TS-Stationary.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutoCluster.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOe+xv/G2ljsoQQOaJPhyJp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ogut77/DataScience/blob/main/TS-Stationary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot\n",
        "fb=pd.read_csv('https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-total-female-births.csv')\n",
        "fb1=fb.set_index('Date')\n",
        "fb1.plot()\n",
        "plt.xticks(rotation=90)\n",
        "pyplot.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "gDEUgVMcPeY9",
        "outputId": "b15a3906-17e9-434c-9d0e-e1002bea2817"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fb1"
      ],
      "metadata": {
        "id": "kcecO2nwF9fN",
        "outputId": "37be083c-f7b5-42dd-f1e6-44f739535069",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Births\n",
              "Date              \n",
              "1959-01-01      35\n",
              "1959-01-02      32\n",
              "1959-01-03      30\n",
              "1959-01-04      31\n",
              "1959-01-05      44\n",
              "...            ...\n",
              "1959-12-27      37\n",
              "1959-12-28      52\n",
              "1959-12-29      48\n",
              "1959-12-30      55\n",
              "1959-12-31      50\n",
              "\n",
              "[365 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d225e6fd-8bd0-45c9-b0e2-d5adbef3d829\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Births</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1959-01-01</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-01-02</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-01-03</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-01-04</th>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-01-05</th>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-12-27</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-12-28</th>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-12-29</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-12-30</th>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-12-31</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>365 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d225e6fd-8bd0-45c9-b0e2-d5adbef3d829')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d225e6fd-8bd0-45c9-b0e2-d5adbef3d829 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-d225e6fd-8bd0-45c9-b0e2-d5adbef3d829');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.graphics import tsaplots\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#plot autocorrelation function\n",
        "fig = tsaplots.plot_acf(fb, lags=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "JvxmacRmCMrc",
        "outputId": "8df05fdf-289f-4bfc-a5e5-892caa9e8557",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#testing for Stationary\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "result = adfuller(fb1['Births'])\n",
        "print('ADF Statistic: %f' % result[0])\n",
        "print('p-value: %f' % result[1])\n",
        "print('Critical Values:')\n",
        "for key, value in result[4].items():\n",
        "\tprint('\\t%s: %.3f' % (key, value))"
      ],
      "metadata": {
        "id": "U4d12H7YCdrm",
        "outputId": "7cb27345-50f9-4fc9-8630-bd562ec463db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistic: -4.808291\n",
            "p-value: 0.000052\n",
            "Critical Values:\n",
            "\t1%: -3.449\n",
            "\t5%: -2.870\n",
            "\t10%: -2.571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ho:Test is  stationary\n",
        "\n",
        "Ha:Test is not stationary \n",
        "\n",
        "If significance level(alpha) is 5%, what is the decision?"
      ],
      "metadata": {
        "id": "gJMtcQMOEJwf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot\n",
        "ap=pd.read_csv('https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv')\n",
        "ap=ap.set_index('Month')\n",
        "ap.plot()\n",
        "pyplot.show()\n"
      ],
      "metadata": {
        "id": "FyDGxIvGA9d6",
        "outputId": "0517e091-9d93-407c-d6fe-ff1c90e2846b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ap"
      ],
      "metadata": {
        "id": "eXpOihsdGnH3",
        "outputId": "1fa9ebce-e53d-4bb0-e35b-7a953be85308",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Passengers\n",
              "Month              \n",
              "1949-01         112\n",
              "1949-02         118\n",
              "1949-03         132\n",
              "1949-04         129\n",
              "1949-05         121\n",
              "...             ...\n",
              "1960-08         606\n",
              "1960-09         508\n",
              "1960-10         461\n",
              "1960-11         390\n",
              "1960-12         432\n",
              "\n",
              "[144 rows x 1 columns]"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>Passengers</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1949-01</th>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1949-02</th>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1949-03</th>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1949-04</th>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1949-05</th>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-08</th>\n",
              "      <td>606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-09</th>\n",
              "      <td>508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-10</th>\n",
              "      <td>461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-11</th>\n",
              "      <td>390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-12</th>\n",
              "      <td>432</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>144 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ffce061f-66b8-400c-be42-e8886806c316')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ffce061f-66b8-400c-be42-e8886806c316 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-ffce061f-66b8-400c-be42-e8886806c316');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = tsaplots.plot_acf(ap, lags=13)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "fAIzB7m9CVKT",
        "outputId": "a526c826-7957-4793-85ef-ba64528266e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing for Stationary\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "result = adfuller(ap['Passengers'])\n",
        "print('ADF Statistic: %f' % result[0])\n",
        "print('p-value: %f' % result[1])\n",
        "print('Critical Values:')\n",
        "for key, value in result[4].items():\n",
        "\tprint('\\t%s: %.3f' % (key, value))"
      ],
      "metadata": {
        "id": "XrpBm_PAGhhS",
        "outputId": "32b3801c-3830-4f21-aa98-eef015af9298",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistic: 0.815369\n",
            "p-value: 0.991880\n",
            "Critical Values:\n",
            "\t1%: -3.482\n",
            "\t5%: -2.884\n",
            "\t10%: -2.579\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ho:Test is not stationary\n",
        "\n",
        "Ha:Test is stationary\n",
        "\n",
        "If significance level(alpha) is 5%, what is the decision?"
      ],
      "metadata": {
        "id": "u-cdBGalGv8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Take differnce for making stationary\n",
        "dap = ap - ap.shift(1)\n",
        "dap.plot()"
      ],
      "metadata": {
        "id": "2o5iuXEgGyU2",
        "outputId": "7978fc93-03df-4d81-b703-8dc577dcbafc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff8e83f9590>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dap=dap.dropna()\n",
        "fig = tsaplots.plot_acf(dap, lags=13)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Df4KWbKmHoGj",
        "outputId": "ccc2f6ba-6663-44d3-a6c9-0b5f5e272c29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q:Is there a seasonality in the data?"
      ],
      "metadata": {
        "id": "IEuq91EnKCD7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "result = adfuller(dap['Passengers'])\n",
        "print('ADF Statistic: %f' % result[0])\n",
        "print('p-value: %f' % result[1])\n",
        "print('Critical Values:')\n",
        "for key, value in result[4].items():\n",
        "\tprint('\\t%s: %.3f' % (key, value))"
      ],
      "metadata": {
        "id": "HO9h3ZoNHs64",
        "outputId": "801d02e6-daf2-4909-dfb6-38d8e2ba5af0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistic: -2.829267\n",
            "p-value: 0.054213\n",
            "Critical Values:\n",
            "\t1%: -3.482\n",
            "\t5%: -2.884\n",
            "\t10%: -2.579\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Ho:Test is not stationary\n",
        "\n",
        "Ha:Test is stationary\n",
        "\n",
        "If significance level(alpha) is 5%, what is the decision?"
      ],
      "metadata": {
        "id": "lkuy9MQ6J9Jr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}