains data stored in Series. If data is a dict, argument order is\n",
              "    maintained.\n",
              "index : array-like or Index (1d)\n",
              "    Values must be hashable and have the same length as `data`.\n",
              "    Non-unique index values are allowed. Will default to\n",
              "    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like\n",
              "    and index is None, then the keys in the data are used as the index. If the\n",
              "    index is not None, the resulting Series is reindexed with the index values.\n",
              "dtype : str, numpy.dtype, or ExtensionDtype, optional\n",
              "    Data type for the output Series. If not specified, this will be\n",
              "    inferred from `data`.\n",
              "    See the :ref:`user guide &lt;basics.dtypes&gt;` for more usages.\n",
              "name : Hashable, default None\n",
              "    The name to give to the Series.\n",
              "copy : bool, default False\n",
              "    Copy input data. Only affects Series or 1d ndarray input. See examples.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.series&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing Series from a dictionary with an Index specified\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "a   1\n",
              "b   2\n",
              "c   3\n",
              "dtype: int64\n",
              "\n",
              "The keys of the dictionary match with the Index values, hence the Index\n",
              "values have no effect.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "x   NaN\n",
              "y   NaN\n",
              "z   NaN\n",
              "dtype: float64\n",
              "\n",
              "Note that the Index is first build with the keys from the dictionary.\n",
              "After this the Series is reindexed with the given Index values, hence we\n",
              "get all NaN as a result.\n",
              "\n",
              "Constructing Series from a list with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = [1, 2]\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "[1, 2]\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `copy` of\n",
              "the original data even though `copy=False`, so\n",
              "the data is unchanged.\n",
              "\n",
              "Constructing Series from a 1d ndarray with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = np.array([1, 2])\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "array([999,   2])\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `view` on\n",
              "the original data, so\n",
              "the data is changed as well.</pre>\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
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        "outputId": "40683294-cc3c-4d89-b27f-9eea3412fa05"
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      "source": [
        "df[df['open'] > 64]"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            date   open    high    low  close   volume  name\n",
              "1087  2017-06-05  64.85  64.870  64.18  64.27  6809284  SBUX\n",
              "1088  2017-06-06  64.22  64.350  64.05  64.16  5448439  SBUX\n",
              "1089  2017-06-07  64.13  64.295  63.34  63.50  8364994  SBUX"
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              "  .colab-df-quickchart-complete:disabled,\n",
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      "source": [
        "df[df['name'] != 'SBUX']"
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      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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          "metadata": {},
          "execution_count": 38
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      "execution_count": 39,
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          "height": 204
        },
        "outputId": "74ee8291-4b80-42fa-ca11-1f3ce75deb9d"
      },
      "source": [
        "type(df['name'] != 'SBUX')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
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              "\n",
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              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.series.Series</b><br/>def __init__(data=None, index=None, dtype: Dtype | None=None, name=None, copy: bool | None=None, fastpath: bool | lib.NoDefault=lib.no_default) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.11/dist-packages/pandas/core/series.py</a>One-dimensional ndarray with axis labels (including time series).\n",
              "\n",
              "Labels need not be unique but must be a hashable type. The object\n",
              "supports both integer- and label-based indexing and provides a host of\n",
              "methods for performing operations involving the index. Statistical\n",
              "methods from ndarray have been overridden to automatically exclude\n",
              "missing data (currently represented as NaN).\n",
              "\n",
              "Operations between Series (+, -, /, \\*, \\*\\*) align values based on their\n",
              "associated index values-- they need not be the same length. The result\n",
              "index will be the sorted union of the two indexes.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : array-like, Iterable, dict, or scalar value\n",
              "    Contains data stored in Series. If data is a dict, argument order is\n",
              "    maintained.\n",
              "index : array-like or Index (1d)\n",
              "    Values must be hashable and have the same length as `data`.\n",
              "    Non-unique index values are allowed. Will default to\n",
              "    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like\n",
              "    and index is None, then the keys in the data are used as the index. If the\n",
              "    index is not None, the resulting Series is reindexed with the index values.\n",
              "dtype : str, numpy.dtype, or ExtensionDtype, optional\n",
              "    Data type for the output Series. If not specified, this will be\n",
              "    inferred from `data`.\n",
              "    See the :ref:`user guide &lt;basics.dtypes&gt;` for more usages.\n",
              "name : Hashable, default None\n",
              "    The name to give to the Series.\n",
              "copy : bool, default False\n",
              "    Copy input data. Only affects Series or 1d ndarray input. See examples.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.series&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing Series from a dictionary with an Index specified\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "a   1\n",
              "b   2\n",
              "c   3\n",
              "dtype: int64\n",
              "\n",
              "The keys of the dictionary match with the Index values, hence the Index\n",
              "values have no effect.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "x   NaN\n",
              "y   NaN\n",
              "z   NaN\n",
              "dtype: float64\n",
              "\n",
              "Note that the Index is first build with the keys from the dictionary.\n",
              "After this the Series is reindexed with the given Index values, hence we\n",
              "get all NaN as a result.\n",
              "\n",
              "Constructing Series from a list with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = [1, 2]\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "[1, 2]\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `copy` of\n",
              "the original data even though `copy=False`, so\n",
              "the data is unchanged.\n",
              "\n",
              "Constructing Series from a 1d ndarray with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = np.array([1, 2])\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "array([999,   2])\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `view` on\n",
              "the original data, so\n",
              "the data is changed as well.</pre>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4WqLOxkVF6y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc81dd38-0b8d-49b8-db1a-77ecd8760d2a"
      },
      "source": [
        "# by the way, this works with Numpy too\n",
        "import numpy as np\n",
        "A = np.arange(10)\n",
        "A"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xRzEsa6jVLqs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "518163a5-cc5e-4855-c1d5-ff125d6ade86"
      },
      "source": [
        "A[A % 2 == 0]"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 4, 6, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNSThpYdJlQx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b315aa6f-f9ea-438d-affe-c8db7cccf5e4"
      },
      "source": [
        "# don't want \"objects\" when we're doing math!\n",
        "df.values"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['2013-02-08', 27.92, 28.325, ..., 28.185, 7146296, 'SBUX'],\n",
              "       ['2013-02-11', 28.26, 28.26, ..., 28.07, 5457354, 'SBUX'],\n",
              "       ['2013-02-12', 28.0, 28.275, ..., 28.13, 8665592, 'SBUX'],\n",
              "       ...,\n",
              "       ['2018-02-05', 55.53, 56.26, ..., 54.69, 16059955, 'SBUX'],\n",
              "       ['2018-02-06', 53.685, 56.06, ..., 55.61, 17415065, 'SBUX'],\n",
              "       ['2018-02-07', 55.08, 55.43, ..., 54.46, 13927022, 'SBUX']],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CptMzuduVak6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e91dd577-8376-487d-906c-25263708e6d0"
      },
      "source": [
        "A = df[['open', 'close']].values\n",
        "A"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[27.92 , 28.185],\n",
              "       [28.26 , 28.07 ],\n",
              "       [28.   , 28.13 ],\n",
              "       ...,\n",
              "       [55.53 , 54.69 ],\n",
              "       [53.685, 55.61 ],\n",
              "       [55.08 , 54.46 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MW_TyI4nVl1f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86a7788f-8197-4341-e76f-b27a7ad58487"
      },
      "source": [
        "type(A)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C42Vx5nFZ6VK"
      },
      "source": [
        "# write a dataframe to file\n",
        "smalldf = df[['open', 'close']]\n",
        "smalldf.to_csv('output.csv')"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4fQSWNJaSd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6258ad45-2851-4046-e6c9-b71fd973a706"
      },
      "source": [
        "!head output.csv"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ",open,close\n",
            "0,27.92,28.185\n",
            "1,28.26,28.07\n",
            "2,28.0,28.13\n",
            "3,28.23,27.915\n",
            "4,27.765,27.775\n",
            "5,27.805,27.17\n",
            "6,27.18,27.225\n",
            "7,27.3,26.655\n",
            "8,26.535,26.675\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQ1cKZFyaWTB"
      },
      "source": [
        "# what if we don't want an index column?\n",
        "smalldf.to_csv('output.csv', index=False)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JR9D6-1safMY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be0c2930-709b-4117-e1fb-e500c0793352"
      },
      "source": [
        "!head output.csv"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "open,close\n",
            "27.92,28.185\n",
            "28.26,28.07\n",
            "28.0,28.13\n",
            "28.23,27.915\n",
            "27.765,27.775\n",
            "27.805,27.17\n",
            "27.18,27.225\n",
            "27.3,26.655\n",
            "26.535,26.675\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZqY_ygGnVXsz"
      },
      "source": [
        "## The apply() function\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bz9o0snTXS3Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "46ee18fb-3296-47ae-cda5-5f9ed664c69a"
      },
      "source": [
        "def date_to_year(row):\n",
        "  return int(row['date'].split('-')[0])\n",
        "\n",
        "df.apply(date_to_year, axis=1)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2013\n",
              "1       2013\n",
              "2       2013\n",
              "3       2013\n",
              "4       2013\n",
              "        ... \n",
              "1254    2018\n",
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              "      <th>1256</th>\n",
              "      <td>2018</td>\n",
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              "      <th>1257</th>\n",
              "      <td>2018</td>\n",
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              "    <tr>\n",
              "      <th>1258</th>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1259 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3OKuaS92YEAy"
      },
      "source": [
        "df['year'] = df.apply(date_to_year, axis=1)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAUR-eO8YI4J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ae796b46-592d-4f30-fdb4-1e457664c63f"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date    open    high     low   close   volume  name  year\n",
              "0  2013-02-08  27.920  28.325  27.920  28.185  7146296  SBUX  2013\n",
              "1  2013-02-11  28.260  28.260  27.930  28.070  5457354  SBUX  2013\n",
              "2  2013-02-12  28.000  28.275  27.975  28.130  8665592  SBUX  2013\n",
              "3  2013-02-13  28.230  28.230  27.750  27.915  7022056  SBUX  2013\n",
              "4  2013-02-14  27.765  27.905  27.675  27.775  8899188  SBUX  2013"
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              "\n",
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              "      <th>high</th>\n",
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              "      <th>close</th>\n",
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              "      <th>0</th>\n",
              "      <td>2013-02-08</td>\n",
              "      <td>27.920</td>\n",
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              "      <td>27.920</td>\n",
              "      <td>28.185</td>\n",
              "      <td>7146296</td>\n",
              "      <td>SBUX</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013-02-11</td>\n",
              "      <td>28.260</td>\n",
              "      <td>28.260</td>\n",
              "      <td>27.930</td>\n",
              "      <td>28.070</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>28.000</td>\n",
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              "      <td>27.975</td>\n",
              "      <td>28.130</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013-02-13</td>\n",
              "      <td>28.230</td>\n",
              "      <td>28.230</td>\n",
              "      <td>27.750</td>\n",
              "      <td>27.915</td>\n",
              "      <td>7022056</td>\n",
              "      <td>SBUX</td>\n",
              "      <td>2013</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013-02-14</td>\n",
              "      <td>27.765</td>\n",
              "      <td>27.905</td>\n",
              "      <td>27.675</td>\n",
              "      <td>27.775</td>\n",
              "      <td>8899188</td>\n",
              "      <td>SBUX</td>\n",
              "      <td>2013</td>\n",
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              "      --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",
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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",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\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-right-color: var(--fill-color);\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } 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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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-af78dc62-1226-4c34-a6ec-ad77c2d9f9fe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1259,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1259,\n        \"samples\": [\n          \"2013-05-30\",\n          \"2017-03-08\",\n          \"2013-04-12\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.389108044534797,\n        \"min\": 26.535,\n        \"max\": 64.85,\n        \"num_unique_values\": 992,\n        \"samples\": [\n          39.05,\n          39.32,\n          49.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.458484718588727,\n        \"min\": 26.82,\n        \"max\": 64.87,\n        \"num_unique_values\": 1032,\n        \"samples\": [\n          34.15,\n          31.5275,\n          37.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.304081842518338,\n        \"min\": 26.26,\n        \"max\": 64.18,\n        \"num_unique_values\": 1043,\n        \"samples\": [\n          60.95,\n          35.085,\n          51.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.391866673432563,\n        \"min\": 26.605,\n        \"max\": 64.57,\n        \"num_unique_values\": 1023,\n        \"samples\": [\n          51.96,\n          38.985,\n          43.995\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4382209,\n        \"min\": 2215418,\n        \"max\": 53454789,\n        \"num_unique_values\": 1259,\n        \"samples\": [\n          8798456,\n          13061632,\n          10418310\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"SBUX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2013,\n        \"max\": 2018,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e-lRsfm4Wycn"
      },
      "source": [
        "## Plotting with Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VdzZLf88Wfuz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "8e6ff00e-0581-4ae8-cb5b-891c430f48aa"
      },
      "source": [
        "df['open'].hist();"
      ],
      "execution_count": 55,
      "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",
      "metadata": {
        "id": "rkLF3t6uWlPg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "5d3d0992-96aa-4d83-c890-926dc0970a9f"
      },
      "source": [
        "df['open'].plot();"
      ],
      "execution_count": 56,
      "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",
      "metadata": {
        "id": "Rux9b8syVn2b"
      },
      "source": [
        "df[['open', 'high', 'low', 'close']].plot.box();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tvi5oWMQWRPt"
      },
      "source": [
        "from pandas.plotting import scatter_matrix\n",
        "scatter_matrix(df[['open', 'high', 'low', 'close']],\n",
        "               alpha=0.2, figsize=(6, 6));"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}